Miracle-dz/UniFAR-FTUs huggingface dataset:
Bovine Staphylococcus aureus Superantigens Stimulate the Entire T Cell Repertoire of Cattle

Superantigens (SAgs) represent a diverse family of bacterial toxins that induce Vβ-specific T cell proliferation associated with an array of important diseases in humans and animals, including mastitis of dairy cows. However, an understanding of the diversity and distribution of SAg genes among bovine Staphylococcus aureus strains and their role in the pathogenesis of mastitis is lacking. ABSTRACT Superantigens (SAgs) represent a diverse family of bacterial toxins that induce Vβ-specific T cell proliferation associated with an array of important diseases in humans and animals, including mastitis of dairy cows. However, an understanding of the diversity and distribution of SAg genes among bovine Staphylococcus aureus strains and their role in the pathogenesis of mastitis is lacking. Population genomic analysis of 195 bovine S. aureus isolates representing 57 unique sequence types revealed that strains encode 2 to 13 distinct SAgs and that the majority of isolates contain 5 or more SAg genes. A genome-scale analysis of bovine reference strain RF122 revealed a complement of 11 predicted SAg genes, which were all expressed in vitro. Detection of specific antibodies in convalescent cows suggests expression of 7 of 11 SAgs during natural S. aureus infection. We determined the Vβ T cell activation profile for all functional SAgs encoded by RF122, revealing evidence for bovine host-specific activity among the recently identified RF122-encoded SAgs SElY and SElZ. Remarkably, we discovered that some strains have evolved the capacity to stimulate the entire T cell repertoire of cattle through an array of diverse SAgs, suggesting a key role in bovine immune evasion.
A Comparative Study of Magnetic Properties of Large Diameter Co Nanowires and Nanotubes

A comparative study of the magnetic properties of the arrays of Co nanowires and nanotubes with large external diameters (180 nm) has been carried out. The nanowires/nanotubes were grown by electrodeposition into the self-assembled pores of anodic alumina membranes. The experimental study of their magnetic behavior was focused on the angular dependence of hysteresis loops and their parameters. In both nanowire and nanotube arrays, from the analysis of experimental data, effective longitudinal magnetic anisotropy is concluded, which is stronger in the case of the nanotube array. In addition, the extremely small remanence observed for all loops indicates the important role played by magnetostatic interactions. Micromagnetic simulations were first performed considering intrinsic shape and magnetocrystalline anisotropy terms, together with an effective easy-plane anisotropy to account for those magnetostatic interactions. A qualitative agreement between experiments and simulations is found despite the complexity introduced by the intrinsic and extrinsic array properties (i.e., large diameters, grain structure, and array configuration). In addition, simulations were also carried out for individual nanowire/nanotube with a particular emphasis to understand their differences at the remanence, due to pure geometry contribution.
Potential Therapeutic Benefits of Herbs and Supplements in Patients with NAFLD

Our aim is to review the efficacy of various herbs and supplements as a possible therapeutic option in the treatment and/or prevention of nonalcoholic fatty liver disease (NAFLD). We performed a systematic review of medical literature using the PubMed Database by searching the chemical names of many common herbs and supplements with “AND (NAFLD or NASH)”. Studies and medical literature that discussed the roles and usage of herbs and supplements in NAFLD and nonalcoholic steatohepatitis (NASH) from inception until 20 June 2018 were reviewed. Many studies have claimed that the use of various herbs and supplements may improve disease endpoints and outcomes related to NAFLD and/or NASH. Improvement in liver function tests were noted. Amelioration or reduction of lobular inflammation, hepatic steatosis, and fibrosis were also noted. However, well-designed studies demonstrating improved clinical outcomes are lacking. Furthermore, experts remain concerned about the lack of regulation of herbs/supplements and the need for further research on potential adverse effects and herb–drug interactions. In conclusion, preliminary data on several herbs have demonstrated promising antioxidant, anti-inflammatory, anti-apoptotic, and anti-adipogenic properties that may help curtail the progression of NAFLD/NASH. Clinical trials testing the safety and efficacy must be completed before widespread use can be recommended.
Boron inhibits aluminum-induced toxicity to citrus by stimulating antioxidant enzyme activity

Abstract Aluminum (Al) toxicity is a major factor limiting plant productivity. The objective of the present study was to develop the mechanisms of boron (B) alleviating aluminum toxicity in citrus. The results showed that aluminum toxicity severely hampered root elongation. Interestingly, under aluminum exposure, boron supply improved superoxide dismutase activity while reducing peroxidase, catalase and polyphenol oxidase activities. Likewise, the contents of H2O2, lipid peroxidation, protein and proline in roots were markedly decreased by boron application under aluminum exposure. Our results demonstrated that boron could alleviate aluminum toxicity by regulating antioxidant enzyme activities in the roots.
HDAC1/2‐mediated regulation of JNK and ERK phosphorylation in bovine mammary epithelial cells in response to TNF‐α

Bovine mammary epithelial cells (MAC‐Ts) are a common cell line for the study of mammary epithelial inflammation; these cells are used to mechanistically elucidate molecular underpinnings that contribute to bovine mastitis. Bovine mastitis is the most prevalent form of disease in dairy cattle that culminates in annual losses of two billion dollars for the US dairy industry. Thus, there is an urgent need for improved therapeutic strategies. Histone deacetylase (HDAC) inhibitors are efficacious in rodent models of inflammation, yet their role in bovine mammary cells remain unclear. HDACs have traditionally been studied in the regulation of nucleosomal DNA, in which deacetylation of histones impact chromatin accessibility and gene expression. Using MAC‐T cells stimulated with tumor necrosis factor α (TNF‐α) as a model for mammary cell inflammation, we report that inhibition of HDACs1 and 2 (HDAC1/2) attenuated TNF‐α‐mediated inflammatory gene expression. Of note, we report that HDAC1/2‐mediated inflammatory gene expression was partly regulated by c‐Jun N‐terminal kinase (JNK) and extracellular signal‐regulated kinase (ERK) phosphorylation. Here, we report that HDAC1/2 inhibition attenuated JNK and ERK activation and thus inflammatory gene expression. These data suggest that HDACs1 and 2 regulate inflammatory gene expression via canonical (i.e., gene expression) and noncanonical (e.g., signaling dependent) mechanisms. Whereas, further studies using primary cell lines and animal models are needed. Our combined data suggest that HDAC1/2‐specific inhibitors may prove efficacious for the treatment of bovine mastitis.
Performance Level and Cortical Atrophy Modulate the Neural Response to Increasing Working Memory Load in Younger and Older Adults

There is evidence that the neural response to increasing working memory (WM) load is modulated by age and performance level. For a valid interpretation of these effects, however, it is important to understand, whether and how they are related to gray matter atrophy. In the current work, we therefore used functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) to examine the association between age, performance level, spatial WM load-related brain activation and gray matter volume in 18 younger high-performers (YHP), 17 younger low-performers (YLP), 17 older high-performers (OHP), and 18 older low-performers (OLP). In multiple sub regions of the prefrontal cortex (PFC), load-related activation followed a linear trend with increasing activation at increasing load in all experimental groups. Results did not reveal differences between the sub groups. Older adults additionally showed a pattern of increasing activation from low to medium load but stable or even decreasing activation from medium to high load in other sub regions of the PFC (quadratic trend). Quadratic trend related brain activation was higher in older than in younger adults and in OLP compared to OHP. In OLP, quadratic trend related brain activation was negatively correlated with both performance accuracy and prefrontal gray matter volume. The results suggest an efficient upregulation of multiple PFC areas as response to increasing WM load in younger and older adults. Older adults and particularly OLP additionally show dysfunctional response patterns (i.e., enhanced quadratic trend related brain activation compared to younger adults and OHP, respectively) in other PFC clusters being associated with gray matter atrophy.
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation dataset only contains 90 short video clips. To solve this problem, we build a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS). Our dataset contains 4,453 YouTube video clips and 94 object categories. This is by far the largest video object segmentation dataset to our knowledge and has been released at this http URL We further evaluate several existing state-of-the-art video object segmentation algorithms on this dataset which aims to establish baselines for the development of new algorithms in the future.
Online Speech Translation System for Tamil

In this paper, we present an application, which recognizes spoken Tamil utterances and speaks out the recognized text in Tamil through our Tamil text-to-speech (TTS) system. Further, we translate the recognized Tamil text to English using google translate and play it through our English TTS. Our Tamil speech recognition system, which can recognize about 75,000 words, has been trained on a 150-hour transcribed speech corpus. We have trained a deep neural network for the acoustic model and employed tri-gram language models to build our recognition system. Our Thirukkural TTS system performs unit-selection based, concatenative speech synthesis, using 2.5 hours of Tamil spoken utterances transcribed at the phone-level. Our English TTS uses 2.7 hours of phone-transcribed utterances. This is a technology demonstration of a complete web application, which, when perfected, could be used to assist Tamil users in learning English, by speaking in Tamil into the system. The playback of the recognized text from Tamil TTS serves to demonstrate the effectiveness of the Tamil ASR to the majority of the conference registrants (who cannot read the recognized Tamil text.
The Effect of Emojis when interacting with Conversational Interface Assisted Health Coaching System

The recent rise of conversational interfaces have made it possible to integrate this technology into various domains, among which is health. Dialogue systems and conversational agents can bring a lot into healthcare to reduce cost, increase efficiency and provide continuing care, albeit its infancy and complexity about building natural dialogues. However, the design guidelines to design dialogues for conversational agents are usually based on common knowledge, and less frequently on empirical evidence. For example, the use of emojis in conversational agent dialogues is still a debated issue, and the added value of adding such graphical elements is mainly anecdotal. In this work, we present an empirical study comparing users feedback when interacting with chatbot applications that use different dialogue styles, i.e., plain text or text with emoji, when asking different health related questions. The analysis found that when participants had to score an interaction with a chatbot that asks personal questions on their mental wellbeing, they rated the interaction with higher scores with respect to enjoyment, attitude and confidence. Differently, participants rated with lower scores a chatbot that uses emojis when asking information on their physical wellbeing compared to a dialogue with plain text. We believe this work can contribute to the research on integrating conversational agents in the health and wellbeing context and can serve as a guidance in the design and development of interfaces for text-based dialogue systems.
A machine learning approach to generate test oracles

One of the essential activities for quality assurance in software development is the software testing. Studies report that Software Testing is one of the most costly activities in the development process, can reach up to 50 percent of its total cost. One of the great challenges of conducting software testing is related to the automation of a mechanism known as "test oracle". This work presents an approach based on machine learning (ML) for automation of the test oracle mechanism in software. The approach uses historical usage data from an application captured by inserting a capture component into the application under test. These data go through a Knowledge Discovery in Database step and are then used for training to generate an oracle suitable for the application under test. Four experiments were executed with web applications to evaluate the proposed approach. The first and second experiments were performed with a fictitious application, with faults inserted randomly in the first experiment, inserted by a developer in the second one and inserted by mutation tests in third one. The fourth experiment was carried out with a large real application in order to assure the results of the preliminary experiments. The experiments presented indications of the suitability of the approach to the solution of the problem.
Genetic variability of Akhal-Teke horses bred in Italy

Background The Akhal-Teke horse (AKH) is native of the modern Turkmenistan area. It was introduced in Italy from 1991 to 2000 mainly as an endurance horse. This paper characterizes the genetic variability of the whole Italian AKH horse population and evaluates their inbreeding level by analyzing microsatellite markers and mitochondrial D-Loop sequences. Methods Seventeen microsatellite marker loci were genotyped on 95 DNA samples from almost all the AKH horses bred in Italy in the last 20 years. Standard genetic variability measures (Ho, He, FIS) were compared against the same variables published on other eight AKH populations. In addition, 397 bp of mtDNA D-loop region were sequenced on a sub-group of 22 unrelated AKH out of the 95 sampled ones, and on 11 unrelated Arab horses. The haplotypes identified in the Italian population were aligned to sequences of AKH (56), Arab (five), Caspian Pony (13), Przewalskii (two) and Barb (15) horses available in GenBank. The Median Joining Network (MJN), Principal Component Analysis (PCA) and Neighbor-joining (NJ) tree were calculated on the total 126 sequences. Results Nucleic markers showed a high degree of polymorphism (Ho = 0.642; He = 0.649) and a low inbreeding level (FIS = 0.016) in Italian horses, compared to other AKH populations (ranged from −0.103 AKH from Estonia to 0.114 AKH from Czech Republic). High variability was also recorded in the D-Loop region. 11 haplotypes were identified with haplotype diversity (hd), nucleotide diversity (π) and average number of nucleotide differences (k) of 0.938, 0.021 and 6.448, respectively. When all the 126 D-Loop sequences were compared, 51 haplotypes were found, and four were here found only in the Italian AKH horses. The 51 haplotypes were conformed to eight recognized mtDNA haplogroups (A, C, F, G, L, M, P and Q) and confirmed by MJN analysis, Italian horses being assigned to five haplogroups (A, C, G, L and M). Using a PCA approach to the same data, the total haplotypes were grouped into two clusters including A+C+M+P and G+F haplogroups, while L and Q haplogroups remained ungrouped. Finally, the NJ algorithm effectively discretizes only the L haplogroup. All the above data univocally indicate good genetic variability and accurate management of the Akhal-Teke population in Italy.
Translating Guidelines for the Diagnosis and Management of Sports-Related Concussion Into Practice

Sports injuries are a significant clinical and public health concern. There is a growing call to improve the translation of available evidence-based and expert-informed sports injury prevention interventions into sustained use in practice by physicians and others (eg, athletic trainers, coaches, and parents) who care for injured athletes. This article provides a brief overview of the current sport injury prevention implementation literature before focusing specifically on the translation of guidelines (including consensus and position statements) developed to assist physicians and others diagnose and manage athletes with sport-related concussion and the associated return-to-play decisions. The outcomes of more than 20 published studies indicate that physician, athletic trainer, coach, parent, and athlete knowledge, use of, and compliance with sport-related concussion guidelines are limited. More concerted, coordinated, and theory-informed efforts are required to facilitate the widespread dissemination, translation, and implementation of such guidelines. An example is provided of how implementation drivers could be used to inform the development of a comprehensive, multilevel implementation strategy targeting the individual, organizational, and system-level changes necessary to support the translation of available sport-related concussion guidelines in both the clinical and sports settings.
Classification of grasping tasks based on EEG-EMG coherence

This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0.8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.
pywindow: Automated Structural Analysis of Molecular Pores

Structural analysis of molecular pores can yield important information on their behavior in solution and in the solid state. We developed pywindow, a python package that enables the automated analysis of structural features of porous molecular materials, such as molecular cages. Our analysis includes the cavity diameter, number of windows, window diameters, and average molecular diameter. Molecular dynamics trajectories of molecular pores can also be analyzed to explore the influence of flexibility. We present the methodology, validation, and application of pywindow for the analysis of molecular pores, metal–organic polyhedra, and some instances of framework materials. pywindow is freely available from github.com/JelfsMaterialsGroup/pywindow.
Structure, Function, and Modulation of γ-Aminobutyric Acid Transporter 1 (GAT1) in Neurological Disorders: A Pharmacoinformatic Prospective

γ-Aminobutyric acid (GABA) Transporters (GATs) belong to sodium and chloride dependent-transporter family and are widely expressed throughout the brain. Notably, GAT1 is accountable for sustaining 75% of the synaptic GABA concentration and entails its transport to the GABAA receptors to initiate the receptor-mediated inhibition of post-synaptic neurons. Imbalance in ion homeostasis has been associated with several neurological disorders related to the GABAergic system. However, inhibition of the GABA uptake by these transporters has been accepted as an effective approach to enhance GABAergic inhibitory neurotransmission in the treatment of seizures in epileptic and other neurological disorders. Here, we reviewed computational methodologies including molecular modeling, docking, and molecular dynamic simulations studies to underscore the structure and function of GAT1 in the GABAergic system. Additionally, various SAR and QSAR methodologies have been reviewed to probe the 3D structural features of inhibitors required to modulate GATs activity. Overall, present review provides an overview of crucial role of GAT1 in GABAergic system and its modulation to evade neurological disorders.
A Wide Residual Network for Sentiment Classification

Recurrent neural network (RNN) is a popular deep learning model for sentiment classification. Most RNN models benefit from the power of the depth of the deep learning network. However, training a sufficiently deep RNN using one word-embedding vector has a problem of feature reuse. In addition, deeper neural networks are more difficult to train. To overcome these problems, in this paper, we describe a sentiment classification model that combines a wide word embedding network architecture and a residual RNN. Firstly, more than one word-embedding vectors are pre-trained to obtain a larger feature space; Secondly, extend the RNN to be a deeper architecture, then employ a residual learning to ease the deep training of networks. Finally, experimental evaluations are conducted on two benchmarks, the results of the proposed networks demonstrate a good applicability for the sentiment classification problem.
FareWell and the How of Lifestyle Medicine

The what of Lifestyle Medicine, including a whole foods dietary pattern, has been well established, but the how has remained elusive. How do we apply what we know in a cost-effective and widely accessible manner to prevent, treat, and even reverse chronic disease? Over the decade ahead, we believe the field of Lifestyle Medicine and the people who need it most will benefit from addressing the how. This article summarizes the founding and operational principles of FareWell Inc. - a digital therapeutics company targeting lifestyle-related cardiometabolic diseases. We outline our current use of mobile health technology and artificial intelligence and describe our early clinical experience, business model, and key anticipated challenges.
Biomedical Applications of Silver Nanoparticles: An Up-to-Date Overview

During the past few years, silver nanoparticles (AgNPs) became one of the most investigated and explored nanotechnology-derived nanostructures, given the fact that nanosilver-based materials proved to have interesting, challenging, and promising characteristics suitable for various biomedical applications. Among modern biomedical potential of AgNPs, tremendous interest is oriented toward the therapeutically enhanced personalized healthcare practice. AgNPs proved to have genuine features and impressive potential for the development of novel antimicrobial agents, drug-delivery formulations, detection and diagnosis platforms, biomaterial and medical device coatings, tissue restoration and regeneration materials, complex healthcare condition strategies, and performance-enhanced therapeutic alternatives. Given the impressive biomedical-related potential applications of AgNPs, impressive efforts were undertaken on understanding the intricate mechanisms of their biological interactions and possible toxic effects. Within this review, we focused on the latest data regarding the biomedical use of AgNP-based nanostructures, including aspects related to their potential toxicity, unique physiochemical properties, and biofunctional behaviors, discussing herein the intrinsic anti-inflammatory, antibacterial, antiviral, and antifungal activities of silver-based nanostructures.