Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Course Responses of Apple Leaf Endophytes to the Infection of Gymnosporangium yamadae

Apple rust, caused by Gymnosporangium yamadae, poses a significant challenge to apple production. Prior studies have underscored the pivotal role played by endophytic microbial communities, intimately linked with the host, in influencing plant diseases and their pathogenic outcomes. The objective of this study is to scrutinize alternations in endophytic microbial communities within apple leaves at different stages of apple rust using high-throughput sequencing technology. The findings revealed a discernible pattern characterized by an initial increase and subsequent decrease in the alpha diversity of microbial communities in diseased leaves. A microbial co-occurrence network analysis revealed that the complexity of the bacterial community in diseased leaves diminished initially and then rebounded during the progression of the disease. Additionally, employing the PICRUSt2 platform, this study provided preliminary insights into the functions of microbial communities at specific disease timepoints. During the spermogonial stage, endophytic bacteria particularly exhibited heightened activity in genetic information processing, metabolism, and environmental information processing pathways. Endophytic fungi also significantly enriched a large number of metabolic pathways during the spermogonial stage and aecial stage, exhibiting abnormally active life activities. These findings establish a foundation for comprehending the role of host endophytes in the interaction between pathogens and hosts. Furthermore, they offer valuable insights for the development and exploitation of plant endophytic resources, thereby contributing to enhanced strategies for managing apple rust.
ITGA11, a Prognostic Factor Associated with Immunity in Gastric Adenocarcinoma

Background Stomach adenocarcinoma (STAD) presents a challenge given its advanced stage at diagnosis and poor prognosis. Integrin subunit alpha 11 (ITGA11) encodes alpha integrin and has been implicated in promoting tumorigenesis and development by participating in cell proliferation and invasion. However, the precise mechanism of ITGA11 in STAD remains unclear. Methods The differences in ITGA11 expression levels between 375 gastric cancer samples and 32 paracancerous tissue samples from the Cancer Genome Atlas (TCGA) database were examined. The relationship between ITGA11 expression and clinical features and ITGA11 diagnostic and prognostic value were evaluated using the chi-square test and receiver operating characteristic (ROC) assay. Differentially expressed genes were identified based on ITGA11 expression. Subsequently, functional enrichment analyses were conducted using Gene Ontology, the Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis. Furthermore, immune infiltration and the expression of ITGA11-associated immune checkpoints in patients with tumors were assessed using CIBERSORT, single-sample gene set enrichment analysis, and the TIMER database. Drug sensitivity associated with ITGA11 expression was analyzed using the R oncoPredict package to guide treatment decisions. Finally, the difference in ITGA11 expression between cancer tissue and the adjacent tissues was validated using quantitative PCR (qPCR) and immunohistochemistry. Results The gastric cancer tissue had significantly upregulated ITGA11 expression compared to paracancerous tissues. ITGA11 demonstrated robust diagnostic and prognostic value in gastric cancer (GC) and was an independent risk factor for adverse outcomes. The patients with STAD with elevated ITGA11 expression levels had heightened immune cell infiltration and increased immune checkpoint marker expression. Notably, patients with increased ITGA11 expression demonstrated reduced responsiveness to oxaliplatin and afatinib. Conclusion The results indicated the pivotal role of ITGA11 in shaping the tumor immune microenvironment, ultimately establishing ITGA11 as an immune-related prognostic predictor within the intricate landscape of STAD.
Chaotic Monkey Algorithm Based Optimal Sensor Placement

Optimal sensor placement (OSP) techniques are of vital importance for the monitoring of large-scale structures or complex mechanical systems. In order to overcome the defects of slow convergence speed and easily falling into local optimum in monkey algorithm (MA), a new MA, called chaotic monkey algorithm (CMA), is proposed by introducing chaos search strategy to implement the OSP. In this algorithm, the initial monkeys are generated by using chaos variable and binary coding to enhance the global search capability, and a greedy strategy is adopted to improve the efficiency of local search. Numerical experiments are conducted on the sensor placement of a suspension bridge. The results verify that the new CMA can solve the OSP problem well and has better search capability than MA.
Multidimensional Spatial Vitality Automated Monitoring Method for Public Open Spaces Based on Computer Vision Technology: Case Study of Nanjing's Daxing Palace Square

Assessing the vitality of public open spaces is critical in urban planning and provides insights for optimizing residents’ lives. However, prior research has fragmented study scopes and lacks fine-grained behavioral data segmentation capabilities and diverse vitality dimension assessments. We utilized computer vision technology to collect fine-grained behavioral data and proposed an automated spatial vitality monitoring framework based on discrete trajectory feature points. The framework supported the transformation of trajectory data into four multidimensional vitality indicators: crowd heat, resident behavior ratio, movement speed, and spatial participation. Subsequently, we designed manual validation mechanisms to demonstrate the monitoring framework’s efficacy and utilized the results to explore the changes in vitality, and the influencing factors, in a small public space. Discrete trajectory feature points effectively addressed the literature’s fragmented study scope and limited sample size issues. Spatial boundaries had a significantly positive impact on spatial vitality, confirming the “boundary effect” theory. The peak spatial vitality periods were from 08:30 to 09:30 and from 17:30 to 18:30. A higher enclosure degree and better rest facilities positively impacted spatial vitality, while a lower enclosure degree did not consistently suppress spatial vitality in all situations. Overall, spatial features and spatial vitality have a complex nonlinear relationship.
Maslach Burnout Inventory – General Survey

Abstract: This study sought to investigate the measurement properties of a “gold standard” instrument for measuring burnout, the Maslach Burnout Inventory – General Survey (MBI-GS), with a systematic literature review and meta-analysis of studies that had as the primary aim its psychometric validation. The search spanned from January 1996 to December 2022 using the following databases: Web of Science, Scopus, PubMed, APA PsychINFO, ScienceDirect, and ProQuest Dissertations and Theses Global. Our search identified 35 eligible studies for inclusion in the systematic review. Of these, 17 were included in the meta-analysis. The meta-analysis of the original 16-item version studies supported a three-dimensional representation of burnout with modest internal consistencies. However, similar to the narrative findings, it also indicated the viability of a two-factor solution. Consequently, the structural validity of the MBI-GS remains unclear, and so does its cross-cultural validity. The criterion validity of the cynicism and personal efficacy scales also raised a few questions. Overall, the findings provided some support for the adequacy of the measurement properties of the MBI-GS as a research tool measuring exhaustion, cynicism, and professional efficacy. However, we also identified challenges and practices of which researchers should be cognisant and that they should consider in future burnout projects.
DSHNet: A Semantic Segmentation Model of Remote Sensing Images Based on Dual Stream Hybrid Network

Semantic segmentation is an important issue in intelligent interpretation of remote sensing, playing an important role in applications such as Earth observation and land data update. However, remote sensing images often contain complex ground objects and the boundaries between them are blurred, which poses a huge challenge to the semantic segmentation task of remote sensing images. This article proposes a dual stream hybrid network (DSHNet) model, which focuses on parallel extraction of semantic and boundary features in remote sensing images, and improves the performance of semantic segmentation by fully integrating dual stream information. In the semantic stream, the ViT model pretrained on remote sensing images is used as the backbone network for feature extraction. In the boundary stream, the boundary detection operator Sobel is used to capture the boundaries of different ground objects in the image, and a boundary enhancement mechanism is taken to optimize and enhance the feature representation of ground object boundaries. In addition, DSHNet designs a feature fusion module to cross-aggregate features from both semantic and boundary streams. Compared with the state-to-art semantic segmentation methods, DSHNet model has achieved the best performance on two datasets of Yellow River Estuary Wetland and Gaofen image dataset.
Acceleration of Sociality-Aware Message Routing in Opportunistic Networks

In this paper, we focus on the message routing in Opportunistic Networks (OppNets), and propose a generic method to enhance the routing performance of existing protocols. Most of the current OppNet protocols calculate the utility value for each node based on their social characteristics such as the degree centricity, and make forwarding decisions by comparing utility values with encountered nodes. However, certain protocols such as PRoPHET and Spray-and-Focus impose strict limitations on the number of message copies, leading to a situation where, once a message copy is transmitted, no additional copies can be made even if a node with a much better utility value is encountered later. Moreover, since existing protocols unconditionally forward messages to the nodes with higher utility values regardless of their distance to the destination, it results in the filling of input buffers with redundant messages that do not contribute to the increase of the message delivery rate. This paper introduces innovative methods to overcome these issues whose performance is experimentally evaluated through simulations.
An empirical study of male and female leadership styles in a segregated work environment in the Kingdom of Saudi Arabia

i DEDICATION . iii ACKNOWLEDGEMENT . iv LIST OF PUBL ICATIONS. v LIST OF CONTENTS . vi LIST OF TABL ES . xii LIST OF FIGURES . xiii LIST OF APPENDICES. xiii LIST OF ABBREVIATIONS. xiv CHAPTER ONE: INTRODUCTION . 1 1.
Gradient Monitored Reinforcement Learning for Jamming Attack Detection in FANETs

Unmanned Aerial Vehicles (UAVs) have several military and civilian applications to perform tasks that do not require a central processing unit or human involvement. There are various vulnerable characteristics, alternatively limitations, in UAV systems such as data loss, signals interference, disabling sensors, misleading weapons, cyber attacks, disrupting services, etc. Jamming attack is one of the cyber threats that likely lead to denial of service that often occurs in wireless communication systems like Flying ad hoc networks (FANETs) and Internet of Drones (IoD). Over years, there are several approaches proposed by researchers to detect jamming attacks such as rule-based jamming attack detection mechanism, Bayesian game-theoretic mechanism, IoD-based protection mechanism, communication channel techniques (channel hopping, spectrum spreading, MIMO-based jamming mitigation, coding, etc), delay tolerant networking technique, and cryptographic algorithms, however, these methods were not suitable for jamming detection in UAV environment. The major challenges are on the delivery efficiency, processing time, accuracy, energy consumption, flight distance, and flight autonomy. In this paper, we introduce a method to detect the jamming attack using Reinforcement Learning-based Gradient Monitored (RLGM) mechanism. RLGM maintains safe regions and reduces gradient variance for intended training and this provides a better accuracy of the learning goal. In addition, RLGM achieves prompt training progress and selects precisely the series of parameters required by the network during the training phase. RLGM produces spontaneous derivation of the essential deep network scale over the training process drawing on automatically unvarying trained weights. Our proposed approach outperforms other reinforcement learning methods such as Federated RL, Deep Q Learning (DQL), in addition to non-machine learning based techniques such as GA-AOMDV.
Nanoparticles of Thiolated Xanthan Gum for the Oral Delivery of Miconazole Nitrate: In Vitro and In Vivo Evaluation

The objective of this research was to develop a mucoadhesive delivery system that improves permeation for the administration of poorly absorbed oral medications. Thiolation of xanthan gum (XGM) was carried out by esterification with mercaptobutyric acid. Fourier-transformed infrared spectroscopy was used to confirm thiol-derivatization. Using Ellman’s technique, it was revealed that the xanthan-mercaptobutyric acid conjugate had 4.7 mM of thiol groups in 2 mg/mL of polymeric solution. Using mucosa of sheep intestine, the mucoadhesive properties of XGM and thiolated xanthan gum (TXGM) nanoparticles were investigated and we found that TXGM had a longer bioadhesion time than XGM. The disulfide link that forms between mucus and thiolated XGM explains why it has better mucoadhesive properties than XGM. A study on in vitro miconazole (MCZ) release using phosphate buffer (pH 6.8) found that TXGM nanoparticles released MCZ more steadily than MCZ dispersion did. A 1-fold increase in the permeation of MCZ was observed from nanoparticles using albino rat intestine compared to MCZ. Albino rats were used to test the pharmacokinetics of MCZ, and the results showed a 4.5-fold increase in bioavailability. In conclusion, the thiolation of XGM enhances its bioavailability, controlled release of MCZ for a long period of time, and mucoadhesive activity.
Fact-Preserved Personalized News Headline Generation

Personalized news headline generation, aiming at generating user-specific headlines based on readers’ preferences, burgeons a recent flourishing research direction. Existing studies generally inject a user interest embedding into an encoder-decoder headline generator to make the output personalized, while the factual consistency of headlines is inadequate to be verified. In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency. In FPG, the similarity between the candidate news to be exposed and the historical clicked news is used to give different levels of attention to key facts in the candidate news, and the similarity scores help to learn a fact-aware global user embedding. Besides, an additional training procedure based on contrastive learning is devised to further enhance the factual consistency of generated headlines. Extensive experiments conducted on a real-world benchmark PENS1 validate the superiority of FPG, especially on the tradeoff between personalization and factual consistency.1https://msnews.github. io/pens.html
Literary Identification of Differentially Hydroxymethylated DNA Regions for Type 2 Diabetes Mellitus: A Scoping Minireview

Type 2 diabetes mellitus (T2DM) is a public health condition where environmental and genetic factors can intersect through hydroxymethylation. It was unclear which blood DNA regions were hydroxymethylated in human T2DM development. We aimed to identify the regions from the literature as designed in the ongoing Twins Discordant for Incident T2DM Study. A scoping review was performed using Medical Subject Headings (MeSH) and keyword methods to search PubMed for studies published in English and before 1 August 2022, following our registered protocol. The keyword and MeSH methods identified 12 and 3 records separately, and the keyword-identified records included all from the MeSH. Only three case-control studies met the criteria for the full-text review, including one MeSH-identified record. Increased global levels of 5-hydroxymethylated cytosine (5hmC) in T2DM patients versus healthy controls in blood or peripheral blood mononuclear cells were consistently reported (p < 0.05 for all). Among candidate DNA regions related to the human SOCS3, SREBF1, and TXNIP genes, only the SOCS3 gene yielded higher 5hmC levels in T2DM patients with high poly-ADP-ribosylation than participants combined from those with low PARylation and healthy controls (p < 0.05). Hydroxymethylation in the SOCS3-related region of blood DNA is promising to investigate for its mediation in the influences of environment on incident T2DM.
Incorporating Feature Penalty in Reinforcement Learning for Ludo Game

Modeling cognitive behavior in AI games has got much attention in recent years. One popular and most commonly used cognitive learning technique is Reinforcement Learning (RL). It is a self-learning ability that empowers game agents to flatter as an autodidact. RL has also got much attention in learning board games. Ludo is one of the popular board games of the Asian subcontinent. Researchers primarily focused on the reward feature of RL. This varies between 0 and 1 (i.e. No reward to maximum reward). However, there is no direct concept of penalty in RL. Focus on penalties leads to efficient learning. Such that, considering the penalty before action selection may decrease the worth of actions leading to the penalty. Keeping this in view, we have proposed a Q-learning-based Intelligent Ludo Agent (ILA) that incorporates both rewards and penalties. Q-Learning is an off-policy RL technique used in Machine Learning (ML). The information employed for learning is gained from Q-learning tables maintaining the history of rewards and penalties gained over the prior iterations. ILA has been trained using two different Q-learning strategies. The first one uses board indices with penalty information for moving a piece. Other uses situation on the board to select the appropriate strategy for moving a piece. Also for evaluating performance various matches of ILA were conducted against humans and random machine players. Human players facilitated in flourishing learning abilities of ILA. Whereas, experimentation revealed comparable learning abilities of autodidact agents. Thereby, moving a step ahead from formerly adopted strategies.
Using Deep Learning to Recognize Fake Faces

—In recent times, many fake faces have been created using deep learning and machine learning. Most fake faces made with deep learning are referred to as “deepfake photos. ” Our study’s primary goal is to propose a useful framework for recognizing deep-fake photos using deep learning and transformative learning techniques. This paper proposed convolutional neural network (CNN) models based on deep transfer learning methodologies in which the designed classifier using global average pooling (GAP), dropout, and a dense layer with two neurons that use SoftMax are substituted for the final fully connected layer in the pretrained models. DenseNet201, the suggested framework, produced the best accuracy of 86.85% for both the deepfake and real picture datasets, while MobileNet produced a lower accuracy of 82.78%. The obtained experimental results showed that the proposed method outperformed other state-of-the-art fake picture discriminators in terms of performance. The proposed architecture helps cybersecurity specialists fight deepfake-related cybercrimes.
A Novel Cre Recombinase Mouse Strain for Cell-Specific Deletion of Floxed Genes in Ribbon Synapse-Forming Retinal Neurons

We generated a novel Cre mouse strain for cell-specific deletion of floxed genes in ribbon synapse-forming retinal neurons. Previous studies have shown that the RIBEYE promotor targets the expression of recombinant proteins such as fluorescently tagged RIBEYE to photoreceptors and retinal bipolar cells and generates fluorescent synaptic ribbons in situ in these neurons. Here, we used the same promotor to generate a novel transgenic mouse strain in which the RIBEYE promotor controls the expression of a Cre-ER(T2) recombinase (RIBEYE-Cre). To visualize Cre expression, the RIBEYE-Cre animals were crossed with ROSA26 tau-GFP (R26-τGFP) reporter mice. In the resulting RIBEYE-Cre/R26 τGFP animals, Cre-mediated removal of a transcriptional STOP cassette results in the expression of green fluorescent tau protein (tau-GFP) that binds to cellular microtubules. We detected robust tau-GFP expression in retinal bipolar cells. Surprisingly, we did not find fluorescent tau-GFP expression in mouse photoreceptors. The lack of tau-GFP reporter protein in these cells could be based on the previously reported absence of tau protein in mouse photoreceptors which could lead to the degradation of the recombinant tau protein. Consistent with this, we detected Cre and tau-GFP mRNA in mouse photoreceptor slices by RT-PCR. The transgenic RIBEYE-Cre mouse strain provides a new tool to study the deletion of floxed genes in ribbon synapse-forming neurons of the retina and will also allow for analyzing gene deletions that are lethal if globally deleted in neurons.
Découverte de Mylodontinae dans un habitat préhistorique daté du Mato Grosso (Brésil): l'abri rupestre de Santa Elina

L'abri de Santa Elina s'ouvre dans des plissements calcaires precambriens situes au Nord du bassin du Paraguai. La fouille, d'une superficie de 40 m 2 au centre de l'abri rupestre, a mis en evidence une succession ininterrompue d'occupations de chasseurs-collecteurs, d'une epoque recente a 6 000 ans. Les sediments fins pulverulents secs de ces couches anthropiques ont particulierement bien conserve de nombreux poteaux et vestiges vegetaux en plus des foyers, des industries lithiques et des restes fauniques. L'assise inferieure, fouillee entre 1,50 et 3,50 m sur une surface reduite de 9 m 2 , est formee de couches sableuses. Celles-ci contiennent des vestiges de megafaune ― Mylodontinae ― associes a des outils et eclats lithiques, dates a 10120 ± 60 ans BP, pour les plus recents
Cascaded Cross Attention for Review-based Sequential Recommendation

In recent years, sequential recommendation (SR) has gained significant attention in the recommender systems community. However, most previous works only consider the (user, item, timestep) interaction sequences, which limits the recommendation performance. To overcome this limitation, some studies have utilized user reviews to enrich the understanding of user preferences. However, existing review-based sequential recommendation (RBSR) methods only use either a user’s review on items or an item’s reviews by users, overlooking their complementary nature. In addition, most existing RBSR methods use a simple dot-product operation between the embeddings of a user and the candidate items for scoring, which may not adequately capture the complex relationships among the item sequence, review sequence and candidate items. To release the potential of RBSR, we propose a novel model called cascaded cross attention (CCA), which utilizes aggregated reviews to compensate for the information that is lacking in individual reviews. Moreover, we propose a cascaded cross-attention layer to better capture the dependency intra a sequence and the relationships between a sequence and the candidate items. Extensive experimental results on three public datasets demonstrate that our CCA outperforms the state-of-the-art methods. Additionally, the case study and visualization results showcase high interpretability of our CCA.