Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor Necrosis Factor-α G-308A Polymorphism and Sporadic IgA Nephropathy: A Meta-Analysis Using a Genetic Model-Free Approach

Tumor necrosis factor-α (TNF-α) is a potent pro-inflammatory cytokine, involved in the pathogenesis and progression of immunoglobulin A nephropathy (IgAN). A bi-allelic polymorphism in the promoter region, at position -308 (G/A) of the TNF-α gene (rs1800629) is associated with an increased TNF-a production. However, several previous association studies of TNF-α G-308A polymorphism and IgAN rendered contradictory findings. The objective of the present study is to shed light on these inconclusive results and clarify the role of TNF-α and any possible contribution of this factor in the development and progression of sporadic IgAN. Therefore, a meta-analysis of all available genetic association studies relating the TNF-α G-308A polymorphism to the risk for development and/or progression of IgAN was conducted. Seven studies were included in the meta-analysis. Three of them included populations of European descent (Caucasians) and four involved Asians. The generalized odds ratio (ORG) was used to estimate the risk for the development and/or progression of the disease. Overall, the meta-analysis did not detect any significant association between the G-308A variant and both the risk of developing IgAN and the risk for progression of IgAN. In conclusion, these results suggest that TNF-α does not constitute a key component in the genetic architecture of sporadic IgAN. However, further evidence deciphering the influence of TNF-α on IgAN is still needed.
The effect of the electric wind on the spatial distribution of chemical species in the positive corona discharge

The electrohydrodynamic air flow generated by a positive corona discharge, and its effect on the spatial distribution of chemical species within a wire-plate corona reactor, have been numerically simulated. The computational model is based on the solutions of the Navier–Stokes equation and the continuity equation of each chemical species generated by the electrical discharge. A simplified analytical expression of the electric force density, which only requires the current density as the input parameter, has been used in the Navier–Stokes equation to obtain the velocity field. For the solution of the continuity equations, a plasma chemistry model that includes the most important reactions between electrons, atoms and molecules in air has been used. Similar to the electric force, the electron density distribution has been approximated by using a semi-analytical expression appropriate for the electrode geometry. The results of the study show that the spatial distribution of chemical species can be very different, and depends on the interplay between the electrohydrodynamic flow, the chemical kinetics of the species and its characteristic lifetime.
Combined AOA and TDOA Target Localization Method Under Distance-Dependent Noise Model

This article focuses on the problem of target localization in 2-D using a combination of angle-of-arrival (AOA) and time-difference-of-arrival (TDOA) measurements. Unlike most existing studies, the measurement noises are assumed as the distance-dependent model in this article. Specifically, the variances of the noises are functions of the distance between the target and the sensor. The maximum likelihood estimator (MLE) and the Cramer–Rao lower bound (CRLB) are derived under the distance-dependent noise. By linearizing the nonlinear measurement equations for the unknown target, an AOA/TDOA pseudo-linear estimator (PLE) is established. Then, a new estimator with a closed-form solution is proposed by taking full advantage of the instrumental variable (IV) approach and the generalized pseudo-linear equation, which can improve the localization performance in the absence of noise variances. The simulation results demonstrate that the proposed estimator is superior to the MLE, IV estimator (IVE), and PLE.
Finger Vein Recognition with Hybrid Deep Learning Approach

Finger vein biometrics is an identification technique based on the vein patterns in fingers, and it has the benefit of being difficult to counterfeit. Due to its high level of security, durability, and performance history, finger vein recognition captures our attention as one of the most significant authentication methods available today. Using a mixed deep learning approach, we investigate the challenge of identifying the finger vein sensor model. Thus far, we use Traditional LSTM architectures for this biometric modality. This work also suggests a brand-new hybrid architecture that shines due to its compactness and a merging with the LSMT layer to be taught. In the experiment, original samples as well as the region of interest data from eight freely available FV-USM datasets are employed. The standard LSTM-based strategy is preferable and produced better outcomes, as seen by the comparison with the earlier approaches. Moreover, the results show that the hybrid CNN and LSTM networks may be used to improve vein detection performance.
Model-Based Stabilisation of Deep Reinforcement Learning

Though successful in high-dimensional domains, deep reinforcement learning exhibits high sample complexity and suffers from stability issues as reported by researchers and practitioners in the field. These problems hinder the application of such algorithms in real-world and safety-critical scenarios. In this paper, we take steps towards stable and efficient reinforcement learning by following a model-based approach that is known to reduce agent-environment interactions. Namely, our method augments deep Q-networks (DQNs) with model predictions for transitions, rewards, and termination flags. Having the model at hand, we then conduct a rigorous theoretical study of our algorithm and show, for the first time, convergence to a stationary point. En route, we provide a counter-example showing that 'vanilla' DQNs can diverge confirming practitioners' and researchers' experiences. Our proof is novel in its own right and can be extended to other forms of deep reinforcement learning. In particular, we believe exploiting the relation between reinforcement (with deep function approximators) and online learning can serve as a recipe for future proofs in the domain. Finally, we validate our theoretical results in 20 games from the Atari benchmark. Our results show that following the proposed model-based learning approach not only ensures convergence but leads to a reduction in sample complexity and superior performance.
A general model for how attributes can reduce polarization in social groups

Polarization makes it difficult to form positive relationships across existing groups. Decreasing polarization may improve political discourse around the world. Polarization can be modeled on a social network as structural balance, where the network is composed of groups with positive links between all individuals in the group and negative links with all others. Previous work shows that incorporating attributes of individuals usually makes structural balance, and hence polarization, harder to achieve. That work examines only a limited number and types of attributes. We present a generalized model and a simulation framework to analyze the effect of any type of attribute, including analytically as long as an expected value can be written for the type of attribute. As attributes, we consider people’s (approximately) immutable characteristics (e.g., race, wealth) and such opinions that change more slowly than relationships (e.g., political preferences). We detail and analyze five classes of attributes, recapitulating the results of previous work in this framework and extending it. While it is easier to prevent than to destabilize polarization, we find that usually the most effective at both are continuous attributes, followed by ordered attributes and, finally, binary attributes. The effectiveness of unordered attributes varies depending on the magnitude of negative impact of having differing attributes but is smaller than of continuous ones. Testing the framework on network structures containing communities revealed that destroying polarization may require introducing local tensions. This model could be used by policymakers, among others, to prevent and design effective interventions to counteract polarization.
Quantum Control of Light and Matter: From the Macroscopic to the Nano Scale

Abstract : We increased - by more than an order of magnitude - the storage time for coherent storage of light pulses in Bose-Einstein condensates. We can now store, sculpt, move, and revive light pulses after storage times of 0.5 minutes! We observed storage of single-photon pulses in the condensates, with unprecedented long storage times and high efficiency for storage at the single photon level. The results achieved allow for powerful processing of quantum information and for distribution of entanglement over long distances, which is essential for quantum cryptography. We have built a compact system for integrating BECs with nanoscale structures and have developed new instrumentation and techniques for nano-fabrication. This has resulted in a unique e-beam ice-lithography setup that allows for non-destructive SEM imaging of carbon-based structures. We developed CVD methods for growing large, single-crystals of graphene and demonstrated graphene suspension over large apertures. This allows development of novel devices including chip-integrated devices for atom interferometry, of importance for navigation, and hybrid bio-graphene devices, incorporating enzymes positioned on graphene, for light-driven bio-fuel production with controlled enzymatic rates.
Two new species of Atopobathynella (Parabathynellidae, Bathynellacea) from the Pilbara region, Australia

Fifteen species of the Gondwanan genus Atopobathynella have been described so far from four countries. The position of the genus within the family Parabathynellidae and its species relationships are controversial due to the different characters and species considered by different authors, however most of the phylogenetic reconstructions are based solely on morphological characters. In the past few decades, the arid zones of Western Australia, including the Pilbara region, have been recognized as a hotspot for subterranean fauna. Material is constantly collected to produce Environmental Impact Assessments to protect and manage the subterranean environment. In 2009 the Cleaverville Iron Formation, in the Pilbara region, northern Western Australia, was proposed to be mined, therefore subterranean fauna sampling was conducted as per legislation. Preliminary molecular studies of stygofauna identified two distinct Parabathynellidae lineages from two ridges, however no morphological descriptions were carried out at that time. In this study, we describe two new species from Yarrie and Callawa ridges, respectively: Atopobathynella yarriensis sp. nov. and A. degreyensis sp. nov. The new species show differences in male antennal organ, ventral tooth of mandible, number of teeth on distal endite of maxillula, absence or presence of epipod of thoracopod I, number of setae on article 4 of thoracopod II, length of distal external seta of exopod of all thoracopods, outer lobe of male thoracopod VIII, number of spines on uropodal sympod and furcal rami, length of the external dorsal plumose seta of the furcal rami. Additionally, we integrated the morphological data with sequences of three genetic loci, Cytochrome Oxidase c Subunit I (COI), 12S rRNA (12S), and nuclear 18S rRNA (18S), calculating p-distances and constructing a multigene molecular phylogeny to support morphology, and explore the monophyletic status of the genus and the relationship amongst its species. The two new species were well supported in our phylogeny, however they appear distantly related. http://zoobank.org/urn:lsid:zoobank.org:act:2D8DA822-6C08-45B5-B707-6F63A1B69DE2
Query-Aware Explainable Product Search With Reinforcement Knowledge Graph Reasoning

Product search is one of the most effective tools for people to browse and purchase products on e-commerce platforms. Recent advances have mainly focused on ranking products by their likelihood to be purchased through retrieval models. However, they overlook the problem that users may not understand why certain products are retrieved for them. The lack of appropriate explanations can lead to an unsatisfactory user experience and further decrease user trust in the platforms. To address this problem, we propose a Query-aware Explainable Product Search with Reinforcement Knowledge Reasoning, namely QEPS, which uses search behaviors related to the current query to reinforce explanations. Specifically, with the aim of retrieving suitable products with explanations, QEPS takes full advantage of the user-product knowledge graph (KG) and develops a reinforcement learning approach, characterized by the demonstration-guided policy network and query-aware rewards, to perform explicit multi-step reasoning on the KG. The reasoning paths between users and products are automatically derived from the current query-related search behavior, which can provide valuable signals as to why the retrieved products are more likely to satisfy the user's search intent. Empirical experiments on four datasets show that our model achieves remarkable performance and is able to generate reasonable explanations for the search results.
Exploring the Potential of a Genome-Reduced Escherichia coli Strain for Plasmid DNA Production

The global demand for nucleic acid-based vaccines, including plasmid DNA (pDNA) and mRNA vaccines, needs efficient production platforms. However, conventional hosts for plasmid production have encountered challenges related to sequence integrity due to the presence of insertion sequences (ISs). In this study, we explored the potential of a genome-reduced Escherichia coli as a host for pDNA production. This strain had been constructed by removing approximately 23% of the genome which were unessential genes, including the genomic unstable elements. Moreover, the strain exhibits an elevated level of NADPH, a coenzyme known to increase plasmid production according to a mathematical model. We hypothesized that the combination of genome reduction and the abundance of NADPH would significantly enhance pDNA production capabilities. Remarkably, our results confirmed a three-fold increase in pDNA production compared to the widely employed DH5α strain. Furthermore, the genome-reduced strain exhibited heightened sensitivity to various antibiotics, bolstering its potential for large scale industrial pDNA production. These findings suggest the genome-reduced E. coli as an exciting candidate for revolutionizing the pDNA industry, offering unprecedented efficiency and productivity.
Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from 18F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent 18F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTVTOT) and whole-body Total Lesion Glycolysis (TLGWB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTVTOT, TLGWB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTVTOT (p = 0.0050), TLGWB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTVTOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTVTOT ≤ 89.5 mL had higher OS than those with MTVTOT > 89.5 mL (p = 0.0063). Combining CoV and MTVTOT, patients with CoV ≤ 0.38 and MTVTOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.
WOMEN’S LANGUAGE IN KIM KARDASHIAN’S INTERVIEW WITH VOGUE

As a subordinate group in society, women should speak and behave politely. This situation makes women have their characteristics when they speak. Kim Kardashian is a celebrity who has influenced many people through her fashion sense and career advancement as a celebrity and businesswoman. This research aims to examine the women's language characteristics that occurred in Kim Kardashian's utterances in her interview with Vogue. The instrument of this research is a document. First, the data were collected from Kim Kardashian’s interview with Vogue. The data were selected and identified the women's language characteristics that appeared in Kim Kardashian's utterances. Then the research showed the research findings by presenting the data and analysis. There are 360 data classified as women's language characteristics, which consist of 106 intensifiers, 84 hedges, 71 rising intonation, 34 emphatic stress, 31 hypercorrect grammar, 20 empty adjectives, five tag questions, 4 super polite forms, 3 specific colours, and 2 avoidances of swear words. An intensifier is a characteristic that Kim Kardashian frequently uses because she wants to express her certainty about her utterances and make them firmer, so the addressee will take her statements seriously and get the point of her statement.
The Maximum Idempotent Separating Congruence on E-inversive E-semigroups

A semigroup S is an E-inversive E-semigroup if for every a ∈ S, there exists an element x ∈ S such that ax is idempotent and the set of all idempotents of S forms a subsemigroup. The aim of this paper is to investigate the maximum idempotent separating congruence on E-inversive E-semigroups by using a full and weakly self-conjugate subsemigroup.
Ridges and umbilics of a sampled smooth surface: a complete picture gearing toward topological coherence

Consider a smooth surface, and at each point which is not an umbilic, respectively paint in blue (red) anything related to the maximum (minimum) principal curvature. Given such a surface, a blue (red) ridge is a curve on the surface such that at each of its points, the principal blue (red) curvature has an extremum along its blue (red) curvature line. Ridges are curves of extremal curvature and therefore encode important informations used in segmentation, registration, matching and surface analysis. Surprisingly, no method developed so far to report ridges from a mesh approximating a smooth surface comes with a careful analysis, which entails that one does not know whether the ridges are reported in a coherent fashion. This paper aims at bridging this gap with the following contributions. First, a careful analysis of the Acute rule - an orientation procedure used in most algorithms - is presented. Second, given a triangulation $T$ approximating a smooth generic surface $S$, we present sufficient conditions on $T$ together with a certified algorithm reporting ridges in a topologically coherent fashion. Third, we develop an algorithm and a filtering procedure aiming at reporting the most salient features of a coarse mesh $T$.
Exploring the Perception and Experiences of Nursing Instructors With E-learning During the COVID-19 Pandemic in Saudi Arabia

Background/objective: Due to the coronavirus disease 2019 (COVID-19) pandemic's effect on the educational system, the integration of e-learning became essential. Consequently, it is necessary to explore nursing instructors' perceptions and experiences with e-learning. This study aims to explore the perception and experiences of nursing instructors in Saudi Arabia regarding e-learning. Methods: A descriptive cross-sectional survey was conducted among nursing instructors at Saudi Arabian universities during the COVID-19 pandemic. They were invited to respond to an online questionnaire. The data collected were analyzed utilizing SPSS version 22 (IBM Corp., Armonk, NY), employing both descriptive statistics to summarize the responses of the participants and inferential statistics to identify significant differences in perception and experience levels based on demographic variables. Results: The results demonstrated that nursing instructors in Saudi universities had a high perception of e-learning, with an average rating of 4.55/5. Instructors aged 41-50 years, of Indian or Saudi nationality, and holding a doctoral degree exhibited significantly higher levels of technological and computer experience compared to others. Moreover, instructors in these categories also had significantly higher levels of perception toward e-learning, with an overall average rating of 4.56/5. Conclusions: The study concluded that nursing instructors at Saudi universities possessed a high level of experience in technology and computer usage, and they exhibited a favorable perception toward e-learning during the COVID-19 pandemic. Instructors aged 41-50 years, of Indian or Saudi nationality, and with a doctoral qualification demonstrated higher levels of experience in technology and computer usage, as well as a more positive perception of e-learning during the pandemic.
Possible effects of obesity on motor development, executive functioning, and academic performance of schoolchildren from a neuropsychopedagogical perspective

Purpose: This study aimed to compare the motor competence, executive functioning, and academic performance of students with adequate weight, overweight, and obesity. Design/Methodology/Approach: A total of 1631 children aged between 06 and 08 (±7.58) years old, of both genders (903 girls and 728 boys), students of the public school system in the state of Rio de Janeiro, participated in this study. To achieve the objective, participants were introduced to protocols that assessed body mass index, Motor Competence, Executive Functioning, Reading Comprehension, and Academic Performance. Findings: When analyzing the results of the protocols related to motor development, cognitive development, and academic performance, the results show a superiority of children with weights within the expected range in relation to obese children. Conclusion: The findings indicate a potential correlation between body mass index (BMI) and the proficiency of children in motor skills, executive functions, and academic performance. Specifically, the results demonstrate that children who are obese exhibit lower scores in all assessed variables compared to children with a BMI within the normal range. Research Limitations and Implications: This study did not assess the participants level of physical activity or their eating habits. Practical Implications: The research results contribute to the formulation of public policies on obesity as a public health problem, since its impacts go far beyond physical health problems. Contribution to the Literature: This research can add to the literature on the impacts of obesity on the cognitive and motor development of children, showing the importance of developing new studies with regard to prevention.