Tree-Inspired Structurally Graded Aerogel with Synergistic Water, Salt, and Thermal Transport for High-Salinity Solar-Powered Evaporation.

Solar-powered interfacial evaporation is an energy-efficient solution for water scarcity. It requires solar absorbers to facilitate upward water transport and limit the heat to the surface for efficient evaporation. Furthermore, downward salt ion transport is also desired to prevent salt accumulation. However, achieving simultaneously fast water uptake, downward salt transport, and heat localization is challenging due to highly coupled water, mass, and thermal transport. Here, we develop a structurally graded aerogel inspired by tree transport systems to collectively optimize water, salt, and thermal transport. The arched aerogel features root-like, fan-shaped microchannels for rapid water uptake and downward salt diffusion, and horizontally aligned pores near the surface for heat localization through maximizing solar absorption and minimizing conductive heat loss. These structural characteristics gave rise to consistent evaporation rates of 2.09 kg m-2 h-1 under one-sun illumination in a 3.5 wt% NaCl solution for 7 days without degradation. Even in a high-salinity solution of 20 wt% NaCl, the evaporation rates maintained stable at 1.94 kg m-2 h-1 for 8 h without salt crystal formation. This work offers a novel microstructural design to address the complex interplay of water, salt, and thermal transport.

Modeling Fertilization Outcome in a Changing World.

Marine organisms have complex life histories. For broadcast spawners, successful continuation of the population requires their small gametes to make contact in the water column for sufficiently long periods for fertilization to occur. Anthropogenic climate change has been shown to impact fertilization success in various marine invertebrates, including sea urchins which are key grazers in their habitats. Gamete performance of both sexes declined when exposed to elevated temperature and/or pCO2 levels. Examples of reduced performance included slower sperm swimming speed and thinning egg jelly coat. However, such responses to climate change stress were not uniform between individuals. Such variations could serve as the basis for selection. Fertilization kinetics has long been modeled as a particle collision process. Here, we present a modified fertilization kinetics model that incorporates individual variations in performance in a more environmentally-relevant regime, and which the performance of groups with different traits can be separately tracked in a mixture. Numerical simulations highlight that fertilization outcome is influenced by changes in gametes traits as they age in sea water and the presence of competition groups (multiple dams or sires). These results highlight the importance of considering multiple individuals and at multiple time points during in-vivo assays. We also applied our model to show that interspecific variation in climate stress vulnerabilities elevates the risk of hybridization. By making a numerical model open-source, we aim to help us better understand the fate of organisms in the face of climate change by enabling the community to consider the mean and variance of the response to capture adaptive potential.

The modulation effect of cognition on mentalization in late-life depression: a study of gaze perception-a potential screening tool for high-risk group of late-life depression.

Introduction: Impairment in mentalization is implicated in the development and maintenance of depression. Major depressive disorders showed significant impairment in social cognition and such impairment appears to be positively associated with the severity of depression. Self-referential gaze perception (SRGP), a measurement of mentalization, was predominantly measured in patients with psychosis but rarely examined in late-life depression (LLD). Methods: To assess the effect of cognition on the interpretation bias of mentalization, 29 LLD patients and 29 healthy controls were asked to judge if various gaze directions were directed to self in SRGP. Results: Patients with better cognition showed less unambiguous-SRGP bias than those with worse cognitive scores; this difference was not found in healthy controls. Global cognition and executive function contributed to the SRGP rate in patients. Conclusion: The current study is the first study to explore the relationship between cognition and SRGP in the LLD population. Our study findings suggested that the cognitive function of LLD patients may contribute to the modulation of interpretation bias, which in turn underlie the role of SRGP bias. Greater SRGP bias in patients may reflect social cognition deterioration, impairing the social interaction and functioning of LLD patients. This highlights the need for early intervention and cognitive decline identification to facilitate better prognosis and treatment effectiveness; thus, further studies could navigate the potential of SRGP task as a screening tool for high-risk group of LLD likely to develop dementia.

Comprehensive identification of pathogenic microbes and antimicrobial resistance genes in food products using nanopore sequencing-based metagenomics.

Foodborne pathogens, particularly antimicrobial-resistant (AMR) bacteria, remain a significant threat to global health. Given the limitations of conventional culture-based approaches, which are limited in scope and time-consuming, metagenomic sequencing of food products emerges as a promising solution. This method provides a fast and comprehensive way to detect the presence of pathogenic microbes and antimicrobial resistance genes (ARGs). Notably, nanopore long-read sequencing provides more accurate bacterial taxonomic classification in comparison to short-read sequencing. Here, we revealed the impact of food types and attributes (origin, retail place, and food processing methods) on microbial communities and the AMR profile using nanopore metagenomic sequencing. We analyzed a total of 260 food products, including raw meat, sashimi, and ready-to-eat (RTE) vegetables. Clostridium botulinum, Acinetobacter baumannii, and Vibrio parahaemolyticus were identified as the top three foodborne pathogens in raw meat and sashimi. Importantly, even with low pathogen abundance, higher percentages of samples containing carbapenem and cephalosporin resistance genes were identified in chicken and RTE vegetables, respectively. In parallel, our results demonstrated that fresh, peeled, and minced foods exhibited higher levels of pathogenic bacteria. In conclusion, this comprehensive study offers invaluable data that can contribute to food safety assessments and serve as a basis for quality indicators.

Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.

Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detection, but their clinical applicability remains underexplored. This study aimed to identify linguistic features associated with suicidal ideation and develop ML models for detection. NLP techniques were applied to clinical interview transcripts (n = 319) to extract relevant features, including four cases of FPSP (subjective, objective, dative, and possessive cases) and first-person plural pronouns (FPPPs). Logistic regression analyses were conducted for each linguistic feature, controlling for age, gender, and depression. Gradient boosting, support vector machine, random forest, decision tree, and logistic regression were trained and evaluated. Results indicated that all four cases of FPSPs were associated with depression (p < 0.05) but only the use of objective FPSPs was significantly associated with suicidal ideation (p = 0.02). Logistic regression and support vector machine models successfully detected suicidal ideation, achieving an area under the curve (AUC) of 0.57 (p < 0.05). In conclusion, FPSPs identified during clinical interviews might be a promising indicator of suicidal ideation in Chinese patients. ML algorithms might have the potential to aid clinicians in improving the detection of suicidal ideation in clinical settings.

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features.

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

Setting research priorities for global pandemic preparedness: An international consensus and comparison with ChatGPT's output.

Background: In this priority-setting exercise, we sought to identify leading research priorities needed for strengthening future pandemic preparedness and response across countries. Methods: The International Society of Global Health (ISoGH) used the Child Health and Nutrition Research Initiative (CHNRI) method to identify research priorities for future pandemic preparedness. Eighty experts in global health, translational and clinical research identified 163 research ideas, of which 42 experts then scored based on five pre-defined criteria. We calculated intermediate criterion-specific scores and overall research priority scores from the mean of individual scores for each research idea. We used a bootstrap (n = 1000) to compute the 95% confidence intervals. Results: Key priorities included strengthening health systems, rapid vaccine and treatment production, improving international cooperation, and enhancing surveillance efficiency. Other priorities included learning from the coronavirus disease 2019 (COVID-19) pandemic, managing supply chains, identifying planning gaps, and promoting equitable interventions. We compared this CHNRI-based outcome with the 14 research priorities generated and ranked by ChatGPT, encountering both striking similarities and clear differences. Conclusions: Priority setting processes based on human crowdsourcing - such as the CHNRI method - and the output provided by ChatGPT are both valuable, as they complement and strengthen each other. The priorities identified by ChatGPT were more grounded in theory, while those identified by CHNRI were guided by recent practical experiences. Addressing these priorities, along with improvements in health planning, equitable community-based interventions, and the capacity of primary health care, is vital for better pandemic preparedness and response in many settings.

Interplay of Ferroptosis and Cuproptosis in Cancer: Dissecting Metal-Driven Mechanisms for Therapeutic Potentials.

Iron (Fe) and copper (Cu), essential transition metals, play pivotal roles in various cellular processes critical to cancer biology, including cell proliferation, mitochondrial respiration, distant metastases, and oxidative stress. The emergence of ferroptosis and cuproptosis as distinct forms of non-apoptotic cell death has heightened their significance, particularly in connection with these metal ions. While initially studied separately, recent evidence underscores the interdependence of ferroptosis and cuproptosis. Studies reveal a link between mitochondrial copper accumulation and ferroptosis induction. This interconnected relationship presents a promising strategy, especially for addressing refractory cancers marked by drug tolerance. Harnessing the toxicity of iron and copper in clinical settings becomes crucial. Simultaneous targeting of ferroptosis and cuproptosis, exemplified by the combination of sorafenib and elesclomol-Cu, represents an intriguing approach. Strategies targeting mitochondria further enhance the precision of these approaches, providing hope for improving treatment outcomes of drug-resistant cancers. Moreover, the combination of iron chelators and copper-lowering agents with established therapeutic modalities exhibits a synergy that holds promise for the augmentation of anti-tumor efficacy in various malignancies. This review elaborates on the complex interplay between ferroptosis and cuproptosis, including their underlying mechanisms, and explores their potential as druggable targets in both cancer research and clinical settings.

Online Modules to Alleviate Burnout and Related Symptoms Among Interdisciplinary Staff in Long-Term Care: A Pre-post Feasibility Study.

BACKGROUND: The rising trend of providing palliative care to residents in Canadian long-term care facilities places additional demands on care staff, increasing their risk of burnout. Interventions and strategies to alleviate burnout are needed to reduce its impact on quality of patient care and overall functioning of healthcare organizations. AIM: To examine the feasibility of implementing online modules with the primary goal of determining recruitment and retention rates, completion time and satisfaction with the modules. A secondary goal was to describe changes in burnout and related symptoms associated with completing the modules. SETTING: This single-arm, nonrandomized feasibility study was conducted in five long-term care sites of a publicly-funded healthcare organization in Vancouver, British Columbia, Canada. Eligible participants were clinical staff who worked at least 1 day per month. RESULTS: A total of 103 study participants consented to participate, 31 (30.1%) of whom were lost to follow-up. Of the remaining 72 participants, 64 (88.9%) completed the modules and all questionnaires. Most participants completed the modules in an hour (89%) and found them easy to understand (98%), engaging (84%), and useful (89%). Mean scores on burnout and secondary traumatic stress decreased by .9 (95% CI: .1-1.8; d = .3) and 1.4 (95% CI: .4-2.4; d = .4), respectively; mean scores on compassion satisfaction were virtually unchanged. CONCLUSIONS: Modules that teach strategies to reduce burnout among staff in long-term care are feasible to deliver and have the potential to reduce burnout and related symptoms. Randomized controlled trials are needed to assess effectiveness and longer-term impact.

Evaluation of a Low-Intensity Online Intervention (LiON) for Reducing Distress and Mental Health Symptoms in Young People.

PURPOSE: Enhancing young people's mental health is crucial given that most adult mental disorders develop before age 24 years. However, it is unclear whether low-intensity interventions delivered online can be effective. This study aimed to provide preliminary evidence on whether a low-intensity online intervention (LiON) can effectively lower young people's distress levels and mental health symptoms. METHODS: We compared the preintervention and postintervention changes in distress level and severity of depression and anxiety symptoms in 137 young people aged 15-24 years who used the LiON service with the three-month changes in a 1:1 propensity score-matched control group of community young people who did not use the service. They participated in one of the following modules for the first time: (1) sleep and relaxation, (2) stress-coping, and (3) problem-solving. RESULTS: Participants who received LiON intervention (mean age 22.88 [standard deviation 3.67] years, 65.7% female) showed significantly greater reductions in distress level (Cohen's f2: 0.079), as well as the severity of depressive symptoms (Cohen's f2: 0.056) and anxiety symptoms (Cohen's f2: 0.044) compared to the control group. DISCUSSION: The findings suggest that the LiON intervention has the potential to effectively reduce distress and mental health symptoms in young people. Future research should aim to confirm these findings through randomized controlled trials and explore the cost-effectiveness of the intervention.

Stigma towards mental illness in Asian nations and low-and-middle-income countries, and comparison with high-income countries: A literature review and practice implications.

Background: Stigma related to mental illness (and its treatment) is prevalent worldwide. This stigma could be at the structural or organizational level, societal level (interpersonal stigma), and the individual level (internalized stigma). Vulnerable populations, for example, gender minorities, children, adolescents, and geriatric populations, are more prone to stigma. The magnitude of stigma and its negative influence is determined by socio-cultural factors and macro (mental health policies, programs) or micro-level factors (societal views, health sectors, or individuals' attitudes towards mentally ill persons). Mental health stigma is associated with more serious psychological problems among the victims, reduced access to mental health care, poor adherence to treatment, and unfavorable outcomes. Although various nationwide and well-established anti-stigma interventions/campaigns exist in high-income countries (HICs) with favorable outcomes, a comprehensive synthesis of literature from the Low- and Middle-Income Countries (LMICs), more so from the Asian continent is lacking. The lack of such literature impedes growth in stigma-related research, including developing anti-stigma interventions. Aim: To synthesize the available mental health stigma literature from Asia and LMICs and compare them on the mental health stigma, anti-stigma interventions, and the effectiveness of such interventions from HICs. Materials and Methods: PubMed and Google Scholar databases were screened using the following search terms: stigma, prejudice, discrimination, stereotype, perceived stigma, associate stigma (for Stigma), mental health, mental illness, mental disorder psychiatric* (for mental health), and low-and-middle-income countries, LMICs, High-income countries, and Asia, South Asian Association for Regional Cooperation/SAARC (for countries of interest). Bibliographic and grey literature were also performed to obtain the relevant records. Results: The anti-stigma interventions in Asia nations and LMICs are generalized (vs. disorder specific), population-based (vs. specific groups, such as patients, caregivers, and health professionals), mostly educative (vs. contact-based or attitude and behavioral-based programs), and lacking in long-term effectiveness data. Government, international/national bodies, professional organizations, and mental health professionals can play a crucial in addressing mental health stigma. Conclusion: There is a need for a multi-modal intervention and multi-sectoral coordination to mitigate the mental health stigma. Greater research (nationwide surveys, cultural determinants of stigma, culture-specific anti-stigma interventions) in this area is required.

Improving Text-Independent Forced Alignment to Support Speech-Language Pathologists with Phonetic Transcription.

Problem: Phonetic transcription is crucial in diagnosing speech sound disorders (SSDs) but is susceptible to transcriber experience and perceptual bias. Current forced alignment (FA) tools, which annotate audio files to determine spoken content and its placement, often require manual transcription, limiting their effectiveness. Method: We introduce a novel, text-independent forced alignment model that autonomously recognises individual phonemes and their boundaries, addressing these limitations. Our approach leverages an advanced, pre-trained wav2vec 2.0 model to segment speech into tokens and recognise them automatically. To accurately identify phoneme boundaries, we utilise an unsupervised segmentation tool, UnsupSeg. Labelling of segments employs nearest-neighbour classification with wav2vec 2.0 labels, before connectionist temporal classification (CTC) collapse, determining class labels based on maximum overlap. Additional post-processing, including overfitting cleaning and voice activity detection, is implemented to enhance segmentation. Results: We benchmarked our model against existing methods using the TIMIT dataset for normal speakers and, for the first time, evaluated its performance on the TORGO dataset containing SSD speakers. Our model demonstrated competitive performance, achieving a harmonic mean score of 76.88% on TIMIT and 70.31% on TORGO. Implications: This research presents a significant advancement in the assessment and diagnosis of SSDs, offering a more objective and less biased approach than traditional methods. Our model's effectiveness, particularly with SSD speakers, opens new avenues for research and clinical application in speech pathology.

Hydroponics: current trends in sustainable crop production.

The combination of Hydroponics with smart technology in farming is novel and has promise as a method for effective and environmentally friendly crop production. This technology eliminates the need for soil and reduces water usage by providing nutrients straight to the plant's roots. The Internet of Things (IoT), sensors, and automation are all used in "smart farming," which allows for constant monitoring of soil conditions, nutrient levels, and plant vitality to facilitate fine-grained management and optimization. The technology-driven strategy improves crop output, quickens growth rates, and keeps conditions ideal all year round regardless of weather or other environmental circumstances. In addition, smart farming lessens the need for organic chemical inputs, promotes environmentally safe methods of pest management, and minimizes the amount of waste produced. This ground-breaking strategy may significantly alter the agricultural sector by encouraging regionalized food production, enhancing food security, and adding to more resilient farming practices. This comprehensive review delves into current trends in Hydroponics, highlighting recent advancements in smart farming systems, such as Domotics, Data Acquisition, Remote Cultivation, and automated AI systems. The review also underscores the various types and advantages of smart farming hydroponic technology, emphasizing the requirements for achieving efficiency in this innovative domain. Additionally, it explores future goals and potential developments, paving the way for further advancements in hydroponic smart farming.

Bridging the gap: Using CHNRI to align migration health research priorities in India with local expertise and global perspectives.

Background: Migration and health are increasingly recognised as a global public health priority, but concerns have been raised on the skewed nature of current research and the potential disconnect between health needs and policy and governance responses. The Migration Health South Asia (MiHSA) network led the first systematic research priority-setting exercise for India, aligned with the global call to develop a clearly defined migration health research agenda that will inform research investments and guide migrant-responsive policies by the year 2030. Methods: We adapted the Child Health and Nutrition Research Initiative (CHNRI) method for this priority setting exercise for migration health. Guided by advisory groups established at international and country levels, we sought research topics from 51 experts from diverse disciplines and sectors across India. We consolidated 223 responses into 59 research topics across five themes and scored them against five predefined criteria: answerability, effectiveness, feasibility, impact, and effect on equity. We then calculated research priority scores (RPS) and average expert agreement (AEA) each research topic and theme. Results: A third of the 59 research topics were on migrants' health and health care access, 12 on social determinants of migrants' health, 10 on policies, law and migration health governance, eight on health systems' responsiveness, and five on migration health discourse. Three of the top five priority topics pertained to migrants' health care access. The policies, law, and governance theme had the highest overall RPS score. Conclusions: There is a noticeable gap between research priorities identified by experts at the country-level and the current research focus and priorities set globally. This disconnect between the global and local perspectives in migration health scholarship hinders the development of context-specific and suitable policy agendas for improving migrants' health. Our co-developed agenda emphasises the need to prioritise research on the capacity of existing systems and policies so as to make them more migration-aware and responsive to migrants' health needs.

Parentage influence on gene expression under acidification revealed through single-embryo sequencing.

The dissolution of anthropogenic carbon dioxide (CO2 ) in seawater has altered its carbonate chemistry in the process of ocean acidification (OA). OA affects the viability of marine species. In particular, calcifying organisms and their early planktonic larval stages are considered vulnerable. These organisms often utilize energy reserves for metabolism rather than growth and calcification as supported by bulk RNA-sequencing (RNA-seq) experiments. Yet, transcriptomic profiling of a bulk sample reflects the average gene expression of the population, neglecting the variations between individuals, which forms the basis for natural selection. Here, we used single-embryo RNA-seq on larval sea urchin Heliocidaris crassispina, which is a commercially and ecologically valuable species in East Asia, to document gene expression changes to OA at an individual and family level. Three paternal half-sibs groups were fertilized and exposed to 3 pH conditions (ambient pH 8.0, 7.7 and 7.4) for 12 h prior to sequencing and oxygen consumption assay. The resulting transcriptomic profile of all embryos can be distinguished into four clusters, with differences in gene expressions that govern biomineralization, cell differentiation and patterning, as well as metabolism. While these responses were influenced by pH conditions, the male identities also had an effect. Specifically, a regression model and goodness of fit tests indicated a significant interaction between sire and pH on the probability of embryo membership in different clusters of gene expression. The single-embryo RNA-seq approach is promising in climate stressor research because not only does it highlight potential impacts before phenotypic changes were observed, but it also highlights variations between individuals and lineages, thus enabling a better determination of evolutionary potential.

Rates and correlates of medication non-adherence behaviors and attitudes in adult patients with early psychosis.

PURPOSE: Literature on antipsychotic medication adherence in early psychosis primarily assessed adherence behaviors and focused on young patients. There is a paucity of research investigating negative medication attitude and its determinants in the early illness course, particularly in older-aged sample. We aimed to examine prevalence and correlates of medication non-adherence behaviors and negative medication attitudes separately in adult patients with early psychosis. METHODS: One hundred ninety-nine Chinese early psychosis patients aged 26-55 years who had received three-year treatment for first psychotic episode in Hong Kong were examined. Assessments encompassing socio-demographics, premorbid adjustment, clinical and treatment profiles, self-stigma and therapeutic alliance were conducted. Patients were evaluated with Medication Compliance Questionnaire, which is a modified Chinese-translated version of Medication Adherence Rating Scale and includes items measuring adherence behaviors and attitudes towards medications. RESULTS: Rates of medication non-adherence and negative attitude towards medications were 38.7% and 50.8%, respectively. Multivariate regression analysis showed that more severe positive symptoms, greater self-stigma and negative medication attitude were independently associated with medication non-adherence. Negative attitude towards medications was significantly associated with younger age, higher educational attainment, diagnosis of other psychotic disorders, poorer insight, greater self-stigma and less satisfaction with communication with healthcare staff, which represented an index reflecting suboptimal therapeutic alliance. CONCLUSION: Antipsychotic non-adherence and negative medication attitudes are frequently observed in adult early psychosis patients. Our findings indicate that poor insight, elevated self-stigma and suboptimal therapeutic alliance may constitute potential treatment targets for promoting medication adherence and rectifying negative medication attitudes in the early illness stage.

A systematic review and meta-analysis of the effectiveness of hypertension interventions in faith-based organisation settings.

Background: Hypertension is the global, leading cause of mortality and is the main risk factor for cardiovascular disease. Community-based partnerships can provide cost-saving ways of delivering effective blood pressure (BP) interventions to people in resource-poor settings. Faith-based organisations (FBOs) prove important potential health partners, given their reach and community standing. This potential is especially strong in hard-to-reach, socio-economically marginalised communities. This systematic review explores the state of the evidence of FBO-based interventions on BP management, with a focus on randomised controlled trials (RCTs) and cluster RCTs (C-RCTs). Methods: Seven academic databases (English = 5, Chinese = 2) and grey literature were searched for C-/RCTs of community-based interventions in FBO settings. Only studies with pre- and post-intervention BP measures were kept for analysis. Random effects models were developed using restricted maximum likelihood estimation (REML) to estimate the population average mean change and 95% confidence interval (CI) of both systolic and diastolic blood pressure (SBP and DBP). The overall heterogeneity was assessed by successively adding studies and recording changes in heterogeneity. Prediction intervals were generated to capture the spread of the pooled effect across study settings. Results: Of the 19 055 titles identified, only 11 studies of fair to good quality were kept for meta-analysis. Non-significant, average mean differences between baseline and follow-up for the intervention and control groups were found for both SBP (0.78 mm of mercury (mmHg) (95% CI = 2.11-0.55)) and DBP (-0.20 mm Hg (95% CI = -1.16 to 0.75)). Subgroup analysis revealed a significant reduction in SBP of -6.23 mm Hg (95% CI = -11.21 to -1.25) for populations with mean baseline SBP of ≥140 mm Hg. Conclusions: The results support the potential of FBO-based interventions in lowering SBP in clinically hypertensive populations. However, the limited evidence was concentrated primarily in Christian communities in the US More research is needed to understand the implications of such interventions in producing clinically meaningful long-term effects in a variety of settings. Further research can illuminate factors that affect success and potential expansion to sites outside the US as well as non-Christian FBOs. Current evidence is inadequate to evaluate the potential of FBO-based interventions in preventing hypertension in non-hypertensive populations. Intervention effects in non-hypertensive population might be better reflected through intermediate outcomes.

The Whole Is Greater Than The Sum of Its Parts: Large-scale Phenomena Arising from Small-Scale Biophysical Processes.

The symposium "Large-scale biological phenomena arising from small-scale biophysical processes" at the SICB 2023 Annual General Meeting focused on the cross-disciplinary exploration of emergent phenomena in biology. Interactions between cells or organisms at small scales within a system can govern patterns occurring at larger scales in space, time, or biological complexity. This theme recurs in many sub-disciplines of biology, including cell and developmental biology, evolution, and ecology. This symposium, and the associated special issue introduced here, showcases a wide range of cross-disciplinary collaborations among biologists, physicists, and engineers. Technological advancements in microscopy and microfluidics, as well as complementary advances in mathematical modelling and associated theory demonstrate the timeliness of this issue. This introduction seeks to provide useful background information to place the studies within this issue in a broader biophysical context and highlight similarities in ideas and approaches across systems and sub-disciplines. We hope to demonstrate that cross-disciplinary research linking small-scale biophysics to larger-scale emergent phenomena can help us understand problems ranging from single-cell behaviors to tissue formation and function, evolution of form, and the dynamics of communities.

Towards a global partnership model in interprofessional education for cross-sector problem-solving.

OBJECTIVES: A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. METHODS: This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students' data. RESULTS: We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest-posttest differences in students' readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students' social interaction anxiety after the IPE simulation. CONCLUSIONS: The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education.

Effect of pH on the Early Development of the Biofouling Ascidian Ciona robusta.

Ocean acidification (OA) impacts the survival, fertilization, and community structure of marine organisms across the world. However, some populations or species are considered more resilient than others, such as those that are invasive, globally distributed, or biofouling. Here, we tested this assumption by investigating the effect of pH on the larval development of one such tunicate, Ciona robusta, which is currently exposed to a wide range of pH levels. Consistent with our hypothesis, C. robusta larvae developed and metamorphosed at a rate comparable to control (pH 8.0) at modest near-future conditions (pH 7.7) over a 58-hour period. However, development was stunted at the extreme low pH of 6.8 such that no embryo progressed beyond late cleavage after 58 hours. Interestingly, piecewise regression of the proportion of embryos at the most advanced stage at a given time point against pH identified a breakpoint with the highest pH (~pH 7.6) at around hatching. The variation in breakpoint pH throughout ontogeny highlighted that the sensitivity to decreasing pH differs significantly between developmental stages. More broadly, our results show that even a cosmopolitan, biofouling, invasive species could be negatively impacted by decreasing pH.