Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution, organization and expression of genes concerned with anaerobic lactate utilization in human intestinal bacteria

Lactate accumulation in the human gut is linked to a range of deleterious health impacts. However, lactate is consumed and converted to the beneficial short-chain fatty acids butyrate and propionate by indigenous lactate-utilizing bacteria. To better understand the underlying genetic basis for lactate utilization, transcriptomic analyses were performed for two prominent lactate-utilizing species from the human gut, Anaerobutyricum soehngenii and Coprococcus catus , during growth on lactate, hexose sugar or hexose plus lactate. In A. soehngenii L2-7 six genes of the lactate utilization (lct) cluster, including NAD-independent d-lactate dehydrogenase (d-iLDH), were co-ordinately upregulated during growth on equimolar d- and l-lactate (dl-lactate). Upregulated genes included an acyl-CoA dehydrogenase related to butyryl-CoA dehydrogenase, which may play a role in transferring reducing equivalents between reduction of crotonyl-CoA and oxidation of lactate. Genes upregulated in C. catus GD/7 included a six-gene cluster (lap) encoding propionyl CoA-transferase, a putative lactoyl-CoA epimerase, lactoyl-CoA dehydratase and lactate permease, and two unlinked acyl-CoA dehydrogenase genes that are candidates for acryloyl-CoA reductase. A d-iLDH homologue in C. catus is encoded by a separate, partial lct, gene cluster, but not upregulated on lactate. While C. catus converts three mols of dl-lactate via the acrylate pathway to two mols propionate and one mol acetate, some of the acetate can be re-used with additional lactate to produce butyrate. A key regulatory difference is that while glucose partially repressed lct cluster expression in A. soehngenii , there was no repression of lactate-utilization genes by fructose in the non-glucose utilizer C. catus . This suggests that these species could occupy different ecological niches for lactate utilization in the gut, which may be important factors to consider when developing lactate-utilizing bacteria as novel candidate probiotics.
Decoding motor imagery tasks using ESI and hybrid feature CNN

Objective. Brain–computer interface (BCI) based on motor imaging electroencephalogram (MI-EEG) can be useful in a natural interaction system. In this paper, a new framework is proposed to solve the MI-EEG binary classification problem. Approach. Electrophysiological source imaging (ESI) technology is used to solve the influence of volume conduction effect and improve spatial resolution. Continuous wavelet transform and best time of interest (TOI) are combined to extract the optimal discriminant spatial-frequency features. Finally, a convolutional neural network with seven convolution layers is used to classify the features. In addition, we also validated several new data augment methods to solve the problem of small data sets and reduce network over-fitting. Main results. The model achieved an average classification accuracy of 93.2% and 95.4% on the BCI Competition III IVa and high-gamma data sets, which is better than most of the published advanced algorithms. By selecting the best TOI for each subject, the classification accuracy rate increased by about 2%. The effects of four data augment methods on the classification results were also verified. Among them, the noise addition and overlap methods are better than the other two, and the classification accuracy is improved by at least 4%. On the contrary, the rotation and flip data augment methods reduced the classification accuracy. Significance. Decoding MI tasks can benefit from combing the ESI technology and the data augment technology, which is used to solve the problem of low spatial resolution and small samples of EEG signals, respectively. Based on the results, the model proposed has higher accuracy and application potential in the task of MI-EEG binary classification.
Examining student well-being: Development and initial validation of the perceived vulnerability and hardiness scale

Abstract Objectives The primary aim was to assess the factor structure and establish measurement invariance across sex for the Perceived Vulnerability and Hardiness Scale. Participants Sample 1 (N = 377) and Sample 2 (N = 401) were volunteers from six large southern universities. Methods Iterative and collaborative survey-based focus groups were used to create the final version of the PVHS. Results A two-factor model was identified through exploratory and confirmatory factor analyses. The psychological hardiness subscale was found to positively correlate with hardiness and resilience, and negatively correlate with psychosocial functioning, depression, and anxiety. The vulnerability subscale was found to positively, strongly correlate with the anxiety, psychological vulnerability, and psychosocial functioning, and negatively correlate with the hardiness and resilience. Men and women interpreted the PVHS in an equivalent manner Conclusions This brief measure provides researchers, counselors, and administrations a method for general assessment, intervention effectiveness, and evaluation of campus climates.
Linking Pain and Motor Control: Conceptualization of Movement Deficits in Patients with Painful Conditions.

When we experience or expect pain, we move differently. Pain-altered movement strategies, collectively described here as pain-related movement dysfunction (PRMD), may persist well after pain resolves and, ultimately, may result in altered kinematics and kinetics, future reinjury, and disability. Although PRMD may manifest as abnormal movements that are often evident in clinical assessment, the underlying mechanisms are complex, engaging sensory-perceptual, cognitive, psychological, and motor processes. Motor control theories provide a conceptual framework to determine, assess, and target processes that contribute to normal and abnormal movement, and thus are important for physical therapy and rehabilitation practice. Contemporary understanding of motor control has evolved from reflex-based understanding to a more complex task-dependent interaction between cognitive and motor systems, each with distinct neuroanatomic substrates. Though experts have recognized the importance of motor control in the management of painful conditions, there is not a comprehensive framework that explicates the processes engaged in the control of goal-directed actions, particularly in the presence of pain. This Perspective outlines sensory-perceptual, cognitive, psychological, and motor processes in the contemporary model of motor control, describing the neural substrates underlying each process and highlighting how pain and anticipation of pain influence motor control processes and consequently contribute to PRMD. Finally, potential lines of future inquiry-grounded in the contemporary model of motor control-are outlined to advance understanding and to improve the assessment and treatment of PRMD. IMPACT This Perspective proposes that approaching PRMD from a contemporary motor control perspective will uncover key mechanisms, identify treatment targets, inform assessments, and innovate treatments across sensory-perceptual, cognitive, and motor domains, all of which have the potential to improve movement and functional outcomes in patients with painful conditions.
LeafNet: A Tool for Segmenting and Quantifying Stomata and Pavement Cells.

Stomata play important roles in gas and water exchange in leaves. The morphological features of stomata and pavement cells are highly plastic and are regulated during development. However, it is very laborious and time-consuming to collect accurate quantitative data from the leaf surface by manual phenotyping. Here, we introduce LeafNet, a tool that automatically localizes stomata, segments pavement cells (to prepare them for quantification), and reports multiple morphological parameters for a variety of leaf epidermal images, especially bright-field microscopy images. LeafNet employs a hierarchical strategy to identify stomata using a deep convolutional network and then segments pavement cells on stomata-masked images using a region merging method. LeafNet achieved promising performance on test images for quantifying different phenotypes of individual stomata and pavement cells compared with six currently available tools, including StomataCounter, Cellpose, PlantSeg, and PaCeQuant. LeafNet shows great flexibility, and we improved its ability to analyze bright-field images from a broad range of species as well as confocal images using transfer learning. Large-scale images of leaves can be efficiently processed in batch mode and interactively inspected with a graphic user interface or a web server (https://leafnet.whu.edu.cn/). The functionalities of LeafNet could easily be extended and will enhance the efficiency and productivity of leaf phenotyping for many plant biologists.
Simultaneous brain, brainstem, and spinal cord pharmacological-fMRI reveals involvement of an endogenous opioid network in attentional analgesia

Pain perception is decreased by shifting attentional focus away from a threatening event. This attentional analgesia engages parallel descending control pathways from anterior cingulate (ACC) to locus coeruleus, and ACC to periaqueductal grey (PAG) – rostral ventromedial medulla (RVM), indicating possible roles for noradrenergic or opioidergic neuromodulators. To determine which pathway modulates nociceptive activity in humans, we used simultaneous whole brain-spinal cord pharmacological-fMRI (N = 39) across three sessions. Noxious thermal forearm stimulation generated somatotopic-activation of dorsal horn (DH) whose activity correlated with pain report and mirrored attentional pain modulation. Activity in an adjacent cluster reported the interaction between task and noxious stimulus. Effective connectivity analysis revealed that ACC interacts with PAG and RVM to modulate spinal cord activity. Blocking endogenous opioids with Naltrexone impairs attentional analgesia and disrupts RVM-spinal and ACC-PAG connectivity. Noradrenergic augmentation with Reboxetine did not alter attentional analgesia. Cognitive pain modulation involves opioidergic ACC-PAG-RVM descending control which suppresses spinal nociceptive activity.
A dynamic simulation model to simulate shifting transients of off-Highway vehicles

This paper describes one method for modeling a power train-vehicle system, giving special consideration and one method for modeling to the dynamics of a transmission unit having five planetary gear sets in gear shifts . All is directed toward computer Simulation model testing optimizing system design parameters and evaluating power train component capability and especially vehicle shift-feel.
Prevalence of and racial/ethnic differences in sexuality disclosure among men who have sex with men in 23 U.S. cities—National HIV Behavioral Surveillance, 2017

Abstract Sexuality disclosure among men who have sex with men (MSM) is key in access to HIV prevention services. We used weighted 2017 data from National HIV Behavioral Surveillance to investigate prevalence of, and racial/ethnic differences in, sexuality disclosure among MSM. Of 10,753 MSM, 89.4% (95% CI: 88.5–90.3%) had disclosed their sexuality to any non-lesbian, gay, or bisexual (LGB) friends, 85.9% (95% CI: 84.8–87.0%) had disclosed their sexuality to any family members, and 82.8% (95% CI: 81.6–83.9%) had disclosed their sexuality to any health care providers. Although most MSM had disclosed, 23.8% (95% CI: 22.4–25.1%) had not disclosed to at least one of the three groups. Black, Hispanic/Latino, or Asian MSM were less likely than White MSM to have disclosed their sexuality to any non-LGB friends, any family members, or any health care providers, after adjusting for age and region. We found high prevalence of sexuality disclosure among MSM, but racial/ethnic differences persist. Strategies and interventions to promote sexuality disclosure among MSM are needed.
Research on the Influencing Factors of the Range-Shifting Impact on HMT

To decrease the range-shifting impact on hydro-mechanical transmission(HMT),theoretical analysis and test-bed experiments of the range-shifting process on one kind of HMT had been done.It can be concluded that the mismatch of the rotating speeds of the three basic elements of the planetary-gear system is the essential factor leading to the range-shifting impact.The existence of the synchronized range-shifting gear ratio was proven and it is only determined by the mechanical structure of the HMT.The variation of the motor speed and output speed during the under-synchronized and over-synchronized range-shifting were analyzed and investigated with test bench experiments.The results show that,the volumetric efficiency of the hydraulic system will cause a sudden shift of the motor speed and further the impact on the planetary-gear system,the distinctness of the engagement and disengagement characteristics of the brakes is one of the reasons resulting in the interrupt of the power transfer.While the two brakes use a common oil source,the mutual influence of the manipulating hydraulic pressure may cause the power transfer interrupt and torque impact.
Stem Cell Therapies for Cardiac Disease: Which Cell Types Are the Best

Stem cell therapy for the treatment of cardiovascular disease is increasingly being researched during the past few decades. Due to the risk of cardiac transplantation surgery and the nature of cardiac cell lines, the regenerative potential of stem cell therapy is being extremely expected. Popular cell lines are discussed in this study, which includes mesenchymal stem cells, bone marrow-derived mononuclear stem cells, embryonic stem cells, hematopoietic stem cells, endothelial progenitor cells, tissue-specific stem cells, umbilical cord blood stem cells, skeletal myoblast and induced pluripotent stem cells. The purpose of this study is to review the current use of various cell types for stem cell therapy in cardiovascular patients, as mentioned above. Additionally, optimal delivery methods of stem cell therapies are also discussed in patients with and without secondary stroke conditions. Literature from the Internet was searched and primary studies with the cell types of interest were carefully examined and included in the study. Although from current literature in the field stem cell therapies have great potential to be used in cardiovascular patients, more extensive research with a greater number is warranted before widespread application in the clinical setting.
Towards Automatic Green Claim Detection

Companies frequently make claims about positive impacts on the environment, but these claims are not always valid. In this sense, “greenwashing” is used when a company states a false claim about its products and practices being environmentally friendly. It is an important issue and has attracted the attention of policymakers to strengthen consumer law and new companies with the mission of helping brands and consumers to become more eco-friendly. However, manual screening of websites and social networks for sustainable claims (green claims) is time-consuming. Automatic detection of green claims is an underexplored problem from a computer science perspective, and thus, we present the design, training and evaluation of different approaches in this study. Our experiments reveal that although pre-trained models present high performance, they also show sensibility to adversarial attacks, such as character-swap-based methods, which are common in social networks. In order to understand the applicability in a real-world scenario, we also evaluated its generalization performance, which showed a notable performance across different domains.
UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu

This study reports the second shared task named as UrduFake@Fire2021 on identifying fake news detection in Urdu language. This is a binary classification problem in which the task is to classify a given news article into two classes: (i) real news, or (ii) fake news. In this shared task, 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE)registered to participate in the shared task, 18 teams submitted their experimental results and 11 teams submitted their technical reports. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The stochastic gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score.
Characterization of second-order mixing effects in reconstructed cross-spectra of random neural fields

Functional connectivity in electroencephalography (EEG) and magnetoencephalography (MEG) data is commonly assessed by using measures that are insensitive to instantaneously interacting sources and as such would not give rise to false positive interactions caused by instantaneous mixing of true source signals (first-order mixing). Recent studies, however, have drawn attention to the fact that such measures are still susceptible to instantaneous mixing from lagged sources (i.e. second-order mixing) and that this can lead to a large number of false positive interactions. In this study we relate first- and second-order mixing effects on the cross-spectra of reconstructed source activity to the properties of the resolution operators that are used for the reconstruction. We derive two identities that relate first- and second-order mixing effects to the transformation properties of measurement and source configurations and exploit them to establish several basic properties of signal mixing. First, we provide a characterization of the configurations that are maximally and minimally sensitive to second-order mixing. It turns out that second-order mixing effects are maximal when the measurement locations are far apart and the sources coincide with the measurement locations. Second, we provide a description of second-order mixing effects in the vicinity of the measurement locations in terms of the local geometry of the point-spread functions of the resolution operator. Third, we derive a version of Lagrange’s identity for cross-talk functions that establishes the existence of a trade-off between the magnitude of first- and second-order mixing effects. It also shows that, whereas the magnitude of first-order mixing is determined by the inner product of cross-talk functions, the magnitude of second-order mixing is determined by a generalized cross-product of cross-talk functions (the wedge product) which leads to an intuitive geometric understanding of the trade-off. All results are derived within the general framework of random neural fields on cortical manifolds.
Poison frog dietary preference depends on prey type and alkaloid load

The ability to acquire chemical defenses through the diet has evolved across several major taxa. Chemically defended organisms may need to balance chemical defense acquisition and nutritional quality of prey items. However, these dietary preferences and potential trade-offs are rarely considered in the framework of diet-derived defenses. Poison frogs (Family Dendrobatidae) acquire defensive alkaloids from their arthropod diet of ants and mites, although their dietary preferences have never been investigated. We conducted prey preference assays with the Dyeing Poison frog (Dendrobates tinctorius) to test the hypothesis that alkaloid load and prey traits influence frog dietary preferences. We tested size preferences (big versus small) within each of four prey groups (ants, beetles, flies, and fly larvae) and found that frogs preferred interacting with smaller prey items of the fly and beetle groups. Frog taxonomic prey preferences were also tested as we experimentally increased their chemical defense load by feeding frogs decahydroquinoline, an alkaloid compound similar to those naturally found in their diet. Contrary to our expectations, overall preferences did not change during alkaloid consumption, as frogs across groups preferred fly larvae over other prey. Finally, we assessed the protein and lipid content of prey items and found that small ants have the highest lipid content while large fly larvae have the highest protein content. Our results suggest that consideration of toxicity and prey nutritional value are important factors in understanding the evolution of acquired chemical defenses and niche partitioning as a whole.
A-Test Method for Quantifying Structural Risk and Learning Capacity of Supervised Machine Learning Methods

This paper presents an original method for studying the performance of the supervised Machine Learning (ML) methods, the A-Test method. The method offers the possibility of investigating the structural risk as well as the learning capacity of ML methods in a quantitating manner. A-Test provides a powerful validation method for the learning methods with small or medium size of the learning data, where overfitting is regarded as a common problem of learning. Such a condition can occur in many applications of bioinformatics and biomedical engineering in which access to a large dataset is a challengeable task. Performance of the A-Test method is explored by validation of two ML methods, using real datasets of heart sound signals. The datasets comprise of children cases with a normal heart condition as well as 4 pathological cases: aortic stenosis, ventricular septal defect, mitral regurgitation, and pulmonary stenosis. It is observed that the A-Test method provides further comprehensive and more realistic information about the performance of the classification methods as compared to the existing alternatives, the K-fold validation and repeated random sub-sampling.
A Call for a Systematic Analysis of the Environmental Impact of Education Technologies

Educational technologies rely on physical devices and infrastructures which consume energy and natural resources as well as generate wastes. Given the size of the education market, resulting environment impacts of the production and use of these technologies should not be ignored, especially in the light of the ongoing global mobilization around the critical state of our environment. So far, environmental impacts in the design process among the different educational technology actors have been largely overlooked. In order to remedy this, we propose to systematically account for environmental impacts in the design process using a cost benefit approach. A case study of a smart classroom model is then investigated to illustrate the method. Peak power, energy consumption and waste generation are used as indicator to assess environmental loss while learning improvement, teaching improvement and education accessibility are used as indicator to assess educational benefits. Results highlight issues mostly associated to the use of interactive screens in this classroom model.
Deep Neural Network based Minimum Length Scheduling in Wireless Powered Communication Networks

Minimization of schedule length is key in ensuring the delay performance of wireless powered communication networks (WPCNs) demanding strict timing and reliability guarantees. Previous solution methodologies proposed for these wireless networks suffer from high run-time complexity, making it very difficult to solve the problem in real time. This paper considers a run-time efficient deep learning based approach for solving minimum length scheduling problem in a full-duplex WPCN. Leveraging upon the universal approximation capability of neural networks, a multi-output feed forward deep neural network based framework is proposed where inputs are the channel coefficients and outputs are the optimal power, transmission length and schedule of users. Simulation results indicate that the proposed deep learning based approach can very well approximate the true outputs with a percentage error below 1% for different network configurations while maintaining a very low run-time complexity.