Results 81 to 90 of about 3,357,696 (317)

Structural insights into lacto‐N‐biose I recognition by a family 32 carbohydrate‐binding module from Bifidobacterium bifidum

open access: yesFEBS Letters, EarlyView.
Bifidobacterium bifidum establishes symbiosis with infants by metabolizing lacto‐N‐biose I (LNB) from human milk oligosaccharides (HMOs). The extracellular multidomain enzyme LnbB drives this process, releasing LNB via its catalytic glycoside hydrolase family 20 (GH20) lacto‐N‐biosidase domain.
Xinzhe Zhang   +5 more
wiley   +1 more source

Thermodynamics and complex dielectric permittivity of mixed crystals of the Rb1-x(NH4)xH2PO4 type

open access: yesCondensed Matter Physics, 2008
We propose a pseudospin model for proton glasses of the Rb1-x(NH4)xH2PO4(Rb1-x(ND4)xD2PO4) type, which takes into account the energy levels of hydrogens (deuterons) around the PO4 group, long-range interactions between the hydrogen bonds, and an internal
R.R.Levitsky   +4 more
doaj   +1 more source

A power approximation for the Kenward and Roger Wald test in the linear mixed model.

open access: yesPLoS ONE, 2021
We derive a noncentral [Formula: see text] power approximation for the Kenward and Roger test. We use a method of moments approach to form an approximate distribution for the Kenward and Roger scaled Wald statistic, under the alternative.
Sarah M Kreidler   +3 more
doaj   +1 more source

Effective Operator Treatment of the Lipkin Model

open access: yes, 2004
We analyze the Lipkin Model using effective operator techniques. We present both analytical and numerical results for effective Hamiltonians. The accuracy of the cluster approximation is investigated.Comment: To appear in Phys.Rev.
J. P. Vary   +4 more
core   +2 more sources

Analytic Approximations to Galaxy Clustering [PDF]

open access: yes, 1998
We discuss some recent progress in constructing analytic approximations to the galaxy clustering. We show that successful models can be constructed for the clustering of both dark matter and dark matter haloes. Our understanding of galaxy clustering and galaxy biasing can be greatly enhanced by these models.
openaire   +2 more sources

Interplay between circadian and other transcription factors—Implications for cycling transcriptome reprogramming

open access: yesFEBS Letters, EarlyView.
This perspective highlights emerging insights into how the circadian transcription factor CLOCK:BMAL1 regulates chromatin architecture, cooperates with other transcription factors, and coordinates enhancer dynamics. We propose an updated framework for how circadian transcription factors operate within dynamic and multifactorial chromatin landscapes ...
Xinyu Y. Nie, Jerome S. Menet
wiley   +1 more source

Charge order and antiferromagnetism in twisted bilayer graphene from the variational cluster approximation

open access: yesSciPost Physics, 2022
We study the possibility of charge order at quarter filling and antiferromagnetism at half-filling in a tight-binding model of magic angle twisted bilayer graphene.
B. Pahlevanzadeh, P. Sahebsara, D. Sénéchal
doaj   +1 more source

Approximate Clustering with Same-Cluster Queries

open access: yes, 2017
Ashtiani et al. proposed a Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to make adaptive queries to a domain expert. The queries are of the kind "do two given points belong to the same optimal cluster?" There are many clustering contexts where such same-cluster queries are feasible. Ashtiani et al.
Ailon, Nir   +3 more
openaire   +4 more sources

Approximate Correlation Clustering Using Same-Cluster Queries [PDF]

open access: yes, 2018
Ashtiani et al. (NIPS 2016) introduced a semi-supervised framework for clustering (SSAC) where a learner is allowed to make same-cluster queries. More specifically, in their model, there is a query oracle that answers queries of the form given any two vertices, do they belong to the same optimal cluster?. Ashtiani et al. showed the usefulness of such a
Ailon, Nir   +2 more
openaire   +2 more sources

Optimal Time Bounds for Approximate Clustering [PDF]

open access: yesMachine Learning, 2004
Clustering is a fundamental problem in unsupervised learning, and has been studied widely both as a problem of learning mixture models and as an optimization problem. In this paper, we study clustering with respect the emph{k-median} objective function, a natural formulation of clustering in which we attempt to minimize the average distance to cluster ...
Mettu, Ramgopal R., Plaxton, C. Greg
openaire   +2 more sources

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