scholarly journals Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

2012 ◽  
Vol 28 (15) ◽  
pp. 1998-2003 ◽  
Author(s):  
C. D. Tekwe ◽  
R. J. Carroll ◽  
A. R. Dabney
Keyword(s):  
Survival Analysis ◽  
Quantitative Analysis ◽  
Proteomics Data ◽  
Analysis Methodology

An innovative quantitative analysis methodology for Natech events triggered by earthquakes in chemical tank farms

2020 ◽  
Vol 128 ◽  
pp. 104744 ◽  
Author(s):  
Kongxing Huang ◽  
Guohua Chen ◽  
Yunfeng Yang ◽  
Peizhu Chen
Keyword(s):  
Quantitative Analysis ◽  
Analysis Methodology

Reasons and timing of cows leaving herd in dairy cows using survival analysis methodology

2007 ◽  
Vol 2007 ◽  
pp. 150-150
Author(s):  
A. Heravi Moussavi ◽  
M. Danesh Mesgaran ◽  
E. Dirandeh ◽  
A. Pirzadeh Naeini ◽  
R. Noorbakhsh
Keyword(s):  
Survival Analysis ◽  
Dairy Cows ◽  
Censored Data ◽  
Reproductive Performance ◽  
Dairy Farm ◽  
Stage Of Lactation ◽  
Analysis Methodology ◽  
Farm Profit ◽  
Health Disorders ◽  
Time Dependent Covariates

Cow longevity is highly related to dairy farm profit. Cows are culled for a variety of reasons. The predominant reasons for culling are reproduction (i.e., failure to conceive), health, and low production (Bascom and Young, 1998). Half of the herd removals occur involuntarily and prematurely because of health disorders (Beaudeau et al., 2000). The decision to cull is a complex one. Farmers may consider many individual (such as age, stage of lactation, milk production, health status, and reproductive performance) and economic (such as milk price, the price of culled cows, and the price and availability of replacement heifers) factors when deciding to cull a cow. On the other hand, the risk of culling is not consistent across all stages of lactation. Cows experience the highest risk shortly after calving (Fetrow et al., 2006). Survival analysis allows for a more appropriate management of censored data and time-dependent covariates. Analyses of the reason and timing of culling is needed to predict herd performance. The objective of this study was to study the reasons and timing of cows leaving herd in two large Holstein dairy farms in Iran.

scholarly journals Survival Analysis Methodology on Ophthalmic Area

2018 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Eunjeong Ji ◽  
Sang Jun Park ◽  
Soyeon Ahn ◽  
Minjung Lee
Keyword(s):  
Survival Analysis ◽  
Analysis Methodology

scholarly journals Validation of a global quantitative analysis methodology of tryptophan metabolites in mice using LC-MS

Talanta ◽  
2019 ◽  
Vol 195 ◽  
pp. 593-598 ◽  
Author(s):  
Antoine Lefèvre ◽  
Sylvie Mavel ◽  
Lydie Nadal-Desbarats ◽  
Laurent Galineau ◽  
Sylvie Attucci ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Analysis Methodology ◽  
Tryptophan Metabolites

scholarly journals Analysis of Diagnostic Images of Artworks and Feature Extraction: Design of a Methodology

2021 ◽  
Vol 7 (3) ◽  
pp. 53
Author(s):  
Annamaria Amura ◽  
Alessandro Aldini ◽  
Stefano Pagnotta ◽  
Emanuele Salerno ◽  
Anna Tonazzini ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Information Systems ◽  
Quantitative Analysis ◽  
Geographic Information Systems ◽  
Digital Images ◽  
Geographic Information ◽  
Gis Application ◽  
Analysis Methodology ◽  
Research Goal ◽  
Short Time

Digital images represent the primary tool for diagnostics and documentation of the state of preservation of artifacts. Today the interpretive filters that allow one to characterize information and communicate it are extremely subjective. Our research goal is to study a quantitative analysis methodology to facilitate and semi-automate the recognition and polygonization of areas corresponding to the characteristics searched. To this end, several algorithms have been tested that allow for separating the characteristics and creating binary masks to be statistically analyzed and polygonized. Since our methodology aims to offer a conservator-restorer model to obtain useful graphic documentation in a short time that is usable for design and statistical purposes, this process has been implemented in a single Geographic Information Systems (GIS) application.

scholarly journals Identification of metabolism-associated genes and construction of a prognostic signature in bladder cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chengquan Shen ◽  
Jing Liu ◽  
Liping Wang ◽  
Zhijuan Liang ◽  
Haitao Niu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Survival Analysis ◽  
Transforming Growth Factor ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
High Morbidity ◽  
Prognostic Signature ◽  
Proteomics Data ◽  
Expression Levels ◽  
Tgf Β1

Abstract Background Bladder cancer (BC) is a commonly diagnosed malignant tumor in the urinary system, with a high morbidity and a high recurrence rate. Current studies indicated that metabolism-associated genes (MAGs) having critical roles in the etiology of BC. The present study aims to identify differentially expressed MAGs and construct a MAGs based prognostic risk signature for BC by using The Cancer Genome Atlas (TCGA) database and proteomics data. Methods RNA-sequence data from the TCGA database and proteomics data from our BC samples were used to identify differentially expressed MAGs and construct a MAGs based prognostic signature in BC. Subsequently, survival analysis and nomogram were used to evaluate the prognostic and predictive value of the MAGs based signature in BC. RNA isolation and reverse transcription‑quantitative PCR (RT-qPCR) were further performed to investigate the expression levels of MAGs in BC cells and explore the relationship between MAGs and M2 tumor associated macrophages (TAMs) secreted transforming growth factor-β1 (TGF-β1) in BC cells. Results A total of 23 differentially expressed MAGs were identified and five MAGs were finally used to construct a MAGs based signature. Survival analysis revealed that the MAGs based signature was closely correlated with the survival outcomes of patients with BC. A nomogram with the MAGs based signature risk score and clinical features was also constructed to facilitate the individualized prediction of BC patients. RT-qPCR showed that five MAGs were significantly differentially expressed and the expression levels of three MAGs were positively correlated with M2 TAMs secreted TGF-β1 in T24 cells. Conclusions Our study identified novel prognostic MAGs and constructed a MAGs based signature, which can be used as an independent factor in evaluating the prognosis of patients with BC. Furthermore, M2 TAMs may promote the expression of MAGs via the TGF-β1 signaling pathway in the microenvironment of BC. Further clinical trials and experimental explorations are needed to validate our observations in BC.

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