Results 81 to 90 of about 13,519,689 (294)
Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
Causally Informative Entropic Inequalities within Families of Distributions with Shared Marginals
EntropyThe joint probability distribution of observable variables from a system is constrained by the underlying causal structure. In the presence of hidden variables, untestable independencies that involve hidden variables lead to testable causally-imposed ...
Daniel Chicharro
doaj +1 more source
Journal of Agricultural Sciences, 2020 The variables affecting the milk productivity have been discussed in various articles through different methods. A recent study using path analysis shows that three variables significantly affect the 305-day milk yield of Holstein Friesian cows ...
Volkan Sevinç +3 more
doaj +1 more source
Designing professional learning [PDF]
, 2014 The Designing Professional Learning report provides a snapshot of the key elements involved in creating effective and engaging professional learning in a globally dispersed market.
Learning Forward
core
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source
A New Algorithm for Learning Large Bayesian Network Structure From Discrete Data
IEEE Access, 2019 Learning the structure of Bayesian networks (BNs) from high dimensional discrete data is common nowadays but a challenging task, due to the large parameter space, the acyclicity constraint placed on the graphical structures and the difficulty in ...
Weiping Zhang +3 more
doaj +1 more source
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
IEEE Access, 2023 Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions–both direct and indirect.
Juhyeon Kim +5 more
doaj +1 more source
Structure‐forward targeting of claudins with synthetic binders
FEBS Letters, EarlyView.Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley +1 more source
Structure Learning-Based Interaction Feature Construction for Bank Failure Prediction
IEEE AccessMachine-learning models for bank failure prediction commonly rely on bank-level financial ratios. Although such models often achieve strong predictive performance, they offer limited insight into the joint signaling of distress by multiple indicators ...
Chiwoo Lee, Sujin Pyo, Minsu Cho
doaj +1 more source
Supervised Learning of Graph Structure [PDF]
, 2011 Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited.
Andrea Torsello, Luca Rossi 0004
openaire +2 more sources