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Institution

Yunnan University

EducationKunming, China
About: Yunnan University is a education organization based out in Kunming, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 16810 authors who have published 15966 publications receiving 200708 citations. The organization is also known as: Yunda & Yunnan daxue.


Papers
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Journal ArticleDOI
TL;DR: Genomic and evolutionary evidence of the occurrence of a SARS-CoV-2-like CoV (named Pangolin-Cov) in dead Malayan pangolins is found and suggests that pangolin species are a natural reservoir of SARS

1,213 citations

Posted ContentDOI
Spyridon Bakas1, Mauricio Reyes, Andras Jakab2, Stefan Bauer3  +435 moreInstitutions (111)
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

1,165 citations

Journal ArticleDOI
TL;DR: A universal energy-alignment trend is observed for a set of transition-metal oxides--representing a broad diversity in electronic properties--with several organic semiconductors, demonstrating that, despite the variance in their electronic properties, oxide energy alignment is governed by one driving force: electron-chemical-potential equilibration.
Abstract: Transition-metal oxides improve power conversion efficiencies in organic photovoltaics and are used as low-resistance contacts in organic light-emitting diodes and organic thin-film transistors. What makes metal oxides useful in these technologies is the fact that their chemical and electronic properties can be tuned to enable charge exchange with a wide variety of organic molecules. Although it is known that charge exchange relies on the alignment of donor and acceptor energy levels, the mechanism for level alignment remains under debate. Here, we conclusively establish the principle of energy alignment between oxides and molecules. We observe a universal energy-alignment trend for a set of transition-metal oxides--representing a broad diversity in electronic properties--with several organic semiconductors. The trend demonstrates that, despite the variance in their electronic properties, oxide energy alignment is governed by one driving force: electron-chemical-potential equilibration. Using a combination of simple thermodynamics, electrostatics and Fermi statistics we derive a mathematical relation that describes the alignment.

865 citations

Journal ArticleDOI
TL;DR: The physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized.
Abstract: Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

793 citations

Journal ArticleDOI
TL;DR: A Gram-positive, motile, short-rod-shaped strain, isolated from a forest-soil sample collected from Lijiang, Yunnan Province, China, and was investigated using a polyphasic taxonomic approach represents a novel species of the genus Georgenia, for which the nameGeorgenia ruanii sp.
Abstract: A Gram-positive, motile, short-rod-shaped strain, designated YIM 004T, was isolated from a forest-soil sample collected from Lijiang, Yunnan Province, China, and was investigated using a polyphasic taxonomic approach. The isolate contained chemotaxonomic markers that corresponded to those of its phylogenetic neighbour, Georgenia muralis, i.e. it possessed peptidoglycan type A4α with lysine as the diagnostic cell-wall diamino acid, the predominant menaquinone was MK-8(H4) and the major fatty acid was ai-C15 : 0. The G+C content of the genomic DNA was 72.9 mol%. Strain YIM 004T exhibited a 16S rRNA gene sequence similarity of 97.3 % and a DNA–DNA relatedness value of 18 % with respect to G. muralis DSM 14418T. On the basis of the phenotypic and genotypic differences between the isolate and G. muralis, strain YIM 004T represents a novel species of the genus Georgenia, for which the name Georgenia ruanii sp. nov. is proposed. The type strain is YIM 004T (=CCTCC AB 204065T=DSM 17458T=KCTC 19029T). In addition, an emended description of the genus Georgenia is presented.

766 citations


Authors

Showing all 16985 results

NameH-indexPapersCitations
Gang Chen1673372149819
Lei Zhang130231286950
Chao Zhang127311984711
Simon A. Wilde11839045547
Jinde Cao117143057881
Zheng Wang110121055478
Jian Zhang107306469715
Jun Wang106103149206
Wei Zhang104291164923
Tao Li102248360947
Jinlong Yang9576535981
Peng Zhang88157833705
François M. Peeters88146742733
Bo Li8389128722
Gehan A. J. Amaratunga8272530988
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202383
2022321
20212,148
20201,807
20191,351
20181,010