Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular dynamics study on the stability of foot-and-mouth disease virus particle in salt solution

ABSTRACT The poor thermostability of the Foot-and-mouth disease virus (FMDV) vaccines is a huge challenge since it requires cold chain transportation and thus significantly increases the cost. To study the theoretical basis for the enhanced stability of FMDV vaccines with the addition of bivalent metal cations in solution, in this work, molecular dynamics simulations were performed between two adjacent protein subassemblies of FMDV capsid under three different solvent environments, including salt-free aqueous solution, CaCl2 solution and CuCl2 solution. Besides analysis of the adsorption properties of Ca2+ and Cu2+ ions, umbrella sampling method was used to calculate the potential of mean force between the protein subassemblies. Moreover, molecular mechanics/Poisson–Boltzmann surface area method was used to calculate the change of free energies with the addition of metal cations. The results suggest that the system in bivalent metal cationic solutions, especially CuCl2 solution, exhibited stronger interactions between protein subassemblies, mainly through manipulation of the electrostatic interactions raised from polar residues on protein molecules, and thus result in better stabilities of the FMDV capsid structure. With this work, we hope to shed some light on the molecular mechanism of the structure manipulation of the virus capsid structure through regulation of the micro-environments.
MKP-3 suppresses LPS-induced inflammatory responses in HUVECs via inhibition of p38 MAPK/NF-κB pathway

ABSTRACT Endothelial cell dysfunction and inflammatory responses play critical roles in the development of atherosclerosis. Recent data on the processes underlying atherogenesis indicate the substantial role of endotoxins (lipopolysaccharides; LPS) of the intestinal microflora in the initiation and progression of atherosclerosis. Mitogen-activated protein (MAP) kinase phosphatase-3 (MKP-3) is a cytoplasmic dual-specificity protein phosphatase that specifically binds to and inactivates MAP kinases in mammalian cells, but its biological function in endothelial cell dysfunction and inflammatory responses remains largely unknown. The aim of the present study was to investigate the role of MKP-3 in endotoxin-induced endothelial inflammation by western blotting, quantitative polymerase chain reaction, and immunofluorescence. The results of our study demonstrated that MKP-3 overexpression markedly inhibited the adhesion of human monocytic THP-1 cells to human umbilical vein endothelial cells (HUVECs) by downregulating the expression of vascular cell adhesion protein 1 (VCAM-1) and pro-inflammatory cytokines. In contrast, MKP-3-encoding gene knockdown by small interfering RNA (siRNA) exacerbated LPS-induced endothelial dysfunction. Additionally, we found that MKP-3 overexpression inhibited LPS-induced p38 MAPK phosphorylation and decreased the nuclear translocation of nuclear factor kappa B (NF-κB) after LPS treatment, suggesting its implication in the LPS/Toll-like receptor 4 (TLR4)/p38/NF-κB pathway. Overall, these observations suggest that MKP-3 plays a protective role in endothelial dysfunction and may be a therapeutic target.
Machine Translation and Minority Languages

Language Engineering (LE) products and resources for the world's "major" languages, including Machine Translation systems, CAT systems, on-line dictionaries, thesauri, and so on, are steadily increasing, but there remains a major gap as regards less widely-used languages. This paper considers the need for LE support for minority languages. The current situation regarding LE resources for the languages in question is reviewed. Some proposals for rectifying this situation are made, including techniques based on adapting existing resources and "knowledge extraction" techniques from machine-readable corpora.
Genetic Confirmation of Predation of an Adult Female Eastern Spotted Skunk by a Barred Owl

Abstract Avian predation is a primary cause of mortality for Spilogale putorius (Eastern Spotted Skunk) and is generally attributed to Bubo virginianus (Great Horned Owl). We report the first confirmed avian predation of an Eastern Spotted Skunk by a Strix varia (Barred Owl). The radio-collar of an adult female Eastern Spotted Skunk was recovered alongside an owl pellet containing Eastern Spotted Skunk remains in eastern West Virginia. DNA sampled from the surface of the pellet was amplified, sequenced, and compared to known Barred Owl and Great Horned Owl sequences. Differences in 2 single nucleotide polymorphisms confirmed a Barred Owl produced the pellet. Our findings show Barred Owls are at least occasionally an avian predator of this species in eastern forest ecosystems.
River Crossings by Two Male Eastern Spotted Skunks in West Virginia

Abstract Spilogale putorius (Eastern Spotted Skunk) populations have declined throughout their range and may now be extirpated from the northeast in Pennsylvania and Maryland. Populations have persisted south of the Potomac River along shared mountain ranges in Virginia and West Virginia. Little is known about the dispersal capabilities of the species, including if large riverine systems inhibit their dispersal. During a radio-telemetry study of 8 Eastern Spotted Skunks in West Virginia, 2 males crossed the South Branch of the Potomac River (SBPR) on 9 occasions. Crossings may have been motivated by breeding movements, as 7 occurred during the breeding season. Stretches of the Potomac River similar in size to the SBPR might not inhibit the natural recolonization of the northeastern range.
Empirical Analysis of AHBS Option Pricing Model-Based on SSE 50ETF Option

In this paper, the implied volatility of options is set as a function of related variables, and the method of parameter estimation is used to analyze the AHBS model pricing of SSE 50ETF options. Through the analysis of the pricing error results of each model, this paper finds that the pricing accuracy of the AHBS model is much higher than that of the traditional BS model, and the AHBS model that takes the remaining time of the option as the model variable has higher pricing accuracy.
Decision Mechanism of Vehicle Autonomous Lane Change Based on Rough Set Theory

As one of the most common driving behaviors, lane changing is a major factor affecting the safety of traffic vehicles and commuting efficiency, and it is also the key to achieving high-level autonomous driving. At present, rule-based decision-making models for lane change are mostly designed through expert experience and lack the corresponding data support. This paper combines rough set theory to extract lane change rules with high relevance and large sample size from natural driving data as decision-making conditions, and implements and verifies the model through Matlab and PreScan. The results show that the decision-making algorithm can fit the actual scene to make anthropomorphic decision-making instructions.
Would Humans Want to Work Side-by-Side with Autonomous Robots: The Effect of Robot Autonomy on Perceived Usefulness, Ease of Use and Desire for Contact

The emergence of automatic machines is a hot topic in society. In this study, we examine the effects of robot autonomy on perceived usefulness, ease of use, and desire for contact. Participants watched a video of robots performing a variety of tasks -the robots were either autonomous and able to ignore human commands, or non-autonomous and could only obey human commands. The results showed that participants who watched videos of autonomous robots generally rated the robots as more useful than non-autonomous robots, but the difference was not statistically significant. On the other hand, people who watched videos of autonomous robots had lower perceived ease of use and less desire for contact than non-autonomous robots. These findings have practical implications for artificial intelligence research and raise new questions about the relationship between robot autonomy and human perception.
A Reinforcement Learning Framework for Optimizing Throughput in DOCSIS Networks

The capacity in a communication network is restricted by the famous Shannon-Hartley theorem, which establishes a relationship between maximum achievable capacity, channel bandwidth, and signal-to-noise ratio of the channel. The state-of-the-art in pushing the achievable capacity close to the theoretical limit revolves around coming up with ever more efficient error correction algorithms combined with assigning the proper modulation and encoding scheme to match the conditions of the spectrum at any given point in time. In cable broadband networks, which operate under the DOCSIS protocol, a Profile Management Application (PMA) system uses telemetry collected from cable modems and cable modem termination systems (CMTSs) to dynamically assign DOCSIS profiles that constitute a combination of Forward Error Correction (FEC) configuration, a Quadrature Amplitude Modulation (QAM) level, and other protocol-based configurations. The objective behind this dynamic assignment is twofold: maximizing capacity and keeping the uncorrectable error rate at a minimal level. The current PMA implementation, adopts a rule-based approach, where pre-defined thresholds govern the decisions for adjusting the profiles. This approach, while proven to be successful, limits opportunities to fully realize optimal DOCSIS configurations to bring system performance closer to the Shannon limit. Through a reinforcement learning (RL) implementation of PMA, it is possible to substitute the pre-defined rules for a system that learns to select the optimal configuration at each decision point, based on past outcomes and potential future rewards. In this paper, we focus on designing an RL-based PMA system to manage DOCSIS 3.0 upstream configurations.
Evasion and Countermeasures Techniques to Detect Dynamic Binary Instrumentation Frameworks

Dynamic Binary Instrumentation (DBI) is a dynamic analysis technique that allows arbitrary code to be executed when a program is running. DBI frameworks have started to be used to analyze malicious applications. As a result, different approaches have merged to detect and avoid them. Commonly referred to as split personality malware or evasive malware are pieces of malicious software that incorporate snippets of code to detect when they are under DBI framework analysis and thus mimic benign behavior. Recent studies have questioned the use of DBI in malware analysis, arguing that it increases the attack surface. In this article, we examine the anti-instrumentation techniques that abuse desktop-based DBI frameworks and existing countermeasures to determine if it is possible to reduce the exploitable attack surface introduced by these DBI frameworks. In particular, we review the related literature to identify (i) the existing set of DBI framework evasion techniques and (ii) the existing set of countermeasures to avoid them. We also analyze and compare the taxonomies introduced in the literature, and propose a new taxonomy that expands and completes the previous taxonomies. Our findings demonstrate that despite advances in DBI framework protections that make them quite suitable for system security purposes, more efforts are needed to reduce the attack surface that they add during application analysis. Only 12 of the 26 evasion techniques covered in this document have countermeasures, threatening the transparency of DBI frameworks. Furthermore, the impact in terms of performance overhead and effectiveness of these countermeasures in real-world situations is unknown. Finally, there are only proofs of concept for 9 of these 26 techniques, which makes it difficult to validate and study how they evade the analysis in order to counter them. We also point out some relevant issues in this context and outline ways of future research directions in the use of DBI frameworks for system security purposes.
A Method For The RNA-Protein Complexes Recognition

It is important to know RNA-protein pair interact or not. It's also important to know where they interact. If knowing the RNA-protein interaction sites, it's also to know the strength of the biding sites. It is necessary to study the algorithm which can predict the RNA-protein interaction automatically. The coding method is used to express protein characteristics by combining DC and PseAA. This coding method will generate eigenvectors with higher dimension yet. In this paper, the method of simplifying feature dimension is adopted. Important eigenvectors are taken from the original eigenvectors. Higher eigenvectors are reduced to lower eigenvectors. At last, we use fuzzy sets algorithm to identify the binding strength. The jackknife test outcomes are very good. The outcomes illustrated that the algorithm proposed in this paper is very suitable for protein classes prediction predicting RNA-protein binding complexes.
Indoor SLAM Algorithm Based on PL-ICP and Map Matching

ICP is a common algorithm for indoor mapping of mobile robots, which completes the process of robot positioning and mapping by constantly matching adjacent scans. Pl-ICP is an optimized version of ICP, which can achieve faster convergence speed and higher accuracy than ordinary ICP. But whether it is ICP or PL-ICP, because the position of the new scan is only related to the previous scan, errors will continue to accumulate, thereby reducing the quality of the final map. This paper proposes a map-matching algorithm based on PL-ICP and TSDF. By extracting the point cloud from the TSDF grid map, the new scan of the laser can be matched with a high-quality TSDF map, so as to solve the problem of error accumulation in PL-ICP. The experiment will be divided into two parts: simulation and real environment. The experiments in these two parts prove the effectiveness of the algorithm in this paper.
Enhanced Customer Analysis Based on Variations of Natural Language Processing Algorithms Implemented on Past E-Commerce Reviews

The study examines the role of the text comments in the ecommerce clothing reviews by proposing a case study of extracting potentially helpful information from the text corpus. Using the obtained dataset of Women's E-commerce Clothing Reviews, the study presents the data in a variety of graph visualizations, from computing the sentiment polarity of the text comments using TextBlob to analyzing the characteristics of a typical reviewer through the e-commerce platform, and elaborated on the regression and classification results of various algorithms: LSTM based neural networks, random forest, logistic regression, decision tree and gradient boosting decision tree. The goal is to implement neural network models on forecasting customer review by reading the text-based comments and to use the regression and classification models for predicting the reviewer's age based on given features and the manually extracted features from text-based comments. The results indicate that big data analysis, namely using machine learning models to process text-based comments and customer information, is particularly conducive to generating more accurate conclusions about the customers and are beneficial for ecommerce platforms to improve their recommendation system as well as overall shopping experience.
A data-driven approach to optimize building energy performance and thermal comfort using machine learning models

Buildings account for 30% of the world's total energy consumption, and heating, ventilation and air conditioning systems account for more than 70% of the world's total energy consumption. People pay more and more attention to the energy efficiency improvement of building energy saving system, especially HVAC system. Open-plan office is one of the most popular office types in recent decades, which can not only improve communication efficiency, but also save considerable construction costs. But it can't satisfy everyone's comfort requirements, especially indoor air temperature and relative humidity. In this paper, a data-driven thermal comfort model is established based on ASHRAE Global Thermal Comfort Database II. Two machine learning algorithms for building thermal comfort models are studied: support vector machine (SVM) and random forest. The model optimizes energy consumption while ensuring thermal comfort of commercial buildings. Under the thermal comfort condition, the purpose of energy saving is achieved by controlling the indoor temperature setting value. We also set up a co-simulation model of building energy consumption, compared the benchmark control strategy with the optimal control strategy by using the data-driven thermal comfort model, and analyzed the economic benefits of the enterprise by using the whole-life cycle cost analysis method. The results have shown the optimized control strategy outperforms the baseline owing to better thermal comfort prediction performances with machine learning. Therefore, this paper contributes to intelligent human building interaction areas with artificial intelligence.
The impact of COIVD-19 on the degree of interdependence of stock markets

This paper examines the impact of the novel coronavirus (COVID-19) on the interdependence of stock markets in China, Japan and U. S. Data is divided into two sub-periods: pre-crisis period (Jul. 1st, 2019-Dec.31st, 2019) and post-crisis period (Jan.1st, 2020-Jun. 30th, 2020). Method of Conditional Maximum Likelihood Estimation is used. The results from copula methods indicate 1) following the COVID-19 outbreak, degree of dependence among stocks have increased; 2) the outbreak has converted left-tailed dependence into a right-tailed dependence; 3) Gumbel copula has better in-sample fitness before crisis while for post-crisis period, Student's T copula is better.
Referencing Race = Racist? Examining Perceptions of References to a Target’s Race as Offensive

We examined majority group members’ perceptions of racial slurs, compared to what we have labeled as combination terms. These combination terms possess the same semantic and pragmatic linguistic functions as racial slurs, functioning to express negative emotion toward, and to describe, a target. Across three studies (total N = 943) racial slurs were not perceived as significantly different from combination terms. We then examined whether participants higher in social dominance beliefs reported greater perceived justification for using combination terms over racial slurs because of their lack of historical denigration of marginalized groups that racial slurs have. Participants, even those higher in socially dominant attitudes, did not perceive greater justification for the use of combination terms than racial slurs. Indeed, an important implication is that race-marking, an understudied area of social psychology, paired with general derogative terms produces terms which may function similarly to racial slurs, but, fortunately, are also similarly vilified in modern society.
Relevance of non-activity representation in traveling user behavior profiling for adaptive gamification

Collaborative location collecting systems (CLCS) are collaborative systems where users collects location-based data. When these systems are gamified and aim to adapt the game elements to each user, it may require a user traveling behavior profile. This work presents two approaches of traveling user behavior profiling: a raw series built up with categorical data that describes the user's activity in a period, and a timed series that is an enhanced version of the first that includes a representation of the non-activity time frames. The profiling of user traveling behavior can be used in adaptive gamification strategies. The approach is evaluated over a behavioral atoms dataset based on a year of Foursquare check-ins. The results showed that both approaches reflex different aspects of traveling user behavior, and also both could be used in a complementary manner.
Phantom Evaluation of Energy-Based Scatter Estimation in an SiPM PET Scanner

PET measurements include a background of scattered radiation which must be estimated as a part of image reconstruction. In this work, an implementation of energy-based scatter (EBS) estimation is described in detail and evaluated in PET/CT phantom studies, including a comparison to standard single-scatter simulation (SSS). The EBS method had no adjustable parameters. An 8-ring Biograph Vision PET/CT system was used. Phantoms provided sinogram regions in which the scatter content was known to be 100%. These were: a cylindrical phantom with a slab insert; a NEMA-94 image quality phantom; a uniform cylinder; and a cardiac phantom. Sinograms were coarsely sampled based on groups of 10×10 crystals. Evaluation criteria included image mean values and image roughness as well as contrast recovery in regions that were completely void of radioactivity. EBS and SSS were comparable. EBS resulted in slightly greater image roughness in one test and better contrast recovery in another test.
Preliminary Evaluation of Artificial Neural Networks as Test Pattern Generators for BIST

In this paper, we display a preliminary evaluation of some artificial neural networks (ANNs) that are used as test pattern generators (TPGs) for the BIST technology. In the evaluation, fault coverages of test patterns by ANN- TPGs and numbers of transistors required for those TPGs are compared with those of LFSRs and ROMs. Computational results display that ANN- TPGs with sufficient hidden nodes can yield higher fault coverages than LFSRs with fewer transistors than ROMs.
Spatial Downscaling of MODIS Land Surface Temperature Based on a Geographically and Temporally Weighted Autoregressive Model

Land surface temperature (LST) is a key parameter in numerous environmental studies. However, currently, there is no satellite sensor that can completely provide LST data with both high spatial and high temporal resolutions simultaneously. LST downscaling is regarded as an effective remedy for improving the temporal and spatial resolutions of LST data. In this article, a geographically and temporally weighted autoregressive (GTWAR) model of LST downscaling is that comprehensively considers the spatial heterogeneity, spatial autoregression, and temporality of LST is newly proposed. The normalized difference water index, the normalized difference built-up index, and the normalized difference vegetation index were selected as explanatory variables to downscale the moderate resolution imaging spectroradiometer (MODIS) LST from 1000 to 100 m, while the Landsat 8 LST was selected as the reference data. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR) and the geographically and temporally weighted regression (GTWR) downscaling methods, the proposed method was superior based on quantitative indices, with the lowest root mean square error (Zhangye: 1.57 °C, Beijing: 1.22 °C) and mean absolute error (Zhangye: 1.06 °C, Beijing: 0.85 °C). The downscaling model of GTWAR will facilitate improvements in the accuracy of downscaling for temporal series of LST data.