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Whether post-transcriptional regulation of gene expression controls differentiation of stem cells for tissue renewal remains unknown. Quiescent stem cells exhibit a low level of protein synthesis 1 , which is key to maintaining the pool of fully functional stem cells, not only in the brain but also in the bone marrow and hair follicles 2 – 6 . Neurons also maintain a subset of messenger RNAs in a translationally silent state, which react ‘on demand’ to intracellular and extracellular signals. This uncoupling of general availability of mRNA from translation into protein facilitates immediate responses to environmental changes and avoids excess production of proteins, which is the most energy-consuming process within the cell. However, when post-transcriptional regulation is acquired and how protein synthesis changes along the different steps of maturation are not known. Here we show that protein synthesis undergoes highly dynamic changes when stem cells differentiate to neurons in vivo. Examination of individual transcripts using RiboTag mouse models reveals that whereas stem cells translate abundant transcripts with little discrimination, translation becomes increasingly regulated with the onset of differentiation. The generation of neurogenic progeny involves translational repression of a subset of mRNAs, including mRNAs that encode the stem cell identity factors SOX2 and PAX6, and components of the translation machinery, which are enriched in a pyrimidine-rich motif. The decrease of mTORC1 activity as stem cells exit the cell cycle selectively blocks translation of these transcripts. Our results reveal a control mechanism by which the cell cycle is coupled to post-transcriptional repression of key stem cell identity factors, thereby promoting exit from stemness. Sequencing of total and ribosome-associated mRNAs enables identification of specific mRNAs that are differentially translationally regulated along the neuronal stem cell lineage, independently of their availability.

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms, and has been widely applied in the biomedical analysis for shared biomarker identification. Although MTL has successfully supported either regression or classification tasks, incorporating mixed types of tasks into a unified MTL framework remains challenging, especially in biomedicine, where it can lead to biased biomarker identification. To address this issue, we propose an improved method of multi-task learning, MTLComb, which balances the weights of regression and classification tasks to promote unbiased biomarker identification. We demonstrate the algorithmic efficiency and clinical utility of MTLComb through analyses on both simulated data and actual biomedical studies pertaining to sepsis and schizophrenia. The code is available at https://github.com/transbioZI/MTLComb .

BackgroundSepsis is the leading cause of death in intensive care units (ICUs), and its timely detection and treatment improve clinical outcome and survival. Systemic inflammatory response syndrome (SIRS) refers to the concurrent fulfillment of at least two out of the following four clinical criteria: tachycardia, tachypnea, abnormal body temperature, and abnormal leukocyte count. While SIRS was controversially abandoned from the current sepsis definition, a dynamic SIRS representation still has potential for sepsis prediction and diagnosis.ObjectiveWe retrospectively elucidate the individual contributions of the SIRS criteria in a polytrauma cohort from the post-surgical ICU of University Medical Center Mannheim (Germany).MethodsWe used a dynamic and prospective SIRS algorithm tailored to the ICU setting by accounting for catecholamine therapy and mechanical ventilation. Two clinically relevant tasks are considered: (i) sepsis prediction using the first 24 h after admission to our ICU, and (ii) sepsis diagnosis using the last 24 h before sepsis onset and a time point of comparable ICU treatment duration for controls, respectively. We determine the importance of individual SIRS criteria by systematically varying criteria weights when summarizing the SIRS algorithm output with SIRS descriptors and assessing the classification performance of the resulting logistic regression models using a specifically developed ranking score.ResultsOur models perform better for the diagnosis than the prediction task (maximum AUROC 0.816 vs. 0.693). Risk models containing only the SIRS level average mostly show reasonable performance across criteria weights, with prediction and diagnosis AUROCs ranging from 0.455 (weight on leukocyte criterion only) to 0.693 and 0.619 to 0.800, respectively. For sepsis prediction, temperature and tachypnea are the most important SIRS criteria, whereas the leukocytes criterion is least important and potentially even counterproductive. For sepsis diagnosis, all SIRS criteria are relevant, with the temperature criterion being most influential.ConclusionSIRS is relevant for sepsis prediction and diagnosis in polytrauma, and no criterion should a priori be omitted. Hence, the original expert-defined SIRS criteria are valid, capturing important sepsis risk determinants. Our prospective SIRS algorithm provides dynamic determination of SIRS criteria and descriptors, allowing their integration in sepsis risk models also in other settings.

Genomic alterations (GA) in PIK3CA leads to the hyper-activation of the phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K) pathway in more than 20% of ovarian cancer (OC) patients. Therefore, PI3K therapies are under clinical evaluation for this subset of patients. Evidently, in clinical trials testing the efficacy of isoform-specific inhibitors of PI3K (PI3Ki), patients having a stable disease eventually relapse, as tumors become resistant to treatment. Hence, there is an urgent clinical need to develop new therapeutic combinations to improve the efficacy of PI3Ki in PIK3CA-driven OC patients. Here we identified the molecular mechanism that limits the efficacy of the beta-sparing PI3Ki, Taselisib (GDC0032), in PIK3CA -mutated OC cell lines (IGROV1 and OAW42) that acquired resistance to GDC0032. By comparing the molecular profile of GDC0032-sensitve and -resistant OC cell lines, we found that AKT/mTOR inhibition is required for GDC0032 efficacy. In resistant cells, the sustained activation of AKT/mTOR was regulated by the upregulation of the insulin growth factor 1 receptor (IGF1R). Knockdown of IGF1R re-sensitized cells to GDC0032 in vitro, and the combination of AEW541, an IGF1R inhibitor, with GDC0032 exhibited potent anti-tumor activity in vitro and in vivo. We further demonstrated that IGF1R regulates tumor cell proliferation in IGROV1 cells, whereas in OAW42, it determines autophagy as well. Overall, our findings suggest that the dual inhibition of PI3K and IGF1R may be considered as a new therapeutic strategy in PIK3CA -driven OC.

Infection can induce granulopoiesis. This process potentially contributes to blood gene classifiers of sepsis in systemic inflammatory response syndrome (SIRS) patients. This study aimed to identify signature genes of blood granulocytes from patients with sepsis and SIRS on intensive care unit (ICU) admission. CD15 + cells encompassing all stages of terminal granulocytic differentiation were analyzed. CD15 transcriptomes from patients with sepsis and SIRS on ICU admission and presurgical controls (discovery cohort) were subjected to differential gene expression and pathway enrichment analyses. Differential gene expression was validated by bead array in independent sepsis and SIRS patients (validation cohort). Blood counts of granulocyte precursors were determined by flow cytometry in an extension of the validation cohort. Despite similar transcriptional CD15 responses in sepsis and SIRS, enrichment of canonical pathways known to decline at the metamyelocyte stage (mitochondrial, lysosome, cell cycle, and proteasome) was associated with sepsis but not SIRS. Twelve of 30 validated genes, from 100 selected for changes in response to sepsis rather than SIRS, were endo-lysosomal. Revisiting the discovery transcriptomes revealed an elevated expression of promyelocyte-restricted azurophilic granule genes in sepsis and myelocyte-restricted specific granule genes in sepsis followed by SIRS. Blood counts of promyelocytes and myelocytes were higher in sepsis than in SIRS. Sepsis-induced granulopoiesis and signature genes of early terminal granulocytic differentiation thus provide a rationale for classifiers of sepsis in patients with SIRS on ICU admission. Yet, the distinction of this process from noninfectious tissue injury-induced granulopoiesis remains to be investigated.

Pneumonia develops frequently after major surgery and polytrauma and thus in the presence of systemic inflammatory response syndrome (SIRS) and organ dysfunction. Immune checkpoints balance self-tolerance and immune activation. Altered checkpoint blood levels were reported for sepsis. We analyzed associations of pneumonia incidence in the presence of SIRS during the first week of critical illness and trends in checkpoint blood levels. Patients were studied from day two to six after admission to a surgical intensive care unit (ICU). Blood was sampled and physician experts retrospectively adjudicated upon the presence of SIRS and Sepsis-1/2 every eight hours. We measured the daily levels of immune checkpoints and inflammatory markers by bead arrays for polytrauma patients developing pneumonia. Immune checkpoint time series were additionally determined for clinically highly similar polytrauma controls remaining infection-free during follow-up. We performed cluster analyses. Immune checkpoint time trends in cases and controls were compared with hierarchical linear models. For patients with surgical trauma and with and without sepsis, selected immune checkpoints were determined in study baseline samples. In polytrauma patients with post-injury pneumonia, eleven immune checkpoints dominated subcluster 3 that separated subclusters 1 and 2 of myeloid markers from subcluster 4 of endothelial activation, tissue inflammation, and adaptive immunity markers. Immune checkpoint blood levels were more stable in polytrauma cases than controls, where they trended towards an increase in subcluster A and a decrease in subcluster B. Herpes virus entry mediator (HVEM) levels (subcluster A) were lower in cases throughout. In unselected surgical patients, sepsis was not associated with altered HVEM levels at the study baseline. Pneumonia development after polytrauma until ICU-day six was associated with decreased blood levels of HVEM. HVEM signaling may reduce pneumonia risk by strengthening myeloid antimicrobial defense and dampening lymphoid-mediated tissue damage. Future investigations into the role of HVEM in pneumonia and sepsis development and as a predictive biomarker should consider the etiology of critical illness and the site of infection.

Critical decisions frequently rely on high-dimensional output from complex computer simulation models that show intricate cross-variable, spatial and temporal dependence structures, with weather and climate predictions being key examples. There is a strongly increasing recognition of the need for uncertainty quantification in such settings, for which we propose and review a general multi-stage procedure called ensemble copula coupling (ECC), proceeding as follows: 1. Generate a raw ensemble, consisting of multiple runs of the computer model that differ in the inputs or model parameters in suitable ways. 2. Apply statistical postprocessing techniques, such as Bayesian model averaging or nonhomogeneous regression, to correct for systematic errors in the raw ensemble, to obtain calibrated and sharp predictive distributions for each univariate output variable individually. 3. Draw a sample from each postprocessed predictive distribution. 4. Rearrange the sampled values in the rank order structure of the raw ensemble to obtain the ECC postprocessed ensemble. The use of ensembles and statistical postprocessing have become routine in weather forecasting over the past decade. We show that seemingly unrelated, recent advances can be interpreted, fused and consolidated within the framework of ECC, the common thread being the adoption of the empirical copula of the raw ensemble. Depending on the use of Quantiles, Random draws or Transformations at the sampling stage, we distinguish the ECC-Q, ECC-R and ECC-T variants, respectively. We also describe relations to the Schaake shuffle and extant copula-based techniques. In a case study, the ECC approach is applied to predictions of temperature, pressure, precipitation and wind over Germany, based on the 50-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble.

Many practical applications of statistical post-processing methods for ensemble weather forecasts require accurate modeling of spatial, temporal, and inter-variable dependencies. Over the past years, a variety of approaches has been proposed to address this need. We provide a comprehensive review and comparison of state-of-the-art methods for multivariate ensemble post-processing. We focus on generally applicable two-step approaches where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are restored via copula functions in a second step. The comparisons are based on simulation studies tailored to mimic challenges occurring in practical applications and allow ready interpretation of the effects of different types of misspecifications in the mean, variance, and covariance structure of the ensemble forecasts on the performance of the post-processing methods. Overall, we find that the Schaake shuffle provides a compelling benchmark that is difficult to outperform, whereas the forecast quality of parametric copula approaches and variants of ensemble copula coupling strongly depend on the misspecifications at hand.

Abstract Motivation Multi-task learning (MTL) enables simultaneous learning of related regression or classification tasks by exploiting shared information. The R package dsMTL provides a computational framework for federated MTL approaches, supporting the analysis of sensitive, individual-level data from geographically distributed data sources using the DataSHIELD platform. While the current architecture provides comprehensive data security mechanisms, these are not specifically tailored to MTL models. In particular, these models may still be vulnerable to membership inference attacks, attempting to determine whether a specific individual was included in a given training set using the model. Results To further enhance the privacy-preserving capabilities of dsMTL and protect against such attacks, differential privacy using the Laplace mechanism is integrated into dsMTL as a novel optional feature. This approach aims to obscure individual-level characteristics from the model while retaining group-level differences. The differential privacy implementation is validated in both simulation studies and a case study identifying schizophrenia patients from gene expression data. For practical utility, it is crucial to find an adequate balance between the degree of privacy protection and the conservation of model performance by choosing a reasonable privacy parameter within the differential privacy mechanism. Availability and implementation dsMTL is open-source and available at https://github.com/transbioZI/dsMTLBase (server-side) and https://github.com/transbioZI/dsMTLClient (client-side).