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Abstract Background Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds. Methods A structured search designed around three concepts—individual decision-maker, decision threshold, and medical decision—was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision. Results Of the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles. Conclusions Our scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

Young men from disadvantaged contexts are the least likely to attend university in Australia; furthermore, when they do attend, they are likely to struggle. This article draws on empirical data documenting the aspirations and resilience of first-in-family young men in Australian higher education, with the aim of nuancing their classed experience of university. Drawing on an exploratory longitudinal study (n = 42) and adopting a mixed method approach, we use the 25-item Connor-Davidson Resilience Scale (CD-RISC) and semi-structured interviews over a three-year period to explore changes in resilience of first-in-family men from the age of 17 to 20. The mixed-method approach employed in this study allows us to draw connections between the participants' subjective experience of resilience and the more objective measures of resilience as captured by the Connor-Davidson Resilience Scale, a psychometrically sound and validated instrument. Quantitative analyses of data enable us to document the trends in resilience over time for different groups of first-in-family men, while qualitative data provide insights structured around three key themes: independence and isolation; managing and adjusting; and using support structures. The article draws on analysis across these data to consider the participants' perceptions of their resilience, and how these perceptions change in reference to their experience, in order to paint a more nuanced picture of first-in-family men's classed experience of higher education.

Understanding the drivers of student dropout from higher education has been a policy concern for several decades. However, the contributing role of certain factors--including student mental health--remains poorly understood. Furthermore, existing studies linking student mental health and university dropout are limited in both methodology and scope--for example, they often rely on small and/or non-representative samples or subjective measures, and focus almost exclusively on main effects. This paper overcomes many of these shortcomings by leveraging unique linked administrative data on the full population of domestic students commencing undergraduate studies at Australian universities between 2012 and 2015 (n = 652,139). Using these data, we document that approximately 15% of students drop out of university within their first academic year. Critically, students receiving treatment for mental health problems are 4.3 (adjusted) to 8.3 (unadjusted) percentage points more likely to drop out of higher education. This association remains in the presence of an encompassing set of potential confounds, and is remarkably uniform across segments of the student population determined by individual, family, and programme characteristics. Altogether, our findings call for increased policy efforts to improve student mental health and to buffer against its deleterious effects on retention.

Driven by the focus on standardised assessment and performance-driven accountability, a considerable body of literature has documented differences in students' academic achievement across school sectors, both internationally and in Australia. However, few studies have to date explored the potential mechanisms underlying such differences, particularly through the lens of school climate and student engagement. And despite extensive literature on school climate and student engagement, including their relationships with achievement, the differences in these patterns across school sectors remain under-studied. In this paper, we leverage nationally representative data from a large-scale longitudinal survey in Australia with linked administrative data on student achievement to reveal different patterns of school climate and student engagement across government, Catholic and independent sectors. Employing multivariable regression analyses, we identify unique school climate and student engagement facets that are associated with improved achievement in each of these sectors, offering important pointers for educational policies.

Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40,000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively.

Background Despite tremendous therapeutic advancements, a significant proportion of coronary artery disease patients suffer from refractory angina pectoris, that is, quality-of-life-compromising angina that is non-manageable with established pharmacological and interventional treatment options. Adenoviral vascular endothelial growth factor-DΔNΔC (AdVEGF-D)-encoding gene therapy (GT) holds promise for the treatment of refractory angina.Methods ReGenHeart is an investigator-initiated, multicentre, randomised, placebo-controlled and double-blinded phase 2 clinical trial that aims to study the safety and efficacy of intramyocardially administered angiogenic AdVEGF-D GT for refractory angina. Patients will be randomised in a 2:1 ratio and blocks of six to receive either AdVEGF-D or placebo. Primary endpoints are improvements in functional capacity assessed with the 6 min walking test and angina symptoms with Canadian Cardiovascular Society class after 6 month follow-up. Secondary endpoints are improvements in myocardial perfusion assessed with either positron emission tomography or single-photon emission CT after 6 month follow-up and functional capacity and angina symptoms after 12 months. In addition, changes in the quality of life, the use of angina medication and the incidence of major adverse cardiac and cerebrovascular events will be evaluated.Conclusions The phase 2 ReGenHeart trial will provide knowledge of the safety and efficacy of AdVEGF-D GT to ameliorate symptoms in refractory angina patients, extending and further testing positive results from the preceding phase 1/2a trial.

The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this paper, we present a development model for the iterative, cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. A neural classifier is trained by exclusively using synthetic images, whose generation process is iteratively refined to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. Our evaluation shows superior generalization results when compared to using non-task-specific real training data and a higher cost efficiency of development compared to traditional synthetic training data. We believe that our approach will help to reduce the cost of synthetic data generation in future applications.

OBJECTIVES:. Anemia has been associated with an increased risk of both cardiac arrest and stroke, frequent complications of COVID-19. The effect of hemoglobin level at ICU admission on a composite outcome of cardiac arrest or stroke in an international cohort of COVID-19 patients was investigated. DESIGN:. Retrospective analysis of prospectively collected database. SETTING:. A registry of COVID-19 patients admitted to ICUs at over 370 international sites was reviewed for patients diagnosed with cardiac arrest or stroke up to 30 days after ICU admission. Anemia was defined as: normal (hemoglobin ≥ 12.0 g/dL for women, ≥ 13.5 g/dL for men), mild (hemoglobin 10.0–11.9 g/dL for women, 10.0–13.4 g/dL for men), moderate (hemoglobin ≥ 8.0 and < 10.0 g/dL for women and men), and severe (hemoglobin < 8.0 g/dL for women and men). PATIENTS:. Patients older than 18 years with acute COVID-19 infection in the ICU. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Of 6926 patients (median age = 59 yr, male = 65%), 760 patients (11.0%) experienced stroke (2.0%) and/or cardiac arrest (9.4%). Cardiac arrest or stroke was more common in patients with low hemoglobin, occurring in 12.8% of patients with normal hemoglobin, 13.3% of patients with mild anemia, and 16.7% of patients with moderate/severe anemia. Time to stroke or cardiac arrest by anemia status was analyzed using Cox proportional hazards regression with death as a competing risk. Covariates selected through clinical knowledge were age, sex, comorbidities (diabetes, hypertension, obesity, and cardiac or neurologic conditions), pandemic era, country income, mechanical ventilation, and extracorporeal membrane oxygenation. Moderate/severe anemia was associated with a higher risk of cardiac arrest or stroke (hazard ratio, 1.32; 95% CI, 1.05–1.67). CONCLUSIONS:. In an international registry of ICU patients with COVID-19, moderate/severe anemia was associated with increased hazard of cardiac arrest or stroke.

Australian children from regional, rural and remote (RRR) areas exhibit lower educational outcomes than their peers in metropolitan areas. The mechanisms driving the comparatively poorer educational outcomes of children in RRR areas, however, are not well understood. This paper proposes and tests two sets of factors that may be responsible for these disparities: population socio-demographic composition and school climate. Using rich survey and linked administrative data from the "Longitudinal Study of Australian Children" (n = 9,248 observations), we estimate the relative contribution of these sets of factors to RRR children's disadvantage in NAPLAN numeracy test scores. Our results indicate that both socio-demographic and school climate factors account for part of the educational disparities between children in RRR and metropolitan areas. These findings suggest that hybrid policy approaches that tackle both the social determinants of educational success and use schools as an intervention site are required to close the achievement gap.

BackgroundRight anterior mini thoracotomy (RAMT) for aortic valve replacement (AVR) is a minimally invasive procedure that avoids sternotomy. Herein, we report the outcomes of patients who underwent redo-cardiac via a RAMT approach for AVR.MethodsThis case series reports the clinical outcomes of 14 consecutive redo operations, done in Calgary (Canada) and Gdansk (Poland) between 2020 and 2023. Primary outcomes were 30-day mortality and disabling stroke. Secondary outcomes included surgical times, hemodynamics, permanent pacemaker implantation (PPM), length of ICU and hospital stay, new post-operative atrial fibrillation (POAF), post-operative blood transfusion, incidence of acute respiratory distress syndrome (ARDS), rate of continuous renal replacement therapy (CRRT) and/or dialysis, and chest tube output in the first 12-hours after surgery.ResultsNine patients were male, and the mean age was 64.36 years. There were no deaths, while one patient had a disabling stroke postoperatively. Mean cardiopulmonary bypass and cross clamp-times were 136 min and 90 min, respectively. Three patients needed a PPM, 3 patients needed blood transfusions, and 2 developed new onset POAF. Median lengths of ICU and hospital stays were 2 and 12 days, respectively. There was no incidence of paravalvular leak greater than trace and the average transvalvular mean gradient was 12.23 mmHg.ConclusionThe number of patients requiring redo-AVR is increasing. Redo-sternotomy may not be feasible for many patients. This study suggests that the RAMT approach is a safe alternative to redo-sternotomy for patients that require an AVR.

This paper presents a new approach for protein generation based on one-shot learning and hybrid quantum neural networks. Given a single protein complex, the system learns how to predict the remaining unknown properties, without resorting to autoregression, from the physicochemical properties of the receptor and a prior on the physicochemical properties of the ligand. In contrast with other approaches, QuantumBound learns from a single instance, not from a large dataset, as is common in deep learning. By knowing half of the properties of the ligand, the system can predict the remaining half with an average relative error of 1.43% for a dataset consisting of one hundred and twenty Covid-19 spikes complexes. To the best of our knowledge, this is the first time that one-shot learning and hybrid quantum computing have been applied to protein generation.

Purpose: Gain of chromosome 8q has been associated with poor prognosis in uveal melanoma (UM), and an increase in the absolute number of 8q-copies correlated with an even shorter survival. Splicing factor 3b subunit 1 (SF3B1)-mutated (SF3B1MUT) tumors display structural chromosomal anomalies and frequently show a partial gain of chromosome 8qter. A recent subset of SF3B1MUT UM with early-onset metastases has been identified, prompting the investigation of the relationship between survival, 8q gain, and SF3B1MUT UM. Design: Retrospective cohort study. Subjects: Patients diagnosed with UM who underwent enucleation or received a biopsy at the Erasmus MC Cancer Institute or the Rotterdam Eye Hospital, The Netherlands were included. Methods: Fifty-nine patients with SF3B1MUT tumors and 211 patients with BRCA1 associated protein 1 (BAP1)-mutated (BAP1MUT) tumors were included in this study. Copy number status and gene expression were assessed using either a single nucleotide polymorphism array, fluorescence in situ hybridization, and karyotyping, or a combination of these techniques. Disease-free survival was determined and a cut-off of 60 months was used to define early-onset metastatic disease. Main Outcome Measures: Disease-free survival. Results: Forty-eight patients with SF3B1MUT UM (81%) had chromosome 8q gain (3 copies, 78%; 4 copies, 22%). Kaplan–Meier analysis of SF3B1MUT UM did not indicate a difference in survival in patients with or without gain of 8q (P = 0.99). Furthermore, the number of 8q copies was not associated with survival when comparing early (P = 0.97) versus late (P = 0.23) metastases group. In contrast, the presence of 8q gain (86%) was correlated with a decreased survival in BAP1MUT UM (P = 0.013). Conclusions: We did not find a correlation between 8q gain and early-onset metastasis in SF3B1MUT tumors. Gain of 8q has no additional predictive value in SF3B1MUT tumors. In contrast, 8q gain is predictive of a worse prognosis in patients with BAP1MUT tumors. Thus, gain of chromosome 8q has additional predictive value for BAP1MUT tumors, but not for SF3B1MUT tumors. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Lifelong machine learning concerns the development of systems that continuously learn from diverse tasks, incorporating new knowledge without forgetting the knowledge they have previously acquired. Multi-label classification is a supervised learning process in which each instance is assigned multiple non-exclusive labels, with each label denoted as a binary value. One of the main challenges within the lifelong learning paradigm is the stability-plasticity dilemma, which entails balancing a model’s adaptability in terms of incorporating new knowledge with its stability in terms of retaining previously acquired knowledge. When faced with multi-label data, the lifelong learning challenge becomes even more pronounced, as it becomes essential to preserve relations between multiple labels across sequential tasks. This scoping review explores the intersection of lifelong learning and multi-label classification, an emerging domain that integrates continual adaptation with intricate multi-label datasets. By analyzing the existing literature, we establish connections, identify gaps in the existing research, and propose new directions for research to improve the efficacy of multi-label lifelong learning algorithms. Our review unearths a growing number of algorithms and underscores the need for specialized evaluation metrics and methodologies for the accurate assessment of their performance. We also highlight the need for strategies that incorporate real-world data from varying contexts into the learning process to fully capture the nuances of real-world environments.

Introduction Cardiovascular diseases are the most numerous group of diseases prevalent in the world. They are a challenge for many health systems, in terms of keeping life comfortable and also economics. The cause of selected disease entities is too much cholesterol in the blood. The most popular treatment for hypercholesterolemia is based on statins. Many patients are affected by intolerance to these drugs, so an important issue is the discovery and improvement of alternatives to statins. Purpose of work The purpose of this review is to collect literature data on the latest treatments for hypercholesterolemia with drugs other than statins and ezetimibe. Materials and methods Materials are from a review of recent literature available in PubMed. To search for articles, we used keywords such as: bempedoic acid, non-statin therapy, cardiovascular risk, inclisiran, alirocumab, cardiovascular disease, dyslipidemia, evolocumab. Summary Treatment of hypercholesterolemia with statins remains the most popular management strategy. Intolerance to treatment with these drugs creates serious clinical problems for patients. Recently, we could see the emergence of new drugs as alternatives to statins. As the results show, the new drugs can effectively replace statins in the hypolipemic treatment especially of patients who cannot be treated with them.

Background Dialysis is a rare but serious complication after transcatheter aortic valve replacement. We analyzed the large multicenter TRITAVI (transfusion requirements in transcatheter aortic valve implantation) registry in order to develop and validate a clinical score assessing this risk. Methods and Results A total of 10 071 consecutive patients were enrolled in 19 European centers. Patients were randomly assigned (2:1) to a derivation and validation cohort. Two scores were developed, 1 including only preprocedural variables (TRITAVIpre) and 1 also including procedural variables (TRITAVIpost). In the 6714 patients of the derivation cohort (age 82±6 years, 48% men), preprocedural factors independently associated with dialysis and included in the TRITAVIpre score were male sex, diabetes, prior coronary artery bypass graft, anemia, nonfemoral access, and creatinine clearance

Background. The aim of this open-label, multicenter, randomized controlled study was to investigate whether the life cycle pharma (LCP)-tacrolimus compared with the extended-release (ER)-tacrolimus formulation results in a difference in the prevalence of posttransplant diabetes, hypertension and chronic kidney disease (CKD) at 12 mo after liver transplantation. Methods. Patients were 1:1 randomized to either of the 2 tacrolimus formulations. The primary endpoint was defined as a composite endpoint of any of 3 events: sustained (>3 mo postrandomization) posttransplant diabetes, new-onset hypertension, and/or CKD, defined as estimated glomerular filtration rate 3 m during the follow-up. Results. In total, 105 patients were included. In the intention-to-treat analysis, a statistically significant lower proportion of liver transplant recipients in the LCP-tacrolimus group reached the composite primary endpoint at 12 mo compared with the ER-tacrolimus group (50.9% [27/53], 95% confidence interval [CI], 37.9%-63.9% versus 71.2% [37/52], 95% CI, 57.7%-81.7%; risk difference: 0.202; 95% CI, 0.002-0.382; P = 0.046). No significant difference was found in the per protocol analysis. In the intention-to-treat and per protocol population, fewer liver transplant recipients in the LCP-tacrolimus group developed CKD and new-onset hypertension compared with the ER-tacrolimus group. No differences in rejection rate, graft and patient survival were found. Conclusions. A statistically significant and clinically relevant reduction in the prevalence of the composite primary endpoint was found in the LCP-tacrolimus group compared with the ER-tacrolimus group in the first year after liver transplantation with comparable efficacy.

Abstract The design of binder proteins for specific target proteins using deep learning is a challenging task that has a wide range of applications in both designing therapeutic antibodies and creating new drugs. Machine learning-based solutions, as opposed to laboratory design, streamline the design process and enable the design of new proteins that may be required to address new and orphan diseases. Most techniques proposed in the literature necessitate either domain knowledge or some appraisal of the target protein’s 3-D structure. This paper proposes an approach for designing binder proteins based solely on the amino acid sequence of the target protein and without recourse to domain knowledge or structural information. The sequences of the binders are generated with two new transformers, namely the AppendFormer and MergeFormer architectures. Because, in general, there is more than one binder for a given target protein, these transformers employ a binding score and a prior on the sequence of the binder to obtain a unique targeted solution. Our experimental evaluation confirms the strengths of this novel approach. The performance of the models was determined with 5-fold cross-validation and clearly indicates that our architectures lead to highly accurate results. In addition, scores of up to 0.98 were achieved in terms of Needleman-Wunsch and Smith-Waterman similarity metrics, which indicates that our solutions significantly outperform a seq2seq baseline model.

Humanitarian migrants are amongst the most marginalised population groups in countries within the Global North, including Australia. An important channel for these migrants to successfully settle into the host society and improve their socio-economic outcomes is participation in the local education system, particularly in higher-education options. However, we know surprisingly little about the socio-demographic factors that structure inequalities in humanitarian migrants' access to (higher) education, with evidence from robust quantitative studies being particularly scarce. The present study fills this important gap in knowledge by analysing Australian longitudinal survey data ("Building a New Life in Australia;" n = 2109 migrants and 8668 person-year observations) by means of random-effect panel regression models. Key results indicated that higher English-language proficiency and pre-arrival education levels are core factors fostering greater engagement with the Australian higher-education system amongst humanitarian migrants. Humanitarian-migrant women in our sample exhibited a greater adjusted likelihood of being a student than humanitarian-migrant men. Altogether, our findings confirmed inequalities in accessing the Australian higher-education system amongst humanitarian migrants, and that policy attention is required to redress this situation. However, they also stress that a 'one size fits all' policy strategy may be neither sufficient nor appropriate to boost their education prospects.

Abstract Introduction: Community health workers (CHWs) stand as critical frontline agents within the Brazilian healthcare system. In this qualitative study, we examined the impact of a community-based behavioral change intervention spearheaded by CHWs. Methods: The intervention focused on promoting healthy behaviors – physical activity, nutrition, and emotional well-being – among individuals aged 50 and older living in a rural community in Brazil. The intervention was designed, implemented, and evaluated in close collaboration with CHWs and local administrators. The implementation of the intervention unfolded in two waves, each lasting 12 months. Interviews with CHWs, health administrators, and intervention participants conducted at post-intervention and 6-year follow-up centered on CHWs as delivery agents and examined the implementation of the intervention in primary care contexts around adoption, implementation, and long-term maintenance. Results: Inductive analysis revealed four themes that highlight CHWs’ motivation to take active roles in health promotion and overcoming challenges such as unfamiliarity with new roles or limited training. In addition, enhanced community bonds, job satisfaction, and trust in CHWs gained through the intervention, empowered CHWs to realize their potential and importance. Another important area relates to the CHWs’ ability to leverage their deep community ties and cultural insights to enhance the intervention’s significance. CHWs’ participation in the program also led to personal benefits and self-care practices, setting an example for the community they serve. Conclusions: This study underscores the positive impact of a community-based intervention led by CHWs. Such programs have the potential for nationwide dissemination, leveraging the CHWs’ widespread presence and deep community integration.

IntroductionIn adults, muscle disease (MD) is typically a chronic long-term condition that can lead to a reduced quality of life (QoL). Previous research suggests that a psychological intervention, in particular Acceptance and Commitment Therapy (ACT), may help improve QoL for individuals living with chronic conditions such as MD.MethodsThis nested qualitative study was incorporated within a randomized controlled trial which evaluated a guided self-help ACT intervention for people living with MD to explore their experiences of the intervention. Semi-structured interviews (n = 20) were conducted with those who had received ACT. Data were analyzed via thematic analysis.ResultsThere were four overarching themes. (1) Views on whether therapy sessions would help with a medical condition: participants' expectations regarding ACT varied. Some participants were skeptical about mindfulness. (2) I was able to look at things in a different way: participants described increased meaningful activity, greater awareness of thoughts and emotions and acceptance or adaptation to mobility problems. Some described improvement in the quality of relationships and a sense of feeling free. (3) Treating the body and the mind together: following the intervention participants noted that a holistic approach to healthcare is beneficial. (4) Intervention delivery: The remote delivery was generally seen as helpful for practical reasons and allowed participants to speak openly. Participants voiced a need for follow-up sessions.DiscussionOverall, the intervention was experienced as acceptable. Suggested improvements included de-emphasizing the role of mindfulness and adding follow-up sessions.

Background: Temporal trends of the impact of social determinants on cardiovascular outcomes of cancer patients has not been previously studied. Objectives: This study examined social disparities in cardiovascular mortality of people with and without cancer in the US population between 1999 and 2019. Methods: Primary cardiovascular deaths were identified from the Multiple Cause of Death database and grouped by cancer status. The cancer cohort was subcategorized into breast, lung, prostate, colorectal, and haematological. The number of cardiovascular deaths, crude cardiovascular mortality rate, cardiovascular age-adjusted mortality rate (AAMR), and percentage change in cardiovascular AAMR were calculated by cancer status and cancer type, and stratified by sex, race, ethnicity, and urban-rural setting. Results: 17.9 million cardiovascular deaths were analysed. Of these, 572,222 occurred in patients with a record of cancer. The cancer cohort were older and included more men and White racial groups. Regardless of cancer status, cardiovascular AAMR was higher in men, rural settings, and Black or African American races. Cardiovascular AAMR declined over time, with greater reduction in those with cancer (−51.6% vs −38.3%); the greatest reductions were in colorectal (−68.4%), prostate (−60.0%), and breast (−58.8%) cancers. Sex, race, and ethnic disparities reduced over time, with greater narrowing in the cancer cohort. There was increase in urban-rural disparities, which appeared greater in those with cancer. Conclusions: While most social disparities narrowed over time, urban-rural disparities widened, with greater increase in those with cancer. Healthcare plans should incorporate strategies for reduction of health inequalities and to promote equitable access to cardio-oncology services.

Some statistical analysis techniques may require complete data matrices, but a frequent problem in the construction of databases is the incomplete collection of information for different reasons. One option to tackle the problem is to estimate and impute the missing data. This paper describes a form of imputation that mixes regression with lower rank approximations. To improve the quality of the imputations, a generalisation is proposed that replaces the singular value decomposition (SVD) of the matrix with a regularised SVD in which the regularisation parameter is estimated by cross-validation. To evaluate the performance of the proposal, ten sets of real data from multienvironment trials were used. Missing values were created in each set at four percentages of missing not at random, and three criteria were then considered to investigate the effectiveness of the proposal. The results show that the regularised method proves very competitive when compared to the original method, beating it in several of the considered scenarios. As it is a very general system, its application can be extended to all multivariate data matrices. • The imputation method is modified through the inclusion of a stable and efficient computational algorithm that replaces the classical SVD least squares criterion by a penalised criterion. This penalty produces smoothed eigenvectors and eigenvalues that avoid overfitting problems, improving the performance of the method when the penalty is necessary. The size of the penalty can be determined by minimising one of the following criteria: the prediction errors, the Procrustes similarity statistic or the critical angles between subspaces of principal components.

Abstract Background Eukaryotes such as fungi and protists frequently accompany bacteria and archaea in microbial communities. Unfortunately, their presence is difficult to study with “shotgun” metagenomic sequencing since prokaryotic signals dominate in most environments. Recent methods for eukaryotic detection use eukaryote-specific marker genes, but they do not incorporate strategies to handle the presence of eukaryotes that are not represented in the reference marker gene set, and they are not compatible with web-based tools for downstream analysis. Results Here, we present CORRAL (for Clustering Of Related Reference ALignments), a tool for the identification of eukaryotes in shotgun metagenomic data based on alignments to eukaryote-specific marker genes and Markov clustering. Using a combination of simulated datasets, mock community standards, and large publicly available human microbiome studies, we demonstrate that our method is not only sensitive and accurate but is also capable of inferring the presence of eukaryotes not included in the marker gene reference, such as novel strains. Finally, we deploy CORRAL on our MicrobiomeDB.org resource, producing an atlas of eukaryotes present in various environments of the human body and linking their presence to study covariates. Conclusions CORRAL allows eukaryotic detection to be automated and carried out at scale. Implementation of CORRAL in MicrobiomeDB.org creates a running atlas of microbial eukaryotes in metagenomic studies. Since our approach is independent of the reference used, it may be applicable to other contexts where shotgun metagenomic reads are matched against redundant but non-exhaustive databases, such as the identification of bacterial virulence genes or taxonomic classification of viral reads. Video Abstract

BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.

Proteins mainly perform their functions by interacting with other proteins. Protein–protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein–protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein’s primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein’s primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein’s amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

Summary: Rats were more frequently used than mice to model human disease before mouse embryonic stem cells (mESCs) revolutionized genetic engineering in mice. Rat ESCs (rESCs) were first reported over 10 years ago, yet they are not as frequently used as mESCs. CRISPR-based gene editing in zygotes is widely used in rats but is limited by the difficulty of inserting or replacing DNA sequences larger than about 10 kb. We report here the generation of germline-competent rESC lines from several rat strains. These rESC lines maintain their potential for germline transmission after serial targeting with bacterial artificial chromosome (BAC)-based targeting vectors, and CRISPR-Cas9 cutting can increase targeting efficiency. Using these methods, we have successfully replaced entire rat genes spanning up to 101 kb with the human ortholog.

Little is known about the dynamics of human cooperation in groups with changing compositions. Using data from a large-scale and long-term online public goods game, this study shows how group changes are associated with temporarily lower cooperation.

Research consistently shows that higher-education participation has positive impacts on individual outcomes. However, few studies explicitly consider differences in these impacts by socio-economic background (SEB), and those which do fail to examine graduate trajectories over the long run, non-labor outcomes and relative returns. We address these knowledge gaps by investigating the short- and long-term socio-economic trajectories of Australian university graduates from advantaged and disadvantaged backgrounds across multiple domains. We use high-quality longitudinal data from two sources: the "Australian Longitudinal Census Dataset and the Household," "Income and Labour Dynamics in Australia Survey." Low-SEB graduates experienced short-term post-graduation disadvantage in employment and occupational status, but not wages. They also experienced lower job and financial security up to 5 years post-graduation. Despite this, low-SEB graduates benefited more from higher education in relative terms--that is, university education improves the situation of low-SEB individuals to a greater extent than it does for high-SEB individuals.

Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective.

In this article, we contribute to understanding of the mechanisms through which students' socio-economic family background can translate into academic performance by focusing on the concept of student engagement. Drawing on theoretical perspectives from disciplines across the social sciences, and a major nationally representative dataset from Australia, which links survey responses with administrative records on school performance, we conduct a series of multiple regression models to investigate the mediating role of student engagement on the relationship between students' socio-economic status (SES) and academic achievement. We find that, first, low-SES students show lower levels of engagement than other students, particularly in terms of behavioural and cognitive aspects; they also have lower achievement levels as measured by standardised test scores. We further find that lower engagement is associated with lower achievement levels, and that the effects of SES on achievement are partially mediated through student engagement. Although there are clearly other mechanisms in place that operate at the same time, it is important to focus on student engagement since it can be directly influenced by teachers and school leaders, as well as curriculum choices and school resources. This makes it a ripe target for government policies aimed at improving educational outcomes for students from low-SES families, compared with approaches targeting the influences of family environment or peer groups.

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein–protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein–protein interaction and protein–ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein–protein interaction and their sites, protein–ligand binding, and protein design.