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Institution

Nazarbayev University

EducationNur-Sultan, Kazakhstan
About: Nazarbayev University is a education organization based out in Nur-Sultan, Kazakhstan. It is known for research contribution in the topics: Population & Optical fiber. The organization has 2539 authors who have published 5345 publications receiving 50483 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates is discussed, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood.
Abstract: This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial a...

782 citations

Journal ArticleDOI
TL;DR: A detailed catalog of zebrafish behaviors that covers both larval and adult models is developed, representing a beginning of creating a more comprehensive ethogram ofZebrafish behavior, which will improve interpretation of published findings, foster cross-species behavioral modeling, and encourage new groups to apply zebra fish neurobehavioral paradigms in their research.
Abstract: Zebrafish (Danio rerio) are rapidly gaining popularity in translational neuroscience and behavioral research. Physiological similarity to mammals, ease of genetic manipulations, sensitivity to pharmacological and genetic factors, robust behavior, low cost, and potential for high-throughput screening contribute to the growing utility of zebrafish models in this field. Understanding zebrafish behavioral phenotypes provides important insights into neural pathways, physiological biomarkers, and genetic underpinnings of normal and pathological brain function. Novel zebrafish paradigms continue to appear with an encouraging pace, thus necessitating a consistent terminology and improved understanding of the behavioral repertoire. What can zebrafish 'do', and how does their altered brain function translate into behavioral actions? To help address these questions, we have developed a detailed catalog of zebrafish behaviors (Zebrafish Behavior Catalog, ZBC) that covers both larval and adult models. Representing a beginning of creating a more comprehensive ethogram of zebrafish behavior, this effort will improve interpretation of published findings, foster cross-species behavioral modeling, and encourage new groups to apply zebrafish neurobehavioral paradigms in their research. In addition, this glossary creates a framework for developing a zebrafish neurobehavioral ontology, ultimately to become part of a unified animal neurobehavioral ontology, which collectively will contribute to better integration of biological data within and across species.

776 citations

Journal ArticleDOI
31 Jan 2019-Nature
TL;DR: A consortium of 11 bacterial strains from the healthy human gut microbiota can strongly induce interferon-γ-producing CD8 T cells in the intestine, and enhance both resistance to bacterial infection and the therapeutic efficacy of immune checkpoint inhibitors in syngeneic tumour models.
Abstract: There is a growing appreciation for the importance of the gut microbiota as a therapeutic target in various diseases. However, there are only a handful of known commensal strains that can potentially be used to manipulate host physiological functions. Here we isolate a consortium of 11 bacterial strains from healthy human donor faeces that is capable of robustly inducing interferon-γ-producing CD8 T cells in the intestine. These 11 strains act together to mediate the induction without causing inflammation in a manner that is dependent on CD103+ dendritic cells and major histocompatibility (MHC) class Ia molecules. Colonization of mice with the 11-strain mixture enhances both host resistance against Listeria monocytogenes infection and the therapeutic efficacy of immune checkpoint inhibitors in syngeneic tumour models. The 11 strains primarily represent rare, low-abundance components of the human microbiome, and thus have great potential as broadly effective biotherapeutics. A consortium of 11 bacterial strains from the healthy human gut microbiota can strongly induce interferon-γ-producing CD8 T cells in the intestine, and enhance both resistance to bacterial infection and the therapeutic efficacy of immune checkpoint inhibitors.

638 citations

Journal ArticleDOI
TL;DR: In this article, a review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion, concluding that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.
Abstract: Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

633 citations

Journal ArticleDOI
TL;DR: In this article, the challenges and recent developments related to rechargeable zinc-ion battery research are presented, as well as recent research trends and directions on electrode materials that can store Zn2+ and electrolytes that can improve the battery performance.
Abstract: The zinc-ion battery (ZIB) is a 2 century-old technology but has recently attracted renewed interest owing to the possibility of switching from primary to rechargeable ZIBs. Nowadays, ZIBs employing a mild aqueous electrolyte are considered one of the most promising candidates for emerging energy storage systems (ESS) and portable electronics applications due to their environmental friendliness, safety, low cost, and acceptable energy density. However, there are many drawbacks associated with these batteries that have not yet been resolved. In this Review, we present the challenges and recent developments related to rechargeable ZIB research. Recent research trends and directions on electrode materials that can store Zn2+ and electrolytes that can improve the battery performance are comprehensively discussed.

612 citations


Authors

Showing all 2606 results

NameH-indexPapersCitations
George F. Smoot8449171884
Neil Collins8332130457
Eric V. Linder6526222666
Woojin Lee5356612120
Prim B. Singh511118329
Mohammad S. Obaidat5084711247
Naofal Al-Dhahir5056412345
Vladimir Brusic5021213727
Ilesanmi Adesida503839732
Vesselin N. Paunov491768353
Jinho Choi4868010659
Jozef Konings431659159
Sergey V. Mikhalovsky422045807
Philippe M. Frossard4214613680
Anton S. Desyatnikov412005724
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202339
2022128
2021956
20201,010
2019865
2018831