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The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability. The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models’ log-likelihood and akaike information criterion scores. In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing. We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.

Global change is expected to expand and shrink species' distributions in complex ways beyond just retraction at warm edges and expansion at cool ones. Detecting these changes is complicated by the need for robust baseline data for comparison. For instance, gaps in species' distributions may reflect long‐standing patterns, recent shifts, or merely insufficient sampling effort. We investigated an apparent gap in the distribution of the American pika, Ochotona princeps, along the North American Sierra Nevada. Historical records from this region are sparse, with ~100 km separating previously documented pika‐occupied sites. Surveys during 2014–2023 confirmed that the gap is currently unoccupied by pikas, and evidence of past occurrence indicates that the gap has expanded over time, likely due to contemporary global change. Sites lacking evidence of past pika occurrence were climatically and geographically more distant from sites with signs of recent (former) occurrence and currently occupied sites. Formerly and currently occupied sites were partially climatically distinct, suggesting either metapopulation‐like dynamics or an extinction debt that may eventually result in further population losses at the edge of suitable climate space. The Feather River gap aligns with one of several “low points” in the otherwise continuous boreal‐like conditions spanning the Cascade Range and Sierra Nevada and is coincident with discontinuities in ranges of other mammals. These results highlight the potential for climate‐driven fragmentation and range retraction in regions considered climatically and geographically interior to a species' overall distribution.

To date, non-pharmacological interventions (NPI) have been the mainstay for controlling the coronavirus disease-2019 (COVID-19) pandemic. While NPIs are effective in preventing health systems overload, these long-term measures are likely to have significant adverse economic consequences. Therefore, many countries are currently considering to lift the NPIs—increasing the likelihood of disease resurgence. In this regard, dynamic NPIs, with intervals of relaxed social distancing, may provide a more suitable alternative. However, the ideal frequency and duration of intermittent NPIs, and the ideal “break” when interventions can be temporarily relaxed, remain uncertain, especially in resource-poor settings. We employed a multivariate prediction model, based on up-to-date transmission and clinical parameters, to simulate outbreak trajectories in 16 countries, from diverse regions and economic categories. In each country, we then modelled the impacts on intensive care unit (ICU) admissions and deaths over an 18-month period for following scenarios: (1) no intervention, (2) consecutive cycles of mitigation measures followed by a relaxation period, and (3) consecutive cycles of suppression measures followed by a relaxation period. We defined these dynamic interventions based on reduction of the mean reproduction number during each cycle, assuming a basic reproduction number ( R 0 ) of 2.2 for no intervention, and subsequent effective reproduction numbers ( R ) of 0.8 and 0.5 for illustrative dynamic mitigation and suppression interventions, respectively. We found that dynamic cycles of 50-day mitigation followed by a 30-day relaxation reduced transmission, however, were unsuccessful in lowering ICU hospitalizations below manageable limits. By contrast, dynamic cycles of 50-day suppression followed by a 30-day relaxation kept the ICU demands below the national capacities. Additionally, we estimated that a significant number of new infections and deaths, especially in resource-poor countries, would be averted if these dynamic suppression measures were kept in place over an 18-month period. This multi-country analysis demonstrates that intermittent reductions of R below 1 through a potential combination of suppression interventions and relaxation can be an effective strategy for COVID-19 pandemic control. Such a “schedule” of social distancing might be particularly relevant to low-income countries, where a single, prolonged suppression intervention is unsustainable. Efficient implementation of dynamic suppression interventions, therefore, confers a pragmatic option to: (1) prevent critical care overload and deaths, (2) gain time to develop preventive and clinical measures, and (3) reduce economic hardship globally.

During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.

Background Prognostication is very important to clinicians and families during the early management of severe traumatic brain injury (sTBI), however, there are no gold standard biomarkers to determine prognosis in sTBI. As has been demonstrated in several diseases, early measurement of serum metabolomic profiles can be used as sensitive and specific biomarkers to predict outcomes. Methods We prospectively enrolled 59 adults with sTBI (Glasgow coma scale, GCS ≤ 8) in a multicenter Canadian TBI (CanTBI) study. Serum samples were drawn for metabolomic profiling on the 1st and 4th days following injury. The Glasgow outcome scale extended (GOSE) was collected at 3- and 12-months post-injury. Targeted direct infusion liquid chromatography-tandem mass spectrometry (DI/LC–MS/MS) and untargeted proton nuclear magnetic resonance spectroscopy ( 1 H-NMR) were used to profile serum metabolites. Multivariate analysis was used to determine the association between serum metabolomics and GOSE, dichotomized into favorable (GOSE 5–8) and unfavorable (GOSE 1–4), outcomes. Results Serum metabolic profiles on days 1 and 4 post-injury were highly predictive (Q 2  > 0.4–0.5) and highly accurate (AUC > 0.99) to predict GOSE outcome at 3- and 12-months post-injury and mortality at 3 months. The metabolic profiles on day 4 were more predictive (Q 2  > 0.55) than those measured on day 1 post-injury. Unfavorable outcomes were associated with considerable metabolite changes from day 1 to day 4 compared to favorable outcomes. Increased lysophosphatidylcholines, acylcarnitines, energy-related metabolites (glucose, lactate), aromatic amino acids, and glutamate were associated with poor outcomes and mortality. Discussion Metabolomic profiles were strongly associated with the prognosis of GOSE outcome at 3 and 12 months and mortality following sTBI in adults. The metabolic phenotypes on day 4 post-injury were more predictive and significant for predicting the sTBI outcome compared to the day 1 sample. This may reflect the larger contribution of secondary brain injury (day 4) to sTBI outcome. Patients with unfavorable outcomes demonstrated more metabolite changes from day 1 to day 4 post-injury. These findings highlighted increased concentration of neurobiomarkers such as N-acetylaspartate (NAA) and tyrosine, decreased concentrations of ketone bodies, and decreased urea cycle metabolites on day 4 presenting potential metabolites to predict the outcome. The current findings strongly support the use of serum metabolomics, that are shown to be better than clinical data, in determining prognosis in adults with sTBI in the early days post-injury. Our findings, however, require validation in a larger cohort of adults with sTBI to be used for clinical practice.

Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.

Childhood adversities are major preventable risk factors for poor mental and physical health. Scientific advances in this area are not matched by clinical gains for affected individuals. We reflect on novel research directions that could accelerate clinical impact.

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Individual patient data from all six eligible randomised controlled trials were used to develop ( = 3, = 1722) and test ( = 3, = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months. Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

The rising scarcity of potable water, coupled with environmental concerns over conventional purification methods, has accelerated the global shift toward renewable energy-based clean water solutions. Solar stills, with their simplicity, sustainability, and low operating costs, present a promising option for decentralized water purification. This study examines three solar still designs—rectangular, conical, and hemispherical—under identical conditions to identify the most efficient configuration based on key performance outcomes. However, the intricate nonlinear relationships among the output parameters and the geometric complexities of the stills pose significant challenges to conventional optimization techniques. To overcome these limitations, a hybrid methodology is employed, incorporating a priority-based clustering model to systematically optimize and identify the ideal solar still design. The integrated analysis reveals strong thermal-performance relationships, with heat transfer coefficient (h) showing high correlations with Tw, Tg, and Ta ( r  > 0.77, Adj R 2 ≈ 0.89), while Qew is strongly linked to Tg ( r  = 0.9284), Tw ( r  = 0.8941), and h ( r  = 0.9221); Mew exhibits an almost perfect correlation with Qew ( r  = 0.9998, Adj R 2 ≈ 0.9971). The ANN model using the Gaussian membership function demonstrated the highest prediction accuracy with R 2 = 0.997, RMSE ≈ 0.14%, and MSE ≈ 0.01%. Priority weighting favoured Mew at 39%, followed by η (28%), Qew (19%), and h (14%), improving clustering accuracy. K-means clustering pinpointed Trial 27 as optimal, with I(t) = 820 W/m 2 , Ta = 37 °C, Tw = 59 °C, and Tg = 53 °C, yielding the best thermal synergy for maximum solar still performance.

Background Machine learning algorithms have been used to predict malaria risk and severity, identify immunity biomarkers for malaria vaccine candidates, and determine molecular biomarkers of antimalarial drug resistance. Developing these prediction models requires large training datasets to ensure prediction accuracy when applied to new individuals in the target population. Learning curves can be used to assess the sample size required for the training dataset by evaluating the predictive performance of a model trained using different dataset sizes. These curves are agnostic to the specific prediction model, but their construction does require existing data. This tutorial demonstrates how to generate and interpret learning curves for malaria prediction models developed using machine learning algorithms. Methods To illustrate the approach, training dataset sizes were evaluated to inform the design of a “mock” prediction modelling study aimed to predict the artemisinin resistance status of Plasmodium falciparum malaria isolates from gene expression data. Data were simulated based on a previously published in vivo parasite gene expression dataset, which contained transcriptomes of 1043  P. falciparum isolates from patients with acute malaria, of which 29% (299/1043) were from slow clearing infections (parasite clearance half-life > 5 h). Learning curves were produced for two machine learning algorithms, sparse Partial Least Squares-Discriminant Analysis plus Support Vector Machines (sPLSDA + SVMs) and random forests. Prediction error was measured using the balanced error rate (average of percentage of slow clearing infections incorrectly predicted as fast and percentage of fast clearing infections predicted as slow). Results For this mock malaria prediction study, the balanced error rate on a test dataset not used for model training (208 samples) was 50% for sPLSDA + SVMs and 50% for random forests on the smallest training dataset evaluated (20 samples) and 14% for sPLSDA + SVMs and 22% for random forests on the largest training dataset evaluated (835 samples). The shape of the learning curves indicates that increasing the training dataset size beyond 835 samples is unlikely to significantly reduce the balanced error rates further. Conclusions Learning curves are a simple tool that can be used to determine the minimum sample size required for future prediction modelling studies of different malaria outcomes that use machine learning algorithms for prediction. These curves need to be generated for each specific prediction modelling application.