Miracle-dz/UniFAR-FTUs huggingface dataset:
Signaling pathways and therapeutic perspectives related to environmental factors associated with multiple sclerosis

Multiple sclerosis (MS) is an immune‐mediated demyelinating disorder of unknown etiology. Both genetic‐susceptibility and environment exposures, including vitamin D deficiency, Epstein‐Barr viral and Herpesvirus (HHV‐6) infections are strongly implicated in the activation of T cells and MS‐pathogenesis. Despite precise knowledge of how these factors could be operating alone or in combination to facilitate and aggravate the disease progression, it is clear that prolonged induction of inflammatory molecules and recruitment of other immune cells by the activated T cells results in demyelination and axonal damage. It is imperative to understand the risk factors associated with MS progression and how these factors contribute to disease pathology. Understanding of the underlying mechanisms of what factors triggers activation of T cells to attack myelin antigen are important to strategize therapeutics and therapies against MS. Current review provides a detailed literature to understand the role of both pathogenic and non‐pathogenic factors on the impact of MS.
The Case for Vigorous Physical Activity in Youth

Although it is widely recognized that regular physical activity is associated with a variety of health-related benefits in youths, the extent to which vigorous physical activity, as opposed to moderate or light physical activity, may be especially beneficial for youths is not completely understood. This review will examine the evidence for the efficacy of vigorous physical activity for promoting the well-being of youths as indicated by body composition, physical fitness, cardiometabolic biomarkers, and cognitive function. Potential caveats associated with the promotion of vigorous physical activity among youths will also be discussed, as will the inclusion of vigorous physical activity in current recommendations by national organizations for physical activity among youths.
Lifestyle Modification for Secondary Stroke Prevention

Of nearly 800 000 strokes that occur annually, 23% are recurrent events. Risk for disability and mortality is higher following a recurrent stroke than following a first-time stroke, which makes secondary stroke prevention a priority. Many risk factors for stroke are modifiable and amenable to improvement through lifestyle modification. Lifestyle modification can be difficult for people with stroke, however, in part because of stroke-related physical and cognitive deficits. Despite these challenges, risk factor management through lifestyle modification is important. This article reviews the multiple cardiovascular and metabolic benefits associated with the modification of several lifestyle behaviors: diet, physical activity, smoking cessation, and alcohol consumption. Health behavior theories and existing lifestyle intervention programs are also reviewed to identify important behavioral and cognitive skills that can be used to facilitate modification of health behaviors, and practical skills and suggestions for health care providers are provided.
TCR: Temporal-CNN for Reviews Based Recommendation System

In recent year, it has become a popular trend that online stores encouraged their users to write review texts for shopping items. Obviously, these collected text-reviews are helpful for understanding item properties and user preferences, as well as improving the quality of recommendation. However, existing works put considerable attentions on the performance of recommendation without using the temporal information, while the customer inclinations are evolving. In this paper, we propose TCR to model user preferences and item properties by using the convolutional neural network (CNN) combined with temporal information. In details, since the item popularity and user preferences are constantly evolving, we then build a time model that to capture the influence of time evolving on the performance of recommendation and integrate the proposed time model to the original CNN recommender. Furthermore, aiming at building an effective model, we carry out the experimental analysis on the influence of four factors (i.e., word vector embedding dimension, word frequency of comment text, the depth and width of CNN model) on the performance of recommender system. Based on the theoretical analysis, we identify the key factors, and use these factors to optimize our TCR model. Finally, we conduct the experiments on the industrial dataset, i.e., Amazon. It demonstrates that our proposed model has achieved better results than the existing models in terms of prediction accuracy.
Active Inverse Reward Design

Reward design, the problem of selecting an appropriate reward function for an AI system, is both critically important, as it encodes the task the system should perform, and challenging, as it requires reasoning about and understanding the agent's environment in detail. As a result, system designers often iterate on the reward function in a trial-and-error process to get their desired behavior. We propose structuring this process as a series of reward design queries, where we actively select the set of reward functions available to the designer. We query with two types of sets: discrete queries, where the system designer chooses from a small set of reward functions, and feature queries, where the system queries the designer for weights on a small subset of features. After each query, we use inverse reward design (IRD) (Hadfield-Menell et al., 2017) to update the distribution over the true reward function from the observed proxy reward function chosen by the designer. Compared to vanilla IRD, we find that our approach not only decreases the uncertainty about the true reward, but also greatly improves performance in unseen environments while only querying for reward functions in a single training environment.
Special Issue: Text Mining and Information Analysis; Retrieving and Clustering Keywords in Neurosurgery Operation Reports Using Text Mining Techniques

Background: To develop a more practical and reasonable classification of surgical procedures, we applied text mining techniques to retrieve and categorize keywords in operation reports. Materials and Methods: Based on neurosurgical operation reports performed in a Taiwan medical center between 2009 and 2012, a corpus containing 3,657 documents was built. A total of 9,906 words were extracted. Initially, we applied term frequency-inverse document frequency (TF-IDF) weighting to automatically select pertinent keywords but the results were unsatisfactory. Then, we manually chose 45 keywords that belong to 3 categories: brain, spine and others. All documents were checked in an automated fashion for the presence of these keywords, producing a binary data matrix, which was used to compute the cosine similarity matrix. Then, we applied 6 variants of agglomerative clustering to build the dendrograms. Results: The document frequencies (DFs) of these 45 keywords ranged from 12 to 1,250, with an average of 444±342. The number of distinctive keywords per document ranged from 0-15, with an average of 5.5±2.5. The similarities between DF vectors are higher between keywords in the same category (brain or spine). The shortest link method and the unweighted pair-group method using the centroid (UPGMC) methods performed best on external and internal evaluation, respectively. Conclusion: The distributions of important keywords in neurosurgery operation reports reveal the localized nature of surgical procedures.
Perlecan/HSPG2: Signaling role of domain IV in chondrocyte clustering with implications for Schwartz‐Jampel Syndrome

Perlecan/heparan sulfate proteoglycan 2 (HSPG2), a large HSPG, is indispensable for the development of musculoskeletal tissues, where it is deposited within the pericellular matrix (PCM) surrounding chondrocytes and disappears nearly completely at the chondro‐osseous junction (COJ) of developing long bones. Destruction of perlecan at the COJ converts an avascular cartilage compartment into one that permits blood vessel infiltration and osteogenesis. Mutations in perlecan are associated with chondrodysplasia with widespread musculoskeletal and joint defects. This study elucidated novel signaling roles of perlecan core protein in endochondral bone formation and chondrocyte behavior. Perlecan subdomains were tested for chondrogenic properties in ATDC5 cells, a model for early chondrogenesis. A region within domain IV of perlecan (HSPG2 IV‐3) was found to promote rapid prechondrocyte clustering. Introduction of the mutation (R3452Q) associated with the human skeletal disorder Schwartz‐Jampel syndrome limited HSPG2 IV‐3‐induced clustering. HSPG2 IV‐3 activity was enhanced when thermally unfolded, likely because of increased exposure of the active motif(s). HSPG2 IV‐3‐induced clustering was accompanied by the deactivation of key components of the focal adhesion complex, FAK and Src, with increased messenger RNA (mRNA) levels of precartilage condensation markers Sox9 and N‐cadherin ( Cdh2), and cartilage PCM components collagen II ( Col2a1) and aggrecan ( Acan). HSPG2 IV‐3 reduced signaling through the ERK pathway, where loss of ERK1/2 phosphorylation coincided with reduced FoxM1 protein levels and increased mRNA levels cyclin‐dependent kinase inhibitor 1C (Cdkn1c) and activating transcription factor 3 ( Atf3), reducing cell proliferation. These findings point to a critical role for perlecan domain IV in cartilage development through triggering chondrocyte condensation.
Towards a Robust Method of Dataset Generation of Malicious Activity on a Windows-Based Operating System for Anomaly-Based HIDS Training

Classical cyber-attack detection methods, based on signatures and rules demonstrate stagnation and inability to fight the zero-day, advancedpersistent-threat and similar attacks, while anomaly-based detection methods, although were exploited for a number of years, are still characterized by huge numbers of false-positives (valid user or application behavior, that has been classified as intrusion) and ability to work in relatively stable conditions. The progress achieved in recent years in the area of deep learning artificial intelligence techniques provide a potential for renewing the research on the topic and for achieving promising results. Anomaly-based intrusion detection systems (IDS) utilize the ability to learn from a training set of legal and malicious actions. In order to train anomaly-based IDS systems enormous amount of data is required. Majority of available datasets used for IDS training are related to the network-level based intrusion detection, while datasets for host-based intrusion detection system (HIDS), which is becoming extremely important, training are not available or incomplete and lack important features. In this article we propose a method for automated system-level anomaly dataset generation that is to be used in further training of artificial intelligence-based HIDS training. Details for method implementation are also presented and test results discussed.
Efficient and Robust Parallel DNN Training through Model Parallelism on Multi-GPU Platform

The training process of Deep Neural Network (DNN) is compute-intensive, often taking days to weeks to train a DNN model. Therefore, parallel execution of DNN training on GPUs is a widely adopted approach to speed up the process nowadays. Due to the implementation simplicity, data parallelism is currently the most commonly used parallelization method. Nonetheless, data parallelism suffers from excessive inter-GPU communication overhead due to frequent weight synchronization among GPUs. Another approach is pipelined model parallelism, which partitions a DNN model among GPUs, and processes multiple mini-batches concurrently. This approach can significantly reduce inter-GPU communication cost compared to data parallelism. However, pipelined model parallelism faces the weight staleness issue; that is, gradients are computed with stale weights, leading to training instability and accuracy loss. In this paper, we present a pipelined model parallel execution method that enables high GPU utilization while maintaining robust training accuracy via a novel weight prediction technique, SpecTrain. Experimental results show that our proposal achieves up to 8.91x speedup compared to data parallelism on a 4-GPU platform while maintaining comparable model accuracy.
Clustering of graph vertex subset via Krylov subspace model reduction

Clustering via graph-Laplacian spectral imbedding is ubiquitous in data science and machine learning. However, it becomes less efficient for large data sets due to two factors. First, computing the partial eigendecomposition of the graph-Laplacian typically requires a large Krylov subspace. Second, after the spectral imbedding is complete, the clustering is typically performed with various relaxations of k-means, which may become prone to getting stuck in local minima and scale poorly in terms of computational cost for large data sets. Here we propose two novel algorithms for spectral clustering of a subset of the graph vertices (target subset) based on the theory of model order reduction. They rely on realizations of a reduced order model (ROM) that accurately approximates the diffusion transfer function of the original graph for inputs and outputs restricted to the target subset. While our focus is limited to this subset, our algorithms produce its clustering that is consistent with the overall structure of the graph. Moreover, working with a small target subset reduces greatly the required dimension of Krylov subspace and allows to exploit the approximations of k-means in the regimes when they are most robust and efficient, as verified by the numerical experiments. There are several uses for our algorithms. First, they can be employed on their own to clusterize a representative subset in cases when the full graph clustering is either infeasible or not required. Second, they may be used for quality control. Third, as they drastically reduce the problem size, they enable the application of more powerful approximations of k-means like those based on semi-definite programming (SDP) instead of the conventional Lloyd's algorithm. Finally, they can be used as building blocks of a divide-and-conquer algorithm for the full graph clustering. The latter will be reported in a separate article.
Application of Object Detection Algorithm in Identification of Rice Weevils and Maize Weevils

Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to the identification of the stored-grain pests. We apply for the first time an object detection model to identify rice weevils and maize weevils, which have always been a challenge in the field of the research of stored-grain pests because of their very similar appearance. To conduct our initial study, we created a pre-training dataset of 4000 images and a object detection dataset of 1600 images. In the experiments, we used Faster R-CNN and R-FCN as object detectors and used VGG16, ResNet101 and Inception-ResNet-v2 as feature extractors. In detail, we pre-trained the object detection models on our pre-training dataset, and fine tuned with our object detection dataset. Finally, we demonstrate that the final object detection model outperforms our baseline and shows a nice detection effect with a high accuracy. It is worth noting that our research will have a revelatory influence on stored-grain pest control and grain storage.
[Optimization of Suitable Drying Processing Methods of Angelica dahurica].

Objective To optimize the suitable drying processing methods of the root of Angelica dahurica by the appearance and the content of active compounds. Methods 19 methods including natural drying in the shade, natural drying in the sun, hot air drying, shortwave infrared radiation drying were studied. HPLC-PDA method was adopted to determine the contents of coumarin compounds. GC-MS with internal standard method was used to determine the content of volatile oil. The appearance of samples was evaluated by the method of composite scores. Results The results of coumarins contents showed that the natural drying in the sun was higher than natural drying in the shade, variable-temperature drying was the best, and the temperature of constant temperature drying was inversely proportional to the contents of coumarins. The results of volatile oils contents showed that variable-temperature drying was the best, and the temperature of constant temperature drying was inversely proportional to the contents of volatile oils. The results of composite scores showed that hot air drying in 50 ℃ was the best, followed by natural drying in the sun, natural drying in the shade and shortwave infrared radiation drying in 50 ℃. Conclusion Therefore,considering the drying time,the appearance,and the content of active compounds,the hot air drying method and hot air machine with temperature and humidity controlled was recommend. The suitable parameters for the machine were that the drying temperature was 50 ℃,and the humidity was 35%. The method could provide the reference for the drying technology standard of roots such as Angelica dahurica.
Neural Allocentric Intuitive Physics Prediction from Real Videos

Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong assumptions about the underlying system, ad-hoc modeling, or annotated datasets, to carry out even simple predictions. To tackle this gap, we propose a new perspective on the problem of learning intuitive physics that is inspired by the spatial memory representation of objects and spaces in human brains, in particular the co-existence of egocentric and allocentric spatial representations. We present a generic framework that learns a layered representation of the physical world, using a cascade of invertible modules. In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture. Then, an allocentric viewpoint transformer removes viewpoint complexity by projecting images to a canonical view. Finally, a novel Recurrent Latent Variation Network (RLVN) architecture learns the dynamics of the objects interacting with the environment and predicts future motion, leveraging the availability of unlimited synthetic simulations. Predicted frames are then projected back to the original camera view and translated back to the real world domain. Experimental results show the ability of the framework to consistently and accurately predict several frames in the future and the ability to adapt to real images.
The Hydrophobin HYTLO1 Secreted by the Biocontrol Fungus Trichoderma longibrachiatum Triggers a NAADP-Mediated Calcium Signalling Pathway in Lotus japonicus

Trichoderma filamentous fungi are increasingly used as biocontrol agents and plant biostimulants. Growing evidence indicates that part of the beneficial effects is mediated by the activity of fungal metabolites on the plant host. We have investigated the mechanism of plant perception of HYTLO1, a hydrophobin abundantly secreted by Trichoderma longibrachiatum, which may play an important role in the early stages of the plant-fungus interaction. Aequorin-expressing Lotus japonicus suspension cell cultures responded to HYTLO1 with a rapid cytosolic Ca2+ increase that dissipated within 30 min, followed by the activation of the defence-related genes MPK3, WRK33, and CP450. The Ca2+-dependence of these gene expression was demonstrated by using the extracellular Ca2+ chelator EGTA and Ned-19, a potent inhibitor of the nicotinic acid adenine dinucleotide phosphate (NAADP) receptor in animal cells, which effectively blocked the HYTLO1-induced Ca2+ elevation. Immunocytochemical analyses showed the localization of the fungal hydrophobin at the plant cell surface, where it forms a protein film covering the plant cell wall. Our data demonstrate the Ca2+-mediated perception by plant cells of a key metabolite secreted by a biocontrol fungus, and provide the first evidence of the involvement of NAADP-gated Ca2+ release in a signalling pathway triggered by a biotic stimulus.
Single mutations in the ε subunit from thermophilic Bacillus PS3 generate a high binding affinity site for ATP

The ε subunit from ATP synthases acts as an ATP sensor in the bacterial cell to prevent ATP hydrolysis and thus the waste of ATP under conditions of low ATP concentration. However, the ATP binding affinities from various bacterial organisms differ markedly, over several orders of magnitude. For example, the ATP synthases from thermophilic Bacillus PS3 and Escherichia coli exhibit affinities of 4 µM and 22 mM, respectively. The recently reported R103A/R115A double mutant of Bacillus PS3 ATP synthase demonstrated an increased binding affinity by two orders of magnitude with respect to the wild type. Here, we used atomic-resolution molecular dynamics simulations to determine the role of the R103A and R115A single mutations. These lead us to predict that both single mutations also cause an increased ATP binding affinity. Evolutionary analysis reveals R103 and R115 substitutions in the ε subunit from other bacillic organisms, leading us to predict they likely have a higher ATP binding affinity than previously expected.
Work Practices and Respiratory Health Status of Appalachian Coal Miners With Progressive Massive Fibrosis

Objective: The aim of this study was to characterize workplace practices and respiratory health among coal miners with large opacities consistent with progressive massive fibrosis (PMF) who received care at a federally funded black lung clinic network in Virginia. Methods: Participants were interviewed about their workplace practices and respiratory health. Medical records were reviewed. Results: Nineteen former coal miners were included. Miners reported cutting rock, working downwind of dust-generating equipment, nonadherence to mine ventilation plans (including dust controls), improper sampling of respirable coal mine dust exposures, working after developing respiratory illness, and suffering from debilitating respiratory symptoms. Conclusion: Consistent themes of suboptimal workplace practices contributing to development of PMF emerged during the interviews. Some of the practices reported were unsafe and unacceptable. Further research is needed to determine the prevalence of these factors and how best to address them.
A Social Recommender System based on Bhattacharyya Coefficient

Recommender systems play a significant role in providing the appropriate data for each user among a huge amount of information. One of the important roles of a recommender system is to predict the preference of each user to some specific data. Some of these systems concentrate on user-item networks that each user rates some items. The main step for item recommendation is to predict the rate of unrated items. Each recommender system utilizes different criteria such as the similarity between users or social relations in the process of rate prediction. As social connections of each user affect his behaviors, it can be a valuable source to use in rate prediction. In this paper, we will provide a new social recommender system which uses Bhattacharyya coefficient in similarity computing to be able to evaluate similarity in sparse data and between users without co-rated items as well as integrating social ties into the rating prediction process.