Results 71 to 80 of about 1,213,359 (335)
Molecular Oncology, EarlyView.Large multidimensional digital images of cancer tissue are becoming prolific, but many challenges exist to automatically extract relevant information from them using computational tools. We describe publicly available resources that have been developed jointly by expert and non‐expert computational biologists working together during a virtual hackathon
Sandhya Prabhakaran +16 more
wiley +1 more source
Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm [PDF]
, 2019 Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably.
Bueso Sánchez, María del Carmen +4 more
core +2 more sources
Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer
Molecular Oncology, EarlyView.This study employed targeted metabolomic profiling to identify 302 distinct metabolites present in platelet‐rich plasma (PRP), revealing aberrant metabolic profiles amongst individuals diagnosed with colorectal cancer (CRC). Compared to carcinoembryonic antigen (CEA) and cancer antigen 19‐9 (CA199), our metabolite panel showed improved sensitivity ...
Zuojian Hu +7 more
wiley +1 more source
Cell‐free DNA aneuploidy score as a dynamic early response marker in prostate cancer
Molecular Oncology, EarlyView.mFast‐SeqS‐based genome‐wide aneuploidy scores are concordant with aneuploidy scores obtained by whole genome sequencing from tumor tissue and can predict response to ARSI treatment at baseline and, at an early time point, to ARSI and taxanes. This assay can be easily performed at low cost and requires little input of cfDNA. Cell‐free circulating tumor
Khrystany T. Isebia +17 more
wiley +1 more source
Molecular Oncology, EarlyView.Prostate cancer is a leading malignancy with significant clinical heterogeneity in men. An 11‐gene signature derived from dysregulated epithelial cell markers effectively predicted biochemical recurrence‐free survival in patients who underwent radical surgery or radiotherapy.
Zhuofan Mou, Lorna W. Harries
wiley +1 more source
Advanced Engineering Materials, EarlyView., 2023 A novel method for tracking structural changes in gels using widely accessible microcomputed tomography is presented and validated for various hydro‐, alco‐, and aerogels. The core idea of the method is to track positions of micrometer‐sized tracer particles entrapped in the gel and relate them to the density of the gel network.
Anja Hajnal +3 more
wiley +1 more source
Experimental Analysis on Friction Characteristics of MW Wind Turbine Generator Yaw Braking System
南方能源建设[Objective] The wind turbine generator (WTG) yaw braking system has application in low-pressure yaw and high-pressure heavy load, and the friction performance between friction pairs is influenced by various parameters between the components of the yaw ...
Ming LIU +4 more
doaj +1 more source
Peripheral blood proteome biomarkers distinguish immunosuppressive features of cancer progression
Molecular Oncology, EarlyView.Immune status significantly influences cancer progression. This study used plasma proteomics to analyze benign 67NR and malignant 4T1 breast tumor models at early and late tumor stages. Immune‐related proteins–osteopontin (Spp1), lactotransferrin (Ltf), calreticulin (Calr) and peroxiredoxin 2 (Prdx2)–were associated with systemic myeloid‐derived ...
Yeon Ji Park +6 more
wiley +1 more source
Dynamic modelling of wind turbine and power system for fault ride-through analysis [PDF]
, 2010 This paper presents a Simulink model of a wind power system for the holistic analysis of wind turbine and power grid during grid faults, aiming to investigate wind turbine Fault Ride-Through performance.
Anaya-Lara, Olimpo +2 more
core
Classification of acute myeloid leukemia based on multi‐omics and prognosis prediction value
Molecular Oncology, EarlyView.The Unsupervised AML Multi‐Omics Classification System (UAMOCS) integrates genomic, methylation, and transcriptomic data to categorize AML patients into three subtypes (UAMOCS1‐3). This classification reveals clinical relevance, highlighting immune and chromosomal characteristics, prognosis, and therapeutic vulnerabilities.
Yang Song +13 more
wiley +1 more source