Global correlates of emerging zoonoses: Anthropogenic, environmental, and biodiversity risk factors

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Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting

Objective: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Active Surveillance for Prostate Cancer: A Systematic Review of the Literature

Context: Prostate cancer (PCa) remains an increasingly common malignancy worldwide. The optimal management of clinically localized, early-stage disease remains unknown, and profound quality of life issues surround PCa interventions. Objective: To systematically summarize the current literature on the management of low-risk PCa with active surveillance (AS), with a focus on patient selection, outcomes, and future research needs. Evidence acquisition: A comprehensive search of the PubMed and Embase databases from 1980 to 2011 was performed to identify studies pertaining to AS for PCa. The search terms used included prostate cancer and active surveillance or conservative management or watchful waiting or expectant management. Selected studies for outcomes analysis had to provide a comprehensive description of entry characteristics, criteria for surveillance, and indicators for further intervention. Evidence synthesis: Data from seven large AS series were reviewed. Inclusion criteria for surveillance vary among studies, and eligibility therefore varies considerably (4-82%). PCa-specific mortality remains low (0-1%), with the longest published-median follow-up being 6.8 yr. Up to one-third of patients receive secondary therapy after a median of about 2.5 yr of surveillance. Surveillance protocols and triggers for intervention vary among institutions. Most patients are treated for histologic reclassification (27-100%) or prostate-specific antigen doubling time <3 yr (13-48%), while 7-13% are treated with no evidence of progression. Repeat prostate biopsy with a minimum of 12 cores appears to be important for monitoring patients for changes in tumor histology over time. Conclusions: AS for PCa offers an opportunity to limit intervention to patients who will likely benefit the most from radical treatment. This approach confers a low risk of disease-specific mortality in the short to intermediate term. An early, confirmatory biopsy is essential for limiting the risk of underestimating tumor grade and amount. (C) 2012 European Association of Urology. Published by Elsevier B. V. All rights reserved

A search for ultra-high-energy photons at the Pierre Auger Observatory exploiting air-shower universality

The Pierre Auger Observatory is the most sensitive detector to primary photons with energies above ∼0.2 EeV. It measures extensive air showers using a hybrid technique that combines a fluorescence detector (FD) with a ground array of particle detectors (SD). The signatures of a photon-induced air shower are a larger atmospheric depth at the shower maximum (X$_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced background. Using observables measured by the FD and SD, three photon searches in different energy bands are performed. In particular, between threshold energies of 1-10 EeV, a new analysis technique has been developed by combining the FD-based measurement of X$_{max}$ with the SD signal through a parameter related to its muon content, derived from the universality of the air showers. This technique has led to a better photon/hadron separation and, consequently, to a higher search sensitivity, resulting in a tighter upper limit than before. The outcome of this new analysis is presented here, along with previous results in the energy ranges below 1 EeV and above 10 EeV. From the data collected by the Pierre Auger Observatory in about 15 years of operation, the most stringent constraints on the fraction of photons in the cosmic flux are set over almost three decades in energy