bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exogenous Methyl Jasmonate Mediated Physiological and Transcriptomic Network Improves Thrips tolerance in alfalfa (Medicago Sativa. L)
Authors: Shuang Shuang , Xiaowei Huo , chen qi , Rui Dai , li Jianwei , yan Jiaxin , jiang xiaohong , tan yao , zhang zhiqiang 
Published: September 2024

Abstract

Exogenous methyl jasmonate is widely acknowledged for its role in triggering plants' defense systems against pest invasions. Nonetheless, there has been a dearth of research exploring the elicitation of defense mechanisms by jasmonic acid in alfalfa. In order to investigate the effect of methyl jasmonate on thrips resistance in alfalfa, Medicago sativa L.cv. Caoyuan No. 4 was exogenously sprayed with different concentrations of methyl jasmonate, and thrips and Orius strigicolli (natural enemies) behavioral choice, physiological and transcriptomic analyses were performed. The results revealed a concentration-dependent inducible effect of methyl jasmonate on the behavioral choice, feeding and oviposition of thrips mediated by volatile organic compounds. Moreover, methyl jasmonate treatment at varying concentrations significantly influenced the activity levels of defense enzymes and secondary metabolites in alfalfa. Notably, the most pronounced induction effect of methyl jasmonate was observed at a concentration of 0.1 mmol/L, particularly evident in the enhanced activity of peroxidase, polyphenol oxidase, lipoxygenase and tannins. Transcriptome analysis showed that differentially expressed genes between methyl jasmonate treatment and CK were mainly enriched in metabolic pathways and plant hormone signal transduction pathways such as terpenoid biosynthesis, linoleic acid metabolism and jasmonate signal transduction. Subsequent pathway analysis elucidated the potential of methyl jasmonate treatment to elevate endogenous jasmonic acid levels and instigate the activation of the jasmonate signaling pathway.
Rice cultivars and non-synthetic phytosanitaries screening for rice blast control in Ebro Delta
Authors: Serrat Xavier , Manikan Brigen , Palma-Guillén Alfred , Pérez-Méndez Néstor , Nadal Ariadna , Nogués Salvador 
Published: September 2024

Abstract

Rice blast (Magnaporthe grisea (Herbert) Barr, anamorph Pyricularia grisea Sacc.) represents one of the worst rice diseases. The application of synthetic fungicides is the main method for blast control, yet not allowed for organic rice farming. As an alternative, non-synthetic sulphur and silica-based commercial fungicides might effectively control rice blast in organic farming without contributing to adverse effects on rice growth, yet their efficacy evidence on a broad range of japonica rice cultivars is still scarce. The objective of this study is to screen blast resistance in six temperate japonica rice cultivars while assessing the efficacy of sulphur and silica-based non-synthetic fungicides on them. A two-years field trial was performed following a completely randomized block design arranged in a single factor with 4 replications. The most blast-sensitive cultivar was Bomba, followed by Argila, Guara, J. Sendra and Montsianell, while V.exp.1 was exceptionally blast tolerant. The only effective non-synthetic fungicide for medium blast sensitivity rice cultivars was sulphur at high doses. In contrast, none of the treatments resulted in blast contol improvement in either the most blast-resistant (V.Exp.1) or the most blast-sensitive (Bomba) cultivars. We conclude that sulphur (Thiopron, 82.5% a.i. L− 1, SC, UPL Iberica) at a 7.5 L·ha− 1 dose has potential to help organic farmers control rice blast. Thiopron is registered and commercialized in Spain for rice cultivation.
A 128 Steps Phase Interpolator Implementation using Coarse and Fine Control Technique
Authors: Arenas Erick Jesús , Espinosa Guillermo , González Víctor Rodolfo , Fuente Gisela De la 
Published: September 2024

Abstract

In modern transmission systems, a critical block used not only in the Phase Locked Loop (PLL) feedback path but also as a crucial circuit in the receiver of a serial link for clock data recovery is the phase interpolator (PI). Its implementation allows for the fine adjustment of phase in very small increments through a digital or analog control code. In this paper, a new phase interpolator topology is presented using a current control circuit that incorporates a fine current control system to improve linearity and reduce control error codes. Besides, the proposed topology was proven over corner process simulations and Montecarlo mismatch in order to demonstrate the robustness added by the fine current control. This circuit was designed in UMC 65nm CMOS Technology, operating at 2.5GHz with 128 steps, providing an angular resolution of 2.8125º per code and a standard delay of 3.125ps.
Improvement of RT-DETR model for ground glass pulmonary nodule detection
Authors: Tang Siyuan , Bao Qiangqiang , Ji Qingyu , Wang Tong , Wang Naiyu , Yang Min , Gu Yu , Wang Siriguleng , zhao jinliang 
Published: September 2024

Abstract

Currently, pulmonary nodules detection work mostly focus on recognition and diagnosis of solid nodules. However,ground glass nodules have higher probability of malignancy, posing greater identification challenges and thus greater value for detection. To achieve rapid and accurate detection of ground glass nodules, This article proposed an algorithm based on RT-DETR model with the following enhancement: 1) optimize the backbone network with FCGE blocks to increase the detection accuracy of small-sized and blurred edge nodules. 2) replace the AIFI module with HiLo-AIFI module to reduce redundant computation and improve the detection accuracy of pure ground glass pulmonary nodules and mixed ground glass pulmonary nodules. 3) replace the DGAK module with CCFF module to address the issue of capturing complex features and recognition of irregularly shaped ground glass nodules.To obtain a more lightweight model, modules are designed for smaller number of parameters and higher computational efficiency. Model are tested on on mixed dataset composed of LIDC-IDRI data and clinical data from cooperating hospitals. Compared to the baseline model, it shows an average precision improvement (mAP50/mAP50:95) of 2.1% and 1.7%, with a reduction parameters by 5.2 million. On a specialized dataset containing both pure and mixed ground glass nodules, our model outperformed the baseline model in all evaluation metrics. In general, the model proposed in this paper achieves improvement on lightweightness and detection accuracy. However, the model exhibits poor noise resistance and robustness, suggesting optimization in future work.
Comparative Genomic Prediction of Resistance to Fusarium Wilt (Fusariumoxysporum f. sp. niveum race 2) in Watermelon: Insights from Parametric and Machine Learning Approaches
Authors: Biswas Anju , Wechter Pat , Ganaparthi Venkat , Jarquin Diego , Kousik Shaker , Branham Sandra , Levi Amnon 
Published: September 2024

Abstract

Complex traits influenced by multiple genes pose challenges for marker-assisted selection (MAS) in breeding. Genomic selection (GS) is a promising strategy for achieving higher genetic gains in quantitative traits by stacking favorable alleles into elite cultivars. Resistance to Fusarium oxysporum f. sp. niveum (Fon) race 2 in watermelon is complex and polygenic with moderate heritability. This study evaluated GS as an alternative or additional approach to quantitative trait loci (QTL) analysis/marker assisted selection (MAS) for enhancing Fon race 2-resistance in elite watermelon cultivars. Objectives were to: 1) assess the accuracy of genomic prediction (GP) models for predicting Fon race 2-resistance in F2 (Pop I) and recombinant inbred line (RIL) (Pop II) populations, 2) rank and select families in each population based on genomic estimated breeding values (GEBVs) for developing testing populations, and 3) verify if major QTL associated with Fon race 2-resistance are present in top selected families with the highest GEBV. Resistance ratings were based on the percentage of healthy plants at the 28-day post-seeding in Fon race 2-inoculated soil. GBS-SNP data from genotyping-by-sequencing (GBS) for 205 F2:3 and 204 RIL families were used, and parental line genome sequences were used as references. Six GS models, including parametric (G-BLUP, BayesB, Bayes_LASSO) and non-parametric (Random Forest, SVM Linear, SVM Radial) methods, were tested. G-BLUP and Random Forest outperformed the other models, with correlations of 0.48 in the F2:3 and 0.68 in the RIL populations, highlighting the GP efficacy in early-stage breeding for improving Fon race 2-resistance in elite watermelon cultivars.
Triboinformatic modeling of wear and friction coefficient of microwave-assisted synthesized g-C3N4/MoS2 nanocomposites using advanced regression models
Authors: SAXENA MUKUL , Saharma Anuj Kumar , Singh Monika 
Published: September 2024

Abstract

Tribological phenomena, encompassing friction, wear, and lubrication, significantly impact the performance and efficiency of mechanical systems across various industries. This research investigates the application of machine learning approaches to minimize wear depth and coefficient of friction in tribometer systems by modeling the effects of applied load, sliding speed, and coating material. Through comprehensive experimentation and analysis, the influence of these critical parameters on the tribological responses is quantified. Several machine learning algorithms, including linear regression, decision trees, random forests, support vector regression, k-nearest neighbors, and neural networks, are employed to capture the complex relationships between the input parameters and the responses. The neural network model achieved the best performance with a low mean squared error (MSE) of 0.0023 and high R-squared of 0.9977 for predicting wear depth, along with an MSE of 567.89 and R-squared of 0.9654 for coefficient of friction predictions. Random forests also exhibited strong performance with testing MSE of 0.0298 and R-squared of 0.9671 for wear depth. Feature importance analysis identifies coating material as the most influential factor for wear depth, while sliding speed is the dominant factor (F-ratio of 8.589) affecting the coefficient of friction. Statistical significance testing confirms a substantial difference between the means (t-statistic of -14.544, p-value of 5.41e-31). Multiple linear regression analysis reveals an optimized input parameter combination of 10 kN applied load, 0.5 m/s sliding speed, and 9 Wt% of gCN, minimizing both tribological responses with low MSEs of 0.0214 (wear depth) and 12.8745 (coefficient of friction) on the training set. The study provides valuable insights into tribological optimization, highlighting the potential of machine learning techniques for enhancing system performance, efficiency, and durability. The findings contribute to a deeper understanding of tribological processes and pave the way for future research in developing advanced coatings, lubricants, and condition monitoring systems for tribological applications,
ROAEN: Reversed dependency graph and Orthogonal-gating strategy Attention-Enhanced Network for Aspect-Level Sentiment Classification
Authors: Chen Jiang , Ye Weijie 
Published: September 2024

Abstract

Aspect-Level sentiment classification aims to determine the sentiment polarity of different aspects within a sentence. Many existing approaches employ Graph Convolutional Networks (GCN) in conjunction with dependency trees to model syntactic structures in sentences but often ignore its limitations in modeling long-distance words. This paper proposes a Reversed dependency graph and Orthogonal-gating strategy Attention-Enhanced Network (ROAEN), which introduces a novel Reversed dependency Graph Convolutional Network combined with Orthogonal projection (RGCN-O) to model long-distance words and syntactic structures simultaneously. Specifically, RGCN-O discards a few words with dependencies, generates vector representations for long-distance words, and models syntactic structures from its orthogonal space. The output of RGCN-O is then used to design a gating strategy that dynamically filters attention noise in a weighted manner. Additionally, we offer a novel total inter-intra loss function to enhance the compactness and distinctiveness of the intra-class and inter-class sample features. Extensive experiments on five benchmark datasets demonstrate the effectiveness of RGCN-O and the new loss function, with ROAEN achieving state-of-the-art performance.
Fascination in the Workplace: Analyzing Work-Related Flow Among Clinical Residents and Professors: A Cross- Sectional Study
Authors: Jamali Negin , Mosavat Seyed Hamdollah , Bazrafshan Amir 
Published: September 2024

Abstract

Background
Flow or fascination is a state of mind in which a person performs a specific activity with peak interest and mental engagement. Researchers have confirmed the existence of flow experience during artistic, sports and work activities. However, due to the limitations of existing studies, the present study was conducted with the aim of investigating the average index of work-related flow in professors and residents of Shiraz University of Medical Sciences.
Methods
This cross-sectional study was conducted on clinical residents and professors at Shiraz University of Medical Sciences. Participants were selected using a weight-stratified sampling method from a population of 1200 individuals, which included 400 professors and 800 residents. Work-related flow was assessed using the Work-related Flow Inventory (WOLF), a 13-item scale rated on a 5-point Likert scale, where higher scores indicate greater work enthusiasm (total scores range from 13 to 65).
Results
In total, 104 professors and 230 residents were included into the study. The average of the total psychological index of work-related flow in professors and residents of medical sciences in Shiraz was equal to 43.31 ± 9.7. The age of the participants had a significant positive correlation with their average score (p < 0.001). The mean score for work-related flow was significantly higher in men than in women, higher in married compared to single ones, and also higher in professors than in residents. Specialized field could influence the work-related flow score that the highest and the lowest score was specified to general surgery and gynecology respectively.
Conclusion
Work-related fascination in clinical residents and professors of Shiraz University of Medical Sciences is different and is affected by their gender, age and specialized field.
Time Series Prediction using Laplace Transform-based Deep RNN- LSTM Approach
Authors: Ambhika C. , Chandrasekaran Raja , Bolla Sreenivasulu , Gangopadhyay Amit , Brahmaiah Madamanchi , Jeyakumar Balajee 
Published: September 2024

Abstract

This study presents a novel hybrid framework employing integrated Laplace transform-based Deep Recurrent Neural Network-Long shortTerm memory (DRNN-LSTM) model for time series prediction. In the presented work, the Ebola Optimization search (EOS) optimization algorithm was utilized to train the unsupervised DRNN-LSTM model. The Laplace transform in the proposed work eliminates the noises or unwanted features and helps to transform the input signal into a required format. Finally, the experimental analysis was performed and validated with the conventional techniques. The experimental analysis demonstrates that the designed model effectively eliminates the noises and also extracts the features accurately and also outperformed all the state-of-art techniques.
Proteomic analysis reveals the sperm protein difference among low, high quality and frozen semen of Sewa sheep in Tibet
Authors: Su Kui-yuan , Fu Han-yu , Xu Ye-fen , Tan Zhan-kun , Yao Yi-long , Xi Guang-yin , Shang Zhen-da , Liu Yang , baima Sangzhu , Liu Bo-hua , Zan Rui-long , Liu Suo-zhu 
Published: September 2024

Abstract

The Tibetan Sewa sheep is a unique breed that inhabits the plateau area at an altitude of approximately 4,800 m. In this study, we conducted DIA quantitative proteomic analysis on high-quality semen (F), frozen semen (C), and low-quality semen (L) from Sewa sheep. A total of 33,073 peptide segments and 3,175 proteins were detected in the three groups of semen. Among them, 3,700 proteins were functionally annotated in the database. Comparison between the C and F groups revealed 163 differentially expressed proteins (DEPs) with 134 upregulated and 29 downregulated proteins. These DEPs were found to be associated with biological regulation and metabolic processes in Gene Ontology (GO) enrichment analysis; they also mapped to 97 pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Comparison between the L and F groups identified 459 DEPs with 319 upregulated and 140 downregulated proteins. These DEPs were found to be associated with metabolic processes and cellular component biogenesis in GO enrichment analysis; they also mapped to 107 pathways in KEGG enrichment analysis. Protein-protein interaction (PPI) analysis based on comparisons between C vs F group and L vs F group identified potential proteins that may affect sperm cryotolerance and sperm vitality; however, their specific functional mechanisms require further investigation. We have elucidated for the first time the differentially expressed proteins in various types of semen from Sewa sheep as well as their involvement in biological pathways, providing a theoretical basis for understanding sperm quality and freezing damage mechanisms in Sewa sheep.
Unveiling Temporal Patterns in Information for Improved Rumor Detection
Authors: Mairaj Omel , Khan Shafiq Ur Rehman 
Published: September 2024

Abstract

Rumor detection is a critical task for addressing the spread of misinformation and maintaining the credibility of information sources. Natural Language Processing (NLP) techniques have been employed to propose efficient and effective methods for rumor detection. In the wake of the widespread COVID-19 pandemic, the world has faced extensive strain on health, economics, and social structures. The dissemination of false or inaccurate information on social media, whether intentionally malicious or unintentional, has had detrimental consequences for individuals and society, particularly during critical situations like real-world emergencies. In this study, we aim to explore the textual and temporal features present in social media posts (specifically tweets) related to COVID-19 to detect rumors as time is unique feature of text and any event can be mapped on timeline. Previous studies utilized the textual features and the temporal features are neglected at large for rumors detection. We utilize both temporal and textual features independently, as well as in combination, to train machine learning and neural network models. The evaluation of multiple algorithms (RNN, LSTM, CNN, DNN, BERT) across various feature sets reveals diverse performance. RNN and LSTM improve with combined textual and temporal features, highlighting temporal information's importance. CNN performs well with textual features but declines with temporal features. DNN maintains consistent performance, while BERT demonstrates moderate effectiveness in classification tasks.
Translucent Color Organic Solar Cells: From Power Generating Windowpanes to Aesthetic Art
Authors: Zhu Furong 
Published: September 2024

Abstract

The photovoltaic performance and artistic perspectives of the translucent color organic solar cells (OSCs), made with the emerging narrowband nonfullerene organic semiconductors are explored. This work reports the effort to develop high-performance translucent color OSCs comprising a Fabry-Pérot microcavity optical coupling layer, with a power conversion efficiency of >15% and a maximum transparency of >20% for the three primary colors. The performance color relationship of the translucent color OSCs was analyzed using a combination of high-throughput optical computing and experimental optimization, allowing light with desired color to pass through, and yet absorbing enough light to generate electricity. A 10×10 cm2-sized replication of Piet Mondrian's artwork "Composition C (1920)" was demonstrated using the translucent OSCs with a wider palette of colors and a high accuracy of hues. The outcome of the work offers an opportunity for translucent color OSCs to serve as both esthetic art and power generating windowpanes for use in our homes, offices, and even greenhouses.
Enrichment of Cysteine S-palmitoylated peptides using Sodium Deoxycholate Acid Precipitation
Authors: Larsen Martin , Jensen Peter , Palmisano Giuseppe , Rhodes Christopher 
Published: September 2024

Abstract

S-palmitoylation is a poorly understood post-translational modification that is gaining more attention as an essential regulator of cellular processes. The reversible nature of S-palmitoylation allows for fine-tuned control of cellular events and adaptation to stimuli. The enrichment of S-palmitoylated proteins and peptides includes the Acyl-Biotin Exchange (ABE) method, Acyl resin-assisted Capture (Acyl-RAC), metabolic labelling, and derivatives thereof. We present a novel method of enrichment of S-palmitoylated peptides termed SDC Acid Precipitation Enrichment (SDC-ACE). Here, S-palmitoylated peptides are enriched by taking advantage of their co-precipitation with Sodium-Deoxycholate (SDC) under acidic conditions, allowing easy and fast separation of lipidated peptides from the sample suspension. We applied our novel method for the characterization of the mouse brain, providing an in-depth analysis of S-palmitoylation events within the brain and comprehensive profile of the mouse brain S-palmitoylome. Furthermore, we applied our method for mapping mouse tissue-specific S-palmitoylation, highlighting the extensive role of S-palmitoylation throughout various organs in the body. Finally, we applied our methods for studying the brain palmitoylome of diabetic db/db mouse, uncovering alterations in the palmitoylation related to obesity and type 2 diabetes. The SDC-ACE method allows fast and easy enrichment of S-palmitoylated peptides, providing a valuable tool for exploring the dynamics and function of S-palmitoylation in diverse biological systems.
Perceived Familial Gender Discrimination, Gender Transcendence and Self-Efficacy: A Study on Indian Female University Students
Authors: Shergill Kanwal , Hooja Himangini Rathore 
Published: September 2024

Abstract

Gender discrimination in familial settings carries significant adverse consequences for females across their life span, due to their status being relegated in the household like a second class citizen, restricting their mobility, leisure, and aspirations. The present paper explores the relationship between perceived familial gender discrimination, gender transcendence and self-efficacy. The study was conducted on 850 female university students aged 18 years and above who were unmarried, unemployed and have male siblings living with them. Gender Discrimination by Parents’ Behaviour subscale (from Gender Discrimination Inventory), Gender Transcendence subscale (from Social Roles Questionnaire) and New General Self-Efficacy scale were administered to the sample. Pearson correlation and linear regression analysis revealed that perceived familial gender discrimination is inversely correlated to both gender transcendence and self-efficacy.