scholarly journals Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

2021 ◽  
Author(s):  
Manik Kuchroo ◽  
Jessie Huang ◽  
Patrick Wong ◽  
Jean-Christophe Grenier ◽  
Dennis Shung ◽  
...  
Keyword(s):  
Coarse Graining ◽  
High Dimensional ◽  
Computational Techniques ◽  
Clinical Markers ◽  
Cell Responses ◽  
Biological Features ◽  
Biological Meaning ◽  
High Level ◽  
Data Topology ◽  
High Dimensional Datasets

Abstract The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16-hi,CD66b-lo neutrophil and IFNγ+,GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

scholarly journals Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

2020 ◽  
Author(s):  
Manik Kuchroo ◽  
Jessie Huang ◽  
Patrick Wong ◽  
Jean-Christophe Grenier ◽  
Dennis Shung ◽  
...  
Keyword(s):  
Th17 Cell ◽  
Coarse Graining ◽  
High Dimensional ◽  
Computational Techniques ◽  
Clinical Markers ◽  
Cell Responses ◽  
Biological Features ◽  
Biological Meaning ◽  
High Level ◽  
High Dimensional Datasets

1SummaryThe biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

A PROSPECTS OF THE SUPER-INTENSIVE UNSUPPORTED GARDENS ESTABLISHMENT OF COLUMNAR APPLE TREE VARIETIES

1970 ◽  
pp. 45-48
Author(s):  
S. А. Korneeva ◽  
Е. N. Sedov ◽  
T. V. Янчук
Keyword(s):  
Economic Efficiency ◽  
Apple Tree ◽  
Maximum Yield ◽  
Biological Features ◽  
Apple Cultivars ◽  
High Level ◽  
Dwarf Rootstocks

Columnar apple cultivars are optimally suited to lay apple tree plantings using intensive technology, which provides for super-dense placement of trees. The article considers a variant of growing columnar apple cultivars on inserts of dwarf rootstocks 3-17-38 and 62-396. The use of dwarf rootstocks 3-17-38 and 62-396 as intercalar inserts in the cultivation of columnar apple cultivars, along with good anchoring of plants, provides high precocity, productivity and economic efficiency of planting. All the costs of laying the orchard and annual works on agrotechnical care of the trees were paid off in the fourth year after planting.The economic and biological features of the columnar cultivars provided not only a quick return of the investments, but also a high level of profitability. The profitability of the studied columnar planting for the 6th year after planting (2020) on average for all cultivars was 106.0 % on the insert of the dwarf rootstock 62-396 and 104.7 % on the insert 3-17-38. The profit received on average for the plantings amounted to 2 378 661 rubles per ha. In the group of the studied cultivars, there is a difference in economic efficiency. The lowest level of productivity and profitability was in the Vostorg cultivar: on average, on two inserts, the yield in 2020 was 27.3 t/ha and the profitability was 66.6%. The Girlyanda cultivar was characterized by the maximum yield and profitability: 88.0 t/ha and 115.8%, respectively.

Fast Algorithms for LS and LAD-Collaborative Regression

2021 ◽  
Author(s):  
Jun Sun ◽  
Lingchen Kong ◽  
Mei Li
Keyword(s):  
Numerical Experiments ◽  
Modern Science ◽  
Alternating Direction Method ◽  
Least Square ◽  
High Dimensional ◽  
Statistical Interpretation ◽  
Absolute Deviation ◽  
Linear Rate ◽  
Alternating Direction ◽  
High Dimensional Datasets

With the development of modern science and technology, it is easy to obtain a large number of high-dimensional datasets, which are related but different. Classical unimodel analysis is less likely to capture potential links between the different datasets. Recently, a collaborative regression model based on least square (LS) method for this problem has been proposed. In this paper, we propose a robust collaborative regression based on the least absolute deviation (LAD). We give the statistical interpretation of the LS-collaborative regression and LAD-collaborative regression. Then we design an efficient symmetric Gauss–Seidel-based alternating direction method of multipliers algorithm to solve the two models, which has the global convergence and the Q-linear rate of convergence. Finally we report numerical experiments to illustrate the efficiency of the proposed methods.

scholarly journals (Auto)Biographical reflections on the contributions of William F. Loomis (1940-2016) to Dictyostelium biology

2019 ◽  
Vol 63 (8-9-10) ◽  
pp. 343-357
Author(s):  
Adam Kuspa ◽  
Gad Shaulsky
Keyword(s):  
Cell Differentiation ◽  
Molecular Biology ◽  
Genetic Control ◽  
Dictyostelium Discoideum ◽  
University Of California ◽  
High Dimensional ◽  
Social Amoeba ◽  
The Social ◽  
The University ◽  
High Dimensional Datasets

William Farnsworth Loomis studied the social amoeba Dictyostelium discoideum for more than fifty years as a professor of biology at the University of California, San Diego, USA. This biographical reflection describes Dr. Loomis’ major scientific contributions to the field within a career arc that spanned the early days of molecular biology up to the present day where the acquisition of high-dimensional datasets drive research. Dr. Loomis explored the genetic control of social amoeba development, delineated mechanisms of cell differentiation, and significantly advanced genetic and genomic technology for the field. The details of Dr. Loomis’ multifaceted career are drawn from his published work, from an autobiographical essay that he wrote near the end of his career and from extensive conversations between him and the two authors, many of which took place on the deck of his beachfront home in Del Mar, California.

scholarly journals Probing Multiscale Disorder in Pyrochlore and Related Complex Oxides in the Transmission Electron Microscope: A Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Jenna L. Wardini ◽  
Hasti Vahidi ◽  
Huiming Guo ◽  
William J. Bowman
Keyword(s):  
Complex Oxides ◽  
Atomic Scale ◽  
High Dimensional ◽  
Detection Systems ◽  
Spatially Resolved ◽  
Chemical Ordering ◽  
Transmission Electron ◽  
The Many ◽  
High Dimensional Datasets

Transmission electron microscopy (TEM), and its counterpart, scanning TEM (STEM), are powerful materials characterization tools capable of probing crystal structure, composition, charge distribution, electronic structure, and bonding down to the atomic scale. Recent (S)TEM instrumentation developments such as electron beam aberration-correction as well as faster and more efficient signal detection systems have given rise to new and more powerful experimental methods, some of which (e.g., 4D-STEM, spectrum-imaging, in situ/operando (S)TEM)) facilitate the capture of high-dimensional datasets that contain spatially-resolved structural, spectroscopic, time- and/or stimulus-dependent information across the sub-angstrom to several micrometer length scale. Thus, through the variety of analysis methods available in the modern (S)TEM and its continual development towards high-dimensional data capture, it is well-suited to the challenge of characterizing isometric mixed-metal oxides such as pyrochlores, fluorites, and other complex oxides that reside on a continuum of chemical and spatial ordering. In this review, we present a suite of imaging and diffraction (S)TEM techniques that are uniquely suited to probe the many types, length-scales, and degrees of disorder in complex oxides, with a focus on disorder common to pyrochlores, fluorites and the expansive library of intermediate structures they may adopt. The application of these techniques to various complex oxides will be reviewed to demonstrate their capabilities and limitations in resolving the continuum of structural and chemical ordering in these systems.

scholarly journals Diabetes and its Complication Prediction using Multi-Task Learning

2020 ◽  
Vol 9 (5) ◽  
pp. 1426-1430
Keyword(s):  
Risk Factors ◽  
Diabetes Patient ◽  
Prediction Performance ◽  
Multitask Learning ◽  
High Dimensional ◽  
Disease Prediction ◽  
Healthcare Applications ◽  
Future Health ◽  
High Dimensional Datasets

Diabetes is a long-term disease that ends up in multiple side-effects. It has now become a reticent exterminator in society because it doesn’t reveal any signs hitherto to the patients until it’s too late. It leads to many complications to other organs, such as kidney, cardiovascular, liver or blood pressure [1]. This work tends to apply a unique multitask learning [2] to synchronously map the relation between manifold complications wherever every task conforms to risks of modelling of complications [3]. It also uses feature selection to reduce the set of risk factors from high-dimensional datasets. Then using the concept of correlation, it finds the degree of relativity among various sideeffects. The proposed method is able to identify the possible future health hazards identified with the diabetes patient. This will enable us to explain medical conditions and can improves healthcare applications which would help to improve disease prediction performance.

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