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Accessible Visual Artworks for Blind and Visually Impaired People: Comparing a Multimodal Approach with Tactile Graphics

Despite the use of tactile graphics and audio guides, blind and visually impaired people still face challenges to experience and understand visual artworks independently at art exhibitions. Art museums and other art places are increasingly exploring the use of interactive guides to make their collections more accessible. In this work, we describe our approach to an interactive multimodal guide prototype that uses audio and tactile modalities to improve the autonomous access to information and experience of visual artworks. The prototype is composed of a touch-sensitive 2.5D artwork relief model that can be freely explored by touch. Users can access localized verbal descriptions and audio by performing touch gestures on the surface while listening to themed background music along. We present the design requirements derived from a formative study realized with the help of eight blind and visually impaired participants, art museum and gallery staff, and artists. We extended the formative study by organizing two accessible art exhibitions. There, eighteen participants evaluated and compared multimodal and tactile graphic accessible exhibits. Results from a usability survey indicate that our multimodal approach is simple, easy to use, and improves confidence and independence when exploring visual artworks.

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and areaconstrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of theCNNcomponent of our system. We parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to lowAnalog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791Wof power while occupying an area of 31.255 mm(2) in a 22 nm FDSOI CMOS process.

Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists' workload and simultaneously reduce misdiagnosis rate. However, the automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method is corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumor localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.

Long-Term Effectiveness of Antiretroviral Therapy in China: An Observational Cohort Study from 2003-2014

In order to assess the effectiveness of the Chinese government's expanded access program, a cohort study on all adult HIV patients in Shenzhen was conducted from December 2003 to February 2014 to estimate the effects of antiretroviral therapy (ART) on mortality, tuberculosis and CD4 cell counts. Marginal structural regression models adjusted for baseline and time-varying covariates. Of the 6897 patients enrolled and followed up for a maximum of 178 months, 44.92% received ART. Among patients who commenced receiving ART during the study, there were 98 deaths and 59 new tuberculosis diagnoses, while there were 410 deaths and 201 new tuberculosis diagnoses among those without ART. ART was associated with both lower mortality (hazard ratio [HR] = 0.18; 95% confidence interval [CI] = 0.11-0.27) and the presence of tuberculosis (HR = 0.27; 95% CI = 0.19-0.37). Each month of ART was associated with an average increase in CD4 cell count of 6.52 cells/mu L (95% CI = 6.08-7.12 cells/mu L). In conclusions, the effectiveness of ART provided by China government health services is the same as that in higher-income countries. Accounting to higher mortality rates from the delay of starting ART, faster expansion and timely imitation of ART are urgent.

Real-Time Facial Affective Computing on Mobile Devices

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

A phenotype-based forward genetic screen identifies Dnajb6 as a sick sinus syndrome gene

Previously we showed the generation of a protein trap library made with the gene-break transposon (GBT) in zebrafish (Danio rerio) that could be used to facilitate novel functional genome annotation towards understanding molecular underpinnings of human diseases (Ichino et al, 2020). Here, we report a significant application of this library for discovering essential genes for heart rhythm disorders such as sick sinus syndrome (SSS). SSS is a group of heart rhythm disorders caused by malfunction of the sinus node, the heart's primary pacemaker. Partially owing to its aging-associated phenotypic manifestation and low expressivity, molecular mechanisms of SSS remain difficult to decipher. From 609 GBT lines screened, we generated a collection of 35 zebrafish insertional cardiac (ZIC) mutants in which each mutant traps a gene with cardiac expression. We further employed electrocardiographic measurements to screen these 35 ZIC lines and identified three GBT mutants with SSS-like phenotypes. More detailed functional studies on one of the arrhythmogenic mutants, GBT411, in both zebrafish and mouse models unveiled Dnajb6 as a novel SSS causative gene with a unique expression pattern within the subpopulation of sinus node pacemaker cells that partially overlaps with the expression of hyperpolarization activated cyclic nucleotide gated channel 4 (HCN4), supporting heterogeneity of the cardiac pacemaker cells.

ROS System Facial Emotion Detection Using Machine Learning for a Low-Cost Robot Based on Raspberry Pi

Facial emotion recognition (FER) is a field of research with multiple solutions in the state-of-the-art, focused on fields such as security, marketing or robotics. In the literature, several articles can be found in which algorithms are presented from different perspectives for detecting emotions. More specifically, in those emotion detection systems in the literature whose computational cores are low-cost, the results presented are usually in simulation or with quite limited real tests. This article presents a facial emotion detection system-detecting emotions such as anger, happiness, sadness or surprise-that was implemented under the Robot Operating System (ROS), Noetic version, and is based on the latest machine learning (ML) techniques proposed in the state-of-the-art. To make these techniques more efficient, and that they can be executed in real time on a low-cost board, extensive experiments were conducted in a real-world environment using a low-cost general purpose board, the Raspberry Pi 4 Model B. The final achieved FER system proposed in this article is capable of plausibly running in real time, operating at more than 13 fps, without using any external accelerator hardware, as other works (widely introduced in this article) do need in order to achieve the same purpose.

A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features

This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal set of co-existed permissions and APIs in comparing to those requested by benign applications. To prove this assumption, the paper created a new dataset of co-existed permissions and API calls at different levels of combinations, which are the second level, the third level, the fourth level and the fifth level. The extracted datasets of co-existed features at different levels were applied on permissions only, APIs only, permissions and APIs, and APIs and APIs frequencies. To extract the most relevant co-existed features, the frequent pattern growth (FP-growth) algorithm, which is an association rule mining technique, was used. The new datasets were extracted using Android APK samples from the Drebin, Malgenome and MalDroid2020 datasets. To evaluate the proposed model, several conventional machine learning algorithms were used. The results show that the model can successfully classify Android malware with a high accuracy using machine learning algorithms and the co-existence of features. Moreover, the results show that the achieved classification accuracy depends on the classifier and the type of co-existed features. The maximum accuracy, which is 98%, was achieved using the Random Forest algorithm and the co-existence of permissions features at the second combination level. Furthermore, the results show that the proposed approach outperforms the state-of-the-art model. Using Malgenome dataset, the proposed approach achieved an accuracy of about 98%, while the state-of-the-art achieved an accuracy of about 87%. In addition, the experiments show that using the Drebin dataset, the proposed approach achieved an accuracy of about 95%, while the state-of-the-art achieved an accuracy of about 93%.

Universal blind image quality assessment using contourlet transform and singular-value decomposition

Most current state-of-the-art blind image quality assessment (IQA) algorithms usually require process training or learning. Here, we have developed a completely blind IQA model that uses features derived from an image's contourlet transform and singular-value decomposition. The model is used to build algorithms that can predict image quality without any training or any prior knowledge of the images or their distortions. The new method consists of three steps: first, the contourlet transform is used on the image to obtain detailed high-frequency structural information from the image; second, the singular values of the just-obtained "structural image" are computed; and finally, two new universal blind IQA indices are constructed utilizing the area and slope of the truncated singular-value curves of the "structural image." Experimental results on three open databases show that the proposed algorithms deliver quality predictions that have high correlations against human subjective judgments and are highly competitive with the state-of-the-art. (C) 2014 SPIE and IS&T

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.

Construction of a novel plasmid for an industrial yeast Candida glycerinogenes by dual-autonomously replicating sequence strategy

Due to the lack of available episomal plasmid, the improvement of many industrial strains, especially exogenous gene expression, is severely restricted. The failure of autonomous replication or low copy number of episomal plasmids is the main reason for the failure of many episomal plasmids construction. In this paper, Candida glycerinogenes, an industrial strain lacking episomal plasmids, was employed as the topic. A series of GFP-based plasmids containing autonomously replicating sequence (ARS) from different strain sources were constructed and analyzed for performance, and it was found that only the panARS from Kluyveromyces lactis compared with other nine low capacity ARSs proved to have the best performance and could be used to construct episomal plasmid. Further, the dual-ARS strategy was used to optimize the episomal plasmid, and the results indicated that only the dual-ARS plasmid +PPARS2 with double different ARSs, not the dual-ARS plasmid +panARS with double same ARSs, showed an improvement in all properties, with an increase in transformation efficiency of about 36% and a synchronous trend of fluorescence intensity and copy number, both by about 40%. In addition, constructed episomal plasmids were used to express the exogenous gene CrGES, and the fact that geraniol was found proved the versatility of the plasmids. The successful construction of episomal plasmids will also substantially facilitate genetic engineering research and industrial use of C. glycerinogenes in the future, as well as providing a feasible approach to create episomal plasmids for industrial strains.

Flagellum tapering and midpiece volume in songbird spermatozoa

In contrast to numerous studies on spermatozoa length, relatively little work focuses on the width of spermatozoa, and particularly the width of the midpiece and flagellum. In flagellated spermatozoa, the flagellum provides forward thrust while energy may be provided via mitochondria in the midpiece and/or through glycolysis along the flagellum itself. Longer flagella may be able to provide greater thrust but may also require stronger structural features and more or larger mitochondria to supply sufficient energy. Here, we use scanning electron microscopy to investigate the ultrastructure of spermatozoa from 55 passerine species in 26 taxonomic families in the Passerides infraorder. Our data confirm the qualitative observation that the flagellum tapers along its length, and we show that longer flagella are wider at the neck. This pattern is similar to mammals, and likely reflects the need for longer cells to be stronger against shearing forces. We further estimate the volume of the mitochondrial helix and show that it correlates well with midpiece length, supporting the use of midpiece length as a proxy for mitochondrial volume, at least in between-species studies where midpiece length is highly variable. These results provide important context for understanding the evolutionary correlations among different sperm cell components and dimensions.

Contactless Biometric Identification Using 3D Finger Knuckle Patterns

Study on finger knuckle patterns has attracted increasing attention for the automated biometric identification. However, finger knuckle pattern is essentially a 3D biometric identifier and the usage or availability of only 2D finger knuckle databases in the literature is the key limitation to avail full potential from this biometric identifier. This paper therefore introduces (first) contactless 3D finger knuckle database in public domain, which is acquired from 130 different subjects in two-session imaging using photometric stereo approach. This paper investigates on the 3D information from the finger knuckle patterns and introduces a new feature descriptor to extract discriminative 3D features for more accurate 3D finger knuckle matching. An individuality model for the proposed feature descriptor is also presented. Comparative experimental results using the state-of-the-art feature extraction methods on this challenging 3D finger knuckle database validate the effectiveness of our approach. Although our feature descriptor is designed for 3D finger knuckle patterns, it is also attractive for other hand-based biometric identifiers with similar patterns such as the palmprint and fingerprint. This observation is validated from the outperforming results, using the state-of-the-art pixel-wise 3D palmprint and 3D fingerprint feature descriptors, on other publicly available datasets.

Kolmogorov's Theory of Computer Science

In the present work, we follow in chronological order the ideas, contributions and discoveries of the greatest Russian mathematician Andrei Kolmogorov in Computer Science. We interpret such Kolmogorov's concepts as algorithm, complexity, komputer mathematics, machine, in the context of the state-of-the art information theories and technologies. We conclude that in broad sense these theories and technologies follow the ways sketched and predicted by Kolmogorov about half century ago.

Curved scene text detection via transverse and longitudinal sequence connection

Curved text detection is a difficult problem that has not been addressed sufficiently. To highlight the difficulties in reading curved text in a real environment, we constructed a curved text dataset called CTW1500, which includes over 10,000 text annotations in 1500 images, and used it to formulate a polygon-based curved text detector that can detect curved text without using an empirical combination. With the seamless integration of recurrent transverse and longitudinal offset connection, our method explores context information instead of predicting points independently, resulting in smoother and more accurate detection. Our approach is designed as a universal method, meaning it can be trained using rectangular or quadrilateral bounding boxes, requiring no extra effort. Experimental results on the CTW1500 dataset and Total-text demonstrated that our method with only a light backbone can outperform stateof-the-art methods by a large margin. Our method also achieved state-of-the-art performance on the MSRA-TD500 dataset, demonstrating its promising generalization ability. Code, datasets, and label-tool are available at https://github.com/Yuliang-Liu/Curve-Text-Detector. (C) 2019 The Authors. Published by Elsevier Ltd.

The Presence and Profile of Neurological Conditions and Associated Psychiatric Comorbidities in U.S. Resettled Refugees: A Retrospective Single Center Study

Refugees are a vulnerable, growing population who confront a myriad of traumas leading to their forced migration. Although psychiatric illnesses of resettled refugees are well-documented, there is a paucity of studies characterizing their neurological disease profiles. This study aimed to characterize the frequency and range of neurological disorders in a sample of resettled refugees receiving care at a community health center in Massachusetts, U.S.A. We performed a retrospective medical record review of adult (≥ 18 years) resettled refugees between May 2001 and December 2012 at a community health center in Northeast Massachusetts. Sociodemographic and clinical characteristics pertaining to neuropsychiatric health were collected from medical records using a standardized data extraction tool. Group comparisons between those with and without neurological illness and associated sociodemographic and psychiatric characteristics were evaluated using χ2 and independent samples two-tailed t-tests. In our sample (n = 779), 53.3% (n = 415) were male and 48.8% (n = 380) were from the African continent. The mean age was 33.2 ± 12.4 years (range 18-85). 36.8% were diagnosed with at least one neurological disorder and 18.1% with more than one neurological illness. The most common diagnoses were headaches (28.3%), sleep disorders (11.2%), cognitive impairment/ dementia (5.5%), and head trauma (5.5%). Exploratory analyses revealed that participants with neurological illness were more likely to be older (p < .001), female (p = .002), and diagnosed with co-morbid psychiatric diagnoses (p < .001) than those without neurological illness. Neurological disorders frequently co-occur with psychiatric comorbidities among U.S. resettled adult refugees. Standard refugee health assessments should incorporate screening and treatment for neurological illnesses.

Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts

Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on natural images, we show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets, possibly including new classes. The approach does not include any fine-tuning or cross-domain learning and is therefore efficient and possibly applicable to arbitrary datasets and classes. We investigate several flavors of the proposed approach, some including multi layers perceptron and polyhedral classifiers. Despite its simplicity, our method shows competitive results on a range of publicly available datasets, including paintings (People-Art, IconArt), watercolors, cliparts and comics and allows to quickly learn unseen visual categories.

Group-Wise Registration of Point Sets for Statistical Shape Models

This paper presents a novel, fast, group-wise registration technique based on establishing soft correspondences between groups of point sets. The registration approach is used to create a statistical shape model, capable of learning the shape variations within a training set. The shape model consists of a mean shape and its transformations to all training shapes. We decouple the procedure into two steps: updating the mean shape and registering it to the training shapes. The algorithm alternates between these two steps until convergence. Following the generation of the statistical shape model, we use the soft correspondence approach to register the model to a new observation. We perform extensive experiments on two data sets: lumbar spine and hippocampi. We compare our algorithm to available state-of-the-art group-wise registration algorithms including feature-based and volume-based approaches. We demonstrate improved generalization, specificity and compactness compared to these algorithms.

Overcoming Electrostatic Interaction via Strong Complexation for Highly Selective Reduction of CN- into N2

Limited by the electrostatic interaction, the oxidation reaction of cations at the anode and the reduction reaction of anions at the cathode in the electrocatalytic system nearly cannot be achieved. This study proposes a novel strategy to overcome electrostatic interaction via strong complexation, realizing the electrocatalytic reduction of cyanide (CN- ) at the cathode and then converting the generated reduction products into nitrogen (N2 ) at the anode. Theoretical calculations and experimental results confirm that the polarization of the transition metal oxide cathodes under the electric field causes the strong chemisorption between CN- and cathode, inducing the preferential enrichment of CN- to the cathode. CN- is hydrogenated by atomic hydrogen at the cathode to methylamine/ammonia, which are further oxidized into N2 by free chlorine derived from the anode. This paper provides a new idea for realizing the unconventional and unrealizable reactions in the electrocatalytic system.

Planar thinned array design by hybrid analytical-stochastic optimisation

The integration of analytical strategies and global optimisation techniques is proposed to address the limitations of current almost difference sets (ADSs) methods for thinning planar apertures. Three design problems associated to these limitations are formulated and the customisation of the optimisation operators to exploit the a-priori information provided by ADS sequences is presented. A numerical validation, including full-wave simulations and comparisons with state-of-the-art methods, is illustrated.

Genetic resistance to infections in sheep

This paper considers genetic resistance to infectious disease in sheep, with appropriate comparison with goats, and explores how such variation may be used to assist in disease control. Many studies have attempted to quantify the extent to which host animals differ genetically in their resistance to infection or in the disease side-effects of infection, using either recorded animal pedigrees or information from genetic markers to quantify the genetic variation. Across all livestock species, whenever studies are sufficiently well powered, then genetic variation in disease resistance is usually seen and such evidence is presented here for three infections or diseases of importance to sheep, namely mastitis, foot rot and scrapie. A further class of diseases of importance in most small ruminant production systems, gastrointestinal nematode infections, is outside the scope of this review. Existence of genetic variation implies the opportunity, at least in principle, to select animals for increased resistance, with such selection ideally used as part of an integrated control strategy. For each of the diseases under consideration, evidence for genetic variation is presented, the role of selection as an aid to disease control is outlined and possible side effects of selection in terms of effects in performance, effects on resistance to other diseases and potential parasite/pathogen coevolution risks are considered. In all cases, the conclusion is drawn that selection should work and it should be beneficial, with the main challenge being to define cost effective selection protocols that are attractive to sheep farmers.

Multi-Source Multi-Destination Hybrid Infrastructure-Aided Traffic Aware Routing in V2V/I Networks

The concept of the "connected car " offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs), the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HI-TAR) that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and similar to 40% more than the state-of-the-art.

Digital camera identification by fingerprint's compact representation

In this paper we deal with the issue of digital camera identification (DCI) based on images. This area matches the digital forensics (DF) research. This topic has attracted many researchers and number of algorithms for DCI have been proposed. However, majority of them focus only on camera identification with high accuracy without taking into account the speed of image processing. In this paper we propose an effective algorithm for much faster camera identification than state-of-the-art algorithms. Experimental evaluation conducted on two large image datasets including almost 14.000 images confirms that the proposed algorithm achieves high classification accuracy of 97 [%] in much shorter time compared with state-of-the-art algorithms which obtained 92.0 - 96.0 [%]. We also perform a statistical analysis of obtained results which confirms their reliability.

A Screening of Antimalarials Extends the Range of Known Escherichia coli AcrB Efflux Substrates and Reveals Two Candidates with Antimicrobial Drug-Enhancing Activity

Efflux by resistance nodulation cell division transporters, such as AcrAB-TolC in Escherichia coli, substantially contributes to the development of Gram-negative multidrug resistance. Therefore, the finding of compounds that counteract efflux is an urgent goal in the fight against infectious diseases. Previously, an efflux inhibitory activity of the antimalarials mefloquine and artesunate was reported. In this study, we have investigated further antimalarials regarding efflux by AcrB, the pumping part of AcrAB-TolC, and their drug-enhancing potency in E. coli. We show that 10 of the 24 drugs tested are substrates of the multidrug efflux pump AcrB. Among them, tafenoquine and proguanil, when used at subinhibitory concentrations, caused an at least 4- and up to 24-fold enhancement in susceptibility to 6 and 14 antimicrobial agents, respectively. Both antimalarials are able to increase the intracellular accumulation of Hoechst 33342, with proguanil showing similar effectiveness as the efflux inhibitor 1-(1-naphthylmethyl)piperazine. In the case of proguanil, AcrB-dependent efflux inhibition could also be demonstrated in a real-time efflux assay. In addition to presenting new AcrB substrates, our study reveals two previously unknown efflux inhibitors among antimalarials. Particularly proguanil appears as a promising candidate and its chemical scaffold might be further optimized for repurposing as antimicrobial drug enhancer.

Allelic variation in the Arabidopsis TNL CHS3/CSA1 immune receptor pair reveals two functional cell-death regulatory modes

Some plant NLR immune receptors are encoded in head-to-head "sensor-executor" pairs that function together. Alleles of the NLR pair CHS3/CSA1 form three clades. The clade 1 sensor CHS3 contains an integrated domain (ID) with homology to regulatory domains, which is lacking in clades 2 and 3. In this study, we defined two cell-death regulatory modes for CHS3/CSA1 pairs. One is mediated by ID domain on clade 1 CHS3, and the other relies on CHS3/CSA1 pairs from all clades detecting perturbation of an associated pattern-recognition receptor (PRR) co-receptor. Our data support the hypothesis that an ancestral Arabidopsis CHS3/CSA1 pair gained a second recognition specificity and regulatory mechanism through ID acquisition while retaining its original specificity as a "guard" against PRR co-receptor perturbation. This likely comes with a cost, since both ID and non-ID alleles of the pair persist in diverse Arabidopsis populations through balancing selection.

Silver nanoparticles from AgNO3-affinin complex synthesized by an ecofriendly route: chitosan-based electrospun composite production

A novel nanofibrous chitosan-based composite containing affinin and silver nanoparticles is obtained by electrospinning. Silver nanoparticles are synthesized by sunlight photoreduction of the metal complex [Ag-2-(affinin)](NO3)(2) in polymeric solution, via a green one-pot methodology, wherein chitosan and affinin act as reducing, dispersing and stabilizing agent.

Investigating the energy-environmental Kuznets curve under panel quantile regression: a global perspective

Energy is regarded as an engine of economic growth and an important ingredient of human survival and development, but it can lead to deterioration of environmental quality. The study investigates the energy environmental Kuznets curve (EEKC) during the 1990-2017 period for 144 countries using models for total energy, renewable energy, and non-renewable energy consumptions. We employ panel mean and quantile regressions, accounting for individual and distributional heterogeneities. It is found that the EEKC sustains among the higher middle-income countries while it cannot be verified at some lower-income quantiles due to the heterogeneous nature of the different groups of countries. The relationship between economic growth, total energy, and non-renewable energy consumption is positive and non-linear. The quantile estimations revealed mixed (positive and non-linear, inverted U-shape, U-shape, and N-shape) EEKC. The maximum and minimum turning values of GDP per capita for total energy consumption (is 43,201.58 and 89,630.49), for renewable energy consumption (53,535.07 and 89,869.41), and for non-renewable energy consumption (42,188.16 and 89,487.71). Urbanization and population growth had positive impacts on energy consumption while these effects become more significant as moving from low to high-income quantiles. The study implies that while the developed nations can adopt energy-efficient policies without compromising on the growth momentum and environment, this might be not recommended for the developing nations and it would be preferable for these countries to "grow first and clean up later." The study indicates the importance of the developed nations to support the developing countries to achieve economic growth along the EEKC by transferring energy-efficient technologies.

Domain Adaptation for Microscopy Imaging

Electron and light microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While machine learning algorithms can be used to help automate this process, they require training data, which is time-consuming to obtain manually, especially in image stacks. Furthermore, due to changing experimental conditions, successive stacks often exhibit differences that are severe enough to make it difficult to use a classifier trained for a specific one on another. This means that this tedious annotation process has to be repeated for each new stack. In this paper, we present a domain adaptation algorithm that addresses this issue by effectively leveraging labeled examples across different acquisitions and significantly reducing the annotation requirements. Our approach can handle complex, nonlinear image feature transformations and scales to large microscopy datasets that often involve high-dimensional feature spaces and large 3D data volumes. We evaluate our approach on four challenging electron and light microscopy applications that exhibit very different image modalities and where annotation is very costly. Across all applications we achieve a significant improvement over the state-of-the-art machine learning methods and demonstrate our ability to greatly reduce human annotation effort.

Breathing is coupled with voluntary initiation of mental imagery

Previous research has suggested that bodily signals from internal organs are associated with diverse cortical and subcortical processes involved in sensory-motor functions, beyond homeostatic reflexes. For instance, a recent study demonstrated that the preparation and execution of voluntary actions, as well as its underlying neural activity, are coupled with the breathing cycle. In the current study, we investigated whether such breathing-action coupling is limited to voluntary motor action or whether it is also present for mental actions not involving any overt bodily movement. To answer this question, we recorded electroencephalography (EEG), electromyography (EMG), and respiratory signals while participants were conducting a voluntary action paradigm including self-initiated motor execution (ME), motor imagery (MI), and visual imagery (VI) tasks. We observed that the voluntary initiation of ME, MI, and VI are similarly coupled with the respiration phase. In addition, EEG analysis revealed the existence of readiness potential (RP) waveforms in all three tasks (i.e., ME, MI, VI), as well as a coupling between the RP amplitude and the respiratory phase. Our findings show that the voluntary initiation of both imagined and overt action is coupled with respiration, and further suggest that the breathing system is involved in preparatory processes of voluntary action by contributing to the temporal decision of when to initiate the action plan, regardless of whether this culminates in overt movements.

Animal pose estimation: A closer look at the state-of-the-art, existing gaps and opportunities

Over the past few years, research on animal pose estimation in computer vision field has grown in many aspects such as 2D and 3D pose estimation, 3D mesh reconstruction, and behavior prediction. Promoted by deep learning, more and more animal pose estimation tools and animal pose datasets have also been made publicly available. However, compared to human pose estimation, which already has high accuracy and high applicability for complex scenes, animal pose estimation is still at a preliminary stage. The huge domain shift between each species, the scarce datasets, and uncooperative research subjects all pose intractable challenges to the development of robust and accurate animal pose estimation algorithms. In this review paper, we summarize the recent (from 2013 to 2021) work in animal pose estimation from computer vision perspective in order to present the state-of-the-art approaches and highlight the challenges they face in this field. We first categorize the various methods of animal pose estimation and present them according to several keywords. Also, we sort and introduce the released annotated image, video, and 3D models of animal poses as well as a promising substitute for real dataset. We also report the performances of the existing algorithms and visualize their results. Finally, we provide an in-depth analysis of the persisting obstacles in this field based on existing work, and offer potential solutions.

Isolation of a diphenylamine-degrading bacterium and characterization of its metabolic capacities, bioremediation and bioaugmentation potential

The antioxidant diphenylamine (DPA) is used in fruit-packaging plants for the control of the physiological disorder apple scald. Its use results in the production of DPA-contaminated wastewater which should be treated before finally discharged. Biological treatment systems using tailored-made microbial inocula with specific catabolic activities comprise an appealing and sustainable solution. This study aimed to isolate DPA-degrading bacteria, identify the metabolic pathway of DPA and evaluate their potential for future implementation in bioremediation and biodepuration applications. A Pseudomonas putida strain named DPA1 able to rapidly degrade and utilize DPA as the sole C and N source was enriched from a DPA-contaminated soil. The isolated strain degraded spillage-level concentrations of DPA in liquid culture (2000 mg L(-1)) and in contaminated soil (1000 mg kg(-1)) and metabolized DPA via the transient formation of aniline and catechol. Further evidence for the bioremediation and biodepuration potential of the P. putida strain DPA1 was provided by its capacity to degrade the post-harvest fungicide ortho-phenylphenol (OPP), concurrently used by the fruit-packaging plants, although at slower rates and DPA in a wide range of pH (4.5-9) and temperatures (15-37 °C). These findings revealed the high potential of the P. putida strain DPA1 for use in future soil bioremediation strategies and/or as start-up inocula in wastewater biodepuration systems.

Estimating the selectivity of LIKE queries using pattern-based histograms

Accurate cost and time estimation of a query is one of the major success indicators for database management systems. SQL allows the expression of flexible queries on text-formatted data. The LIKE operator is used to search for a specified pattern (e.g., LIKE "luck%") in a string database. It is vital to estimate the selectivity of such flexible predicates for the query optimizer to choose an efficient execution plan. In this paper, we study the problem of estimating the selectivity of a LIKE query predicate over a bag of strings. We propose a new type of pattern-based histogram structure to summarize the data distribution in a particular column. More specifically, we first mine sequential patterns over a given string database and then construct a special histogram out of the mined patterns. During query optimization time, pattern-based histograms are exploited to estimate the selectivity of a LIKE predicate. The experimental results on a real dataset from DBLP show that the proposed technique outperforms the state of the art for generic LIKE queries like %s(1)%s(2)%...%s(n) % where s(i) represents one or more characters. What is more, the proposed histogram structure requires more than two orders of magnitude smaller memory space, and the estimation time is almost an order of magnitude less in comparison to the state of the art.

Confined Excitons in Spherical-Like Halide Perovskite Quantum Dots

Quantum dots (QDs) offer unique physical properties and novel application possibilities like single-photon emitters for quantum technologies. While strongly confined III-V and II-VI QDs have been studied extensively, their complex valence band structure often limits clear observations of individual transitions. In recently emerged lead-halide perovskites, band degeneracies are absent around the bandgap reducing the complexity of optical spectra. We show that for spherical-like CsPbBr3 QDs with diameters >6 nm, excitons confine with respect to their center-of-mass motion leading to well-pronounced resonances in their absorption spectra. Optical pumping of the lowest-confined exciton with femtosecond laser pulses not only bleaches all excitons but also reveals a series of distinct induced absorption resonances which we attribute to exciton-to-biexciton transitions and are red-shifted by the biexciton binding energy (∼40 meV). The temporal dynamics of the bleached excitons further support our exciton confinement model. Our study provides the first insight into confined excitons in CsPbBr3 QDs and gives a detailed understanding of their linear and nonlinear optical spectra.

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.

Prospects of Botanical Compounds and Pesticides as Sustainable Management Strategies Against Spodoptera frugiperda

Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) (fall armyworm) is an extremely destructive insect pest that causes crop losses, especially cereal production across the world. Its management is challenged by its high migratory ability, polyphagous nature, high fecundity level, and short life cycle. It has become a serious threat across the globe that requires proactive and coordinated regional and global interventions. Although synthetic insecticides have been widely utilized to control the pest, there are numerous inherent challenges associated with the overreliance and overuse of these chemicals, e.g., toxicity to humans, destruction of natural pest enemies and pollinators, environmental and food contamination, pest resurgence, secondary pest outbreaks, and resistance development. Plant-derived pesticides such as Azadirachta indica, Eucalyptus globulus, Jatropha curcas, Lantana camara, Phytolacca dodecandra, and Piper guineense have been evaluated under laboratory, greenhouse, and field conditions to control S. frugiperda. We are certain that the substantial potential of these plants under field conditions could be enhanced and promoted together with existing plant-based products (registered) for use against S. frugiperda as an alternative in integrated pest management schemes. Therefore, this review highlights challenges and prospects that will help refocus and increase research attention on the development and application of botanical pesticides under field conditions rather than only under laboratory and control conditions to increase the commercialization and adoption rate of this technology across the globe.

Artificial Intelligence in Acute Kidney Injury Prediction

The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.

Occluded thermal face recognition using BoCNN and radial derivative Gaussian feature descriptor

In this work, we propose a Radial derivative Gaussian feature (RDGF) descriptor, a novel handcrafted feature descriptor for disguised thermal face recognition. The feature encoding has been done so that the performance is least affected by noise and works well over challenging datasets. We propose a cascaded framework that combines two modules, namely BoCNN and the RDGF descriptor. The cascading architecture estimates the performance of BoCNN before classification. It also uses a dynamic classifier selector in run time to choose between handcrafted features and the CNN framework to enhance the overall performance. We also propose a thermal face dataset with partial occlusion. We have compared the performance of the RDGF descriptor with state-of-the-art descriptors on the IIIT-Delhi disguised thermal face dataset and our proposed dataset. RDGF exhibits better performance compared to other state-of-the-art descriptors. Our proposed descriptor shows relative increment of 56.84%, 64.92%, 67.25%, 64.03%, 48.06%, and 7.28% on IIIT-D Occluded Thermal Dataset when compared with LBP, LDP, LBDP, LVP, LGHP, and HOG, respectively. A similar enhancement of accuracy has been observed on our proposed dataset as well. An exhaustive comparison based on the performance of the cascaded framework with state-of-the-art CNN models has also been done in a similar fashion. (c) 2023 Elsevier B.V. All rights reserved.

Sketch Recognition: What Lies Ahead?

What is the state-of-the-art in sketch recognition and what are some important future research directions in matching sketches with digital face images? This opinion paper focuses on answering these questions through proposing three important steps that need to move the field forward: (i) create a large, real world forensic sketch database, (ii) develop fundamental understanding of human cognition of processing sketches, and (iii) develop improved algorithms for matching sketches with mugshot photos. (C) 2016 Elsevier B.V. All rights reserved.

Citric acid can enhance the uptake and accumulation of organophosphate esters (OPEs) in Suaeda salsa rhizosphere: Potential for phytoremediation

Bioaccumulation of organophosphate esters (OPEs) by plants has been widely studied, but how root exudates influence their bioavailability to plants is poorly understood. Here, we examined whether root exudates could promote desorption of OPEs, thereby enhancing bioavailability and subsequent accumulation potential. Root exudate components exert great influences on the sorption/desorption isotherms of OPEs in soils, resulting in activating OPEs and enhanced bioavailability. Among root exudate components, citric acid was confirmed to play a crucial role in driving OPEs, with 77.7-90.3 % attribution. Citric acid at rhizosphere levels (0.01-0.4 mM) can successfully reduce OPEs sorption to soils by decreasing electrostatic interaction, ligand exchange, and hydrophobic force. Pot experiments indicated that the addition of citric acid can significantly increase OPEs dissolution and bioaccumulation from the rhizosphere soil to Suaeda salsa. A higher level of citric acid in rhizosphere soil resulted in a higher accumulation of OPEs in Suaeda salsa, which was partly attributed to the enhanced OPEs mobility, and the increased root lengths (13.4-29.0 %) and tip numbers (60.2-120 %), promoting OPEs uptake by roots. Our findings suggest the activation process of OPEs in soils by citric acid at rhizosphere levels and provide insights into designing LMWOAs-enhanced phytoremediation techniques in natural environment.

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Relational diversity in social portfolios predicts well-being

We document a link between the relational diversity of one's social portfolio-the richness and evenness of relationship types across one's social interactions-and well-being. Across four distinct samples, respondents from the United States who completed a preregistered survey (n = 578), respondents to the American Time Use Survey (n = 19,197), respondents to the World Health Organization's Study on Global Aging and Adult Health (n = 10,447), and users of a French mobile application (n = 21,644), specification curve analyses show that the positive relationship between social portfolio diversity and well-being is robust across different metrics of well-being, different categorizations of relationship types, and the inclusion of a wide range of covariates. Over and above people's total amount of social interaction and the diversity of activities they engage in, the relational diversity of their social portfolio is a unique predictor of well-being, both between individuals and within individuals over time.

SATE: Providing Stable and Agile Adaptation in HTTP-Based Video Streaming

Online video streaming service, such as Youtube and Netflix, is emerging as a killer application that constitutes most IP traffic. Users want high-quality video streaming service; however, network band-width cannot keep up user's demand. In the HTTP-based video streaming technology, the video provider divides the video into multiple chunks, encodes it at various bitrates, and stores it in the media server. The client requests a video chunk using an adaptive bitrate (ABR) algorithm, considering the network conditions. While there are many works to maximize user satisfaction, the state-of-the-art ABR algorithms are still vulnerable to rebuffering due to inaccurate estimates and decisions. To address this problem, we first analyze the real-world traces and find several design inferences for an efficient ABR algorithm. Then, we introduce SATE, an ABR algorithm that provides a stable and agile adaptation even in bandwidth constraint and dramatically changing network conditions. We evaluate the performance of SATE in various network settings and demonstrate its efficacy in practice. SATE provides a stable adaptation and dramatically reduced rebuffering while delivering a similar average bitrate compared to the state-of-the-art ABR algorithms.

A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected "things"and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.

Wireless Power Transmission for Power Supply: State of Art

The wireless power supply is motivated by simple and comfortable use of many small electric appliances with low power input. This paper reviews the concepts that are suitable for wireless power transmission with respect to power supply of such appliances in small areas. The categorization of the concepts is made. The efficiency of the concepts is discussed on general base. The reference levels for exposure to electric and magnetic fields are mentioned, and maximal power delivered to an appliance by fulfillment of these levels is considered.

Current Issues within the Perinatal Mental Health Care System in Aichi Prefecture, Japan: A Cross-Sectional Questionnaire Survey

Mental illnesses commonly occur in the reproductive age. This study aimed to identify the issues that exist within the perinatal mental health care system. A cross-sectional survey was conducted in Aichi Prefecture in central Japan. Questionnaires on the situation between 2016 and 2018 were mailed to the head physicians of 128 maternity care units, 21 neonatal intensive care units (NICUs), and 40 assisted reproductive technology (ART) units. A total of 82 (52.6 per 100,000 births) women were admitted to mental health care units during the perinatal period, and 158 (1.0 per 1000 births) neonates born to mothers with mental illness were admitted to NICUs. Approximately 40% of patients were hospitalized in psychiatric hospitals without maternity care units. Eighty-four (71.1%) and 76 (64.4%) maternity care units did not have psychiatrists or social workers, respectively. Moreover, 20-35% of the head physicians in private clinics, general hospitals, and ART units endorsed the discontinuation of psychotropic drug use during pregnancy. However, the corresponding figures were only 5% among those in maternal-fetal centers. Resources for perinatal mental illness might be limited. Perspectives on psychotropic drug use differed based on the type of facilities where the doctors were working.

Toward Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

The success of convolutional neural networks (CNNs) in computer vision applications has been accompanied by a significant increase of computation and memory costs, which prohibits their usage on resource-limited environments, such as mobile systems or embedded devices. To this end, the research of CNN compression has recently become emerging. In this paper, we propose a novel filter pruning scheme, termed structured sparsity regularization (SSR), to simultaneously speed up the computation and reduce the memory overhead of CNNs, which can be well supported by various off-the-shelf deep learning libraries. Concretely, the proposed scheme incorporates two different regularizers of structured sparsity into the original objective function of filter pruning, which fully coordinates the global output and local pruning operations to adaptively prune filters. We further propose an alternative updating with Lagrange multipliers (AULM) scheme to efficiently solve its optimization. AULM follows the principle of alternating direction method of multipliers (ADMM) and alternates between promoting the structured sparsity of CNNs and optimizing the recognition loss, which leads to a very efficient solver (2.5x to the most recent work that directly solves the group sparsity-based regularization). Moreover, by imposing the structured sparsity, the online inference is extremely memory-light since the number of filters and the output feature maps are simultaneously reduced. The proposed scheme has been deployed to a variety of state-of-the-art CNN structures, including LeNet, AlexNet, VGGNet, ResNet, and GoogLeNet, over different data sets. Quantitative results demonstrate that the proposed scheme achieves superior performance over the state-of-the-art methods. We further demonstrate the proposed compression scheme for the task of transfer learning, including domain adaptation and object detection, which also show exciting performance gains over the state-of-the-art filter pruning methods.

Single-cell analysis of skeletal muscle macrophages reveals age-associated functional subpopulations

Tissue-resident macrophages represent a group of highly responsive innate immune cells that acquire diverse functions by polarizing toward distinct subpopulations. The subpopulations of macrophages that reside in skeletal muscle (SKM) and their changes during aging are poorly characterized. By single-cell transcriptomic analysis with unsupervised clustering, we found 11 distinct macrophage clusters in male mouse SKM with enriched gene expression programs linked to reparative, proinflammatory, phagocytic, proliferative, and senescence-associated functions. Using a complementary classification, membrane markers LYVE1 and MHCII identified four macrophage subgroups: LYVE1-/MHCIIhi (M1-like, classically activated), LYVE1+/MHCIIlo (M2-like, alternatively activated), and two new subgroups, LYVE1+/MHCIIhi and LYVE1-/MHCIIlo. Notably, one new subgroup, LYVE1+/MHCIIhi, had traits of both M2 and M1 macrophages, while the other new subgroup, LYVE1-/MHCIIlo, displayed strong phagocytic capacity. Flow cytometric analysis validated the presence of the four macrophage subgroups in SKM and found that LYVE1- macrophages were more abundant than LYVE1+ macrophages in old SKM. A striking increase in proinflammatory markers (S100a8 and S100a9 mRNAs) and senescence-related markers (Gpnmb and Spp1 mRNAs) was evident in macrophage clusters from older mice. In sum, we have identified dynamically polarized SKM macrophages and propose that specific macrophage subpopulations contribute to the proinflammatory and senescent traits of old SKM.

A Coulomb Force Inspired Loss Function for High-Performance Pedestrian Detection

Pedestrian detection has received considerable research interest due to its wide application and has made significant progress along with the development of deep neural networks. However, crowd occlusion still remains a significant challenge to current state-of-the-art pedestrian detectors due to the complication in formulating interactions between occluded instances. Inspired by the Coulomb force, we in this work set each proposal as a single electric charge and define the attractive and repulsive forces to model the interaction between ground truths and assigned proposals. This design is driven by two motivations: the attractive force pulls bounding boxes toward their assigned targets, aggregating them compactly around the ground truths. The repulsive force pushes bounding boxes away from other instances, preventing them from shifting to surrounding pedestrians. With this insight, we propose a novel bounding box regression loss and achieve more robust localization performance in crowded scenes without introducing any computational overhead. Extensive experimental evaluations on the CityPersons and CrowdHuman benchmarks demonstrate consistent state-of-the-art performance.

EOVNet: Earth-Observation Image-Based Vehicle Detection Network

Vehicle detection from earth-observation (EO) image has been attracting remarkable attention for its critical value in a variety of applications. Encouraged by the stunning success of deep learning techniques based on convolutional neural networks (CNNs), which have revolutionized the visual data processing community and obtained the state-of-the-art performance in a variety of classification and recognition tasks on benchmark datasets, we propose a network, called EOVNet (EO image-based vehicle detection network), to bridge the gap between the advanced deep learning research of object detection and the specific task of vehicle detection in EO images. Our network has integrated nearly all advanced techniques including very deep residual networks for feature extraction, feature pyramid to fuse multiscale features, network for proposal generation with feature sharing, and hard example mining. Moreover, our novel designs of probability-based localization and homography-based data augmentation have been investigated, resulting in further improvement of the detection performance. For performance evaluation, we have collected nearly all the representative EO datasets associated with vehicle detection. Extensive experiments on the representative datasets demonstrate that our method outperforms the state-of-the-art object detection approach Faster R-CNN++ (which is based on the Faster R-CNN framework, but with significant improvement) with 5% average precision improvement. The source code will be made available after the review process.

Epilepsy and innate immune system: A possible immunogenic predisposition and related therapeutic implications

Recent experimental studies and pathological analyses of patient brain tissue samples with refractory epilepsy suggest that inflammatory processes and neuroinflammation plays a key-role in the etiopathology of epilepsy and convulsive disorders. These inflammatory processes lead to the secretion of pro-inflammatory cytokines responsible for blood-brain-barrier disruption and involvement of resident immune cells in the inflammation pathway, occurring within the Central Nervous System (CNS). These elements are produced through activation of Toll-Like Receptors (TLRs) by exogenous and endogenous ligands thereby increasing expression of cytokines and co-stimulatory molecules through the activation of TLRs 2, 3, 4, and 9 as reported in murine studies.It has been demonstrated that IL-1β intracellular signaling and cascade is able to alter the neuronal excitability without cell loss. The activation of the IL-1β/ IL-1β R axis is strictly linked to the secretion of the intracellular protein MyD88, which interacts with other cell surface receptors, such as TLR4 during pathogenic recognition. Furthermore, TLR-signaling pathways are able to recognize molecules released from damaged tissues, such as damage-associated molecular patterns/proteins (DAMPs). Among these molecules, High-mobility group box-1 (HMGB1) is a component of chromatin that is passively released from necrotic cells and actively released by cells that are subject to profound stress. Moreover, recent studies have described models of epilepsy induced by the administration of bicuculline and kainic acid that highlight the nature of HMGB1-TLR4 interactions, their intracellular signaling pathway as well as their role in ictiogenesis and epileptic recurrence.The aim of our review is to focus on different branches of innate immunity and their role in epilepsy, emphasizing the role of immune related molecules in epileptogenesis and highlighting the research implications for novel therapeutic strategies.

Novel One-Dimensional Sampling Method to Calculate Two-Dimensional Diamond-Shaped Discrete Frequency Distributions

A novel 1-D sampling method is proposed to calculate 2-D diamond-shaped discrete frequency distributions, which are often treated in the harmonic balance method. The proposed method fills up the entire 2-D discrete frequency plane with diamond-shaped rhombic tiles and makes the number of sampling points equal to that of the necessary frequency components.

Low Complexity VLSI Architecture Design Methodology for Wigner Ville Distribution

In this brief, we propose a low complexity VLSI architecture design Methodology for Wigner Ville Distribution (WVD) computation. The proposed methodology performs both auto and cross WVD computations using only the half number of Fast Fourier transform (FFT) computations as opposed to the state of the art methodologies. The FPGA implementation for proposed methodology was performed for 16 bit fixed point and 32 bit single precision floating point numbers on the Xilinx Virtex-7 FPGA (XC7vx485tffg). The proposed methodology saves 49% energy consumption when compared with the state of the art methodology. However it can be noted that the proposed methodology is independent of the VLSI implementation platform and technology node.

Tracking fiducial markers with discriminative correlation filters

In the last few years, squared fiducial markers have become a popular and efficient tool to solve monocular local-ization and tracking problems at a very low cost. Nevertheless, marker detection is affected by noise and blur: small camera movements may cause image blurriness that prevents marker detection. The contribution of this paper is two-fold. First, it proposes a novel approach for estimating the location of markers in images using a set of Discriminative Correlation Filters (DCF). The proposed method outperforms state-of-the-art methods for marker detection and standard DCFs in terms of speed, precision, and sensitivity. Our method is robust to blur and scales very well with image resolution, obtaining more than 200fps in HD im-ages using a single CPU thread. As a second contribution, this paper proposes a method for camera localization with marker maps employing a predictive approach to detect visible markers with high precision, speed, and robustness to blurriness. The method has been compared to the state-of-the-art SLAM methods obtaining, better accuracy, sensitivity, and speed. The proposed approach is publicly available as part of the ArUco library. (c) 2020 Elsevier B.V. All rights reserved.

Recognizing shipbuilding parts using artificial neural networks and Fourier descriptors

A pattern recognition system is described for recognizing shipbuilding parts using artificial neural networks and Fourier descriptors. The system uses shape contour information that is invariant of size, translation, and rotation. Fourier descriptors provide information, and the neural networks make decisions about the shapes. A brief review of the current state of the art is included, and results from testing show that the system distinguished between various shapes and proved to be a valid approach.

Nonrigid Registration of Joint Histograms for Intensity Standardization in Magnetic Resonance Imaging

A major disadvantage of magnetic resonance imaging (MRI) compared to other imaging modalities like computed tomography is the fact that its intensities are not standardized. Our contribution is a novel method for MRI signal intensity standardization of arbitrary MRI scans, so as to create a pulse sequence dependent standard intensity scale. The proposed method is the first approach that uses the properties of all acquired images jointly (e.g., T1- and T2-weighted images). The image properties are stored in multidimensional joint histograms. In order to normalize the probability density function (pdf) of a newly acquired data set, a nonrigid image registration is performed between a reference and the joint histogram of the acquired images. From this matching a nonparametric transformation is obtained, which describes a mapping between the corresponding intensity spaces and subsequently adapts the image properties of the newly acquired series to a given standard. As the proposed intensity standardization is based on the probability density functions of the data sets only, it is independent of spatial coherence or prior segmentations of the reference and current images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The evaluation was done using two different settings. First, MRI head images were used, hence the approach can be compared to state-of-the-art methods. Second, whole body MRI scans were used. For this modality no other normalization algorithm is known in literature. The Jeffrey divergence of the pdfs of the whole body scans was reduced by 45%. All used data sets were acquired during clinical routine and thus included pathologies.

Safety evaluation of the food enzyme β-galactosidase from the non-genetically modified Neobacillus sp. strain AE-LT

The food enzyme β-galactosidase (EC 3.2.1.23) is produced with the non-genetically modified Neobacillus sp. strain AE-LT by Amano Enzyme Inc. The strain is not cytotoxic and does not harbour any known virulence factor or antimicrobial resistance gene. The presence of viable cells of the production strain in the food enzyme could not be excluded, but the likelihood of this being a hazard is considered low. The food enzyme is intended to be used for lactose hydrolysis in milk processing and the manufacture of galacto-oligosaccharides (GOS). The dietary exposure to the food enzyme-total organic solids (TOS) was estimated to be up to 2.971 mg TOS/kg body weight (bw) per day in European populations. Genotoxicity tests did not raise a safety concern. The systemic toxicity was assessed by means of a repeated dose 90-day oral toxicity study in rats. The Panel identified a no observed adverse effect level of 1,223 mg TOS/kg bw per day, the highest dose tested, which when compared with the estimated dietary exposure, results in a margin of exposure of at least 412. A search for similarity of the amino acid sequence of the food enzyme to known allergens was made and no match was found. The Panel considered that, under the intended conditions of use, the risk of allergic reactions by dietary exposure cannot be excluded, but the likelihood for this to occur is low. Based on the data provided, the Panel concluded that this food enzyme does not give rise to safety concerns under the intended conditions of use.

Reconstitution of Human Cytomegalovirus-Specific CD4+ T Cells is Critical for Control of Virus Reactivation in Hematopoietic Stem Cell Transplant Recipients but Does Not Prevent Organ Infection

The relative contribution of human cytomegalovirus (HMCV)-specific CD4(+) and CD8(+) T cells to the control of HCMV infection in hematopoietic stem cell transplant (HSCT) recipients is still controversial. HCMV reactivation and HCMV-specific CD4(+) and CD8(+) T cell reconstitution were monitored for 1 year in 63 HCMV-seropositive patients receiving HSCT. HCMV reactivation was detected in all but 2 patients. In 20 of 63 (31.7%) patients (group 1) HCMV infection resolved spontaneously, whereas 32 of 63 (50.8%) patients (group 2) controlled the infection after a single short-course of pre-emptive therapy and the remaining 9 (14.3%) patients (group 3) suffered from relapsing episodes of HCMV infection, requiring multiple courses of antiviral therapy. The kinetics and magnitude of HCMV-specific CD8(+) T cell reconstitution were comparable among the 3 groups, but HCMV-specific CD4(+) T cells were lower in number in patients requiring antiviral treatment. HCMV-seronegative donors, as well as unrelated donors (receiving antithymocyte globulin) and acute graft-versus-host disease (GVHD) were associated with both delayed HCMV-specific CD4(+) T cell reconstitution and severity of infection. Conversely, these risk factors had no impact on HCMV-specific CD8(+) T cells. Eight patients with previous GVHD suffered from HCMV gastrointestinal disease, although in the presence of HCMV-specific CD4(+) and CD8(+) systemic immunity and undetectable HCMV DNA in blood. Reconstitution of systemic HCMV-specific CD4(+) T cell immunity is required for control of HCMV reactivation in adult HSCT recipients, but it may not be sufficient to prevent late-onset organ localization in patients with GVHD. HCMV-specific CD8(+) T cells contribute to control of HCMV infection, but only after HCMV-specific CD4(+) T cell reconstitution.

Early life exposure to chronic unpredictable stress induces anxiety-like behaviors and increases the excitability of cerebellar neurons in zebrafish

Anxiety is a common emotional disorder in children. To understand its underlying mechanisms, chronic unpredictable stress (CUS) has been established as a stress model in zebrafish. By using the tall tank test, the stress response reliability could be improved in adult fish which has not been confirmed in larvae. In addition, the increasing evidences have shown that cerebellum plays important roles in anxiety. Whether CUS will affect cerebellar neuronal activity remains unknown. We found that CUS exposure to larvae (from 10 to 17 days post fertilization) induced anxiety-like behaviors and social cohesion impairments within 1-2 d after CUS, including a prolonged freezing time, an increased time spent at the bottom of tank, an increased thigmotaxis index, and an increased interindividual distance. Our results showed that the four behavioral tests were homogeneous, especially the tall tank test either anxiety-like behaviors or the basal locomotion. Furthermore, we found that CUS enhanced the excitability of cerebellar neurons, as the amplitude, frequency, time to peak and half-width of spontaneous firing significantly decreased, as well as the amplitude of excitatory post-synaptic current when compared with the control group. CUS also activated hyperpolarization-activated cyclic nucleotide-gated and potassium channels of cerebellar neurons. Multiple linear regression analysis showed that the total distance in bottom (tall tank test) was correlated positively with outward Na+-K+ currents (r = 0.848, P = 0.016), and the thigmotaxis index (open field test) correlated with action potential amplitude (r = 0.854, P = 0.030). Altogether, early life CUS transiently induced an anxiety-like behavior which could be more accurately assessed by combining the tall tank test with other behavior tests in young zebrafish. CUS increased the excitability of cerebellar neurons might provide new targets to treat emotional diseases such as anxiety.

Current and prognostic overview on the strategic exploitation of anaerobic digestion and digestate: A review

The depletion of fossil fuels and increasing demand for energy are encountered by generating renewable biogas. Anaerobic digestion (AD) produces not only biogas, also other value-added products from the digestate using various organic, municipal and industrial wastes which have several benefits like remediating waste, reduces greenhouse gas emissions, renewable energy generation and securing socio-economic status of bio-based industries. This review work critically analyzes the biorefinery approaches on AD process for the production of biogas and digestate, and their direct and indirect utilization. The left-out residue obtained from AD is called 'digestate' which enriched with organic matter, nitrogen, heavy metals and other valuable micronutrients. However, the direct disposal of digestate to the land as fertilizer/landfills creates various environmental issues. Keeping this view, the digestate should be upgraded or transformed into high valued products such as biofertilizer, pyrochar, biodiesel, syngas and soil conditioner that can aid to enrich the soil nutrients and ensures the safe environment as well. In this context, the present review focused to illustrate the current techniques and different strategic exploitations on AD proper management of digestate products for storage and further applications. Such a technology transfer provides a proven strategic mechanism towards the enhancement of the sustainability of bio-based industries, attaining the energy demand, safest waste management, protection of environment and reduces the socio-economic issues of the industrial sector.

EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

Ordering-Based Kalman Filter Selective Ensemble for Classification

This paper investigates Kalman Filter-based Heuristic Ensemble (KFHE), which is a new perspective on multi-class ensemble classification with performance significantly better or at least as good as the state-of-the-art algorithms. We prove that the sample weight tuning method used in KFHE is a version of adaptive boosting, and the weight distribution does not change anymore and leads to redundant classifiers when the algorithm iterates enough times. This motivates us to select a sub-ensemble to alleviate the redundancy and improve the performance of the ensemble. An Ordering-based Kalman Filter Selective Ensemble (OKFSE) is proposed in this paper to select a sub-ensemble using the margin distance minimization approach. We demonstrate the effectiveness and robustness of OKFSE through extensive experiments on 20 real-world UCI datasets, and the statistical test shows that OKFSE significantly outperforms the state-of-the-art KFHE and clustering-based pruning methods on these datasets with 5% and 10% class label noise.

State of the art of numerical modeling for induction processes

Purpose - To provide a selective bibliography for researchers and graduate students who have an interest in induction processes applied to the electromagnetic processing of materials. Design/methodology/approach - The objective is to provide references that identify seminal, early work, and references that represent the current state of the art. References are listed in categories that cover the broad range of induction modeling and application issues. Findings - A brief over-view of the key areas in induction processing of materials is provided, but greater emphasis and space is devoted to the references provided. Research limitations/implications - The middle years of each topic area are not covered. Practical implications - A very comprehensive coverage of material is provided to those with an interest in induction processing of materials. Originality/value - This paper fulfils an identified information/resources need.

A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.

Natech risk and management: an assessment of the state of the art

The present state-of-the-art for natech risk and management is discussed. Examples of recent natechs include catastrophic oil spills associated with Hurricane Katrina and hazardous chemical releases in Europe during the heavy floods of 2002. Natechs create difficult challenges for emergency responders due to the geographical extent of the natural disaster, the likelihood of simultaneous releases, emergency personnel being preoccupied with response to the natural disaster, mitigation measures failing due to the effects of the natural disaster, and others. Recovery from natechs may be much more difficult than for "normal" chemical accidents, as the economic and social conditions of the industrial facility and the surrounding community may have been drastically altered by the natural disaster. Potential safeguards against natechs include adoption of stricter design criteria, chemical process safeguards, community land use planning, disaster mitigation and response planning, and sustainable industrial processes, but these safeguards are only sporadically applied. Ultimately, the public must engage in a comprehensive discussion of acceptable risks for natechs.

Thinking the Future of Agricultural Worker Health on a Warming Planet and an Automating Farm

Over the last 20 years, earth's increasing surface temperature has dramatically altered local climates and risks associated with agricultural work. In parallel, increasing automation has continued to be a hallmark of innovation in agriculture, promising to lower the economic and health externalities of labor in food production by reducing worker demand and hazardous exposure. However, many of these automations neither eliminate labor nor ameliorate climate change pressures on farms. As a result of the confluence between automation and environmental change, empirical studies into the social determinants of agricultural health and safety in rapidly automating industries impacted by local effects of climate change are essential for a responsive agricultural health and safety science. In this commentary, I suggest that looking outside of our disciplinary boundaries to the lessons learned from rural studies (RS), environmental social science (ESS), and science and technology studies (STS) can lend useful theoretical framing for the development of new research trajectories in the areas of automation and climate change as they impact agricultural health and safety.

Baseline water quality of the Gold Coast Broadwater, southern Moreton Bay (Australia)

This study establishes baseline water quality characteristics for the Gold Coast Broadwater, southern Moreton Bay (Australia) utilising routinely monitored parameters between 2016 and 2021, across 18 sites. Combined site mean concentrations of NOx-N, NH3-N and total nitrogen were 11.4 ± 33.4 μg/L, 12.7 ± 27.2 μg/L, and 169 ± 109 μg/L, respectively, whilst PO4-P and total phosphorous were 7.30 ± 5.10 μg/L and 21.7 ± 14.1 μg/L. Additionally, total suspended solids and turbidity combined site means were 6.6 ± 6.0 mg/L and 3.4 ± 2.9 NTU, respectively. During high rainfall periods nutrient concentrations increased by up to >200-, >150-, 15-, 12- and >12-fold for NOx-N, NH3-N, TN, PO4-P and TP, respectively, compared to quiescent conditions. Furthermore, TSS and NTU values increased by up to 15- and 40-fold during periods of measured rainfall compared to quiescent conditions.

Context Embedding Based on Bi-LSTM in Semi-Supervised Biomedical Word Sense Disambiguation

Word sense disambiguation (WSD) is a basic task of natural language processing (NLP) and its purpose to choose the correct sense of an ambiguous word according to its context. In biomedical WSD, recent research has used context embeddings built by concatenating or averaging word embeddings to represent the sense of a context. These simple linear operations on neighbor words ignore the information about the sequence and may cause their models to be flawed in semantic representation. In this paper, we present a novel language model based on Bi-LSTM to embed an entire sentential context in continuous space by taking account of word order. We demonstrate that our language model can generate high-quality context representations in an unsupervised manner. Unlike the previous work that directly predicts the word senses, our model classifies a word in a context by building sense embeddings and this helps us set a new state-of-the-art result (macro/micro average) on both MSH and NLM datasets. In addition, with the same language model, we propose semi-supervised learning based on label propagation (LP) to reduce the dependence on biomedical data. The results show that this method can nearly approach the state-of-the-art results produced by our Bi-LSTM when reducing the labeled training data.

Design and Characterization of Hyperpolarized 15N-BBCP as a H2O2-Sensing Probe

Hydrogen peroxide (H2O2) is a type of reactive oxygen species that regulates essential biological processes. Despite the central role of H2O2 in pathophysiological states, available molecular probes for assessing H2O2 in vivo are still limited. This work develops hyperpolarized 15N-boronobenzyl-4-cyanopyridinium (15N-BBCP) as a rationally designed molecular probe for detecting H2O2. The 15N-BBCP demonstrated favorable physicochemical and biochemical properties for H2O2 detection and dynamic nuclear polarization, allowing noninvasive detection of H2O2. In particular, 15N-BBCP and the products possessed long spin-lattice relaxation times and spectrally resolvable 15N chemical shift differences. The performance of hyperpolarized 15N-BBCP was demonstrated both in vitro and in vivo with time-resolved 15N-MRS. This study highlights a promising approach to designing a reaction-based 15N-labeled molecular imaging agent for detecting oxidative stress in vivo.

Brain-wide connectivity map of mouse thermosensory cortices

In the thermal system, skin cooling is represented in the primary somatosensory cortex (S1) and the posterior insular cortex (pIC). Whether S1 and pIC are nodes in anatomically separate or overlapping thermal sensorimotor pathways is unclear, as the brain-wide connectivity of the thermal system has not been mapped. We address this using functionally targeted, dual injections of anterograde viruses or retrograde tracers into the forelimb representation of S1 (fS1) and pIC (fpIC). Our data show that inputs to fS1 and fpIC originate from separate neuronal populations, supporting the existence of parallel input pathways. Outputs from fS1 and fpIC are more widespread than their inputs, sharing a number of cortical and subcortical targets. While, axonal projections were separable, they were more overlapping than the clusters of input cells. In both fS1 and fpIC circuits, there was a high degree of reciprocal connectivity with thalamic and cortical regions, but unidirectional output to the midbrain and hindbrain. Notably, fpIC showed connectivity with regions associated with thermal processing. Together, these data indicate that cutaneous thermal information is routed to the cortex via parallel circuits and is forwarded to overlapping downstream regions for the binding of somatosensory percepts and integration with ongoing behavior.

Does environmental, social, and governance performance mitigate earnings management practices? Evidence from US commercial banks

Environmental, social, and governance (ESG) performance has attracted debates of regulatory bodies and the academic community. Previous studies highlighted the relationship between corporate social responsibility (CSR) disclosure index and earnings management (EM) for non-financial firms. In this paper, we examine the relationship between the ESG performance and EM practices for a sample of US commercial banks over the period 2010-2019. We use two proxies for earnings management: abnormal loan loss provisions (ALLP) and EM to meet the threshold of reporting small positive profit or avoiding losses (SPOS). Consistent with the transparent financial reporting hypothesis, we find that banks reporting higher ESG performance are less likely engaged in income-increasing practice through ALLP. However, no evidence supports that ESG score mitigates EM through loss avoidance. Furthermore, we disaggregate the ESG score into its main three components: environmental, social, and governance. Our findings show that the governance pillar effectively mitigates EM practice under its two proxies. Specifically, the social pillar also seems to be an efficient constraint of banks' EM through income-increasing abnormal loan loss provisions and loss avoidance activity. However, no supporting evidence of a mitigating role for the environmental pillar is provided. Taken together, our results show that, except the environmental pillar, ESG performance score acts as an efficient mitigating tool for EM practices for US banks. Our findings provide a better understanding of banks' earnings management practices. Our findings are helpful for managers when undertaking long-term investment strategies in ESG reporting practices, regulators when issuing new standards, and banks' stakeholders when assessing both the financial and non-financial performance of such entities.

A Case of Pulmonary Nodular Schistosomiasis

Bilharzia is a parasitic infection particularly affecting the digestive tract and urinary tract. Lung involvement is rarely reported. We report a case of pulmonary bilharzioma of nodular type surrounded by ground glass opacities diagnosed on CT-scan and associated with a hepatic nodule, in a 41-year-old woman. The disappearance of the pulmonary nodule under antischistosomal treatment made it possible to make the diagnosis a posteriori without going through an invasive process.

Four decades of experience with carcinoid heart disease: An analysis of 84 patients

Carcinoid heart disease (CHD) is a serious cardiac condition which is caused by elevated serotonin in the systemic circulation, secreted by neuroendocrine tumours (NET). It mostly affects the right-sided heart valves, where it causes fibrotic disturbances and is associated with worse survival. In this study, we describe a large cohort of patients with CHD and provide an insight into their survival over the past decades. All consecutive patients with a serotonin producing NET and CHD referred to the Netherlands Cancer Institute that presented with CHD or developed CHD during their follow up time were included from 1984 until 2021. Patients were divided into three time periods: 1984-2000, 2000-2010 and 2010-2018. Median N-terminal pro B-type natriuretic protein (NT-proBNP) and serum serotonin levels were stratified according to tricuspid regurgitation severity. Kaplan-Meier curves and log rank test were used for visualisation of survival. Cox regression was used for identification of the characteristics associated with disease specific mortality (DSM). A total of 84 patients with CHD were included of whom 49 (58.3%) were male. Median age at NET diagnosis was 62.3 (range 23.9-81.7) years, and median time to development of CHD was 1.1 (range 0-24.2) years. NT-proBNP was significantly higher when more severe tricuspid regurgitation (TR) was present (p = .027). Median survival from CHD diagnosis for 1984-2000, 2000-2010 and 2010-2018 were 1.3 (confidence interval [CI]: 0.9-1.6), 1.9 (CI: 1.2-2.6) and 3.9 (CI: 1.7-6.2) years (p = .025). Valve replacement surgery (VSR) occurred more frequent in later time periods. VSR (hazard ratio [HR] 0.33, p = .005) and NT-proBNP (HR 1.003, 1.00-1.005, p = .036) were significantly associated with DSM. The prognosis of patients with CHD has improved over the past decades, possibly caused by more VSR. NT-proBNP is a valuable biomarker in patients with CHD. Clinical practice should be aimed at timely diagnosis and intervention of CHD.

Severe hypertriglyceridemia in an infant on chronic hemodialysis

Severe hyperlipidemia is a risk factor for cardiovascular disease. Children with chronic kidney disease and end stage renal disease are at risk for development of hyperlipidemia. In this report, we describe a 7-month-old male infant with Denys-Drash syndrome who was found to have a "milky-layer" floating on the deaerator of the hemodialysis machine. Investigations showed severe hypertriglyceridemia of >1000 mg/dl. The patient had been on chronic continuous manual peritoneal dialysis until 6 months of age and recently had been switched to hemodialysis. Management included lowering of caloric intake and addition of medium chain triglyceride with reduction of the serum triglyceride levels to 300-400 mg/dl. Close monitoring of serum lipids and timely intervention is important to prevent serious complications associated with dyslipidemia. Observation of the "milky layer" in the deaerator of the hemodialysis machine may be an interesting visual clue of underlying severe hypertriglyceridemia.

Multifocal cutaneous tuberculosis coexisting with pulmonary tuberculosis

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and it can affect multiple organ systems. Cutaneous TB, a less common type of extrapulmonary TB can coexist with TB of other organs. Here, we describe a case of multifocal cutaneous TB suggestive of two different morphological types with concomitant miliary pulmonary TB.

Multi-Exponential Relaxometry Using l(1)-Regularized Iterative NNLS (MERLIN) With Application to Myelin Water Fraction Imaging

A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art $\ell _{1}$ -regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel $\ell _{2}$ -regularized NNLS (rNNLS) and a multi-voxel extension with spatial priors (rNNLS SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.

A Kernel Classification Framework for Metric Learning

Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LNINN) and information theoretic metric learning (ITNII,), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions is proposed for pairs of doublets or triplets. Then, a kernel classification framework is established to generalize many popular metric learning methods such as LNINN and ITML. The proposed framework can also suggest new metric learning methods, which can he efficiently implemented, interestingly, using the standard support vector machine (SVNI) solvers. Two novel metric learning methods, namely, doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods but with significantly less training time.

Incidence and risk factors for anxiety disorders in young adults: A population-based prospective cohort study

Anxiety disorders are among the most common psychiatric disorders in the general population. Our objective was to describe the cumulative incidence and risk factors of anxiety disorders, including obsessive-compulsive disorder (OCD) and posttraumatic stress disorder (PTSD), in a follow-up of young adults over a five-year period. This is a prospective cohort conducted in two waves. The first took place from 2007 to 2009, in which 1,560 young adults aged between 18 and 24 years were evaluated using the Mini-International Neuropsychiatric Interview (MINI). Subjects were invited to participate in the second wave, which wave took place from 2012 to 2014, where 1,244 young adults were evaluated using the MINI-Plus. Our findings showed a cumulative incidence of 10.9% for any anxiety disorder, 6.5% for generalized anxiety disorder, 6.0% for agoraphobia, 2.0% for OCD, 1.6% for panic disorder, 1.1% for social anxiety and 0.7% for PTSD. Being female and having had a depressive episode were risk factors to develop any anxiety disorder. We observed a high cumulative incidence of anxiety disorders in a population-based sample of young adults. Our data highlights the importance of the early identification of these disorders as this could lead to early illness detection, early illness management and a reduced burden of disease.

Game-Based Virtual Reality System for Upper Limb Rehabilitation After Stroke in a Clinical Environment: Systematic Review and Meta-Analysis

The use of virtual reality (VR) for stroke rehabilitation has been implemented during the last decade. At present, most studies still focus on the effects of VR on upper limb rehabilitation, and few studies have explored VR games, VR system designs, and rehabilitation modes for upper limb rehabilitation. This study aims to (1) evaluate the rehabilitation effect of stroke patients using a game-based VR upper limb rehabilitation system in clinical settings; (2) investigate the impact of custom and commercial VR games on patients in clinical settings; and (3) review VR upper limb rehabilitation modes. The PubMed, ScienceDirect, Scopus, Web of Science, and IEEE Xplore databases were searched, and related literature published through December 2021 was included. A total of 4700 articles were retrieved according to the search strategy. We identified 24 studies, including 793 patients. We conducted a systematic search for randomized controlled trials with adult stroke patients to analyze the effect of game-based VR upper limb rehabilitation systems. A meta-analysis was conducted to compare the effects of upper limb function, hand dexterity, daily living ability, and cognitive function between the experimental group (EG, using VR for upper limb rehabilitation) and control group (CG, receiving conventional rehabilitation, including physical therapy and occupational therapy). We also conducted an analysis of both custom and commercial games. The results of the meta-analysis proved that game-based VR upper limb rehabilitation therapy for cerebral apoplexy is an effective method of rehabilitation in clinical settings and is more effective than traditional rehabilitation in improving patients' upper limb function and hand mobility. Custom games heal better than commercial games. This study only includes nonimmersive device rehabilitation modes due to research constraints and classified them into four categories. The mode of VR games combined with rehabilitation instruments may solve the problem that patients with severe upper limb dysfunction cannot operate games. Whether the use of immersive VR devices and the fun of games will affect patients' rehabilitation motivation and effect is the direction of future research.

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision trees to enable resource-efficient classification on a neural implant. By integrating model compression with probabilistic routing and implementing cost-aware learning, our proposed model could significantly reduce the memory and hardware cost compared to state-of-the-art models, while maintaining the classification accuracy. We trained the resource-efficient oblique tree with power-efficient regularization (ResOT-PE) on three neural classification tasks to evaluate the performance, memory, and hardware requirements. On seizure detection task, we were able to reduce the model size by 3.4x and the feature extraction cost by 14.6x compared to the ensemble of boosted trees, using the intracranial EEG from 10 epilepsy patients. In a second experiment, we tested the ResOT-PE model on tremor detection for Parkinson's disease, using the local field potentials from 12 patients implanted with a deep-brain stimulation (DBS) device. We achieved a comparable classification performance as the state-of-the-art boosted tree ensemble, while reducing the model size and feature extraction cost by 10.6x and 6.8x, respectively. We also tested on a 6-class finger movement detection task using ECoG recordings from 9 subjects, reducing the model size by 17.6x and feature computation cost by 5.1x. The proposed model can enable a low-power and memory-efficient implementation of classifiers for real-time neurological disease detection and motor decoding.

Sustainability and Tourism Marketing: A Bibliometric Analysis of Publications between 1997 and 2020 Using VOSviewer Software

Several studies have empirically explored the association between practices in sustainable tourism and their impact on tourism marketing. However, bibliometric studies that organize the production in this field are still scarce. The objective of this study is thus to provide a bibliometric analysis of research on sustainable practices in tourism related to marketing, identifying the state of the art, trends and other indicators, by monitoring the articles published on the Web of Science (WoS) platform. A sample of 694 materials was obtained. The data were processed and the results graphically illustrated using the VOSviewer software. The study analyzed the simultaneous occurrence of publications by year, keyword trends, cocitations, bibliographic coupling and analysis of coauthorship, countries and institutions, and indicates that the literature on tourism sustainability issues in the field of tourism marketing is growing at a quick pace; merely five papers accounted for more than 2193 citations, but there are several prolific authors. Of the 694 sources included in the review, the most important ones published 40.34% of the papers; Spain is the leading country in this topic. This research provides insight about the state of the art and identifies gaps and research opportunities in sustainability and tourism marketing.

Lateness minimization with Tabu search for job shop scheduling problem with sequence dependent setup times

We tackle the job shop scheduling problem with sequence dependent setup times and maximum lateness minimization by means of a tabu search algorithm. We start by defining a disjunctive model for this problem, which allows us to study some properties of the problem. Using these properties we define a new local search neighborhood structure, which is then incorporated into the proposed tabu search algorithm. To assess the performance of this algorithm, we present the results of an extensive experimental study, including an analysis of the tabu search algorithm under different running conditions and a comparison with the state-of-the-art algorithms. The experiments are performed across two sets of conventional benchmarks with 960 and 17 instances respectively. The results demonstrate that the proposed tabu search algorithm is superior to the state-of-the-art methods both in quality and stability. In particular, our algorithm establishes new best solutions for 817 of the 960 instances of the first set and reaches the best known solutions in 16 of the 17 instances of the second set.

Maximum Isometric and Dynamic Strength of Mixed Martial Arts Athletes According to Weight Class and Competitive Level

Mixed martial arts (MMA) athletes must achieve high strength levels to face the physical demands of an MMA fight. This study compared MMA athletes' maximal isometric and dynamic strength according to the competitive level and weight class. Twenty-one male MMA athletes were divided into lightweight professional (LWP; n = 9), lightweight elite (LWE; n = 4), heavyweight professional (HWP; n = 4), and heavyweight elite (HWE; n = 4). The handgrip and isometric lumbar strength tests assessed the isometric strength, and the one-repetition maximum (1RM) bench press and 4RM leg press the dynamic strength. Univariate ANOVA showed differences between groups in absolute and relative 1RM bench press and absolute isometric lumbar strength. Post hoc tests showed differences in 1RM bench press between HWE and LWE (117.0 +/- 17.8 kg vs. 81.0 +/- 10.0 kg) and HWE and LWP athletes (117.0 +/- 17.8 kg vs. 76.7 +/- 13.7 kg; 1.5 +/- 0.2 kg center dot BW-1 vs. 1.1 +/- 0.2 kg center dot BW-1). In addition, there was a correlation between 1RM bench press and isometric lumbar strength for absolute (r = 0.67) and relative values (r = 0.50). This study showed that the 1RM bench press and isometric lumbar strength were associated and could differentiate MMA athletes according to their competitive level and weight class. Therefore, optimizing the force production in the upper body and lower back seems important in elite and professional MMA athletes.

Reanalysis of unclear CMV status results from buccal swab samples of potential stem cell donors is an efficient donor registry strategy

Many stem cell donor registries determine the cytomegalovirus (CMV) IgG serostatus at donor recruitment as it is an important marker for donor selection in the context of hematopoietic stem cell transplantation. To make sample collection less uncomfortable for the donor, we have developed a method that allows CMV status determination from buccal swab samples, thus avoiding blood drawing. However, the determination fails in some cases which leads to new donors being listed for donor search without CMV status, thus hindering donor searches. In this work, we evaluated the success rate of repeating CMV status analysis from a new swab. Our results show that about 90% of the samples could be successfully determined. Due to the great importance of the CMV status in donor search, we consider the retesting approach to be highly recommended for stem cell donor registries.

Human Endogenous Retroviruses and Toll-Like Receptors

Human endogenous retroviruses (HERVs) are estimated to comprise ∼8% of the entire human genome, but the vast majority of them remain transcriptionally silent in most normal tissues due to accumulated mutations. However, HERVs can be frequently activated and detected in various tissues under certain conditions. Nucleic acids or proteins produced by HERVs can bind to pattern recognition receptors of immune cells or other cells and initiate an innate immune response, which may be involved in some pathogenesis of diseases, especially cancer and autoimmune diseases. In this review, we collect studies of the interaction between HERV elements and Toll-like receptors and attempt to provide an overview of their role in the immunopathological mechanisms of inflammatory and autoimmune diseases.

Buckling considerations and cross-sectional geometry development for topology optimised body in white

This paper will investigate how current state-of-the-art structural optimisation algorithms, with an emphasis on topology optimisation, can be used to rapidly develop lightweight body in white (BIW) concept designs, based on a computer aided design envelope. The optimisation models included in the paper will primarily focus on crashworthiness and roof crush scenarios as specified in the Federal Motor Vehicle Safety Standards (FMVSS) 216 standard. This paper is a continuation of a previously published paper, which investigated the potential effects of recently proposed changes to FMVSS 216 upon BIW mass and architecture using topology optimisation. The paper will investigate the possibilities of including buckling considerations of roof members directly into current state-of-the-art topology optimisation algorithms. This paper will also demonstrate the potential for developing a detailed BIW design including cross-sectional properties based on a styling envelope.

Revisiting AES SBox Composite Field Implementations for FPGAs

Composite fields are used for implementing the advanced encryption standard (AES) SBox when compact and side-channel resistant constructions are required. The prior art has investigated efficient implementations of such SBoxes for application specific integrated circuit (ASIC) platforms. On field programmable gate arrays (FPGAs); however, due to the considerably different structure compared with ASICs, these implementations perform poorly. In this letter, we revisit composite field AES SBox implementations for FPGAs. We show how design choices and optimizations can be made to better suit the granular look-up tables that are present in modern FPGAs. We investigate 2880 SBox constructions and show that about half of them are better than the state-of-the-art composite field implementation. Our best SBox implementation is 18% smaller compared with the state-of-the-art implementation on an FPGA.

Localization of stomatal lineage proteins reveals contrasting planar polarity patterns in Arabidopsis cotyledons

Many plant cells exhibit polarity, revealed by asymmetric localization of specific proteins within each cell.1,2,3,4,5,6 Polarity is typically coordinated between cells across a tissue, raising the question of how coordination is achieved. One hypothesis is that mechanical stresses provide cues.7 This idea gains support from experiments in which cotyledons were mechanically stretched transversely to their midline.8 These previously published results showed that without applied tension, the stomatal lineage cell polarity marker, BREVIS RADIX-LIKE 2 (BRXL2), exhibited no significant excess in the transverse orientation. By contrast, 7 h after stretching, BRXL2 polarity distribution exhibited transverse excess, aligned with the stretch direction. These stretching experiments involved statistical comparisons between snapshots of stretched and unstretched cotyledons, with different specimens being imaged in each case.8 Here, we image the same cotyledon before and after stretching and find no evidence for reorientation of polarity. Instead, statistical analysis shows that cotyledons contain a pre-existing transverse excess in BRXL2 polarity orientation that is not significantly modified by applied tension. The transverse excess reflects BRLX2 being preferentially localized toward the medial side of the cell, nearer to the cotyledon midline, creating a weak medial bias. A second polarity marker, BREAKING OF ASYMMETRY IN THE STOMATAL LINEAGE (BASL), also exhibits weak medial bias in stomatal lineages, whereas ectopic expression of BASL in non-stomatal cells exhibits strong proximal bias, as previously observed in rosette leaves. This proximal bias is also unperturbed by applied tension. Our findings therefore show that cotyledons contain two near-orthogonal coordinated biases in planar polarity: mediolateral and proximodistal.

Band pass filter design against interrupted-sampling repeater jamming based on time-frequency analysis

The interrupted-sampling repeater jamming (ISRJ) is coherent with an emitted signal, and significantly limits radar's ability to detect, track and recognise targets. This study focuses on the research of ISRJ suppression for linear frequency modulation radars. A new band pass filter design method based on time frequency (TF) analysis is proposed. A function named 'max-TF' is constructed from the TF energy distribution of the de-chirped signal, reflecting the changes of the maximum signal component amplitude with respect to time. Based on the 'max-TF' function, jamming-free signal segments are automatically and accurately extracted to generate the filter, which is smoothed subsequently. After filtering, jamming signal peaks in pulse compression results are suppressed while real targets are retained simultaneously. Comparing with the state-of-the-art filtering method, the proposed method has improved jamming suppression ability and extended the feasible scope of signal-to-noise ratio and jamming-to-signal ratio conditions. Simulations have validated the improvements and demonstrated how the parameters affect performance. The average signal to jamming improvement and average radar detection rate of the proposed method is about 7.4 dB and 23% higher than those of the state-of-the-art filtering method, respectively. The direction of further works is inferred.

Removal of Canvas Patterns in Digital Acquisitions of Paintings

We address the removal of canvas artifacts from high-resolution digital photographs and X-ray images of paintings on canvas. Both imaging modalities are common investigative tools in art history and art conservation. Canvas artifacts manifest themselves very differently according to the acquisition modality; they can hamper the visual reading of the painting by art experts, for instance, in preparing a restoration campaign. Computer-aided canvas removal is desirable for restorers when the painting on canvas they are preparing to restore has acquired over the years a much more salient texture. We propose a new algorithm that combines a cartoon-texture decomposition method with adaptive multiscale thresholding in the frequency domain to isolate and suppress the canvas components. To illustrate the strength of the proposed method, we provide various examples, for acquisitions in both imaging modalities, for paintings with different types of canvas and from different periods. The proposed algorithm outperforms previous methods proposed for visual photographs such as morphological component analysis and Wiener filtering and it also works for the digital removal of canvas artifacts in X-ray images.

Deep Relation Learning for Regression and Its Application to Brain Age Estimation

Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative relation," "relative relation," "maximal relation" and "minimal relation." These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).

The general ventilation multipliers calculated by using a standard Near-Field/Far-Field model

In conceptual exposure models, the transmission of pollutants in an imperfectly mixed room is usually described with general ventilation multipliers. This is the approach used in the Advanced REACH Tool (ART) and Stoffenmanager (R) exposure assessment tools. The multipliers used in these tools were reported by Cherrie (1999; http://dx.doi.org/10.1080/104732299302530) and Cherrie et al. (2011; http://dx.doi.org/10.1093/annhyg/mer092) who developed them by positing input values for a standard Near-Field/Far-Field (NF/FF) model and then calculating concentration ratios between NF and FF concentrations. This study revisited the calculations that produce the multipliers used in ART and Stoffenmanager and found that the recalculated general ventilation multipliers were up to 2.8 times (280%) higher than the values reported by Cherrie (1999) and the recalculated NF and FF multipliers for 1-hr exposure were up to 1.2 times (17%) smaller and for 8-hr exposure up to 1.7 times (41%) smaller than the values reported by Cherrie et al. (2011). Considering that Stoffenmanager and the ART are classified as higher-tier regulatory exposure assessment tools, the errors is general ventilation multipliers should not be ignored. We recommend revising the general ventilation multipliers. A better solution is to integrate the NF/FF model to Stoffenmanager and the ART.

Adaptive Wireless Power Transfer and Backscatter Communication for Perpetual Operation of Wireless Brain-Computer Interfaces

Brain-computer interfaces (BCIs) are neural prosthetics that enable closed-loop electrophysiology procedures. These devices are currently used in fundamental neurophysiology research, and they are moving toward clinical viability for neural rehabilitation. State-of-the-art BCI experiments have often been performed using tethered (wired) setups in controlled laboratory settings. Wired tethers simplify power and data interfaces but restrict the duration and types of experiments that are possible, particularly for the study of sensorimotor pathways in freely behaving animals. To eliminate tethers, there is significant ongoing research to develop fully wireless BCIs having wireless uplink of broadband neural recordings and wireless recharging for long-duration deployment, but significant challenges persist. BCIs must deliver complex functionality while complying with tightly coupled constraints in size, weight, power, noise, and biocompatibility. In this article, we provide an overview of recent progress in wireless BCIs and a detailed presentation of two emerging technologies that are advancing the state of the art: ultralow-power wireless backscatter communication and adaptive inductive resonant (AIR) wireless power transfer (WPT).

Garment-based motion capture (GaMoCap): high-density capture of human shape in motion

This paper presents a new motion capture (MoCap) system, the garment-based motion capture system-GaMoCap. The key feature is the use of an easily wearable garment printed with colour-coded pattern and a generic multicamera setup with standard video cameras. The coded pattern allows a high-density distribution of markers per unit of surface (about 40 markers per 100 cm), avoiding markers-swap errors. The high density of markers reconstructed makes possible a simultaneous reconstruction of shape and motion, which gives several concurrent advantages with respect to the state of the art and providing performances comparable with previous marker-based systems. In particular, we provide effective solutions to counter the soft-tissue artefact which is a common problem for garment-based techniques. This effect is reduced using Point Cluster Technique to filter out the points strongly affected by non-rigid motion. Uncertainty of motion estimation has been experimentally quantified by comparing with a state-of-the-art commercial system and numerically predicted by means of a Monte Carlo Method procedure. The experimental evaluation was performed on three different articulated motions: shoulder, knee and hip flexion-extension. The results shows that for the three motion angles estimated with GaMoCap, the system provides comparable accuracies against a commercial VICON system.

Preparation of Highly Bloating Ceramsite from "White Mud" and Oil Shale with Incorporation of Black Cotton Soil

Highly bloating ceramsite has been successfully prepared from forthcoming industrial wastes, the white mud (WM), which is the residue from H2SO4-treated coal fly ash obtained from our previous industrial art. In this art the oil shale (OS) and black cotton soil (BCS) were used as foaming and fluxing agents, respectively. The products were demonstrated to reach two types of ceramsite defined by GB/T17431.2-2010. The best dosages were found at the mass ratio of WM:BCS:OS = 7:7:6. The optimal sintering temperature is 1230 degrees C, and a two-stage sintering strategy was chosen to prepare better qualified ceramsite. The total porosity reaches 52.55%, which affords a low bulk density of 318 kg/m(3). The formation mechanism was indicated to be bloated by OS, and pores were formed by the sealing effect of vitrified BCS. The WM works as scaffolds to provide mechanical strength. The ternary diagram of SiO2-Al2O3-alkaline/alkaline earth was rectified concerning the composition area in preparing super-light-weight ceramsite.

Antimicrobial Activity of Ceftazidime-Avibactam and Comparators Against Fluoroquinolone-Resistant Klebsiella pneumoniae Collected Globally from Antimicrobial Testing Leadership and Surveillance: 2018-2019

This study assessed the in vitro antimicrobial activity of ceftazidime-avibactam (CAZ-AVI) and a panel of comparator agents, including aztreonam, cefepime, ceftazidime, meropenem, imipenem, colistin, piperacillin-tazobactam, and tigecycline against isolates of fluoroquinolone-resistant (FQ-R) Klebsiella pneumoniae collected in 2018 and 2019 from the Antimicrobial Testing Leadership and Surveillance (ATLAS) program. Susceptibility and minimum inhibitory concentration were determined using broth microdilution for all antimicrobial agents by a central reference laboratory according to the Clinical and Laboratory Standards Institute guidelines and European Committee on Antimicrobial Susceptibility Testing guidelines. Of all the K. pneumoniae isolates (n = 10,906), 44.1% (4,814/10,906) were FQ-R. Of these, 71.3% (3,432/4,814) were extended-spectrum β-lactamase (ESBL)-positive, and 10.4% (499/4,814) were CAZ-AVI-resistant. CAZ-AVI showed high susceptibility (>87%) against all the FQ-R K. pneumoniae isolates. However, metallo- β-lactamase-positive isolates showed low susceptibility (3.8%; 18/470) to CAZ-AVI. Among the different geographical regions, CAZ-AVI showed the highest activity against isolates collected from North America (98.2%, 216/220) and lowest against those collected from Asia Pacific (APAC) (81.7%; 882/1,079). Among comparator agents, carbapenems showed a relatively lower susceptibility (<71.5%), while only tigecycline and colistin were active (>85%) across all isolates. In conclusion, CAZ-AVI may be a potential treatment option for FQ-R K. pneumoniae isolates. However, increasing CAZ-AVI resistance among ESBL-positive and metallo-β-lactamase-positive isolates and in isolates from APAC warrants continuous surveillance.

A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots

The state-of-the-art reinforcement learning (RL) techniques have made innumerable advancements in robot control, especially in combination with deep neural networks (DNNs), known as deep reinforcement learning (DRL). In this article, instead of reviewing the theoretical studies on RL, which were almost fully completed several decades ago, we summarize some state-of-the-art techniques added to commonly used RL frameworks for robot control. We mainly review bioinspired robots (BIRs) because they can learn to locomote or produce natural behaviors similar to animals and humans. With the ultimate goal of practical applications in real world, we further narrow our review scope to techniques that could aid in sim-to-real transfer. We categorized these techniques into four groups: 1) use of accurate simulators; 2) use of kinematic and dynamic models; 3) use of hierarchical and distributed controllers; and 4) use of demonstrations. The purposes of these four groups of techniques are to supply general and accurate environments for RL training, improve sampling efficiency, divide and conquer complex motion tasks and redundant robot structures, and acquire natural skills. We found that, by synthetically using these techniques, it is possible to deploy RL on physical BIRs in actuality.

sEMG Onset Detection via Bidirectional Recurrent Neural Networks With Applications to Sports Science

Surface electromyography (sEMG) provides physiological information that can be used in sports science. In many applications, sEMG signal activity, i.e., contractions, needs to be detected in the stream of sensor recordings. During sports exercises, the impact of any collision on the body due to an athlete's movement (e.g., jump) forms an additive noise called motion-induced artifact (MIA) in sEMG recordings. This study proposes a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) to automatically identify sEMG signal activity in measurements that include MIA. The proposed model is compared with the state-of-the-art techniques that are envelope, sample entropy (SampEn), modified adaptive linear energy detector (M-ALED), and adaptive contraction detection (ACD). As hamstring strain injuries (HSIs) are the most frequent and recurring injuries in professional football, this article uses sEMG data of different hamstring exercises performed by first-team players of the Leeds United Football Club. On data recorded using state-of-the-art sensors, the classification accuracy of the proposed solution is 96.73%, while the other methods reach 61.41% (sEMG envelope), 84.95% (SampEn), 58.86% (M-ALED), and 65.54% (ACD).

Tm4sf1-marked Endothelial Subpopulation Is Dysregulated in Pulmonary Arterial Hypertension

The identification and role of endothelial progenitor cells in pulmonary arterial hypertension (PAH) remain controversial. Single-cell omics analysis can shed light on endothelial progenitor cells and their potential contribution to PAH pathobiology. We aim to identify endothelial cells that may have stem/progenitor potential in rat lungs and assess their relevance to PAH. Differential expression, gene set enrichment, cell-cell communication, and trajectory reconstruction analyses were performed on lung endothelial cells from single-cell RNA sequencing of Sugen-hypoxia, monocrotaline, and control rats. Relevance to human PAH was assessed in multiple independent blood and lung transcriptomic data sets. Rat lung endothelial cells were visualized by immunofluorescence in situ, analyzed by flow cytometry, and assessed for tubulogenesis in vitro. A subpopulation of endothelial cells (endothelial arterial type 2 [EA2]) marked by Tm4sf1 (transmembrane 4 L six family member 1), a gene strongly implicated in cancer, harbored a distinct transcriptomic signature enriched for angiogenesis and CXCL12 signaling. Trajectory analysis predicted that EA2 has a less differentiated state compared with other endothelial subpopulations. Analysis of independent data sets revealed that TM4SF1 is downregulated in lungs and endothelial cells from patients and PAH models, is a marker for hematopoietic stem cells, and is upregulated in PAH circulation. TM4SF1+CD31+ rat lung endothelial cells were visualized in distal pulmonary arteries, expressed hematopoietic marker CD45, and formed tubules in coculture with lung fibroblasts. Our study uncovered a novel Tm4sf1-marked subpopulation of rat lung endothelial cells that may have stem/progenitor potential and demonstrated its relevance to PAH. Future studies are warranted to further elucidate the role of EA2 and Tm4sf1 in PAH.

Blockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0

Traditional federated learning (FL) adopts a client-server architecture where FL clients (e.g., IoT edge devices) train a common global model with the help of a centralized orchestrator (cloud server). However, current approaches are moving away from centralized orchestration toward a decentralized one in order to fully adapt FL for a cross-silo configuration with multiple organizations acting as clients. State-of-the-art decentralized FL mechanisms make at least one of the following assumptions: 1) clients are trusted organizations and cannot inject low-quality model updates for aggregation and 2) client local models can be shared with other clients or a third party for verification of low-quality updates. This article proposes a Blockchain-based decentralized framework for scenarios where participatory organizations are believed to be fully capable of injecting low-quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralized FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organizations in proportion to their local model quality updates. The framework is flexible and unlike state-of-the-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use cases: 1) predictive maintenance and 2) product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed framework's utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.

Estimated specific antibody-based true sero-prevalences of canine filariosis in dogs in Central Europe and the UK

Dirofilariosis is a vector-borne disease mainly caused by Dirofilaria immitis and Dirofilaria repens. In contrast to the known endemicity of dirofilariosis in southern and south-eastern Europe, information on the distribution of D. repens in Central-Europe is fragmentary. We tested 8877 serum samples from dogs from Austria, Denmark, Germany, Italy, Lithuania, Poland, Switzerland and the UK using an ELISA detecting filarial-specific antibodies, hypothesising higher occurrence of D. repens. Based on two overlapping frequency distributions, presumed negative samples had a mean optical density (OD) value of 0.097, representing 97.45% of all samples. Presumed positive samples, representing 2.55% of all sera, had a mean OD value of 0.287. Test prevalence based on the calculated cut-off was 3.51% for all sera (4.36% for Austria, 1.94% for Denmark, 1.39% for Germany, 3.37% for Italy, 6.90% for Lithuania, 6.99% for Poland, 0.77% for Switzerland and 0.0% for the UK, respectively). The bimodal distribution, representing overlapping distributions of OD values from positive and negative dogs, enabled the assignment of a probability of true infection status to each dog. Mean probabilities of true infection status across groups, based on the postal codes of origin, allowed us to estimate and map true prevalences. For all countries, except the UK, the true prevalence was lower than the test prevalence. The large number of serum samples and the use of a non-gold standard analytical method allowed us to create a more realistic picture of the distribution of D. repens in Central Europe and the UK.