Avian enthusiast basks in bird life By YUAN HUI in Hohhot and CHEN MEILING | China Daily | Updated: 2021-02-24 09:36 Share CLOSE A flock of gray cranes flies over the Baotou Yellow River Wetland in Baotou, Inner Mongolia autonomous region. CHINA DAILY
Liu Li felt excited as he picked birds dung from ice holes at the Yellow River wetland in Baotou, Inner Mongolia autonomous region, on a day early in spring 2018.
The zoologist walked carefully on the soft ice as he approached the holes. To prevent bird flu infection, he wore a face mask and gloves as he bagged the samples, which would be dried and analyzed later. He didn t realize he had become deeply stuck in the mud until he turned around to go back.
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PHILADELPHIA - A machine learning-based tool was able to predict the risk of malignancy among patients presenting with multiple pulmonary nodules and outperformed human experts, previously validated mathematical models, and a previously established artificial intelligence tool, according to results published in
Clinical Cancer Research, a journal of the American Association for Cancer Research.
Tools currently available can predict malignancy in patients with single nodules; predictive tools for patients presenting with multiple nodules are limited. With the adoption of widespread use of thoracic computed tomography (CT) for lung cancer screening, the detection of multiple pulmonary nodules has become increasingly common, said study author Kezhong Chen, MD, vice professor in the Department of Thoracic Surgery at Peking University People s Hospital in China. Among patients presenting with a pulmonary nodule on a CT scan in a previous lung cancer screening trial, rough
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A former post-doctoral fellow at the McGovern Medical School, part of the University of Texas (UT) Health Science Center, admitted to committing research misconduct by “knowingly and intentionally falsifying, fabricating, and plagiarizing data and text” in six papers and eight manuscripts, according to the HHS Office of Research Integrity (ORI). In its Feb. 4
Federal Register notice, ORI said Yibin Lin “falsely created fictitious author names and affiliations without listing himself as an author to disguise himself from being the offender, and submitted them for publication in
bioRxiv and
medRxiv, open access preprint repositories, by falsely assembling random paragraphs of text, tables, and figures from previous publications and manuscripts to improve his citation metrics.”
Credit: American Chemical Society
Plants and animals can rapidly respond to changes in their environment, such as a Venus flytrap snapping shut when a fly touches it. However, replicating similar actions in soft robots requires complex mechanics and sensors. Now, researchers reporting in
ACS Applied Materials & Interfaces have printed liquid metal circuits onto a single piece of soft polymer, creating an intelligent material that curls under pressure or mechanical strain. Watch a video of the smart material here.
Ideally, soft robots could mimic intelligent and autonomous behaviors in nature, combining sensing and controlled movement. But the integration of sensors and the moving parts that respond can be clunky or require an external computer. A single-unit design is needed that responds to environmental stimuli, such as mechanical pressure or stretching. Liquid metals could be the solution, and some researchers have already investigated their use in soft robots. These materials
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