scholarly journals Soil Metagenome Sequences from a Geothermal Site at Mount Melbourne in Antarctica

2021 ◽  
Vol 10 (10) ◽  
Author(s):  
Mira Park ◽  
Yong-Hoe Choe ◽  
Mincheol Kim
Keyword(s):  
Soil Microbes ◽  
Soil Microbiome ◽  
Data Sets ◽  
Prokaryotic Diversity ◽  
Soil Metagenome ◽  
Insight Into

ABSTRACT Here, we examined two soil metagenome data sets obtained from a geothermal site at the summit of Mount Melbourne in Antarctica. The geothermal soil microbiome exhibits very unique features of prokaryotic diversity and functions, which will provide deeper insight into the adaptation and evolution of soil microbes in Antarctic geothermal habitats.

scholarly journals High-Performance Computing of BEAST/BEAGLE in Bayesian Phylogenetics using SDumont Hybrid Resources

2020 ◽  
Author(s):  
Kary Ocaña ◽  
Micaella Coelho ◽  
Guilherme Freire ◽  
Carla Osthoff
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Current Knowledge ◽  
Data Sets ◽  
Length Estimation ◽  
Computationally Intensive ◽  
Mcmc Chain ◽  
Performance Computing ◽  
Insight Into

Bayesian phylogenetic algorithms are computationally intensive. BEAST 1.10 inferences made use of the BEAGLE 3 high-performance library for efficient likelihood computations. The strategy allows phylogenetic inference and dating in current knowledge for SARS-CoV-2 transmission. Follow-up simulations on hybrid resources of Santos Dumont supercomputer using four phylogenomic data sets, we characterize the scaling performance behavior of BEAST 1.10. Our results provide insight into the species tree and MCMC chain length estimation, identifying preferable requirements to improve the use of high-performance computing resources. Ongoing steps involve analyzes of SARS-CoV-2 using BEAST 1.8 in multi-GPUs.

scholarly journals Informative Frailty Indices from Binarized Biomarkers

2020 ◽  
Author(s):  
Garrett Stubbings ◽  
Spencer Farrell ◽  
Arnold Mitnitski ◽  
Kenneth Rockwood ◽  
Andrew Rutenberg
Keyword(s):  
Medical Knowledge ◽  
Data Sets ◽  
Canadian Study ◽  
Adverse Health Outcomes ◽  
Health And Nutrition ◽  
Health Measure ◽  
Upper Limits ◽  
Insight Into ◽  
Better Than ◽  
Biomarker Data

AbstractFrailty indices (FI) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FI from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey (NHANES) and the Canadian Study of Health and Aging (CSHA) data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, the upper limits of the FI observed in various data sets, and highlights general difficulties in obtaining absolute scales for comparing FI between studies.

scholarly journals Study of Oak Ridge soils using BONCAT-FACS-Seq reveals that a large fraction of the soil microbiome is active

2018 ◽  
Author(s):  
Estelle Couradeau ◽  
Joelle Sasse ◽  
Danielle Goudeau ◽  
Nandita Nath ◽  
Terry C. Hazen ◽  
...  
Keyword(s):  
Soil Microbes ◽  
Sequence Similarity ◽  
Large Fraction ◽  
Amplicon Sequencing ◽  
Bulk Soil ◽  
Soil Microbiome ◽  
Active Fraction ◽  
Soil Microbial Diversity ◽  
Oak Ridge

AbstractThe ability to link soil microbial diversity to soil processes requires technologies that differentiate active subpopulations of microbes from so-called relic DNA and dormant cells. Measures of microbial activity based on various techniques including DNA labelling have suggested that most cells in soils are inactive, a fact that has been difficult to reconcile with observed high levels of bulk soil activities. We hypothesized that measures of in situ DNA synthesis may be missing the soil microbes that are metabolically active but not replicating, and we therefore applied BONCAT (Bioorthogonal Non Canonical Amino Acid Tagging) i.e. a proxy for activity that does not rely on cell division, to measure translationally active cells in soils. We compared the active population of two soil depths from Oak Ridge (TN) incubated under the same conditions for up to seven days. Depending on the soil, a maximum of 25 – 70% of the cells were active, accounting for 3-4 million cells per gram of soil type, which is an order of magnitude higher than previous estimates. The BONCAT positive cell fraction was recovered by fluorescence activated cell sorting (FACS) and identified by 16S rDNA amplicon sequencing. The diversity of the active fraction was a selected subset of the bulk soil community. Excitingly, some of the same members of the community were recruited at both depths independently from their abundance rank. On average, 86% of sequence reads recovered from the active community shared >97% sequence similarity with cultured isolates from the field site. Our observations are in line with a recent report that, of the few taxa that are both abundant and ubiquitous in soil, 45% are also cultured – and indeed some of these ubiquitous microorganisms were found to be translationally active. The use of BONCAT on soil microbiomes provides evidence that a large portion of the soil microbes can be active simultaneously. We conclude that BONCAT coupled to FACS and sequencing is effective for interrogating the active fraction of soil microbiomes in situ and provides new perspectives to link metabolic capacity to overall soil ecological traits and processes.

scholarly journals Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

scholarly journals ASSESSING LIDAR TRAINING DATA QUANTITIES FOR CLASSIFICATION MODELS

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 101-106
Author(s):  
O. Majgaonkar ◽  
K. Panchal ◽  
D. Laefer ◽  
M. Stanley ◽  
Y. Zaki
Keyword(s):  
Point Cloud ◽  
Classification Performance ◽  
Training Data ◽  
Lidar Data ◽  
Data Sets ◽  
Classification Models ◽  
Fundamental Properties ◽  
Point Cloud Classification ◽  
The Impact ◽  
Insight Into

Abstract. Classifying objects within aerial Light Detection and Ranging (LiDAR) data is an essential task to which machine learning (ML) is applied increasingly. ML has been shown to be more effective on LiDAR than imagery for classification, but most efforts have focused on imagery because of the challenges presented by LiDAR data. LiDAR datasets are of higher dimensionality, discontinuous, heterogenous, spatially incomplete, and often scarce. As such, there has been little examination into the fundamental properties of the training data required for acceptable performance of classification models tailored for LiDAR data. The quantity of training data is one such crucial property, because training on different sizes of data provides insight into a model’s performance with differing data sets. This paper assesses the impact of training data size on the accuracy of PointNet, a widely used ML approach for point cloud classification. Subsets of ModelNet ranging from 40 to 9,843 objects were validated on a test set of 400 objects. Accuracy improved logarithmically; decelerating from 45 objects onwards, it slowed significantly at a training size of 2,000 objects, corresponding to 20,000,000 points. This work contributes to the theoretical foundation for development of LiDAR-focused models by establishing a learning curve, suggesting the minimum quantity of manually labelled data necessary for satisfactory classification performance and providing a path for further analysis of the effects of modifying training data characteristics.

scholarly journals Reconditioning Degraded Mine Site Soils With Exogenous Soil Microbes: Plant Fitness and Soil Microbiome Outcomes

2019 ◽  
Vol 10 ◽  
Author(s):  
Benjamin Moreira-Grez ◽  
Miriam Muñoz-Rojas ◽  
Khalil Kariman ◽  
Paul Storer ◽  
Anthony G. O’Donnell ◽  
...  
Keyword(s):  
Soil Microbes ◽  
Soil Microbiome ◽  
Plant Fitness ◽  
Mine Site

scholarly journals Response of Barley Plants to Drought Might Be Associated with the Recruiting of Soil-Borne Endophytes

2020 ◽  
Vol 8 (9) ◽  
pp. 1414
Author(s):  
Luhua Yang ◽  
Peter Schröder ◽  
Gisle Vestergaard ◽  
Michael Schloter ◽  
Viviane Radl
Keyword(s):  
Drought Stress ◽  
Bacterial Communities ◽  
Soil Microbes ◽  
Plant Biomass ◽  
Management Strategies ◽  
Plant Performance ◽  
Soil Microbiome ◽  
Bacterial Endophytes ◽  
Natural Conditions

Mechanisms used by plants to respond to water limitation have been extensively studied. However, even though the inoculation of beneficial microbes has been shown to improve plant performance under drought stress, the inherent role of soil microbes on plant response has been less considered. In the present work, we assessed the importance of the soil microbiome for the growth of barley plants under drought stress. Plant growth was not significantly affected by the disturbance of the soil microbiome under regular watering. However, after drought stress, we observed a significant reduction in plant biomass, particularly of the root system. Plants grown in the soil with disturbed microbiome were significantly more affected by drought and did not recover two weeks after re-watering. These effects were accompanied by changes in the composition of endophytic fungal and bacterial communities. Under natural conditions, soil-derived plant endophytes were major colonizers of plant roots, such as Glycomyces and Fusarium, whereas, for plants grown in the soil with disturbed microbiome seed-born bacterial endophytes, e.g., Pantoea, Erwinia, and unclassified Pseudomonaceae and fungal genera normally associated with pathogenesis, such as Gibberella and Gaeumannomyces were observed. Therefore, the role of the composition of the indigenous soil microbiota should be considered in future approaches to develop management strategies to make plants more resistant towards abiotic stress, such as drought.

scholarly journals Toward Psychoinformatics: Computer Science Meets Psychology

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Christian Montag ◽  
Éilish Duke ◽  
Alexander Markowetz
Keyword(s):  
Computer Science ◽  
Online Social Network ◽  
Large Data ◽  
Research Field ◽  
Large Data Sets ◽  
Future Research ◽  
Data Sets ◽  
Psychological Traits ◽  
Scientific Methods ◽  
Insight Into

The present paper provides insight into an emerging research discipline calledPsychoinformatics. In the context ofPsychoinformatics, we emphasize the cooperation between the disciplines of psychology and computer science in handling large data sets derived from heavily used devices, such as smartphones or online social network sites, in order to shed light on a large number of psychological traits, including personality and mood. New challenges await psychologists in light of the resulting “Big Data” sets, because classic psychological methods will only in part be able to analyze this data derived from ubiquitous mobile devices, as well as other everyday technologies. As a consequence, psychologists must enrich their scientific methods through the inclusion of methods from informatics. The paper provides a brief review of one area of this research field, dealing mainly with social networks and smartphones. Moreover, we highlight how data derived fromPsychoinformaticscan be combined in a meaningful way with data from human neuroscience. We close the paper with some observations of areas for future research and problems that require consideration within this new discipline.

A snapshot of microarray-generated gene expression signatures associated with ovarian carcinoma

2008 ◽  
Vol 18 (6) ◽  
pp. 1215-1233 ◽  
Author(s):  
B. Györffy ◽  
M. Dietel ◽  
T. Fekete ◽  
H. Lage
Keyword(s):  
Gene Expression ◽  
Ovarian Carcinoma ◽  
Therapy Response ◽  
Global Gene Expression ◽  
Diagnostic Tools ◽  
Data Sets ◽  
Current State ◽  
Technology Platforms ◽  
Array Technology ◽  
Insight Into

It was hypothesized that analysis of global gene expression in ovarian carcinoma can identify dysregulated genes that can serve as molecular markers and provide further insight into carcinogenesis and provide the basis for development of new diagnostic tools as well as new targeted therapy protocols. By applying bioinformatics tools for screening of biomedical databases, a gene expression profile databank, specific for ovarian carcinoma, was constructed with utilizable data sets published in 28 studies that applied different array technology platforms. The data sets were divided into four compartments: (i) genes associated with carcinogenesis: in 14 studies, 1881 genes were extracted, 75 genes were identified in more than one study, and only 4 genes (PRKCBP1, SPON1, TACSTD1, and PTPRM) were identified in three studies. (ii) Genes associated with histologic subtypes: in four studies, 463 genes could be identified, but none of them was identified in more than a single study. (iii) Genes associated with therapy response: in seven studies, 606 genes were identified from which 38 were differentially regulated in at least two studies, 3 genes (TMSB4X, GRN, and TJP1) in three studies, and 1 gene (IFITM1) in four studies. (iv) Genes associated with prognosis and progression: 254 genes were found in seven studies. From these genes, merely three were identified in at least two different studies. This snapshot of available gene expression data not only provides independently described potential diagnostic and therapeutic targets for ovarian carcinoma but also emphasizes the drawbacks of the current state of global gene expression analyses in ovarian cancer.

scholarly journals Biogeochemical climatology for the Southern Benguela Upwelling System, constructed from <i>in situ</i> monitoring data

2018 ◽  
Author(s):  
Stephanie de Villiers
Keyword(s):  
Monitoring Data ◽  
Data Sets ◽  
Profile Data ◽  
Data Archiving ◽  
Upwelling System ◽  
In Situ Data ◽  
Benguela Upwelling ◽  
Phosphate Nitrate ◽  
Insight Into

Abstract. An annual and a seasonal biogeochemical climatology had been constructed for the Southern Benguela Upwelling System, from in situ data collected along a 12 station monitoring line, sampled at monthly intervals from 2001 to 2012. The monitoring line reaches a maximum offshore distance of almost 190 km, with monitoring station depths ranging from 27 to 1465 m. In addition to temperature, salinity and oxygen CTD profile data, archived monitoring data for the macro-nutrients (phosphate, nitrate + nitrite, silicate) and chlorophyll-a was evaluated. The climatologies exhibit clear spatial and seasonal variability patterns for all parameters, that yield important insight into the SBUS upwelling cycle. These data sets comprise valuable additions to our knowledge base, and will aid both future modelling efforts and studies of biogeochemical processes in upwelling systems. Data for the constructed climatologies have been made available via the PANGAEA Data Archiving and Publication database at http://doi.pangaea.de/10.1594/PANGAEA.882218.

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