Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood HDAC4 Variation Links With Disease Activity and Response to Tumor Necrosis Factor Inhibitor and Regulates CD4+ T Cell Differentiation in Ankylosing Spondylitis

Purpose Histone deacetylase 4 (HDAC4) regulates the progression of autoimmune diseases. This study aimed to further investigate the correlation between HDAC4 and Th cells, inflammation, disease activity, and treatment response in patients with ankylosing spondylitis (AS). Methods A total of 132 active patients with AS were enrolled, of whom 54 patients received TNF inhibitor (TNFi) and 78 patients received NSAID. Serum HDAC4 was measured by ELISA in patients with AS before treatment (W0) and at week (W)4, W8, and W12 after treatment. Meanwhile, serum HDAC4 was detected in 30 patients with osteoarthritis and in 30 healthy controls (HCs) by ELISA. Besides, naïve CD4+ T cells from patients with AS were isolated, followed by modulation of HDAC4 and then polarization toward Th1, Th2, and Th17. Results Histone deacetylase 4 was reduced in patients with AS compared with HCs and patients with osteoarthritis (both P < 0.01). In patients with AS, HDAC4 was negatively correlated with TNF (P < 0.001), IL-1β (P = 0.003), Th17 proportion (P = 0.008), C-reactive protein (P < 0.001), and ASDAS (P = 0.038), but not with IL-6, Th1 proportion, or other characteristics. Meanwhile, HDAC4 increased from W0 to W12 (P < 0.001); HDAC4 at W8 (P = 0.014) and W12 (P = 0.006) was raised in ASAS40-response patients than ASAS40-non-response patients; further subgroup analysis showed that HDAC4 at W12 was higher in ASAS40-response patients than ASAS40-non-response patients (P = 0.016) in the TNFi-treated group, but not in the NSAID-treated group. In addition, HDAC4 negatively regulated the polarization of naïve CD4+ T cells toward Th17 (P < 0.01), but not Th1 or Th2. Conclusion Histone deacetylase 4 is associated with lower inflammation, and the disease activity negatively regulates Th17 polarization, whose increment after treatment reflects favorable outcomes in patients with AS.
Ability Orientation or Good Character? Moderated Mediation Mechanism to Determine the Impact of Telepresence on Consumer Purchasing Intention in Cross-Border E-Commerce

A research model is proposed based on the telepresence theory to determine the long-term mechanism for generating consumer purchasing intention and explore potential information haze caused by information overload. A total of 406 usable samples were collected from the consumers of several cross-border e-commerce (CBEC) platforms in China. The results showed that telepresence has a positive effect on benevolence trust, integrity trust, and ability trust. As expected, benevolence trust and ability trust were found to exhibit significant mediation effects, while integrity trust did not have a significant mediation role. The moderated mediation mechanism shows that product information (description, display, and content) restrain mediation paths. The findings provide new perspectives on the information haze caused by information overload. The results suggest that promoting an ability-oriented (ability trust) business philosophy and instilling good corporate character (benevolence trust) are vital in achieving sustainable development in CBEC platforms. Eliminating information haze could also help strengthen the activation effect of telepresence and promote the guiding role of customer trust on purchasing intention. This study's theoretical and management contributions extend our knowledge of optimizing management strategies for CBEC platforms.
Predicting links in a social network based on recognised personalities

The problem of link prediction concerns the inference of network edges. Its adaptation for the task of friendship recommendation has been gaining considerable attention from computer scientists and psychologists alike, especially within the context of online social networks (OSNs). Although one's personality is known to influence social relationships, its impact within OSNs is oftentimes overlooked. The main components in this research are the personality recognition tool and the link prediction boosting component, referred to as PALP-boost. The former extracts a user's personality traits from their set of micro-blog postings, while the latter incorporates personality preferences towards their followee recommendations. The best performing personality recogniser model was found to be a Support Vector Machine with a Pearson VII function kernel, attaining a Mean Absolute Error of 0.105. The model achieved competitive results to previous state-of-the-art approaches. A real-world Twitter network was utilised to evaluate PALP-boost. Using the developed personality recogniser, user personalities were extracted from their collected tweets. Results suggested that the PALP-boost component enhanced link prediction models by 10% (in terms of Average Precision) in the best case.
Disentangling temporal and amplitude variations in ECG synthesis using anchored GANs

Electrocardiogram (ECG) synthesis is a challenging task due to the complex dynamic nature of ECG signals. In this paper, we present a novel approach for ECG synthesis based on Generative Adversarial Networks. We study how to incorporate prior knowledge on morphological ECG patterns into synthetic data generation. We demonstrate how to improve the modeling of the complex dynamics of ECG signals by separating between modeling temporal and amplitude variations. To evaluate our proposal, we consider ECG signals taken from the MIT-BIH arrhythmia database for training our models. The obtained experimental results demonstrate the efficacy of our approach for generating synthetic ECG signals and improving ECG classification.
SAGA-Net: efficient pointcloud completion with shape-assisted graph attention neural network

In this paper, we propose a real-time shape-assisted graph attention neural network to perform local pointcloud repairment. The orderless pointclouds require an effective shape encoder to distill local and global geometric feature descriptors. Previous work has attempted to convert pointcloud representation into a voxelized shape or perform grid-transformations. While these approaches can subsequently allow common convolution operations on the structured data, they either pose additional computational cost or disrupt the local geometric information. We present SAGA-Net, an efficient graph attention neural network framework with a prior shape inquiry protocol that effectively extracts local geometric information, locates the descriptor for the missing region and accurately reconstructs the local region in a real-time manner. Our framework has been validated on a benchmark dataset, ShapeNet. We demonstrate that our framework can repair each partial pointcloud with accuracy surpassing other frameworks in most object categories, and improve the computational efficiency by orders of magnitude in terms of time cost.
PITracker: Detecting Android PendingIntent Vulnerabilities through Intent Flow Analysis

Intent is an essential inter-component communication mechanism of Android OS, which can be used to request an action from another app component. The security of its design and implementation attracts lots of attention. However, the security of PendingIntent, a kind of delayed-triggered Intent, was neglected by most previous research, and the related analysis techniques are still imperfect. In this paper, we design a novel automated tool, PITracker, to detect the PendingIntent vulnerabilities in Android apps. It achieves the Intent flow tracking technique proposed by us, figuring out how an Intent is created and where it goes. In the real-world evaluations, PITracker discovered 2,939 potential threats in 10,000 third-party apps and 214 in 1,412 pre-installed apps. Among them, 11 exploitable vulnerabilities have been confirmed and acknowledged by the corresponding vendors.
The impact of different facial expression intensities on the performance of pre-trained emotion recognition models

Facial Expression Recognition (FER) has improved a great deal with the advances of machine learning, mainly due to the development of deep learning methods for automatic facial expression classification. Due to the availability of big datasets required for the training processes, the amount of commercial and open-source solutions that use transfer learning and pre-trained machine learning models have increased. However, there is not enough information about the performance of these models in non-standard scenarios, in view of a set of images with variations of intensity in emotional facial expressions. In this article, an evaluation of the performance of pre-trained open-source models used for facial expressions recognition of emotions was carried out on images with different facial expression intensities. A total of 1512 video frames from the ADFES-BIV dataset were submitted to five pre-trained machine learning models for performance evaluation. The ADFES-BIV dataset contains representations of seven facial expressions (anger, fear, disgust, happiness, neutral, sadness, surprise) considering three intensity levels (low, intermediate and high). The highest accuracy values (60% to 74%) were obtained for images of apex frames from high intensity facial expression videos. The lowest accuracy values (17% to 29%) were observed for low intensity frames. The results show that the tested pre-trained machine learning models are very susceptible to variations in intensity of facial expressions. In addition, a large recall variability rate was observed among different facial expressions. Therefore, before adopting pre-trained models, their performance should be carefully analyzed in order to meet the requirements of each specific application.
Leveraging hardware-dependent knowledge extraction with multiple program analysis techniques

Hardware-dependent systems must follow coding rules which are specific to the employed hardware. Among these rules, coding rules related to register-access are frequently required. Verifying these coding rules is challenging because of their variances. This paper proposed a practical approach to handle this problem. The approach is to extract the information related to register-access, called hardware-dependent knowledge, in the source code beforehand. The extracted knowledge is convenient to adapt for the variances of the coding rules. The process of extracting this knowledge is performed by combining multiple program analysis techniques: pattern matching, abstract interpretation-based static program analysis, bounded model checking, and counterexample-guided abstraction refinement. The proposed approach is evaluated by applying for an industrial source code. The result shows that combining multiple program analysis techniques is effective in extracting hardware-dependent knowledge.
Context Matters! Identifying Argumentative Relations in Essays

Context is crucial for identifying semantic relations between input sequences embedded in a document. This paper studies how different levels of context, coming from discourse markers or from a paragraph-wide context window, impact the capability of language models to identify inference relations. Empirical results for the task of argumentative relation identification show that the proposed paragraph-aware model provides considerable improvements over a context-agnostic baseline, surpassing the usage of discourse markers alone.
Dissimilarity‐based test case prioritization through data fusion

Test case prioritization (TCP) aims at scheduling test case execution so that more important test cases are executed as early as possible. Many TCP techniques have been proposed, according to different concepts and principles, with dissimilarity‐based TCP (DTCP) prioritizing tests based on the concept of test case dissimilarity: DTCP chooses the next test case from a set of candidates such that the chosen test case is farther away from previously selected test cases than the other candidates. DTCP techniques typically only use one aspect/granularity of the information or features from test cases to support the prioritization process. In this article, we adopt the concept of data fusion to propose a new family of DTCP techniques, data‐fusion‐driven DTCP (DDTCP), which attempts to use different information granularities for prioritizing test cases by dissimilarity. We performed an empirical study involving 30 versions of five subject programs, investigating the testing effectiveness and efficiency by comparing DDTCP against DTCP techniques that use a dissimilarity granularity. The experimental results show that not only does DDTCP have better fault‐detection rates than single‐granularity DTCP techniques, but it also appears to only incur similar prioritization costs. The results also show that DDTCP remains robust over multiple system releases.
Oral and long‐acting antipsychotics for relapse prevention in schizophrenia‐spectrum disorders: a network meta‐analysis of 92 randomized trials including 22,645 participants

According to current evidence and guidelines, continued antipsychotic treatment is key for preventing relapse in people with schizophrenia‐spectrum disorders, but evidence‐based recommendations for the choice of the individual antipsychotic for maintenance treatment are lacking. Although oral antipsychotics are often prescribed first line for practical reasons, long‐acting injectable antipsychotics (LAIs) are a valuable resource to tackle adherence issues since the earliest phase of disease. Medline, EMBASE, PsycINFO, CENTRAL and CINAHL databases and online registers were searched to identify randomized controlled trials comparing LAIs or oral antipsychotics head‐to‐head or against placebo, published until June 2021. Relative risks and standardized mean differences were pooled using random‐effects pairwise and network meta‐analysis. The primary outcomes were relapse and dropout due to adverse events. We used the Cochrane Risk of Bias tool to assess study quality, and the CINeMA approach to assess the confidence of pooled estimates. Of 100 eligible trials, 92 (N=22,645) provided usable data for meta‐analyses. Regarding relapse prevention, the vast majority of the 31 included treatments outperformed placebo. Compared to placebo, “high” confidence in the results was found for (in descending order of effect magnitude) amisulpride‐oral (OS), olanzapine‐OS, aripiprazole‐LAI, olanzapine‐LAI, aripiprazole‐OS, paliperidone‐OS, and ziprasidone‐OS. “Moderate” confidence in the results was found for paliperidone‐LAI 1‐monthly, iloperidone‐OS, fluphenazine‐OS, brexpiprazole‐OS, paliperidone‐LAI 1‐monthly, asenapine‐OS, haloperidol‐OS, quetiapine‐OS, cariprazine‐OS, and lurasidone‐OS. Regarding tolerability, none of the antipsychotics was significantly worse than placebo, but confidence was poor, with only aripiprazole (both LAI and OS) showing “moderate” confidence levels. Based on these findings, olanzapine, aripiprazole and paliperidone are the best choices for the maintenance treatment of schizophrenia‐spectrum disorders, considering that both LAI and oral formulations of these antipsychotics are among the best‐performing treatments and have the highest confidence of evidence for relapse prevention. This finding is of particular relevance for low‐ and middle‐income countries and constrained‐resource settings, where few medications may be selected. Results from this network meta‐analysis can inform clinical guidelines and national and international drug regulation policies.
Learning of new associations invokes a major change in modulations of cortical beta oscillations in human adults

Large-scale cortical beta (β) oscillations have been implicated in the learning processes but their exact role is debated. We explored the dynamics of β-oscillations while 25 adult participants learned, through trial and error, novel associations between four auditory pseudowords and movements of four body extremities. We used MEG to evaluate learninginduced changes in beta modulation accompanying cue-triggered movements. Our findings showed that spatial-temporal characteristics of movement-related β-oscillations underwent a major transition as learning proceeded. Early in learning, suppression of β-power in multiple cortical areas occurred long before movement initiation and sustained throughout the whole behavioral trial. As learning advanced and task performance reached asymptote, βsuppression was replaced by a widespread and prolonged rise in β-power. The β-power rise started shortly after the initiation of correct motor response and mainly comprised the prefrontal and medial temporal regions of the left hemisphere. This post-decision β-power predicted trial-by-trial response times (RT) at both stages of learning (before and after the rules become familiar) but in opposite ways. When a subject started to acquire associative rules and gradually improved task performance, a decrease in RT was correlated with the increase in the post-decision β-band power. Repeatedly correct implementation of the learned rules reversed this correlation in the opposite direction with faster (more confident) responses associated with the weaker post-decision β-band synchronization. Our findings suggest that maximal beta activity is pertinent to a distinct stage of learning and may serve to strengthen the newly learned association in a distributed memory network.
Global genomic epidemiology, resistome, virulome and plasmidome of the Extraintestinal Pathogenic Escherichia coli (ExPEC) ST38 lineage

Most of the extraintestinal human infections worldwide are determined by some specific Extraintestinal Pathogenic Escherichia coli (ExPEC) lineages, which also present a zoonotic character. One of these lineages belongs to ST38, which is considered a uropathogenic/enteroaggregative E. coli hybrid and a high-risk globally disseminated ExPEC. To get insights on the aspects of ST38 epidemiology and evolution as a multidrug resistant and pathogenic lineage, this study performed a global comparative genomic analysis on ST38 genomes. A total of 376 genomes recovered from environments, humans, livestock, wild and domestic animals in all continents throughout three decades were analyzed considering the ST38 phylogenomics, resistome, plasmidome, and virulome. In general, our analyses revealed that, independently of host sources and geographic origin, these genomes were distributed in two clusters comprising clonal and non-clonal genomes. Moreover, the ST38 accessory genome was not strictly associated with clusters and sub-clusters. Interestingly, the High Pathogenicity Island (HPI) prevailed in the major cluster (cluster 2), which comprised most of the genomes from human origin recovered worldwide (2000 to 2020). In addition, the ExPEC ST38 harbors a huge and diverse plasmidome that might contribute to the evolution of this lineage, reflecting in ST38 ability to live in a diversity of niches as a commensal or pathogenic organism.
Identification of sex-determining loci in hybridizing Catostomus fish species

Despite the near-universality of gonochorism (separate sexes) in eukaryotic organisms, the underlying mechanisms of sex determination are poorly understood and highly variable in some taxa. Sex determination mechanisms may promote or impede reproductive isolation depending on whether mechanisms are similar between related species, so identifying genomic regions associated with sex determination is especially relevant for understanding evolutionary diversification and hybridization between closely related species. In Catostomus fishes, contemporary hybridization is variable and extensive, but factors influencing hybridization dynamics are not fully understood. In the present study, we aim to describe the genetic basis of sex determination in bluehead (Catostomus discobolus) and white suckers (Catostomus commersonii ) to understand the potential impact of sex determination on reproductive isolation. We used genotyping-by-sequencing data from Catostomus species and their hybrids to identify regions of the genome associated with sex using a genome-wide association study and the identification of sex-specific loci. We identified a genetic basis of sex determination in Catostomus fishes, with a region of the genome significantly associating with sex in bluehead suckers. This region is suggestive of a master sex-determining region in bluehead suckers but is not significant in white suckers, implying that either the sex-determining region of the genome differs in these two species that hybridize, or that sample size was insufficient to identify this genomic region in white suckers. By describing and comparing sex-determination systems across Catostomus fish species, we highlight the relationship between sex determining systems and hybridization in closely related fish species.
A common framework for discriminability and perceived intensity of sensory stimuli

The perception of sensory attributes is often quantified through measurements of discriminability (the ability to detect small stimulus changes), as well as through direct judgements of appearance or intensity. Despite their ubiquity, the relationship between these two measurements remains controversial and unresolved. Here, we propose a framework in which they arise from different aspects of a common representation. Specifically, we assume that judgements of stimulus intensity (e.g., through rating scales) reflect the mean value of an internal representation, and discriminability reflects the ratio between the derivative of the mean value and the internal noise amplitude, as quantified by the measure of Fisher Information. A unique identification of internal representation properties can be achieved by combining the two measurements. As a central example, we show that Weber’s Law of perceptual discriminability can co-exist with Stevens’ power-law scaling of intensity ratings (for all exponents), when the noise amplitude increases in proportion to the representational mean. We extend this result beyond Weber’s range by incorporating a more general and physiology-inspired form of noise, and show that the combination of noise properties and discriminability measurements accurately predicts intensity ratings across a variety of sensory modalities and attributes. Our framework unifies two major perceptual measurements - discriminability and rating scales, and proposes a neural interpretation for the underlying representations. Additionally, our framework teases two super-threshold perceptual measurements apart – rating scales and super-threshold perceptual distance, which were thought to measure the same perceptual aspect, and generated decades of mixed experimental results in the literature. Significance Statement Perceptual measurements of stimulus sensitivity (discriminability) and stimulus appearance (intensity) are ubiquitous in the study of perception. However, the relationship between these two seemingly disparate measurements remains unclear. Existing proposals have been made since 60 years ago, but generally lack support from perceptual or physiological measurements, and at the same time, new theories have still been proposed each year. This problem is central to perceptual studies, and lacking understanding of which have resulted in many other conundrums in the literature remain unresolved. Here, we provide a simple and elegant framework with which to interpret and unify perceptual discrimination and intensity measurements, and the framework gained empirical support from data in multiple perceptual domains.