Results 141 to 150 of about 2,818,371 (325)

Prevalence and epidemic pattern of ecdemic multidrug-resistant tuberculosis during 2012–2022 in Hangzhou, China: implication for public health strategies

BMC Public Health
Background To assess the prevalence and epidemic pattern of multidrug-resistant tuberculosis in Hangzhou City, Zhejiang Province, China during 2012–2022.
Qingchun Li, Yifei Wu, Qinglin Cheng, Min Lu, Yinyan Huang, Xuexin Bai, Qingjun Jia, Zijian Fang, Liyun Ai, Nan Jiang, QiuFeng Lao, Li Xie, Junfang Chen   +12 more
doaj   +1 more source

A Novel Approach to Developing a Supervised Spatial Decision Support System for Image Classification: A Study of Paddy Rice Investigation [PDF]

, 2014
Shih-Hsun Chang
openalex   +1 more source

Bank Regulation and Supervision: What Works Best?

, 2001
James R. Barth, G. Caprio, Ross Levine
semanticscholar   +1 more source

Developing evidence‐based, cost‐effective P4 cancer medicine for driving innovation in prevention, therapeutics, patient care and reducing healthcare inequalities

Molecular Oncology, EarlyView.
The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg, Joachim von Braun, Julio Celis, Anton Berns, Michael Baumann, Tit Albreht, Nancy Abou‐Zeid, Vanderlei Bagnato, Christian Brandts, Chien‐Jen Chen, Massimiliano di Pietro, Manjit Dosanjh, Thomas Dubois, Alexander Eggermont, Angelika Eggert, Ingemar Ernberg, Sara Faithfull, Johannes Förner, Stefan Fröhling, Manuel Heitor, Leroy Hood, Wei Jiang, Bengt Jönsson, Ravi Kannan, Maria Leptin, Su Li, Peter Lindgren, Douglas Lowy, Jun Ma, Alex Markham, Péter Nagy, Simon Oberst, M. Iqbal Parker, Danielle Rodin, Kevin Ryan, Joachim Schüz, Richard Sullivan, Josep Tabernero, Peter Turkson, Oliver Várhelyi, Harold Varmus, Chijie Wang, Elisabete Weiderpass, Nils Wilking   +43 more
wiley   +1 more source

$S^3$Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data [PDF]

, 2020
Bin Cheng, Inderjot Singh Saggu, Raunak Shah, Gaurav Bansal, Dinesh Bharadia   +4 more
openalex   +1 more source

Frequency-Supervised MR-to-CT Image Synthesis [PDF]

, 2021
Zenglin Shi, Pascal Mettes, Guoyan Zheng, Cees G. M. Snoek   +3 more
openalex   +1 more source

Cell surface interactome analysis identifies TSPAN4 as a negative regulator of PD‐L1 in melanoma

Molecular Oncology, EarlyView.
Using cell surface proximity biotinylation, we identified tetraspanin TSPAN4 within the PD‐L1 interactome of melanoma cells. TSPAN4 negatively regulates PD‐L1 expression and lateral mobility by limiting its interaction with CMTM6 and promoting PD‐L1 degradation.
Guus A. Franken, Andrea Abel Gutierrez, Imke van Rossum, Cornelia G. Spruijt, Michiel Vermeulen, Guido van Mierlo, Blanca Scheijen, Annemiek B. van Spriel   +7 more
wiley   +1 more source

MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection [PDF]

, 2021
JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak   +5 more
openalex   +1 more source

LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro

Molecular Oncology, EarlyView.
Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix, Daniel Samaga, Katharina Gehr, Benedek Dankó, Mohamed Schumann, Guido Drexler, Ahmed Alnatsha, Georg Beyer, Ujjwal Mahajan, Martin Selmansberger, Julia Mayerle, Claus Belka, Horst Zitzelsberger, Kirsten Lauber   +13 more
wiley   +1 more source

Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning

, 2019
Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to produce impressive results on a ...
openaire   +2 more sources