Automating the measurements of galaxy redshifts and ISM properties using CNN

Studying the effects that the local environments of galaxies have on their interstellar medium (ISM) properties is crucial for understanding galaxy evolution and large scale structure of the universe. In order to do that we need precise measurements of ISM properties like Star Formation Rate (SFR), metallicity (Z), ionization parameter (U), gas pressure, and extinction. Accurate estimation of redshift and emission line fluxes from a galaxy\u27s spectrum is the first step in measuring these ISM properties. Current techniques for these measurements still rely on time-consuming manual efforts or error-prone cross-correlation codes that are already struggling to process the vast quantities of spectroscopic data that currently exist. With future NASA missions like JWST, Euclid, Roman, and SPHEREx expected to produce even larger amounts of spectroscopic data, a fast and reliable alternative to the current techniques of spectroscopic measurements is the need of the hour. To that end, we train a Convolutional Neural Network (CNN) to estimate redshift directly from an input spectrum. We generate a library of synthetic spectra spanning a wide range of parameter values and use it to train the CNN and later evaluate its performance. We obtain a normalized mean absolute deviation (NMAD) value of 0.0086 and an outlier fraction of 5.36% for our test set. This accuracy and precision is comparable to the current best photometric redshifts estimated using SED fitting codes and is lower than the subset of high quality spectroscopic data estimated using time and labour-intensive techniques. In comparison, our CNN is able to process ~30,000 spectra in around five seconds giving it an important advantage over the current methods of redshift estimation. We plan to extend this technique to estimating other ISM properties from galaxy spectra in the future

Reporting trends, practices, and resource utilization in neuroendocrine tumors of the prostate gland: a survey among thirty-nine genitourinary pathologists

Background: Neuroendocrine differentiation in the prostate gland ranges from clinically insignificant neuroendocrine differentiation detected with markers in an otherwise conventional prostatic adenocarcinoma to a lethal high-grade small/large cell neuroendocrine carcinoma. The concept of neuroendocrine differentiation in prostatic adenocarcinoma has gained considerable importance due to its prognostic and therapeutic ramifications and pathologists play a pivotal role in its recognition. However, its awareness, reporting, and resource utilization practice patterns among pathologists are largely unknown. Methods: Representative examples of different spectrums of neuroendocrine differentiation along with a detailed questionnaire were shared among 39 urologic pathologists using the survey monkey software. Participants were specifically questioned about the use and awareness of the 2016 WHO classification of neuroendocrine tumors of the prostate, understanding of the clinical significance of each entity, and use of different immunohistochemical (IHC) markers. De-identified respondent data were analyzed. Results: A vast majority (90%) of the participants utilize IHC markers to confirm the diagnosis of small cell neuroendocrine carcinoma. A majority (87%) of the respondents were in agreement regarding the utilization of type of IHC markers for small cell neuroendocrine carcinoma for which 85% of the pathologists agreed that determination of the site of origin of a high-grade neuroendocrine carcinoma is not critical, as these are treated similarly. In the setting of mixed carcinomas, 62% of respondents indicated that they provide quantification and grading of the acinar component. There were varied responses regarding the prognostic implication of focal neuroendocrine cells in an otherwise conventional acinar adenocarcinoma and for Paneth cell-like differentiation. The classification of large cell neuroendocrine carcinoma was highly varied, with only 38% agreement in the illustrated case. Finally, despite the recommendation not to perform neuroendocrine markers in the absence of morphologic evidence of neuroendocrine differentiation, 62% would routinely utilize IHC in the work-up of a Gleason score 5 + 5 = 10 acinar adenocarcinoma and its differentiation from high-grade neuroendocrine carcinoma. Conclusion: There is a disparity in the practice utilization patterns among the urologic pathologists with regard to diagnosing high-grade neuroendocrine carcinoma and in understanding the clinical significance of focal neuroendocrine cells in an otherwise conventional acinar adenocarcinoma and Paneth cell-like neuroendocrine differentiation. There seems to have a trend towards overutilization of IHC to determine neuroendocrine differentiation in the absence of neuroendocrine features on morphology. The survey results suggest a need for further refinement and development of standardized guidelines for the classification and reporting of neuroendocrine differentiation in the prostate gland

COSMOS-Web: An Overview of the JWST Cosmic Origins Survey

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hour treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg$^2$ NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5$\sigma$ point source depths ranging $\sim$27.5-28.2 magnitudes. In parallel, we will obtain 0.19 deg$^2$ of MIRI imaging in one filter (F770W). COSMOS-Web will build on the rich heritage of multiwavelength observations and data products available in the COSMOS field. The design of COSMOS-Web is motivated by three primary science goals: (1) to discover thousands of galaxies in the Epoch of Reionization ($64$ and place constraints on the formation of the Universe's most massive galaxies ($M_\star>10^{10}$ M$_\odot$), and (3) directly measure the evolution of the stellar mass to halo mass relation using weak gravitational lensing out to $z\sim2.5$ and measure its variance with galaxies' star formation histories and morphologies. In addition, we anticipate COSMOS-Web's legacy value to reach far beyond these scientific goals, touching many other areas of astrophysics, such as the identification of the first direct collapse black hole candidates, ultracool sub-dwarf stars in the Galactic halo, and possibly the identification of $z>10$ pair-instability supernovae. In this paper we provide an overview of the survey's key measurements, specifications, goals, and prospects for new discovery

COSMOS-Web: An Overview of the JWST Cosmic Origins Survey

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hour treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg$^2$ NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5$\sigma$ point source depths ranging $\sim$27.5-28.2 magnitudes. In parallel, we will obtain 0.19 deg$^2$ of MIRI imaging in one filter (F770W). COSMOS-Web will build on the rich heritage of multiwavelength observations and data products available in the COSMOS field. The design of COSMOS-Web is motivated by three primary science goals: (1) to discover thousands of galaxies in the Epoch of Reionization ($64$ and place constraints on the formation of the Universe's most massive galaxies ($M_\star>10^{10}$ M$_\odot$), and (3) directly measure the evolution of the stellar mass to halo mass relation using weak gravitational lensing out to $z\sim2.5$ and measure its variance with galaxies' star formation histories and morphologies. In addition, we anticipate COSMOS-Web's legacy value to reach far beyond these scientific goals, touching many other areas of astrophysics, such as the identification of the first direct collapse black hole candidates, ultracool sub-dwarf stars in the Galactic halo, and possibly the identification of $z>10$ pair-instability supernovae. In this paper we provide an overview of the survey's key measurements, specifications, goals, and prospects for new discovery

COSMOS-Web: An Overview of the JWST Cosmic Origins Survey

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hour treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg$^2$ NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5$\sigma$ point source depths ranging $\sim$27.5-28.2 magnitudes. In parallel, we will obtain 0.19 deg$^2$ of MIRI imaging in one filter (F770W). COSMOS-Web will build on the rich heritage of multiwavelength observations and data products available in the COSMOS field. The design of COSMOS-Web is motivated by three primary science goals: (1) to discover thousands of galaxies in the Epoch of Reionization ($64$ and place constraints on the formation of the Universe's most massive galaxies ($M_\star>10^{10}$ M$_\odot$), and (3) directly measure the evolution of the stellar mass to halo mass relation using weak gravitational lensing out to $z\sim2.5$ and measure its variance with galaxies' star formation histories and morphologies. In addition, we anticipate COSMOS-Web's legacy value to reach far beyond these scientific goals, touching many other areas of astrophysics, such as the identification of the first direct collapse black hole candidates, ultracool sub-dwarf stars in the Galactic halo, and possibly the identification of $z>10$ pair-instability supernovae. In this paper we provide an overview of the survey's key measurements, specifications, goals, and prospects for new discovery

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use