Browse Articles

Secure healthcare data management using multimodal image fusion and dual watermarking

Party realignment and single-issue voters

This paper studies the effect of a single-issue referendum on political party realignment. We consider the Conservative Party in the United Kingdom in the aftermath of a referendum on the British exit from the European Union. We look at the change in positions of Conservative Members of Parliament using a novel dataset tracking their opinions on British membership of the EU and their election outcomes between 2015 and 2022. Our findings show that MPs who resisted switching to a pro-Leave position faced significantly higher electoral losses. We then consider various models of switching, showing that safe seats are strong predictors. These results highlight the sensitivity of political parties to single-issue politics, underscoring how such environments can contribute to broader populist movements in modern democracies.

Spillover effects of work–family conflict on job consequences influencing work attitudes

The impact of acute bike desk usage before encoding and during early consolidation on memory task performance in university students and use case evaluation in an educational setting

This study examined the impact of using bike desks on cognitive function and memory among university students. Physical activity during adolescence offers enduring health benefits, yet sedentary behaviors prevail among young adults, posing significant health risks. Bike desks, integrating stationary cycling with ergonomic desk designs, aim to mitigate sedentary behavior while enhancing cognitive performance. Research indicates that acute aerobic exercise improves executive functions, memory, and attention, which is particularly beneficial in educational settings. The study employed the verbal paired associates learning task (VPAT) to assess memory performance when either bike desk usage at moderate intensity (intervention) or rest (control condition) occurred before encoding and during early consolidation in 26 young adult medicine and nursing students in a library setting. We hypothesised that bike desk usage will enhance memory encoding and consolidation compared to the control condition of rest. The results of our study showed no significant differences in VPAT scores or response latency between seated and bike desk conditions. Supplementary analysis, including a multiple linear regression model (R2: 0.773, Adjusted R2: 0.651, p <  0.001) revealed that higher BMI, more frequent bicycle or stationary bike usage, and higher physical activity category were associated with improved VPAT performance with the bike desk, while higher vigorous MET minutes per week negatively impacted performance. This analysis suggests there are potentially numerous uncharacterized modulators of the impact of exercise on memory, warranting further research to identify and understand these factors.

Evaluating employee performance in smart work environment with focus on psychological distance and process versus outcome-centric approaches

Evaluating AI-based breastfeeding chatbots: quality, readability, and reliability analysis

Background In recent years, expectant and breastfeeding mothers commonly use various breastfeeding-related social media applications and websites to seek breastfeeding-related information. At the same time, AI-based chatbots-such as ChatGPT, Gemini, and Copilot-have become increasingly prevalent on these platforms (or on dedicated websites), providing automated, user-oriented breastfeeding guidance. Aim The goal of our study is to understand the relative performance of three AI-based chatbots: ChatGPT, Gemini, and Copilot, by evaluating the quality, reliability, readability, and similarity of the breastfeeding information they provide. Methods Two researchers evaluated the information provided by three different AI-based breastfeeding chatbots: ChatGPT version 3.5, Gemini, and Copilot. A total of 50 frequently asked questions about breastfeeding were identified and used in the study, divided into two categories (Baby-Centered Questions and Mother-Centered Questions), and evaluated using five scoring criteria, including the Quality Information Provision for Patients (EQIP) scale, the Simple Measure of Gobbledygook (SMOG) scale, the Similarity Index (SI), the Modified Dependability Scoring System (mDISCERN), and the Global Quality Scale (GQS). Results The evaluation of AI chatbots’ answers showed statistically significant differences across all criteria (p <  0.05). Copilot scored highest on the EQIP, SMOG, and SI scales, while Gemini excelled in mDISCERN and GQS evaluations. No significant difference was found between Copilot and Gemini for mDISCERN and GQS scores. All three chatbots demonstrated high reliability and quality, though their readability required university-level education. Notably, ChatGPT displayed high originality, while Copilot exhibited the greatest similarity in responses. Conclusion AI chatbots provide reliable answers to breastfeeding questions, but the information can be hard to understand. While more reliable than other online sources, their accuracy and usability are still in question. Further research is necessary to facilitate the integration of advanced AI in healthcare.

Comparative analysis of endoscopic discectomy for demanding lumbar disc herniation

Disparities in self-reported mental health, physical health, and substance use across sexual orientations in Canada

Background While prior studies have shown LGB individuals have elevated risk of poor mental health, poor physical health, and substance use, existing study designs may be improved by using representative samples, wider ranges of health outcomes, heterosexual comparison groups, and disaggregated data. The goal of this study is to provide estimates of multiple health disparities across sexual orientations in Canada based on these principles. Methods Using data from 2009-2014 Canadian Community Health Surveys, a sample of 19,980,000 weighted individuals was created. Outcomes included mental health, physical health, binge drinking, illicit drug use, and cannabis use. The study used logistic regression models adjusted by covariates, stratified by sex, to estimate health disparities across sexual orientations over time. Results Among LGB individuals, there was evidence for elevated risk of poor mental health (i.e. gay men, bisexual men, bisexual women), poor physical health (i.e. bisexual men, bisexual women), binge drinking (i.e. lesbians, bisexual women), illicit drug use (i.e. lesbians, bisexual women), and cannabis use (i.e. lesbians, bisexual women) relative to their heterosexual counterparts. Those identifying as ‘don’t know’ or ‘refuse’ showed reduced odds of substance use. Bisexual women exhibited highest disparities in health outcomes, e.g. OR=3.3, 95% 2.58 to 4.22 for poor mental health. Trends over time showed worsening mental health among bisexual women (relative to changes in heterosexual women), and decreasing substance use in gay and bisexual men, and lesbians. Conclusion This study highlights health disparities across sexual orientations in Canada, especially bisexual women, calling for targeted interventions (e.g. increased training of service providers in working with bisexual women and community outreach against biphobia). Future research should aim to explore these disparities longitudinally while also including the use of administrative-linked health data to reduce potential bias in self-reported data.

Prognostic factors for survival after allogeneic transplantation in acute myeloid leukemia in Iran using censored quantile regression model

Relative telomere length in dairy calves and dams undergoing two different methods of weaning and separation after three months of contact

Telomere length (i.e., the length of the repeated sequences of DNA at the end of chromosomes) is a promising indicator of overall stress. Our study aimed to compare the effects of a stress-inducing separation process between dams and their calves, with either a gradual or a nose-flap separation method after a three-months dam-calf contact since calving, on relative telomere length (RTL). Due to their nature, the nose-flap and gradual separation method have different effects on behaviour, stress hormone levels and physical development during and after dam-calf separation, which requires an overall measure of the weaning and separation stress during both procedures. We also investigated correlations between behavioural and other physiological stress indicators on RTL. We found no significant effect of the weaning and separation method on RTL in dairy calves after weaning and separation from their dams, but a tendency for shorter RTL in gradually separated dams compared to nose-flap separated dams. No correlations between behavioural and other physiological stress indicators and RTL were found, which may be due to a short interval between the two RTL measurement points. Future studies should aim to analyse the effect of various separation methods over a longer period and preferably include a non-separation group as reference.

Optimization of alumina leaching from microcline using particle swarm optimization and response surface methodology

Analysis of the Kuznets curve relationship between economic development and ecological environment in Aba Prefecture

Background The Yangtze and Yellow River basins play a critical role in China’s economic and social development as well as ecological security. As a key ecological barrier in the upper reaches of these rivers, Aba Prefecture has faced questions about whether its economic development after the 2008 earthquake has impacted its ecological environment. Objective This study uses the Environmental Kuznets Curve (EKC) to analyze the relationship and characteristics between per capita GDP and the ecological environment in Aba Prefecture from 2010 to 2021. The aim is to provide scientific evidence for promoting the coordinated development of the economy and environment. Methods The analysis employed the ADF test, Engle-Granger cointegration analysis, and correlation tests on the data. Finally, regression curve fitting was used to derive the relationship between per capita GDP and key environmental indicators in Aba Prefecture. Results Per capita GDP in Aba Prefecture is highly negatively correlated with industrial wastewater discharge, highly positively correlated with domestic sewage discharge, positively correlated with industrial solid waste generation, and uncorrelated with industrial dust emissions. There exists a long-term equilibrium relationship between per capita GDP and both industrial wastewater discharge and industrial solid waste generation, indicating mutual influence and co-variation over time. The EKC curve in Aba Prefecture does not fully align with the traditional EKC model. Specifically, the relationship between per capita GDP and industrial wastewater discharge exhibits an “N” shape, while the relationships with industrial solid waste generation and domestic sewage discharge are linear and upward. There is no relationship between per capita GDP and industrial dust emissions. Conclusion The relationship between the economy and the environment in Aba Prefecture exhibits a certain level of complexity. To achieve a long-term win-win outcome of both economic growth and environmental protection, it is essential to further deepen the transformation of the industrial structure, strengthen environmental governance measures, and optimize policy implementation.

Risk factors for non-responsiveness to 5-aminosalicylic acid in patients with ulcerative colitis identified using retrospective clinical study and Mendelian randomization

Finite element analysis of the lens profile during accommodation

The magnitude of zonular forces required to change the shape of the human lens while focusing at near; i.e., accommodating, is still under investigation. During accommodation, ciliary muscle contraction induces a large increase in lens central optical power (COP). Here we used finite element (FE) analysis to evaluate the correlation between zonular forces and lens surface curvatures, central thickness, COP, overall lens shape and longitudinal spherical aberration (LSA). Fresh isolated lenses from donors aged 20, 24, 26, and 30 years were the basis for the analyses. Lens nucleus elastic moduli were specified as equal to, 2, 3, 10, 20 and 30 times greater than its cortex. When equatorial zonular (Ez) force was increased in 3.125 x 10-6 N steps while the anterior zonular (Az) and posterior zonular (Pz) forces were decreased in 3.125 x 10-6 N steps, COP was evaluated. Independent of the increase in lens nuclear modulus, less than 0.02 N of Ez force was required to increase COP 10 diopters while Az and Pz forces were decreased. The lens peripheral surfaces flattened, central surfaces steepened, central lens thickness increased, COP increased and LSA shifted in the negative direction consistent with published in vivo accommodation studies. The minimal Ez force required to obtain 10 diopters of COP increase supports that increasing Ez force with decreasing Az and Pz force is the basis for the change in lens shape during accommodation. Since the COP increase was independent of increasing elastic modulus of the nucleus, stiffening of the lens nucleus is not the etiology of the universal age-related decline in accommodative amplitude that results in presbyopia in the fifth decade of life. Increased Ez zonular tension during accommodation has implications for the development and potential treatments of myopia, glaucoma, presbyopia, cortical cataracts and accommodative intraocular lens design.

Comprehensive understanding of context-specific functions of PHF2 in lipid metabolic tissues

Correction: Cross-reactive inhibitory antibody and memory B cell responses to variant strains of Duffy binding protein II at post-Plasmodium vivax infection

Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning

Abstract During the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were imposed all over Europe with the intent to reduce infection spread. However, reports on the effectiveness of those measures across different European countries are inconclusive up to now. Moreover, attempts to predict the effect of NPIs in a prospective and dynamical manner with the aim to support decision makers in future global health emergencies are largely lacking. Here, we explore causal machine learning to isolate causal effects of NPIs in observational public health data from seven EU countries, taking into account specific challenges like their sequential nature, effect heterogeneity, time-dependent confounding and lack of robustness due to violated assumptions. In a pseudo-prospective scenario planning analysis, we investigate which recommendations our model would have made during the second wave of the pandemic in Germany, demonstrating its capacity to generalize to the near future and identifying effective NPIs. In retrospect, our approach indicates that a wide range of response measures curbed COVID-19 across countries, especially in the early phases of the pandemic. Interestingly, this includes controversial interventions like strict school and border closures, but also recommendation-based policies in Sweden. Finally, we discuss important data- and modeling-related considerations that may optimize causal effect estimation in future pandemics.

Correction: Network analysis reveals abnormal functional brain circuitry in anxious dogs

Structural inequalities exacerbate infection disparities

Abstract During the COVID-19 pandemic, the world witnessed a disproportionate infection rate among marginalized and low-income groups. Despite empirical evidence suggesting that structural inequalities in society contribute to health disparities, there has been little attempt to offer a computational and theoretical explanation to establish its plausibility and quantitative impact. Here, we focus on two aspects of structural inequalities: wealth inequality and social segregation. Our computational model demonstrates that (a) due to the inequality in self-quarantine ability, the infection gap widens between the low-income and high-income groups, and the overall infected cases increase, (b) social segregation between different socioeconomic status (SES) groups intensifies the disease spreading rates, and (c) the second wave of infection can emerge due to a false sense of safety among the medium and high SES groups. By performing two data-driven analyses, one on the empirical network and economic data of 404 metropolitan areas of the United States and one on the daily Covid-19 data of the City of Chicago, we verify that higher segregation leads to an increase in the overall infection cases and higher infection inequality across different ethnic/socioeconomic groups. These findings together demonstrate that reducing structural inequalities not only helps decrease health disparities but also reduces the spread of infectious diseases overall.

Large language models can consistently generate high-quality content for election disinformation operations

Advances in large language models have raised concerns about their potential use in generating compelling election disinformation at scale. This study presents a two-part investigation into the capabilities of LLMs to automate stages of an election disinformation operation. First, we introduce DisElect, a novel evaluation dataset designed to measure LLM compliance with instructions to generate content for an election disinformation operation in localised UK context, containing 2,200 malicious prompts and 50 benign prompts. Using DisElect, we test 13 LLMs and find that most models broadly comply with these requests; we also find that the few models which refuse malicious prompts also refuse benign election-related prompts, and are more likely to refuse to generate content from a right-wing perspective. Secondly, we conduct a series of experiments (N = 2 , 340) to assess the “humanness” of LLMs: the extent to which disinformation operation content generated by an LLM is able to pass as human-written. Our experiments suggest that almost all LLMs tested released since 2022 produce election disinformation operation content indiscernible by human evaluators over 50% of the time. Notably, we observe that multiple models achieve above-human levels of humanness. Taken together, these findings suggest that current LLMs can be used to generate high-quality content for election disinformation operations, even in hyperlocalised scenarios, at far lower costs than traditional methods, and offer researchers and policymakers an empirical benchmark for the measurement and evaluation of these capabilities in current and future models.