Scientific Data Research Articles

Management of the Expected Difficult Airway with Planned One-Lung Ventilation: A Retrospective Analysis of 44 Cases.

Difficult airway management is essential in anesthesia practice. Particular challenges are posed to patients who require intraoperative one-lung ventilation. Specific guidelines for these scenarios have been lacking. The recent update of German guidelines incorporates recommendations for securing the airway in anticipated difficult airway scenarios in patients requiring one-lung ventilation. However, scientific data on this specific topic is rare. A retrospective analysis was conducted on adult patients undergoing thoracic surgery with one-lung ventilation from 2016 to 2021. During these years, the standard of practice has been in line with the now published guidelines. Patients with anticipated difficult airways were identified, and airway management strategies were analyzed. Among 3,197 anesthetic procedures, 44 cases involved anticipated difficult airways, primarily due to prior head and neck tumor treatment. Nasal bronchoscopic awake intubation followed by oral reintubation under videolaryngoscopic inspection and the use of bronchial blockers was the standard procedure. No severe complications were recorded, and one-lung ventilation was maintained successfully in all cases. The study highlights the challenges of managing difficult airways during thoracic surgery. Recommendations align with recent guidelines, emphasizing the importance of tailored approaches. The use of single-lumen tubes with bronchial blockers appears favorable over double-lumen tubes, offering comparable ventilation quality with reduced risks. Despite limitations, the study underscores the safety and efficacy of tailored airway management strategies during one-lung ventilation in patients with anticipated difficult airways. The presented approach offers patient safety and practicability. Further multicenter studies are warranted to validate these findings and refine clinical approaches.

Pathways for Enhancing the Teaching Quality of Financial Education through Research Practices

Objective: This study investigates how integrating research practices into financial education can enhance students' learning outcomes, practical skills, and innovation capacity. With the rapid evolution of the financial sector due to globalization and technological advancements, financial education must move beyond traditional theoretical approaches to prepare students for complex real-world challenges. The objective of this research is to explore specific pathways for incorporating research-driven practices into financial curricula to better align education with industry demands. Methods: The study employed a mixed-method approach, combining quantitative and qualitative research methods. Surveys were distributed to 461 students and 121 faculty members from four universities—two research-intensive institutions and two applied colleges—aiming to capture diverse perspectives on the integration of research into teaching. The data were analyzed using descriptive statistics, regression models, and qualitative analysis of interviews with selected participants. The regression analysis focused on the relationship between the frequency of research participation and the development of practical skills and innovation capacity. Results: The regression analysis revealed a significant positive correlation between research participation and improved learning outcomes (β=0.52, p<0.01), practical skills (β=0.47, p<0.01), and innovation capacity (β=0.43, p<0.01). Students who participated in research projects demonstrated a deeper understanding of financial theories, stronger data analysis and decision-making skills, and greater creativity in problem-solving. These results suggest that research engagement significantly enhances students' ability to apply theoretical knowledge in practical contexts. Conclusion: Integrating research practices into financial education bridges the gap between theory and practice, fostering critical thinking, problem-solving skills, and innovation among students. Universities should prioritize research-based learning, offering opportunities for interdisciplinary research, university-business collaborations, and digital platforms to expand student access to research experiences. Future studies should explore the role of interdisciplinary research in further enhancing financial education outcomes, particularly through fields such as data science, behavioral economics, and artificial intelligence.

A local convergent ecological inference algorithm for RxC tables

ABSTRACT Over the years, a number of methods have been proposed to forecast the unknown inner-cell values of a set of related RxC contingency tables when only their margins are known. This is a classical problem that emerges in many areas, from economics to quantitative history, being particularly ubiquitous when dealing with electoral data in sociology and political science. However, the two current major algorithms to solve this problem, based on Bayesian statistics and iterative linear programming depend on adjustable (hyper-)parameters and do not yield a unique solution: their estimates tend to fluctuate (when convergence is reached) around a stationary distribution. Within the linear programming framework, this paper proposes a new algorithm (lclphom) that always converges to a unique solution, having no adjustable parameters. This characteristic makes it easy to use and robust to claims of hacking. Furthermore, after assessing lclphom with real and simulated data, lclphom is found to yield estimates of (almost) similar accuracy to the current major solutions, being more preferable to the other lphom-family algorithms the more heterogeneous the row-fraction distributions of the tables are. Interested practitioners can easily use this new algorithm as it has been programmed in the R-package lphom.

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Assessing the Impact and Cost-Effectiveness of Exposome Interventions on Alzheimer’s Disease: A Review of Agent-Based Modeling and Other Data Science Methods for Causal Inference

Background: The exposome (e.g., totality of environmental exposures) and its role in Alzheimer’s Disease and Alzheimer’s Disease and Related Dementias (AD/ADRD) are increasingly critical areas of study. However, little is known about how interventions on the exposome, including personal behavioral modification or policy-level interventions, may impact AD/ADRD disease burden at the population level in real-world settings and the cost-effectiveness of interventions. Methods: We performed a critical review to discuss the challenges in modeling exposome interventions on population-level AD/ADRD burden and the potential of using agent-based modeling (ABM) and other advanced data science methods for causal inference to achieve this. Results: We describe how ABM can be used for empirical causal inference modeling and provide a virtual laboratory for simulating the impacts of personal and policy-level interventions. These hypothetical experiments can provide insight into the optimal timing, targeting, and duration of interventions, identifying optimal combinations of interventions, and can be augmented with economic analyses to evaluate the cost-effectiveness of interventions. We also discuss other data science methods, including structural equation modeling and Mendelian randomization. Lastly, we discuss challenges in modeling the complex exposome, including high dimensional and sparse data, the need to account for dynamic changes over time and over the life course, and the role of exposome burden scores developed using item response theory models and artificial intelligence to address these challenges. Conclusions: This critical review highlights opportunities and challenges in modeling exposome interventions on population-level AD/ADRD disease burden while considering the cost-effectiveness of different interventions, which can be used to aid data-driven policy decisions.

Abstract Su705: Predictive Model and Factors for Out-of-Hospital Cardiac Arrest in Geriatric Patients with insomnia: A Longitudinal Study

Background: Previous studies have shown that geriatric insomnia can impact the quality of life and lead to various health risks and comorbidities, which may further result in out-of-hospital cardiac arrest (OHCA). Therefore, assessing the risk of OHCA in elderly patients with insomnia is crucial. Hypothesis: We hypothesized that a predictive model incorporating demographic, clinical, and treatment adherence factors can effectively estimate the risk of OHCA among geriatric patients with insomnia. Aim: This study aimed to develop and validate a predictive model for OHCA in geriatric patients with insomnia using a large-scale population-based analysis. Method: A retrospective cohort study was conducted using data from Taiwan's Health and Welfare Data Science Center spanning from 2011 to 2020. The study included elderly patients diagnosed with insomnia who received medication. Predictors of OHCA, such as age, sex, comorbidities, medication adherence, and psychotherapy, were analyzed using multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) with both internal and temporal validation. Results: Among 438,147 eligible elderly patients with insomnia, 7,666 experienced OHCA. Using data from 2011 to 2018 for model development, the AUROC for predicting OHCA within 7 days, 30 days, and 90 days ranged from 0.749 to 0.762 in the training set and 0.768 to 0.784 in the validation set. Key predictors included older age, male sex, prior medical resource use, hemodialysis, pre-existing comorbidities, medication possession ratio, medication changes, and recent psychotherapy (Figure 1). The model was also tested in 2019 and 2020 dataset (began of the COVID-19 in Taiwan), respectively, demonstrating consistent predictive performance (Figure 2). Specifically, the 7-day model in 2019 showed a sensitivity of 0.776, specificity of 0.632, and accuracy of 0.632, with a positive likelihood ratio (PLR) of 2.11, and negative likelihood ratio (NLR) of 0.36. In 2020, it achieved a sensitivity of 0.739, specificity of 0.661, accuracy of 0.661, PLR of 2.18, NLR of 0.40. Conclusion: The study developed a reliable predictive model for OHCA in geriatric patients with insomnia, highlighting significant predictors such as age, comorbidities, medication adherence, and recent psychotherapy. This model can aid clinicians in identifying high-risk patients and improving preventative care strategies.

Instagram data for validating Nothofagus pumilio distribution mapping in the Southern Andes: A novel ground truthing approach

The availability of valid, non-biased species occurrence data has always been a major challenge for biodiversity research and modelling studies. Data from open-source databases or remote sensing are promising approaches to increase the availability of species occurrence data. However, these data may contain spatial, temporal, and taxonomic biases or require ground truthing. In recent years, social media has received attention for its contribution to species occurrence data sampling and ground truthing approaches. The wide reach of social media platforms allows for rapid and large-scale analyses. Here we introduce a novel Instagram ground truthing approach to validate the occurrence mapping of Nothofagus pumilio across its entire distribution range. The treeline species of the southern Andes has been extensively studied in small-scale studies, but large-scale modelling approaches are largely missing due to limited accessibility to treeline sites resulting in a lack of occurrence data. The content posted on the social media platform Instagram consists of images and videos in which the species N. pumilio and its location can be identified. By searching for suitable posts using hashtags and location tags, we created 1238 Instagram ground truthing points. We compared the performance of our dataset with open-source data from the Global Biodiversity Information Facility (GBIF) through visual, quantitative, and bias analyses, acknowledging that both social media-based and Citizen Science data are subject to sampling and spatial biases due to collection in human-accessible areas. The Instagram ground truthing points were subsequently used to validate remote sensing occurrence data, generated using Sentinel-2 level 2A data and Supervised Classification. The combined approach – Instagram ground truthing and remote sensing – allows for the collection of occurrence data over the entire latitudinal range of N. pumilio, covering approximately 2000 km. The use of social media content provides potentially important contributions to species occurrence data sampling and ground truthing In our study we introduce a novel ground truthing approach for species occurrence data sampling based on Instagram data Instagram ground truthing points, combined with Supervised Classification generate species occurrence data of Nothofagus pumilio over its entire distribution range in the southern Andes The performance of the Instagram ground truthing points is evaluated by comparison with existing data from the GBIF database. Our Instagram ground truthing approach demonstrates a new way of sampling species occurrence data and can be applied to other suitable species and study areas.

Using Google Earth Engine open-source code for land surface temperature estimation from Landsat data in Chuong My district, Hanoi city, Vietnam

Land surface temperature (LST) in association with urbanization has significantly influenced on local climates and terrestrial surface processes. By using multi-temporal Landsat data in the GEE platform, the study has found that there have been changes in land covers due to the main drivers of industrial development, urban and built-up expansion. This study estimated land surface temperature (LST) in Chuong My district from Landsat 5, 7, 8 and 9 thermal infrared sensors, using different surface emissivity sources. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, offers the estimation of LST products, covering the time period from 2000 to 2024 in study site. RF algorithm for land cover classification and mapping is suggested to be applied in Chuong My district, with overall accuracy of land cover mapping in 2024 being 91.9% with Kappa coefficient of 0.89. The period 2000-2010 showed that 327.0 ha of land were converted to land for industrial development, urban and residential land, while the period 2010-2024 continued to witness a continued conversion of land to other land use purposes, with a decrease in agricultural land area (65.7 ha) and an increase in industrial land use (65.2 ha), but the rate of conversion was much lower than the previous period (2000-2024). The periods of 2000-2010 and 2010-2024 showed that there have been significant changes in land surface temperature. Most of the surface temperature in the entire region increased by over 20C, especially in the period 2003-2015 when a total area of 1424 ha had the temperature increased by above 30C. This is a consequence of the process of urbanization and industrialization, which began to take place in 2008, along with the process of changing the land cover status of the study area. Further studies are needed to better understand the thermal conductivity of surface materials and to plan how to reduce LST in such areas.

Examining Teachers’ Professional Learning in an Online Asynchronous System: Personalized Supports for Growth and Engagement in Learning to Teach Statistics and Data Science

Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning.

Terahertz time-domain investigation of atacamite: Spectral analysis and theoretical insights for cultural heritage applications

Spectroscopic methods based on terahertz (THz) radiation can represent an innovative approach in the Cultural Heritage field. Specifically, contemporary and historical pigments can exhibit unique fingerprints. However, despite the high selectiveness of terahertz technique, the origin of spectral characteristics is not often known. In this work, the characteristic terahertz-time-domain spectroscopy spectrum of atacamite is reported in the spectral range 0.1–3.7 THz along with ab initio calculation of lattice dynamics to assess the nature of experimental features. The obtained results enable the acquisition of reliable new data in Heritage sciences, which is essential for developing accurate reference pigment databases.

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists.

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

Acute Decompensation of Chronic Cardiac Failure in Elderly and Senile Persons: Focus on Renal Dysfunction

The heart and kidneys, as well as their synergy, are necessary conditions for maintaining hemodynamic homeostasis in the human body. Since the beginning of the 19th century, it has been recognized that cardiovascular and renal diseases often coexist and have a mutually aggravating effect on each other. During the aging process, involutive changes in organs and body systems, especially the cardiovascular and urinary systems, lead to many physiological and functional changes that can increase the risk of developing acute complications, including acute decompensation of chronic heart failure (CHF). The prevalence of CHF is increasing among the population of older age groups all over the world, which is explained not only by involutive changes in the cardiovascular system of the body, but also by the high frequency of concomitant diseases, in particular chronic kidney disease. Age-related changes in the urinary system also play an important role in maintaining homeostasis of the body, however, during the aging process, along with a decrease in the number of nephrons, a decrease in the concentration function of the kidneys is observed, as well as a change in their structural integrity. These involutive changes in renal tissue may complicate the diagnosis and treatment of conditions associated with acute decompensation of CHF. Therefore, consideration of age-related changes in the cardiovascular and urinary systems and their contribution to the development of acute decompensation of CHF represents an important area for further research and practical applications aimed at improving medical care and quality of life in older people. The review article examines scientific literature data on cardiorenal relationships in acute decompensation of chronic heart failure in elderly and senile people.

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

Time Efficient & Cost Effective Online teaching tool with iConcepts in Orthodontics for DDS students @ UoM

Background Traditional teaching methods in Orthodontics include plaster models and photos which have been used for decades, however due to the nature of being static still images its difficult for 3-Dimensional (3D) changes in Orthodontics teaching, which has posed immense challenges as the learner is unable to clear concepts on the different planes that affect the final tooth positions not to mention the protracted treatment time ranges from 12 months (simple cases) to 36 months (complex cases). In 2024 with variety of platforms such as videos, 3D animations, interactive changes in face and teeth can be added to teaching tools for Orthodontics. Furthermore, orthodontic movements can pose difficulty in understanding the changes particularly in growing children adding to the 4th dimension. At UQ (Naser-ud-Din, 2015) and internationally (Bridges, 2015) provided experience with creating online teaching modules highlighting its strengths of flexibility, ease of access on demand and global presence. UQ developed SBLi -Scenario Based Learning interactive for Postgraduate Orthodontic students who found it highly engaging, with self-reflective and self-assessment elements useful. (Khoo et al., 2023; Naser-ud-Din, 2016). Generally simulations can be expensive to create (Kröger et al., 2017) and it’s essential to explore cost effective simulation teaching tools with industry partners for authentic learning experience. Aims There is a gap in the dental education sector, particularly with emerging CADCAM (Computer Assisted Design and Computer Assisted Manufacture), which can be used to enhance the learning of core concepts in biomechanics with the aid of 3D simulated online learning. This is reflected particularly for the university students in undergraduate courses who need to feel confident and clinically ready on graduation as Dentists. Thus, greater expectations to be aligned with current technological processes in dentistry. Over the past 5 years, in particular, there has been exponential improvements by the industry providing 3D simulations for treatment planning and patient communication, commonly referred to as the GPS for smile transformations. The aim of this presentation is to highlight the time efficiency and cost effectiveness of the learning tool in sync with contemporary demands in Orthodontics to provide authentic learning platform that prepares the clinician for real patients. Material & Methods Currently the CAD CAM technology provide 3D simulations as open access to general public and doctors portal which can be utilized for teaching core concepts related to biomechanics foundations for student learning, engagement and self-assessment. This project envisages to create a novel forum encompassing education revolution as massive changes in delivery will occur with speed and efficiency internationally with onlinelearning demands. Hence, robust online presence with an interactive textbook such as iConcepts in Orthodontics under the banner of UoM to assist students in Doctor of Dental Surgery at MDS shall pave the way to learning needs for the future. Results The purpose of iConcepts a digital interactive online textbook is to create lifelong learning opportunities in safe space for visual, auditory and kinesthetic with quizzes for interactive learning of concepts without pressure in class learning and feeling judged by peers. Interactive 3D Orthodontic learning directly translates into clinical applications for various orthodontic cases. In the past decade CAD CAM has become clinically relevant particularly with Clear Aligner Therapy adding to higher precision and patient satisfaction. Moreover, it is imperative to have Long Term Retention (LTR) (Irvine, 2020) of learning new tasks. It is essential that students in dentistry are aware of the digital workflows and have clinical preparedness on graduation as it’s the future and certainly here to stay. Both qualitative and quantitative data on student experience shall be collected and analysed to seek out the best practices and processes for instruction of delivery in Orthodontics for DDS cohort in particular and lifelong learning for all ages in general encompassing time efficiency and cost effectiveness. Conclusion The current iConcepts is developed by the author with Apple Education and prototype is being assessed with MSc Data Science cohort at UoM. Furthermore, it is refined by Master of Science Software Engineering students at UoM. Future Recommendations Novel teaching tools shall be marketed to developing universities internationally assisting the dissemination of information a flagship for UoM and revenue generation for department of Education at UoM. In future there will be more and more demand towards interactive concepts clarification (Poblete et al., 2020) and self-assessments hence iConcepts provides that ideal platform. Such technologies will become more prevalent in the future particularly with AI and translated into different languages/ sectors of education. Various faculties that showcase start to finish particularly lengthy/complex outcomes can benefit with timelapse videos and 3D CADCAM for clarity and comprehension for the entire process such as patient journey throughout treatment in Orthodontics.

Do Open Access Articles Have a Citation Advantage Over Toll Access Articles? A Comparative Analysis of Articles Published in the Journal of Craniofacial Surgery From 2019 to 2023 Based on Web of Science Data.

This study aimed to compare the citation rates of open access (OA), and toll access (TA) articles published in the Journal of Craniofacial Surgery between 2019 and 2023, and to examine whether OA articles received more citations. Using the Web of Science database, articles published in the Journal of Craniofacial Surgery between 2019 and 2023 were listed, excluding those with corrections or retractions. Articles were divided into 2 groups: OA and TA. The number of articles and citation counts for each group were analyzed by year. Citation rates were compared using the independent sample t test, with a p-value of <0.05 considered statistically significant. A total of 4691 articles were analyzed, of which 7.14% were OA and 92.86% were TA. Published in 2021, 2020, and 2019, OA articles had statistically significantly higher citation counts than TA articles. OA articles were found to be funded at a higher rate than TA articles. This study demonstrated that OA articles received more citations over time and were more likely to be funded. Although it is not definitively clear whether the citation advantage stems from the quality of the articles or their open-access status, the citation advantage of OA articles is striking.

Machine Learning in Healthcare: Developing an AI Assistant Doctor for Symptom-Based Disease Prediction

The rapid advancements in data science and machine learning have revolutionized various sectors, particularly healthcare. This research introduces an innovative AI Assistant Doctor system that utilizes machine learning techniques, specifically a Support Vector Classifier (SVC), to predict potential diseases based on user-reported symptoms. The system aims to bridge the gap between symptom recognition and understanding their significance, promoting early intervention and improved health outcomes. The AI Assistant Doctor offers a comprehensive healthcare tool that goes beyond disease prediction, providing users with detailed information about illnesses, preventive measures, drug recommendations, nutritional guidance, and personalized exercise suggestions. The SVC model is trained on a diverse dataset of symptoms and diseases, enabling accurate predictions. The system is designed to be accessible through a user-friendly web interface, allowing individuals to input their symptoms and receive immediate insights. In addition to disease prediction, the AI Assistant Doctor equips users with a thorough understanding of the predicted condition, including its symptoms, causes, and typical progression. It also offers preventive strategies, suggested medications, dietary advice, and customized physical activity recommendations to help users manage their health effectively. By leveraging machine learning algorithms and web-based frameworks, this research presents a cutting-edge approach to disease prediction and healthcare management, addressing the growing need for accessible medical information and timely interventions. The AI Assistant Doctor has the potential to alleviate pressure on healthcare systems, improve individual health outcomes, and support medical practitioners in delivering precise diagnoses and personalized treatment plans. Keywords: Machine learning, Disease Prediction, Support Vector Classifier (SVC), Healthcare, Symptoms, Prevention Personalized recommendations.

Roadmap for a European Open Science Alliance for ATM Research

To meet the future challenges of air transport and air traffic management (ATM), higher levels of transparency and access to the underlying data will be a key enabler. We here propose an Open Science Alliance for ATM, advocating for open data in such a framework. The benefits of adopting an open science approach are to be found, inter alia, through the independent verification and validation of reported impacts/results and achieved performance levels, i.e., through reproducibility. We consider that this can only be achieved through: (1) open access to scientific methods and data utilised; (2) open access to (analytical) code and methods; (3) open review of reported analyses/research. The proper application of such practices will reduce the innovation cycles in ATM, which is much needed by industry and society. Steps for forming an Open Science Alliance for ATM are described. We propose further initial, specific recommendations for supporting open data and improved access for research.

A Study on the Water Management Knowledge of Traditional Villages from the Perspective of Stormwater Resilience—A Case Study of Changqi Ancient Village in Guangdong, China

With the advancement of resilience concepts, enhancing resilience capacity has become an effective approach to addressing rainwater and flooding issues. Most rural planning and construction efforts adopt urban planning models from economically developed regions, often leading to surface hardening, which subsequently causes drainage difficulties and severe surface water accumulation during the rainy season. In contrast, traditional Lingnan villages, exemplified by Guangdong’s Changqi Ancient Village, continue to function normally in flood-prone areas, suggesting that their water management knowledge merits investigation. Previous research on rainwater management in traditional Chinese villages has predominantly been qualitative, lacking scientific data support. This study employs an eco-social resilience perspective, combining field surveys and interviews with villagers, and utilizes the SWMM (Storm Water Management Model) software to conduct both qualitative and quantitative analyses of Changqi Ancient Village. The findings reveal the following: (1) The SWMM effectively quantifies rainwater and flood management in traditional villages. (2) From an ecological resilience perspective, the village’s geographical location is crucial. The topography, along with a rainwater regulation system comprising rivers, ponds, ditches, and permeable pavements, significantly influences the village’s drainage performance. (3) From a social resilience perspective, community participation is vital to the long-term stable development of traditional villages. This includes post-disaster collective fundraising by villagers for the restoration of rainwater and flood management facilities, the formulation of village regulations, and the construction and restoration of spiritual sites. (4) From an eco-social resilience perspective, the eco-social resilience system exhibits adaptive cyclical characteristics, where the geographical environment and the local economy significantly shape the ecological spatial patterns of Changqi, while positive interaction between nature and human society ensures the system’s dynamic equilibrium.

Insights on therapeutic approaches of natural anti-Alzheimer's agents in the management of Alzheimer's disease: A future perspective.

In the current scenario, Alzheimer's disease is a complex, challenging, and arduous health issue, and its prevalence, together with comorbidities, is accelerating around the universe. Alzheimer's disease is becoming a primary concern that significantly impacts an individual's status in life. The traditional treatment of Alzheimer's disease includes some synthetic drugs, which have numerous dangerous side effects, a high risk of recurrence, lower bioavailability, and limited treatment. Hence, the current article is a detailed study and review of all known information on plant-derived compounds as natural anti-Alzheimer's agents, including their biological sources, active phytochemical ingredients, and a possible mode of action. With the help of a scientific data search engine, including the National Center for Biotechnology Information (NCBI/PubMed), Science Direct, and Google Scholar, analysis from 2001 to 2024 has been completed. This article also described clinical studies on phytoconstituents used to treat Alzheimer's disease. Plant-derived compounds offer promising alternatives to synthetic drugs in treating Alzheimer's disease, with the potential for improving cognitive function and slowing down the progression of the disease. Further research and clinical trials are needed to fully explore their therapeutic potential and develop effective strategies for managing this complex condition.

Understanding complex systems through differential causal networks

In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes. Resolved to reach this ambitious goal, we introduce Differential Causal Networks (DCNs), a novel framework that represents differences between two existing CNs. A DCN is obtained from experimental data by comparing two CNs, and it is a power tool for highlighting differences in causal relations. After a careful definition and design of DCNs, we test our algorithm to model possible differential causal relationships between genes responsible for the onset of type 2 diabetes mellitus-related pathologies considering patients’ sex at the tissue level. DCNs allowed us to shed light on causal differences between sexes across nine tissues. We also compare differences among three possible definitions of DCNs to highlight similarities and differences of biological importance. Code, Data and Supplementary Information are available at https://github.com/hguzzi/DifferentialCausalNetworks.