Transcriptome analysis of the binucleate ciliate with asynchronous nuclear cell cycles.

harbors two functionally and physically distinct nuclei within a shared cytoplasm. During vegetative growth, the "cell cycles" of the diploid micronucleus and polyploid macronucleus are offset. Micronuclear S phase initiates just before cytokinesis and is completed in daughter cells before onset of macronuclear DNA replication.

Morpholino-driven blockade of Dkk-1 in osteosarcoma inhibits bone damage and tumour expansion by multiple mechanisms.

Background: Osteosarcoma (OS) is the most common primary bone malignancy. Chemotherapy plays an essential role in OS treatment, potentially doubling 5-year event-free survival if tumour necrosis can be stimulated. The canonical Wnt inhibitor Dickkopf-1 (Dkk-1) enhances OS survival in part through upregulation of aldehyde-dehydrogenase-1A1 which neutralises reactive oxygen species originating from nutritional stress and chemotherapeutic challenge.

Sex differences in stroke outcome correspond to rapid and severe changes in gut permeability in adult Sprague-Dawley rats.

Background: Sex differences in experimental stroke outcomes are well documented, such that adult males have a greater infarct volume, increased stroke-induced mortality, and more severe sensory-motor impairment. Based on recent evidence that the gut is an early responder to stroke, the present study tested the hypothesis that sex differences in stroke severity will be accompanied by rapid and greater permeability of the gut-blood barrier and gut dysbiosis in males as compared to females.

Method: Male and female Sprague-Dawley rats (5-7 months of age) were subject to endothelin (ET)-1-induced middle cerebral artery occlusion (MCAo).

SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes.

Copy number variants are duplications and deletions of the genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications.

Author Correction: RNA-seq of serial kidney biopsies obtained during progression of chronic kidney disease from dogs with X-linked hereditary nephropathy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

RNA-seq of serial kidney biopsies obtained during progression of chronic kidney disease from dogs with X-linked hereditary nephropathy.

Dogs with X-linked hereditary nephropathy (XLHN) have a glomerular basement membrane defect that leads to progressive juvenile-onset renal failure. Their disease is analogous to Alport syndrome in humans, and they also serve as a good model of progressive chronic kidney disease (CKD). However, the gene expression profile that affects progression in this disease has only been partially characterized.

Evaluation of Sterilized Artificial Diets for Mass Rearing the Lucilia sericata (Diptera: Calliphoridae).

The impact of six sterilized diets (blood-yeast agar diet, decomposed beef liver diet, powdered beef liver diet, powdered fish diet, milk-based diet, and a chemically defined diet) on Lucilia sericata (Meigen) larvae reared at three densities (10 larvae, 20 larvae, and 40 larvae on 20 g diet) was determined in comparison to fresh beef liver as a control. Specifically, the effects of these diets on the following traits of L. sericata were measured: 1) pupal weight, 2) pupation percentage, 3) eclosion percentage, as well as 4) adult longevity.

Transitioning to Patient-Centered Medical Homes: Associations With Appointment Availability.

Introduction: Recent implementation of the Patient-Centered Medical Home (PCMH) in military primary care has gained significant traction and attention from leadership and policy makers. The study objective was to measure the rate of change in appointment availability before and after primary care clinics were certified as a medical home. Access to care is one core tenet of the medical home and appointment availability is an important indicator of access.

Military Deployments and Mental Health Utilization Among Spouses of Active Duty Service Members.

Since the onset of the wars in Iraq and Afghanistan attention has increased on the importance of mental health with military service members. An integral component, although far less studied, are the ties between mental health and military spouses. Military deployments place considerable stress on military families.

Predicting "heart age" using electrocardiography.

Knowledge of a patient's cardiac age, or "heart age", could prove useful to both patients and physicians for better encouraging lifestyle changes potentially beneficial for cardiovascular health. This may be particularly true for patients who exhibit symptoms but who test negative for cardiac pathology. We developed a statistical model, using a Bayesian approach, that predicts an individual's heart age based on his/her electrocardiogram (ECG).

Diet complexity and estrogen receptor β status affect the composition of the murine intestinal microbiota.

Intestinal microbial dysbiosis contributes to the dysmetabolism of luminal factors, including steroid hormones (sterones) that affect the development of chronic gastrointestinal inflammation and the incidence of sterone-responsive cancers of the breast, prostate, and colon. Little is known, however, about the role of specific host sterone nucleoreceptors, including estrogen receptor β (ERβ), in microbiota maintenance. Herein, we test the hypothesis that ERβ status affects microbiota composition and determine if such compositionally distinct microbiota respond differently to changes in diet complexity that favor Proteobacteria enrichment.

Normalization and missing value imputation for label-free LC-MS analysis.

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

DanteR: an extensible R-based tool for quantitative analysis of -omics data.

Motivation: The size and complex nature of mass spectrometry-based proteomics datasets motivate development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab.

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

Motivation: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features.

A hybrid approach to protein differential expression in mass spectrometry-based proteomics.

Motivation: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics.

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach.

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects.

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer.

An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++.

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information.

Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.

Motivation: LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference.

A statistical framework for protein quantitation in bottom-up MS-based proteomics.

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level.

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content.

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement.

Optimality driven nearest centroid classification from genomic data.

Nearest-centroid classifiers have recently been successfully employed in high-dimensional applications, such as in genomics. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is frequently carried out by computing univariate scores for each feature individually, without consideration for how a subset of features performs as a whole.

Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships.

In normalizing two-channel expression arrays, the ANOVA approach explicitly incorporates the experimental design in its model, and the MA plot-based approach accounts for intensity-dependent biases. However, both approaches can lead to inaccurate normalization in fairly common scenarios. We propose a method called efficient Common Array Dye Swap (eCADS) for normalizing two-channel microarrays that accounts for both experimental design and intensity-dependent biases.

A new approach to intensity-dependent normalization of two-channel microarrays.

A two-channel microarray measures the relative expression levels of thousands of genes from a pair of biological samples. In order to reliably compare gene expression levels between and within arrays, it is necessary to remove systematic errors that distort the biological signal of interest. The standard for accomplishing this is smoothing "MA-plots" to remove intensity-dependent dye bias and array-specific effects.