scholarly journals Inference of Chromosome-length Haplotypes using Genomic Data of Three to Five Single Gametes

2018 ◽  
Author(s):  
Ruidong Li ◽  
Han Qu ◽  
Jinfeng Chen ◽  
Shibo Wang ◽  
John M. Chater ◽  
...  
Keyword(s):  
Cost Efficiency ◽  
Human Disease ◽  
Large Scale ◽  
Genomic Data ◽  
Cost Effective ◽  
Chromosome Length ◽  
Recombination Activity ◽  
Innovative Method ◽  
Human Reproductive ◽  
The Relationship

AbstractKnowledge of chromosome-length haplotypes will not only advance our understanding of the relationship between DNA and phenotypes, but also promote a variety of genetic applications. Here we present Hapi, an innovative method for chromosomal haplotype inference using only 3 to 5 gametes. Hapi outperformed all existing haploid-based phasing methods in terms of accuracy, reliability, and cost efficiency in both simulated and real gamete datasets. This highly cost-effective phasing method will make large-scale haplotype studies feasible to facilitate human disease studies and plant/animal breeding. In addition, Hapi can detect meiotic crossovers in gametes, which has promise in the diagnosis of abnormal recombination activity in human reproductive cells.

scholarly journals Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes

2020 ◽  
Vol 37 (12) ◽  
pp. 3684-3698 ◽  
Author(s):  
Ruidong Li ◽  
Han Qu ◽  
Jinfeng Chen ◽  
Shibo Wang ◽  
John M Chater ◽  
...  
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Genomic Data ◽  
Cost Effective ◽  
R Package ◽  
Chromosome Length ◽  
Sequencing Data ◽  
Experimental Phasing ◽  
Wide Range ◽  
Haplotype Data

Abstract Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor’s chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring chromosome-length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.

DO INNOVATIVE ORGANISATIONS COMPETE ON SINGLE OR MULTIPLE OPERATIONAL CAPABILITIES?

2014 ◽  
Vol 18 (03) ◽  
pp. 1440001
Author(s):  
ALKA ASHWINI NAND ◽  
PRAKASH J. SINGH ◽  
ANANYA BHATTACHARYA
Keyword(s):  
Cost Efficiency ◽  
Large Scale ◽  
Trade Off ◽  
Manufacturing Plants ◽  
Operational Level ◽  
Operational Capabilities ◽  
Quality Of Products ◽  
Operations Capabilities ◽  
The Relationship

Organisations lack clear guidance on how they can become more innovative at the operational level. The operations strategy literature shows that organisations compete on four generic capabilities: cost efficiency, quality of products or services, speed of delivery, and flexibility of operations. Should organisations choose between these capabilities, i.e., engage in trading-off these capabilities and focussing on one capability ("trade-off" model), or combine them, thereby competing on multiple capabilities simultaneously ("cumulative capabilities" model), remains an unresolved issue. Our paper addresses this by empirically testing the relationship between the four operations capabilities and innovation performance through a large-scale global study of manufacturing plants. Our results show support for the cumulative capabilities model and not the trade-off model. Furthermore, both delivery and flexibility capabilities are comparatively stronger predictors of innovativeness than cost efficiency and quality capabilities. This study provides interesting insights for practitioners and managers in generating clearer guidelines as to what organisations need to do with their key operational capabilities, in order to become more innovative.

scholarly journals dbgap2x: An R package to explore and extract data from the database of Genotypes and Phenotypes (dbGaP)

2019 ◽  
Author(s):  
Grégoire Versmée ◽  
Laura Versmée ◽  
Mikaël Dusenne ◽  
Niloofar Jalali ◽  
Paul Avillach
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Genomic Data ◽  
R Package ◽  
National Institutes Of Health ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Complex Procedure ◽  
Range Of Functions ◽  
The Relationship

Abstract Summary Based on the Genomic Data Sharing Policy issued in August 2007, the National Institutes of Health (NIH) has supported several repositories such as the database of Genotypes and Phenotypes (dbGaP). dbGaP is an online repository that provides access to large-scale genetic and phenotypic datasets with more than 1,000 studies. However, navigating the website and understanding the relationship between the studies are not easy tasks. Moreover, the decryption of the files is a complex procedure. In this study we propose the dbgap2x R package that covers a broad range of functions for searching dbGaP studies, exploring the characteristics of a study and easily decrypting the files from dbGaP. Availability and implementation dbgap2x is an R package with the code available at https://github.com/gversmee/dbgap2x. A containerized version including the package, a Jupyter server and with a Notebook example is available at https://hub.docker.com/r/gversmee/dbgap2x. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Saturating Single-Cell atlas Datasets

2017 ◽  
Author(s):  
Aparna Bhaduri ◽  
Tomasz J. Nowakowski ◽  
Alex A. Pollen ◽  
Arnold R. Kriegstein
Keyword(s):  
Population Structure ◽  
Single Cell ◽  
Mouse Brain ◽  
Large Scale ◽  
Single Cells ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Number ◽  
Cell Type ◽  
The Relationship

AbstractHigh throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index”, which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage”, the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.Recent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.

Effect of the dyeing process on thermal and dyeing properties of poly(butylene terephthalate) fibers

2020 ◽  
pp. 004051752094816
Author(s):  
Shuqiang Zhao ◽  
Zhe Gao ◽  
Gaoming Jiang ◽  
Jiankang Wang ◽  
Xuhong Miao ◽  
...  
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Cost Effective ◽  
Decomposition Temperature ◽  
Dye Uptake ◽  
Color Strength ◽  
Dyeing Properties ◽  
Butylene Terephthalate ◽  
Dyeing Process ◽  
The Relationship

A medium- and low-temperature disperse dye was applied as a dyeing material for poly(butylene terephthalate) (PBT) fibers using a cost-effective and scalable approach. The relationship between the dyeing process and thermal or dyeing properties, such as the dye uptake percentage and color fastness properties, was systematically investigated. Interestingly, with the increase of the C. I. Disperse Red 167 concentration from 2% o.w.f. (on weight of fiber) to 5% o.w.f. and dyeing temperature, the dyed PBT fibers correspondingly gained better color strength (28.7), an indication of a suitable dye uptake of 93.15%. Furthermore, the incorporation of the dye into PBT fibers improved the decomposition temperature and melting or crystallization temperature, and the storage modulus was higher than that of undyed PBT fibers using this simple approach. Therefore, these promising results would be a significant component to enhance or regulate the significant thermal stability of PBT fibers in the dyeing field. Also, they would go a long way to support the idea that the present approach is useful for further industrialization of the dyeing process because of its low cost and suitability for a large-scale process.

Relationship between total plasma homocysteine and the risk of aneurysms – a meta-analysis

VASA ◽  
2020 ◽  
pp. 1-6
Author(s):  
Hanji Zhang ◽  
Dexin Yin ◽  
Yue Zhao ◽  
Yezhou Li ◽  
Dejiang Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Single Species ◽  
Total Plasma ◽  
Control Groups ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
The Difference ◽  
The Relationship ◽  
Healthy Participants

Summary: Our meta-analysis focused on the relationship between homocysteine (Hcy) level and the incidence of aneurysms and looked at the relationship between smoking, hypertension and aneurysms. A systematic literature search of Pubmed, Web of Science, and Embase databases (up to March 31, 2020) resulted in the identification of 19 studies, including 2,629 aneurysm patients and 6,497 healthy participants. Combined analysis of the included studies showed that number of smoking, hypertension and hyperhomocysteinemia (HHcy) in aneurysm patients was higher than that in the control groups, and the total plasma Hcy level in aneurysm patients was also higher. These findings suggest that smoking, hypertension and HHcy may be risk factors for the development and progression of aneurysms. Although the heterogeneity of meta-analysis was significant, it was found that the heterogeneity might come from the difference between race and disease species through subgroup analysis. Large-scale randomized controlled studies of single species and single disease species are needed in the future to supplement the accuracy of the results.

New ''Cold War''

2020 ◽  
pp. 27-34
Author(s):  
Vladimir Batiuk
Keyword(s):  
Cold War ◽  
Nuclear Weapons ◽  
Armed Conflict ◽  
Large Scale ◽  
Arms Race ◽  
Ideological Struggle ◽  
Nuclear Arms Race ◽  
The Cold War ◽  
The Relationship ◽  
Nuclear Arms

In this article, the ''Cold War'' is understood as a situation where the relationship between the leading States is determined by ideological confrontation and, at the same time, the presence of nuclear weapons precludes the development of this confrontation into a large-scale armed conflict. Such a situation has developed in the years 1945–1989, during the first Cold War. We see that something similar is repeated in our time-with all the new nuances in the ideological struggle and in the nuclear arms race.

Investigating Diseases and Chemicals in COVID-19 Literature with Text Mining (Preprint)

2020 ◽  
Author(s):  
Amir Karami ◽  
Brandon Bookstaver ◽  
Melissa Nolan
Keyword(s):  
Text Mining ◽  
Literature Review ◽  
Topic Modeling ◽  
Large Scale ◽  
Clinical Manifestations ◽  
International Health ◽  
Research Papers ◽  
Strategic Plans ◽  
Funding Agencies ◽  
The Relationship

BACKGROUND The COVID-19 pandemic has impacted nearly all aspects of life and has posed significant threats to international health and the economy. Given the rapidly unfolding nature of the current pandemic, there is an urgent need to streamline literature synthesis of the growing scientific research to elucidate targeted solutions. While traditional systematic literature review studies provide valuable insights, these studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, incapable of trend analysis, and lack of data-driven tools. OBJECTIVE This study fills the mentioned restrictions in the literature and practice by analyzing two biomedical concepts, clinical manifestations of disease and therapeutic chemical compounds, with text mining methods in a corpus containing COVID-19 research papers and find associations between the two biomedical concepts. METHODS This research has collected papers representing COVID-19 pre-prints and peer-reviewed research published in 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling to find the relationship between the two biomedical concepts. RESULTS We analyzed 9,298 research papers published through May 5, 2020 and found 3,645 disease-related and 2,434 chemical-related articles. The most frequent clinical manifestations of disease terminology included COVID-19, SARS, cancer, pneumonia, fever, and cough. The most frequent chemical-related terminology included Lopinavir, Ritonavir, Oxygen, Chloroquine, Remdesivir, and water. Topic modeling provided 25 categories showing relationships between our two overarching categories. These categories represent statistically significant associations between multiple aspects of each category, some connections of which were novel and not previously identified by the scientific community. CONCLUSIONS Appreciation of this context is vital due to the lack of a systematic large-scale literature review survey and the importance of fast literature review during the current COVID-19 pandemic for developing treatments. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, to journals for exploring most discussed disease symptoms and pharmaceutical targets, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

Pound Wise and Penny Foolish? Weight Loss and The Dynamics of Health Care Spending

2011 ◽  
Vol 14 (2) ◽  
Author(s):  
Thomas G Koch
Keyword(s):  
Weight Loss ◽  
Cost Savings ◽  
Cost Effective ◽  
Economic Consequences ◽  
Health Care Spending ◽  
Order Of Magnitude ◽  
Dynamic Measures ◽  
The Cost ◽  
The Impact ◽  
The Relationship

Current estimates of obesity costs ignore the impact of future weight loss and gain, and may either over or underestimate economic consequences of weight loss. In light of this, I construct static and dynamic measures of medical costs associated with body mass index (BMI), to be balanced against the cost of one-time interventions. This study finds that ignoring the implications of weight loss and gain over time overstates the medical-cost savings of such interventions by an order of magnitude. When the relationship between spending and age is allowed to vary, weight-loss attempts appear to be cost-effective starting and ending with middle age. Some interventions recently proven to decrease weight may also be cost-effective.

scholarly journals Strategic resources and smallholder performance at the bottom of the pyramid

2019 ◽  
Vol 22 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Matthias Olthaar ◽  
Wilfred Dolfsma ◽  
Clemens Lutz ◽  
Florian Noseleit
Keyword(s):  
Large Scale ◽  
Business Environment ◽  
Agricultural Economics ◽  
Local Market ◽  
Bottom Of The Pyramid ◽  
Fine Grained ◽  
Strategic Resources ◽  
Relative Contribution ◽  
The Relationship ◽  
Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

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