Results 41 to 50 of about 195,059 (253)

Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins

Advanced Science, 2023
Domain insertion engineering is a promising approach to recombine the functions of evolutionarily unrelated proteins. Insertion of light‐switchable receptor domains into a selected effector protein, for instance, can yield allosteric effectors with light‐
Jan Mathony, Sabine Aschenbrenner, Philipp Becker, Dominik Niopek   +3 more
doaj   +1 more source

Psychosocial Functioning After Pediatric Bone Sarcoma: Generic and Survivor‐Specific Outcomes in Adolescent and Young Adult Patients

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric bone sarcoma patients and survivors may experience psychosocial challenges related to childhood cancer after their intensive, body‐altering treatment. This cross‐sectional study aimed to evaluate generic and survivor‐specific psychosocial outcomes in a national cohort of pediatric bone sarcoma patients and survivors, and ...
Hinke van der Hoek, Heleen Maurice‐Stam, Laura R. Beek, Jennifer van Dijk, Marjolein E. M. Langemeijer, Relinde W. Slooff‐Lentink, Alied M. van der Aa‐van Delden, Anne Westerweel, Marloes van Gorp, Hendrik W. B. Schreuder, Lianne M. Haveman, W. Peter Bekkering, Leonie G. Tigelaar, Johannes H. M. Merks, Martha A. Grootenhuis   +14 more
wiley   +1 more source

Human-Annotated Label Noise and Their Impact on ConvNets for Remote Sensing Image Scene Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Human-labeled training datasets are essential for convolutional neural networks (ConvNets) in satellite image scene classification. Annotation errors are unavoidable due to the complexity of satellite images. However, the distribution of real-world human-
Longkang Peng, Tao Wei, Xuehong Chen, Xiaobei Chen, Rui Sun, Luoma Wan, Jin Chen, Xiaolin Zhu   +7 more
doaj   +1 more source

CODA: Constructivism Learning for Instance-Dependent Dropout Architecture Construction

CoRR, 2021
Dropout is attracting intensive research interest in deep learning as an efficient approach to prevent overfitting. Recently incorporating structural information when deciding which units to drop out produced promising results comparing to methods that ignore the structural information.
openaire   +2 more sources

Instance-dependent Label-noise Learning under a Structural Causal Model

CoRR, 2021
Label noise will degenerate the performance of deep learning algorithms because deep neural networks easily overfit label errors. Let X and Y denote the instance and clean label, respectively. When Y is a cause of X, according to which many datasets have been constructed, e.g., SVHN and CIFAR, the distributions of P(X) and P(Y|X) are entangled.
Yu Yao 0005, Tongliang Liu, Mingming Gong, Bo Han 0003, Gang Niu 0001, Kun Zhang 0001   +5 more
openaire   +3 more sources

Inpatient Exposure, Confidence, and Knowledge in Pediatric Hematology/Oncology: Evaluating General Pediatric Residents During 2025 ACGME Curriculum Change

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background General pediatricians often evaluate hematologic and oncologic presentations before subspecialty consultation, yet the 2025 Accreditation Council for Graduate Medical Education (ACGME) pediatric requirements reduce inpatient pediatric hematology/oncology (PHO) time, raising questions about resident readiness.
Colburn Yu, Rohini Jain
wiley   +1 more source

Consensus Standards and Recommendations for Developmental and Cognitive Surveillance, Screening, and Evaluation in Sickle Cell Disease: Executive Summary From the National Alliance of Sickle Cell Centers Neurocognitive Workgroup

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Neurodevelopmental and neurocognitive difficulties are prevalent among individuals with sickle cell disease and warrant prompt identification and support. This Special Report provides an executive summary of standards and recommendations for surveillance, screening, and evaluation for development and cognition across the lifespan developed by ...
Alyssa M. Schlenz, Donna Murdaugh, Andrew M. Heitzer, Jennifer N. Longoria, Wendy Santos‐Modesitt, Arianna Martin, Judy Cavazos, Jeff Schatz, Catherine R. Hoyt, Alison King, Eboni I. Lance, Steven Hardy, Marsha Treadwell   +12 more
wiley   +1 more source

Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers. However, it is very challenging to estimate the transition matrix T(x), where x denotes the instance, because it is unidentifiable under the instance-dependent noise(IDN).
De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang 0001, Bo Han 0003, Gang Niu 0001, Xinbo Gao 0001, Masashi Sugiyama   +7 more
openaire   +2 more sources

Change in Mental Health and Resilience in Childhood Cancer Survivors After Attending a Person‐Centred State‐of‐the‐Art Late Effects Clinic—on Behalf of the PanCareFollowUp Consortium

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Person‐centred follow‐up care based on evidence‐based clinical practice guidelines and providing individualised information should help to inform and reassure survivors about their medical and psychosocial situation and provide treatment and support where needed.
Gisela Michel, Mieke Rijken, Marietta Kokla, Charlotte Sleurs, Jurgen Lemiere, Line Elmerdahl Frederiksen, Luzius Mader, Cecilia Follin, Sara Oberti, Jeanette Falck Winther, Selina R. van den Oever, Leontien C. M. Kremer, Helena J. H. van der Pal, Peter Spreeuwenberg, Eline Bouwman, Jeroen te Dorsthorst, Samira Essiaf, Elizabeth A. M. (Lieke) Feijen, Hannah Gsell, Riccardo Haupt, Maria van Helvoirt, Rosella P. M. G. Hermens, Lars Hjorth, Tomas Kepak, Katerina Kepakova, Anita Kienesberger, Monica Muraca, Marleen Renard, Carina Schneider, Roderick Skinner, Anne Uyttebroeck, Deveny Vanrusselt, Sabine Verschueren, Jacqueline Loonen, Saskia M. F. Pluijm, Katharina Roser, the PanCareFollowUp consortium   +36 more
wiley   +1 more source

Typicality- and instance-dependent label noise-combating: a novel framework for simulating and combating real-world noisy labels for endoscopic polyp classification

Visual Computing for Industry, Biomedicine, and Art
Learning with noisy labels aims to train neural networks with noisy labels. Current models handle instance-independent label noise (IIN) well; however, they fall short with real-world noise.
Yun Gao, Junhu Fu, Yuanyuan Wang, Yi Guo
doaj   +1 more source