By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.
Abstract Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.
L. Maier-Hein, M. Eisenmann, Duygu Sarikaya
et al.
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral methods refer to a collection of algorithms built upon the eigenvalues (resp. singular values) and eigenvectors (resp. singular vectors) of some properly designed matrices constructed from data. A diverse array of applications have been found in machine learning, data science, and signal processing. Due to their simplicity and effectiveness, spectral methods are not only used as a stand-alone estimator, but also frequently employed to initialize other more sophisticated algorithms to improve performance. While the studies of spectral methods can be traced back to classical matrix perturbation theory and methods of moments, the past decade has witnessed tremendous theoretical advances in demystifying their efficacy through the lens of statistical modeling, with the aid of non-asymptotic random matrix theory. This monograph aims to present a systematic, comprehensive, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large-scale applications. In particular, our exposition gravitates around several central questions that span various applications: how to characterize the sample efficiency of spectral methods in reaching a target level of statistical accuracy, and how to assess their stability in the face of random noise, missing data, and adversarial corruptions? In addition to conventional $\ell_2$ perturbation analysis, we present a systematic $\ell_{\infty}$ and $\ell_{2,\infty}$ perturbation theory for eigenspace and singular subspaces, which has only recently become available owing to a powerful "leave-one-out" analysis framework.
Abstract Klebsiella pneumoniae (K. pneumoniae) is a Gram-negative bacterium that causes severe community- and hospital-acquired infections. Its rising multidrug resistance complicates therapy, highlighting the need for novel drugs with broad-spectrum, multi-target potential. Leveraging the traditional use and therapeutic evidence of Sesbania grandiflora, this study performed structure-based computational screening of its phytochemicals against K. pneumoniae targets. Initially, 93 proteins with high annotation scores and resolved X-ray structures were identified. Six key therapeutic targets, including LpxH, fabG, KPC-2, GlmU, chbG, and ompA, were prioritized for their pathogenic role. Molecular docking revealed that 59 of 73 compounds interacted with all six targets with high affinity, while the remaining 14 compounds interacted with five targets. Network pharmacology indicated KPC-2, fabG, and ompA had the highest connectivity (73 compounds), followed by chbG and LpxH (72), and GlmU (61). ‘3′,6-di-O-feruloylsucrose’ had the strongest affinity for ompA, LpxH, and GlmU, while ‘Acarbose hydrate’ ranked top for chbG, fabG, and KPC-2. Out of 47 drug-like compounds, 9 passed ADMET filters. Sonchuionoside A was selected for molecular dynamics simulations, demonstrating stable binding to all targets. This suggests S. grandiflora phytoconstituents as multi-target regulators against K. pneumoniae and highlights Sonchuionoside A as a promising lead for further validation.
Abdullah Ahmad Zubair, Fatima Ahmad Zubair, Syed Muhammad Abdal Hussain Shah Gillani
Objective: To find the relationship between sleep quality and psychological distress among undergraduate
medical college students; to investigate the influence of demographic variables such as age, gender, year of
study, residence, BMI, screen time, exercise, smoking status, and hostel/day scholar status on sleep quality and
psychological distress; and to identify which component of psychological distress (anxiety, depression, or
stress) shows the strongest correlation with poor sleep quality.
Study Design: Cross-sectional study.
Place and Duration of Study: The study was conducted at the Department of Community Medicine, HBS
Medical and Dental College, Islamabad, Pakistan from August 2024 to July 2025.
Methods: A total of 320 undergraduate medical students were enrolled. A stratified random sampling
technique was applied. Validated scales were used to assess sleep quality and psychological distress. Data were
collected using a structured proforma and analyzed using SPSS version 25.0.
Results: Among 320 undergraduate medical students, 55.0% were females, and the mean age was 21.37 ±
1.546 years. The correlation between sleep quality and demographic variables was significant for BMI, exercise,
and screen time. Sleep quality also showed a significant association with psychological distress components,
including depression, anxiety, and stress. Multiple linear regression indicated that, among demographic
factors, age, exercise, and screen time, and among psychological variables, depression, anxiety, and stress were
significant predictors of sleep quality.
Conclusion: The study concluded that poor sleep quality was prevalent among undergraduate medical
students and was significantly correlated with psychological distress. Depression, anxiety, and stress were
found to be important contributing factors influencing sleep quality.
How to cite this: Zubair AA, Zubair FA, Gillani SMAHS. Sleep Quality and Psychological Distress Among Undergraduate Students at a Private Medical and Dental College of Islamabad: A Cross-Sectional Study. Life and Science. 2025; 6(4): 455-463. doi: http://doi.org/10.37185/LnS.1.1.1023
Abstract Background There is an emerging understanding of the increased risk of stroke in patients with immune thrombocytopenic purpura (ITP) and immune thrombotic thrombocytopenic purpura (iTTP). We aimed to determine the prevalence and characteristics of acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) in patients with ITP and iTTP in a systematic review and meta-analysis. Methods We used PubMed, Embase, Cochrane, Web of Science, and Scopus using text related to ITP, iTTP, stroke, AIS, and ICH from inception to 11/3/2023. Our primary outcome was to determine prevalence of AIS and/or ICH in a cohort of ITP or iTTP patients (age > 18). Our secondary outcomes were to determine stroke type associated with thrombopoietin receptor agonists (TPO-RAs) in ITP patients, as well as risk factors associated with stroke in ITP and iTTP patients. Results We included 42 studies with 118,019 patients (mean age = 50 years, 45% female). Of those, 27 studies (n = 116,334) investigated stroke in ITP patients, and 15 studies (n = 1,685) investigated stroke in iTTP patients. In all ITP patients, the prevalence of AIS and ICH was 2.1% [95% Confidence Interval (CI) 0.8-4.0%] and 1.5% (95% CI 0.9%-2.1%), respectively. ITP patients who experienced stroke as an adverse event (AE) from TPO-RAs had an AIS prevalence of 1.8% (95% CI 0.6%-3.4%) and an ICH prevalence of 2.0% (95% CI 0.2%-5.3%). Prevalence of stroke did not significantly differ between all ITP patients and those treated with TPO-RAs. iTTP patients had a prevalence of AIS and ICH of 13.9% (95% CI 10.2%-18.1%) and 3.9% (95% CI 0.2%-10.4%), respectively. Subgroup analysis revealed the prevalence of AIS and ICH was greater in iTTP patients vs. all ITP patients (p < 0.01 and p = 0.02, respectively). Meta-regression analysis revealed none of the collected variables (age, sex, history of diabetes or hypertension) were risk factors for stroke in all ITP patients, although there were high levels of data missingness. Conclusions Prevalence of different stroke types was lower in all ITP patients vs. iTTP patients. Additionally, ITP patients experienced a similar prevalence of stroke regardless of if they were specifically denoted to have been treated with TPO-RAs or not, supporting the continued use of TPO-RAs in management. Risk factors for stroke remain unclear, and future studies should continue to investigate this relationship.
Neurosciences. Biological psychiatry. Neuropsychiatry, Neurology. Diseases of the nervous system
PURPOSE The ACR Data Science Institute conducted its first annual survey of ACR members to understand how radiologists are using artificial intelligence (AI) in clinical practice and to provide a baseline for monitoring trends in AI use over time. METHODS The ACR Data Science Institute sent a brief electronic survey to all ACR members via e-mail. Invitees were asked for demographic information about their practice and if and how they were currently using AI as part of their clinical work. They were also asked to evaluate the performance of AI models in their practices and to assess future needs. RESULTS Approximately 30% of radiologists are currently using AI as part of their practice. Large practices were more likely to use AI than smaller ones, and of those using AI in clinical practice, most were using AI to enhance interpretation, most commonly detection of intracranial hemorrhage, pulmonary emboli, and mammographic abnormalities. Of practices not currently using AI, 20% plan to purchase AI tools in the next 1 to 5 years. CONCLUSION The survey results indicate a modest penetrance of AI in clinical practice. Information from the survey will help researchers and industry develop AI tools that will enhance radiological practice and improve quality and efficiency in patient care.
Stock market process is full of uncertainty; hence stock prices forecasting very important in finance and business. For stockbrokers, understanding trends and supported by prediction software for forecasting is very important for decision making. This paper proposes a data science model for stock prices forecasting in Indonesian exchange based on the statistical computing based on R language and Long Short-Term Memory (LSTM). The first Covid-19 (Coronavirus disease-19) confirmed case in Indonesia is on 2 March 2020. After that, the composite stock price index has plunged 28% since the start of the year and the share prices of cigarette producers and banks in the midst of the corona pandemic reached their lowest value on March 24, 2020. We use the big data from Bank of Central Asia (BCA) and Bank of Mandiri from Indonesia obtained from Yahoo finance. In our experiments, we visualize the data using data science and predict and simulate the important prices called Open, High, Low and Closing (OHLC) with various parameters. Based on the experiment, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1 year) with high epoch in training phase rather than using 3 years training data.
Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling, and so on are referred to as data science pipelines. To facilitate research and practice on data science pipelines, it is essential to understand their nature. What are the typical stages of a data science pipeline? How are they connected? Do the pipelines differ in the theoretical representations and that in the practice? Today we do not fully understand these architectural characteristics of data science pipelines. In this work, we present a three-pronged comprehensive study to answer this for the state-of-the-art, data science in-the-small, and data science in-the-large, Our study analyzes three datasets: a collection of 71 proposals for data science pipelines and related concepts in theory, a collection of over 105 implementations of curated data science pipelines from Kaggle competitions to understand data science in-the-small, and a collection of 21 mature data science projects from GitHub to understand data science in-the-large. Our study has led to three representations of data science pipelines that capture the essence of our subjects in theory, in-the-small, and in-the-large.
Surgical data science (SDS) aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis, and modeling of procedural data. As data capture has increased and artificial intelligence (AI) has advanced, SDS can help to unlock augmented and automated coaching, feedback, assessment, and decision support in surgery. We review major concepts in SDS and AI as applied to surgical education and surgical oncology.
Omnichannel is not just a marketing, e-commerce, or customer support buzzword. This future customer engagement platform helps businesses communicate with customers through centralized channels on a smart interface. It is difficult to achieve customer loyalty when the risk in online transactions, which creates anxiety, exists in all transaction processes in an omnichannel system. Hence, the purpose of this research was to analyze the influence of anxiety on relationships when clients purchase from an omnichannel platform using the stimulus–organism–response (SOR) paradigm. To fulfill study aims, qualitative and quantitative research approaches were used. In-depth interviews and focus group discussions were used to acquire qualitative data, while survey responses from 485 participants were used to collect quantitative data. This study’s results revealed relationships between consumer psychology factors such as perceived mental benefits, hedonic value, and anxiety. Moreover, customer anxiety in omnichannel can be measured as a novel and exact concept in marketing science and have a moderating role in the effect of perceived mental benefits on electronic loyalty and perceived mental benefits on hedonic value in omnichannel systems. As a result, enterprises were also offered various managerial implications to develop their omnichannel system.
Maryam Hajishafiee, Kostas Kapellas, Stefan Listl
et al.
Abstract Background As part of the Global Strategy on Oral health, the World Health Organization (WHO) is exploring cost-effective interventions for oral health, including taxation on sugar-sweetened beverages (SSBs). To inform this process, this umbrella review aimed to identify the best available estimates pertaining to the impact of SSB taxation on the reduction of sugars intake, and the sugars-caries dose–response, such that estimates of the impact of SSB taxation on averting dental caries in both high (HIC) and low and middle (LMIC) countries be available. Methods The questions addressed were: (1) what are the effects of SSB taxation on consumption of SSBs and (2) sugars? (3) What is the effect on caries of decreasing sugars? and (4) what is the likely impact of a 20% volumetric SSB tax on the number of active caries prevented over 10 years? Data sources included PubMed, Embase, Web of Science, Scopus, CINAHL, Dentistry and Oral Sciences Source, Cochrane Library, Joanna Briggs Institute (JBI) Systematic Review Register, and PROSPERO. The review was conducted with reference to JBI guidelines. The quality of included systematic reviews was assessed using AMSTAR to identify best evidence. Results From 419 systematic reviews identified for questions 1 & 2, and 103 for question 3, 48 (Questions 1 & 2) and 21 (Question 3) underwent full text screening, yielding 14 and five included reviews respectively. Best available data indicated a 10% tax would reduce SSB intake by 10.0% (95% CI: -5.0, 14.7%) in HIC and by 9% (range -6.0 to 12.0%) in LMIC, and that a 20% tax would reduce free sugars intake on average by 4.0 g/d in LMIC and 4.4 g/d in HIC. Based on best available dose response data, this could reduce the number of teeth with caries per adults (HIC and LMIC) by 0.03 and caries occurrence in children by 2.7% (LMIC) and 2.9% (HIC), over a 10-year period. Conclusion Best available data suggest a 20% volumetric SSB tax would have a modest impact on prevalence and severity of dental caries in both HIC and LMIC.
Julia R. Varshavsky, Swati D. G. Rayasam, Jennifer B. Sass
et al.
Abstract A key element of risk assessment is accounting for the full range of variability in response to environmental exposures. Default dose-response methods typically assume a 10-fold difference in response to chemical exposures between average (healthy) and susceptible humans, despite evidence of wider variability. Experts and authoritative bodies support using advanced techniques to better account for human variability due to factors such as in utero or early life exposure and exposure to multiple environmental, social, and economic stressors. This review describes: 1) sources of human variability and susceptibility in dose-response assessment, 2) existing US frameworks for addressing response variability in risk assessment; 3) key scientific inadequacies necessitating updated methods; 4) improved approaches and opportunities for better use of science; and 5) specific and quantitative recommendations to address evidence and policy needs. Current default adjustment factors do not sufficiently capture human variability in dose-response and thus are inadequate to protect the entire population. Susceptible groups are not appropriately protected under current regulatory guidelines. Emerging tools and data sources that better account for human variability and susceptibility include probabilistic methods, genetically diverse in vivo and in vitro models, and the use of human data to capture underlying risk and/or assess combined effects from chemical and non-chemical stressors. We recommend using updated methods and data to improve consideration of human variability and susceptibility in risk assessment, including the use of increased default human variability factors and separate adjustment factors for capturing age/life stage of development and exposure to multiple chemical and non-chemical stressors. Updated methods would result in greater transparency and protection for susceptible groups, including children, infants, people who are pregnant or nursing, people with disabilities, and those burdened by additional environmental exposures and/or social factors such as poverty and racism.
Industrial medicine. Industrial hygiene, Public aspects of medicine
We present the opportunities and limitations of administrative benefits data held by local authorities for data linkage projects. Whilst the richness of this data has been exploited by practitioners for administration, its potential remains little explored by researchers. We discuss data quality, sample selection and legal gateways for data sharing.
Drawing on our experience working with over 40 local authorities, we present the structure of three datasets: the Council Tax Reduction Scheme, the Single Housing Benefits Extract and the Universal Credit Data Share. We show what variables are usually included, under which legal gateways this data can be shared and how the cohorts represented within the data compare with the low-income population. We discuss how these datasets can be linked at the household level with a number of other data held by local authorities such as social rent and Council Tax arrears, Housing Benefit overpayments and Discretionary Housing Payments (DHPs).
Administrative benefits data provides a comprehensive snapshot of a household’s financial situation. Local authorities can proactively use and share this data with external data processors to fulfil their statutory duties if a legal gateway allows. By identifying households at risk of cash shortfalls before they reach a crisis point, councils can target support when administering local welfare schemes and preventing homelessness. By assessing eligibility for benefits, they can run data-driven uptake campaigns. This data captures a proportion of the population on national and local benefits within a local authority at several points in time. Attrition is of concern since households may leave datasets over time. Some will see their income rise and no longer qualify for benefits. Others will move out of the constituency.
Local authorities routinely process longitudinal data on households receiving means-tested benefits by administering housing benefits, council tax support, and discretionary support funds. This data provides a unique real-time insight into the socioeconomic situation of low-income households. Yet, we show that its promising potential for policy research remains largely untapped.
Abstract Manufacturing has adopted technologies such as automation, robotics, industrial Internet of Things (IoT), and big data analytics to improve productivity, efficiency, and capabilities in the production environment. Modern manufacturing workers not only need to be adept at the traditional manufacturing technologies but also ought to be trained in the advanced data-rich computer-automated technologies. This study analyzes the data science and analytics (DSA) skills gap in today's manufacturing workforce to identify the critical technical skills and domain knowledge required for data science and intelligent manufacturing-related jobs that are highly in-demand in today's manufacturing industry. The gap analysis conducted in this paper on Emsi job posting and profile data provides insights into the trends in manufacturing jobs that leverage data science, automation, cyber, and sensor technologies. These insights will be helpful for educators and industry to train the next generation manufacturing workforce. The main contribution of this paper includes (1) presenting the overall trend in manufacturing job postings in the U.S., (2) summarizing the critical skills and domain knowledge in demand in the manufacturing sector, (3) summarizing skills and domain knowledge reported by manufacturing job seekers, (4) identifying the gaps between demand and supply of skills and domain knowledge, and (5) recognize opportunities for training and upskilling workforce to address the widening skills and knowledge gap.