Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the interplay between posttraumatic stress disorder, gut microbiota, and inflammatory biomarkers: a comprehensive meta-analysis

Introduction Posttraumatic stress disorder (PTSD) is the most common mental health disorder to develop following exposure to trauma. Studies have reported conflicting results regarding changes in immune biomarkers and alterations in the abundance of bacterial taxa and microbial diversity in patients with PTSD. Aim The purpose of this meta-analysis is to summarize existing studies examining gut microbiota characteristics and changes in immune biomarkers in patients with PTSD. Methods Relevant studies were systematically searched in PubMed, Scopus, and Embase, published in English between January 1, 1960, and December 1, 2023. The outcomes included changes in abundance and diversity in gut microbiota (gut microbiota part) and changes in immune biomarkers (immune part). Results The meta-analysis included a total of 15 studies, with 9 focusing on changes in inflammatory biomarkers and 6 focusing on changes in gut microbiota composition in patients with PTSD. No differences were observed between groups for all inflammatory biomarkers (P≥0.05). Two of the six studies found that people with PTSD had less alpha diversity. However, the overall Standardized Mean Difference (SMD) for the Shannon Diversity Index was not significant (SMD 0.27, 95% CI -0.62–0.609, p = 0.110). Regarding changes in abundance, in two of the studies, a significant decrease in Lachnospiraceae bacteria was observed. Conclusion This meta-analysis provides a comprehensive overview of gut microbiota characteristics in PTSD, suggesting potential associations with immune dysregulation. Future research should address study limitations, explore causal relationships, and consider additional factors influencing immune function in individuals with PTSD. Systematic review registration https://www.crd. york.ac. uk, identifier CRD42023476590.
Cooling Capacity Prediction for Safety-Guaranteed Optimization in IoT-Enabled Data Center

The increasing demand for computation and storage has led to a significant rise in energy consumption and carbon emissions in data centers. Optimizing cooling systems to reduce Power Usage Effectiveness (PUE) has become a key concern due to its significant impact on the energy consumption of data centers. Thanks to the development of Internet of Things (IoT) techniques, previous studies have employed machine learning methods, which are representative data-driven techniques that are superior to traditional methods, to predict the energy consumption of the data center for assisting optimization. However, these methods have not addressed the safety issues that may arise during the optimization process. In this paper, we investigate the problem of predicting cooling capacity to ensure safety-guaranteed PUE optimization in IoT-enabled data center by leveraging machine learning. Specifically, we develop a safety-guaranteed PUE optimization framework for IoT-enabled data center, and utilize a neural network to predict the cooling capacity, ensuring safety during the optimization process. To further reduce the complexity of prediction models and improve their performance, we propose a general supervised autoencoder for dimension reduction and feature extraction. Extensive experiments demonstrate the effectiveness and superiority of the proposed neural network and supervised autoencoder over other machine learning methods.
Identification of PLK1-PBD Inhibitors from the Library of Marine Natural Products: 3D QSAR Pharmacophore, ADMET, Scaffold Hopping, Molecular Docking, and Molecular Dynamics Study

PLK1 is found to be highly expressed in various types of cancers, but the development of inhibitors for it has been slow. Most inhibitors are still in clinical stages, and many lack the necessary selectivity and anti-tumor effects. This study aimed to create new inhibitors for the PLK1-PBD by focusing on the PBD binding domain, which has the potential for greater selectivity. A 3D QSAR model was developed using a dataset of 112 compounds to evaluate 500 molecules. ADMET prediction was then used to select three molecules with strong drug-like characteristics. Scaffold hopping was employed to reconstruct 98 new compounds with improved drug-like properties and increased activity. Molecular docking was used to compare the efficient compound abbapolin, confirming the high-activity status of [(14S)-14-hydroxy-14-(pyridin-2-yl)tetradecyl]ammonium,[(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium. Molecular dynamics simulations and MMPBSA were conducted to evaluate the stability of the compounds in the presence of proteins. An in-depth analysis of [(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium identified them as potential candidates for PLK1 inhibitors.
Patterns and trends of group-based inequality in Brazil

In this paper, we analyse the patterns and trends of group-based inequalities in Brazil in the past 30 years. Using data from the last four demographic censuses (1980, 1991, 2000, and 2010), we estimate numerous measures to analyse inequalities between different â€˜ethnicâ€™ groups. Our results show that the trend toward greater equality in Brazil shown in other analyses of vertical inequality is also found in terms of horizontal inequalities along racial, gender, and regional lines between 1980 and 2010. Nevertheless, horizontal inequalities in terms of race and gender in particular remain pronounced; as shown using various measures, race is highly correlated with income and education. We show that municipalities with low ethnic diversity and low income and education inequality tend to be located in the South region. In regression analysis, we note that ethnic diversity negatively affects the institutional quality of Brazilian municipalities.
CNN-Based Synergetic Beamforming for Symbiotic Secure Transmissions in Integrated Satellite-Terrestrial Network

The integrated satellite-terrestrial network has received keen attention in the 6G wireless communication network, as it can significantly improve network coverage and system transmission efficiency. However, it is susceptible to eavesdropping threats. Physical layer security serves as an endogenous means to counter eavesdropping, but due to constraints such as communication link similarity and resource limitations, it fails to substantially enhance secrecy rates. The flourishing development of new technologies like machine learning has introduced novel opportunities for enhancing physical layer security. In the paper, we propose a synergetic beamforming scheme based on convolutional neural network (CNN) to achieve symbiotic security among heterogeneous downlinks in integrated satellite-terrestrial network. This method divides the acquired channel state information into real and imaginary parts, serving as inputs to the network, and outputs beamforming vectors for both the base station and satellite. During the training process, we incorporate constraint conditions as penalty terms into the loss function and correlate training parameters with iteration count, leading to improved training performance. Lastly, extensive simulation verification is conducted to demonstrate the effectiveness of this method in enhancing secrecy rate.
OsLRR-RLP2 Gene Regulates Immunity to Magnaporthe oryzae in Japonica Rice

Rice is an important cereal crop worldwide, the growth of which is affected by rice blast disease, caused by the fungal pathogen Magnaporthe oryzae. As climate change increases the diversity of pathogens, the disease resistance genes (R genes) in plants must be identified. The major blast-resistance genes have been identified in indica rice varieties; therefore, japonica rice varieties with R genes now need to be identified. Because leucine-rich repeat (LRR) domain proteins possess R-gene properties, we used bioinformatics analysis to identify the rice candidate LRR domain receptor-like proteins (OsLRR-RLPs). OsLRR-RLP2, which contains six LRR domains, showed differences in the DNA sequence, containing 43 single-nucleotide polymorphisms (SNPs) in indica and japonica subpopulations. The results of the M. oryzae inoculation analysis indicated that indica varieties with partial deletion of OsLRR-RLP2 showed susceptibility, whereas japonica varieties with intact OsLRR-RLP2 showed resistance. The oslrr-rlp2 mutant, generated using clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), showed increased pathogen susceptibility, whereas plants overexpressing this gene showed pathogen resistance. These results indicate that OsLRR-RLP2 confers resistance to rice, and OsLRR-RLP2 may be useful for breeding resistant cultivars.
Integrated UPLC/Q-TOF-MS/MS Analysis and Network Pharmacology to Reveal the Neuroprotective Mechanisms and Potential Pharmacological Ingredients of Aurantii Fructus Immaturus and Aurantii Fructus

Aurantii Fructus (AF) and Aurantii Fructus Immaturus (AFI) have been used for thousands of years as traditional Chinese medicine (TCM) with sedative effects. Modern studies have shown that Citrus plants also have protective effects on the nervous system. However, the effective substances and mechanisms of action in Citrus TCMs still remain unclear. In order to explore the pharmacodynamic profiles of identified substances and the action mechanism of these herbs, a comprehensive approach combining ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS/MS) analysis and network pharmacology was employed. Firstly, UNIFI 2.1. 1 software was used to identify the chemical characteristics of AF and AFI. Secondly, the SwissTargetPrediction database was used to predict the targets of chemical components in AF and AFI. Targets for neuroprotection were also collected from GeneCards: The Human Gene Database (GeneCards-Human Genes|Gene Database|Gene Search). The networks between targets and compounds or diseases were then constructed using Cytoscape 3.9. 1. Finally, the Annotation, Visualization and Integrated Discovery Database (DAVID) (DAVID Functional Annotation Bioinformatics Microarray Analysis) was used for GO and pathway enrichment analysis. The results showed that 50 of 188 compounds in AF and AFI may have neuroprotective biological activities. These activities are associated with the regulatory effects of related components on 146 important signaling pathways, derived from the KEGG (KEGG: Kyoto Encyclopedia of Genes and Genomes), such as neurodegeneration (hsa05022), the Alzheimer’s disease pathway (hsa05010), the NF-kappa B signaling pathway (hsa04064), the hypoxia-inducible factor (HIF)-1 signaling pathway (hsa04066), apoptosis (hsa04210), the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor resistance signaling pathway (hsa01521), and others, by targeting 108 proteins, including xanthine dehydrogenase (XDH), glutamate ionotropic receptor NMDA type subunit 2B (GRIN2B), and glucose-6-phosphate dehydrogenase (G6PD), among others. These targets are thought to be related to inflammation, neural function and cell growth.
UAP Attack for Radio Signal Modulation Classification Based on Deep Learning

Automatic Modulation Classification (AMC), which is based on deep learning, has been extensively implemented in wireless communication systems. Universal adversarial perturbation (UAP), which is a type of sample-agnostic adversarial attack, can add to all natural samples to change most of their predicted labels. In this paper, we aim to achieve universal adversarial attacks on AMC models, and thus we proposed a method based on AutoEncoder, a novel strategy that takes advantage of the feature extraction capability and dimensionality reduction of AutoEncoder to generate UAP. Firstly, we used white-box and black-box methods to generate universal adversarial perturbations. Secondly, we proved that small perturbations to the original input can significantly reduce the accuracy of the model. Moreover, once the UAP generated, the adversarial attack will no longer depend on their input and only a small number of samples are needed to generate a powerful UAP. This property eases the attack procedure for real-time applications. Finally, the UAP generated by our method can attack across models with similar structures, exhibiting strong transferability.
Multi-STMT: Multi-Level Network for Human Activity Recognition Based on Wearable Sensors

Human activity recognition (HAR) has gained significant interest in recent years, specifically in the context of wearable sensors. This approach utilizes the abundant sensory information provided by multimode embedded sensors like accelerometers and gyroscopes to deduce and identify human activities. How to extract rich representation features from these data to capture subtle differences between different activities is the key challenge of this technology. Although the HAR technology based on deep learning has achieved satisfactory results, most methods still have problems such as insufficient feature extraction and insufficient attention to key features. Therefore, this article designs a multilevel network called Multi-STMT, which is based on the spatiotemporal attention mechanism and multiscale temporal embedding. The network integrates a convolutional neural network (CNN) module and a bidirectional gated recurrent unit (BiGRU) module and introduces an attention mechanism in each module to fully capture the temporal and spatial dependence of multimodal signals. Comparing previous methods, we have achieved state-of-the-art recognition accuracy on four publicly available datasets. Specifically, on the DSADS, SisFall, HCI-HAR, and KU-HAR, the proposed method’s recognition accuracy has reached 99.48%, 91.85%, 96.67%, and 97.99%, respectively, hence indicating the advantage of the proposed method.
Proper orthogonal decomposition analysis on longitudinal streaks in channel flow laden with micro-bubbles

The streaky structures in the fluid laden with and without micro-bubbles of channel turbulent boundary layer flow were experimentally studied by using particle image velocimetry technique at a Reynolds number of Re = 9.3 × 104. Micro-bubbles were produced by the electrolysis of water through platinum wires in a low speed circulating water tunnel. The velocity fields in the streamwise-spanwise plane were obtained at three different heights (i.e. y = 3 mm, 5 mm and 7 mm) along the wall-normal direction. The two-point autocorrelation of velocity fluctuations was carried out to identify the scale of the streaky structures in the physical space statistically. The proper orthogonal decomposition (POD) method was employed to analyze the turbulent kinetic energy contents and the streaky structures in mode space. The results show that the streak intensity is decreased and the structures become indistinct in the presence of micro-bubbles at a relatively lower height of the boundary layer in physical space. The ratios of the energy corresponding to leading-order modes to the total energy of the turbulent for the mixed fluid (fluid laden with micro-bubbles) are smaller than those for the single-phase fluid (fluid without micro-bubbles) at these three heights. The effect on the energy content is decayed with the increase of POD mode number as well as the normal height in POD mode space for the mixed fluid. Through comparing the streaky and non-streaky turbulent activities in the presence of micro-bubbles, it is found that the streaky structure turbulent activities are weakened while the non-streaky turbulent activities are enhanced for the mixed fluid.
Chapter 4 Infinite Supermodularity and Preferences

This chapter studies the ordinal content of supermodularity on lattices. This chapter is a generalization of the famous study of binary relations over finite Boolean algebras obtained by Wong, Yao and Lingras. We study the implications of various types of supermodularity for preferences over finite lattices. We prove that preferences on a finite lattice merely respecting the lattice order cannot disentangle these usual economic assumptions of supermodularity and infinite supermodularity. More precisely, the existence of a supermodular representation is equivalent to the existence of an infinitely supermodular representation. In addition, the strict increasingness of a complete preorder on a finite lattice is equivalent to the existence of a strictly increasing and infinitely supermodular representation. For wide classes of binary relations, the ordinal contents of quasisupermodularity, supermodularity and infinite supermodularity are exactly the same. In the end, we extend our results from finite lattices to infinite lattices.
Biotechnology and Bioengineering

Funding information Canadian Institutes of Health Research; Canada Foundation for Innovation; The Quebec Network for Cell, Tissue and Gene Therapy ‐ ThéCell; the Centre québécois sur les matériaux fonctionnels (CQMF); PROTEO, The Quebec Network for Research on Protein Function Abstract The expansion of human peripheral blood endothelial progenitor cells to obtain therapeutically relevant endothelial colony‐forming cells (ECFCs) has been commonly performed on xeno‐derived extracellular matrix proteins. For cellular therapy applications, xeno‐free culture conditions are desirable to improve product safety and reduce process variability. We have previously described a novel fluorophore‐tagged RGD peptide (RGD‐ TAMRA) that enhanced the adhesion of mature endothelial cells in vitro. To investigate whether this peptide can replace animal‐derived extracellular matrix proteins in the isolation and expansion of ECFCs, peripheral blood mononuclear cells from 22 healthy adult donors were seeded on RGD‐TAMRA‐modified polystyrene culture surfaces. Endothelial colony formation was significantly enhanced on RGD‐TAMRA‐modified surfaces compared to the unmodified control. No phenotypic differences were detected between ECFCs obtained on RGD‐TAMRA compared to ECFCs obtained on rat‐tail collagen‐coated surfaces. Compared with collagen‐coated surfaces and unmodified surfaces, RGD‐TAMRA surfaces promoted ECFC adhesion, cell spreading, and clonal expansion. This study presents a platform that allows for a comprehensive in vitro evaluation of peptide‐based biofunctionalization as a promising avenue for ex vivo ECFC expansion.
The Community Adherence and Support Group Strategy in Niassa Province, Mozambique: A Process Evaluation of Group Formation Dynamics

In Mozambique, where 11.5% of the population is infected with HIV, there is an ongoing struggle to scale-up HIV care and treatment including the expansion of ART access and improvement of coverage rates. The health system in Mozambique, as in other high HIV prevalence countries in SSA, must contend with a crippling health-care workforce shortage and weak infrastructure, among other challenges. To achieve the ambitious goal of scaling up ART coverage (while assuring high rates of ART adherence and retention in care), it is necessary to explore new models for HIV service delivery that account for practical challenges such as understaffed health facilities and physical and non-physical barriers to patient access and adherence to ART. A new model known as the Community and Adherence Support Group (CASG) strategy, which involves stable patients on ART forming groups to share treatment distribution responsibilities, was piloted by the Ministry of Health in 2010. This research was a part of a process evaluation that generated formative, qualitative data around group formation dynamics at 3 study sites of the national pilot. This research took place in Niassa Province. University of Washington
Hyperparameter Tuning of Identity Block Uses an Imbalance Dataset with Hyperband Method

Visual pattern recognition, selection of appropriate image processing techniques, and network architecture are key factors in achieving optimal model performance. This article focuses on the application of Identity Blocks in the context of image processing, especially on unbalanced datasets. Three different datasets, namely Plant Diseases, Rock & Paper Scissors, and Animal Faces, are used in this study, each with unique characteristics. Identity Block, implemented in the ResNet network architecture, helps to overcome the gradient loss problem that often occurs in deep neural networks (DNN) with deep layers. This research specifically explores Identity Block optimization using the hyperband method to improve model performance. The average performance improvement of all optimized models is 4.45% in accuracy, 5.39% in precision, 6.4% in recall, and 6.48% in F1-score. These results show that model optimization is very good at improving identity block performance using the hyperband method.
Plant Health Assessment Based on Precision IoT Leaf Phytometry Techniques

Leaf Phytometry supersedes conventional Agri-IoT techniques because of its ability to monitor plant data from leaves, more directly, accurately, and extensively, compared to typical indirect methods that only target monitoring environment variables of a plant. It belongs to precision agriculture which is a fundamental method for plant quality and health assessment, optimization, and resource savings and has been the impetus behind agricultural success despite the extreme climate conditions of Middle Eastern countries. Four leaf phytometric techniques were researched and implemented that measure; leaf thickness via magnetic field measurement, chlorophyll level, and nitrogen level estimation via spectrophotometry, leaf capacitance via measuring capacitance, and leaf surface temperature compared to environment temperature, on the experiment plant i.e., Cabbage (Brassica sp.) to obtain highly accurate relative measurements and, rote-experiment and estimation-based absolute measurements. Four effective and low-cost leaf clips were developed for the above cases based on external research and utilizing alternative resources. Finally, a Neural Network based ML model was trained to identify plant stress from the output of leaf clips.
Weighted, Circular and Semi-Algebraic Proofs

In recent years there has been an increasing interest in studying proof systems stronger than Resolution, with the aim of building more efficient SAT solvers based on them. In defining these proof systems, we try to find a balance between the power of the proof system (the size of the proofs required to refute a formula) and the difficulty of finding the proofs. In this paper we consider the proof systems circular Resolution, Sherali-Adams, Nullstellensatz and Weighted Resolution and we study their relative power from a theoretical perspective. We prove that circular Resolution, Sherali-Adams and Weighted Resolution are polynomially equivalent proof systems. We also prove that Nullstellensatz is polynomially equivalent to a restricted version of Weighted Resolution. The equivalences carry on also for versions of the systems where the coefficients/weights are expressed in unary. The practical interest in these systems comes from the fact that they admit efficient algorithms to find proofs in case these have small width/degree.
Visible Light Positioning System Based on Quantum Well Diode

Quantum Well Diodes (QWD) have been proven to possess the fundamental physical phenomenon of simultaneous light emission and detection, making them suitable for sensing requirements in the era of the Internet of Things (IoT). The QWD chip itself offers several advantages, including miniaturization, low cost, strong resistance to electromagnetic interference, and high confidentiality, which contribute to its broad application prospects. By utilizing an array of QWD chips in a 2×2 configuration, combined with hardware circuits and software algorithms, we can achieve real-time localization of spatial light sources. This provides a low-cost and miniaturized technical approach for light source positioning.