Tracing early stellar evolution with asteroseismology: pre-main sequence stars in NGC 2264

Asteroseismology has been proven to be a successful tool to unravel details of the internal structure for different types of stars in various stages of their main sequence and post-main sequence evolution. Recently, we found a relation between the detected pulsation properties in a sample of 34 pre-main sequence (pre-MS) delta Scuti stars and the relative phase in their pre-MS evolution. With this we are able to demonstrate that asteroseismology is similarly powerful if applied to stars in the earliest stages of evolution before the onset of hydrogen core burning.Comment: CoRoT Symposium 3 / Kepler KASC-7 joint meeting, Toulouse, July 2014. To be published by EPJ Web of Conference

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data

The expected growth in wind energy capacity requires efficient and accurate models for wind farm layout optimization, control, and annual energy predictions. Although analytical wake models are widely used for these applications, several model components must be better understood to improve their accuracy. To this end, we propose a Bayesian uncertainty quantification framework for physics-guided data-driven model enhancement. The framework incorporates turbulence-related aleatoric uncertainty in historical wind farm data, epistemic uncertainty in the empirical parameters, and systematic uncertainty due to unmodelled physics. We apply the framework to the wake expansion parameterization in the Gaussian wake model and employ historical power data of the Westermost Rough offshore wind farm. We find that the framework successfully distinguishes the three sources of uncertainty in the joint posterior distribution of the parameters. On the one hand, the framework allows for wake model calibration by selecting the maximum a posteriori estimators for the empirical parameters. On the other hand, it facilitates model validation by separating the measurement error and the model error distribution. In addition, the model adequacy and the effect of unmodelled physics are assessable via the posterior parameter uncertainty and correlations. Consequently, we believe that the Bayesian uncertainty quantification framework can be used to calibrate and validate existing and upcoming physics-guided models

Feature engineering for ICU mortality prediction based on hourly to bi-hourly measurements

Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56%, sensitivity of 90.59%, precision of 86.52% and F-1-score of 88.50%. The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs

Cortical Neurogenesis Requires Bcl6-Mediated Transcriptional Repression of Multiple Self-Renewal-Promoting Extrinsic Pathways.

During neurogenesis, progenitors switch from self-renewal to differentiation through the interplay of intrinsic and extrinsic cues, but how these are integrated remains poorly understood. Here, we combine whole-genome transcriptional and epigenetic analyses with in vivo functional studies to demonstrate that Bcl6, a transcriptional repressor previously reported to promote cortical neurogenesis, acts as a driver of the neurogenic transition through direct silencing of a selective repertoire of genes belonging to multiple extrinsic pathways promoting self-renewal, most strikingly the Wnt pathway. At the molecular level, Bcl6 represses its targets through Sirt1 recruitment followed by histone deacetylation. Our data identify a molecular logic by which a single cell-intrinsic factor represses multiple extrinsic pathways that favor self-renewal, thereby ensuring robustness of neuronal fate transition

Vital signs prediction and early warning score calculation based on continuous monitoring of hospitalised patients using wearable technology

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration

Patch test results with the European baseline series, 2019/20-Joint European results of the ESSCA and the EBS working groups of the ESCD, and the GEIDAC

BACKGROUND Continual analyses of patch test results with the European baseline series (EBS) serve both contact allergy surveillance and auditing the value of included allergens. OBJECTIVES To present results of current EBS patch testing, obtained in 53 departments in 13 European countries during 2019 and 2020. METHODS Anonymised or pseudonymised individual data, and partly aggregated data on demographic/clinical characteristics and patch test rest results with the EBS were prospectively collected and centrally pooled and analysed. RESULTS In 2019 and 2020, 22581 patients were patch tested with the EBS. Sensitization to nickel remained most common (19.8 (19.2-20.4)% positivity (95% confidence interval)). Fragrance mix I and Myroxylon pereirae yielded very similar results with 6.80 (6.43-7.19)% and 6.62 (6.25-7.00)% positivity, respectively. Formaldehyde at 2% aq. yielded almost one percentage point more positive reactions than 1% concentration (2.49 (2.16-2.85)% vs. 1.59 (1.33-1.88)); methylchloroisothiazolinone/methylisothiazolinone (MCI/MI) and MI alone up to around 5% positives. Among the new additions, propolis was most commonly positive (3.48 (3.16-3.82)%), followed by 2-hydroxyethyl methacrylate (2.32 (2.0-2.68)%). CONCLUSIONS Ongoing surveillance on the prevalence of contact sensitization contributes to an up-to-date baseline series containing the most frequent and/or relevant contact sensitizers for routine patch testing in Europe. This article is protected by copyright. All rights reserved

Expression of phosphorylated ribosomal protein S6 in mesothelioma patients - correlation with clinico-pathological characteristics and outcome: results from the European Thoracic Oncology Platform (ETOP) Mesoscape project

Pleural mesothelioma (PM) is an aggressive malignancy with poor prognosis. Although histology and pathologic stage are important prognostic factors, better prognostic biomarkers are needed. The ribosomal protein S6 is a downstream target of the phosphatidylinositol 3-kinase (PI3K) pathway involved in protein synthesis and cell proliferation. In previous studies, low phosphorylated S6 (pS6) immunoreactivity was significantly correlated with longer progression-free survival (PFS) and overall survival (OS) in PM patients. We aimed to correlate pS6 expression to clinical data in a large multi-centre PM cohort as part of the European Thoracic Oncology Platform (ETOP) Mesoscape project. Tissue Micro Arrays (TMAs) of PM were constructed and expression of pS6 was evaluated by a semi-quantitatively aggregate H-score. Expression results were correlated to patient characteristics as well as OS/PFS. pS6 IHC results of 364 patients from 9 centres, diagnosed between 1999 and 2017 were available. The primary histology of included tumours was epithelioid (70.3%), followed by biphasic (24.2%) and sarcomatoid (5.5%). TMAs included both treatment-naïve and tumour tissue taken after induction chemotherapy. High pS6 expression (181 patients with H-score>1.41) was significantly associated with less complete resection. In the overall cohort, OS/PFS were not significantly different between pS6-low and pS6-high patients. In a subgroup analysis non-epithelioid (biphasic and sarcomatoid) patients with high pS6 expression showed a significantly shorter OS (p < 0.001, 10.7 versus 16.9 months) and PFS (p < 0.001, 6.2 versus 10.8 months). In subgroup analysis, in non-epithelioid PM patients high pS6 expression was associated with significantly shorter OS and PFS. These exploratory findings suggest a clinically relevant PI3K pathway activation in non-epithelioid PM which might lay the foundation for future targeted treatment strategies