Results 161 to 170 of about 643,643 (305)

Noninvasive Characterization of Tumor Heterogeneity in HNSCC: From Clinical Utility to Biological Correlates

Advanced Science, EarlyView.
This study establishes a CT‐based radiomics framework to quantify intratumoral heterogeneity (ITH) in HNSCC. Using unsupervised clustering, tumor ROIs and VOIs are analyzed to calculate 2D/3D ITH scores. The score shows strong predictive value for prognosis and immunotherapy response, and is associated with tumor metabolism and immune microenvironment,
Xinwei Chen, Rui Liu, Yuan Zhou, Yin Xiao, Chengmin Feng, Lin Chen, Yanshi Li, Mengna Wang, Zhaobo Cheng, Yuxi Luo, Long Liu, Lijing Niu, Yue Xiang, Juan Peng, Guohua Hu, Min Pan   +15 more
wiley   +1 more source

Burden of colon and rectum cancer attributable to a diet high in red meat in the United States, 1990-2021. [PDF]

Front Nutr
Huang L, Li M.
europepmc   +1 more source

Hierarchical Twin Networks Enable Exceptional Strength and Fracture Toughness in Titanium

Advanced Science, EarlyView.
A commercial‐purity titanium with hierarchical ultrafine twin structures delivers an exceptional combination of fracture toughness (KJIc) of 187 MPa·m1/2 and specific yield strength of 140 MPa·cm3·g−1, in which pre‐engineered high‐density twin boundaries stimulate massive and dislocations to blunt crack tip.
Xiao‐Wei Zou, Ting Zhu, En Ma, Wei‐Zhong Han   +3 more
wiley   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

Advanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura, Ryo Iguchi, Kazuya Terabe, Takashi Tsuchiya   +6 more
wiley   +1 more source

Mini-review: Processed red meat intake and risk of neurodegenerative diseases. [PDF]

Front Nutr
Chen KQ, Cao WJ, Liu Z, Liu RZ.
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Association between red meat intake and diabetes: a cross-sectional analysis of a nationally representative sample of US adults (NHANES 2003-2016). [PDF]

Br J Nutr
Ba DM, Zhang Y, Qiu T, Raja-Khan N, Pichardo-Lowden A, Gao X, Chinchilli VM.   +6 more
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Lipidomic Markers of Processed Meat and Unprocessed Red Meat Intake and Risk of Diabetes in American Indians. [PDF]

Diabetes Care
Wen X, Miao G, Fretts AM, Chen M, Zhang Y, Umans JG, Cole SA, Best LG, Fiehn O, Zhao J.   +9 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source