Expert Analysis Of Data Research Articles

Building capacity for maternal, newborn and child health research in low-income country settings: A research fellowship experience in Ethiopia.

There is a need to build research capacity to improve maternal, newborn, and child health (MNCH) in low- and middle-income countries (LMICs). To address this gap, we co-designed the HaSET (meaning 'happiness' in Amharic) MNCH Research Fellowship programme for academics and policymakers in collaboration with the Ministry of Health (MOH) and academic institutions in Ethiopia. Based on interviews and focus group discussions regarding a landscape analysis of the MNCH research environment, we developed an innovative 'learning by doing' model in which fellows identified research questions, developed proposals, obtained institutional review board (IRB) approvals, conducted research, analysed data, disseminated their findings, and developed policy briefs. Postdoctoral fellows were paired with policymakers and health professionals at the MoH to facilitate the translation of research findings into policy and programmes. Each pair received mentorship from a member of the HaSET's scientific advisory group (SAG) who had expertise in research methods, data analysis, dissemination, and translation of evidence into policy. The HaSET MNCH Research Fellowship curriculum included 10 modules covering topics from biostatistics to study operations and professional development. From March 2021 to July 2023, five postdoctoral fellows from local universities and four policymakers from the MoH and government research institutes underwent the HaSET programme, where they learned to gather high-quality evidence on priority research questions and guide the implementation of national policies and programmes. Leveraging existing data, the fellows produced 15 manuscripts and 11 policy briefs. The programme established a functional research link between the MoH, regional health bureaus, and local universities, while utilising the SAG's expertise in mentorship. This robust and comprehensive HaSET MNCH Research Fellowship produced the first cohort of dedicated fellows trained in evidence-based medicine and mentored them to become effective public health professionals. They conducted high-quality studies to inform policy decisions on MNCH interventions in Ethiopia. Given its sustainability and scalability, researchers and academic institutions can further adapt the fellowship curriculum within capacity-building programmes to educate the next generation of research leaders in LMICs.

Analysis of signals from air conditioner compressors with ordinal patterns and machine learning

Most machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ordinal Patterns, a symbolic transformation of time series, which allows for the visual assessment of the type of operation. Ordinal Patterns are chosen because they can transform intricate time series into simple and intuitive symbolic representations. The technique is visually appealing, generating points on a plane whose positions reveal hidden dynamics. This approach makes it easier to identify recurring or abnormal patterns in machine operations that may indicate wear or impending failure. Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. We compare two machines under different operational conditions with six measured variables. We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. Integrating machine learning methods with a symbolic transformation offers a robust approach for the early and accurate diagnosis of potential failures, enhancing the predictive maintenance of air conditioning equipment.

Friend and Foe. An Analysis of Expert Advice on Educating for Data Literacy in Journalism and the Creative Industries

Analysis of practical advice in regard of developing a data literacy course for students in higher education and for early-career professionals in interviews with 56 experts in data analysis, offered an unexpected shared story among the experts around the perception, affect and power of data. By attending to what data are felt to be and do to you unintentionally, the analysis delivered a new story of how to teach for data literacy. Rather than (only) focus on formal skills, it points to how building a relationship with data may help more students and young professionals become curious, critical and engaged while recognizing the strengths and the fickleness of data sets.

DNA Logical Computing on a Transistor for Cancer Molecular Diagnosis.

Given the high degree of variability and complexity of cancer, precise monitoring and logical analysis of different nucleic acid markers are crucial for improving diagnostic precision and patient survival rates. However, existing molecular diagnostic methods normally suffer from high cost, cumbersome procedures, dependence on specialized equipment and the requirement of in-depth expertise in data analysis, failing to analyze multiple cancer-associated nucleic acid markers and provide immediate results in a point-of-care manner. Herein, we demonstrate a transistor-based DNA molecular computing (TDMC) platform that enables simultaneous detection and logical analysis of multiple microRNA (miRNA) markers on a single transistor. TDMC can perform not only basic logical operations such as "AND" and "OR", but also complex cascading computing, opening up new dimensions for multi-index logical analysis. Owing to the high efficiency, sensing and computations of multi-analytes can be operated on a transistor at a concentration as low as 2×10-16 M, reaching the lowest concentration for DNA molecular computing. Thus, TDMC achieves an accuracy of 98.4 % in the diagnosis of hepatocellular carcinoma from 62 serum samples. As a convenient and accurate platform, TDMC holds promise for applications in "one-stop" personalized medicine.

DNA Logical Computing on a Transistor for Cancer Molecular Diagnosis

AbstractGiven the high degree of variability and complexity of cancer, precise monitoring and logical analysis of different nucleic acid markers are crucial for improving diagnostic precision and patient survival rates. However, existing molecular diagnostic methods normally suffer from high cost, cumbersome procedures, dependence on specialized equipment and the requirement of in‐depth expertise in data analysis, failing to analyze multiple cancer‐associated nucleic acid markers and provide immediate results in a point‐of‐care manner. Herein, we demonstrate a transistor‐based DNA molecular computing (TDMC) platform that enables simultaneous detection and logical analysis of multiple microRNA (miRNA) markers on a single transistor. TDMC can perform not only basic logical operations such as “AND” and “OR”, but also complex cascading computing, opening up new dimensions for multi‐index logical analysis. Owing to the high efficiency, sensing and computations of multi‐analytes can be operated on a transistor at a concentration as low as 2×10−16 M, reaching the lowest concentration for DNA molecular computing. Thus, TDMC achieves an accuracy of 98.4 % in the diagnosis of hepatocellular carcinoma from 62 serum samples. As a convenient and accurate platform, TDMC holds promise for applications in “one‐stop” personalized medicine.

FinTech services from BigTech companies

AbstractThe so‐called BigTech companies Amazon, Alphabet, Apple and Meta and are constantly attempting to grow into new business areas. Due to their expertise in data analysis, they have managed to quickly establish themselves in many industries as digital ecosystems and, as part of this strategy, are also expanding their footprint in financial services. However, the biggest challenge that BigTech companies are facing in financial services is customer acceptance. This paper contributes to the emerging field of digital ecosystems and the acceptance of sensitive services by users. Although the BigTech firms have gained much attention, only little empirical analysis is available. This paper aims to shed light on the determinants of customer acceptance of BigTech banking services. Based on a survey in Switzerland this research develops an analytic model to identify and test the relevant determinants. The results indicate that the strongest significant influences were found in subjective risk and trust which clearly demonstrates the still‐existing incumbents' advantage over the BigTech companies.

Purchasing Patterns Analysis in E-commerce: A Big Data-driven Approach and Methodological

This research aims to analyze purchasing patterns in e-commerce using a Big Data-based approach and data analysis methods. Leveraging advanced technology and analytical methodology, this research explores consumer behavior, market trends, and factors influencing purchasing decisions in e-commerce. Through collecting transaction data from leading e-commerce platforms and applying rigorous data analysis techniques, this research identifies significant patterns and provides valuable insights for e-commerce companies. This research shows that big data has great potential in understanding consumer behavior and market trends in e-commerce. In contrast, sophisticated data analysis methods are essential in interpreting the large and complex data generated by e-commerce. The findings of this research significantly contribute to the development of the e-commerce industry by helping companies improve their marketing strategies and business decision-making. However, this research also needs help, as it requires data analysis skills and privacy issues. To overcome these challenges, collaboration between researchers, e-commerce companies, and governments is needed to develop the necessary data analysis expertise and ensure that consumer data is managed securely and ethically. Thus, this research provides a more holistic view of consumer behavior and market dynamics in e-commerce, assisting companies and policymakers in addressing challenges and opportunities in an ever-evolving landscape.

Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

Single-molecule magnetic tweezers to probe the equilibrium dynamics of individual proteins at physiologically relevant forces and timescales.

The reversible unfolding and refolding of proteins is a regulatory mechanism of tissue elasticity and signalling used by cells to sense and adapt to extracellular and intracellular mechanical forces. However, most of these proteins exhibit low mechanical stability, posing technical challenges to the characterization of their conformational dynamics under force. Here, we detail step-by-step instructions for conducting single-protein nanomechanical experiments using ultra-stable magnetic tweezers, which enable the measurement of the equilibrium conformational dynamics of single proteins under physiologically relevant low forces applied over biologically relevant timescales. We report the basic principles determining the functioning of the magnetic tweezer instrument, review the protein design strategy and the fluid chamber preparation and detail the procedure to acquire and analyze the unfolding and refolding trajectories of individual proteins under force. This technique adds to the toolbox of single-molecule nanomechanical techniques and will be of particular interest to those interested in proteins involved in mechanosensing and mechanotransduction. The procedure takes 4 d to complete, plus an additional 6 d for protein cloning and production, requiring basic expertise in molecular biology, surface chemistry and data analysis.

A methodology for the 3D characterization of surfaces using X-ray computed tomography: Application to additively manufactured parts

Many studies highlight the significance of three-dimensional surface topography characterization in assessing its effect on the mechanical or functional properties of materials. This is especially obvious for parts made by additive manufacturing (AM), known for their complex shape and surface topographies. However, a vast majority of 3D characterizations have constraints regarding the macroscopic geometry of the parts they can probe. At the microscale, they are also unable to account for hidden surface features, e.g. notches hidden by unmelted powder particles. Even with the use of X-ray Computed Tomography (XCT) - a tool with the potential to circumvent these issues -data is often reduced to 2D or 2.5D formats for easier analysis, but this leads to a loss of information. This underscores the need for XCT data post-treatment tools to perform thorough 3D surface characterizations. Herein, we introduce a methodology for local roughness and curvature characterization of surfaces of complex shapes using XCT. This method has been designed to be user-friendly, especially for those without extensive data analysis expertise. It provides a comprehensive 3D characterization and efficiently tackles the issues caused by hidden features. After a detailed description of our methodology, we give a first illustrative example based on architected structures fabricated by Electron Powder Bed Fusion (E-PBF). By integrating roughness and curvature metrics, we also derive a parameter indicative of the stress concentrations caused by surface irregularities.

Digital Transformation in Accounting and Auditing: Insights from The ChatGPT Example

This research delves into the evolving role of the accounting and auditing professions in the digital age, with a focus on the implications of ChatGPT, a sophisticated artificial intelligence tool developed by OpenAI. The study employs a novel approach by engaging ChatGPT to predict the future of these professions, further probing with targeted inquiries to deepen the understanding. The analysis reveals that despite the rapid digital transformation and integration of AI in accounting processes, the core accounting profession is likely to persist. However, it underscores a significant shift in skill requirements, emphasizing the importance for accountants to enhance their expertise in data analysis, cyber security, and technological adaptability. The study also explores AI's potential applications in auditing, suggesting that AI can augment efficiency, accuracy, and risk assessment, while also highlighting the irreplaceable value of human judgment in complex scenarios. Interestingly, the insights generated through interactions with ChatGPT show a strong alignment with existing literature, indicating the tool's utility and reliability in professional contexts. The findings suggest that embracing technological advancements and continuous learning are imperative for accountants to remain relevant and competitive in this rapidly evolving digital landscape.

A multiplicative Holt–Winters model and autoregressive moving-average for hyponatremia mortality rates

This study presents the effective utilization of data analysis in efficiently managing medical resources — a crucial factor in ensuring high-quality healthcare delivery and positive patient outcomes. We employ a multiplicative Holt-Winters model and a linear model with an autoregressive moving-average time series error term and analyze time series mortality rates associated with hyponatremia, a condition that leads to a systemic electrolyte imbalance. The time series data consistently reveals noticeable trends and recurring seasonal patterns. To address these intricate patterns, the proposed data analysis approaches adeptly incorporate these features, thereby yielding relatively accurate point forecasts and reliable forecast intervals. Beyond mere forecasting, the autoregressive moving-average model approach offers insights into the overarching trend and facilitates the comparison of seasonal components. One of the approaches is well-suited for the specific time series related to hyponatremia and holds the potential for extension to incorporate more complex trends beyond linearity. The research demonstrates the synergistic relationship between data analysis and medical expertise, working in tandem to achieve the optimal allocation of medical resources. We strongly advocate for the routine integration of statistical analyses as an essential practice to foster this harmonious synergy.

Paucity of Health Data in Africa: An Obstacle to Digital Health Implementation and Evidence-Based Practice.

Background: Among the numerous challenges that Africa faces in improving its healthcare systems, the paucity of health data stands out as paramount. This study aims to examine the challenges related to the paucity of health data in Africa and its impact on the implementation of digital health and evidence-based practice. The findings of the study reveal that health data availability in Africa is both limited and frequently of poor quality. Several factors contribute to this concerning situation, encompassing inadequate infrastructure, a shortage of resources, and cultural barriers. Furthermore, the available data, despite its limitations, is often underutilized due to a lack of capacity and expertise in data analysis and interpretation. Policy Options and Recommendations: To improve healthcare delivery in Africa, we recommend implementing novel strategies for data collection. It's important to recognize that effective information technology service is crucial for enhancing healthcare delivery, and a holistic approach is necessary to achieve this. Conclusion: This brief presents information to help policymakers develop long-term solutions to Africa's health data poverty. Taking action based on this evidence can assist in addressing the problem.

A Review paper on the Impact of Artificial Intelligence on the Job Market

Artificial intelligence (AI) has revolutionized various industries, and its impact on the job market is significant. While AI has the potential to automate repetitive and time-consuming tasks, it also presents new job opportunities that require expertise in data analysis, software engineering, and machine learning. The paper provides an overview of the current state of the job market and the challenges it faces, and explains how AI is transforming the job market, including its impact on job creation and destruction, skill requirements, and wage distribution. The paper analyzes the job market trends from 2019 to 2023 based on the latest research and reports, and discusses the implications of AI for the workforce and policymakers. The paper concludes with some recommendations for future research and policy interventions. It discusses the benefits of AI, such as increased productivity, efficiency, and accuracy, and highlights the challenges that AI poses, including the displacement of certain jobs and the need for retraining workers. The review paper also discusses how AI is impacting workplace safety and health, including both the potential benefits and the new hazards that AI presents. Finally, the review paper explores the policies and regulations needed to ensure that AI is used ethically and responsibly in the job market. Overall, this review paper aims to provide a comprehensive understanding of the impact of AI on the job market and its implications for workers, businesses, and society

Representation of an Interdisciplinary Approach for Evaluation of Precision of a Neurocognitive Screening Bundle: A Proposal

Neurocognitive disorders encompass a range of conditions characterized by cognitive impairments, presenting significant challenges to individuals, families, and healthcare systems worldwide. Traditional paper-and-pencil assessments have limitations, including restricted access, limited frequency, and subjective interpretation. This research proposal aims to address these limitations by developing a reliable digital platform, a neurocognitive screening bundle, for year-round monitoring of neurocognitive symptom domains. The proposed research project adopts an interdisciplinary approach by integrating knowledge and expertise from the fields of neurology, psychology, cognitive neuroscience, and computer science. Neurology provides clinical context and expertise in understanding neurocognitive symptom domains, ensuring that the chosen screening bundle aligns with clinical needs. Psychology contributes insights into human behavior and cognitive processes, aiding in the selection, adaptation, or development of assessments targeting specific neurocognitive functions. Additionally, psychologists contribute expertise in data analysis and interpretation, enabling the identification of meaningful patterns within the collected data. Cognitive neuroscience offers a deeper understanding of the underlying neural mechanisms associated with neurocognitive symptom domains. By identifying the neural networks, brain regions, and cognitive processes involved in specific cognitive functions, cognitive neuroscientists bridge the gap between cognitive assessment and neurobiological underpinnings, aiding in the selection and interpretation of neurocognitive measures. Computer science plays a pivotal role in developing and evaluating the digital platform for neurocognitive screening. Computer scientists leverage their expertise in software development, data analysis, and machine learning to design algorithms for data processing, ensure data security and privacy, and develop user-friendly interfaces for assessments. By integrating technical skills with knowledge from other disciplines, computer scientists enhance the accuracy, reliability, and usability of the digital platform. This interdisciplinary research project aims to provide a comprehensive, accessible, and reliable tool for year-round monitoring of neurocognitive symptom domains, surpassing the limitations of traditional assessment methods. By leveraging the expertise from neurology, psychology, cognitive neuroscience, and computer science, this study aims to advance the field and improve clinical outcomes for individuals with neurocognitive disorders. The findings of this research project have the potential to revolutionize neurocognitive assessments, leading to more effective diagnosis and monitoring of these challenging conditions.

Metabolic Transformations in the Area of Municipal Solid Waste Management in Russian Megalopolises: The City of Moscow Case

Based on secondary data analysis and semi-structured expert interviews (n = 90), the study analyses metabolic transformations in the area of municipal solid waste (MSW) management in Russian megalopolises using the city of Moscow as a case study. The findings suggest that the key node that triggered numerous interdependent processes involved the changes introduced in the legislation on MSW management and the decision to shut down several large MSW landfills without the simultaneous implementation of other landfills. These actions affected the health of the population and contributed to environmental inequality. The authors provide recommendations for the mitigation of risks associated with MSW generation, recycling, and storage in large Russian cities.

Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization

The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analysis and instant decision making using efficient computer vision algorithms need researchers' attentions. In this research, to the best of our knowledge, we are the first to introduce a process control technique: control charts for surveillance video data analysis. The control charts concept is merged with a novel deep learning-based violence detection framework. Different from the existing methods, the proposed technique considers the importance of spatial information, as well as temporal representations of the input video data, to detect human violence. The spatial information are fused with the temporal dimension of the deep learning model using a multi-scale strategy to ensure that the temporal information are properly assisted by the spatial representations at multi-levels. The proposed frameworks' results are kept in the history-maintaining module of the control charts to validate the level of risks involved in the live input surveillance video. The detailed experimental results over the existing datasets and the real-world video data demonstrate that the proposed approach is a prominent solution towards automated surveillance with the pre- and post-analyses of violent events.

Preparing European Nephrology for the next pandemic: lessons from the ERACODA collaboration.

Owing to the vulnerability of patients with chronic kidney disease to infectious diseases, the coronavirus disease 2019 (COVID-19) pandemic has been particularly devastating for the nephrology community. Unfortunately, the possibility of future COVID-19 waves or outbreaks of other infectious diseases with pandemic potential cannot be ruled out. The nephrology community made tremendous efforts to contain the consequences of the COVID-19 pandemic. Despite this, the COVID-19 pandemic has highlighted several shortcomings in our response to the pandemic and has taught us important lessons that can be utilized to improve our preparedness for any future health crises of a similar nature. In this article we draw lessons from the European Renal Association COVID-19 Database (ERACODA) project, a pan-European collaboration initiated in March 2020 to understand the prognosis of COVID-19 in patients on kidney function replacement therapy. We discuss the challenges faced in generating timely and robust evidence for informed management of patients with kidney disease and give recommendations for our preparedness for the next pandemic in Europe. Limited collaboration, the absence of common data architecture and the sub-optimal quality of available data posed challenges in our response to COVID-19. Aligning different research initiatives, strengthening electronic health records, and involving experts in study design and data analysis will be important in our response to the next pandemic. The European Renal Association may take a leading role in aligning research initiatives via its engagement with other scientific societies, national registries, administrators and researchers.

Evaluating the efficacy of sonification for signal detection in univariate, evenly sampled light curves using astronify

ABSTRACT Sonification is the technique of representing data with sound, with potential applications in astronomy research for aiding discovery and accessibility. Several astronomy-focused sonification tools have been developed; however, efficacy testing is extremely limited. We performed testing of astronify, a prototype tool for sonification functionality within the Barbara A. Mikulski Archive for Space Telescopes. We created synthetic light curves containing zero, one, or two transit-like signals with a range of signal-to-noise ratios (SNRs = 3–100) and applied the default mapping of brightness to pitch. We performed remote testing, asking participants to count signals when presented with light curves as a sonification, visual plot, or combination of both. We obtained 192 responses, of which 118 self-classified as experts in astronomy and data analysis. For high SNRs (=30 and 100), experts and non-experts performed well with sonified data (85–100 per cent successful signal counting). At low SNRs (=3 and 5), both groups were consistent with guessing with sonifications. At medium SNRs (=7 and 10), experts performed no better than non-experts with sonifications but significantly better (factor of ∼2–3) with visuals. We infer that sonification training, like that experienced by experts for visual data inspection, will be important if this sonification method is to be useful for moderate SNR signal detection within astronomical archives and broader research. None the less, we show that even a very simple, and non-optimized, sonification approach allows users to identify high SNR signals. A more optimized approach, for which we present ideas, would likely yield higher success for lower SNR signals.