Advanced Analysis Methods Research Articles

Advances and Challenges in Analytical Strategies for Fruity Thiol Profiling in Beer: From Sensitivity Optimization to Ecologically Responsible Methodologies

The fruity thiols in beer, such as 3SH, 3SHA, 4MMP, and 3S4MP, constitute significant components of its volatile profile, playing a pivotal role in shaping the overall flavor alongside hydrocarbons and oxygen-containing compounds. This paper, drawing extensively on relevant literature, presents a comprehensive summary of the composition, aroma contribution, challenges in thiol detection, and advancements in analytical methods and technologies for these compounds. It outlines the limitations and attempted improvements of direct measurement approaches, the innovations and evolution of derivatization methods, and the integration of various specific sample preparation techniques (e.g., SPE, SPME, LLE, and derivatization) with modern, advanced detection equipment, accompanied by an evaluation of the precision and inclusivity of these methods. Concluding the discussion, the article offers a perspective on the future refinement of thiol determination in the brewing industry. This review aims to serve the objective of more precise beer flavor modulation.

Early evidence of an archaeological dyed textile using scale-insects: The Cave of Skulls, Israel

The color red has held significant historical symbolism and meaning throughout history and can be produced from various sources, including plants and animals. Some of the most luxurious red dyes were derived from various species of scale insects, particularly those of the Coccoidae superfamily. Today, advanced analytical methods allow to trace the origins of dyes even after thousands of years, providing insights into the technological capabilities of the ancient world as well as past economic and social conditions. In our research on textiles discovered in the Cave of Skulls in the Judean Desert, we conducted dye analyses using High-Pressure Liquid Chromatography (HPLC) and identified one unique, red-dyed textile, which was dyed with scale insects. Our analysis enables to pinpoint the precise source of the red color in archaeological textiles down to the species level, and it can be determined with a high degree of certainty that the dye used was Kermes vermilio. Direct radiocarbon dating places this textile in the Middle Bronze Age (1954–1767 BCE), shedding light on the early use of scale-insect dyes in ancient textile production.

Modern developments and analysis of household electricity utilization by applying smart meter and its findings

The efficient utilization of household electricity is pivotal in the context of rising energy demands and environmental concerns. This study addresses the critical issue of electricity wastage in Chinese households by applying advanced smart meter technology. Aiming to analyze and optimize electricity consumption patterns, the research utilizes the Smart Energy Utilization Model (SEUM) over a period from January 2020 to December 2022, incorporating methodologies such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Least Squares Support Vector Regression (LS-SVR), and deep learning. The findings reveal that households equipped with smart meters demonstrated a 15 % reduction in overall energy consumption, a 10 % decrease in peak-hour usage, and a notable improvement in energy-saving behaviors among users. Additionally, the integration of smart meters facilitated better load management and contributed to reducing the carbon footprint. The study also found that smart meter data analytics helped in identifying and rectifying inefficient appliances, resulting in a 12 % reduction in energy waste. Moreover, the increased awareness and real-time feedback provided by smart meters led to a 20 % increase in user engagement with energy-saving initiatives. These findings underscore the potential of smart meter technology, enhanced by advanced analytical methods, to enhance energy efficiency and support sustainable development policies.

Comprehensive source identification of heavy metals in atmospheric particulate matter in a megacity: A case study of Hangzhou

Megacities face significant pollution challenges, particularly the elevated levels of heavy metals (HMs) in particulate matter (PM). Despite the advent of interdisciplinary and advanced methods for HM source analysis, integrating and applying these approaches to identify HM sources in PM remains a hurdle. This study employs a year-long daily sampling dataset for PM1 and PM1-10 to examine the patterns of HM concentrations under hazy, clean, and rainy conditions in Hangzhou City, aiming to pinpoint the primary sources of HMs in PM. Contrary to other HMs that remained within acceptable limits, the annual average concentrations of Cd and Ni were found to be 20.6 ± 13.6 and 46.9 ± 34.8 ng/m³, respectively, surpassing the World Health Organization's limits by 4.1 and 1.9 times. Remarkably, Cd levels decreased on hazy days, whereas Ni levels were observed to rise on rainy days. Using principal component analysis (PCA), enrichment factor (EF), and backward trajectory analysis, Fe, Mn, Cu, and Zn were determined to be primarily derived from traffic emissions, and there was an interaction between remote migration and local emissions in haze weather. Isotope analysis reveals that Pb concentrations in the Hangzhou region were primarily influenced by emissions from unleaded gasoline, coal combustion, and municipal solid waste incineration, with additional impact from long-range transport; it also highlights nuanced differences between PM1 and PM1-10. Pb isotope and PCA analyses indicate that Ni primarily stemmed from waste incineration emissions. This explanation accounts for the observed higher Ni concentrations on rainy days. Backward trajectory cluster analysis revealed that southern airflows were the primary source of high Cd concentrations on clean days in Hangzhou City. This study employs a multifaceted approach and cross-validation to successfully delineate the sources of HMs in Hangzhou's PM. It offers a methodology for the precise and reliable analysis of complex HM sources in megacity PM.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

Exploring impacts of thermal and lighting conditions on office workers’ subjective evaluations, cognitive performance and EEG features in multi-person offices

Modern office workers spend lots of time in multi-person offices. For human-centered building design and operation, it is essential to investigate the influence of coupled physical environments on occupants' well-being and task performance. In this study, to assess impacts of thermal and lighting environments on brainworkers, experiments were conducted under 64 conditions of different temperature, correlated color temperature, and illumination in a multi-person office. Electroencephalogram (EEG) signals were collected to measure the mental state of brainworkers and neurobehavioral tests were utilized to replicate cognitive tasks. The results indicated that only the interaction between the temperature and illumination regarding the sensation of CCT was significant. Primarily, lower illumination in colder environments made subjects perceive the lighting was warmer. Furthermore, cross-domain effects influenced office workers' sensation via synesthetic effects. And the validity of the hue-heat hypothesis on thermal sensation depended on whether subjects received enough stimulation. Besides, for brainworkers, visual comfort peaked at 5000 K and 750 lux but the optimal temperature varied based on specific requirements, with 24 °C promoting overall comfort and 26 °C enhancing executive cognitive performance. The study preliminarily explored the effects among acceptable levels of T, CCT, and I on EEG features, revealing that temperature could adjust occupants’ mental load. However, the further investigation requests equipment with finer temporal granularity, cost-effective techniques and advanced analytical methods to guide office building design and operation.

Recent solid-phase microextraction-based analytical approaches for the profiling of biliary bile acids in pre-transplant assessments of liver grafts subjected to normothermic ex vivo liver perfusion

BackgroundLiver transplantation is the definitive treatment for end-stage liver failure, but the scarcity of donor organs remains a significant challenge. Leveraging organs from extended criteria donors (ECD) offers a potential avenue to address worldwide shortages, though these organs are more susceptible to post-reperfusion injury. This study explores the use of normothermic ex vivo liver perfusion (NEVLP) as a method for organ preservation – an approach that sustains liver metabolism and facilitates pre-transplant assessments of organ viability via bile analysis. The focal point of this study revolves on the development of analytical methods for determining the bile acid profile throughout the peritransplantation period as a potential indicator of liver function and viability. ResultsThe study optimized and validated a high-throughput analytical method to quantify selected bile acids in bile samples using a thin-film microextraction-liquid chromatography-mass spectrometry (TFME-LC-MS) platform. Furthermore, it introduced a solid-phase microextraction-microfluidic open interface-mass spectrometry (SPME-MOI-MS) method for rapid direct analysis of bile acid isobar groups. In the animal study, discernible variations in the concentrations of specific bile acids were observed between donors after circulatory death (DCD) and heart-beating donors (HBD), particularly following normothermic perfusion and reperfusion. Noteworthy fluctuations in individual bile acid concentrations were observed throughout the entire organ transplantation process, with taurocholic acid (TCA), glycocholic acid (GCA), and glycochenodeoxycholic acid (GCDCA) emerging as promising indicators of organ quality. The efficacy of the SPME-MOI-MS platform in corroborating these trends highlights its potential for real-time bile acid analysis during liver transplantation procedures. SignificanceOur findings underscore the efficacy of NEVLP in tandem with advanced bile acid analysis methods as a reliable strategy for pre-transplant assessments of organ viability, potentially increasing the use of ECD organs and reducing organ shortages. The ability to monitor bile acid profiles in real-time provides crucial insights into liver function and ischemic injury, making significant strides in improving transplant outcomes and patient survival rates.

Temporal Analysis of Pharmaceuticals as Emerging Contaminants in Surface Water and Wastewater Samples: A Case Study.

Pharmaceuticals in the environment are a global concern, with studies in all continents highlighting their widespread occurrence and potential ecological impacts, revealing their presence, fate, and associated risks in aquatic ecosystems. Despite typically occurring at low concentrations (ranging from ng/L to µg/L), advancements in analytical methods and more sensitive equipment have enabled the detection of a higher number of pharmaceuticals. In this study, surface and wastewater samples were extracted using solid phase extraction and analyzed using ultra-high-performance liquid chromatography with tandem mass spectrometry. Among the therapeutic classes investigated, nonsteroidal anti-inflammatory drugs/analgesics, antibiotics, and psychiatric drugs showed a higher number of detected pharmaceuticals. Concentrations ranged from below method detection limit (<MDL) to 3.20 µg/L (caffeine) and <MDL to 639 µg/L (hydroxyibuprofen) in 2018, and from <MDL to 0.848 µg/L (diclofenac) and <MDL to 53.0 µg/L (caffeine) in 2019 for river water and wastewater samples. Temporal analysis showed an increase in the sum of pharmaceutical concentrations over the study years, highlighting the importance of monitoring pharmaceuticals in the environment and their potential accumulation over time.

An overview of the study designs and statistical methods used in the determination of predictors of melioidosis mortality in Malaysia: 2010-2021

&lt;b&gt;Background:&lt;/b&gt; In Malaysia, the mortality from melioidosis infection was reported to be higher than in other infectious diseases. The research on melioidosis is still limited in Malaysia but slightly increasing.&lt;br /&gt; &lt;b&gt;Objectives: &lt;/b&gt;The objective of the study was to give an overview of the study designs, statistical methods, and comparison of research in identifying the predictors of melioidosis mortality in Malaysia between January 2010 to December 2021.&lt;br /&gt; &lt;b&gt;Data sources:&lt;/b&gt; Pubmed/Medline.&lt;br /&gt; &lt;b&gt;Study eligibility criteria:&lt;/b&gt; Original English-language articles were abstracted. The articles that identified the predictors of melioidosis from mortality in Malaysia only included. Letters to the editor, editorials, reviews, systematic reviews, meta-analysis, case reports, and any other ineligible articles were excluded.&lt;br /&gt; &lt;b&gt;Results:&lt;/b&gt; A total of eight studies were identified related to predictors of melioidosis mortality in Malaysia. From the selected articles, 87.5% were retrospectively collected. Five out of eight articles (62.5%) used the logistic regression in identifying the predictors of melioidosis mortality. Only one (12.5%) used advanced survival analysis methods of Cox regression analysis. Another 25.0% used Chi-square test.&lt;br /&gt; &lt;b&gt;Conclusions:&lt;/b&gt; Logistic regression methods remain the most common methods of analysis in publications on predictors of melioidosis mortality in Malaysia while retrospective research designs are preferred. There is a limitation of research in predictors of melioidosis mortality and the use of advanced statistical techniques reported using the melioidosis data in Malaysia. More published research on melioidosis will provide input to the clinicians on a more detailed understanding of how to improve the diagnosis of melioidosis and the prognosis factors of this disease.

Advancements and greenification potential of magnetic molecularly imprinted polymers for chromatographic analysis of veterinary drug residues in milk.

Milk, as a widely consumed nutrient-rich food, is crucial for bone health, growth, and overall nutrition. The persistent application of veterinary drugs for controlling diseases and heightening milk yield has imparted substantial repercussions on human health and environmental ecosystems. Due to the high demand, fresh consumption, complex composition of milk, and the potential adverse impacts of drug residues, advanced greener analytical methods are necessitated. Among them, functional materials-based analytical methods attract wide concerns. The magnetic molecularly imprinted polymers (MMIPs), as a kind of typical functional material, possess excellent greenification characteristics and potencies, and they are easily integrated into various detection technologies, which have offered green approaches toward analytes such as veterinary drugs in milk. Despite their increasing applications and great potential, MMIPs' use in dairy matrices remains underexplored, especially regarding ecological sustainability. This work reviews recent advances in MMIPs' synthesis and application as efficient sorbents for veterinary drug extraction in milk followed by chromatographic analysis. The uniqueness and effectiveness of MMIPs in real milk samples are evaluated, current limitations are addressed, and greenification opportunities are proposed. MMIPs show promise in revolutionizing green analytical procedures for veterinary drug detection, aligning with the environmental goals of modern food production systems.

Enhancing Ecological Security in Ili River Valley: Comprehensive Approach

The growing tension between economic development and ecological preservation in the Ili River Valley underscores the need for advanced analytical methods to effectively balance these interests. In this study, we utilized the InVEST model to quantify ecosystem services, combined with an analysis of ecological sensitivity, to comprehensively assess the ecological health of the region. By applying circuit theory, the research identified key ecological components such as sources, corridors, and critical nodes, alongside barriers; thus, mapping an ecological security pattern tailored specifically for the wetland oasis of the Ili River Valley. The analysis identified 15 ecological source sites covering 43,221.17 km2, 31 ecological corridors totaling 782 km in length, and 32 vital ecological nodes each exceeding 1 km2. Notably, 81.8% of these ecological source areas exhibited high ecological resilience, thus emphasizing their crucial role in maintaining the region’s ecological balance. The findings provide essential guidance for the ecological stewardship and management of the Ili River Valley and underscore the importance of incorporating ecological considerations within economic planning frameworks in arid regions.

Modeling Residential Energy Consumption Patterns with Machine Learning Methods Based on a Case Study in Brazil

Developing efficient energy conservation and strategies is relevant in the context of climate change and rising energy demands. The objective of this study is to model and predict the electrical power consumption patterns in Brazilian households, considering the thresholds for energy use. Our methodology utilizes advanced machine learning methods, such as agglomerative hierarchical clustering, k-means clustering, and self-organizing maps, to identify such patterns. Gradient boosting, chosen for its robustness and accuracy, is used as a benchmark to evaluate the performance of these methods. Our methodology reveals consumption patterns from the perspectives of both users and energy providers, assessing the corresponding effectiveness according to stakeholder needs. Consequently, the methodology provides a comprehensive empirical framework that supports strategic decision making in the management of energy consumption. Our findings demonstrate that k-means clustering outperforms other methods, offering a more precise classification of consumption patterns. This finding aids in the development of targeted energy policies and enhances resource management strategies. The present research shows the applicability of advanced analytical methods in specific contexts, showing their potential to shape future energy policies and practices.

Detection of autism spectrum disorder using multi‐scale enhanced graph convolutional network

AbstractMagnetic Resonance Imaging (MRI) based Autism Spectrum Disorder (ASD) detection approaches face various challenges due to variations in brain connectivity patterns, limited sample sizes, and heterogeneity of available data. These challenges make it hard to find consistent imaging markers. To address these issues, researchers have focused on advanced analysis methods, such as multi‐modal imaging techniques and graph‐based approaches to gain a comprehensive understanding of ASD neurobiology. However, existing graph‐based approaches for ASD detection have primarily focused on pairwise similarities between individuals, neglecting individual characteristics and features. A novel framework to detect ASD using a Multi‐Scale Enhanced Graph Convolutional Network (MSE‐GCN). The framework combines the functional connectivity of resting‐state functional MRI (rs‐fMRI) with non‐imaging phenotype data from Autism Brain Imaging Data Exchange‐I (ABIDE‐I). The framework uses MSE‐GCN to represent individuals as node in a population graph. Each node corresponds to an individual and connects to feature vectors from imaging data. Edge weights between nodes are assigned to integrate phenotypic information. Then, the multiple parallel GCN layers are designed using random walk embedding. The output of these GCN layers is then combined in the fully connected layer to detect ASD effectively. The performance of the framework is evaluated using the ABIDE‐I dataset. In addition, Recursive Feature Elimination and Multilayer Perceptron are utilised for feature selection. The outcome of this approach shows more than 10% advancement in accuracy, achieving an accuracy of 83% by incorporating phenotypic data in conjunction with MRI data within a GCN.

Big Data Technology and Financial Performance of Listed Firms in Palestine: Mediating Role of Accounting Information Systems

The purpose of this paper is to examine the mediating effect of the rate of quality of accounting information systems on the relationship between big data technology and firms’ financial performance in firms listed on the Palestine Stock Exchange. The researchers conducted an account of the previous studies in this field. The researcher used the deductive approach in studying and analyzing previous studies related to big data by relying on books, periodicals, theses, and accounting standards related to the subject of the research. The researcher applied an inductive approach when conducting the field study and testing the statistical hypotheses related to the study of the relationship between the use of big data technology and firms’ financial performance. The findings show a correlation coefficient of (0.54) and a coefficient of determination of (48%), indicating that big data analytics positively affects the rate of return on assets, and that there is a statistically significant relationship between the advancement of accounting information systems and the enhancement of financial performance in big data technology, as measured by the rate of return on equity and the rate of return on assets, which have correlation rates of (0.53) and (42%), respectively. This relationship is reflected in the data on the existence of a statistically significant relationship between the use of big data technology and the enhancement of financial performance with big data technology. The intention of big data, as well as the absence of fundamental differences between the sample individuals, states that the use of big data technology leads to improved performance through the development of various accounting practices and good inventory management by predicting customer behaviour, thus increasing the competitiveness of competition and improving the reputation of the establishment on social media. This is reflected in the company’s sales and its survival in the market, as well as the development of analytical models and advanced methods of analysis that limit fraud and help control it, which is one of the establishment’s goals at present. This paper contributes to the literature by showing that the use of big data leads to a change in methods of preparing the final accounts, especially the financial position, and displaying them at fair value, which increases investor confidence. The study offers insights into the necessity of holding training courses for accountants concerning technology related to digital transformation and big data analysis for use in developing accounting practices.

Social Media Influencers: Customer Attitudes and Impact on Purchase Behaviour

Social media marketing has become a crucial component of contemporary business strategies, significantly influencing brand visibility, customer engagement, and sales growth. The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and, on that basis, to explore their effects on purchase intentions regarding advertised products or services. A total of 376 filled-in questionnaires from an online survey were analysed. The main characteristics of digital influencers’ behaviour that affect consumer perceptions have been systematized and categorized through a combination of both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning and multi-criteria decision-making (MCDM) methods were selected to uncover the hidden dependencies between variables from the perspective of social media users. The developed models elucidate the underlying relationships that shape the acceptance mechanism of influencers’ messages. The obtained results provide specific recommendations for stakeholders across the social media marketing value chain. Marketers can make informed decisions and optimize influencer marketing strategies to enhance user experience and increase conversion rates. Working collaboratively, marketers and influencers can create impactful and successful marketing campaigns that resonate with the target audience and drive meaningful results. Customers benefit from more tailored and engaging influencer content that aligns with their interests and preferences, fostering a stronger connection with brands and potentially affecting their purchase decisions. As the perception of customer satisfaction is an individual and evolving process, stakeholders should organize regular evaluations of influencer marketing data and explore the possibilities to ensure the continuous improvement of this e-marketing channel.

Efficacious conditions for sorption of 85Sr (II) and 152+154Eu (III) onto natural composite beads

Radionuclides Sr-85 and Eu-152 + 154 are by-products of nuclear fission, presenting significant applications alongside potential health hazards, necessitating precise separation methods. In response, a sorption technique utilizing gypsum Alginate-Starch (GA-St) beads was innovated and meticulously characterized through advanced analytical methods including Energy Dispersive X-ray (EDX), Scanning Electron Microscopy (SEM), and Fourier Transform Infrared Spectroscopy (FT-IR). Under optimized sorption conditions, the saturation capacities reached 37.89 mg g−1 for 85Sr (II) and 75.87 mg g−1 for 152+154Eu (III) after 12 and 34 cycles, respectively. These conditions, achieved at pH 6.5 and 3.5 with equilibrium times of 1440 and 300 min for 85Sr (II) and 152+154Eu (III), respectively, underscore ambient temperature's role. The sorption mechanism conforms to the Freundlich model and pseudo nth-order kinetics, with thermodynamic assessments revealing the spontaneity and endothermic nature of the sorption processes. Desorption experiments exhibited remarkable recovery rates of 88.94 % for 85Sr (II) and 99.8 % for 152+154Eu (III) using 0.1 mol L−1 HNO3 from the loaded GA-St beads. These findings underscore the efficacy of GA-St beads as sorbents for both radionuclides, offering promising avenues for addressing challenges in nuclear waste management.

METABOLOMIC TECHNIQUE IN GREEN TEA RESEARCH: A REVIEW

Tea, consumed by two-thirds of the global population, had a production of 5.68 million tons in 2017. The cultivation and processing of tea are crucial variables that have a substantial impact on the final product. As a result, many types of tea, such as black, oolong, and green, are produced, each with its own unique flavors and health advantages. Metabolomic techniques, which employ advanced analytical methods, are employed to characterize chemical composition of tea, thereby enabling the assessment of its quality, origin, and bioactivity. This review focused on the application of these techniques to the green tea research. Analytical methods such as UPLC-MS and 1H NMR, when employed in conjunction with multivariate analysis, have proven effective in correlating green tea quality with its chemical constituents, identifying key metabolites such as EGCG, ECG, and caffeine. These metabolomic techniques permit rapid and accurate assessments, enabling the differentiation between high- and low-quality green tea and enhancing the comprehension of its chemical composition and sensory attributes. In addition, a metabolomic approach was employed, utilizing HPLC-TOF-MS, to differentiate between various types of green tea based on their bioactivities. These methods have the capacity to reveal significant variations in metabolite profiles and bioactivities. The application of multivariate analysis enabled the identification of specific bioactive cultivars, thereby demonstrating the efficacy of the technique in evaluating the health-promoting effects of different tea samples. Thus, metabolomic methods represent an optimal approach for a comprehensive and expeditious investigation of the research related to green tea.

Parent attitudes towards data sharing in developmental science.

Data sharing in developmental science is increasingly encouraged, supported by funder and publisher mandates for open data access. Data sharing can accelerate discovery, link researchers with high quality analytic expertise to researchers with large datasets and democratise the research landscape to enable researchers with limited funding to access large sample sizes. However, there are also significant privacy and security concerns, in addition to conceptual and ethical considerations. These are particularly acute for developmental science, where child participants cannot consent themselves. As we move forward into a new era of data openness, it is essential that we adequately represent the views of stakeholder communities in designing data sharing efforts. We conducted a comprehensive survey of the opinions of 195 parents on data sharing in developmental science. Survey themes included how widely parents are willing to share their child's data, which type of organisations they would share the data with and the type of consent they would be comfortable providing. Results showed that parents were generally supportive of curated, but not open, data sharing. In addition to individual privacy and security concerns, more altruistic considerations around the purpose of research were important. Parents overwhelmingly supported nuanced consenting models in which preferences for particular types of data sharing could be changed over time. This model is different to that implemented in the vast majority of developmental science research and is contrary to many funder or publisher mandates. The field should look to create shared repositories that implement features such as dynamic consent and mechanisms for curated sharing that allow consideration of the scientific questions addressed. Better communication and outreach are required to build trust in data sharing, and advanced analytic methods will be required to understand the impact of selective sharing on reproducibility and representativeness of research datasets.

Advanced Technology and Analytical Methods for Assessing the Impact of Anticancer Drug Metabolites on Drug Efficacy and Toxicity

Advanced Technology and Analytical Methods for Assessing the Impact of Anticancer Drug Metabolites on Drug Efficacy and Toxicity

Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE2AT Center study protocol

IntroductionAfrican cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand...