Results 191 to 200 of about 306,037 (248)

Multi‐Omics Insights Into the Mechanisms of Early Muscle Fiber Difference and Transformation Between Lean‐Type and Chinese Indigenous Pigs

Advanced Science, EarlyView.
Multi‐omics analyses uncover breed‐specific cis‐regulatory landscapes and higher‐order chromatin architectural differences that underlie early postnatal muscle fiber divergence in pigs. A super‐enhancer upstream of PPP3CB recruits MEF2C to activate PPP3CB transcription, while the PPP3CB–MEF2C positive feedback loop promotes oxidative muscle fiber ...
Shuailong Zheng, Hainan Wu, Min Liu, Kunpeng Wu, Shuntao Huang, Zulfiqar Ahmed, Lenox Pius, Mengjin Zhu, Dequan Xu   +8 more
wiley   +1 more source

Mechanically Robust Biodegradable Stents With Theragenerative Vascular Responses via Combined 3D Printing and Janus Nanoengineering

Advanced Science, EarlyView.
This work introduces a theragenerative 3D‐printed biodegradable stent with Janus nanoarchitecture for spatially controlled vascular healing. A luminal tantalum ion–implanted surface accelerates endothelialization and hemocompatibility, while an abluminal sirolimus/poly‐L‐lactic acid–tantalum layer provides sustained drug release and suppresses smooth ...
Jong Hwa Seo, Dong‐Sung Won, Hyun Lee, Seojoon Bang, Hyeong Seok Kang, Ju Yeong Gwon, Chan Ho Moon, Ji Won Kim, Yubeen Park, Minho Kang, Dong Yun Lee, Donghyun Lim, Kisuk Yang, Gi Doo Cha, Soo‐Hong Lee, Tae‐Sik Jang, Seokbeom Kim, Jun‐Kyu Park, Jung‐Hoon Park, Hyun‐Do Jung   +19 more
wiley   +1 more source

Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment

Advanced Science, EarlyView.
Accurate prediction of early recurrence in pancreatic ductal adenocarcinoma is vital for optimizing treatment. A novel, integrated radiomics‐pathology machine learning model successfully forecasts recurrence risks by analyzing preoperative CT images and computational pathology.
Sihang Cheng, Fuze Cong, Shenbo Zhang, Rui Lv, Wenjia Zhang, Xinyi Ke, Juncheng Wu, Zhonghe Zhao, Kui Zhao, Di Dong, Ruofan Zhang, Zhengyu Jin, Max Seidensticker, Zhiwei Wang, Huanwen Wu, Xianlin Han, Nan Hong, Huadan Xue   +17 more
wiley   +1 more source

Reinnervation of Muscle Targets Enhances the Separability of Motor Unit Signals Following Peripheral Nerve Transfers

Advanced Science, EarlyView.
Injured or cut peripheral nerves can be surgically rerouted to reinnervate new muscle targets. This study demonstrates reinnervated muscles exhibit enhanced separability between individual motor unit signals, which can simplify signal recording and decomposition. These findings highlight the potential of reinnervated muscle to serve as a key biological
Kiara N Quinn, Siyu Wang, Lang Qin, Alessandro Ascani Orsini, Kenzi Griffith, Rachana Suresh, Fausto Kang, Pierce L Perkins, Neha Joshi, Alexis L Lowe, Sami Tuffaha, Nitish V Thakor   +11 more
wiley   +1 more source

Kruskal Wallis Test with Chi-Squre Approximation and Small Samples

Primate Research, 2014
openaire   +2 more sources

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Privacy-preserving Kruskal–Wallis test

Computer Methods and Programs in Biomedicine, 2013
Statistical tests are powerful tools for data analysis. Kruskal-Wallis test is a non-parametric statistical test that evaluates whether two or more samples are drawn from the same distribution. It is commonly used in various areas. But sometimes, the use of the method is impeded by privacy issues raised in fields such as biomedical research and ...
Suxin Guo, Sheng Zhong, Aidong Zhang
exaly   +3 more sources

The Kruskal-Wallis Test and Stochastic Homogeneity

Journal of Educational and Behavioral Statistics, 1998
For the comparison of more than two independent samples the Kruskal-Wallis H test is a preferred procedure in many situations. However, the exact null and alternative hypotheses, as well as the assumptions of this test, do not seem to be very clear among behavioral scientists.
András Vargha, Harold D Delaney
exaly   +3 more sources

Power study of anova versus Kruskal-Wallis test

Journal of Statistics and Management Systems, 2012
Abstract This paper describes the comparison of the anova and the Kruskal-Wallis test by means of the power when violating the assumption about normally distributed populations. The permutation method is used as a simulation method to determine the power of the test.
exaly   +2 more sources

Estimation of the Power of the Kruskal‐Wallis Test

Biometrical Journal, 1996
AbstractPower calculations of a statistical test require that the underlying population distribution(s) be completely specified. Statisticians, in practice, may not have complete knowledge of the entire nature of the underlying distribution(s) and are at a loss for calculating the exact power of the test.
Mahoney, Michelle, Magel, Rhonda
openaire   +1 more source

Methodology and Application of the Kruskal-Wallis Test

Applied Mechanics and Materials, 2014
This paper describes the methodology and application of the very popular nonparametric test which is a rank based test named as Kruskal-Wallis. This test is useful as a general nonparametric test for comparing more than two independent samples. It can be used to test whether such samples come from the same distribution.
Eva Ostertagová, Oskar Ostertag, Jozef Kováč   +2 more
openaire   +1 more source