Online Datasets Research Articles

Sexual Health and Psychological Well-Being: An Examination of the Interactions between Sexual Satisfaction, Relationship Dynamics, And Mental Health

This study comprehensively examines the relationships between sexual health and psychological well-being, focusing on sexual satisfaction, relationship dynamics, and mental health. The primary objective of this research is to determine how individuals' levels of sexual satisfaction impact their psychological well-being and to understand the dynamics of this relationship. While existing literature widely discusses the positive effects of sexual satisfaction on individuals' overall mental health and quality of life, this research aims to analyze these interactions from a more unique perspective. Utilizing quantitative methods, including statistical analyses such as independent sample t-tests, the study compares face-to-face and online data sets. The findings reveal that sexual satisfaction has a significant and robust impact on psychological well-being. It was observed that sexual satisfaction enhances individuals' capacity to cope with stress, reduces anxiety levels, and improves overall mental health. Additionally, relationship dynamics, particularly factors such as emotional alignment and closeness with a partner, significantly strengthen the interaction between sexual satisfaction and psychological well-being. In this context, the study sheds light on the interdependent dynamics of sexual health and psychological well-being, offering critical insights for clinical practice in this area. Enhancing sexual satisfaction could substantially improve individuals' quality of life and mental health. Therefore, it is recommended that sexual health topics receive greater emphasis in psychotherapy and counseling processes.

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.

From unimodal to multimodal dynamics of verbal and nonverbal cues during unstructured conversation.

Conversations encompass continuous exchanges of verbal and nonverbal information. Previous research has demonstrated that gestures dynamically entrain each other and that speakers tend to align their vocal properties. While gesture and speech are known to synchronize at the intrapersonal level, few studies have investigated the multimodal dynamics of gesture/speech between individuals. The present study aims to extend our comprehension of unimodal dynamics of speech and gesture to multimodal speech/gesture dynamics. We used an online dataset of 14 dyads engaged in unstructured conversation. Speech and gesture synchronization was measured with cross-wavelets at different timescales. Results supported previous research on intrapersonal speech/gesture coordination, finding synchronization at all timescales of the conversation. Extending the literature, we also found interpersonal synchronization between speech and gesture. Given that the unimodal and multimodal synchronization occurred at similar timescales, we suggest that synchronization likely depends on the vocal channel, particularly on the turn-taking dynamics of the conversation.

PARD6A promotes lung adenocarcinoma cell proliferation and invasion through Serpina3.

Par6α encoded by PARD6A is a member of the PAR6 family and is reported to promote cancer initiation and progression. PARD6A is frequently upregulated in different types of cancers, but its regulatory role in lung cancer progression is yet to be established. In this study, we analyzed the PARD6A expression in biopsies from lung adenocarcinoma (LUAD) patients, and the survival probability using LUAD tissue microarray (TMA) and online datasets from TCGA and GEO. We conducted in vitro and in vivo assays to assess the role of PARD6A in regulating lung cancer progression, including proliferation, wound healing, transwell, RNA-seq, and subcutaneous tumor mice models. Our findings revealed that PARD6A is highly expressed in cancer tissues from LUAD patients and is associated with poor prognosis in LUAD patients. In vitro assays showed that PARD6A promoted cell proliferation, migration, and invasion. The transcriptome sequencing identified Serpina3 as one of the key downstream molecules of PARD6A. Ectopic expression of Serpina3 rescued impaired proliferation, migration, and invasion in PARD6A-knocking down H1299 cells, whereas silencing Serpina3 impeded enhanced proliferation, migration, and invasion in PARD6A-overexpressing H1975 cells. Our findings suggest that PARD6A promotes lung cancer progression by inducing Serpina3, which may be a promising therapeutic target.

Integrating Quantum Computing into Business Analytics: Opportunities and Challenges

This article explores the transformative potential of quantum computing in the field of business analytics. It begins with an introduction to quantum computing, explaining its fundamental principles and recent advancements. The study highlights the limitations of current business analytics methods and demonstrates how quantum computing could address these limitations by offering enhanced data processing capabilities, advanced algorithms, and solutions to complex optimization problems. A comprehensive literature review is conducted to provide context and identify gaps in the existing research. The article then outlines a research design that incorporates both real-world and simulated data, using online datasets and quantum computing frameworks for analysis. The findings reveal significant opportunities for quantum computing to revolutionize business analytics, including improved efficiency, accuracy, and the ability tosolve previously intractable problems. However, the article also addresses key challenges such as technical limitations, cost, accessibility, and integration issues. The discussion highlights emerging trends and provides strategic recommendations for businesses considering the adoption of quantum computing. The article concludes with a summary of the implications of integrating quantum computing into business analytics and reflects onfuture potential and challenges.

Drones in Action: A Comprehensive Analysis of Drone-Based Monitoring Technologies

Unmanned aerial vehicles (UAVs), commonly referred to as drones, are extensively employed in various real-time applications, including remote sensing, disaster management and recovery, logistics, military operations, search and rescue, law enforcement, and crowd monitoring and control, owing to their affordability, rapid processing capabilities, and high-resolution imagery. Additionally, drones mitigate risks associated with terrorism, disease spread, temperature fluctuations, crop pests, and criminal activities. Consequently, this paper thoroughly analyzes UAV-based surveillance systems, exploring the opportunities, challenges, techniques, and future trends of drone technology. It covers common image preprocessing methods for drones and highlights notable one- and two-stage deep learning algorithms used for object detection in drone-captured images. The paper also offers a valuable compilation of online datasets containing drone-acquired photographs for researchers. Furthermore, it compares recent UAV-based imaging applications, detailing their purposes, descriptions, findings, and limitations. Lastly, the paper addresses potential future research directions and challenges related to drone usage

ADCY5 act as a putative tumor suppressor in glioblastoma: An integrated analysis

BackgroundAdenylyl cyclase (AC) isoforms played a key role in the multiple cancer pathology, However, the expression, prognostic value and function of ADCY5 in Glioblastoma (GBM) have not been reported yet. This research intends to discover the expression, epigenetic alteration and biological function of ADCY5 in GBM and its value on patients' prognosis. Methods① Transcriptional level, epigenetic alteration, prognostic value and molecular network of ADCY5 were analyzed by using of public online datasets. ② The mRNA expression profile of ADCY5 was explored by using GEPIA database and protein expression levels were detected by HPA Database. ③ The prognostic value of ADCY5 was determined by Kaplan-Meier Plotter, GEPIA and CGGA database. ④ The epigenetic characteristics of ADCY5 were determined by DiseaseMeth database. ⑤ Identification of genes co-expressed with ADCY5 and potential mechanism analyses were performed by using DAVID cBioPorta and STRING. ⑥ Reverse transcription-polymerase chain reaction (RT-PCR), cell counting kit-8 (CCK-8), colony formation, wound-healing scratch and transwell assay were applied to detect relative mRNA expression and biological function of ADCY5 in GMB cells. ResultsADCY5 mRNA and protein were downregulated in GBM compared with normal tissues. Analysis of the genetics and epigenetics of ADCY5 suggested that its expression was negatively correlated with DNA methylation. High expression of ADCY5 was significantly associated with age, grade, IDH mutation, 1p19q_codeletion, radiotherapy and chemotherapy and acted as an independent prognostic factor in GBM. ADCY5 mRNA also down-expressed in GBM cell lines and re-expressed of ADCY5 could inhibit cell proliferation, viability, migration/invasion and epithelial-mesenchymal transition (EMT) in vitro. In the analysis of genes co-expressed with ADCY5, we found that cAMP/AKT pathway, cGMP-PKG pathway, Wnts pathway were dissimilarly enriched. ConclusionOur study indicated that ADCY5 could act as an epigenetic biomarker in GBM, as well as a prognosis target in patients with GBM.

Wearable bracelet and machine learning for remote diagnosis and pandemic infection detection

The COVID-19 pandemic has highlighted that effective early infection detection methods are essential, as they play a critical role in controlling the epidemic spread. In this work, we investigate the use of wearable sensors in conjunction with machine learning (ML) techniques for pandemic infection detection. We work on designing a wristband that measures various vital parameters such as temperature, heart rate, and SPO2, and transmits them to a mobile application using Bluetooth Low Energy. The accuracy of the wristband measurements is shown to be within 10% of the readings of existing commercial products. The measured data can be used and analyzed for various purposes. To benefit from the existing online datasets related to COVID-19, we use this pandemic as an example in our work. Hence, we also develop ML-based models that use the measured vital parameters along with cough sounds in order to determine whether a case is COVID-19 positive or not. The proposed models are shown to achieve remarkable results, exceeding 90% accuracy. One of our proposed models exceeds 96% performance in terms of accuracy, precision, recall, and F1-Score. The system lends itself reasonably for amendment to deal with future pandemics by considering their specific features and designing the ML models accordingly. Furthermore, we design and develop a mobile application that shows the data collected from the wristband, records cough sounds, runs the ML model, and provides feedback to the user about their health status in a user-friendly, intuitive manner. A successful deployment of such an approach would decrease the load on hospitals and prevent infection from overcrowded spaces inside the hospital.

Identification of key genes and long non‑coding RNA expression profiles in osteoporosis with rheumatoid arthritis based on bioinformatics analysis

BackgroundAlthough rheumatoid arthritis (RA) is a chronic systemic tissue disease often accompanied by osteoporosis (OP), the molecular mechanisms underlying this association remain unclear. This study aimed to elucidate the pathogenesis of RA and OP by identifying differentially expressed mRNAs (DEmRNAs) and long non-coding RNAs (lncRNAs) using a bioinformatics approach.MethodsExpression profiles of individuals diagnosed with OP and RA were retrieved from the Gene Expression Omnibus database. Differential expression analysis was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway enrichment analyses were performed to gain insights into the functional categories and molecular/biochemical pathways associated with DEmRNAs. We identified the intersection of common DEmRNAs and lncRNAs and constructed a protein-protein interaction (PPI) network. Correlation analysis between the common DEmRNAs and lncRNAs facilitated the construction of a coding-non-coding network. Lastly, serum peripheral blood mononuclear cells (PBMCs) from patients with RA and OP, as well as healthy controls, were obtained for TRAP staining and qRT-PCR to validate the findings obtained from the online dataset assessments.ResultsA total of 28 DEmRNAs and 2 DElncRNAs were identified in individuals with both RA and OP. Chromosomal distribution analysis of the consensus DEmRNAs revealed that chromosome 1 had the highest number of differential expression genes. GO and KEGG analyses indicated that these DEmRNAs were primarily associated with " platelets (PLTs) degranulation”, “platelet alpha granules”, “platelet activation”, “tight junctions” and “leukocyte transendothelial migration”, with many genes functionally related to PLTs. In the PPI network, MT-ATP6 and PTGS1 emerged as potential hub genes, with MT-ATP6 originating from mitochondrial DNA. Co-expression analysis identified two key lncRNA-mRNA pairs: RP11 − 815J21.2 with MT − ATP6 and RP11 − 815J21.2 with PTGS1. Experimental validation confirmed significant differential expression of RP11-815J21.2, MT-ATP6 and PTGS1 between the healthy controls and the RA + OP groups. Notably, knockdown of RP11-815J21.2 attenuated TNF + IL-6-induced osteoclastogenesis.ConclusionsThis study successfully identified shared dysregulated genes and potential therapeutic targets in individuals with RA and OP, highlighting their molecular similarities. These findings provide new insights into the pathogenesis of RA and OP and suggest potential avenues for further research and targeted therapies.

Adapting and extending multimodal (inter)action analysis to investigate synchronous multimodal online language teaching

Abstract Multimodal (inter)action analysis offers a powerful and robust methodology for the study of action and interaction between social actors, their environment, and the objects and tools within. Yet its implementation in the analysis of synchronous multimodal online data sets, e.g. (inter)actions via videoconferencing, is limited. Drawing on our research in understanding teacher-learner (inter)actions in instruction-giving fragments in synchronous multimodal online language lessons, we describe and illustrate the ways in which we adapted and extended some of the methodological and analytical tools. These include (1) the use of a grounded theory approach in delineating and identifying higher-level actions, (2) the embodiment and disembodiment of frozen actions, (3) electronic print mode, (4) semiotic lag, (5) semiotic (mis)alignment, (6) modal density (mis)alignment, and (7) how modal density can be achieved by brisk modal shifts in addition to through modal intensity and complexity. We conclude by a call for further educational research in online teaching platforms using the framework to have richer understandings of the (inter)actions between social actors with particular roles and identities (teachers-learners), their environment, and the objects and tools within, which bring their “own material properties, feel and techniques of use, affordances and limitations” (Chun, Dorothy, Richard Kern & Bryan Smith. 2016. Technology in language use, language teaching, and language learning. The Modern Language Journal 100. 64–80: 65).

A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease

Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback’s quasi-refraction position for further improving the optimal position’s quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.

A Machine Learning Based Approach for the Detection of DDoS Attacks on Internet of Things Using CICDDoS2019 Dataset - PortMap

In today's technological era, the Internet has become ubiquitous, playing a vital role in our daily lives. With the exponential growth of IoT innovation, millions of interconnected IoT-enabled devices rely on cloud services to communicate over the Internet. However, this rapid development also exposes these devices to various threats, with DDoS (Distributed Denial of Service) and DoS (Denial of Service) attacks being particularly potent and destructive. DDoS attacks present a unique challenge as they are extremely difficult to detect using conventional intrusion detection frameworks and traditional methodologies. Fortunately, advancements in machine learning have provided a promising solution by enabling accurate differentiation between DDoS attacks and other forms of data. This study proposes a DDoS detection model based on machine learning algorithms. To conduct this study, we utilized the most recent and freely available online dataset called CICDDoS2019. Various machine learning-based techniques were explored, aiming to identify the characteristics associated with accurate classification. Among the algorithms tested, AdaBoost and XGBoost demonstrated exceptional performance. As part of future work, a hybrid approach will be incorporated into this model, further improving its capabilities. It is worth noting that this model will be continuously updated with new data on DDoS attacks, ensuring its relevance and effectiveness in combating emerging threats. By leveraging machine learning techniques, this approach enhances the detection of DDoS attacks on Internet of Things networks, safeguarding the integrity and security of connected devices and the overall IoT ecosystem.

Diagnosis and Molecular Characterization of Potential RNA Binding Protein Involved in the Pathogenesis of Liver Ischemia Reperfusion Injury.

Liver ischemia-reperfusion is one of the common complications after liver surgery. Uncontrolled liver ischemia-reperfusion will lead to many serious consequences such as surgical failure. It is an urgent clinical problem to search for diagnostic markers and explore its potential pathogenesis. In this study, we focus on 1411 candidate RNA binding protein. Through several GEO (Gene Expression Omnibus) online datasets, we construct a diagnostic model and perform interactive validation. We evaluate the efficacy of the prognostic model. Using bioinformatics methods, we predicted the relevant signaling pathways of liver ischemia-reperfusion and key genes. We also evaluated the association of RNA binding protein with immune cell infiltration. Single cell sequencing datasets were used to explore the expression profiles of key genes at the single cell level. Machine learning algorithm is used to predict key gene RNA binding domains. ROC (Receiver Operating Characteristic) and DCA (Decision Curve Analysis) curves showed that the above diagnostic model had good and stable diagnostic efficacy and clinical practicability. We identified three key genes (BTG2, CCNL1 and DNAJB1) in liver ischemia-reperfusion. DNAJB1, BTG2 and CCNL1 are mainly expressed in immune cells such as macrophages and T cells, and are closely related to inflammatory pathways such as TNF-α, highlighting their importance in hepatic ischemia reperfusion. We identified RNA-binding domains of the above three genes. We found that the expression of DNAJB1, CCNL1 and BTG2 in the ischemia-reperfusion group were significantly higher than those in the sham operation group. Our study revealed the importance of the candidate RNA binding protein in liver ischemia reperfusion injury and provided new insights into the therapeutic of hepatic ischemia-reperfusion injury.

Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail

In the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models are preferred. In this manuscript, an effective Bi-GRU is proposed for accurate sales forecasting related to E-commerce companies. Initially, retail sales data are acquired from two benchmark online datasets: Rossmann dataset and Walmart dataset. From the acquired datasets, the unreliable samples are eliminated by interpolating missing data, outlier’s removal, normalization, and de-normalization. Then, feature engineering is carried out by implementing the Adaptive Particle Swarm Optimization (APSO) algorithm, Recursive Feature Elimination (RFE) technique, and Minimum Redundancy Maximum Relevance (MRMR) technique. Followed by that, the optimized active features from feature engineering are given to the Bi-Directional Gated Recurrent Unit (Bi-GRU) model for precise retail sales forecasting. From the result analysis, it is seen that the proposed Bi-GRU model achieves higher results in terms of an R2 value of 0.98 and 0.99, a Mean Absolute Error (MAE) of 0.05 and 0.07, and a Mean Square Error (MSE) of 0.04 and 0.03 on the Rossmann and Walmart datasets. The proposed method supports the retail sales forecasting by achieving superior results over the conventional models.

Heart Sound Processing for Early Diagnostic of Heart Abnormalities using Support Vector Machine

This paper addresses the critical issue of cardiovascular disease (CVD), the leading cause of global mortality, emphasizing the imperative for effective and early detection to mitigate CVD-related deaths. The research problem underscores the urgency of developing advanced diagnostic tools to identify heart abnormalities promptly. The primary objective is to create a Support Vector Machine (SVM) algorithm for accurate classification of different heart conditions, namely Normal heart, Mitral Stenosis, and Mitral Regurgitation. To achieve this objective, the study utilizes a dataset of heart sounds available online using a 10-fold cross-validation method. The focus is on evaluating the efficacy of various kernel functions within the SVM framework for heart sound classification. The findings demonstrate that the linear kernel exhibits superior accuracy and robustness in effectively classifying heart conditions. Notably, the proposed classification method attains an impressive 96% accuracy, highlighting its potential as a reliable tool for early detection of cardiovascular diseases. This research contributes to the ongoing efforts to enhance diagnostic capabilities and ultimately reduce the global burden of CVD-related fatalities.

Identifying drug candidates for pancreatic ductal adenocarcinoma based on integrative multiomics analysis.

Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis. We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes. Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered. Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.

Bridging the Gap in Cancer Research: Sulfur Metabolism of Leukemic Cells with a Focus on L-Cysteine Metabolism and Hydrogen Sulfide-Producing Enzymes.

Leukemias are cancers of the blood-forming system, representing a significant challenge in medical science. The development of leukemia cells involves substantial disturbances within the cellular machinery, offering hope in the search for effective selective treatments that could improve the 5-year survival rate. Consequently, the pathophysiological processes within leukemia cells are the focus of critical research. Enzymes such as cystathionine beta-synthase and sulfurtransferases like thiosulfate sulfurtransferase, 3-mercaptopyruvate sulfurtransferase, and cystathionine gamma-lyase play a vital role in cellular sulfur metabolism. These enzymes are essential to maintaining cellular homeostasis, providing robust antioxidant defenses, and supporting cell division. Numerous studies have demonstrated that cancerous processes can alter the expression and activity of these enzymes, uncovering potential vulnerabilities or molecular targets for cancer therapy. Recent laboratory research has indicated that certain leukemia cell lines may exhibit significant changes in the expression patterns of these enzymes. Analysis of the scientific literature and online datasets has confirmed variations in sulfur enzyme function in specific leukemic cell lines compared to normal leukocytes. This comprehensive review collects and analyzes available information on sulfur enzymes in normal and leukemic cell lines, providing valuable insights and identifying new research pathways in this field.

A lifelong meta-learning approach for learning deep grey-box representative thermal dynamics models for residential buildings

Thermal dynamics models of residential buildings are crucial for managing energy use and maintaining desirable indoor environment quality, in the context of decarbonizing the electric grid. Despite recent developments in data-driven models, the implementation with a massive number of buildings in the real world remains infeasible because the computation is expensive and the thermostat data of buildings can be online and sparse. A representative model for aggregated study and acceleration of personalizing models is needed for many downstream tasks. In addition, most existing data-driven models lack physical interpretability questioning the representativeness of any aggregated model. In this paper, we present an online framework for learning meta-models of thermal dynamics across various residential buildings. The meta-models are physically explainable according to the building types and can accelerate fine-tuning personalized models for any building saving data and computation. Experiments suggest that our method performs well with a real-world online dataset that produces representative models of different climate zones and effective initialization for training individual models.

DY131 activates ERRγ/TFAM axis to protect against metabolic disorders and acute kidney injury.

Renal tubular injury is considered as the main pathological feature of acute kidney injury (AKI), and mitochondrial dysfunction in renal tubular cells is implicated in the pathogenesis of AKI. The estrogen-related receptor γ (ERRγ) is a member of orphan nuclear receptors which plays a regulatory role in mitochondrial biosynthesis, energy metabolism and many metabolic pathways. Online datasets showed a dominant expression of ERRγ in renal tubules, but the role of ERRγ in AKI is still unknown. In the present study, we investigated the role of ERRγ in the pathogenesis of AKI and the therapeutic efficacy of ERRγ agonist DY131 in several murine models of AKI. ERRγ expression was reduced in kidneys of AKI patients and AKI murine models along with a negative correlation to the severity of AKI. Consistently, silencing ERRγ in vitro enhanced cisplatin-induced tubular cells apoptosis, while ERRγ overexpression in vivo utilizing hydrodynamic-based tail vein plasmid delivery approach alleviated cisplatin-induced AKI. ERRγ agonist DY131 could enhance the transcriptional activity of ERRγ and ameliorate AKI in various murine models. Moreover, DY131 attenuated the mitochondrial dysfunction of renal tubular cells and metabolic disorders of kidneys in AKI, and promoted the expression of the mitochondrial transcriptional factor A (TFAM). Further investigation showed that TFAM could be a target gene of ERRγ and DY131 might ameliorate AKI by enhancing ERRγ-mediated TFAM expression protecting mitochondria. These findings highlighted the protective effect of DY131 on AKI, thus providing a promising therapeutic strategy for AKI.

Augmenting Surveys with Social Media Data: A Probabilistic Framework for LinkedIn Data Linkage.

Introduction &amp; BackgroundLinkedIn, with its extensive global network of over 900 million members across more than 200 countries, presents a unique repository for examining labour market dynamics, professional development, and the impact of social networking on employment opportunities. Despite its potential, LinkedIn's wealth of data on professional trajectories, skills, and labour market outcomes remains largely untapped in survey research due to challenges in data collection. Objectives &amp; ApproachThis paper introduces a novel methodology for integrating LinkedIn data with survey responses using data from the fourteenth wave of the Innovation Panel (IP14) of Understanding Society: The UK Household Longitudinal Study (UKHLS), conducted in 2021. In IP14, we probed the extent of LinkedIn usage among the UK population and assessed users' willingness to link their LinkedIn profiles with their survey responses. Those consenting to link their accounts were asked for specific details — namely their first and last names, employer, and job title — to enable profile identification on LinkedIn. Faced with the unavailability of a unique platform identifier and the cessation of LinkedIn’s API, this information was crucial for matching profiles accurately. We crafted a framework using PhantomBuster for ethical data extraction and a probabilistic string-matching technique to ensure precise linkage between survey responses and LinkedIn profiles. PhantomBuster, a cloud-based tool, efficiently scrapes dynamic content using JavaScript in a headless browser environment, sidestepping IP-related restrictions while adhering to website terms of service. It streamlines the data collection process. Identified profiles were subjected to an iterative probabilistic string matching, using respondent-provided metadata alongside supplementary data, to maximize the accuracy of matching the profiles to our survey participants. Relevance to Digital FootprintsThe described method advances digital footprint research in data collection and linkage. It automates the retrieval of vast online data sets; compiles information efficiently in an organized format; saves time and labour by mechanizing monotonous tasks; circumvents platform-imposed IP restrictions; and imposes fewer barriers to entry as it requires less technical skill than other scraping tools like Selenium. Conclusions &amp; ImplicationsThis approach not only facilitates the precise identification and collection of LinkedIn profile data but also sets a precedent for ethical considerations in web scraping practices. By documenting this methodology, we aim to equip researchers with a scalable and replicable tool for future studies, enriching the analysis of labour market outcomes and the interplay between formal education, informal training, and professional success through the integration of LinkedIn and survey data.