Explore the vast range of titles available.

Mass loss is one of the key parameters that determine stellar evolution. Despite the progress we have achieved over the last decades we still cannot match the observational derived values with theoretical predictions. Even worse, there are certain phases, such as the B[e] supergiants (B[e]SGs) and the Luminous Blue Variables (LBVs), where significant mass is lost through episodic or outburst activity. This leads to various structures forming around them that permit dust formation, making these objects bright IR sources. The ASSESS project aims to determine the role of episodic mass in the evolution of massive stars, by examining large numbers of cool and hot objects (such as B[e]SGs/LBVs). For this purpose, we initiated a large observation campaign to obtain spectroscopic data for ∼1000 IR-selected sources in 27 nearby galaxies. Within this project we successfully identified seven B[e] supergiants (one candidate) and four Luminous Blue Variables of which six and two, respectively, are new discoveries. We used spectroscopic, photometric, and light curve information to better constrain the nature of the reported objects. We particularly noted the presence of B[e]SGs at metallicity environments as low as 0.14 Z⊙.

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic mass loss although definitely present observationally, is not included in the models, while its role is currently undetermined. A major hindrance is the lack of large enough samples of classified stars. We attempted to address this by applying an ensemble machine-learning approach using color indices (from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining the probabilities from three different algorithms. We trained on M31 and M33 sources with known spectral classification, which we grouped into Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet and background galaxies/AGNs. We then applied the classifier to about one million Spitzer point sources from 25 nearby galaxies, spanning a range of metallicites (). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

Abstract Background Early clinical severity assessments during the 2009 influenza A H1N1 pandemic (pH1N1) overestimated clinical severity due to selection bias and other factors. We retrospectively investigated how to use data from the International Network for Strategic Initiatives in Global HIV Trials, a global clinical influenza research network, to make more accurate case fatality ratio (CFR) estimates early in a future pandemic, an essential part of pandemic response. Methods We estimated the CFR of medically attended influenza (CFRMA) as the product of probability of hospitalization given confirmed outpatient influenza and the probability of death given hospitalization with confirmed influenza for the pandemic (2009-2011) and post-pandemic (2012-2015) periods. We used literature survey results on health-seeking behavior to convert that estimate to CFR among all infected persons (CFRAR). Results During the pandemic period, 5.0% (3.1%-6.9%) of 561 pH1N1-positive outpatients were hospitalized. Of 282 pH1N1-positive inpatients, 8.5% (5.7%-12.6%) died. CFRMA for pH1N1 was 0.4% (0.2%-0.6%) in the pandemic period 2009-2011 but declined 5-fold in young adults during the post-pandemic period compared to the level of seasonal influenza in the post-pandemic period 2012-2015. CFR for influenza-negative patients did not change over time. We estimated the 2009 pandemic CFRAR to be 0.025%, 16-fold lower than CFRMA. Conclusions Data from a clinical research network yielded accurate pandemic severity estimates, including increased severity among younger people. Going forward, clinical research networks with a global presence and standardized protocols would substantially aid rapid assessment of clinical severity. Clinical Trials Registration NCT01056354 and NCT010561. We demonstrate how to use baseline and prospective data from global clinical research networks to rapidly assess the severity of an emerging influenza pandemic.

Introduction There is a lack of data on potential gender differences in the use of interventions to prevent and treat cardiovascular disease (CVD) in HIV‐positive individuals. We investigated whether such differences exist in the D:A:D study. Materials and Methods Follow‐up was from 01/02/99 until the earliest of death, 6 months after last visit or 01/02/13. Rates of initiation of lipid‐lowering drugs (LLDs), angiotensin‐converting enzyme inhibitors (ACEIs), anti‐hypertensives and receipt of invasive cardiovascular procedures (ICPs; bypass, angioplasty, endarterectomy) were calculated in those without a myocardial infarction (MI) or stroke at baseline, overall and in groups known to be at higher CVD risk: (i) age >50, (ii) total cholesterol >6.2 mmol/l, (iii) triglyceride >2.3 mmol/l, (iv) hypertension, (v) previous MI, (vi) diabetes, or (vii) predicted 10‐year CVD risk >10%. Poisson regression was used to assess whether rates of initiation were higher in men than women, after adjustment for these factors. Results At enrolment, women (n=13,039; median (interquartile range) 34 (29–40) years) were younger than men (n=36,664, 39 (33–46) years, p=0.001), and were less likely to be current smokers (29% vs. 39%, p=0.0001), to have diabetes (2% vs. 3%, p=0.0001) or to have hypertension (7% vs. 11%, p=0.0001). Of 49,071 individuals without a MI/stroke at enrolment, 0.6% women vs. 2.1% men experienced a MI while 0.8% vs. 1.3% experienced a stroke. Overall, women received ICPs at a rate of 0.07/100 person‐years (PYRS) compared to 0.29/100 PYRS in men. Similarly, the rates of initiation of LLDs (1.28 vs. 2.46), anti‐hypertensives (1.11 vs. 1.38) and ACEIs (0.82 vs. 1.37) were all significantly lower in women than men (Table 1). As expected, initiation rates of each intervention were higher in the groups determined to be at moderate/high CVD risk; however, within each high‐risk group, initiation rates of most interventions (with the exception of anti‐hypertensives) were generally lower in women than men. These gender differences persisted after adjustment for potential confounders (Table 1). Conclusion Use of most CVD interventions was lower among women than men in the D:A:D study. Our findings suggest that actions should be taken to ensure that both men and women are monitored for CVD and, if eligible, receive appropriate CVD interventions.

Mass loss is one of the key parameters that determine stellar evolution. Despite the progress we have achieved over the last decades we still cannot match the observational derived values with theoretical predictions. Even worse, there are certain phases, such as the B[e] supergiants (B[e]SGs) and the Luminous Blue Variables (LBVs), where significant mass is lost through episodic or outburst activity. This leads to various structures around them that permit dust formation, making these objects bright IR sources. The ASSESS project aims to determine the role of episodic mass in the evolution of massive stars, by examining large numbers of cool and hot objects (such as B[e]SGs/LBVs). For this, we initiated a large observing campaign to obtain spectroscopic data for \\(\\sim\\)1000 IR selected sources in 27 nearby galaxies. Within this project we successfully identified 7 B[e] supergiants (one candidate) and 4 Luminous Blue Variables of which 6 and 2, respectively, are new discoveries. We used spectroscopic, photometric, and light curve information to better constrain the nature of the reported objects. We particularly note the presence of B[e]SGs at metallicity environments as low as 0.14 Z\\(_{\\odot}\\).

(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified sources stars spanning a range of metallicity environments. We aim to remedy the situation by applying machine learning techniques to recently available extensive photometric catalogs. We used IR/Spitzer and optical/Pan-STARRS, with Gaia astrometric information, to compile a large catalog of known massive stars in M31 and M33, which were grouped in Blue, Red, Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and background galaxies. Due to the high imbalance, we implemented synthetic data generation to populate the underrepresented classes and improve separation by undersampling the majority class. We built an ensemble classifier using color indices. The probabilities from Support Vector Classification, Random Forests, and Multi-layer Perceptron were combined for the final classification. The overall weighted balanced accuracy is ~83%, recovering Red supergiants at ~94%, Blue/Yellow/B[e] supergiants and background galaxies at ~50-80%, Wolf-Rayets at ~45%, and Luminous Blue Variables at ~30%, mainly due to their small sample sizes. The mixing of spectral types (no strict boundaries in their color indices) complicates the classification. Independent application to IC 1613, WLM, and Sextans A galaxies resulted in an overall lower accuracy of ~70%, attributed to metallicity and extinction effects. The missing data imputation was explored using simple replacement with mean values and an iterative imputor, which proved more capable. We also found that r-i and y-[3.6] were the most important features. Our method, although limited by the sampling of the feature space, is efficient in classifying sources with missing data and at lower metallicitites.

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic mass loss although definitely present observationally, is not included in the models, while its role is currently undetermined. A major hindrance is the lack of large enough samples of classified stars. We attempted to address this by applying an ensemble machine-learning approach using color indices (from IR/Spitzer and optical/Pan-STARRS photometry) as features and combining the probabilities from three different algorithms. We trained on M31 and M33 sources with known spectral classification, which we grouped into Blue/Yellow/Red/B[e] Supergiants, Luminous Blue Variables, classical Wolf-Rayet and background galaxies/AGNs. We then applied the classifier to about one million Spitzer point sources from 25 nearby galaxies, spanning a range of metallicites (\\(1/15\\) to \\(\\sim3~Z_{\\odot}\\)). Equipped with spectral classifications we investigated the occurrence of these populations with metallicity.

We assemble the most complete and clean red supergiant (RSG) sample (2,121 targets) so far in the Small Magellanic Cloud (SMC) with 53 different bands of data to study the MLR of RSGs. In order to match the observed spectral energy distributions (SEDs), a theoretical grid of 17,820 Oxygen-rich models (``normal'' and ``dusty'' grids are half-and-half) is created by the radiatively-driven wind model of the DUSTY code, covering a wide range of dust parameters. We select the best model for each target by calculating the minimal modified chi-square and visual inspection. The resulting MLRs from DUSTY are converted to real MLRs based on the scaling relation, for which a total MLR of \\(6.16\\times10^{-3}\\) \\(M_\\odot\\) yr\\(^{-1}\\) is measured (corresponding to a dust-production rate of \\(\\sim6\\times10^{-6}\\) \\(M_\\odot\\) yr\\(^{-1}\\)), with a typical MLR of \\(\\sim10^{-6}\\) \\(M_\\odot\\) yr\\(^{-1}\\) for the general population of the RSGs. The complexity of mass-loss estimation based on the SED is fully discussed for the first time, indicating large uncertainties based on the photometric data (potentially up to one order of magnitude or more). The Hertzsprung-Russell and luminosity versus median absolute deviation diagrams of the sample indicate the positive relation between luminosity and MLR. Meanwhile, the luminosity versus MLR diagrams show a ``knee-like'' shape with enhanced mass-loss occurring above \\(\\log_{10}(L/L_\\odot)\\approx4.6\\), which may be due to the degeneracy of luminosity, pulsation, low surface gravity, convection, and other factors. We derive our MLR relation by using a third-order polynomial to fit the sample and compare our result with previous empirical MLR prescriptions. Given that our MLR prescription is based on a much larger sample than previous determinations, it provides a more accurate relation at the cool and luminous region of the H-R diagram at low-metallicity compared to previous studies.

Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. However, their use is currently hindered by their low frequency, tedious manual scoring and extensive cell heterogeneities. Those challenges limit the effectiveness of classical machine-learning methods for automated CTC analysis. Here, we combine autoencoding convolutional neural networks with advanced visualization techniques. This provides a very informative view on the data that opens the way for new biomedical research questions. We unravel hidden information in the raw image data of fluorescent images of blood samples enriched for CTCs. Our network classifies fluorescent images of single cells in five different classes with an accuracy, sensitivity and specificity of over 96%, and the obtained CTC counts predict the overall survival of cancer patients as well as state-of-the-art manual counts. Moreover, our network excelled in identifying different important subclasses of objects. Deep learning was faster and superior to classical image analysis approaches and enabled the identification of new biological phenomena. Counting different types of circulating tumour cells can give valuable information on the severity of the disease and on whether treatments are effective for a specific patient. In this work, the authors show that their method based on autoencoders can identify and count cells more accurately and faster than human experts.

In the CARD study, cabazitaxel significantly improved radiographic progression-free survival and overall survival versus abiraterone or enzalutamide in patients with metastatic castration-resistant prostate cancer previously treated with docetaxel and the alternative androgen signalling-targeted inhibitor. Here, we report the quality-of-life outcomes from the CARD study. CARD was a randomised, multicentre, open-label, phase 4 study involving 62 clinical sites across 13 European countries. Patients (aged ≥18 years, Eastern Cooperative Oncology Group (ECOG) performance status ≤2) with confirmed metastatic castration-resistant prostate cancer were randomly assigned (1:1) by means of an interactive voice–web response system to receive cabazitaxel (25 mg/m2 intravenously every 3 weeks, 10 mg daily prednisone, and granulocyte colony-stimulating factor) versus abiraterone (1000 mg orally once daily plus 5 mg prednisone twice daily) or enzalutamide (160 mg orally daily). Stratification factors were ECOG performance status, time to disease progression on the previous androgen signalling-targeted inhibitor, and timing of the previous androgen signalling-targeted inhibitor. The primary endpoint was radiographic progression-free survival; here, we present more detailed analyses of pain (assessed using item 3 on the Brief Pain Inventory-Short Form [BPI-SF]) and symptomatic skeletal events, alongside preplanned patient-reported outcomes, assessed using the Functional Assessment of Cancer Therapy—Prostate (FACT-P) questionnaire and the EuroQoL—5 dimensions, 5 level scale (EQ-5D-5L). Efficacy analyses were done in the intention-to-treat population. Pain response was analysed in the intention-to-treat population with baseline and at least one post-baseline assessment of BPI-SF item 3, and patient-reported outcomes (PROs) were analysed in the intention-to-treat population with baseline and at least one post-baseline assessment of either FACT-P or EQ-5D-5L (PRO population). Analyses of skeletal-related events were also done in the intention-to-treat population. The CARD study is registered with ClinicalTrials.gov, NCT02485691, and is no longer enrolling. Between Nov 17, 2015, and Nov 28, 2018, of 303 patients screened, 255 were randomly assigned to cabazitaxel (n=129) or abiraterone or enzalutamide (n=126). Median follow-up was 9·2 months (IQR 5·6–13·1). Pain response was observed in 51 (46%) of 111 patients with cabazitaxel and 21 (19%) of 109 patients with abiraterone or enzalutamide (p<0·0001). Median time to pain progression was not estimable (NE; 95% CI NE–NE) with cabazitaxel and 8·5 months (4·9–NE) with abiraterone or enzalutamide (hazard ratio [HR] 0·55, 95% CI 0·32–0·97; log-rank p=0·035). Median time to symptomatic skeletal events was NE (95% CI 20·0–NE) with cabazitaxel and 16·7 months (10·8–NE) with abiraterone or enzalutamide (HR 0·59, 95% CI 0·35–1·01; log-rank p=0·050). Median time to FACT-P total score deterioration was 14·8 months (95% CI 6·3–NE) with cabazitaxel and 8·9 months (6·3–NE) with abiraterone or enzalutamide (HR 0·72, 95% CI 0·44–1·20; log-rank p=0·21). There was a significant treatment effect seen in changes from baseline in EQ-5D-5L utility index score in favour of cabazitaxel over abiraterone or enzalutamide (p=0·030) but no difference between treatment groups for change from baseline in EQ-5D-5L visual analogue scale (p=0·060). Since cabazitaxel improved pain response, time to pain progression, time to symptomatic skeletal events, and EQ-5D-5L utility index, clinicians and patients with metastatic castration-resistant prostate cancer can be reassured that cabazitaxel will not reduce quality of life when compared with treatment with a second androgen signalling-targeted inhibitor. Sanofi.