bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bioinformatics identification of key microRNA correlated genes for the development and prognosis of hepatocellular carcinoma
Authors: Su Guomiao , Li Yanxi , Wang Juan , Liu Shiyue , Pan Guoqing , Zhan Dong 
Published: February 2024

Abstract

Hepatocellular carcinoma (HCC) is the most common histological subtype in primary liver cancer. This study aimed to develop a miRNA-correlated (MIRcor) gene signature, which provided a theoretical foundation for the prognosis and therapy of patients with HCC. The MIRcor genes in HCC were obtained using correlation analysis of the miRNA-mRNA relationship pairs extracted from three databases. Subsequently, consensus clustering was performed in HCC samples based on the MIRcor genes and the differentially expressed genes (DEGs) between HCC and normal samples. The MIRcor-related differentially expressed genes (MIRcor-DEGs) in patients with HCC were identified by overlapping with the DEGs above. Additionally, the univariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analysis extracted the prognostic genes. The risk model was constructed using the TCGA-HCC dataset and validated using the ICGC-HCC dataset. Independent prognostic predictors were authenticated using Cox analysis. Furthermore, immune microenvironment analysis between high- and low-risk groups was performed. On analysing MIRcor-DEGs, we identified five prognostic genes which were used to construct a MIRcor-DEGs-related gene signature. Furthermore, we analysed the expression of five prognostic genes at protein and mRNA levels through Western Blot and RT-PCR. The risk score and T stage were demonstrated as credible independent prognostic predictors using Cox regression analysis. Through our study, 11 kinds of immune cells were significantly different between the high- and low-risk groups. Totally, five prognostic genes were identified to be highly expressed in the normal group compared to the HCC group. A risk model of patients with HCC was constructed using these prognostic genes, which provided a theoretical basis and reference value for HCC management.
A SHAP-based controversy analysis through communities on Twitter
Authors: Benslimane Samy , Papastergiou Thomas , Azé Jérôme , Bringay Sandra , Servajean Maximilien , Mollevi Caroline 
Published: February 2024

Abstract

Controversy encompasses content that draws diverse perspectives, along with positive and negative feedback on a specific event, resulting in the formation of distinct user communities. Research on controversy can be broadly categorized into two domains: controversy detection/quantification, which aims to measure controversy on a topic, and controversy explainability, which seeks to comprehend the reasons behind a topic’s controversial nature. This paper primarily contributes to the realm of controversy explainability. We conduct an analysis of topic discussions on Twitter from a community perspective, investigating the role of text in accurately classifying tweets into their respective communities. To achieve this, we introduce a SHAP-based pipeline designed to quantify the influence of impactful text features on the predictions of three tweet classi-fiers. Our approach involves leveraging various text features, including BERT , TF − IDF, and LIWC. The results, derived from both SHAP plots and statistical analyses, distinctly reveal the substantial impact of certain text features in tweet classification. Furthermore, our findings underscore the significance of this study and underscore the potential advantages of combining text and user interactions for a comprehensive understanding of controversy quantification.
Lessons Learned on Social Health Integration: Evaluating a Novel Social Health Integration and Social Risk-Informed Care Online Continuing Medical Education Course for Primary Care Providers
Authors: Bojkov Elizabeth , Papajorgji-Taylor Dea , Paolino Andrea R. , Dorsey Caitlin N. , Barnes Katheen A. , Brown Meagan C. 
Published: February 2024

Abstract
Background
Adjusting clinical care to account for social risks and needs is vital to patient-centered care, but little attention has been paid to implementing it in routine practice. Kaiser Permanente co-designed and developed a continuing medical education (CME) course to orient providers to adjustment activities, or social risk-informed care. We evaluated the dissemination and implementation of this course.
Methods
We evaluated the dissemination and implementation of the online CME using the RE-AIM implementation framework and the Kirkpatrick model of evaluation for training and learning programs. Administrative records and completion reports were generated to track dissemination and completion. A pre- and post-survey design was utilized to assess provider changes in knowledge, attitudes, beliefs, and self-efficacy in delivering social risk-informed care, and semi-structured interviews were conducted to describe effectiveness of the online CME, adoption of social risk-informed care, and sustainability of the online CME and other KP social health integration initiatives.
Results
From April 2022-February 2023, 82 individuals completed the online CME; 52 participants completed the pre-survey and 38 completed the post-survey. A total of 17 interviews were conducted over two phases of qualitative data collection (passive dissemination versus active dissemination). Interviewees felt the online CME provided foundational knowledge in social health and social risk-informed care but requested more region- and role-specific resources. They also identified several systems-level barriers to social health integration.
Conclusion
Co-designing medical education courses with various stakeholders is vital to ensuring relevant and effective educational material. However, high-quality, intentionally designed educational material needs to be complemented with multifaceted and targeted implementation strategies to achieve intended provider behavior change and improved patient outcomes.
Novel Analysis of Nonlinear Seventh Order Kaup-Kupershmidt Equation via the Caputo Operator
Authors: Ganie Abdul Hamid , Mallik Saurav , AlBaidani Mashael M. , Khan Adnan , Shah Mohd Asif 
Published: February 2024

Abstract
In this work, we use two unique methodologies namely the homotopy perturbation transform method and Yang transform decomposition method to solve the fractional nonlinear seventh order Kaup-Kupershmidt (KK) problem. The physical phenomena that arise in chemistry, physics, and engineering are mathematically explained in this equation. In particular, nonlinear optics, quantum mechanics, plasma physics, fluid dynamics and so on. The provided methods are used to solve the fractional nonlinear seventh order KK problem, along with the Yang transform and the fractional Caputo derivative. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approaches and the fractional operator in this situation to develop satisfactory approximations to the offered problem. To solve the fractional KK equation, we first use the Yang transform and the fractional Caputo derivative. When the suggested approaches are used, the results are compared to the exact solution. By comparing the outcomes with the precise solution using graphs and tables, we can verify the efficacy of the offered strategies. Also, the outcomes of using the suggested methods at various fractional orders are examined, demonstrating that the findings get more accurate as the value moves from fractional order to integer order. Moreover, the offered methods are innovative, simple, and quite accurate, demonstrating that they are effective approaches for resolving any differential equations.
Enhancing Emotional State Detection Using EEG Data With A Multi-Model Deep Learning Approach
Authors: immanuel Rajeswari , SKB Sangeetha 
Published: February 2024

Abstract
Human emotion is the mind's reaction to external stimuli. Since human emotions are dynamic and hard to predict in the real world, studies focusing on this area have gained a lot of importance. Emotion recognition using EEG(electroencephalogram) signals has recently seen prevalent use of many deep learning and machine learning techniques.In this paper, we have used a real time dataset which includes 15 subjects (7 Males and 8 Females) and their EEG signals are recorded using video stimuli. The real time data is preprocessed and features are extracted from the preprocessed data using different feature extraction methods. The accuracy and loss of model are calculated and compared with raw and preprocessed data. The proposed model - EEGEM (Electroencephalogram Ensemble Model) is compared with other machine and deep learning techniques. EEGEM is a ensemble model with the combination of LSTM and CNN together to achieve the desired output. The accuracy achieved using this model is 95.56% and it has outperformed other existing models.
Application of preoperative ultrasound-guided injection of nano-carbon for central lymph node dissection in papillary thyroid carcinoma
Authors: Wang Ying , Si Yan , Wu Jing , Lu Hui , Wei Jifu , Ding Qiang 
Published: February 2024

Abstract

Objective: To explore a better tracing of central lymph nodes in papillary thyroid carcinoma patients by comparing the differences in number and staining of central lymph nodes and in nano-carbon extravasation between preoperative ultrasound- guided injection and intraoperative injection of nano-carbon.
Methods: Totally 302 patients were randomly divided into a preoperative ultrasound- guided injection group and an intraoperative injection group. The number and staining of lymph nodes in each sub-area of the central region were recorded and analyzed, including linea alba cervical lymph nodes, Delphian lymph nodes, and pretracheal and paratracheal lymph nodes.
Results: Nano-carbon is prone to infiltration to the extraglandular area when injected intraoperatively. Significantly more linea alba cervical lymph nodes and pretracheal and paratracheal lymph nodes were detected in the preoperative injection group. Preoperative injection of nano-carbon can accelerate the staining and detection of central lymph nodes in patients complicated with Hashimoto's thyroiditis, with clinically lymph node positivity (cN1) assessed before surgery, or with tumor diameter >1 cm.
Conclusion: Preoperative ultrasound-guided injection of nano-carbon can reduce dye spillover and improve the staining rate and detection rate of central lymph nodes.
Parallel Genetic Algorithm Interface II: A novel computational tool for accelerated simulation-based optimization
Authors: Furda Patrik , Variny Miroslav , Myšiak Juraj 
Published: February 2024

Abstract

The ever increasing power of computational tools encouraged by the general need for development of more sustainable technologies fuels the interest in modern optimization approaches. While simulation-based optimization has been receiving considerable attention in the past decades, it still struggles to overcome some challenges, namely excessive computation time. This study proposes a novel optimization interface, the Parallel Genetic Algorithm Interface II (PAGAN-II), which utilizes parallelization of flowsheet simulations to drastically reduce the optimization time without the need to use clustered CPUs and/or modified optimization algorithms. Results of a detailed performance study showed up to 2100% increase in computation rate when optimizing demanding process flowsheets; and approximately 300% increase when optimizing simple ones. Capabilities of the proposed interface were demonstrated by optimization of a 5 MTPA C3MR LNG technology processing 12 different feedstocks, where a 15–30% decrease in the specific energy consumption was achieved. At the same time, the algorithm increased the optimization speed 13-fold compared to the traditional approach. This translates into a reduction of optimization time from 69 days of non-stop computation to approximately 7 days.
The therapeutic effects of probiotics on core and associated behavioral symptoms of autism spectrum disorders: a systematic review and meta-analysis
Authors: Lee Jen-Chin , Chen Chia-Min , Sun Cheuk-Kwan , Tsai I-Ting , Cheng Yu-Shian , Chiu Hsien‐Jane , Wang Ming Yu , Tang Yen-Hsiang , Hung Kuo-Chuan 
Published: February 2024

Abstract
Background
The current study aimed at investigating the efficacies of probiotics in alleviating the core and associated symptoms of autism spectrum disorder (ASD), which was not adequately addressed in the literature.
Methods
Randomized placebo-controlled trials were identified through searching major electronic databases from inception to Nov 2023. The outcome of interests included improvements in the total and associated symptoms of ASD. Outcomes were quantitatively expressed as effect size (ES) based on standardized mean difference (SMD) with 95% confidence interval (CI).
Results
Ten studies with 522 participants (mean age: 8.11) were included in this meta-analysis. The primary results revealed a significant improvement in the total symptoms of ASD in individuals receiving probiotics compared with the controls (SMD= -0.19, p = 0.03, ten studies with 522 participants) but not in the core symptoms of ASD including repetitive restricted behaviors and problems with social behaviors or communication. Subgroup analyses demonstrated an association between a significant improvement in the total symptoms of ASD and the use probiotics relative to their controls only in studies that used multiple-strain probiotics (SMD= -0.26, p = 0.03, five studies with 288 participants) but not in those using single-strain probiotics. Our results on secondary outcomes only showed a significantly greater improvement in adaptation (SMD = 0.37, p = 0.03, three studies with 139 participants) and a trend of greater improvement in symptoms of anxiety in the probiotics group than that in the control group (SMD= -0.29, 95% CI: -0.60 to 0.02, p = 0.07, three studies with 163 participants) but failed to show greater improvement in the probiotics group regarding the symptoms of irritability/aggression, hyperactivity/impulsivity, inattention and parental stress.
Conclusions
Our study supported the use of probiotics for alleviating the overall behavioral symptoms of ASD, mainly in those receiving multiple-strain probiotics as supplements. However, rather than improvement in the core symptoms of ASD, probiotics use was only associated with greater improvement in adaptation and perhaps anxiety. Nevertheless, our results, which were derived from a limited number of available trials, warrant further large-scale clinical investigations for verification.
The species and abundance of gut bacteria jointly mediate a positive impact on Phortica okadai behavior
Authors: Li Di , Wang Lingjun , Wang Liang , Gou Yanting , Luo Bo , Yan Rong , Liu Hui 
Published: February 2024

Abstract

Background
Gut bacteria, serving as essential modulators, exert a significant impact on insect physiology and behavior, holding substantial potential for application in pest management. The dynamics of gut bacteria and the impact on Phortica okadai behavior remain unclear.
Methods
In this study, the dynamics of gut bacterial at different developmental stages in P. okadai was analyzed using 16S rRNA gene sequencing and the species and abundance of gut bacteria that affect host behavior were examined by behavioral experiments.
Results
As a result, a total of 19 phyla, 29 classes, 74 orders, 101 species, and 169 genera were identified. The results of the behavioral experiments indicated that the species, including Lactiplantibacillus argentoratensis, Acetobacter tropicalis, Leuconostoc citreum, and Levilactobacillus brevis, exhibited an effective influence on the feeding preference of P. okadai and the single-bacteria-seeded P. okadai exhibited feeding preferences distinct from germ-free and wild-type.
Conclusions
Overall, this study not only demonstrates the dynamics of gut bacteria in P. okadai but, significantly, also posits novel evidence. It suggests that beyond the gut bacteria species, the abundance also positively influences host behavior. These findings contribute to the development of novel pest control strategies for preventing agricultural pests and vectors.
Effect of hepatitis B viral loads on gestational abnormal alanine aminotransferase modified by age
Authors: Tao Xing-Ran , Yan Qiao , Yu Wen-Jing , Han Guorong 
Published: February 2024

Abstract
Background
Little research has been conducted to investigate whether age can modify the impact of hepatitis B virus (HBV) replication on alanine aminotransferase (ALT) levels during pregnancy in women with chronic hepatitis B (CHB). We initially hypothesized that maternal age might modify the relationship between HBV DNA levels and gestational ALT levels.
Methods
In a retrospective cohort study, 1205 pregnant women with CHB delivered at the Second Hospital of Nanjing between January 2021 and January 2023. Our objective was to analyze the association between different levels of HBV DNA and the risk of gestational abnormal ALT levels, adjusting for age using modified Poisson regression.
Results
Our research indicated that individuals with high HBV DNA levels of 2000 IU/ml or higher were more probable to experience abnormal ALT with a relative risk of 2.64 (P < 0.01) and a high ALT level (RR = 1.25, P < 0.01) after adjusting for covariates. Considering the age, women with high HBV DNA aged 35 and above had an even higher risk of gestational abnormal ALT (RR = 3.70, P < 0.01) and a high level of ALT (RR = 1.44, P < 0.01). However, the risk of ALT abnormality in women with low HBV DNA would not modify by age.
Conclusion
Women with CHB may experience significant gestational ALT abnormalities, so they should frequently monitor ALT during pregnancy and receive timely treatment.
Real-time implementation of IoT Enabled Cyber Attack Detection System (IoT-E-CADS) in Advanced Metering Infrastructure (AMI) using Machine Learning Technique (MLT)
Authors: K Naveeda , S.M.H. Sithi Shameem Fathima 
Published: February 2024

Abstract
The introduction of Internet of Things (IoT) and Smart Energy Meters (SEM) in power grid expects high level cyber security for properly operating the system. This paper proposes an IoT Enabled Cyber Attack Detection System (IoT-E-CADS) in Advanced Metering Infrastructure (AMI) using Machine Learning Technique (MLT). The proposed Bi-level IoT-E-CADS is working in the industry standards for the detection of two types of attacks in the smart grid environment. In the first level, the Isolation Forest machine learning algorithm is used to find the cyber attacks and anomaly detection in real time system. In the second level, Decision Tree machine learning algorithm is used to find the cyber attacks and False Data Injection in a real time system. The designed hardware is implemented and tested at Quantanics Techserv Pvt. Ltd., located in Madurai, Tamil Nadu, India. This company has AMI facility with 10 smart meters, one data concentrator and dedicated server system. The company energy profile and all electrical parameters are monitored and stored using AMI facility. The proposed IoT-E-CADS successfully implemented in this location and detect the manually created two types of cyber attacks. Based on the obtained results, it is observed that the IoT-E-CADS is able to detect cyber threats with the accuracy level of 95% and provides a complete cyber security solutions for secured monitoring unit in commercial environment
Current GPS Deformation Characteristics of Southern North China and its seismicity
Authors: Gao Jiayi , Lu Xiaofei , Zhang Yang , Sun Jie 
Published: February 2024

Abstract

Using GPS data from 1998 to 2020, this study investigates the characteristics of the current crustal horizontal velocity field in Southern North China and adjacent areas. The relationship between deformation characteristics and earthquake activity, as well as the activity of major faults in the study area, are analyzed based on the spatial variations of regional crustal main strain rate field and surface dilatation strain rate field, and the distribution of earthquakes with magnitudes greater than or equal to 3. The results show that: (1) The GPS velocity field has an increasing trend from the northern North China Basin to the southern North China Basin and the Qinling-Dabie Orogen, but the overall movement rate is lower than that of the South China Block. (2) The distribution of earthquakes with magnitudes 3.0-5.9 is closely related to fault structures, while earthquakes with magnitudes greater than 6 are more closely related to regional crustal deformation characteristics, mainly occurring in the transition zone between areal expansion and areal compression. (3) The crustal deformation field can more intuitively indicate the movement characteristics of major faults in the study area. Major faults with strike-slip properties correspond to compressive deformation characteristics, while significant normal faults mainly exhibit extensional deformation characteristics. The segmentation and changes in the extension direction of faults are closely related to the transformation of crustal deformation characteristics. (4) These findings can provide a scientific basis for urban planning and site selection of important engineering projects, in addition to assessing the risk of earthquakes in the study area.
Impact of Dividend Regulation on Islamic Banking Performance in Pakistan
Authors: Iqbal Muhammad Saeed , Fikri Sofi Mohd 
Published: February 2024

Abstract
Islamic banks operate in a similar environment but have different dividend regulations. There appears to be a lack of agreement regarding Pakistan's dividend regulations. Dividend distribution is essential to a company's financial success, but it has not yet been resolved in the finance field. In Pakistan, this study was undertaken to determine whether dividend distribution regulation has a significant impact on Islamic banking's financial performance. This study has the following specific objectives: to examine the impact of RIBA on Islamic banking performance in Pakistan; and to determine the impact of dividend payout ratio on that performance. A theoretical framework was developed based on dividend irrelevance and signal impact. The study was conducted with a descriptive research design A study of all five Islamic financial institutions registered with SBP was performed. During (2019-2023), data was collected. Using a secondary data collection sheet of paper, secondary data was obtained from the State Bank of Pakistan's Supervisory Reports. Secondary data was also collected from Islamic banks. Data processing and analysis were performed with STATA software version 13. Panel data regression and descriptive analysis were conducted to analyze the data. The results were presented in tables and figures. The data were analyzed using multiple regression models, and SPSS version 21 was selected as the data analysis program. Statistics show 76.9% of Islamic banks fail to generate sufficient income to cover financing costs. There are three types of operating costs: total operating costs, loan loss reserves, and financing costs excluding capital costs. According to the analysis of variance (ANOVA) table, the predictor factors significantly impacted the predictor variable at the 5% level of significance. Islamic banking organizations perform by 0.347 points for every unit change in dividend payout ratio or RIBA, respectively. Consequently, Islamic banking institutions must take advantage of all available borrowing opportunities in order to continue to operate in the lending sector. As well as reinvesting earnings in their business, they must also grow it.