Systematic Data Analysis Research Articles

Assessing attitudes toward research and plagiarism among medical students: a multi-site study.

Research involves the systematic collection and analysis of data to enhance understanding of a particular phenomenon. Participation in medical research is crucial for advancing healthcare practices. However, there has been limited focus on understanding the factors that motivate medical students to engage in research. Additionally, in the era of e-learning, the easy accessibility of online resources has contributed to a widespread 'copy-paste culture' among digital-native students, which is recognized in academia as plagiarism. Existing studies suggest that a contributing factor to the increasing prevalence of plagiarism is students' limited understanding of this act. The purpose of this study was to assess medical students' attitudes toward research and plagiarism, and to evaluate the psychometric properties of the Attitudes Toward Research (ATR) and Attitudes Toward Plagiarism (ATP) questionnaires. This was a multicenter study conducted among medical undergraduate and postgraduate students attending the three medical universities who were involved in research. Students' attitudes toward research and plagiarism were assessed using the ATR and ATP questionnaires. The research instruments underwent translation and cultural adaptation in accordance with internationally accepted methodology. The psychometric properties of the ATR and ATP, including validity and reliability, were assessed. Confirmatory factor analysis was used to test the model's fit to the data. The ATR and ATP questionnaires were completed by 793 medical students who were involved in research (647 undergraduates and 146 PhD students). Cronbach's alpha coefficients of 0.917 and 0.822 indicated excellent and good scale reliability for the ATR and ATP questionnaires, respectively. The five-and three- factor structures of ATR and ATP have been validated with maximum likelihood confirmatory analysis, and the results demonstrated an adequate level of model fit (TLI = 0.930, CFI = 0.942 and TLI = 0.924, CFI = 0.943, respectively). Medical students showed a high degree of positive attitudes toward research and favorable scores across all three domains of attitudes toward plagiarism. In multivariate regression models, age was found to be positively associated with favorable attitudes of research usefulness, positive attitudes, relevance to life subscales and total ATR scale (p < 0.001), while PhD study level was related to research anxiety (p < 0.001) and favorable attitudes across all three ATP domains (p < 0.001). Medical students who were involved in research showed a high degree of favorable attitudes toward research and plagiarism. Adjusting medical school curricula to include research courses would broaden the students' interest in scientific research and maximize their impact on the full preservation of research ethics and integrity.

Self-testing for human papillomavirus as a screening method for identifying risk groups

Due to the high prevalence of cervical cancer, it is necessary to take measures aimed at reducing its frequency through vaccination, screening, detection and treatment of precancerous conditions. The article presents a systematic analysis of scientific data on the use of testing for self-sampling for human papillomavirus as a tool to improve the effectiveness of screening and detection of precancerous diseases of the cervix. It has been shown that the use of such tests, which are based on polymerase chain reaction (PCR), have similar clinical accuracy compared to sampling by a doctor, and thus this option is promising for expanding the coverage of cervical cancer screening. The presence of expected economic efficiency also determines the relevance and prospects for these tests, but further research is needed to assess their implementation in routine practice.

Unveiling the hidden hands: Analysis of corporate ownership of industrial tuna fishing vessels in the Eastern Pacific Ocean

Distant-water fishing (DWF) refers to fishing operations conducted by companies in waters beyond their national exclusive economic zones (EEZs), often targeting the EEZs of other coastal states or international waters. Research on DWF typically emphasizes the flag states of vessels, rather than the nationalities of the corporations that own them, despite evidence of efforts to obscure ownership. This paper examines the corporate owners of 1648 industrial and semi-industrial tuna fishing vessels in the Eastern Pacific Ocean (EPO) using Orbis data, analyzing ownership at three levels: flag state, direct corporate owners, and ultimate corporate owners. Results show that flag state data alone understate the true fishing capacity of key DWF nations. Shifts in ownership nationality across levels are significant, notably for Taiwanese and Spanish corporations owning vessels through proxies in the Global South. These ownership shifts impact 6 % of vessels and 14 % of gross tonnage in the registered EPO tuna fleet. Furthermore, spatial analysis using Global Fishing Watch data highlights the Galapagos Islands EEZ as a critical fishing zone, frequently accessed by foreign fishing corporations through Ecuadorian intermediaries. This study underscores the need to incorporate ownership data into fisheries governance for greater transparency and accountability. The systematic collection and analysis of ownership data would allow fisheries managers to better monitor capacity, address power concentration, and promote policies that ensure fairer distribution of resources and benefits.

Effect of Teachers Attitudes towards Teaching on Students’ Performance in Mathematics Subject in Public Day Secondary Schools in Rwanda: A Case of Huye District

The study investigated how teachers' attitudes toward teaching influenced students' academic progress in mathematics in a public secondary school in Huye District. Specifically, the research identifies the teachers' attitudes toward teaching that could influence the achievement of pupils in Mathematics-related subjects in public day secondary schools in Huye district. Guided by the Theory of Planned Behavior, this study used a combined method approach to collect both quantitative and qualitative data to examine how teachers' attitudes influence their teaching practices in Mathematics. Purposive and random sampling were applied to select sectors, schools, and participants, allowing for systematic data collection and analysis on teachers' attitudes and their influence on students’ performance. The target population for this study was 200, including 3 sector educational officers, 5 head teachers, 25 mathematics teachers and 167 Mathematics students; and the Yamane method was used to calculate a sample size of 134 respondents including 2 sector educational officers, 3 Head teachers, 15 teachers and 114 students. Primary data sources included surveys, while secondary data came from observation and interview methods. This study used purposive, stratified and simple random sampling procedures to create a sample group of respondents. The research collected and analyzed data using both quantitative and qualitative approaches. Content analysis helped with qualitative data analysis, while quantitative data was presented in SPSS Version 21.0 utilizing statistical methods such as descriptive (frequency, percentage, imply and deviation from the mean) and inferential statistics (correlational and regression analysis). For the study objective, the results show that 76.3% strongly agreed that teachers' communication skills, 69.3% strongly agreed that effective goal-setting, 64.9% strongly agreed that lesson preparation, 61.4% strongly agreed that lesson preparation, 78.1% strongly accepted that professional commitment, and 68.0% highly agreed that adaptability and flexibility reflect my teachers' attitudes toward teaching. 64.9% strongly agreed that early arrival in class during the teaching lesson reflects the teachers' attitudes toward teaching. The study found a strong positive correlation between exams, test results, and factors like interpersonal skills, goal-setting, lesson preparation, and professional commitment, which all contributed to improved student performance. The researcher concluded that early attendance and communication skills of teachers were also linked to positive educational outcomes, with tests enhancing performance across subjects. With a p-value below 0.05, the study showed that active class participation boosts effectiveness. Recommendations included improving teacher attitudes towards math, enhancing confidence, making Math enjoyable, investing in teacher training, providing resources and encouraging community involvement in Math careers. Regular follow-ups and ongoing professional development for teachers were also advised.

Main epidemiological indicators of type 1 diabetes mellitus in children in the Russian Federation for 2014-2023

Type 1 diabetes mellitus (T1DM) is the most common form of diabetes mellitus in childhood, where, unlike in adults, it accounts for more than 90% of all cases of diabetes. The constant change in the epidemiology of T1DM with significant differences in populations and regions requires systematic data collection and analysis for timely monitoring of T1DM trends. Analysis of the main epidemiological indicators of T1DM in children in the Russian Federation over the past 10 years - from 2014 to 2023. The object of the study was the data obtained from the federal statistical observation form No. 12 «Information on the number of diseases registered in patients living in the service area of a medical organization» for the period from 2014 to 2023. The prevalence (total number of registered cases) and incidence (cases with a diagnosis established for the first time) of T1DM (ICD-10 code: E10) were analyzed in children in three age groups: from 0 to 14 years, from 15 to 17 years, and combined from 0 to 17 years (inclusive). Over the analyzed period, the prevalence of T1DM increased steadily from 238.6 in 2014 to 374.2 cases per 100,000 children in 2023. The prevalence of T1DM in adolescents from 15 to 17 years was higher than in children and amounted to 120.3-203.2 cases per 100,000 adolescents, while in children under 14 years of age, the prevalence was 100.1-172.2 cases per 100,000 children. The annual increase in the prevalence of T1DM averaged 6.3% (95% CI 4.9-7.8). The incidence of T1DM during the analyzed period was 19.1-27.2 cases per 100,000 children and also had a general tendency toward an annual increase in new cases. At the same time, over the past three years, there has been a relative stabilization of incidence rates at 26.5-27.2 per 100,000 children. The annual increase in incidence averaged 4.9% (95% CI 0.9-8.9). The greatest increase in the incidence of T1DM was observed in regions with low incidence. The epidemiology of T1DM in the Russian Federation is characterized by significant regional and dynamic changes. Over the period 2014-2023, the incidence of type 1 diabetes in children has increased significantly, increasing annually by an average of 5%, while there has been a relative stabilization of incidence rates over the past three years.

GLP-1: A Prospective Guardian for Comprehensive Myocardial Perfusion.

To investigate the role of glucagon-like peptide 1 (GLP-1) in myocardial perfusion, focusing on its effects on coronary microcirculation and cardiovascular outcomes. Review of foundational research and large-scale clinical trials, including Cardiovascular Outcome Trials (CVOTs), examining the cardiovascular effects of GLP-1. Systematic analysis of trial data to assess the impact of GLP-1 therapy on myocardial infarction, composite cardiovascular events, and stroke incidence. GLP-1 therapy was found to significantly reduce myocardial infarction and composite cardiovascular events. Additionally, GLP-1 receptor agonists were observed to reduce stroke incidence, suggesting systemic effects on panvascular diseases. While direct protective effects on coronary microvasculature have been less studied, growing evidence supports GLP-1's role in comprehensive myocardial perfusion. GLP-1 is a promising therapeutic agent for improving myocardial perfusion and reducing cardiovascular events. Its protection is likely associated with comprehensive improvements in myocardial perfusion, including effects on coronary microcirculation. Further research is needed to fully elucidate its mechanisms of action and potential clinical applications.

Disentangling hydrodynamic drivers of the Southern Venice (Italy) coastal aquifer via frequency decomposition analysis: Insights, challenges, and limitations

Study regionShallow coastal aquifer located in the southern part of the Venice lagoon (Italy). Study focusThis study aims to improve the understanding of coastal aquifers’ hydrodynamics by implementing systematic time-series analyses of data. A collection of non-intrinsically consistent time series from hydrological (surface water and groundwater) and meteo-mareographic monitoring networks was obtained from different institutions. Each signal was broken down through a frequency decomposition analysis, isolating the main driving forces to focus on phenomena that occur at different time and spatial scales. New hydrological insights for the regionResults highlighted that the aquifer is highly connected with the Venice lagoon, with a clear fluctuation of piezometric heads induced by tidal major constituents, decreasing landward. Besides, the effects exerted by reclamation canals and pumping stations were also determined and found to increase landward. Despite the relatively simple behaviour of piezometric heads, the groundwater salinity is influenced by additional local factors, like probe depth, wells’ screen length, and vertical salinity distribution along the aquifer. These findings suggested how to make use of limited and sparse data to enhance the conceptual model of coastal aquifer hydrodynamics, while highlighting the limitations of existing monitoring networks. This outcome justified the need for an intrinsically-consistent network of dedicated multi-level samplers to avoid intra-borehole mixing and reliably characterize the groundwater salinity distribution.

Progress on clay swelling inhibitors: a comprehensive review

Relevance. It is known that swelling of a well wall due to the presence of water negatively affects drilling operations, causing a significant increase in their material costs. The use of various inhibitors prevents this effect and the drilling mud reacting on wellbore narrowing. In this sense, the problems of developing polymer, amino-, ionic liquid and shale inhibitors of surfactants considered in the article are very relevant and of practical interest to oil engineers in terms of the prospects for fundamental and applied researches of these issues. Aim. To study the possibilities of existing approaches and methods that determine the characteristics of the main clay swelling inhibitors, the regularities of changes in the properties of traditional and non-traditional clay materials, as well as the mechanisms of clay swelling with the identification of the most characteristic group of these inhibitors. Object. Processes of minimizing the effect of wall narrowing as a result of exposure to inhibitors. Methods. Systematic, critical and comprehensive analysis of modern data on clay swelling inhibition in the field of drilling muds. Results. The authors have carried out the detailed analysis of clay swelling. The paper discusses the characteristics of inhibitors required to prevent swelling, in the structure of which nitrogen and ionic liquids are contained. It is noted that a high-quality clay swelling inhibitor should include in its base a water-soluble bulk molecule with a distinct hydrophobic and hydrophilic structure; have characteristics, which allow replacing the hydrated cations of the intermediate layer with some hydrophobic cations, such as ammonium ions instead of sodium ions; differ in the ability to disperse multiple cations well. It was found that functional groups that support inhibitor binding to the siloxane groups of the clay are able to significantly expand clay swelling.

The effectiveness of Google Forms in assessing and evaluating online learning outcomes: Meta-analysis study

The use of Google Forms as a learning tool in certain subjects is massively used in several schools because of its efficiency. This research aims to identify, classify, and observe literature, synthesize several studies that have been carried out, analyze the contributions that have been made, and propose related research updates. This study uses a methodology supported by a research approach, documentation, and procedural models from systematic literature review and data analysis using quantitative and qualitative descriptive analysis techniques. The results of this study show the effectiveness of Google Forms on the assessment and eval_uation of student learning outcomes, which from the table has proven that there are 36 studies (90%) that agree that the use of Google Forms has a positive or effective influence on success in assessing and eval_uating learning outcomes. This conclusion underscores the importance of digital marketing in the modern technology era. Digital marketing allows businesses to effectively reach global audiences through social media, SEO, and content. In this study, it was seen that a good digital strategy can increase brand awareness and customer loyalty. By utilizing proper data analysis, companies can optimize their digital marketing campaigns for better results. In conclusion, digital marketing is the key to winning the competition in an increasingly competitive market.

Unbiased complexome profiling and global proteomics analysis reveals mitochondrial impairment and potential changes at the intercalated disk in presymptomatic R14Δ/+ mice hearts.

Phospholamban (PLN) is a sarco-endoplasmic reticulum (SER) membrane protein that regulates cardiac contraction/relaxation by reversibly inhibiting the SERCA2a Ca2+-reuptake pump. The R14Δ-PLN mutation causes severe cardiomyopathy that is resistant to conventional treatment. Protein complexes and higher-order supercomplexes such as intercalated disk components and Ca+2-cycling domains underlie many critical cardiac functions, a subset of which may be disrupted by R14Δ-PLN. Complexome profiling (CP) is a proteomics workflow for systematic analysis of high molecular weight (MW) protein complexes and supercomplexes. We hypothesize that R14Δ-PLN may alter a subset of these assemblies, and apply CP workflows to explore these changes in presymptomatic R14Δ/+ mice hearts. Ventricular tissues from presymptomatic 28wk-old WT and R14Δ/+ mice were homogenized under non-denaturing conditions, fractionated by size-exclusion chromatography (SEC) with a linear MW-range exceeding 5 MDa, and subjected to quantitative data-independent acquisition mass spectrometry (DIA-MS) analysis. Unfortunately, current workflows for the systematic analysis of CP data proved ill-suited for use in cardiac samples. Most rely upon curated protein complex databases to provide ground-truth for analysis; however, these are derived primarily from cancerous or immortalized cell lines and, consequently, cell-type specific complexes (including cardiac-specific machinery potentially affected in R14Δ-PLN hearts) are poorly covered. We thus developed PERCOM: a novel CP data-analysis strategy that does not rely upon these databases and can, furthermore, be implemented on widely available spreadsheet software. Applying PERCOM to our CP dataset resulted in the identification of 296 proteins with disrupted elution profiles. Hits were significantly enriched for mitochondrial and intercalated disk (ICD) supercomplex components. Changes to mitochondrial supercomplexes were associated with reduced expression of mitochondrial proteins and maximal oxygen consumption rate. The observed alterations to mitochondrial and ICD supercomplexes were replicated in a second cohort of "juvenile" 9wk-old mice. These early-stage changes to key cardiac machinery may contribute to R14Δ-PLN pathogenesis.

A Bibliometric Studies: Digital Learning and Teacher Teaching Quality

This paper presents a comprehensive bibliometric analysis aimed at exploring the intersection between digital learning and teacher teaching quality. In recent years, the landscape of education has been significantly influenced by advancements in digital technology, leading to the widespread adoption of digital learning platforms. An overview of the importance of digital learning and high-quality teacher instruction in modern education is given in the introduction section. The problem statement outlines the main obstacles and gaps in the research on digital learning and the calibre of teaching methods. By elucidating these issues, the study aims to address pertinent questions concerning effective pedagogical strategies in the digital era and ways to enhance teachers’ effectiveness. Methodologically, this study employs bibliometric analysis techniques and VOSviewer version 1.16.20 to examine a corpus of scholarly publications retrieved from reputable databases. The focus is on identifying relevant literature, analysing citation patterns, and mapping out thematic trends and research trajectories. Through systematic data extraction and analysis, this approach enables the synthesis of existing knowledge and the identification of prominent themes and research clusters. The results of the bibliometric analysis reveal significant insights into the scholarly landscape of digital learning and teacher teaching quality. Findings include the identification of seminal works, influential authors, key research themes, and emerging trends. In conclusion, this study offers valuable insights into the current state of research on digital learning and teacher teaching quality. By synthesizing existing knowledge and identifying research gaps, it provides a foundation for future research endeavours aimed at advancing our understanding of effective educational practices in the digital age.

Multi-Class Imbalanced Data Handling with Concept Drift in Fog Computing: A Taxonomy, Review, and Future Directions

A network of actual physical objects or “IoT components” linked to the internet and equipped with sensors, electronics, software, and network connectivity is known as the Internet of Things (IoT). This ability of the IoT components to gather and share data is made possible by this network connectivity. Many IoT devices are currently operating, which generate a lot of data. When these IoT devices started collecting data, the cloud was the only place to analyze, filter, pre-process, and aggregate it. However, when it comes to IoT, the cloud has restrictions regarding latency and a more centralized method of distributing programs. A new form of computing called Fog computing has been proposed to address the shortcomings of current cloud computing. In an IoT context, sensors regularly communicate signal information, and edge devices process the data obtained from these sensors using Fog computing. The sensors’ internal or external problems, security breaches, or the integration of heterogeneous equipment contribute to the imbalanced data, i.e., comparatively speaking, one class has more instances than the other. As a result of this data, the pattern extraction is imbalanced . Recent attempts have concentrated heavily on binary-class imbalanced concerns with exactly two classes. However, the classification of multi-class imbalanced data is an issue that needs to be fixed in Fog computing, even if it is widespread in other fields, including text categorization, human activity detection, and medical diagnosis. The study intends to deal with this problem. It presents a systematic, thorough, and in-depth comparative analysis of several binary-class and multi-class imbalanced data handling strategies for batch and streaming data in IoT networks and Fog computing. There are five major objectives in this study. First, reviewing the Fog computing concept. Second, outlining the optimization metric used in Fog computing. Third, focusing on binary and multi-class batch data handling for IoT networks and Fog computing. Fourth, reviewing and comparing the current imbalanced data handling methodologies for multi-class data streams. Fifth, explaining how to cope with the concept drift, including novel and recurring classes, targeted optimization measures, and evaluation tools. Finally, the best performance metrics and tools for concept drift, binary-class (batch and stream) data, and multi-class (batch and stream) data are highlighted.

The Quality Improvement Review Board: An Innovative Approach to Oversight of Projects That Do Not Meet Criteria of Human Subject Research.

This article describes the development of an institutional quality improvement review board (QIRB) as an effective and efficient method for reviewing and overseeing institutional quality improvement (QI) initiatives. QI projects involve the systematic collection and analysis of data and the implementation of interventions designed to improve the quality of clinical care and/or educational programs for a distinct population in a specific setting. QI projects are fundamentally distinct from human subjects research (HuSR); however, the differences between them are subtle and highly nuanced. Determining whether a project meets the definition of QI or qualifies as HuSR, thus requiring institutional review board (IRB) review, can be confusing and frustrating. Nevertheless, this distinction is highly consequential due to the heavy regulatory requirements involved in HuSR and IRB oversight. Making the correct determination of a project's regulatory status is essential before the project begins. Project leaders may not realize that their work meets the definition of HuSR and, therefore, might conduct the project without appropriate IRB review. Therefore, best practices dictate that project leaders should not decide which type of institutional review is appropriate for their projects. In addition, when QI project teams attempt to disseminate the results of their work, documentation of formal review and approval is generally required by peer-reviewed journals and professional organizations. However, institutional review mechanisms are rarely available. Projects that do not meet the definition of HuSR fall outside the purview of IRBs and most institutions do not have an alternative review body. This creates frustration for both project leaders and IRB administrators. Apart from IRB review, a separate process for reviewing QI projects offers several benefits. These include (1) relieving the burden on busy IRB staff; (2) promoting scholarly activity; (3) protecting the institution, project leaders, and participants from HuSR conducted outside of appropriate IRB review; and (4) promoting rigorous QI methods.

Applying information mining technology in online entrepreneurship training course

The purpose of this study is to deeply explore the application of information mining technology in online entrepreneurship training courses, and to improve students’ learning effects and entrepreneurship success rate through systematic analysis and optimization of key data in the teaching process. With the development of online education, how to effectively use big data technology to meet personalized learning needs has become an important issue. This study takes several online entrepreneurship training courses as the research object, and uses information mining technology to extract and analyze students’ behavioral data during course participation, including data on study time, interaction frequency, assessment results, etc. Through machine learning algorithms and association rule mining, the research revealed the main factors that affect students’ learning effects and entrepreneurial success, and designed targeted teaching strategies, such as dynamically adjusting learning content, providing personalized feedback, optimizing learning paths, etc. Experimental results show that online courses using information mining technology significantly improve students’ knowledge mastery and entrepreneurial success rate, especially in terms of personalized learning experience and teaching efficiency. In addition, this study also explores the application prospects of information mining technology in future online education. It is believed that through the combination with artificial intelligence (AI) technology, the intelligence and adaptability of online courses can be further enhanced to meet more diversified learning needs.

ENSURING SAFETY AND PROTECTION OF PERSONAL DATA ON THE INTERNET: ISSUES OF SELECTION OF OPTIMAL RESEARCH METHODOLOGY

This article explores the methodologies and legal challenges associated with personal data protection within the global information space. We examine the limitations of relying solely on specialized research methods and techniques. We argue that personal data protection issues require a more comprehensive, multidisciplinary methodological approach. The primary goal is to analyze the selected methodological strategies and evaluate their effectiveness. The subject of the study is to analyze the issues of methodological approaches in legal research on personal data protection. We analyze a philosophical framework that seeks to deepen the understanding of personal data protection issues, addressing legal and ethical dimensions. We aim to contribute to the broader discourse on data security and privacy through various lenses, including legal, technological, and philosophical perspectives. The study focuses on four analytical methods for developing legal technologies to protect personal data internationally. A systematic analysis of personal data as a legal phenomenon, functional analysis within the framework of legal regulation, sociological and psychological approach, and axiological analysis to understand the meaning and evolution of personal data protection rights. These approaches aim to create a unified concept of legal technology for personal data protection, which makes it possible to identify new types of data, study the components of legal technology in detail, and increase the efficiency of legal regulation.

The impact of technology on Brazilian social work: potentials and challenges in ethical and socio-educational intervention

The integration of technology into Brazilian social work has presented both potentials and challenges. Social workers, historically engaged in socio-educational interventions from various perspectives, have experienced changes in their working conditions and tools due to technological innovations, especially following the COVID-19 pandemic. The analysis presented reveals that while technology provides greater access to information and connections, it can also be used to amplify neoliberal influences, prioritising low-cost intervention methods at the expense of service quality and distancing professionals from the population they serve. Social work must adopt a critical and proactive approach, guided by its ethical-political project, to identify the challenges and possibilities of this process. Ignoring technological advances can lead to a detachment from social reality or submission to institutional and neoliberal demands. Methodologically, a systematic literature review and qualitative analysis of secondary data were employed, guided by contemporary critical perspectives within social work.

Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data.

Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genome-wide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the data's high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived -values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimer's disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.

From the ground up: stakeholders’ representations of the Irish longitudinal study on ageing (TILDA)

ABSTRACT Healthy ageing, which is the target of life’ s later developmental stage, can be achieved through building a wise perspective towards life and existence. However, it may not be achievable for older people when the societal sources are limited. The TILDA project aimed to evaluate the associated factors with healthy ageing and to transfer that knowledge into practice. Hence, determining the perspectives of older people support and advocacy organisations on the enablers and facilitators of the healthy ageing strategies of TILDA is essential to gain a better understanding of the project and plan future strategies. This study aimed to investigate how the TILDA project has influenced or impacted upon these organisations from education, policy, or practice perspectives. The participants (n = 15) included in the study’s sample were reached through representative organisations acting to support older people in the Republic of Ireland. Semi-structured interviews were conducted online via Zoom. A systematic thematic data analysis procedure was followed, and three themes emerged from the qualitative data, revealing the perceptions of participants about the TILDA project: (1) Limitations of TILDA, (2) Contributions of TILDA, and (3) Future recommendations for TILDA. In conclusion, among the disadvantages of TILDA, the most significant is not being representative and visible enough; it is evident that it is pivotal to develop a more inclusive culture of TILDA with close cooperation and effective marketing strategies. It is also apparent that TILDA has several advantages that include providing insights into ageing and rich data to plan future support for older people.

Assessment and Evaluation Methods for 21st Century Education: Measuring What Matters

In modern education, not only teaching methods, but also assessment methods play an important role. An adequate assessment system is essential to the development of skills that are necessary for successful careers of applicants. Therefore, the purpose of the research is the analysis of modern assessment methods in the educational process and their significance for the formation of 21st century skills. The PRISMA approach was used to search for relevant materials. Google Scholar was chosen as the main database for literature search. Works written in English or with English annotations were taken into analysis. 10 criteria for the inclusion of literature were developed, which related to the relevance, practical importance and scientific novelty of the research. Therefore, 40 positions were selected. The study used the coding method for the systematic analysis of textual data. Hence, the results showed that the main approaches to assessment in modern education are summative and formative assessment. Summative assessment summarizes student achievement after the learning process is complete, while formative assessment improves learning outcomes in real time through feedback. Special attention is paid to the active use of digital technologies, such as computer adaptive testing, e-portfolios and platforms for online testing. The conclusions emphasize that innovative assessment should contribute to the development of key skills of the 21st century. The development of digital literacy is especially important, as it allows all participants of the study to actively use technologies and understand them.

An enhancer-AAV toolbox to target and manipulate distinct interneuron subtypes.

In recent years, we and others have identified a number of enhancers that, when incorporated into rAAV vectors, can restrict the transgene expression to particular neuronal populations. Yet, viral tools to access and manipulate fine neuronal subtypes are still limited. Here, we performed systematic analysis of single cell genomic data to identify enhancer candidates for each of the cortical interneuron subtypes. We established a set of enhancer-AAV tools that are highly specific for distinct cortical interneuron populations and striatal cholinergic neurons. These enhancers, when used in the context of different effectors, can target (fluorescent proteins), observe activity (GCaMP) and manipulate (opto- or chemo-genetics) specific neuronal subtypes. We also validated our enhancer-AAV tools across species. Thus, we provide the field with a powerful set of tools to study neural circuits and functions and to develop precise and targeted therapy.