Results 131 to 140 of about 15,987,795 (287)

Mutant p53R273H disrupts PDPK1 homodimerization and contributes to PDPK1 activation

Molecular Oncology, EarlyView.
How mutant p53R273H drives AKT signaling is unclear. We show that p53R273H, but not wild‐type, directly binds PDPK1 via a mutation‐dependent conformational change. This interaction disrupts inhibitory PDPK1 homodimerization and enhances AKT phosphorylation.
Mei Chee Lim, Alvin Kunyao Guo, Yoko Itahana, Srinivasaraghavan Kannan, Valerie Goh, Mingmin Ong, Terina Yan Wen Tay, Weiying Ye, Sabhashina Radha Krishnan, Jun Nishiyama, Chandra S Verma, Koji Itahana   +11 more
wiley   +1 more source

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys. [PDF]

Sensors (Basel), 2020
Wu J, Wu Q, Shen J, Cai C.
europepmc   +1 more source

A light‐triggered Time‐Resolved X‐ray Solution Scattering (TR‐XSS) workflow with application to protein conformational dynamics

FEBS Open Bio, EarlyView.
Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei, Fredrik Orädd, Konstantinos Magkakis, Magnus Andersson   +3 more
wiley   +1 more source

On the Possibility of Time Travel [Elektronisk resurs]

, 2010
Time travel can be defined as an experience and social practice in the present that evokes a past (or future) reality. In this paper I introduce popular time travel as a significant phenomenon of the Experience or Dream Society and the fast developing ...
Holtorf, Cornelius,
core   +3 more sources

MULTI-OUTPUT BUS TRAVEL TIME PREDICTION USING CONVOLUTIONAL LSTM NEURAL NETWORKS

Scientific Journal of Astana IT University
Ensuring accurate and dependable predictions of bus arrival times is essential to improving public transportation services and maintaining their appeal in urban settings. Such predictions, whether displayed on electronic boards or integrated into mobile
Akmaral Kuatbayeva, Muslim Sergaziyev , Didar Yedilkhan , Daniyar Issenov , Assylbek Gizatov   +4 more
doaj   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

A Dual-Attentional Gated Residual Framework for Robust Travel Time Prediction

ISPRS International Journal of Geo-Information
Travel time prediction (TTP) is a fundamental pillar of intelligent transportation systems (ITS). However, deploying highly parameterized deep learning models in data-scarce environments—referred to as the “cold-start” problem—remains a critical ...
Jiajun Wu, Yongchuan Zhang, Yiduo Bai, Jun Xia, Yong He   +4 more
doaj   +1 more source

Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment. [PDF]

Sensors (Basel), 2020
Tang R, Kanamori R, Yamamoto T.
europepmc   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

An Empirical Comparison of Urban Road Travel Time Prediction Methods—Deep Learning, Ensemble Strategies and Performance Evaluation

Applied Sciences
Urban road travel time prediction is a critical component of intelligent transportation systems. However, accurate prediction remains challenging due to the high volatility and randomness of urban road traffic.
Yizhe Wang, Yangdong Liu, Xiaoguang Yang
doaj   +1 more source