scholarly journals Creation of a federated database of blood proteins: a powerful new tool for finding and characterizing biomarkers in serum

2021 ◽  
Author(s):  
John Marshall ◽  
Peter Bowden ◽  
Jean Claude Schmit ◽  
Fay Betsou
Keyword(s):  
Blood Proteins ◽  
Protein Biomarkers ◽  
Disease Biomarkers ◽  
Protein Mass ◽  
Protein Mass Spectrometry ◽  
Statistical Analysis System ◽  
Sql Database ◽  
Special Programs ◽  
Federated Database ◽  
Analysis System

Protein biomarkers offer major benefits for diagnosis and monitoring of disease processes. Recent advances in protein mass spectrometry make it feasible to use this very sensitive technology to detect and quantify proteins in blood. To explore the potential of blood biomarkers, we conducted a thorough review to evaluate the reliability of data in the literature and to determine the spectrum of proteins reported to exist in blood with a goal of creating a Federated Database of Blood Proteins (FDBP). A unique feature of our approach is the use of a SQL database for all of the peptide data; the power of the SQL database combined with standard informatic algorithms such as BLASTand the statistical analysis system (SAS) allowed the rapid annotation and analysis of the database without the need to create special programs to manage the data. Our mathematical analysis and review shows that in addition to the usual secreted proteins found in blood, there are many reports of intracellular proteins and good agreement on transcription factors, DNA remodelling factors in addition to cellular receptors and their signal transduction enzymes. Overall, we have catalogued about 12,130 proteins identified by at least one unique peptide, and of these 3858 have 3 or more peptide correlations. The FDBP with annotations should facilitate testing blood for specific disease biomarkers.

scholarly journals Creation of a federated database of blood proteins: a powerful new tool for finding and characterizing biomarkers in serum

2021 ◽  
Author(s):  
John Marshall ◽  
Peter Bowden ◽  
Jean Claude Schmit ◽  
Fay Betsou
Keyword(s):  
Blood Proteins ◽  
Protein Biomarkers ◽  
Disease Biomarkers ◽  
Protein Mass ◽  
Protein Mass Spectrometry ◽  
Statistical Analysis System ◽  
Sql Database ◽  
Special Programs ◽  
Federated Database ◽  
Analysis System

Protein biomarkers offer major benefits for diagnosis and monitoring of disease processes. Recent advances in protein mass spectrometry make it feasible to use this very sensitive technology to detect and quantify proteins in blood. To explore the potential of blood biomarkers, we conducted a thorough review to evaluate the reliability of data in the literature and to determine the spectrum of proteins reported to exist in blood with a goal of creating a Federated Database of Blood Proteins (FDBP). A unique feature of our approach is the use of a SQL database for all of the peptide data; the power of the SQL database combined with standard informatic algorithms such as BLASTand the statistical analysis system (SAS) allowed the rapid annotation and analysis of the database without the need to create special programs to manage the data. Our mathematical analysis and review shows that in addition to the usual secreted proteins found in blood, there are many reports of intracellular proteins and good agreement on transcription factors, DNA remodelling factors in addition to cellular receptors and their signal transduction enzymes. Overall, we have catalogued about 12,130 proteins identified by at least one unique peptide, and of these 3858 have 3 or more peptide correlations. The FDBP with annotations should facilitate testing blood for specific disease biomarkers.

Applied plant science experimental design and statistical analysis using the SAS® OnDemand for Academics

2021 ◽  
Keyword(s):  
Statistical Analysis ◽  
Agricultural Production ◽  
Science Research ◽  
Plant Science ◽  
Data Sets ◽  
Statistical Analysis System ◽  
Key Topics ◽  
Analysis System ◽  
User Friendly ◽  
Enormous Number

Abstract The correct design, analysis and interpretation of plant science experiments is imperative for continued improvements in agricultural production worldwide. The enormous number of design and analysis options available for correctly implementing, analyzing and interpreting research can be overwhelming. Statistical Analysis System (SAS®) is the most widely used statistical software in the world and SAS® OnDemand for Academics is now freely available for academic insttutions. This is a user-friendly guide to statistics using SAS® OnDemand for Academics, ideal for facilitating the design and analysis of plant science experiments. It presents the most frequently used statistical methods in an easy-to-follow and non-intimidating fashion, and teaches the appropriate use of SAS® within the context of plant science research. This book contains 21 chapters that covers experimental designs and data analysis protocols; is presented as a how-to guide with many examples; includes freely downloadable data sets; and examines key topics such as ANOVA, mean separation, non-parametric analysis and linear regression.

Assessment of plant population on growth, fibre and seed yield of kenaf (Hibiscus cannabinus L.) in Ibadan western Nigeria

2020 ◽  
Vol 18 (1) ◽  
pp. 50-56
Author(s):  
S.O. Olanipekun ◽  
A.O. Togun ◽  
S.A. Adejumo ◽  
O.N. Adeniyan ◽  
A.K. Adebayo
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Hibiscus Cannabinus ◽  
Growth And Yield ◽  
Plant Spacing ◽  
Bast Fibre ◽  
Statistical Analysis System ◽  
Fibre Yield ◽  
Analysis System ◽  
Seed Yields

Kenaf is a multi-purpose crop with numerous industrial uses. Its production is constrained by poor cultural and agronomic practices which reduce yield. Inappropriate spacing among others could result in low yield. Effect of plant spacing on growth and yield of kenaf was investigated in Ibadan, Nigeria. Kenaf seed was sown (2 plants/stand) at three plant spacing: 50×15, 50×20, 50×25 cm was assessed for seed and bast fibre yields using randomized complete block design (RCBD) with three replicates. The analysis was done using statistical analysis system (SAS). Plant spacing differed significantly for bast fibre and seed yields. Highest bast fibre yield (0.9±0.03) and seed yield (0.5±0.01) were obtained at 50×20 cm and 50×25 cm spacing, respectively, while the lowest bast fibre yield (0.7±0.01) and seed yield (0.3±0.01) were obtained at 50×15 cm spacing. Spacing of 50 × 15 cm and 50 × 20 cm are appropriate when planting for fibre while 50 × 25cm is appropriate for seed production. Keywords: Kenaf, Spacing, Fibre and Seed yield.

scholarly journals Qualidade de vida em sujeitos respiradores orais e oronasais

Revista CEFAC ◽  
2012 ◽  
Vol 15 (1) ◽  
pp. 105-110 ◽  
Author(s):  
Mirian Hideko Nagae ◽  
Marcelo Corrêa Alves ◽  
Renato Lyuti Kinoshita ◽  
Zélia Z L de Camargo Bittencourt ◽  
Heloisa Gagliardo
Keyword(s):  
Statistical Analysis ◽  
Statistical Analysis System ◽  
Analysis System

OBJETIVO: investigar a qualidade de vida de sujeitos com respiração oral ou oronasal. MÉTODO: compuseram a amostra 49 voluntários, distribuídos em dois grupos: grupo de respiradores orais com 24 sujeitos e grupo de respiradores oronasais com 25 sujeitos, com faixa etária ente 18 e 38 anos de idade, de ambos os sexos. O protocolo utilizado foi o WHOQOL-BREF (versão em português) e o registro e análise dos dados foi realizado pelo sistema Statistical Analysis System. RESULTADOS: os resultados obtidos foram submetidos aos testes de qui-quadrado de Mantel-Haenszele análise de variância. A qualidade de vida dos respiradores mistos se revelou inferior nos domínios ambiental, físico e psicológico em relação aos respiradores orais. CONCLUSÃO: com a detecção das diferenças obtidas, o estudo chama a atenção para a necessidade de aprofundar investigações sobre o perfil dos respiradores oronasais e sua relevância. Para que condutas como desconsiderar o grupo de respiradores oronasais ou agrupá-los como respiradores orais não sejam adotadas de forma inadequada.

scholarly journals Analysis of the coefficient of variation in shear and tensile bond strength tests

2005 ◽  
Vol 13 (3) ◽  
pp. 243-246 ◽  
Author(s):  
Fábio Lourenço Romano ◽  
Gláucia Maria Bovi Ambrosano ◽  
Maria Beatriz Borges de Araújo Magnani ◽  
Darcy Flávio Nouer
Keyword(s):  
Coefficient Of Variation ◽  
Mean Value ◽  
Scientific Article ◽  
A Value ◽  
Statistical Analysis System ◽  
Strength Tests ◽  
Analysis System ◽  
Very High

The coefficient of variation is a dispersion measurement that does not depend on the unit scales, thus allowing the comparison of experimental results involving different variables. Its calculation is crucial for the adhesive experiments performed in laboratories because both precision and reliability can be verified. The aim of this study was to evaluate and to suggest a classification of the coefficient variation (CV) for in vitro experiments on shear and tensile strengths. The experiments were performed in laboratory by fifty international and national studies on adhesion materials. Statistical data allowing the estimation of the coefficient of variation was gathered from each scientific article since none of them had such a measurement previously calculated. Excel worksheet was used for organizing the data while the sample normality was tested by using Shapiro Wilk tests (alpha = 0.05) and the Statistical Analysis System software (SAS). A mean value of 6.11 (SD = 1.83) for the coefficient of variation was found by the data analysis and the data had a normal distribution (p>0.05). A range classification was proposed for the coefficient of variation from such data, that is, it should be considered low for a value lesser than 2.44; intermediate for a value between 2.44 and 7.94, high for a value between 7.94 and 9.78, and finally, very high for a value greater than 9.78. Such classification can be used as a guide for experiments on adhesion materials, thus making the planning easier as well as revealing precision and validity concerning the data.

scholarly journals Intervalo de partos em rebanho Nelore na Amazônia Oriental

2000 ◽  
Vol 29 (5) ◽  
pp. 1327-1331 ◽  
Author(s):  
Francisco Aloísio Cavalcante ◽  
Raimundo Martins Filho ◽  
Claudio Cabral Campello ◽  
Raimundo Nonato Braga Lobo ◽  
Gabrimar Araujo Martins
Keyword(s):  
Statistical Analysis ◽  
Statistical Analysis System ◽  
Analysis System

O estudo foi conduzido para avaliar intervalo de partos (IDP) na raça Nelore, utilizando 578 dados de intervalo de partos de animais criados em regime de pasto, no período de 1988 a 1996. Para as análises estatísticas dos dados, foi usado o Programa SAS (Statistical Analysis System). Foram incluídos no modelo o efeito aleatório do pai da vaca, os efeitos fixos de grupo genético da cria anterior, de ano do parto anterior e do mês do parto anterior e as interações entre grupo genético anterior e mês do parto anterior, grupo genético anterior e ano do parto anterior, além da idade da vaca ao parto como covariável. O IDP foi influenciado pelo pai da vaca e pelo ano do parto anterior. A média estimada por quadrado mínimo e seu respectivo erro-padrão (EP) e coeficiente de variação (CV) foram 431,83 ± 142,76 dias e 33,06%, respectivamente. O intervalo de partos, além do efeito de pai, sofreu influência de ambiente. O desempenho reprodutivo do rebanho estudado, para as condições de clima tropical, pode ser considerado como satisfatório, estando de acordo com os parâmetros obtidos por outros autores em diferentes regiões.

scholarly journals Sisvar: a Guide for its Bootstrap procedures in multiple comparisons

2014 ◽  
Vol 38 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Daniel Furtado Ferreira
Keyword(s):  
Scientific Community ◽  
Type I Error ◽  
Multiple Comparisons ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Statistical Analysis System ◽  
Scientific Results ◽  
Analysis System ◽  
Multiple Comparison Procedures

Sisvar is a statistical analysis system with a large usage by the scientific community to produce statistical analyses and to produce scientific results and conclusions. The large use of the statistical procedures of Sisvar by the scientific community is due to it being accurate, precise, simple and robust. With many options of analysis, Sisvar has a not so largely used analysis that is the multiple comparison procedures using bootstrap approaches. This paper aims to review this subject and to show some advantages of using Sisvar to perform such analysis to compare treatments means. Tests like Dunnett, Tukey, Student-Newman-Keuls and Scott-Knott are performed alternatively by bootstrap methods and show greater power and better controls of experimentwise type I error rates under non-normal, asymmetric, platykurtic or leptokurtic distributions.

scholarly journals Methodology and Applications of Disease Biomarker Identification in Human Serum

2007 ◽  
Vol 2 ◽  
pp. 117727190700200 ◽  
Author(s):  
Ziad J. Sahab ◽  
Suzan M. Semaan ◽  
Qing-Xiang Amy Sang
Keyword(s):  
Human Serum ◽  
Protein Separation ◽  
High Sensitivity ◽  
Disease Diagnosis ◽  
Plasma Lipoproteins ◽  
Great Promise ◽  
Protein Biomarkers ◽  
Serum Samples ◽  
Disease Biomarkers ◽  
Diagnosis And Prognosis

Biomarkers are biomolecules that serve as indicators of biological and pathological processes, or physiological and pharmacological responses to a drug treatment. Because of the high abundance of albumin and heterogeneity of plasma lipoproteins and glycoproteins, biomarkers are difficult to identify in human serum. Due to the clinical significance the identification of disease biomarkers in serum holds great promise for personalized medicine, especially for disease diagnosis and prognosis. This review summarizes some common and emerging proteomics techniques utilized in the separation of serum samples and identification of disease signatures. The practical application of each protein separation or identification technique is analyzed using specific examples. Biomarkers of cancers of prostate, breast, ovary, and lung in human serum have been reviewed, as well as those of heart disease, arthritis, asthma, and cystic fibrosis. Despite the advancement of technology few biomarkers have been approved by the Food and Drug Administration for disease diagnosis and prognosis due to the complexity of structure and function of protein biomarkers and lack of high sensitivity, specificity, and reproducibility for those putative biomarkers. The combination of different types of technologies and statistical analysis may provide more effective methods to identify and validate new disease biomarkers in blood.

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