Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

Modern Applied Statistics With S

1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Classification and diagnosis of diabetes

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.

Whole genomes redefine the mutational landscape of pancreatic cancer.

Understanding immune memory to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for improving diagnostics and vaccines and for assessing the likely future course of the COVID-19 pandemic. We analyzed multiple compartments of circulating immune memory to SARS-CoV-2 in 254 samples from 188 COVID-19 cases, including 43 samples at ≥6 months after infection. Immunoglobulin G (IgG) to the spike protein was relatively stable over 6+ months. Spike-specific memory B cells were more abundant at 6 months than at 1 month after symptom onset. SARS-CoV-2-specific CD4+ T cells and CD8+ T cells declined with a half-life of 3 to 5 months. By studying antibody, memory B cell, CD4+ T cell, and CD8+ T cell memory to SARS-CoV-2 in an integrated manner, we observed that each component of SARS-CoV-2 immune memory exhibited distinct kinetics.

Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection.

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

BACKGROUND The healthy human metabolome, including its physiological responses after meal consumption, remains incompletely understood One major research gap is the limited literature assessing how human metabolomic profiles differ between fasting and postprandial states after physiological challenges OBJECTIVES Our study objective was to evaluate alterations in high-resolution metabolomic profiles following a standardized meal challenge, relative to fasting, in Guatemalan adults METHODS We studied 123 Guatemalan adults without obesity, hypertension, diabetes, metabolic syndrome, or comorbidities Every participant received a standardized meal challenge (520 kcal, 674 g carbohydrates, 243 g fat, 80 g protein) and provided blood samples while fasting and at 2 h postprandial Plasma samples were assayed by high-resolution metabolomics with dual-column LC [C18 (negative electrospray ionization), hydrophilic interaction LC (HILIC, positive electrospray ionization)] coupled to ultra-high-resolution MS Associations between metabolomic features and the meal challenge timepoint were assessed in feature-by-feature multivariable linear mixed regression models Two algorithms (mummichog, gene set enrichment analysis) were used for pathway analysis, and P values were combined by the Fisher method RESULTS Among participants (626% male, median age 430 y), 1130 features (C18: 777; HILIC: 353) differed between fasting and postprandial states (all false discovery rate-adjusted q < 005) Based on differing C18 features, top pathways included: tricarboxylic acid cycle (TCA), primary bile acid biosynthesis, and linoleic acid metabolism (all Pcombined < 005) Mass spectral features included: taurine and cholic acid in primary bile acid biosynthesis; and fumaric acid, malic acid, and citric acid in the TCA HILIC features that differed in the meal challenge reflected linoleic acid metabolism (Pcombined < 005) CONCLUSIONS Energy, macronutrient, and bile acid metabolism pathways were responsive to a standardized meal challenge in adults without cardiometabolic diseases Our findings reflect metabolic flexibility in disease-free individuals

Macronutrient, Energy, and Bile Acid Metabolism Pathways Altered Following a Physiological Meal Challenge, Relative to Fasting, among Guatemalan Adults

Background
High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership.

/pdf/network-based-modular-latent-structure-analysis-kntuo8eclw.pdf

Network-based modular latent structure analysis.

The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

/pdf/dnlc-differential-network-local-consistency-analysis-bf37xrwzw8.pdf

DNLC: differential network local consistency analysis.

To determine if individuals, from HIV-1 serodiscordant couple cohorts from Rwanda and Zambia, who become HIV-positive have a distinct inflammatory biomarker profile compared to individuals who remain HIV-negative, we compared levels of biomarkers in plasma of HIV-negative individuals who either seroconverted (pre-infection) and became HIV-positive or remained HIV-negative (uninfected). We observed that individuals in the combined cohort, as well as those in the individual country cohorts, who later became HIV-1 infected had significantly higher baseline levels of multiple inflammatory cytokines/chemokines compared to individuals who remained HIV-negative. Genital inflammation/ulceration or schistosome infections were not associated with this elevated profile. Defined levels of ITAC and IL-7 were significant predictors of later HIV acquisition in ROC predictive analyses, whereas the classical Th1 and Th2 inflammatory cytokines such as IL-12 and interferon-γ or IL-4, IL-5 and Il-13 were not. Overall, the data show a significant association between increased plasma biomarkers linked to inflammation and immune activation and HIV acquisition and suggests that pre-existing conditions that increase systemic biomarkers represent a factor for increased risk of HIV infection.

/pdf/elevated-levels-of-inflammatory-plasma-biomarkers-are-18p37kdj2k.pdf

Elevated levels of inflammatory plasma biomarkers are associated with risk of HIV infection.

Probabilistic association discovery aims at identifying the association between random vectors, regardless of number of variables involved or linear/nonlinear functional forms. Recently, applications in high-dimensional data have generated rising interest in probabilistic association discovery. We developed a framework based on functions on the observation graph, named MeDiA (Mean Distance Association). We generalize its property to a group of functions on the observation graph. The group of functions encapsulates major existing methods in association discovery, e.g. mutual information and Brownian Covariance, and can be expanded to more complicated forms. We conducted numerical comparison of the statistical power of related methods under multiple scenarios. We further demonstrated the application of MeDiA as a method of gene set analysis that captures a broader range of responses than traditional gene set analysis methods.

/pdf/media-mean-distance-association-and-its-applications-in-51141uxbsq.pdf

Tianwei Yu

Papers

Macronutrient, Energy, and Bile Acid Metabolism Pathways Altered Following a Physiological Meal Challenge, Relative to Fasting, among Guatemalan Adults

Network-based modular latent structure analysis.

DNLC: differential network local consistency analysis.

Elevated levels of inflammatory plasma biomarkers are associated with risk of HIV infection.

MeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set Analysis.