bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of miRNA-disease Association Based on Multi-Source Inductive Matrix Completion
Authors: Wang YaWei , Yin ZhiXiang 
Published: July 2024

Abstract

MicroRNAs (miRNAs) are endogenous non-coding RNAs of about 23 nucleotides in length that play important roles in a variety of cellular biochemical processes. A large number of studies have demonstrated that miRNAs are involved in the regulation of many human diseases. Accurate and efficient prediction and identification of the association between miRNAs and human diseases will have great significance for the early diagnosis, treatment and prognosis assessment of human diseases. In this paper, we propose a model called Autoencoder Inductive Matrix Completion (AEIMC) to identify potential miRNA-disease associations. Specifically, we first capture the interaction features of miRNA-disease associations based on multi-source similarity networks, including miRNA functional similarity network features, miRNA sequence similarity features, disease semantic similarity features, disease ontology similarity features, and Gauss interaction spectral kernel similarity features between disease and miRNA. Secondly, autoencoders are used to capture more complex and abstract data representations of miRNA and disease. Finally, the learned high-level features are used as inputs to the induction matrix completion model to obtain the miRNA-disease association prediction matrix. At the end of the artical, an ablation experiment was performed to confirm the validity and necessity of introducing miRNA sequence similarity and disease ontology similarity for the first time.
Identification of Trolling Memes in Kannada and Tulu - Under-resourced Dravidian Languages
Authors: Hegde Asha , Lakshmaiah Shashirekha Hosahalli 
Published: July 2024

Abstract

On social media platforms, information, ideas, or other forms of expressions are created and/or shared among people in an interactive manner. During exchange of information, users may encounter humorous, funny, offensive, trolling, or malicious content targeting the individuals, groups, or communities. One common way of trolling on social media is to create memes by combining an image with textual information - usually a catchy phrase often obscured by humor or sarcasm, and share it on social media. Memes shared with the intention of trolling need to be filtered out from social media as they may hurt the sentiments of people and create an unhealthy atmosphere in the society. The increasing number of social media users and the increasing number of trolls on social media complicates the task of identifying the trolling memes manually. Hence, there is a demand for the tools to automatically identify the trolling memes. However, this task is challenging due to the unavailability of annotated data. The complexity of the task gets intensified if the text is written in code-mixed under-resourced regional languages like Kannada or Tulu - the languages of south India. To tackle the unavailability of annotated data and tools to identify trolling memes in under-resourced languages - Kannada and Tulu, we created two datasets: i)~\textit{KAmemes} - a meme dataset embedded code-mixed text in Kannada and ii)~\textit{TUmemes} - a meme dataset embedded code-mixed text in Tulu, consisting of memes labeled as \lq Troll' and \lq Not\_Troll'. To benchmark these datasets, Uni-modal and Multi-modal models are proposed to classify a given meme as \lq Troll' or \lq Not\_Troll'. While the uni-modal approaches consider only text or only image to classify a given meme, multi-modal approaches explore both text and image modalities. Several ML and DL baselines are implemented for uni-modal and multi-modal models. The proposed baselines are also evaluated on the available \textit{TamilMemes} dataset to illustrate their efficacy. Among the proposed baselines, a multi-modal joint representation based dual encoder model achieved the best macro F1 scores of 0.90, 0.78, and 0.58 for \textit{TUmemes}, \textit{KAmemes}, and \textit{TamilMemes} datasets respectively.
Digital Forensic Analysis for Vehicle Infotainment Systems based on Packet Fingerprinting
Authors: Shin Yeonghun , Yu Geon , Shon Taeshik 
Published: July 2024

Abstract

With the increasing number of in-vehicle computing systems and rapid development of technologies, such as autonomous driving technology, various IoT technologies are being incorporated into vehicles. In these scenarios, a vehicle is typically connected to a smartphone or various sensors to exchange information based on wireless communication. While this is convenient for the driver, from a security standpoint, it means exposing the vehicle to a new cyberattack surface: wireless communication attacks. Therefore, active research on security inspection and improvement for wireless communication in vehicular environments is required. Some studies in this regard have raised security issues, but little digital forensic research has been conducted on the issues raised. Against this background, we conducted a case study based on packet fingerprinting to improve the level of security in wireless communication in a vehicular environment (i.e., in-vehicle wireless communications). Packet fingerprinting was applied to 11 in-vehicle infotainment systems. Consequently, devices and services in use were identified from wireless network packets. Images of internal storage data were acquired from three in-vehicle infotainment systems, and a file system-based analysis was performed on the images to derive digital forensic artifacts related to the packets stored in the vehicle systems. Further analysis was conducted by combining the derived artifacts with the packet fingerprinting results. Our findings indicate that the security level of wireless communication in various in-vehicle infotainment system environments can be evaluated and improved. Moreover, we provide various identification information and digital forensic artifacts derived from various in-vehicle infotainment systems.
Direct to Psychology Stepped Care Model in the Multidisciplinary Sleep Service: Study protocol
Authors: Winter Sara , Crocker Sara , Rolls Tricia , Curtin Deanne , Haratsis Jessica , Rautenberg Tamlyn , Szollosi Irene 
Published: July 2024

Abstract

Background: Sleep disorders are common and costly to society. Guidelines from the American Academy of Sleep Medicine (AASM) and the Australasian Sleep Association (ASA) recommend Cognitive Behaviour Therapy for Insomnia (CBTi) as the best treatment modality for insomnia disorder in the long term, however, access issues due to medical first referral pathways and reduced availability of psychologists in the community results in increased wait times, reduces efficient care for patients, and adds burden to specialist medical services.
Method: This phase of a broader single-site, multiphase, mixed methods project will implement, and evaluate a service model of care re-design incorporating a new Direct to Psychology referral pathway for insomnia presentations and a Stepped Care treatment approach in the multidisciplinary sleep service. The RE-AIM framework and Standards for intervention Reporting Implementation Studies(StaRI) will be used to guide the development, implementation, evaluation and reporting of this service change. Quantitative data measuring outcomes of the new models of care on key indices of patient clinical outcomes, patient/clinician service satisfaction, and service efficiencies will be collected pre- and post-service change.
Discussion: The overarching aim of this project is implement and evaluate a model of care change to the service model in a public tertiary hospital multidisciplinary sleep service. The new Direct to Psychology Stepped Care Model incorporates a Direct to Psychology Insomnia referral pathway along with a Stepped Care approach to treatment, flexibly delivered across the continuum of care. The project will measure the impact of the service change in our target population. The new model of care will be compared to the old service model for key outcomes. We anticipate that the new Model of Care will be acceptable to stakeholders, will produce non-inferior patient outcomes, and will improve patient flow and service efficiency.
Trial Registration: The Protocol was registered on 5/08/2022 with the Australian and New Zealand Clinical Trials Registry (ACTRN12622001086752).
The potential antitumor activity of polysaccharide extracted from polygonatumsibiricum on human prostate cancer PC-3 cells
Authors: Zhao Guobin , Zhou Yan , Tang Yuhong , Abbas Muhammad , Dong Shaowen , Zhao Xinyang , Liu Xin , Wang Xinmei , Li Chen , Liu Chenxi 
Published: July 2024

Abstract

Polysaccharide extracted from polygonatum sibiricum (PSP) is a traditional herbal medicine component thought to have therapeutic effects on many diseases. In our study, we initially investigated the role of PSP in the development of human prostate cancer PC-3 cells and explored the potential mechanisms. First, the different concentrations of PSP were used to act on human prostate cancer cells PC-3, It was found that the proliferation, invasiveness, and migratory ability of PC-3 cells were significantly inhibited with increasing concentrations of PSP; the cell apoptosis rate and expression of caspase-3 increased with increasing concentrations of PSP; and the cell cycle was arrested in the S phase. In addition, we found that the expression of the MDR-1 gene and its encoding protein P-gp in PC-3 cells decreased after the treatment with PSP, so PSP might reverse multidrug resistance in PC-3 cells. Second, the possible mechanism of PSP action on PC-3 cells has been investigated, and the results showed that phosphorylated P65, PI3K, and AKT decreased in a concentration-dependent manner, as P65, PI3K, and AKT are molecules of the NF-kB and PI3K/Akt signaling pathways, so the potential mechanism of PSP on prostate cancer cells might be mediated by the PI3K/Akt and NF-kB signaling pathway simultaneously.
Optimizing cannabis cultivation: an efficient in vitro system for flowering induction
Authors: Lavie Orly , Buxdorf Kobi , Williams Leor Eshed 
Published: July 2024

Abstract

Background
Cannabis sativa is a versatile medicinal plant known for its therapeutic properties, derived from its diverse array of secondary metabolites synthesized primarily in female flower organs. Breeding cannabis is challenging due to its dioecious nature, strict regulatory requirements, and the need for photoperiod control to trigger flowering, coupled with highly dispersible pollen that can easily contaminate nearby female flowers. This study aimed to develop a protocol for in vitro flowering in cannabis, investigate factors affecting in vitro flower production, and generate viable in vitro seeds, potentially offering a method for producing sterile cannabinoids or advancing breeding techniques.
Results
We show that the life cycle of cannabis can be fully completed in tissue culture; plantlets readily produce inflorescences and viable seeds in vitro. Our findings highlight the superior performance of DKW medium with 2% sucrose in a filtered vessel and emphasize the need for low light intensity during flower induction to optimize production. The improved performance in filtered vessels suggests that plants conduct photosynthesis in vitro, highlighting the need for future investigations into the effects of forced ventilation to refine this system. All tested lines readily developed inflorescences upon induction, with a 100% occurrence rate, including male flowering. We revealed the non-dehiscent trait of in vitro anthers, which is advantageous as it allows for multiple crosses to be conducted in vitro without concerns about cross-contamination.
Conclusion
The current work developed and optimized an effective protocol for in vitro flowering and seed production in cannabis, potentially providing a platform for sterile cannabinoid production and an efficient tool for breeding programs. This system allows for the full and consistent control of plant growth conditions year-round, potentially offering the reliable production of sterile molecules suitable for pharmacological use. As a breeding strategy, this method overcomes the complex challenges of breeding cannabis, such as the need for large facilities, by enabling the production of hundreds of lines in a small facility. By offering precise control over factors such as plant growth regulators, light intensity, photoperiod, and temperature, this system also serves as a valuable tool for studying flowering aspects in cannabis.
A geological event tree on volcanic islands:case study of the Galápagos islands
Authors: Mulas Maurizio , Larreta Erwin , Menoscal Melanie , Bravo Gianella , Rosado Victoria , Capa Diego , Garces Daniel 
Published: July 2024

Abstract

The Galápagos Islands face recurrent geological hazards such as volcanic eruptions, flooding, and seismic activity due to their location in the Pacific Ring of Fire. Due to climate change, the frequency and intensity of natural events have increased in recent years, triggering cascading multi-hazard effects that threaten the development of the population and wildlife of the islands. Therefore, it is necessary to solve its management by studying the relationship of its Socio-Ecological System (SES) with its geological component. To effectively address this problem, it is essential to propose a methodological design based on historical records of hazards that have affected the archipelago, obtaining a reference diagram for decision-making in the face of multi-hazard events and their possible environmental and socio-ecological effects. The methodology is planned in different phases: i) description of the SSE and its ecosystem services; ii) collection of historical data on events that have affected the island; iii) identification of the hazards triggered by a main event; iv) elaboration of an "event tree" type diagram; v) determination of environmental and socio-ecological affectations. The present work seeks to obtain a multi-hazard diagram for active and inactive volcanic islands, considering their environmental parameters and socio-ecological hazards. The importance of this analysis is to provide a tool to entities such as municipalities, Red Cross and Geological Services that allows them to evaluate the existing preventive measures against possible hazards, identifying their cascading effects and minimizing the response time in emerging situations.
A Dual Band Adhesive Planar Antenna for Biomedical Applications
Authors: Yeap Kim Ho , Voon Chow Shen , Oh Zi Xin , Dakulagi Veerendra , Nisar Humaira 
Published: July 2024

Abstract

We present the design of a compact dual-band adhesive planar antenna which operates as a gateway for biomedical applications. Operating in the ISM band (2.4 GHz – 2.5 GHz) and the IEEE 802.11 WLAN band (5.2 GHz to 5.3 GHz), the antenna is useful in the context of Wireless Body Area Networks (WBAN) for body-signal monitoring. The ISM band is meant for in-body communication with the implanted medical devices; whereas the WLAN band is for off-body communication with the base station. The measurement result shows that the antenna has 10 dB impedance bandwidths of 1 GHz (1.64–2.64 GHz) and 800 MHz (5.16–5.96 GHz) to cover the ISM bands and IEEE 802.11 WLAN bands, respectively. The proposed antenna is printed on a flexible Duroid 5880 RT epoxy substrate and it occupies a compact volume of 18 mm × 18 mm × 1 mm. To the best of our knowledge, this size is the smallest among antennas which features a similar nature. The simulated and measured performance of the antenna confirms its omnidirectional radiation patterns and dual-band operation with high return losses.
Tilte：SNRPA may mediate transcription and splicing of genesassociated with HCC via binding special RNAs
Authors: Chang Qingyao , Wang Yidi , Xu Jun 
Published: July 2024

Abstract

Small nuclear ribonucleoprotein polypeptide A (SNRPA) was screened as an important RNA-binding protein based on its correlation with survival in Hepatocellular carcinoma(HCC) patients. SNRPA-overexpressed model was established in HepG2 cells. RNA-seq analysis revealed extensive differentially expressed genes (DEGs) expression profiles and the occurrence of regulated alternative splicing events (RASEs). Furthermore, we employed iRIP-seq and integrated the resulting data with RNA-seq data to identify SNRPA-binding RNAs, including CEMIP, SLC4A11, and GTF2IP7. SNRPA also binds and modulates alternative splicing(AS) of genes including RNA splicing, DNA transcription, and cell division, including HNRNPH1, EIF4A2, PPP6R2, FN1, and GNAS. These discoveries creatively reveal the potential molecular regulatory mechanisms of SNRPA in HCC, thereby significantly contributing to the progress and enhancement of research and treatment strategies for HCC.
Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome
Authors: Xue Weirong , Jin Yingliang , Li Shengli , Xu Bing , Wang Hui , Zhu Xiaoxiao , Qin Jiajia , Yu Peilin 
Published: July 2024

Abstract

Background
Budd-Chiari syndrome (BCS) is a rare condition worldwide with a high recurrence rate. The existing prognostic scoring models have shown limited predictive efficacy for recurrence of BCS patients.The study aim to establish a more effective machine learning model based on multiple kernel learning for predicting the recurrence of Budd-Chiari syndrome patients within three years.
Methods
The dataset was obtained from patients diagnosed with BCS admitted to the Affiliated Hospital of Xuzhou Medical University between January 2015 and July 2022.The data were divided into training, validation, and test sets in a 6:2:2 ratio. We established respective model based on traversal of all combinations of four kernel functions in training set, and selected best hyperparameters for each model by particle swarm optimization (PSO) algorithm in validation set. Test set was conducted for comparasion of kernel function combinations, with AUC (area under the curve), sensitivity, specificity, and accuracy used as evaluation indexs. The optimal model, utilizing the best-selected kernel combination, was then compared with three other machine learning models to further assess its performance.
Result
A kernel combination incorporating all four basic kernels achieved the highest average AUC, specificity, and accuracy, as well as a slightly lower mean but more stable sensitivity across all combinations. In comparison with other classical machine learning models, our model also achieved significant advantages in performance. Furthermore, it outperformed previous studies with similar objectives.
Conclusion
We have explored risk factors influencing relapse of BCS patients and demonstrated our proposed MKSVRB model is superior to previous prediction methods and other machine learning models, showcasing its significant potential in early detection, determination, and prevention of relapse in patients with Budd-Chiari syndrome.
Utilizing Machine Learning and DSAS to Analyze Historical Trends and Forecast Future Shoreline Changes Along the River Niger
Authors: Eteh Desmond Rowland , Moses Paaru , Egobueze Francis Emeka , Okpobiri Okechukwu 
Published: July 2024

Abstract

This study investigates shoreline changes along the River Niger in Nigeria over a 70-year period (1974–2044). We employ remote sensing data, machine learning, and the DSAS tool to analyze historical changes (1974–2024) and predict future trends (2024–2044). Landsat imagery obtained from the United States Geological Survey (USGS) through the Google Earth Engine API is analyzed using ArcGIS, DSAS 5.0 and 6.0 software and rainfall data acquired from the Center for Hydrometeorology and Remote Sensing (CHRS). Findings indicate notable spatial and temporal variations in shoreline dynamics across Bayelsa, Delta, and Anambra States. Around 51.47% of the transects experienced erosion, while 48.53% underwent accretion, with an average annual shoreline change rate of 1.66 meters. Despite the equilibrium, erosion exhibits a more significant impact, with a mean rate of -2.26 meters per year compared to an accretion rate of 3.92 meters per year. The study identifies a total shoreline change envelope (SCE) of 442.86 meters and a net shoreline movement (NSM) of 92.33 meters, indicating substantial overall shoreline advancement. Looking ahead, projections for 2024–2044 show varying erosion and accretion patterns across different sections. Section D faces the most significant threat, with 80% of transects experiencing erosion at a rate of -2.96 meters/year. Rainfall data analysis suggests a strong correlation (R² = 0.7576) between precipitation and shoreline change, highlighting the crucial influence of climate on coastal dynamics. These findings emphasize the need for integrated coastal management strategies that account for rainfall variability and prioritize mitigating erosion, particularly in vulnerable sections.
Comparative Study on the Association Between Types of Physical Activity, Physical Activity Levels, and the Incidence of Osteoarthritis in Adults：The NHANES 2007–2020
Authors: Huang Chenyang , Guo Ziyu , Gu Enpeng , Feng Zhixiao , Xu Jiankang , Pan ZhiCheng , Liu Wangxin 
Published: July 2024

Abstract

Background: It is known that physical activity is beneficial to the prevention of osteoarthritis, but specific discussions on which types and levels of physical activity are more effective in reducing the incidence of osteoarthritis are restricted. Purpose: This study is aimed at exploring the correlation concerning the types of physical activity, levels of physical activity, and the incidence of osteoarthritis by assessing the participation in five typical forms of physical activity (vigorous work activity, vigorous recreational activity, moderate work activity, moderate recreational activity, and walking or bicycling). Study Design: Cross-sectional study was conducted.
Methods: Self-reported data on specific domains of physical activity were obtained from individuals in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2020 with the use of the Physical Activity Questionnaire (PAQ). The incidence of osteoarthritis was assessed through the "Health Conditions" questionnaire section of NHANES. Weighted logistic regression analysis was employed to study the correlation between physical activity types and levels, and the incidence of osteoarthritis.
Conclusion: Different kinds of physical activity and physical activity levels have varying impacts on the incidence of osteoarthritis. Among the types of physical activity, vigorous recreational activity and moderate recreational activity are correlated with a reduced risk of osteoarthritis, while moderate work activity may contribute to a rising risk. Regarding physical activity levels, engaging in medium physical activity level appears to be protective against osteoarthritis.