bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepMiRBP: A Hybrid Model for Predicting MicroRNA-Protein Interactions Based on Transfer Learning and Cosine Similarity
Authors: Azizian Sasan , Cui Juan 
Published: September 2024

Abstract

Background Interactions between microRNAs and RNA-binding proteins are crucial for microRNA-mediated gene regulation and sorting. The molecular mechanisms underlying these protein interactions with small RNAs remain largely under-studied, apart from sequence motifs identified on microRNAs. To date, only a limited number of microRNA-binding proteins have been confirmed, typically through labor-intensive experimental procedures. Advanced bioinformatics tools are urgently needed to facilitate this research.
Methods DeepMiRBP is a novel hybrid deep learning model designed to predict microRNA-binding proteins by modeling molecular interactions. This innovation approach is the first to target the interactions between small RNAs and proteins specifically. DeepMiRBP consists of two main components. The first component employs bidirectional long short-term memory (Bi-LSTM) neural networks to capture sequential dependencies and context within RNA sequences, attention mechanisms to enhance the model’s focus on the most relevant features and transfer learning to apply knowledge gained from a large dataset of RNA-protein binding sites to the specific task of predicting microRNA-protein interactions. Cosine similarity is applied to assess RNA similarities. The second component utilizes Convolutional Neural Networks (CNNs) to process the spatial data inherent in protein structures based on Position-Specific Scoring Matrices (PSSM) and contact maps to generate detailed and accurate representations of potential microRNA-binding sites and assess protein similarities.
Results Our DeepMiRBP method reported a prediction accuracy of 87.4 via training and 85.4 using testing, with an F score of 0.860. Additionally, we have applied three case studies to further validate the method using recently reported evidence on miRNA interactions, including miR-451, -19b, -23a, -21, -223, and -let-7d. Using DeepMiRBP, we accurately predict known microRNA interactions with recently discovered RNA binding proteins identified in different exosomes, including AGO, YBX1, and FXR2.
Conclusions Our proposed DeepMiRBP strategy represents the first of its kind designed for microRNA-protein interaction prediction. Its promising performance underscores the model’s potential to identify novel interactions involved in small RNA sorting and packaging and infer new RNA transporter proteins. The methodologies and insights from DeepMiRBP offer a scalable template for future small RNA research, from mechanistic discovery to modeling disease-related cell-to-cell communication, emphasizing its adaptability and potential for developing novel small RNA-centric therapeutic interventions and personalized medicine.
Research on the mechanism of water-jet guided laser micro-hole machining of GH4169 superalloy
Authors: Yu Shuo , Zhao Guoyong , Zhao Yugang , Gao Yingqi , Xu Shuming , Liu Huayi 
Published: September 2024

Abstract

The air film holes in aero-engine turbine blades are closely arranged, with small hole diameters, and high requirements for surface quality and positional accuracy, which makes it difficult for traditional machining to meet its manufacturing accuracy requirements. To solve this problem, micro-hole machining simulation is carried out in the paper, using water-jet guided laser technology with GH4169 superalloy as the research object. First of all, the three-dimensional transient temperature field and material removal model of GH4169 superalloy was established based on the simulation software of Comsol, and the material removal mechanism of water-jet guided laser micro-hole machining was revealed. Then, comparative experiments between water-jet guided laser and traditional laser micro-hole machining were carried out with different laser powers. Finally, through the measurement of the morphology and roughness of the hole wall, it is possible to verify the feasibility of water-jet guided laser micro-hole machining and to obtain the optimal machining parameters. The experimental results show that the micro-hole surface roughness obtained by the processing is 1.362µm with a smaller heat-affected zone in the laser power of 200W, pulse frequency of 6000Hz, and water beam pressure of 2.0MPa, while the micro-hole surface roughness obtained by the traditional dry laser under the same processing parameters is 13.517µm. Therefore, water-jet guided laser processing can make a better balance of the uneven heat transfer phenomenon in the processing, and improve the processing efficiency with better surface morphology.
The Multifaceted Effects of Migraine on Syrian Society: Insights from a Multicenter Cross- Sectional Study
Authors: Ataya Jamal , Bannoud Nour , Daaboul Rawan , Hasan Yaman Haj , Hanifa Hamdah , Watfa Masa , Alnabi Nour Alhuda Abd , Salama Raghad , Mackieh Rustum 
Published: September 2024

Abstract

Background
Migraine is the most common primary headache. It's alone responsible for 1.3% of disability in the world. Migraine both worsen quality of life of individuals and place a significant burden on the society. Despite this, there exists a knowledge gap regarding its repercussions on the Syrian population.
Methods
This cross-sectional study enrolled 679 migraine-diagnosed patients from all over Syria through electronic questionnaires disseminated on official social media platforms. Depending on demographic characteristics, physical and mental debility assessed using the Chalder scale, the Migraine Specific Quality of Life Questionnaire (MSQ) and the Generalized Anxiety Disorder 2-item (GAD_2) scale.
Results
There are many demographic and biological factors impact migraine's incidence. Being female is a significant risk factor for migraines especially during puberty and menarche. Age also plays a role in migraine incidence, peaking between 30 and 39 years. Environmental factors include urbanization, healthcare accessibility, and air pollution contribute to the increasing prevalence of migraines among younger individuals. In addition to many systemic diseases such as depression, anxiety, irritable bowel syndrome, fibromyalgia, sleep disturbances, chronic fatigue, and cognitive impairments.
Conclusions
This study aimed to elucidate the multifaceted impact of migraines on daily life, considering diverse demographic variables to enhance diagnostic accuracy, treatment efficacy, and disease management strategies.
Tracing Localised Nutrient Pollution Events to Posidonia oceanica Epiphyte Community Assemblages, in the Eastern Aegean Sea
Authors: Naccarato Gian Luca , Cierjacks Arne , Malcolm-McKay Alice , García María Moran , Macrina Laura , Miliou Anastasia 
Published: September 2024

Abstract

The occurrence of localised eutrophication events in small coastal communities of the Mediterranean threatens seagrass ecosystem functionality, necessitating the reliable identification of the onset and magnitude of these processes. This study examined the suitability of epiphyte assemblages on Posidonia oceanica leaves as an indicator of anthropogenic nutrient enrichment, for the first time in the eastern Aegean Sea. Seagrass biometric and meadow parameters were studied at control and potentially disturbed sites (n=3), in addition to epiphyte community composition and structure at a depth of 5 m during winter 2022. Sites were classified using a modified version of the Land Use Simplified Index. Potentially disturbed meadows exhibited decreased leaf length and shoot density, and increased epiphyte cover and biomass. Epiphyte community composition shifted significantly, with clear differences observed in red coralline algae, filamentous brown algae, bryozoans, and annelids. Results highlighted that reduced water quality, which is discontinuous and therefore difficult to tackle with a single direct measurement in the water column, from point and diffuse sources including a sewage outlet, agricultural activity and tourism, strongly affected seagrass bed condition. Here, substantial epiphyte cover was observed, causing direct (shading) and indirect (intensified grazing; physical leaf instability) effects. The study revealed that epiphyte communities, in combination with the index, serve as accurate and cost-effective indicators of coastal water pollution. Regular monitoring could aid in detecting higher nutrient loading at an early stage and potentially prevent long-term meadow decline.
Fruit and Pulse Synergy: Evaluating Vigna mungo performance in Himalayan wild pomegranate based agroforestry systems
Authors: Bhatia Avinash Kumar , Pant K S , Prakash Prem , Kumar Praveen , Sharma Harish , Chauhan Saakshi , Bishnoi Prakash , Kumari Babita 
Published: September 2024

Abstract

The integration of fruit-based agroforestry models with pulse cultivation, supported by integrated nutrient management practices, presents a sustainable approach for developing nations aiming to enhance productivity while maintaining soil and human health. This study evaluated the impact of planting conditions and integrated nutrient management on the growth, yield, and soil attributes of Vigna mungo (mash) under a wild pomegranate-based agroforestry system. The experiment was designed using a randomized block design factorial approach with twelve treatments involving organic and inorganic manures (T1: RDF (Recommended dose of Fertilizer), T2: FYM (100% N equivalent basis), T3: Vermicompost (100% N equivalent basis), T4: Goat manure (100% N equivalent basis), T5: Jeevamrut @ 500 l/ha, T6: 50% RDF + 50% FYM, T7: 50% RDF + 50% VC, T8: 50% RDF + 50% GM, T9: 50% RDF + 50% Jeevamrut, T10: 50% RDF + 25% FYM + 25% VC, T11: 50% RDF + 25% GM + 25% Jeevamrut and T12: Control) and two planting conditions (under wild pomegranate canopy and open conditions), each replicated thrice. The results indicated that Vigna mungo exhibited better growth and yield parameters in open conditions compared to the wild pomegranate canopy. Among the nutrient treatments, T6 (50% RDF + 50% FYM) demonstrated superior growth and yield. Post-harvest soil analysis revealed improved physical and chemical properties under the agroforestry system with 100% FYM application (T2). Economically, the wild pomegranate + Vigna mungo system yielded the highest net returns (US$ 1094.09 ha− 1) and benefit-cost ratio (2.26) compared to sole cropping of Vigna mungo. This study accentuates the potential of integrating fruit trees with pulse crops under appropriate nutrient management to achieve sustainable agricultural practices, improved farmer incomes, and better soil and human health.
Weed Tolerance and Suppressive Ability of Potato Cultivars to Natural Weed Infestations
Authors: Siddiqui Ahmad Omid , Jabran Khawar 
Published: September 2024

Abstract

The concerns such as herbicide-resistant weeds and environmental pollution have increased interest in using weed competitive crop cultivars for weed management. Potato plants are highly vulnerable to weed competition, especially during early and late growing season. In this context, a two-year field experiment was performed to assess weed tolerance and suppressive ability of 15 genetically diverse potato cultivars to natural weed. The results showed that weed interference severely reduced potato plant growth and yield. For instance, weed competition decreased potato plant height by 10–37%, plant fresh weight by 21–44%, plant dry weight by 15–40% and yield by 15–48%. Potato cultivars in the study differed in their ability to suppress and tolerate weeds, with weed dry weight ranging from 146.7 g m− 2 in Lady Olympia to 428.3 g m− 2 in Arizona and tuber yield from 13.84 t ha− 1 in Electra to 25.15 t ha− 1 in Agria under weedy conditions. Key traits of weed-suppressive cultivars included early emergence, rapid canopy development, and greater plant biomass. Cultivars with greater plant height and higher yields were generally more weed tolerant. Among the evaluated cultivars, Lady Olympia demonstrated the highest weed suppression, reducing weed dry weight by 60% or more, making it suitable for organic farming systems. Agria exhibited superior weed tolerance with minimal yield loss, making it suitable for conventional high-yield farming systems.
Robust Dynamic Event-Triggered Sliding ModeControl of Lateral Dynamics of Intelligent ElectricVehicle with Designable Triggering Characteristics
Authors: Liu Zhi , Chu Xing , Mao Suqin , Ding Pengfei , Yao Shaowen , Lei Jilin , Cheng Shouhu 
Published: September 2024

Abstract

This paper proposes a dynamic event triggering mechanism (ETM) whose triggering characteristics can be designed for a novel sliding mode controller to solve the lateral motion control problem of intelligent electric vehicle (IEV) in the presence of bounded matched disturbances. The motivation of current work mainly aims to alleviate unnecessary computing and communication of energy-constrained IEV control systems by a large margin while ensuring its stability and robustness. The main contributions of the paper are summarized as follows. We design a continuous-time robust sliding mode controller (RSMC) which is proved to have the capability to effectively suppress the bounded matched disturbances. We derive a dynamic event triggering rule, under which the positive minimum value and the evolution characteristics of the inter-event times are able to be designed. We analyze the stability via the Lyapunov theory, exclude Zeno behavior and prove that the semi-global robust event-separation property (RESP) holds for the above constructed event-triggered control system theoretically. We numerically evaluate the effectiveness of our theoretical results and the advantages of the proposed methods which is highlighted in an overtaking case for the IEV. To the best of our knowledge, this is the first paper which studies lateral motion control of IEV with the consideration of the robustness not only for the stability but also for the positive minimum inter-event times.
Advanced Influential Node Identification in Social Networks: A Hybrid Copula and Quantum-Based Deep Learning Model
Authors: P Vimal Kumar , C Balasubramanian 
Published: September 2024

Abstract

Social networks are one the foremost origins of information transmission at present. Nevertheless, not all nodes in social networks are indistinguishable. As a matter of fact, certain nodes are said to be more influential than others, or to be more specific, their information gravitates to proliferate more. Identifying the most influential nodes in a social network called as the Influence Maximization problem remains one of the hot issue with the evolution of Internet and social media. Several methods have been proposed to identify influential nodes in composite networks, ranging from parallel algorithm to distribution difference and non-overlapping communities. However, most of the previous methods do not consider error involved in overlapping communities in social network. To solve the problem, we propose a method called, Copula Tubular Quantum Based Spearman Deep Neural for influential node tracing in social network (CTQ-SDNINT) is proposed. Besides, three algorithms are used in our work to find the influential node in social network with maximum influence spread, minimal time, and minimal error rate namely Copula Probability Node Pre-processing algorithm and Tubular Neighborhood Intensity-based Feature extraction algorithm Network Curb Quantum-based Spearman Deep Neural Influential Node Tracing algorithm respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on Performance metrics such as sensitivity, specificity, response time, accuracy, and influence spread are used to compare the proposed methods against existing ones. Results indicate that our approach significantly improves the accuracy and efficiency of influential node tracing in social networks.
Machine Learning Enhanced Autonomous ADCS for an IoT CubeSat
Authors: S Amaresh , S Anaswara B , Albert Aleeta , S Abhirami S , Daniel Sanil K 
Published: September 2024

Abstract

This paper discusses the design and development ofa hardware prototype of an Autonomous Attitude Determination and Control Subsystem (ADCS) for an Internet of Space Things (IoST) CubeSat with Machine Learning (ML) integrated into the system. It is a novel design to the existing ADCS by integrating ML into it and making the whole system autonomous thereby improving reliability and efficiency. The prototype developed is based on the design and structure of a one-unit (1U) CubeSat which are widely used in the IoST domain. A CubeSat encompasses several subsystems, out of them ADCS holds significant importance. The ADCS must be designed within stringent mass, size and energy constraints. Additionally, CubeSat typically has limited sensing, computing, and communication capabilities, highlighting the necessity for autonomous and computationally efficient algorithms. In such constrained environments, developing an effective attitude control system presents a significant challenge. The proposed design discussed in the paper upgrades the existing ADCS to a better efficient and reliable one with optimum energy consumption and less failure rate with the integration of ML technologies and enabling it to be autonomous. The selection of hardware and algorithms takes into account system requirements, limitations, past experience, and both theoretical and practical considerations. The paper thoroughly explores the design methodologies, hardware components, and algorithms utilized in the experimental hardware prototype.
Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages
Authors: Filip Tomas , Pavlicek Martin , Sosik Petr 
Published: September 2024

Abstract

The aspect-based sentiment analysis (ABSA) is a standard NLP task with numerous approaches and benchmarks, where large language models (LLM) represent the current state-of-the-art. We focus on ABSA subtasks based on Twitter/X data in underrepresented languages. On such narrow tasks, small tuned language models can often outperform universal large ones, providing available and cheap solutions.

We fine-tune several LLMs (BERT, BERTweet, Llama2, Llama3, Mistral) for classification of sentiment towards Russia and Ukraine in the context of the ongoing military conflict. The training/testing dataset was obtained from the academic API from Twitter/X during 2023, narrowed to the languages of the V4 countries (Czech Republic, Slovakia, Poland, Hungary). Then we measure their performance under a variety of settings including translations, sentiment targets, in-context learning and more, using GPT4 as a reference model. We document several interesting phenomena demonstrating, among others, that some models are much better fine-tunable on multilingual Twitter tasks than others, and that they can reach the SOTA level with a very small training set. Finally we identify combinations of settings providing the best results.
Assessing landslide velocity scales with acoustic emission active waveguides for early warning system
Authors: Kumar Deepak , Mahapatro Ajit K. , Singh Sushil Kumar 
Published: September 2024

Abstract

This study proposes a strategy to predict the different landslide velocity scales of susceptible slopes by analysing the acoustic emission (AE) behaviour of the active waveguide system (AWS). Laboratory compression tests were conducted on models of AWS utilizing a universal testing machine to induce strain-induced interactions within the backfill material, resulting in the generation of AE signals. AE characteristics of AWS has been analysed at deformation rates ranging from slow (0.003 mm/min) to rapid (30.0 mm/min) rates of Varnes’ landslide velocity scales. Two intermediate scales (0.03 and 3.0 mm/min) have been introduced between slow, moderate, and rapid rates of landslide velocity scales. AE characteristics, including signal duration, counts, acoustic signal level, amplitude, signal strength, and their derivatives were meticulously analysed for each velocity scale. A strong proportionality relationship was observed between cumulative AE counts and deformation rate of AWS. Quadratic correlation was established between AE signal strength and amplitude. AE activity of the AWS for different velocity scales were also analysed. Significant results observed and correlations were validated using another different set of AWS. Additionally, one test consisting all the velocity scales in sequential order was conducted on the AWS and results were reverified. This study can significantly contribute to developing real-time landslide early warning systems that issue alerts based on varying landslide velocities and slope instability stages, as reflected in the AE data of AWS.
GPU-Based Acceleration for Conflict Resolution in Prioritized DL-Lite Knowledge Bases
Authors: Tarek ABABSA , Adelmoutia TELLI 
Published: September 2024

Abstract

Computing of conflicting elements in prioritized \dllite{} knowledge bases when the assertions are provided by multiple and conflicting sources is an important task to repair these kinds of knowledge bases. For this purpose, several algorithms have been proposed in the literature for computing one minimal conflicts set in \dllite{} knowledge base. Even though, the proposed algorithms for repairing \dllite{} knowledge bases have proven their effectiveness, they are still lacking in efficiency. In this paper, we demonstrate how GPUs can accelerate the selection of a set assertional base conflicting (contradictory elements), leading to further reduction in runtime. This way of programming uses data parallelism to minimize execution time compared to sequential programming. Our experimental studies demonstrate the potential to achieve a speed-up of up to 12.68\(\times\) by employing modern GPUs for computing conflicting sets under inconsistency in lightweight knowledge bases.
RETRACTED: Assessment of Workplace Resilience in Reducing Burnout Among Postgraduates Across Multiple Medical Specialties
Authors: Karoli Ritu , Karoli Ritu , Karoli Ritu , Karoli Ritu , Karoli Ritu , Gupta Ashok Kumar , Dixit Sumeet 
Published: September 2024

Abstract

The authors have requested that this preprint be removed from Research Square.
RETRACTED: Assessment of Workplace Resilience in Reducing Burnout Among Postgraduates Across Multiple Medical Specialties
Authors: Srivast Prof )Shetanshu , Sehgal Garima , Kaur Apjit , Rai Priyanka , Karoli Ritu , Gupta Ashok Kumar , Dixit Sumeet 
Published: September 2024

Abstract

The authors have requested that this preprint be removed from Research Square.
T_SELEX program: Theoretical SELEX tool for Rational Design and Selection of RNA Aptamers Targeting Macromolecules
Authors: Mokgopa Kabelo Phuti , Lobb Kevin Alan , Tshiwawa Tendamdzimu 
Published: September 2024

Abstract

Selection of aptamers is crucial iterative process which is significant for many applications from biosensors to biomedical applications. The process of selecting aptamers is called systematic evolution of ligands by exponential enrichment (SELEX). This process is costly, time consuming and labour-intensive. In this communication we present T_SELEX API together with its algorithms and application. This program is an autonomous robust python package specifically engineered for designing aptamers and screening of RNA aptamers against macromolecular targets. The tool is evaluated from generation of sequence library of RNA aptamers, multiscale folding or secondary structure predictions, multiscale tertiary structure predictions, virtual screening and detailed analysis. The application was tested by screening a generated aptamer library against HIV-1 protease. The source code of the T_SELEX tool is available on GitHub [https://github.com/CMCDD/T_SELEX] and can be used directly following instructions on the readme file.