Miracle-dz/UniFAR-FTUs huggingface dataset:
Hippocampal Dysfunction Provoked by Mercury Chloride Exposure: Evaluation of Cognitive Impairment, Oxidative Stress, Tissue Injury and Nature of Cell Death

Mercury (Hg) is a highly toxic metal, which can be found in its inorganic form in the environment. This form presents lower liposolubility and lower absorption in the body. In order to elucidate the possible toxicity of inorganic Hg in the hippocampus, we investigated the potential of low doses of mercury chloride (HgCl2) to promote hippocampal dysfunction by employing a chronic exposure model. For this, 56 rats were exposed to HgCl2 (0.375 mg/kg/day) via the oral route for 45 days. After the exposure period, the animals were submitted to the cognitive test of fear memory. The hippocampus was collected for the measurement of total Hg levels, analysis of oxidative stress, and evaluation of cytotoxicity, apoptosis, and tissue injury. It was observed that chronic exposure to inorganic Hg promotes an increase in mercury levels in this region and damage to short- and long-term memory. Furthermore, we found that this exposure model provoked oxidative stress, which led to cytotoxicity and cell death by apoptosis, affecting astrocytes and neurons in the hippocampus. Our study demonstrated that inorganic Hg, even with its low liposolubility, is able to produce deleterious effects in the central nervous system, resulting in cognitive impairment and hippocampal damage when administered for a long time at low doses in rats.
PEMFC power prediction based on deep auto-encoder and LS-SVMR

A hybrid method based on deep auto-encoder and Least Squares Support Vector Machines Regression(LS-SVMR) is proposed in this paper to anticipate failures in proton exchange membrane fuel cell (PEMFC). Deep learning to deal with high-dimensional data and Support Vector Machines (SVM) to classification accuracy are combined. Through training, a Deep Neural Network of Auto-Encoder (DNNAE) is used to reduce the training data without losing essential information and PEMFC power is then predicted by LS-SVMR using the reduced data. Experimental results show that PEMFC power can be predicted with high accuracy using health monitoring data. Compared with LS-SVMR without using DNNAE, the result indicates that a training data size same as 25% original dimension allows to improve the forecasting accuracy.
Does Trade Policy Explain Total Factor Productivity Differences Across Countries

This study examines whether variations in restrictiveness of trade policy on the flow of international trade explain Total Factor Productivity (TFP) differences across countries. The study employs Trade Restrictiveness Indices (TRIs) to measure trade policy. The TRIs are aggregated using data at the tariff line level, which enable the study to overcome the aggregation bias characterizing the commonly used trade policy measures such as average tariff and import-weighted average tariff. TRIs for Non-Tariff Barriers on imports (NTB), import tariffs and export restrictions are used to show the relative restrictiveness of various types of trade policy on TFP. In line with the political economy literature, the trade restrictiveness measure based on NTB is instrumented using past trade shares while identifying the former’s effect on TFP. Using IV regression, the study shows that trade restrictiveness based on NTB explains a significant variation in TFP across countries while trade restrictiveness based on import tariff or export restrictions does not have a significant effect on TFP. Hence, countries should reduce NTBs on their imports to allow a gain in TFP associated with trade. Besides, the findings suggest that countries should substitute the more restrictive and less transparent trade policy, i.e., NTBs with the less restrictive and more transparent trade policy, i.e., Tariffs.
3 4 6 5 7 1 A B C 2 9 8 Social Network Influence Maximization 3 8 A Seeds Selection

Inuence maximization (IM) targets at maximizing the number of users being aware of a product by nding a set of seed users to expose in a social network. Previous IM models mainly focus on optimizing the spread of product consumption, which assumes that all users are potential customers and more exposures lead to beer prot. However, in the real-world scenario, some people may not like the product and may express negative opinions aer consuming, which damage the product reputation and harm the long-term prot. Only a portion of users in the social network, called the target user, is the potential customer that likes the product and will spread positive opinion. In this paper, we consider a problem called AcTive Opinion Maximization (Atom), where the goal is to nd a set of seed users to maximize the overall opinion spread toward a target product in a multi-round campaign. Dierent from previous works, we do not assume the user opinion is known before consumption, but should be derived from user preference data. e ATOM problem has essential applications in viral marketing, such as reputation building and precision advertising. Given its signicance, ATOM problem is profoundly challenging due to the hardness of estimating user opinion in a multi-round campaign. Moreover, the process of opinion estimation and inuence propagation intertwine with each other, which requires the model to consider the two components collectively. We propose an active learning framework called Cone (aCtive OpinioN Estimator) to address above challenges. Experimental results on two real-world datasets demonstrate that Cone improves the total opinion spread in a social network. CCS CONCEPTS •Applied computing →Economics;
ZFP36 RNA-binding proteins restrain T cell activation and anti-viral immunity

Dynamic post-transcriptional control of RNA expression by RNA-binding proteins (RBPs) is critical during immune response. ZFP36 RBPs are prominent inflammatory regulators linked to autoimmunity and cancer, but functions in adaptive immunity are less clear. We used HITS-CLIP to define ZFP36 targets in T-cells, which revealed unanticipated actions in regulating T-cell activation, proliferation, and effector functions. Transcriptome and ribosome profiling showed that ZFP36 represses mRNA target abundance and translation, notably through a novel class of AU-rich sites in coding sequence. Functional studies revealed that ZFP36 regulates early T-cell activation kinetics in a cell autonomous manner, by attenuating activation marker expression, limiting T-cell expansion, and promoting apoptosis. Strikingly, loss of ZFP36 in vivo accelerated T-cell responses to acute viral infection, and enhanced anti-viral immunity. These findings uncover a critical role for ZFP36 RBPs in restraining T-cell expansion and effector functions, and suggest ZFP36 inhibition as a novel strategy to enhance immune-based therapies.
Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras

This paper demonstrates the effectiveness of our customized deep learning based video analytics system in various applications focused on security, safety, customer analytics and process compliance. We describe our video analytics system comprising of Search, Summarize, Statistics and real-time alerting, and outline its building blocks. These building blocks include object detection, tracking, face detection and recognition, human and face sub-attribute analytics. In each case, we demonstrate how custom models trained using data from the deployment scenarios provide considerably superior accuracies than off-the-shelf models. Towards this end, we describe our data processing and model training pipeline, which can train and fine-tune models from videos with a quick turnaround time. Finally, since most of these models are deployed on-site, it is important to have resource constrained models which do not require GPUs. We demonstrate how we custom train resource constrained models and deploy them on embedded devices without significant loss in accuracy. To our knowledge, this is the first work which provides a comprehensive evaluation of different deep learning models on various real-world customer deployment scenarios of surveillance video analytics. By sharing our implementation details and the experiences learned from deploying customized deep learning models for various customers, we hope that customized deep learning based video analytics is widely incorporated in commercial products around the world.
Application of MEMS sensors for the automation of a laboratory stand for the measurement of the flow resistance of porous materials

The paper describes a laboratory stand for the measurement of flow resistance of porous materials. The stand was modernized by introducing MEMS sensors. The measurement procedure was described, together with the data analysis method. The modernization of the stand was verified by comparing the results of the measurements obtained at the stand with the results of the measurements in an impedance tube for two different materials. The modernization of the stand allowed an increase of the accuracy of the airflow resistance measurement.
Human butyrylcholinesterase knock-out equivalent: Potential to assess role in Alzheimer's disease

Butyrylcholinesterase (BChE) is an enzyme which has been shown to be involved in the pathogenesis, treatment and prognosis of Alzheimer’s disease. In its current form, however, evidence is equivocal with all of the associations. Variant forms of the protein exist, where the enzymatic function is lost to varying degrees. We performed in silico evaluation of these variants. Bioinformatics and molecular modeling, based on data from ESTHER database and Protein Data Bank (RCSB), were used for in silico predictions of the structures of the silent variants that involve a single amino acid change. Variants with loss of enzyme activity were evaluated for structural changes near the active site and the thermodynamic stability of the variants was estimated. The results indicated that the loss of activity of the variants can, in most cases, be attributed to structural changes in the active site or to lower thermodynamic stability. Our results showed that the loss of enzyme activity may be due to changes in the active site, oligomerization or loss of structural stability. Individuals with loss of function mutation of BChE can be studied and followed up for their proneness or resistance to cognitive decline with aging.
A Pilot Study Providing Evidence for a Relationship between a Composite Lifestyle Score and Risk of Higher Carotid Intima-Media Thickness: Is There a Link to Oxidative Stress?

Lifestyle behaviours have been closely linked to the progressive cell damage associated with oxidative stress (OS) and the development of cardiovascular disease (CVD). Early detection of lifestyle-linked OS may therefore be useful in the early identification of prodromal disease. To test this hypothesis, this study assessed the relationship between a comprehensive redox balance lifestyle score (RBLS) and carotid intima-media thickness (CIMT), a recognized marker for CVD, and plasma biomarkers of OS. In a cross-sectional study design, 100 apparently healthy middle-aged participants were asked to complete a comprehensive lifestyle questionnaire, followed by DXA scanning, CIMT ultrasonography, and blood collection. The RBLS was composed of lifestyle components with pro- and antioxidant properties with a higher score indicative of lower oxidative activity. Multiple linear regression and logistic regression analysis were performed for statistical analysis. The RBLS was significantly associated with the risk for increased CIMT that was independent of conventional CVD risk factors (χ2(9) = 35.60, P ≤ 0.001). The adjusted model explained 42.4% of the variance in CIMT. Participants with RBLS below the median were at significantly increased risk of higher CIMT compared to participants with RBLS above the median (OR = 3.60, 95% CI: 1.19–10.88, P = 0.023). Significant associations were also observed between the RBLS, plasma total antioxidant capacity (TAC) (r(99) = 0.28, P = 0.006), hydroperoxide (HPX) (rs(99) = −0.28, P = 0.005), TAC/HPX ratio (r(98) = 0.41, P ≤ 0.001), γ-glutamyltransferase (r(97) = −0.23, P = 0.024), uric acid (r(98) = −0.20, P = 0.045), and inflammatory C-reactive protein (rs(97) = −0.25, P = 0.012) and interleukin-1β (r(97) = −0.21, P = 0.040). These findings highlight the importance of identifying the collective influence of lifestyle behaviours on OS activity and its potential to remodel the vascular endothelium.
Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”

LaManna et al. (Reports, 30 June 2017, p. 1389) found higher conspecific negative density dependence in tree communities at lower latitudes, yielding a possible mechanistic explanation for the latitudinal diversity gradient. We show that their results are artifacts of a selective data transformation and a forced zero intercept in their fitted model. A corrected analysis shows no latitudinal trend.
Designing for Motivation, Engagement and Wellbeing in Digital Experience

Research in psychology has shown that both motivation and wellbeing are contingent on the satisfaction of certain psychological needs. Yet, despite a long-standing pursuit in human-computer interaction (HCI) for design strategies that foster sustained engagement, behavior change and wellbeing, the basic psychological needs shown to mediate these outcomes are rarely taken into account. This is possibly due to the lack of a clear model to explain these needs in the context of HCI. Herein we introduce such a model: Motivation, Engagement and Thriving in User Experience (METUX). The model provides a framework grounded in psychological research that can allow HCI researchers and practitioners to form actionable insights with respect to how technology designs support or undermine basic psychological needs, thereby increasing motivation and engagement, and ultimately, improving user wellbeing. We propose that in order to address wellbeing, psychological needs must be considered within five different spheres of analysis including: at the point of technology adoption, during interaction with the interface, as a result of engagement with technology-specific tasks, as part of the technology-supported behavior, and as part of an individual's life overall. These five spheres of experience sit within a sixth, society, which encompasses both direct and collateral effects of technology use as well as non-user experiences. We build this model based on existing evidence for basic psychological need satisfaction, including evidence within the context of the workplace, computer games, and health. We extend and hone these ideas to provide practical advice for designers along with real world examples of how to apply the model to design practice.
Review of Methods for the Evaluation of Human Body Balance

Review of Methods for the Evaluation of Human Body Balance The aim of this review paper is to thoroughly present all main tests used today in the field of body balance/equilibrium assessment and evaluation. After the introduction of some basic biomechanical and movement regulation concepts, a short revision of the metric characteristics that each test should contain is discussed. The latter encompasses validity, objectivity, repeatability, sensitivity, and some other elements that are of crucial importance for the practical use of every assessment. The major part of text is dedicated to the critical research based review of the body balance tests of different levels of technical and other complexity. Pros and cons of the presented assessment methods are discussed. First, the field motor tests and simple clinical tests are presented. Their primary use in school physical education, sports medicine, and rehabilitation is pointed out. Second, laboratory tests for the static balance assessment are described in details, including all the measured parameters, their informational value and limitations. And third, laboratory tests for the evaluation of dynamic balance are presented in an analogue way. In the discussion, we compare different tests through the scope of usefulness, economy, metric characteristics and informational value. The paper closes with a summary of the state-of-the-art on the field of balance and proposals for future research work.
The Progress of Methylation Regulation in Gene Expression of Cervical Cancer

Cervical cancer is one of the most common gynecological tumors in females, which is closely related to high-rate HPV infection. Methylation alteration is a type of epigenetic decoration that regulates the expression of genes without changing the DNA sequence, and it is essential for the progression of cervical cancer in pathogenesis while reflecting the prognosis and therapeutic sensitivity in clinical practice. Hydroxymethylation has been discovered in recent years, thus making 5-hmC, the more stable marker, attract more attention in the field of methylation research. As markers of methylation, 5-hmC and 5-mC together with 5-foC and 5-caC draw the outline of the reversible cycle, and 6-mA takes part in the methylation of RNA, especially mRNA. Furthermore, methylation modification participates in ncRNA regulation and histone decoration. In this review, we focus on recent advances in the understanding of methylation regulation in the process of cervical cancer, as well as HPV and CIN, to identify the significant impact on the prospect of overcoming cervical cancer.
GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics

This paper describes our system submission to the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Given two concepts and an attribute, the task is to determine whether the attribute is semantically related to one concept and not the other. In this work we assume that discriminative attributes can be detected by discovering the association (or lack of association) between a pair of words. The hypothesis we test in this contribution is whether the semantic difference between two pairs of concepts can be treated in terms of measuring the distance between words in a vector space, or can simply be obtained as a by-product of word co-occurrence counts.
Achieving End-to-End Reliability of Mission-Critical Traffic in Softwarized 5G Networks

Network softwarization is a major paradigm shift, which enables programmable and flexible system operation in challenging use cases. In the fifth-generation (5G) mobile networks, the more advanced scenarios envision transfer of high-rate mission-critical traffic. Achieving end-to-end reliability of these stringent sessions requires support from multiple radio access technologies and calls for dynamic orchestration of resources across both radio access and core network segments. Emerging 5G systems can already offer network slicing, multi-connectivity, and end-to-end quality provisioning mechanisms for critical data transfers within a single software-controlled network. Whereas these individual enablers are already in active development, a holistic perspective on how to construct a unified, service-ready system as well as understand the implications of critical traffic on serving other user sessions is not yet available. Against this background, this paper first introduces a softwarized 5G architecture for end-to-end reliability of the mission-critical traffic. Then, a mathematical framework is contributed to model the process of critical session transfers in a softwarized 5G access network, and the corresponding impact on other user sessions is quantified. Finally, a prototype hardware implementation is completed to investigate the practical effects of supporting mission-critical data in a softwarized 5G core network, as well as substantiate the key system design choices.
Measuring Program Success

Growth in the use of programs has led to a requirement of understanding what constitutes program success. A measurement construct for program success, which comprises four dimensions—delivery capability, organizational capability, marketing capability, and innovative capability—was developed based on 172 responses to a web-based questionnaire to program managers. Analysis of variance (ANOVA) and canonical correlation analysis were applied to test for the relationship between program success and program context. Results showed that the measurement construct for program success was stable over different types of program contexts. It provides a tool for further investigation into program success assessment.
Big Data Linkage for Product Specification Pages

An increasing number of product pages are available from thousands of web sources, each page associated with a product, containing its attributes and one or more product identifiers. The sources provide overlapping information about the products, using diverse schemas, making web-scale integration extremely challenging. In this paper, we take advantage of the opportunity that sources publish product identifiers to perform big data linkage across sources at the beginning of the data integration pipeline, before schema alignment. To realize this opportunity, several challenges need to be addressed: identifiers need to be discovered on product pages, made difficult by the diversity of identifiers; the main product identifier on the page needs to be identified, made difficult by the many related products presented on the page; and identifiers across pages need to beresolved, made difficult by the ambiguity between identifiers across product categories. We present our RaF (Redundancy as Friend) solution to the problem of big data linkage for product specification pages, which takes advantage of the redundancy of identifiers at a global level, and the homogeneity of structure and semantics at the local source level, to effectively and efficiently link millions of pages of head and tail products across thousands of head and tail sources. We perform a thorough empirical evaluation of our RaF approach using the publicly available Dexter dataset consisting of 1.9M product pages from 7.1k sources of 3.5k websites, and demonstrate its effectiveness in practice.
You Shall Not Pass! (Once Again): An IoT Application of Post-quantum Stateful Signature Schemes

This paper presents an authentication protocol specifically tailored for IoT devices that inherently limits the number of times that an entity can authenticate itself with a given key pair. The protocol we propose is based on a stateful hash-based digital signature system called eXtended Merkle Signature Scheme (XMSS), which has increased its popularity of late due to its resistance to quantum-computer-aided attacks. We propose a 1-pass authentication protocol that can be customized according to the server capabilities to keep track of the key pair state. In addition, we present results when ported to ARM Cortex-M3 and M0 processors.