Pro-environmental behavior, personality and emotional intelligence in adolescents: a systematic review.

Introduction: Human behavior significantly contributes to environmental problems, making the study of pro-environmental behavior an important task for psychology. In this context, it is crucial to understand the pro-environmental behavior of adolescents, as young people play a fundamental role in facilitating long-term changes in environmental consciousness and encouraging decision-makers to take action. However, little is currently known about the pro-environmental behavior of adolescents. Recently, there has been growing interest in examining the influence of personality traits and emotional intelligence on pro-environmental behavior. Methods: We conducted a systematic review to enhance our understanding of adolescent pro-environmental behavior. Thus, this systematic review was designed to enhance understanding of adolescent's pro-environmental behavior by summarizing existing evidence on how it relates to personality and emotional intelligence. Results: Our findings suggest associations between specific personality traits and dimensions of emotional intelligence with adolescent pro-environmental behavior, aligning with similar studies conducted on adults. Discussion: While our findings offer valuable insights, further research is needed to establish causality and deepen our understanding of the interplay between multiple variables influencing pro-environmental behavior among adolescents. Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023387836], identifier [CRD42023387836].

People with obesity exhibit losses in muscle proteostasis that are partly improved by exercise training.

This pilot experiment examines if a loss in muscle proteostasis occurs in people with obesity and whether endurance exercise positively influences either the abundance profile or turnover rate of proteins in this population. Men with (n = 3) or without (n = 4) obesity were recruited and underwent a 14-d measurement protocol of daily deuterium oxide (D2 O) consumption and serial biopsies of vastus lateralis muscle. Men with obesity then completed 10-weeks of high-intensity interval training (HIIT), encompassing 3 sessions per week of cycle ergometer exercise with 1 min intervals at 100% maximum aerobic power interspersed by 1 min recovery periods. The number of intervals per session progressed from 4 to 8, and during weeks 8-10 the 14-d measurement protocol was repeated. Proteomic analysis detected 352 differences (p < 0.05, false discovery rate < 5%) in protein abundance and 19 (p < 0.05) differences in protein turnover, including components of the ubiquitin-proteasome system. HIIT altered the abundance of 53 proteins and increased the turnover rate of 22 proteins (p < 0.05) and tended to benefit proteostasis by increasing muscle protein turnover rates. Obesity and insulin resistance are associated with compromised muscle proteostasis, which may be partially restored by endurance exercise.

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.

Efficacy and Safety of Anticoagulants in Patients with Atrial Fibrillation and History of Falls or Risk of Falls: A Systematic Review and Multilevel Meta-Analysis.

INTRODUCTION: Atrial fibrillation (AF) is a major cause of stroke. Anticoagulants substantially reduce risk of stroke but are also associated with an increased risk of bleeding. Because of that, many patients do not receive anticoagulants, particularly patients at risk of falls. This systematic review and meta-analysis aims to compare anticoagulant treatment options for the management of atrial fibrillation patients at risk of falls or with a history of falls. METHODS: We conducted a PRISMA systematic review (until March 2022), including studies evaluating safety and efficacy of different anticoagulants (vitamin K antagonist [VKA] versus non-vitamin K antagonist oral anticoagulant [NOAC]). Outcomes were ischemic stroke, major bleeding, intracranial hemorrhage, hemorrhagic stroke, myocardial infarction, gastrointestinal bleeding, cardiovascular and all-cause mortality. A multilevel meta-analysis was conducted adjusting for clustering effects within studies examining more than one effect size. RESULTS: A total of 919 articles were identified, 848 after removing duplicates. The full text of 155 were screened and 10 articles were retained for final quantitative synthesis. Risk of bias was moderate to serious for the included studies. In meta-analysis, NOACs were associated with superior effectiveness compared with VKA for ischemic stroke/systemic embolism (hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.69-0.98; p < 0.05) and safety (HR 0.53, 95% CI 0.40-0.71; p < 0.05) for intracranial hemorrhage. There were no differences in other outcomes. CONCLUSION: NOACs were associated with less intracranial hemorrhages and ischemic strokes/systemic embolisms than VKAs in AF patients at risk of falls. These findings suggesting preferred use of NOACs over VKAs have clinical implications for physicians, patients and policy makers.

Breast cancer patient characterisation and visualisation using deep learning and fisher information networks.

Breast cancer is the most commonly diagnosed female malignancy globally, with better survival rates if diagnosed early. Mammography is the gold standard in screening programmes for breast cancer, but despite technological advances, high error rates are still reported. Machine learning techniques, and in particular deep learning (DL), have been successfully used for breast cancer detection and classification. However, the added complexity that makes DL models so successful reduces their ability to explain which features are relevant to the model, or whether the model is biased. The main aim of this study is to propose a novel visualisation to help characterise breast cancer patients using Fisher Information Networks on features extracted from mammograms using a DL model. In the proposed visualisation, patients are mapped out according to their similarities and can be used to study new patients as a 'patient-like-me' approach. When applied to the CBIS-DDSM dataset, it was shown that it is a competitive methodology that can (i) facilitate the analysis and decision-making process in breast cancer diagnosis with the assistance of the FIN visualisations and 'patient-like-me' analysis, and (ii) help improve diagnostic accuracy and reduce overdiagnosis by identifying the most likely diagnosis based on clinical similarities with neighbouring patients.

Externally validated models for first diagnosis and risk of progression of knee osteoarthritis.

OBJECTIVE: We develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST). MATERIALS AND METHODS: The diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively. RESULTS: The classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721-0.774) and 0.670 (0.631-0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325-0.7439) and in external validation 0.72 (0.7190-0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data. DISCUSSION: The models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years. CONCLUSION: Modelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients.

The management of anticoagulants in patients with atrial fibrillation and history of falls or risk of falls: protocol for a systematic review and meta-analysis.

BACKGROUND: Atrial fibrillation affects an estimated 33 million individuals worldwide and is a major cause of stroke, heart failure, and death. Anticoagulants substantially reduce the risk of stroke but are also associated with an increased risk of bleeding and especially intracranial hemorrhage which is the most concerning complication. Because of this, many patients are not offered anticoagulants, particularly patients at risk of falls or with a history of falls. It is unclear what anticoagulant treatment these patients should be offered. The Liverpool AF-Falls project aims to investigate this area, and this protocol for a systematic review and meta-analysis aims to define what is the most appropriate anticoagulant treatment option for the management of atrial fibrillation patients at risk of falls or with a history of falls. METHODS: This systematic review and meta-analysis will include randomized and non-randomized studies evaluating the safety and efficacy of different anticoagulant treatments (vitamin K antagonist and non-vitamin K antagonist oral anti-coagulant). Bibliographic databases (Cochrane Central Register of Controlled Trials, CINAHL, ClinicalTrials.gov , Embase, MEDLINE, Scopus and Web of Science) will be searched according to a pre-specified search strategy. Titles, abstracts, and full texts will be assessed by two independent reviewers and disagreements resolved with a third independent reviewer. The Cochrane Risk of Bias tool 2 (RoB 2) will be used to assess the risk of bias in randomized trials, and the Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) tool will be used for non-randomized studies. A pairwise meta-analysis based on the fixed and random-effects models will be conducted. Publication bias will be evaluated with a funnel plot and Egger's test. Heterogeneity will be assessed with the I2 statistic. If conditions for indirect comparison are met and sufficient data are available, a network meta-analysis will be conducted using frequentist and Bayesian methodologies. DISCUSSION: This review will be the first to summarize direct and indirect evidence on the safety and efficacy of anticoagulant treatments in atrial fibrillation patients at risk of falls or with a history of falls. The findings will be important to patients, clinicians, and health policy-makers to inform best practices in the use of these treatments. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registry number: CRD42020201086.

Analysis of malaria clinical-epidemiological predictors in individuals from Brazilian Amazon.

This research aims to determine whether the combination of epidemiological and clinical features can predict malaria. Diagnostic investigation detected 22.3% of individuals with Plasmodium vivax (P. vivax) malaria, with significant predominance of the male gender. The malaria triad (fever, chills and headache) had a more expressive frequency (81.1%) in individuals with positive thick blood than those with negative thick blood smear (65.1%), although there was no statistical significance. Among the variables analysed as predictive for positive thick blood smear, it was observed that personal history of travel to an endemic malaria area and past malaria infection (PMI) were significantly associated with malaria, even in multiple logistic regression. Fever had the higher sensitivity (94.6%) and past malaria history had the greater specificity (68.2%), with accuracy of 23.5% and 67.5%, respectively. In combined analysis, fever with chills had the highest sensitivity (91.9%), but low accuracy (38.5%). High specificity (91.5%) was found in the association of malaria triad, PMI and history of travel to endemic malaria area (which along with anorexia, was higher 94.6%), with good accuracy (80.7%), suggesting that the screening of patients for performing thick blood smear can be based on these data. The epidemiological features and the malaria triad (fever, chills and headache) can be predictors for identification of malaria patients, concurring to precocious diagnosis and immediate treatment of individuals with malaria.

Evaluation of drug-induced liver injury as etiology for acute liver failure in Brazil.

INTRODUCTION AND OBJECTIVES: Little is known about the etiology of acute liver failure (ALF) in Latin America. The objective of this paper is to investigate the main etiologies of ALF in Brazil, including Drug Induced Liver Injury (DILI) using stringent causality criteria. PATIENTS OR MATERIAL AND METHODS: All the cases of individuals who underwent liver transplantation (LT) in 12 centers in Brazil for ALF were reviewed. When DILI was stated as the cause of ALF, causality criteria were applied on site by the main investigator in order to rule out other etiologies. RESULTS: 325 individuals had ALF mainly for unknown reasons (34%), DILI (27%) and AIH (18%). Reassessment of the 89 cases of DILI, using stringent causality criteria, revealed that in only 42 subjects could DILI be confirmed as the cause of ALF. Acetaminophen (APAP) toxicity (n = 3) or DILI due to herbal and dietary supplements (HDS) (n = 2) were not commonly observed. CONCLUSIONS: Undetermined etiology and DILI are the main causes of ALF in Brazil. However, APAP toxicity and DILI due to HDS are mostly uncommon.

Prediction of Balance Perturbations and Falls on Stairs in Older People Using a Biomechanical Profiling Approach: A 12-Month Longitudinal Study.

BACKGROUND: Stair falls are a major health problem for older people, but presently, there are no specific screening tools for stair fall prediction. The purpose of the present study was to investigate whether stair fallers could be differentiated from nonfallers by biomechanical risk factors or physical/psychological parameters and to establish the biomechanical stepping profile posing the greatest risk for a stair fall. METHODS: Eighty-seven older adults (age: 72.1 ± 5.2 years) negotiated an instrumented seven-step staircase and performed a range of physical/psychological tasks. k-Means clustering was used to profile the overall stair negotiation behavior with biomechanical parameters indicative of fall risk as input. Falls and events of balance perturbation (combined "hazardous events") were then monitored during a 12-month follow-up. Cox-regression analysis was performed to examine whether physical/psychological parameters or biomechanical outcome measures could predict future hazardous events. Kaplan-Meier survival curves were obtained to identify the stepping strategy posing a risk for a hazardous event. RESULTS: Physical/psychological parameters did not predict hazardous events and the commonly used Fall Risk Assessment Tool classified only 1/17 stair fallers at risk for a fall. Single biomechanical risk factors could not predict hazardous events on stairs either. On the contrary, two particular clusters identified by the stepping profiling method in stair ascent were linked with hazardous events. CONCLUSION: This highlights the potential of the stepping profiling method to predict stair fall risk in older adults against the limited predictability of single-parameter approaches currently used as screening tools.

Biomechanical loading during running: can a two mass-spring-damper model be used to evaluate ground reaction forces for high-intensity tasks?

Running impact forces expose the body to biomechanical loads leading to beneficial adaptations, but also risk of injury. High-intensity running tasks, especially, are deemed highly demanding for the musculoskeletal system, but loads experienced during these actions are not well understood. To eventually predict GRF and understand the biomechanical loads experienced during such activities in greater detail, this study aimed to (1) examine the feasibility of using a simple two mass-spring-damper model, based on eight model parameters, to reproduce ground reaction forces (GRFs) for high-intensity running tasks and (2) verify whether the required model parameters were physically meaningful. This model was used to reproduce GRFs for rapid accelerations and decelerations, constant speed running and maximal sprints. GRF profiles and impulses could be reproduced with low to very low errors across tasks, but subtler loading characteristics (impact peaks, loading rate) were modelled less accurately. Moreover, required model parameters varied strongly between trials and had minimal physical meaning. These results show that although a two mass-spring-damper model can be used to reproduce overall GRFs for high-intensity running tasks, the application of this simple model for predicting GRFs in the field and/or understanding the biomechanical demands of training in greater detail is likely limited.

Clinical Characteristics of COVID-19 Patients in a Regional Population With Diabetes Mellitus: The ACCREDIT Study.

Objective: To identify clinical and biochemical characteristics associated with 7- & 30-day mortality and intensive care admission amongst diabetes patients admitted with COVID-19. Research Design and Methods: We conducted a cohort study collecting data from medical notes of hospitalised people with diabetes and COVID-19 in 7 hospitals within the Mersey-Cheshire region from 1 January to 30 June 2020. We also explored the impact on inpatient diabetes team resources. Univariate and multivariate logistic regression analyses were performed and optimised by splitting the dataset into a training, test, and validation sets, developing a robust predictive model for the primary outcome. Results: We analyzed data from 1004 diabetes patients (mean age 74.1 (± 12.6) years, predominantly men 60.7%). 45% belonged to the most deprived population quintile in the UK. Median BMI was 27.6 (IQR 23.9-32.4) kg/m2. The primary outcome (7-day mortality) occurred in 24%, increasing to 33% by day 30. Approximately one in ten patients required insulin infusion (9.8%). In univariate analyses, patients with type 2 diabetes had a higher risk of 7-day mortality [p < 0.05, OR 2.52 (1.06, 5.98)]. Patients requiring insulin infusion had a lower risk of death [p = 0.02, OR 0.5 (0.28, 0.9)]. CKD in younger patients (<70 years) had a greater risk of death [OR 2.74 (1.31-5.76)]. BMI, microvascular and macrovascular complications, HbA1c, and random non-fasting blood glucose on admission were not associated with mortality. On multivariate analysis, CRP and age remained associated with the primary outcome [OR 3.44 (2.17, 5.44)] allowing for a validated predictive model for death by day 7. Conclusions: Higher CRP and advanced age were associated with and predictive of death by day 7. However, BMI, presence of diabetes complications, and glycaemic control were not. A high proportion of these patients required insulin infusion warranting increased input from the inpatient diabetes teams.

Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.

The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to be relevant for their characterisation. The data set is bootstrapped in order to maximise the robustness of feature selection for nominated target variables. Specifically, Conditional Independence maps (CI-maps) built from the data and their derived Bayesian networks have been used. A Directed Acyclic Graph (DAG) is built from CI-maps, being a major challenge the minimization of errors in the graph structure. This work presents empirical evidence on how to reduce false positive errors via the False Discovery Rate, and how to identify appropriate parameter settings to improve the False Negative Reduction. In addition, several node ordering policies are investigated that transform the graph into a DAG. The obtained results show that ordering nodes by strength of mutual information can recover a representative DAG in a reasonable time, although a more accurate graph can be recovered using a random order of samples at the expense of increasing the computation time.

Adaptation of rat fast-twitch muscle to endurance activity is underpinned by changes to protein degradation as well as protein synthesis.

Muscle adaptations to exercise are underpinned by alterations to the abundance of individual proteins, which may occur through a change either to the synthesis or degradation of each protein. We used deuterium oxide (2 H2 O) labeling and chronic low-frequency stimulation (CLFS) in vivo to investigate the synthesis, abundance, and degradation of individual proteins during exercise-induced muscle adaptation. Independent groups of rats received CLFS (10 Hz, 24 h/d) and 2 H2 O for 0, 10, 20, or 30 days. The extensor digitorum longus (EDL) was isolated from stimulated (Stim) and contralateral non-stimulated (Ctrl) legs. Proteomic analysis encompassed 38 myofibrillar and 46 soluble proteins and the rates of change in abundance, synthesis, and degradation were reported in absolute (ng/d) units. Overall, synthesis and degradation made equal contributions to the adaptation of the proteome, including instances where a decrease in protein-specific degradation primarily accounted for the increase in abundance of the protein.

Fractional Synthesis Rates of Individual Proteins in Rat Soleus and Plantaris Muscles.

Differences in the protein composition of fast- and slow-twitch muscle may be maintained by different rates of protein turnover. We investigated protein turnover rates in slow-twitch soleus and fast-twitch plantaris of male Wistar rats (body weight 412 ± 69 g). Animals were assigned to four groups (n = 3, in each), including a control group (0 d) and three groups that received deuterium oxide (D2O) for either 10 days, 20 days or 30 days. D2O administration was initiated by an intraperitoneal injection of 20 µL of 99% D2O-saline per g body weight, and maintained by provision of 4% (v/v) D2O in the drinking water available ad libitum. Soluble proteins from harvested muscles were analysed by liquid chromatography-tandem mass spectrometry and identified against the SwissProt database. The enrichment of D2O and rate constant (k) of protein synthesis was calculated from the abundance of peptide mass isotopomers. The fractional synthesis rate (FSR) of 44 proteins in soleus and 34 proteins in plantaris spanned from 0.58%/day (CO1A1: Collagen alpha-1 chain) to 5.40%/day NDRG2 (N-myc downstream-regulated gene 2 protein). Eight out of 18 proteins identified in both muscles had a different FSR in soleus than in plantaris (p < 0.05).

A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities.

Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple body-worn sensors, limiting their ecological validity, or aim to estimate discrete force parameters, limiting their ability to assess overall biomechanical load. This paper presents a neural network method to predict GRF time series from a single, commonly used, trunk-mounted accelerometer. The presented method uses a principal component analysis and multilayer perceptron (MLP) to obtain predictions. Time-series r2 coefficients with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r2 was 0.74 across activities, comparing favourably with correlation analysis approaches. Several modifications, such as subject-specific training of the MLP, may help to improve results further, but the presented method can accurately predict GRF from trunk accelerometry data without requiring additional information. Results demonstrate the scope of machine learning to exploit common wearable technologies to estimate GRF in sport-specific environments.

Stair negotiation behaviour of older individuals: Do step dimensions matter?

Stair falls are a major health problem for older people. Most studies on identification of stair fall risk factors are limited to staircases set in given step dimensions. However, it remains unknown whether the conclusions drawn would still apply if the dimensions had been changed to represent more challenging or easier step dimensions encountered in domestic and public buildings. The purpose was to investigate whether the self-selected biomechanical stepping behaviours are maintained when the dimensions of a staircase are altered. Sixty-eight older adults (>65 years) negotiated a seven-step staircase set in two step dimensions (shallow staircase: rise 15 cm, going 28 cm; steep staircase: rise 20 cm, going 25 cm). Six biomechanical outcome measures indicative of stair fall risk were measured. K-means clustering profiled the overall stair-negotiating behaviour and cluster profiles were calculated. A Cramer's V measured the degree of association in membership between clusters. The cluster profiles revealed that the biomechanically risky and conservative factors that characterized the overall behaviour in the clusters did not differ for the majority of older adults between staircases for ascent and descent. A strong association of membership between the clusters on the shallow staircase and the steep staircase was found for stair ascent (Cramer's V: 0.412, p < 0.001) and descent (Cramer's V: 0.380, p = 0.003). The findings indicate that manipulating the demand of the task would not affect the underpinning mechanism of a potential stair fall. Therefore, for most individuals, detection of stair fall risk might not require testing using a staircase with challenging step dimensions.

Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.

Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype (i.e., disease trait). GWAS typically use a p-value threshold of 5*10-8 to identify highly ranked SNPs. While this approach has proven useful for detecting disease-susceptible SNPs, evidence has shown that many of these are, in fact, false positives. Consequently, there is some ambiguity about the most suitable threshold for claiming genome-wide significance. Many believe that using lower p-values will allow us to investigate the joint epistatic interactions between SNPs and provide better insights into phenotype expression. One example that uses this approach is multifactor dimensionality reduction (MDR), which identifies combinations of SNPs that interact to influence a particular outcome. However, computational complexity is increased exponentially as a function of higher-order combinations making approaches like MDR difficult to implement. Even so, understanding epistatic interactions in complex diseases is a fundamental component for robust genotype-phenotype mapping. In this paper, we propose a novel framework that combines GWAS quality control and logistic regression with deep learning stacked autoencoders to abstract higher-order SNP interactions from large, complex genotyped data for case-control classification tasks in GWAS analysis. We focus on the challenging problem of classifying preterm births which has a strong genetic component with unexplained heritability reportedly between 20-40 percent. A GWAS data set, obtained from dbGap is utilised, which contains predominantly urban low-income African-American women who had normal and preterm deliveries. Epistatic interactions from original SNP sequences were extracted through a deep learning stacked autoencoder model and used to fine-tune a classifier for discriminating between term and preterm births observations. All models are evaluated using standard binary classifier performance metrics. The findings show that important information pertaining to SNPs and epistasis can be extracted from 4,666 raw SNPs generated using logistic regression (p-value = 5*10-3) and used to fit a highly accurate classifier model. The following results (Sen = 0.9562, Spec = 0.8780, Gini = 0.9490, Logloss = 0.5901, AUC = 0.9745, and MSE = 0.2010) were obtained using 50 hidden nodes and (Sen = 0.9289, Spec = 0.9591, Gini = 0.9651, Logloss = 0.3080, AUC = 0.9825, and MSE = 0.0942) using 500 hidden nodes. The results were compared with a Support Vector Machine (SVM), a Random Forest (RF), and a Fishers Linear Discriminant Analysis classifier, which all failed to improve on the deep learning approach.

Reliability of Protein Abundance and Synthesis Measurements in Human Skeletal Muscle.

The repeatability of dynamic proteome profiling (DPP), which is a novel technique for measuring the relative abundance (ABD) and fractional synthesis rate (FSR) of proteins in humans, is investigated. LC-MS analysis is performed on muscle samples taken from male participants (n = 4) that consumed 4 × 50 mL doses of deuterium oxide (2 H2 O) per day for 14 days. ABD is measured by label-free quantitation and FSR is calculated from time-dependent changes in peptide mass isotopomer abundances. One-hundred one proteins have at least one unique peptide and are used in the assessment of protein ABD. Fifty-four of these proteins meet more stringent criteria and are used in the assessment of FSR data. The median (M), lower-, (Q1 ) and upper-quartile (Q3 ) values for protein FSR (%/d) are M = 1.63, Q1  = 1.07, and Q3  = 3.24, respectively. The technical CV of ABD data has a median value of 3.6% (Q1 1.7% to Q3 6.7%), whereas the median CV of FSR data is 10.1% (Q1 3.5% to Q3 16.5%). These values compare favorably against other assessments of technical repeatability of proteomics data, which often set a CV of 20% as the upper bound of acceptability.

A Novel Approach to Detecting Epistasis using Random Sampling Regularisation.

Epistasis is a progressive approach that complements the 'common disease, common variant' hypothesis that highlights the potential for connected networks of genetic variants collaborating to produce a phenotypic expression. Epistasis is commonly performed as a pairwise or limitless-arity capacity that considers variant networks as either variant vs variant or as high order interactions. This type of analysis extends the number of tests that were previously performed in a standard approach such as Genome-Wide Association Study (GWAS), in which False Discovery Rate (FDR) is already an issue, therefore by multiplying the number of tests up to a factorial rate also increases the issue of FDR. Further to this, epistasis introduces its own limitations of computational complexity and intensity that are generated based on the analysis performed; to consider the most intense approach, a multivariate analysis introduces a time complexity of O(n!). Proposed in this paper is a novel methodology for the detection of epistasis using interpretable methods and best practice to outline interactions through filtering processes. Using a process of Random Sampling Regularisation which randomly splits and produces sample sets to conduct a voting system to regularise the significance and reliability of biological markers, SNPs. Preliminary results are promising, outlining a concise detection of interactions. Results for the detection of epistasis, in the classification of breast cancer patients, indicated eight outlined risk candidate interactions from five variants and a singular candidate variant with high protective association.