How climate policy commitments influence energy systems and the economies of US states.

In the United States, state governments have been the locus of action for addressing climate change. However, the lack of a holistic measure of state climate policy has prevented a comprehensive assessment of state policies' effectiveness. Here, we assemble information from 25 individual policies to develop an aggregate index of state climate policies from 2000-2020.

Quantification of Epicardial Adipose Tissue Volume and Attenuation for Cardiac CT Scans Using Deep Learning in a Single Multi-Task Framework.

Background: Recent studies have shown that epicardial adipose tissue (EAT) is an independent atrial fibrillation (AF) prognostic marker and has influence on the myocardial function. In computed tomography (CT), EAT volume (EATv) and density (EATd) are parameters that are often used to quantify EAT. While increased EATv has been found to correlate with the prevalence and the recurrence of AF after ablation therapy, higher EATd correlates with inflammation due to arrest of lipid maturation and with high risk of plaque presence and plaque progression.

Remarkable Intracranial Response to Sotorasib in a Patient With -Mutated Lung Adenocarcinoma and Untreated Brain Metastases: A Case Report.

Sotorasib is a inhibitor that recently received approval for use in locally advanced or metastatic -mutated NSCLC. CodeBreaK100, the phase 2 clinical trial leading to the approval of sotorasib, excluded patients with untreated brain metastases; there have been no reports describing efficacy of sotorasib on untreated brain metastases. We present a case of a patient with active untreated brain metastases with resulting disorientation and weakness who has radiographic response and complete resolution of neurologic symptoms with sotorasib.

The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.