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Abstract Background The global discourse on future health care emphasises that learning to collaborate across professions is crucial to assure patient safety and meet the changing demands of health care. The research on interprofessional education (IPE) is diverse but with gaps in curricula design and how IPE is enacted in practice. Purpose and aims This research project will identify. 1) how IPE in clinical placements emerges, evolves, and is enacted by students when embedded in local health care practices, 2) factors critical for the design of IPE for students at clinical placements across the four countries. Methods A study involving four countries (Sweden, Norway, Australia and New Zealand) using the theory of practice architectures will be undertaken between 2023 and 2027. The project is designed as an international, collaborative multiple-case ethnographic study, using the theoretical framework of practice architectures (TPA). It will include four ethnographic case studies of IPE, one in each country. Data will be collected in the following sequence: (1) participant observation of students during interprofessional placements, (2) interviews with students at clinical placement and stakeholders/professionals, (3) Non-clinical documents may be used to support the analysis, and collection of photos may be use as memory aids for documenting context. An analysis of “sayings, doings and relatings” will address features of the cultural- discursive, material-economic, social-political elements making up the three key dimensions of TPA. Each of the four international cases will be analysed separately. A cross case analysis will be undertaken to establish common learning and critical IPE design elements across the four collaborating universities. Discussion The use of TPA framework and methodology in the analysis of data will make it possible to identify comparable dimensions across the four research sites, enabling core questions to be addressed critical for the design of IPE. The ethnographic field studies will generate detailed descriptions that take account of country-specific cultural and practice contexts. The study will also generate new knowledge as to how IPE can be collaboratively researched.

Abstract: Large-scale metagenomic and data mining efforts have uncovered an expansive diversity of bacteriophages (phages) within the human gut1–3. These insights include broader phage populational dynamics such as temporal stability4, interindividual uniqueness5,6and potential associations to specific disease states7,8. However, the functional understanding of phage-host interactions and their impacts within this complex ecosystem have been limited due to a lack of cultured isolates for experimental validation. Here we characterise 125 active prophages originating from 252 diverse human gut bacterial isolates using seven different induction conditions to substantially expand the experimentally validated temperate phage-host pairs originating from the human gut. Importantly, only 17% of computationally predicted prophages were induced with common induction agents and these exhibited distinct gene patterns compared to non-induced predictions. Active Bacteroidota prophages were among the most prevalent members of the gut virome, with extensive use of diversity generating retroelements and exhibiting broad host ranges. Moreover, active polylysogeny was present in 52% of studied gut lysogens and led to coordinated prophage induction across diverse conditions. This study represents a substantial expansion of experimentally validated gut prophages, providing key insights into their diversity and genetics, including a genetic pathway for prophage domestication and demonstration that differential induction was complex and influenced by divergent prophage integration sites. More broadly, it highlights the importance of experimental validation alongside genomic based computational prediction to enable further functional understanding of these commensal viruses within the human gut.

Psoriasis is an immune-mediated inflammatory skin disease typically characterized by erythematous and scaly plaques. It affects 3% of the Newfoundland population while only affecting 1.7% of the general Canadian population. Recent genome-wide association studies (GWAS) in psoriasis have identified more than 63 genetic susceptibility loci that individually have modest effects. Prior studies have shown that a genetic risk score (GRS) combining multiple loci can improve psoriasis disease prediction. However, these prior GRS studies have not fully explored the association of GRS with patient clinical characteristics. In this study, we calculated three types of GRS: one using all known GWAS SNPs (GRS-ALL), one using a subset of SNPs from the HLA region (GRS-HLA), and the last using non-HLA SNPs (GRS-noHLA). We examined the relationship between these GRS and a number of psoriasis features within a well characterized Newfoundland psoriasis cohort. We found that both GRS-ALL and GRS-HLA were significantly associated with early age of psoriasis onset, psoriasis severity, first presentation of psoriasis at the elbow or knee, and the total number of body locations affected, while only GRS-ALL was associated with a positive family history of psoriasis. GRS-noHLA was uniquely associated with genital psoriasis. These findings clarify the relationship of the HLA and non-HLA components of GRS with important clinical features of psoriasis.

Background: There is increasing evidence that air pollution and noise may have detrimental psychological impacts, but there are few studies evaluating adolescents, ground-level ozone exposure, multi-exposure models, or metrics beyond outdoor residential exposure. This study aimed to address these gaps. Methods: Annual air pollution and traffic noise exposure at home and school were modelled for adolescents in the Greater London SCAMP cohort (N=7555). Indoor, outdoor and hybrid environments were modelled for air pollution. Cognitive and mental health measures were self-completed at two timepoints (baseline aged 11–12 and follow-up aged 13–15). Associations were modelled using multi-level multivariate linear or ordinal logistic regression. Results: This is the first study to investigate ground-level ozone exposure in relation to adolescent executive functioning, finding that a 1 interquartile range increase in outdoor ozone corresponded to −0.06 (p

This paper presents the generalized Pell $ p- $numbers and provides some related results. A new sequence is defined using the characteristic polynomial of the Pell $ p- $numbers and generalized Mersenne numbers. Two algorithms for Diffie-Hellman key exchange are given as an application of these sequences. They are illustrated via numerical examples and shown to be secure against attacks. Thus, these new sequences are practical for encryption and constructing private keys.

Abstract Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1160 (95% CI 1140–1180) metric tons (mt) per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median = 2.89 X 107 L/10-year) and southwest (median = 5.56 × 107 L/10-year) regions of the state, influencing lithium yield estimates of individual wells in southwest [2.90 (95% CI 2.80–2.99) mt/10-year] and northeast [1.96 (CI 1.86–2.07) mt/10-year] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66–7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3–20.7). These estimates indicate substantial lithium yields from Marcellus PW, though regional variability in chemistry and production may impact recovery efficiencies.

Weiyi Wang,1,2 John Gulliver,3 Sean Beevers,4,5 Anna Freni Sterrantino,1,6 Bethan Davies,1,2 Richard W Atkinson,3 Daniela Fecht1,2 1UK Small Area Health Statistics Unit, MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; 2National Institute for Health and Care Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, School of Public Health, Imperial College London, London, UK; 3Population Health Research Institute, St George’s, University of London, London, UK; 4MRC Centre for Environment and Health, Environmental Research Group, School of Public Health, Imperial College London, London, UK; 5National Institute for Health and Care Research Health Protection Research Unit in Environmental Exposures and Health, School of Public Health, Imperial College London, London, UK; 6The Alan Turing Institute, London, UKCorrespondence: Weiyi Wang, MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK, Email weiyi.wang14@imperial.ac.ukBackground: There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2. We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England.Methods: We conducted a time-stratified case-crossover study including 111,766 emergency hospital admissions for asthma in children (aged 0– 14 years) between 1st January 2011 and 31st December 2015 in England. Daily NO2 levels were predicted at the patients’ place of residence using spatio-temporal models by combining land use data and chemical transport model estimates. Conditional logistic regression models were used to obtain the odds ratios (OR) and confidence intervals (CI) after adjusting for temperature, relative humidity, bank holidays, and influenza rates. The effect modifications by age, sex, season, area-level income deprivation, and region were explored in stratified analyses.Results: For each 10 μg/m³ increase in NO2 exposure, we observed an 8% increase in asthma-related emergency admissions using a five-day moving NO2 average (mean lag 0– 4) (OR 1.08, 95% CI 1.06– 1.10). In the stratified analysis, we found larger effect sizes for male (OR 1.10, 95% CI 1.07– 1.12) and during the cold season (OR 1.10, 95% CI 1.08– 1.12). The effect estimates varied slightly by age group, area-level income deprivation, and region.Significance: Short-term exposure to NO2 was significantly associated with an increased risk of asthma emergency admissions among children in England. Future guidance and policies need to consider reflecting certain proven modifications, such as using season-specific countermeasures for air pollution control, to protect the at-risk population.Keywords: asthma, nitrogen dioxide, children, hospital admissions, case-crossover

This paper considers the transceiver design in a multiuser multiple-input multiple-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay downlink communication system, where users simultaneously transmit data via an FD relay node. The design incorporates an imperfect loop interference (LI) cancellation which results in a residual LI. Linear precoders are employed at the sources and relay, and minimum mean-squared-error (MMSE) combiners are employed at the destinations to mitigate the effect of the residual LI. The corresponding design problem is highly nonconvex, so a closed-form solution is intractable. Thus, an iterative method is developed to solve this optimization problem. Simulation results are presented which show that the proposed iterative algorithm provides better performance than the corresponding half-duplex (HD) solution in terms of the achievable rate under residual LI.

A robust body of work has demonstrated that a reduction in cAMP‐specific 3′,5′‐cyclic phosphodiesterase 4D isoform 7 (PDE4D7) is linked with negative prostate cancer outcomes; however, the exact molecular mechanism that underpins this relationship is unknown. Epigenetic profiling has shown that the PDE4D gene can be hyper‐methylated in transmembrane serine protease 2 (TMPRSS2)–ETS transcriptional regulator ERG (ERG) gene‐fusion‐positive prostate cancer (PCa) tumours, and this inhibits messenger RNA (mRNA) expression, leading to a paucity of cellular PDE4D7 protein. In an attempt to understand how the resulting aberrant cAMP signalling drives PCa growth, we immunopurified PDE4D7 and identified binding proteins by mass spectrometry. We used peptide array technology and proximity ligation assay to confirm binding between PDE4D7 and ATP‐dependent RNA helicase A (DHX9), and in the design of a novel cell‐permeable disruptor peptide that mimics the DHX9‐binding region on PDE4D7. We discovered that PDE4D7 forms a signalling complex with the DExD/H‐box RNA helicase DHX9. Importantly, disruption of the PDE4D7–DHX9 complex reduced proliferation of LNCaP cells, suggesting the complex is pro‐tumorigenic. Additionally, we have identified a novel protein kinase A (PKA) phosphorylation site on DHX9 that is regulated by PDE4D7 association. In summary, we report the existence of a newly identified PDE4D7–DHX9 signalling complex that may be crucial in PCa pathogenesis and could represent a potential therapeutic target.

UNLABELLED: Green spaces may be protective against COVID-19 incidence. They may provide outdoor, ventilated, settings for physical and social activities and therefore decrease transmission risk. We examined the association between neighborhood greenness and COVID-19-like illness incidence using individual-level data. METHODS: The study population includes participants enrolled in the COVID Symptom Study smartphone application in the United Kingdom and the United States (March-November 2020). All participants were encouraged to report their current health condition and suspected risk factors for COVID-19. We used a validated symptom-based classifier that predicts COVID-19-like illness. We estimated the Normalized Difference Vegetation Index (NDVI), for each participants reported neighborhood of residence for each month, using images from Landsat 8 (30 m2). We used time-varying Cox proportional hazards models stratified by age, country, and calendar month at study entry and adjusted for the individual- and neighborhood-level risk factors. RESULTS: We observed 143,340 cases of predicted COVID-19-like illness among 2,794,029 participants. Neighborhood NDVI was associated with a decreased risk of predicted COVID-19-like illness incidence in the fully adjusted model (hazard ratio = 0.965, 95% confidence interval = 0.960, 0.970, per 0.1 NDVI increase). Stratified analyses showed protective associations among U.K. participants but not among U.S. participants. Associations were slightly stronger for White individuals, for individuals living in rural neighborhoods, and for individuals living in high-income neighborhoods compared to individuals living in low-income neighborhoods. CONCLUSIONS: Higher levels of greenness may reduce the risk of predicted COVID-19-like illness incidence, but these associations were not observed in all populations.

Objective Digital interventions can be effective in preventing and treating common mental health conditions among university students. Incorporating student experiences and perspectives in the design and implementation of these programmes may improve uptake and engagement. This qualitative study explored university students’ perspectives of a low-intensity video-based mental health intervention, their recommendations for implementing the programme in university settings, and their views and recommendations to address barriers to engagement. Methods Participants (N = 115) were students (mean = 20.63 years, SD = 2.10) with elevated distress from 31 Australian universities drawn from a randomised controlled trial of the Uni Virtual Clinic-Lite (UVC-Lite). Data from students randomised to the intervention condition were collected via semi-structured interviews (n = 12) and open-ended questions during post-intervention surveys (n = 103). Data were analysed using content analysis. Results Participants generally reported positive views of the intervention, and most felt it should be offered to students as a universal intervention. Multiple methods of disseminating the intervention were suggested, including through university counselling, official platforms (e.g. student support services) and informal channels (e.g. word-of-mouth promotion). Difficulty integrating the programme into everyday life, pre-existing beliefs about mental health and technology-related factors were highlighted as barriers to engagement. Conclusion A low-intensity video-based mental health intervention was generally considered to be acceptable and appropriate for students with mild to moderate distress. Participants provided several suggestions to encourage uptake of the intervention and possible pathways to disseminate the intervention to students. The effectiveness of these should be examined in future trials.

BackgroundNumerous studies have demonstrated the effectiveness of digital interventions for improving the mental health of university students. However, low rates of engagement with these interventions are an ongoing challenge and can compromise effectiveness. Brief, transdiagnostic, web-based video interventions are capable of targeting key mental health and related issues affecting university students and may be more engaging and accessible for this population. ObjectiveThis study used a 2-arm randomized controlled trial to evaluate the effectiveness of Uni Virtual Clinic-Lite (UVC-Lite), a fully automated, transdiagnostic, web-based video intervention, relative to an attention-control condition. The primary outcomes were symptoms of depression and generalized anxiety disorder. The secondary outcomes included psychological distress, social anxiety symptoms, body appreciation, quality of life, well-being, functioning, general self-efficacy, academic self-efficacy, and help seeking. Program use (intervention uptake and engagement) and satisfaction were also assessed. MethodsUniversity students (n=487) with mild to moderate symptoms of distress were recruited from universities across Australia and randomly allocated to receive access to the UVC-Lite intervention or an attention-control condition targeting general health for a period of 6 weeks. UVC-Lite includes 12 modules, each comprising a brief animated video and an accompanying exercise. Of the 12 modules, 7 also included a brief symptom screening quiz. Outcomes were assessed at baseline, postintervention, and 3- and 6-months postintervention. ResultsThe primary and secondary outcomes were analyzed on an intention-to-treat basis using mixed models repeated measures ANOVA. The intervention was not found to be effective relative to the control condition on any of the primary or secondary outcomes. While 67.9% (114/168) of participants accessed at least 1 module of the intervention, module completion was extremely low. Subgroup analyses among those who engaged with the program (completed at least 1 video) and those with higher baseline distress (Distress Questionnaire-5 score ≥15) did not reveal any differences between the conditions over time. However, uptake (accessing at least 1 video) and engagement (completing at least 1 video) were higher among those with higher baseline symptoms. Satisfaction with the intervention was high. ConclusionsThe UVC-Lite intervention was not effective relative to a control program, although it was associated with high satisfaction among students and was not associated with symptom deterioration. Given the challenges faced by universities in meeting demand for mental health services, flexible and accessible interventions such as UVC-Lite have the potential to assist students to manage symptoms of mental health problems. However, low uptake and engagement (particularly among students with lower levels of symptomatology) are significant challenges that require further attention. Future studies should examine the effectiveness of the intervention in a more highly symptomatic sample, as well as implementation pathways to optimize effective engagement with the intervention. Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12621000375853; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380146

Objectives To investigate the associations of traffic-related air pollution exposures in early pregnancy with birth outcomes and infant neurocognitive development.Design Cohort study.Setting Eligible women attended six visits in the maternity clinics of two centres, the First Affiliated Hospital of Chongqing Medical University and Chongqing Health Centre for Women and Children.Participants Women who were between 20 and 40 years of age and were at 11–14 weeks gestation with a singleton pregnancy were eligible for participation. Women were excluded if they had a history of premature delivery before 32 weeks of gestation, maternal milk allergy or aversion or severe lactose intolerance. 1273 pregnant women enrolled in 2015–2016 and 1174 live births were included in this analysis.Exposures Air pollution concentrations at their home addresses, including particulate matter with diameter ≤2.5 µm (PM2.5) and nitrogen dioxide (NO2), during pre-conception and each trimester period were estimated using land-use regression models.Outcome measures Birth outcomes (ie, birth weight, birth length, preterm birth, low birth weight, large for gestational age and small for gestational age (SGA) status) and neurodevelopment outcomes measured by the Chinese version of Bayley Scales of Infant Development.Results An association between SGA and per-IQR increases in NO2 was found in the first trimester (OR: 1.57, 95% CI: 1.06 to 2.32) and during the whole pregnancy (OR: 1.33, 99% CI: 1.01 to 1.75). Both PM2.5 and NO2 exposure in the 90 days prior to conception were associated with lower Psychomotor Development Index scores (β: −6.15, 95% CI: −8.84 to –3.46; β: −2.83, 95% CI: −4.27 to –1.39, respectively). Increased NO2 exposure was associated with an increased risk of psychomotor development delay during different trimesters of pregnancy.Conclusions Increased exposures to NO2 during pregnancy were associated with increased risks of SGA and psychomotor development delay, while increased exposures to both PM2.5 and NO2 pre-conception were associated with adverse psychomotor development outcomes at 12 months of age.Trial registration number ChiCTR-IOR-16007700

Demand for natural gas continues to climb in the United States, having reached a record monthly high of 104.9 billion cubic feet per day (Bcf/d) in November 2023. Hydraulic fracturing, a technique used to extract natural gas and oil from deep underground reservoirs, involves injecting large volumes of fluid, proppant, and chemical additives into shale units. This is followed by a “shut-in” period, during which the fracture fluid remains pressurized in the well for several weeks. The microbial processes that occur within the reservoir during this shut-in period are not well understood; yet, these reactions may significantly impact the structural integrity and overall recovery of oil and gas from the well. To shed light on this critical phase, we conducted an analysis of both pre-shut-in material alongside production fluid collected throughout the initial production phase at the Hydraulic Fracturing Test Site 2 (HFTS 2) located in the prolific Wolfcamp formation within the Permian Delaware Basin of west Texas, USA. Specifically, we aimed to assess the microbial ecology and functional potential of the microbial community during this crucial time frame. Prior analysis of 16S rRNA sequencing data through the first 35 days of production revealed a strong selection for a Clostridia species corresponding to a significant decrease in microbial diversity. Here, we performed a metagenomic analysis of produced water sampled on Day 33 of production. This analysis yielded three high-quality metagenome-assembled genomes (MAGs), one of which was a Clostridia draft genome closely related to the recently classified Petromonas tenebris. This draft genome likely represents the dominant Clostridia species observed in our 16S rRNA profile. Annotation of the MAGs revealed the presence of genes involved in critical metabolic processes, including thiosulfate reduction, mixed acid fermentation, and biofilm formation. These findings suggest that this microbial community has the potential to contribute to well souring, biocorrosion, and biofouling within the reservoir. Our research provides unique insights into the early stages of production in one of the most prolific unconventional plays in the United States, with important implications for well management and energy recovery.

The development of 6G wireless networks is driven by the pressing need for reliable connectivity in the increasingly intelligent Internet of Things (IoT) ecosystem. The goal of these networks is to seamlessly connect individuals, devices, vehicles, and resources such as the cloud. However, the heterogeneity and complexity of 6G due to the proliferation of devices, diverse applications, and the need for green and sustainable communication networks, pose significant Resource Management (RM) challenges. Furthermore, the stringent requirements of 6G networks for Quality-of-Service (QoS), scalability, intelligence, and security can make traditional RM approaches ineffective, particularly considering Energy Efficiency (EE). In response to these challenges, Artificial Intelligence (AI) has been considered to provide green RM. AI techniques can be used to efficiently manage network resources, balance energy demands, optimize EE, and integrate Energy Harvesting (EH). This paper examines 6G networks from an AI perspective to optimize resource allocation, minimize energy consumption, and maximize network performance. The focus is on RM within these networks considering Radio Resource Management (RRM), Computing and Caching Resource Management (CCRM), and Communication Network Resource Management (CNRM). The emphasis is on RM within the Cellular Network Infrastructure (CNI) and Machine Type Communications (MTC). AI models for efficient resource utilization to enhance EE and network performance are investigated. It is shown that AI plays a pivotal role in achieving green RM within 6G networks. Future research directions are outlined for intelligent networks to meet the growing demands and emerging challenges.

Abstract The impact of fog on visibility is a major factor affecting traffic congestion and safety. This paper proposes a microscopic traffic model that captures the features of traffic in foggy weather and characterizes it based on visibility. The intelligent driver (ID) model is based on a constant acceleration exponent and produces similar traffic behaviour for all conditions, which is unrealistic. The performance of the ID and proposed models is evaluated on a 2.2 km ring road for 250 s with a platoon of 51 vehicles. Results are presented which show that the proposed model characterizes traffic realistically with lower acceleration and deceleration compared to the ID model. Further, it does not create stop‐and‐go waves and is stable even during foggy weather. The proposed model can be used to reduce fuel consumption and pollution resulting from traffic congestion.

Many face-to-face accounting classes were canceled in response to the COVID-19 pandemic and were subsequently delivered exclusively in an online and distance learning format. This paper investigates the impact of this migration on accounting students' engagement and satisfaction in the early stages of the pandemic. A survey was administered to all the students registered in the undergraduate and graduate accounting programs of a large state-funded university in Canada. The study finds that the variables stress/anxiety, social interactions, instructor strategy, technological accessibility and delivery mode flexibility are related to accounting student engagement, while social interactions, instructor strategy, and engagement affect satisfaction. Using factors previously studied in non-pandemic settings, the study shows what drives the extent of accounting students' engagement and satisfaction during a health emergency. Practical implications include the importance of providing emotional/psychological support to anxious/stressed students, fostering frequent and easy interactions with instructors, and offering added flexibility through asynchronous instruction.

The applications of non-square binary matrices span many domains including mathematics, error-correction coding, machine learning, data storage, navigation signals, and cryptography. In particular, they are employed in the McEliece and Niederreiter public-key cryptosystems. For the parity check matrix of these cryptosystems, a systematic non-square binary matrix H with dimensions m × n , n > m , m = n − k , there exist 2 m ( n − m ) distinct inverse matrices. This article presents an algorithm to generate these matrices as well as a method to construct a random inverse matrix. Then it is extended to non-square matrices in arbitrary fields. This overcomes the limitations of the Moore-Penrose and Gauss-Jordan methods. The application to public-key cryptography is also discussed.

BackgroundMost people with mental health problems do not seek help, with delays of even decades in seeking professional help. Lack of engagement with professional mental health services can lead to poor outcomes and functional impairment. However, few effective interventions have been identified to improve help-seeking in adults, and those that exist are not widely implemented to deliver public health impact. Co-designing interventions with people with lived experience of mental ill-health and other relevant stakeholders is critical to increase the likelihood of uptake and engagement with these programs. ObjectiveThis study aims to (1) test the effectiveness of a co-designed help-seeking program on increasing professional help-seeking intentions in employees in a workplace setting; (2) determine whether the program reduces mental illness stigma and improves help-seeking intentions and behavior, mental health literacy, mental health symptoms, and work and activity functioning relative to the control condition; (3) explore factors that facilitate broader implementation of the co-designed program; and (4) explore the cost-effectiveness of the co-designed program compared to the control condition over 6 months. MethodsA 2-arm cluster randomized controlled trial will be conducted (target sample: N=900 from 30 to 36 workplaces, with n=25 to 35 participants per workplace). The trial will compare the relative effectiveness of an enhanced interactive program (intervention condition) with a standard psychoeducation-alone program (active control condition) on the primary outcome of professional help-seeking intentions as measured by the General Help-Seeking Questionnaire. Secondary outcomes include the impact on mental illness stigma; mental health literacy; help-seeking attitudes and behavior; work and activity functioning; quality of life; and symptoms of mental ill-health including depression, anxiety, and general psychological distress. ResultsFacilitators of and risks to the trial are identified and addressed in this protocol. Recruitment of workplaces is scheduled to commence in the first quarter of 2024. ConclusionsIf effective, the program has the potential to be ready for rapid dissemination throughout Australia, with the potential to increase appropriate and efficient service use across the spectrum of evidence-based services. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12623000270617p; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=385376 International Registered Report Identifier (IRRID)PRR1-10.2196/55529

Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an information-theoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a single-class classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication.

Researchers have examined how extreme weather experiences influence climate change attitudes, beliefs, and behaviors, with mixed results. However, limited research has explored how extreme weather experiences may affect climate-related perceptions and behaviors among climate activists. Given the significant role activism plays in climate action, as well as frequent dropout and burnout among activists, it is important to understand to what extent, how, and why extreme weather may influence individual climate activism. This study explores reported influences of extreme weather experiences on climate perceptions and activism through interviews with 33 Australian adults who directly experienced bushfires and previously engaged in climate activism. All participants felt more vulnerable to climate change after experiencing bushfires. Fifteen participants (45%) increased their activism; 13 (39%) maintained the same activism level; and 5 (15%) decreased their activism. Participants who increased their activism sought to share their bushfire stories with news media, policymakers, and through artistic projects. Climate activism helped several participants cope with bushfire-related trauma, whereas several other participants reduced their activism because their experiences undermined self-efficacy (perception that one can act on climate change). These findings show the divergent ways individuals may respond to extreme weather experiences and have implications for climate action mobilization strategies.

BackgroundBreast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.Objective and methodsThis study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.ResultsA total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.ConclusionThis study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.

BackgroundHidradenitis suppurativa (HS) is associated with comorbidities that contribute to poor health, impaired life quality, and mortality risk.ObjectiveTo provide evidence-based screening recommendations for comorbidities linked to HS.MethodsSystematic reviews were performed to summarize evidence on the prevalence and incidence of 30 comorbidities in patients with HS relative to the general population. The screening recommendation for each comorbidity was informed by the consistency and quality of existing studies, disease prevalence, and magnitude of association, as well as benefits, harms, and feasibility of screening. The level of evidence and strength of corresponding screening recommendation were graded by using the Strength of Recommendation Taxonomy (SORT) criteria.ResultsScreening is recommended for the following comorbidities: acne, dissecting cellulitis of the scalp, pilonidal disease, pyoderma gangrenosum, depression, generalized anxiety disorder, suicide, smoking, substance use disorder, polycystic ovary syndrome, obesity, dyslipidemia, diabetes mellitus, metabolic syndrome, hypertension, cardiovascular disease, inflammatory bowel disease, spondyloarthritis, and sexual dysfunction. It is also recommended to screen patients with Down syndrome for HS. The decision to screen for specific comorbidities may vary with patient risk factors. The role of the dermatologist in screening varies according to comorbidity.LimitationsScreening recommendations represent one component of a comprehensive care strategy.ConclusionsDermatologists should support screening efforts to identify comorbid conditions in HS.

Abstract Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn’s disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.

Abstract Introduction Harnessing positive plant–soil feedbacks via crop rotations is a promising strategy for sustainable agriculture. These feedbacks are often context‐dependent, and how soil heterogeneity explains this variation is unknown. Plants influence soil properties, including microbes, by exuding specialized metabolites. Benzoxazinoids, specialized metabolites released by cereals such as wheat and maize, can alter rhizosphere microbiota and performance of plants subsequently growing in the exposed soils and are thus an excellent model to study agriculturally relevant plant–soil feedbacks. Materials and Methods To understand local variation in soil properties on benzoxazinoid‐mediated plant–soil feedbacks, we conditioned plots with wild‐type maize and benzoxazinoid‐deficient bx1 mutants in a grid pattern across a field, and we then grew winter wheat in the following season. We determined accumulation of benzoxazinoids, root‐associated microbial communities, abiotic soil properties and wheat performance in each plot and then assessed their associations. Results We detected a marked gradient in soil chemistry and microbiota across the field. This gradient resulted in significant differences in benzoxazinoid accumulation, which were explained by differential benzoxazinoid degradation rather than exudation. Benzoxazinoid exudation modulated microbial diversity in root and rhizospheres during maize growth, but not during subsequent wheat growth, while the chemical fingerprint of benzoxazinoids persisted. Averaged across the field, we did not detect feedbacks on wheat performance and defence, apart from a transient decrease in biomass during vegetative growth. Closer analysis, however, revealed significant feedbacks along the chemical and microbial gradient of the field, with effects gradually changing from negative to positive along the gradient. Conclusion Overall, this study revealed that plant–soil feedbacks differ in strength and direction within a field and that this variation can be explained by standing chemical and microbial gradients. Understanding within‐field soil heterogeneity is crucial for the future exploitation of plant–soil feedbacks in sustainable precision agriculture.

This study explores patterns of alcohol drinking within a representative New Zealand sample (2887 participants (1464 female, 1423 male)). Alcohol use and drinking patterns across the population are described. Multivariable logistic regressions document associations between alcohol use and drinking patterns and the likelihood of experiencing different health outcomes. Alcohol use, early drinking initiation, frequent drinking, and heavy episodic drinking (HED) are prevalent in New Zealand and vary in relation to gender, age, and socioeconomic characteristics. Those who reported alcohol-related problems were more likely to report poor mental health (AOR: 2.21; 95% CI: 1.42–3.46) and disability (AOR: 1.79, 95% CI: 1.06–3.00), and less likely to experience positive mental health (AOR: 0.28, 95% CI: 0.18–0.42). Those who reported HED were also less likely to experience good general health (AOR: 0.61, 95% CI: 0.47–0.81) and positive mental health (AOR: 0.67, 95% CI: 0.53–0.84). Younger age cohorts were more likely to engage in early drinking, and those who initiated regular drinking before age 18 were more likely to report HED and alcohol-related problems. Findings indicate that problem drinking and HED are not only associated with poor physical health, but also reduce the likelihood of individuals experiencing positive mental health. This provides information to enable public health practitioners to target alcohol prevention strategies at the entire population of drinkers.

Abstract This paper seeks to examine how Australian environmental non-governmental organisations (ENGOs) communicate about and mobilise their supporters for climate justice. ENGOs play an important role in raising awareness and changing values, attitudes and behaviours related to climate justice. However, while many Australian ENGOs have begun incorporating language around climate justice in their communications, it remains unclear how this concept is framed and enacted in practice. Using data collected from 619 ENGO websites and 149 grant applications, we examine how ENGOs describe climate justice and the collective action frames they use to mobilise action. We found that while few ENGOs provided detailed explanations of climate justice on their websites, they primarily framed climate injustice as a procedural and distributive problem. The fossil fuel sector was most commonly identified as the cause of climate injustice, and First Nations communities most commonly affected. ENGOs linked different climate justice dimensions to diverse causes, issues and actions, indicating a nuanced understanding of how climate justice can be enacted in different contexts. However, they primarily proposed incremental tactics involving education, solidarity and allyship behaviours rather than radical actions through which to drive a transformative agenda of social, political or economic change. We conclude the paper with a discussion of applied implications for ENGOs and suggestions for future research.

Abstract Introduction Numerous frameworks for defining and supporting co‐created research exist. The practicalities of designing and conducting co‐created research are clearly important, yet the utility of these frameworks and their operationalisation within local contexts and involving a diversity of stakeholders and interests are currently not well‐researched. Methods Using an instrumental case study approach, we examined the utility of a published systematic framework designed to improve clarity about co‐creation as a concept and approach. The framework is explored based on the first two processes that correspond to our own work to date: co‐ideation and co‐design. Results Our study showed that diverse stakeholders bring challenges regarding research priorities, methods, language and the distribution of power within co‐creation processes. Co‐creation activities were incremental, adaptable, responsive and made best use of established relationships, structures and collective leadership to meet the competing demands of funders and human research ethics committees, while ensuring the meaningful participation of multiple stakeholders. Conclusion The findings highlight the iterative, fluid and deeply relational nature of co‐created research. Rather than seeking to categorise these processes, we argue that the social relations of research production that provide the structures within which all co‐created knowledge is generated are more important drivers of effective knowledge mobilisation and implementation. Thus, close attention to these social relations is needed in co‐created research. Patient or Public Contribution People with lived experience of emotional distress and/or suicidal crisis, including academic researchers, service and peer workers, carers and advocates were involved in the co‐ideation and co‐design of this research. All authors identify as people with lived experience, from both academic and nonresearch backgrounds.

Abstract Background Over the last decade there has been considerable research into the treatment, management, and quality of life of people living with multiple myeloma. However, there has been limited investigation into topics deemed important to patients and caregivers within this community. We conducted a James Lind Alliance Priority Setting Partnership to establish the ‘Top 10 Priorities for Myeloma Research’, informed by patient and public partners. Methods A research team and steering group were established in 2019 to conduct the myeloma priority setting partnership. Steering group members included patients, caregivers, and healthcare providers who advised the research team and oversaw the scope of the project, grounded on their lived experience. Following the James Lind Alliance guidelines for identification and ranking of research questions, we used surveys and a virtual workshop to collect and prioritize questions posed by myeloma patients, caregivers, and healthcare providers across Canada. Results The Top 10 list of priorities for myeloma research was finalized at the consensus-building workshop and encompassed questions related to diagnosis, treatment, management, and living well with myeloma. A final participant evaluation survey elicited a positive response. Interpretation The myeloma priority setting partnership identified the research priorities of people living with myeloma, caregivers, and healthcare providers to inform clinical research on this disease going forward. This project underscores the importance of patient and public engagement in the identification of research questions, highlighting the concerns of people affected by myeloma to ultimately improve the lives of people living with this disease.

Abstract Digital signature schemes are used for the authentication and verification of signatures. The Courtois–Finiasz–Sendrier (CFS) digital signature is a well‐known code‐based digital signature scheme based on the Niederreiter cryptosystem. However, it is not widely used due to the computation time of the signing algorithm. Most code‐based digital signature schemes are based on the Niederreiter cryptosystem. This paper proposes a new code‐based digital signature that is based on the McEliece cryptosystem. Key generation, signing, and verification algorithms are presented. The key generation algorithm constructs a public key using random inverse matrices. The signing algorithm has lower complexity and requires less computation time than the CFS scheme to sign a document. The verification algorithm is able to detect forgeries. It is shown that the proposed scheme is secure against public key structural attacks.

The neutropenic diet (ND) is often recommended to people with cancer to reduce infection risk despite recommendations of clinical guidelines advising against its use. While recent literature suggests the ND does not reduce infection risk, other outcomes related to health, nutrition, and lifestyle are unknown. The aim of this review is to systematically scope the literature on the ND in people with cancer for all outcomes related to clinical health, nutrition, and lifestyle. Scientific databases were systematically searched. Eligible studies were in English, people with any cancer type, consuming an ND, any age group, date, or setting. Eligible study types were randomised control trials, observational studies, systematic reviews, and meta-analyses. Twenty-one studies met the inclusion criteria. Outcomes of interest found were infection rates, fever, mortality, antibiotic use, gastrointestinal side effects, comorbidities, biochemistry, hospitalisation, nutritional status, quality of life (QoL), well-being, and financial costs. Most research has focused on infection and mortality rates with few assessing hospitalisation rates, nutritional status, financial costs, and QoL. Most included studies found no significant differences between ND and comparator diet for mortality, antibiotics use, comorbidities, and QoL; however, several studies reported the ND significantly increased the risk of infection. Gaps in the literature included effect of ND on QoL in an adult population, microbiome, lifestyle changes, and financial burden. Further research is needed regarding how the ND affects the microbiome and QoL of its consumers, but in the interim, it is important for hospitals providing an ND to their patients to liberalise the ND wherever possible.