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作者: Turina, Massimo ; Ergünay, Koray ; Maes, Piet ; de la Torre, Juan Carlos ; Hughes, Holly R. ; Brown, Katherine ; Kuhn, Jens H. ; Sasaya (笹谷孝英), Takahide ; Firth, Andrew E. ; Wolf, Yuri I. ; Marklewitz, Marco ; Zhang (张永振), Yong-Zhen ; Adkins, Scott ; Palacios, Gustavo ; Shi (施莽), Mang ; Lambert, Amy J. ; Digiaro, Michele ; Junglen, Sandra

ABSTRACT Prior to 2017, the family Bunyaviridae included five genera of arthropod and rodent viruses with tri-segmented negative-sense RNA genomes related to the Bunyamwera virus. In 2017, the International Committee on Taxonomy of Viruses (ICTV) promoted the family to order Bunyavirales and subsequently greatly expanded its composition by adding multiple families for non-segmented to polysegmented viruses of animals, fungi, plants, and protists. The continued and accelerated discovery of bunyavirals highlighted that an order would not suffice to depict the evolutionary relationships of these viruses. Thus, in April 2024, the order was promoted to class Bunyaviricetes . This class currently includes two major orders, Elliovirales ( Cruliviridae , Fimoviridae , Hantaviridae , Peribunyaviridae , Phasmaviridae , Tospoviridae , and Tulasviridae ) and Hareavirales ( Arenaviridae , Discoviridae , Konkoviridae , Leishbuviridae , Mypoviridae , Nairoviridae , Phenuiviridae , and Wupedeviridae ), for hundreds of viruses, many of which are pathogenic for humans and other animals, plants, and fungi.

2024-10-08·Environmental Science & Technology Letters

Revealing Chemical Trends: Insights from Data-Driven Visualization and Patent Analysis in Exposomics Research

Article

作者: Schymanski, Emma L. ; Thiessen, Paul A. ; Pang, Jun ; Aurich, Dagny ; de Jesus Matias, Flavio

Understanding historical chemical usage is crucial for assessing current and past impacts on human health and the environment and for informing future regulatory decisions. However, past monitoring data are often limited in scope and number of chemicals, while suitable sample types are not always available for remeasurement. Data-driven cheminformatics methods for patent and literature data offer several opportunities to fill this gap. The chemical stripes were developed as an interactive, open source tool for visualizing patent and literature trends over time, inspired by the global warming and biodiversity stripes. This paper details the underlying code and data sets behind the visualization, with a major focus on the patent data sourced from PubChem, including patent origins, uses, and countries. Overall trends and specific examples are investigated in greater detail to explore both the promise and caveats that such data offer in assessing the trends and patterns of chemical patents over time and across different geographic regions. Despite a number of potential artifacts associated with patent data extraction, the integration of cheminformatics, statistical analysis, and data visualization tools can help generate valuable insights that can both illuminate the chemical past and potentially serve toward an early warning system for the future.

2024-10-01·Medical Image Analysis

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Article

作者: Summers, Ronald M ; Kim, Dongkyun ; Wang, Song ; Ryu, Jongbin ; Kim, Changhyun ; Peng, Yifan ; Zhou, Yiliang ; Lu, Zhiyong ; Park, Wongi ; Jaiswal, Ajay ; Lin, Mingquan ; Celi, Leo Anthony ; Tran, Minh-Triet ; Kang, Myungjoo ; Wang, Zhangyang ; Nguyen-Mau, Trong-Hieu ; Zhuge, Sherry ; Yamagishi, Yosuke ; Verma, Arsh ; Seo, Hyeryeong ; Yang, Yuzhe ; Hong, Feng ; Holste, Gregory ; Jeong, Jaehyup ; Shih, George

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

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