Intervention strategies to prevent mental health problems and improve resilience in employed parents from conception until the child is 5 years of age: a scoping review

Aim: To understand the extent and type of evidence in relation to the effectiveness of intervention strategies targeting working pregnant women, and their partners, for the prevention of mental health problems (depression, anxiety) and improving resilience, from conception until the child is 5 years of age. Methods: A scoping review was conducted searching Pubmed (including Medline), Embase, Web of Science Core Collection and Scopus. Inclusion criteria were based on population (employed parents), context (from -9 months to 5 years postpartum) and concept (mental health problems, resilience and prevention/ preventative interventions). Results: Of the 17,699 papers screened, 3 full text papers were included. Studies focused on intervention strategies for working parents which showed a relationship with a reduction in mental health problems (depression and/or anxiety). The intervention strategies extracted from the literature referred to ‘social support’. Social support provided by both the social and the work environment correlated with prenatal stress and depressive symptoms in the postpartum period, and supports a healthy work-family balance. Conclusion: Social support seems to have a positive association with the reduction of mental health problems. However, there are still important gaps in the literature such as a lack of RCT designs to test effectiveness of interventions and systematic reviews. Findings from this study may provide a roadmap for future research to close these gaps in knowledge

Reinforcement Learning-Based Time-Slotted Protocol: A Reinforcement Learning Approach for Optimizing Long-Range Network Scalability

The Internet of Things (IoT) is revolutionizing communication by connecting everyday objects to the Internet, enabling data exchange and automation. Low-Power Wide-Area networks (LPWANs) provide a wireless communication solution optimized for long-range, low-power IoT devices. LoRa is a prominent LPWAN technology; its ability to provide long-range, low-power wireless connectivity makes it ideal for IoT applications that cover large areas or where battery life is critical. Despite its advantages, LoRa uses a random access mode, which makes it susceptible to increased collisions as the network expands. In addition, the scalability of LoRa is affected by the distribution of its transmission parameters. This paper introduces a Reinforcement Learning-based Time-Slotted (RL-TS) LoRa protocol that incorporates a mechanism for distributing transmission parameters. It leverages a reinforcement learning algorithm, enabling nodes to autonomously select their time slots, thereby optimizing the allocation of transmission parameters and TDMA slots. To evaluate the effectiveness of our approach, we conduct simulations to assess the convergence speed of the reinforcement learning algorithm, as well as its impact on throughput and packet delivery ratio (PDR). The results demonstrate significant improvements, with PDR increasing from 0.45–0.85 in LoRa to 0.88–0.97 in RL-TS, and throughput rising from 80–150 packets to 156–172 packets. Additionally, RL-TS achieves 82% reduction in collisions compared to LoRa, highlighting its effectiveness in enhancing network performance. Moreover, a detailed comparison with conventional LoRa and other existing protocols is provided, highlighting the advantages of the proposed method

Robust inference for the unification of confidence intervals in meta-analysis

Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from each study could make the final estimation unreliable. In the situation when K is small, it is not realistic to assume the random effect follows Gaussian distribution. In this paper, we present a novel empirical likelihood method for combining confidence intervals under the meta-analysis framework. This method is free of the Gaussian assumption in effect size estimates from individual studies and from the random effects. We establish the large sample properties of the nonparametric estimator and introduce a criterion governing the relationship between the number of studies, K, and the sample size of each study, (Formula presented.). Our methodology supersedes conventional meta-analysis techniques in both theoretical robustness and computational efficiency. We assess the performance of our proposed methods using simulation studies and apply our proposed methods to two examples

A Critical Exploration of Social Transformation Through Occupation

Background: In occupational therapy and occupational science, scholars and practitioners advocate for a stronger focus on social transformation. However, there is limited evidence on how occupation-based interventions contribute to addressing inequalities and fostering change for marginalised populations. Methods: This study applied critical occupational science and realist theoretical frameworks to examine the use of arts-based occupations for social transformation. It comprised three strands: (1) a narrative review of arts-based occupations in social change, (2) a realist review of participatory photography, and (3) a realist evaluation of a socially engaged art group in primary schools. Findings: Arts-based occupations have been employed by grassroots activists and researchers to drive social change, but their impact is often ameliorative at the meso level rather than transformative at the structural level. Participation in these interventions carries risks, including the reinforcement of injustices. A refined theory of change in participatory photography was developed, identifying key mechanisms such as psychological safety and perception of burden, which interact with contextual factors to produce both intended and unintended outcomes. The realist evaluation generated 10 programme theories explaining how and why participation in the art group was initiated and sustained, emphasising the role of perceived value and trusting relationships in enabling engagement with occupations otherwise inaccessible to seldom- heard communities. The research culminated in the ‘ReSTART’ conceptual platform, outlining key processes in effective social transformation through arts-based occupations. Conclusion: Achieving social transformation through arts-based interventions is complex and demands advanced skills from practitioners. Adoption of the ReSTART platform will support occupational therapists in socially transformative practice. In addition, to fulfil the Royal College of Occupational Therapists\u27 vision of occupational therapists as changemakers in population health, enhanced education and workforce development are essential

A five-year retrospective analysis (2017-2022) of reported incidents from a primary care-based education provider

Background Patient safety incident reporting and analysis are often confined to secondary care, despite 95% of dentistry occurring in primary care. Peninsula Dental Social Enterprise (PDSE) delivers primary care dentistry in education-based settings and uses a report-review-action process to underpin its patient safety framework. Aim This article analyses trends in clinical incident data, reflecting on learning to improve overall patient safety. Methods A retrospective observational study was employed to analyse incidents over a five-year period (2017-2022) using anonymised data from the PDSE reporting system. Results Over the five-year reporting period, there were an average of 13.1 total incidents per 1,000 appointments. Sub-analysis of reported incidents revealed 1.5 clinical incidents and 0.9 ‘near miss\u27 incidents. A soft-tissue injury rate of 0.6, a contamination injury rate of 0.9, and 0.3 written complaints were reported per 1,000 appointments. Conclusion Patient safety is a key component of quality dental care, especially when delivering clinical dental education. PDSE fosters an environment of transparency, enabling the provider to monitor incidents and learn from them. This results in systems improvements sitting at the heart of the clinical service. With a lack of data published from similar settings, comparison to the sector is limited. Further sharing of data is encouraged to enable standardisation and quality benchmarking

Deficits in Motion and Form Perception in Infantile Nystagmus Syndrome

Purpose: Visual deficits in infantile nystagmus syndrome (INS) could be a result of retinal blur from excessive eye movements and/or cortical changes from visual deprivation. We measured global motion and form sensitivity in INS to compare deficits between motion and form perception and to decipher the role of internal noise (local deficit such as eye movement) and sampling efficiency (global cortical deficit).Methods: A total of 30 participants (14.40 ± 4.83 years) with INS and 30 age-matched controls discriminated the direction of motion and orientation of physically equivalent translational random dot kinematograms (RDKs) and Glass patterns. Both stimuli consisted of 240 black dots (RDKs) and 120 pairs of dipoles (Glass patterns) with a display duration of 1.0 second. Two experimental paradigms were employed: coherence threshold (random noise) and equivalent noise (at five external noise levels).Results: The mean motion coherence thresholds at 5°/s and 10°/s were higher in INS (50.55% ± 21.33% and 31.87% ± 14.69% respectively) compared to controls (24.04% ± 13.22% and 20.65% ± 12.89, respectively) (P \u3c 0.01). The mean orientation coherence thresholds were also higher in INS (12.23% ± 0.32% vs. 7.88% ± 0.33%; P \u3c 0.01). For the equivalent noise paradigm, thresholds were higher for INS at no noise and 3 lower noise levels (P \u3c 0.01), but similar at the highest noise level (P \u3e 0.01). Higher internal noise best explained the difference in performance between INS and controls for both motion and form (P \u3e 0.05).Conclusions: INS results in lower sensitivity to both motion and form perception. These deficits are due to higher internal noise, which could arise from early areas of visual processing such as primary visual cortex as a result of abnormal eye movement or effect of early visual deprivation

Impact of growth conditions on the abundance and diversity of cultivable bacteria recovered from Pheronema carpenteri and investigation of their antimicrobial potential

The deep sea is a largely unexplored extreme environment supporting a diverse biological community adapted to low temperatures and high pressures. Such environments support microbial life that may be a source of novel antibiotics and other drugs. Whilst this is often the case, many species with bioactive capabilities may be missed with traditional culturing methods. In this study, a total of 16 different concentrations and types of media were employed, to culture 389 bacterial isolates using Dilution to Extinction methods and Actinobacteria Directed Cultivation techniques. This generated 72 (18.6%) isolates with narrow and broad-spectrum activity against ESKAPE pathogens including Escherichia coli (E. coli), methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococci. We also report that an early-stage ‘One Strain Many Compounds’ approach can reveal a greater number of bioactive isolates that otherwise would have been missed; 12 isolates initially deemed ‘inactive’ were seen to exhibit activity towards S. aureus and/or E. coli. We emphasize the importance of a thorough initial screening method to capture bioactive isolates and show how selecting only morphologically distinct isolates for screening may result in species with promising bioactivity being overlooked. Our findings justify on-going investigation of Pheronema sponges for bioactive microbiota

Global manta and devil ray population declines: closing policy and management gaps to reduce fisheries mortality

Significant progress has been made in advancing priority actions to conserve manta and devil rays, yet implementation and enforcement of protective measures often fall short, leaving most mobulid populations at risk of overfishing. Drawing on a literature review, fisheries databases, agency reports, and expert interviews, we assess global trends in mobulid catch and mortality. We examine both targeted and incidental catch, in small (\u3c15 m, ‘SV’) and large (\u3e15 m, ‘LV’) vessel fisheries to identify hotspots with the highest risk of fisheries-related mortality and population decline. We estimate global fisheries catch at 264,520 (184,407–344,987) mobulids per year, with SV fisheries accounting for 87 % of global mortality. The highest-risk hotspots, based on mortality and declines, are located in India, Indonesia, Sri Lanka, Peru, and Myanmar. Mobulid retention is driven by demand, with higher mortality rates observed in countries exporting gill plates, and to a lesser extent, in those trading meat domestically or internationally. We recommend urgent implementation and enforcement of mobulid listings under the Convention on International Trade in Endangered Wild Flora and Fauna (CITES), the Convention on the Conservation of Migratory Species of Wild Animals (CMS), and national protective measures, including (i) uplisting mobulids to CITES Appendix I, (ii) full legislative protection for all mobulid species in high-risk fishing nations to reduce demand, (iii) avoiding fishing in critical habitats through permanent or temporary targeted area closures, or management, (iv) limiting drift gillnet effort, and (v) involving fishers in management decisions and implementation

Life cycle assessment comparing electric and petrol-powered rigid inflatable boats for recreational and harbour-master applications

This study presents the first cradle-to-grave life cycle assessment (LCA), focusing exclusively on environmental impacts, comparing electric and petrol-powered rigid inflatable boats (RIBs) under low-usage recreational and high-usage harbour-master scenarios. The study follows ISO-compliant LCA methodology and emphasizes transparency in data and assumptions. Ten environmental impact categories were analysed, including global warming potential (GWP), particulate matter formation, acidification, eutrophication, ecotoxicity, and resource scarcity. Results show that switching to electric RIBs reduces GWP by 34 % for recreational and 52 % for harbour-master scenarios, despite higher production-phase impacts across all categories. The electric RIB delivers notable operational benefits in GWP and fossil resource use, while the petrol RIB shows lower operational impacts in other categories. Thus, the key advantage of electrification lies in reducing fossil fuel reliance and GWP. Sensitivity analysis reflecting the UK\u27s evolving electricity grid mix from 2014 to 2023 further revealed GWP reductions of 22 % and 25 % for electric RIBs in recreational and harbour-master scenarios. These findings highlight the growing benefits of electrification as the UK decarbonises its grid. A break-even analysis showed that electric RIBs become environmentally preferable after 900 kWh of annual energy use, equivalent to 45 h at cruising speed, emphasizing their sustainability advantage in high-utilization settings

Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived from cardiopulmonary exercise testing

Accurate preoperative risk assessment is of great value to both patients and clinical teams. Several risk scores have been developed but are often not calibrated to the local institution, limited in terms of data input into the underlying models, and/or lack individual precision. Machine Learning (ML) models have the potential to address limitations in existing scoring systems. A database of 1190 elderly patients who underwent major elective surgery was analyzed retrospectively. Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). Shapley-Additive-exPlanations (SHAP) was subsequently used to analyze feature contributions. Model performance was benchmarked against existing risk prediction scores, namely the Portsmouth-Physiological-and-Operative-Severity-Score-for-the-Enumeration-of-Mortality-and-Morbidity (PPOSSUM) and the Duke-Activity-Status-Index, as well as linear regression using CPET features. A model was also developed for the same task using data directly extracted from the CPET time-series. The incorporation of cardiorespiratory fitness data enhanced the performance of all models for predicting postoperative morbidity by 20% compared to sole reliance on clinical data. Cardiorespiratory fitness features demonstrated greater importance than clinical features in the SHAP analysis. Models utilizing data taken directly from the CPET time-series demonstrated a 12% improvement over the cardiorespiratory fitness models. MOSR model surpassed all other models in every experiment, demonstrating excellent robustness and generalization capabilities. Integrating cardiorespiratory fitness data with ML models enables improved preoperative prediction of postoperative morbidity in elective surgical patients. The MOSR model stands out for its capacity to pinpoint essential features and build models that are both simple and accurate, showing excellent generalizability