Results 141 to 150 of about 4,229,330 (247)

Early Retinal UCHL1 Dysregulation Coupled With Synaptic Loss Reflects Alzheimer's Disease Severity

Advanced Science, EarlyView.
This study identifies synapse‐enriched deubiquitinase UCHL1 as an early Aβ‐responsive regulator of retinal synaptopathy in Alzheimer's disease. Retinal UCHL1 loss accompanies excitatory synapse degeneration, p75NTR activation, and neuroinflammation, and predicts Braak stage and cognitive decline. Aβ42 fibrils trigger synaptic and UCHL1 depletion before
Altan Rentsendorj, Jean‐Philippe Vit, Alexandre Hutton, Yosef Koronyo, Saba Shahin, Edward Robinson, Ernesto Barron, Anthony Rodriguez, Ousman Jallow, Julia Sheyn, Bhakta Prasad Gaire, Lalita Subedi, Miyah R. Davis, Barathan Gnanabharathi, Alexander V. Ljubimov, Stuart L. Graham, Vivek K. Gupta, Mehdi Mirzaei, Debra Hawes, Katlin Silm, Keith L. Black, Lon S. Schneider, Alfredo A. Sadun, Jesse G. Meyer, Dieu‐Trang Fuchs, Maya Koronyo‐Hamaoui   +25 more
wiley   +1 more source

Why are some forest fires more intense?

, 2014
This archived document is maintained by the Oregon State Library as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Title from PDF caption (viewed on August 15, 2014)Part of ...

core  

Multidimensional and Multifunctional Laser‐Induced Graphene (LIG) for Point‐of‐Care and Wearable Biosensing, Theranostics, and Bioactive Interfaces Toward Personalized Healthcare and Regenerative Medicine

Advanced Science, EarlyView.
Multidimensional laser‐induced graphene (LIG) spanning from 0D to 3D architectures is comprehensively reviewed for multifunctional biomedical platforms, including biosensing, theranostics, and bioactive interface applications, which highlights its potentials for point‐of‐care diagnostics, wearable health monitoring, smart drug delivery, and tissue ...
Li Zhang, Yurou Yuan, Xiaohong Ding, Wing Cheung Mak   +3 more
wiley   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

USDA Forest Service resource bulletin SE.

, 1968
Some issues have title: U.S.D.A.
Southeastern Forest Experiment Station (Asheville, N.C.)
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Generic Composition, Structure and Diversity of Secondary Forests at Amisconde, the Pacific Slope of the Cordillera de Talamanca, Costa Rica

Revista de Biología Tropical, 1998
Most Costa Rican forests have been intensively studied in recent years. One exception is the transition zone from lowland wet forest to the high elevation Quercus forest belt at the pacific slopes of the Cordillera de Talamanca. An inventory of secondary
D.A.P. Hooftman
doaj  

Benchmark maps of 33 years of secondary forest age for Brazil. [PDF]

Sci Data, 2020
Silva Junior CHL, Heinrich VHA, Freire ATG, Broggio IS, Rosan TM, Doblas J, Anderson LO, Rousseau GX, Shimabukuro YE, Silva CA, House JI, Aragão LEOC.   +11 more
europepmc   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Start, speed, and direction of secondary forest succession. [PDF]

Sci Adv
Matsuo T, Hordijk I, Amissah L, Laurance SGW, Martínez-Ramos M, Meave JA, Bongers F, van der Sande MT, Kok J, Poorter L.   +9 more
europepmc   +1 more source