Results 221 to 230 of about 3,767,744 (333)

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al2O3/HfO2 Dielectrics

Advanced Functional Materials, EarlyView.
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon, Dong Ho Lee, Seyoung Oh, Jongwon Yoon, Hyo‐Bae Kim, Ji‐Hoon Ahn, Woojin Park, Han Seul Kim, Byungjin Cho   +8 more
wiley   +1 more source

Mining umami peptides in lager and multidimensional sensory evaluation of the beer body integrating computational biology with modern sensomics. [PDF]

Food Chem X
Wu Y, Yin R, Gao Z, Guo L, Song Y, Zhao D, Sun J, Huang M, Sun B.   +8 more
europepmc   +1 more source

Computational development of regulatory gene set networks for systems biology applications

, 2014
Chayaporn Suphavilai
openalex   +1 more source

Accelerating Systems Biology Computation: Enhanced Sampling of Spatially Realistic Stochastic Models using the Weighted Ensemble Approach [PDF]

, 2015
Rory Donovan-Maiye
openalex   +1 more source

Emerging 2D Materials and Their Hybrid Nanostructures for Label‐Free Optical Biosensing: Recent Progress and Outlook

Advanced Functional Materials, EarlyView.
This review highlights recent advances in label‐free optical biosensors based on 2D materials and rationally designed mixed‐dimensional nanohybrids, emphasizing their synergistic effects and novel functionalities. It also discusses multifunctional sensing platforms and the integration of machine learning for intelligent data analysis.
Xinyi Li, Yonghao Fu, Yuehe Lin, Dan Du
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Curved Nanographene–Graphite Hybrid Anodes with Sequential Li+ Insertion for Fast‐Charging and Long‐Life Li‐Ion Batteries

Advanced Functional Materials, EarlyView.
A hybrid anode composed of Cl‐functionalized curved nanographene and graphite enables ultra‐fast charging and long cycle life through an engineered morphology and sequential Li+ insertion. It delivers 100 mAh g−1 at 5 C with 70% capacity retention after 1000 cycles and maintains stable performance over 2000 cycles in pouch cells, providing a practical ...
Hyunji Cha, Minsung Kang, Seung Hak Oh, Jee Ho Ha, Jeongmi Joo, Dae Hyeon Kwon, Sang Kyu Kwak, Seokhoon Ahn, Seok Ju Kang   +8 more
wiley   +1 more source

ECCB2024: The 23rd European Conference on Computational Biology. [PDF]

Bioinformatics
Kukkonen-Macchi A, Hautaniemi S, Heil KF, Heinäniemi M, Jensen LJ, Junttila S, Käll L, Laiho A, Maccallum P, Nykter M, Persson B, Suomi T, Van Den Bossche T, Nyrönen TH, Elo LL.   +14 more
europepmc   +1 more source

Computational Methods in Systems Biology

, 2009
Pierpaolo Degano, Roberto Gorrieri
openalex   +2 more sources

An integrative computational systems biology approach identifies differentially regulated dynamic transcriptome signatures which drive the initiation of human T helper cell differentiation [PDF]

, 2012
Tarmo Äijö, Sanna Edelman, Tapio Lönnberg, Antti Larjo, Henna Kallionpää, Soile Tuomela, Emilia Engström, Riitta Lahesmaa, Harri Lähdesmäki   +8 more
openalex   +1 more source