Miracle-dz/UniFAR-FTUs huggingface dataset:
Nsp1α of Porcine Reproductive and Respiratory Syndrome Virus Strain BB0907 Impairs the Function of Monocyte-Derived Dendritic Cells via the Release of Soluble CD83

ABSTRACT Porcine reproductive and respiratory syndrome virus (PRRSV), a virulent pathogen of swine, suppresses the innate immune response and induces persistent infection. One mechanism used by viruses to evade the immune system is to cripple the antigen-processing machinery in monocyte-derived dendritic cells (MoDCs). In this study, we show that MoDCs infected by PRRSV express lower levels of the major histocompatibility complex (MHC)-peptide complex proteins TAP1 and ERp57 and are impaired in their ability to stimulate T cell proliferation and increase their production of CD83. Neutralization of sCD83 removes the inhibitory effects of PRRSV on MoDCs. When MoDCs are incubated with exogenously added sCD83 protein, TAP1 and ERp57 expression decreases and T lymphocyte activation is impaired. PRRSV nonstructural protein 1α (Nsp1α) enhances CD83 promoter activity. Mutations in the ZF domain of Nsp1α abolish its ability to activate the CD83 promoter. We generated recombinant PRRSVs with mutations in Nsp1α and the corresponding repaired PRRSVs. Viruses with Nsp1α mutations did not decrease levels of TAP1 and ERp57, impair the ability of MoDCs to stimulate T cell proliferation, or increase levels of sCD83. We show that the ZF domain of Nsp1α stimulates the secretion of CD83, which in turn inhibits MoDC function. Our study provides new insights into the mechanisms of immune suppression by PRRSV. IMPORTANCE PRRSV has a severe impact on the swine industry throughout the world. Understanding the mechanisms by which PRRSV infection suppresses the immune system is essential for a robust and sustainable swine industry. Here, we demonstrated that PRRSV infection manipulates MoDCs by interfering with their ability to produce proteins in the MHC-peptide complex. The virus also impairs the ability of MoDCs to stimulate cell proliferation, due in large part to the enhanced release of soluble CD83 from PRRSV-infected MoDCs. The viral nonstructural protein 1 (Nsp1) is responsible for upregulating CD83 promoter activity. Amino acids in the ZF domain of Nsp1α (L5-2A, rG45A, G48A, and L61-6A) are essential for CD83 promoter activation. Viruses with mutations at these sites no longer inhibit MoDC-mediated T cell proliferation. These findings provide novel insights into the mechanism by which the adaptive immune response is suppressed during PRRSV infection.
ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets

This paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term Memory neural networks. This system has been used with slight modifications for the two tasks addressed both for English and Spanish. Finally, the results obtained in the competition are reported and discussed.
Effects of genetic variants on serum parathyroid hormone in hyperparathyroidism and end-stage renal disease patients

Background: Parathyroid hormone (PTH) is one of the principal regulators of calcium homeostasis, crucial for normal functioning of the kidneys, bones, heart, and nervous system. Different pathologic conditions can affect serum PTH level resulting in hyperparathyroidism or hypoparathyroidism. Our study assessed the association of previously reported polymorphisms with the level of PTH (expressed in pg/mL) among individuals with different pathologic conditions affecting PTH level. Methods: We searched Web of Science, MEDLINE, and Scopus to identify relevant articles published up to July 2017. The search yielded 6967 publications of which 44 fulfilled the inclusion criteria. We conducted meta-analyses for calcium-sensing receptor gene (CaSR) rs1801725 polymorphism in patients with primary hyperparathyroidism and vitamin D receptor gene (VDR) rs1544410 polymorphism in patients with end-stage renal disease (ESRD). Results: None of the polymorphisms were significantly associated with PTH levels in the overall population. In subgroup analysis by ethnicity for VDR rs1544410 gene polymorphism, we found significant differences under dominant model (SMD: −0.18 [−0.32, −0.05], P < .01) and AA versus GG comparison (SMD: −0.29 [−0.52, −0.06], P < .01) in Asian patients with ESRD, while nominally significant results (P < .05) were observed for AG versus GG and AA versus GG comparisons in European individuals with ESRD. Conclusion: Scientific evidence of genetic association of serum PTH level among individuals with different pathologic conditions remains deficient and published results provide weak evidence. Further well-conducted studies on larger sample sets designed according to evidence-based principles are warranted to assure clinically applicable findings.
Developing Metrics to Guide Sustainable Development of Arctic Cities : Progress & Challenges

This article describes the preliminary results of an effort to produce an Arctic Urban Sustainability Index that will have applications for researchers and policymakers. The project aims to help policymakers define and implement sustainability poli cies by measuring progress towards sustainability, compare across cities, and trace development over time. Existing studies within the region provide little analysis specifically addressing urban development. This study, under the auspices of the National Science Foundation’s Partnerships for International Research and Education (PIRE) project, aims to fill this gap in Arctic research by promoting urban sustainability, with a focus on optimal city planning and management to ensure the interests of future generations. Collecting the data to prepare the Index has proven challenging across a number of dimensions and efforts to address those challenges are discussed. While the Index described here remains a work in progress, we believe the process of thinking through issues related to measuring sustainability systematically will ultimately deliver useful results for researchers and policymakers.
Incremental Natural Language Processing: Challenges, Strategies, and Evaluation

Incrementality is ubiquitous in human-human interaction and beneficial for human-computer interaction. It has been a topic of research in different parts of the NLP community, mostly with focus on the specific topic at hand even though incremental systems have to deal with similar challenges regardless of domain. In this survey, I consolidate and categorize the approaches, identifying similarities and differences in the computation and data, and show trade-offs that have to be considered. A focus lies on evaluating incremental systems because the standard metrics often fail to capture the incremental properties of a system and coming up with a suitable evaluation scheme is non-trivial.
Examining the Quality of Technology Implementation in STEM Classrooms: Demonstration of an Evaluative Framework

Abstract Technology applications aligned with science, technology, engineering, and math (STEM) workplace practices can engage students in real-world pursuits but also present dramatic challenges for classroom implementation. We examined the impact of teacher professional development focused on incorporating these workplace technologies in the classroom. Because existing measures primarily use only presence or type of technology as proxies for implementation quality, we developed an expanded framework that incorporated (a) the type of technology used; (b) the degree of alignment to STEM practices; (c) the use of student-centered pedagogical practices; and (d) the degree of relevance to real-world contexts. While our framework successfully described the variation in technology implementation in our study group, we found no statistically significant difference between teachers with and without extensive training on STEM workplace technologies. Our results provide evidence that the framework captures quality of technology use and point to the need for additional research on effective teacher education around technology applications.
A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions

This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial aims to provide an accessible introduction to these techniques. We will introduce Gaussian processes which generate distributions over functions used for Bayesian non-parametric regression, and demonstrate their use in applications and didactic examples including simple regression problems, a demonstration of kernel-encoded prior assumptions and compositions, a pure exploration scenario within an optimal design framework, and a bandit-like exploration-exploitation scenario where the goal is to recommend movies. Beyond that, we describe a situation modelling risk-averse exploration in which an additional constraint (not to sample below a certain threshold) needs to be accounted for. Lastly, we summarize recent psychological experiments utilizing Gaussian processes. Software and literature pointers are also provided.
Time-Lapse Mechanical Characterization of Zona Pellucida Using a Cell Carrier Chip

The aim of this paper is to measure the changes in the mechanical characteristics of the zona pellucida (ZP) of an oocyte over time. To measure the mechanical characteristics such as Young’s modulus during cultivation, it is necessary to achieve the cultivation and the measurement of the mechanical characteristics of target oocytes. Generally, measurement systems are constructed on a microscope for observing the deformation of the oocytes, but it is difficult to integrate such a system with a conventional incubator. Therefore, we propose a cell carrier chip for the time-lapse mechanical characterization. The cell carrier chip has a probe as well as a force sensor to measure the mechanical characteristics of the ZP. In addition, the chip enables us to carry the target oocyte from the measurement system to the incubator. To calculate the Young’s modulus of the ZP, we used an elastic spherical shell model and measured the Young’s modulus of the ZP of cryopreserved oocytes at 0.5, 1, 2, 3, 4, 5, and 6 h after being thawed. The result indicates that the Young’s modulus of the ZP tends to increase with the cultivation time. [2017–0133]
Characterization of EMC/LF interfacial fracture toughness using the advanced mixed-mode bending method

Tests on epoxy molding compound (EMC) and copper lead frame (LF) interfaces in form of bimaterial beams have been carried out with the Advanced Mixed-mode Bending (AMB) test method. The AMB test is a fracture mechanical test which enables the loading of the interface by a superposition of the opening mode (mode I) and the in-plane shear mode (mode II). In this work the test specimens are extracted from mold maps consisting of two different EMC systems on Cu lead frames. Additionally, in order to study the effect of the assembly process, beams cut from an unlabeled package without a die were experimentally tested. The experimental fracture tests, performed with the AMB, are numerical simulated by means of finite element method (FEM). For specific fractured states of the beam, the crack length is determined using digital image correlation (DIC) algorithm. For the evaluation of the energy release rate G versus mode-mixity ψ FEM analysis were carried out accounting for actual test data, crack length, geometry and linear elastic material data. The extraction of G and ψ was implemented by the crack surface displacement extrapolation method (CSDEM). The presented results summarize normalized G(ψ) curves of two molding compounds used in the two different sample configurations, which are the mold map and the package specimen. The data consists of energy release rate values in a mode-mixity range of ψ = −25° to 90°. Each curve is resulting from tests of 3–11 samples proofing the capability of the AMB to produce reliable measurement data with lowest number of specimens. A qualitative comparison of the achieved G(ψ) values is discussed with respect to existing curve fitting models and examined effect of the lead frame surface finish.
Towards Affective Drone Swarms: A Preliminary Crowd-Sourced Study

Drone swarms are teams of autonomous un-manned aerial vehicles that act as a collective entity. We are interested in humanizing drone swarms, equipping them with the ability to emotionally affect human users through their non-verbal motions. Inspired by recent findings in how observers are emotionally touched by watching dance moves, we investigate the questions of whether and how coordinated drone swarms' motions can achieve emotive impacts on general audience. Our preliminary study on Amazon Mechanical Turk led to a number of interesting findings, including both promising results and challenges.
The EPFL Logic Synthesis Libraries

We present a collection of modular open source C++ libraries for the development of logic synthesis applications. The alice library is a lightweight wrapper for shell interfaces, which is the typical user interface for most logic synthesis and design automation applications. It includes a Python interface to support scripting. The lorina library is a parsing library for simple file formats commonly used in logic synthesis. It includes several customizable parsing algorithms and a flexible diagnostic engine. The kitty library is a truth table library for explicit representation and manipulation of Boolean functions. It requires less overhead compared to symbolic counterparts such as binary decision diagrams, but is limited by the number of variables of the Boolean function to represent. Finally, percy is an exact synthesis library with multiple engines to find optimum logic networks. All libraries are well documented and well tested. Furthermore, being header-only, the libraries can be readily used as core components in complex logic synthesis systems.
Design of a Super Twisting Sliding Mode Controller for an MR Damper-Based Semi-Active Prosthetic Knee

A large number of transfemoral amputees living in low-income countries could not access a much-needed prosthesis. Hence, affordable semi-active prosthetic knees have been designed in recent years. As the swing phase of the gait cycle is unstable as compared to the stance phase, these designs could not perfectly mimic this phase of a healthy human being. In contribution toward such a gap, this study proposes the modeling and design of a robust controller for magnetorheological (MR) damper-based semi-active prosthetic knee. A dynamic model representation for the swing phase of the single-axis knee is derived first. Subsequently, an MR damper valve model is developed. Then, a higher-order sliding mode controller is designed and evaluated for its stability and performance. The numerical simulation results show that the super twisting sliding mode controller improves the semi-active prosthetic knee’s tracking efficiency. The design exhibited the finest performance, providing a low normalized mean square error as compared to previous designs. The variable speed performance and robustness evaluation for this controller also showed its ability to continue providing excellent performance in the presence of disturbances.
MEMS accelerometer in hexapod intellectual control

The paper presents the results of three-axis MEMS accelerometer practical Introduction into the hexapod control system to solve the problem of classifying its states in real time. An experiment was conducted in which machine learning various methods possibilities were studied to solve the problem of classifying the current state of a walking robot. The best results for the accuracy parameter were shown by the method Medium KNN.
Bounded Authority: Expanding “Appropriate” Police Behavior Beyond Procedural Justice

This paper expands previous conceptualizations of appropriate police behavior beyond procedural justice. The focus of the current study is on the notion of bounded authority—that is, acting within the limits of one’s rightful authority. According to work on legal socialization, U.S. citizens come to acquire three dimensions of values that determine how authorities ought to behave: (a) neutral, consistent, and transparent decision-making; (b) interpersonal treatment that conveys respect, dignity, and concern; and (c) respecting the limits of one’s rightful power. Using survey data from a nationally representative sample of U.S. adults, we show that concerns over bounded authority, respectful treatment, and neutral decision-making combine to form a strong predictor of police and legal legitimacy. We also find that legal legitimacy is associated with greater compliance behavior, controlling for personal morality and perceived likelihood of sanctions. We discuss the implications of a boundary perspective with respect to ongoing debates over the appropriate scope of police power and the utility of concentrated police activities. We also highlight the need for further research specifically focused on the psychological mechanisms underlying the formation of boundaries and why they shape the legitimacy of the police and law.
Factors affecting intra- and inter-specific variations in the difference between alert distances and flight distances for birds in forested habitats

Studies of escape from predators have usually focused on fleeing. We studied intra- and inter-specific variations in a previous level of decision-making during predatorprey encounters by determining the difference between the distance at which a predator is detected and the distance at which the prey flees from the predator (buffer distance). We measured buffer distances of four bird species (ground foragers) living in forested habitats (wooded recreational parks) to approaching humans. Buffer distances increased with group size and temperature, and this was probably related to dilution of the predation risk and a higher risk of heat stress, respectively. Buffer distances decreased with shrub and coniferous cover, probably because of increased visual obstruction, and increased with tree height, probably because of the increased security provided by taller trees. Grass cover increased buffer distances of wood pigeons (Columba palumbus) but decreased those of house sparrows (Passer domesticus) and magpies .
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017

Adverse drug reactions (ADRs) are unwanted or harmful effects experienced after the administration of a certain drug or a combination of drugs, presenting a challenge for drug development and drug administration. In this paper, we present a set of taggers for extracting adverse drug reactions and related entities, including factors, severity, negations, drug class and animal. The systems used a mix of rule-based, machine learning (CRF) and deep learning (BLSTM with word2vec embeddings) methodologies in order to annotate the data. The systems were submitted to adverse drug reaction shared task, organised during Text Analytics Conference in 2017 by National Institute for Standards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.
Question Answering Systems : Measuring Task Performance

This research seeks to understand the user perspective in evaluating web question answering technology. Previous research concentrated in the design and development of question answering systems but left out the user’s perspective. Web question answering technology is important because it represents a way to search for information on the web and it is argued that it can improve task performance. This research proposes to study the relationship between the use of search technology and task performance. A research model is presented and a set of experiments are designed to test the hypotheses.
Access control of cloud users credible behavior based on IPv6

Cloud computing security issues become increasingly serious. Trust is a trust relationship entity acts in accordance with its past experience and interactions built up, may be direct or indirect interaction through a third party recommended interactions. User behavior trust evaluation using AHP, access to users by collecting historical behavior information behavior trust evidence, according to the characteristics of trust evidence will be divided into different trust property among the recalculation by users through analytic hierarchy process and the relative weight of each trust property behavior trust value. Combined with IPv6 protocol, the use of IPv6 extension headers, will assess the value placed header, transmission and validation, so that the program is practical.
RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem

Written communication lacks the multimodal features such as posture, gesture and gaze that make it easy to model affective states. Especially in social media such as Twitter, due to the space constraints, the sources of information that can be mined are even more limited due to character limitations. These limitations constitute a challenge for understanding short social media posts. In this paper, we present an approach that utilizes multiple binary classifiers that represent different affective categories to model Twitter posts (e.g., tweets). We train domain-independent recurrent neural network models without any outside information such as affect lexicons. We then use these domain independent binary ranking models to evaluate the applicability of such deep learning models on the affect identification task. This approach allows different model architectures and parameter settings for each affect category instead of building one single multi-label classifier. The contributions of this paper are two-folds: we show that modeling tweets with a small training set is possible with the use of RNNs and we also prove that formulating affect identification as a binary classification task is highly effective.
Statistical Analytic Tool for Dimensionalities in Big Data : The role of Hierarchical Cluster Analysis

An interest for presenting this paper rose because of massive increase information with a very high dimensional from different sources in this era of globalization. Data are produced continuously and are unstructured (1). This paper is confined to literature review search for big data issue and challenges of several scopes in data. It brings a detailed discussion on the problem on these data and analysis done using the effective multivariate statistical tool namely clustering analysis technique as a data reduction technique. It is used as a base for discussion for existing challenge of multi-dimensionalities of data. The findings indicated that, the world is noisy due to massive flow of information continuously. Findings revealed that data emanating from face book, you tube and twitter can be used to predict the epidemic of influenza and even market trend (2 and 3). With face book data is used to predict the people`s interest. However, data from different sources have been proved to be useful in decision making efficiently and effectively for public as well as private sector. Cluster analysis technique sorts data/alike things into groups, to see if there a high natural degree association among members of the same group and low between members of different groups. Finally, this technique has proved failure to handle such heap of data with varied sources. With regards to data stored, it remains to be a challenge in terms of analysis among researchers and scientists. Therefore, it calls for advanced statistical software to cater for such an existing challenges.