BornAgain: software for simulating and fitting grazing-incidence small-angle scattering.

BornAgain is a free and open-source multi-platform software framework for simulating and fitting X-ray and neutron reflectometry, off-specular scattering, and grazing-incidence small-angle scattering (GISAS). This paper concentrates on GISAS. Support for reflectometry and off-specular scattering has been added more recently, is still under intense development and will be described in a later publication. BornAgain supports neutron polarization and magnetic scattering. Users can define sample and instrument models through Python scripting. A large subset of the functionality is also available through a graphical user interface. This paper describes the software in terms of the realized non-functional and functional requirements. The web site https://www.bornagainproject.org/ provides further documentation.

Analysis of Readmissions Following Hospitalization for Cellulitis in the United States.

Importance: Cellulitis commonly results in hospitalization. Limited data on the proportion of cellulitis admissions associated with readmission are available. Objective: To characterize the US national readmission rate associated with hospitalization for treatment of cellulitis. Design, Setting, and Participants: This retrospective cohort analysis of cellulitis admissions from the nationally representative 2014 Nationwide Readmissions Database calculated readmission rates for all cellulitis admissions and subsets of admissions. The multicenter population-based cohort included adult patients admitted for conditions other than obstetrical or newborn care. Data were collected from January 1 through November 30, 2014, and analyzed from February 1 through September 18, 2018. Bivariate logistic regression models were used to assess differences in readmission rates by patient characteristics. Costs were calculated for all readmissions after discharge from hospitalization for cellulitis (hereinafter referred to as cellulitis discharge) and by readmission diagnosis. Exposures: Admission with a primary diagnosis of cellulitis. Main Outcomes and Measures: Proportion of cellulitis admissions associated with nonelective readmission within 30 days, characteristics of patients readmitted after cellulitis discharge, and costs associated with cellulitis readmission. Results: A total of 447 080 (95% CI, 429 927-464 233) index admissions with a primary diagnosis of cellulitis (53.8% male [95% CI, 53.5%-54.2%]; mean [SD] age, 56.1 [18.9] years) were included. Overall 30-day all-cause nonelective readmission rate after cellulitis discharge was 9.8% (95% CI, 9.6%-10.0%). Among patients with cellulitis, age (odds ratio for 45-64 years, 0.78; 95% CI, 0.75-0.81; P = .001) and insurance status (odds ratio for Medicare, 2.45; 95% CI, 2.33-2.58; P < .001) were associated with increased readmission rates. The most common diagnosis of readmissions included skin and subcutaneous tissue infections. The total cost associated with nonelective readmissions attributed to skin and subcutaneous infections within 30 days of a cellulitis discharge during the study period was $114.4 million (95% CI, $106.8-$122.0 million). Conclusions and Relevance: Readmission after hospitalization for cellulitis is common and costly and may be preventable with improved diagnostics, therapeutics, and discharge care coordination.

Revertant mosaicism in genodermatoses.

Inherited monogenic skin disorders include blistering disorders, inflammatory disorders, and disorders of differentiation or development. In most cases, the skin is broadly involved throughout the affected individual's lifetime, but rarely, appearance of normal skin clones has been described. In these cases of revertant mosaicism, cells undergo spontaneous correction to ameliorate the effects of genetic mutation. While targeted reversion of genetic mutation would have tremendous therapeutic value, the mechanisms of reversion in the skin are poorly understood. In this review, we provide an overview of genodermatoses that demonstrate widespread reversion and their corrective mechanisms, as well as the current research aimed to understand this "natural gene therapy".

Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma.

Although human tumours are shaped by the genetic evolution of cancer cells, evidence also suggests that they display hierarchies related to developmental pathways and epigenetic programs in which cancer stem cells (CSCs) can drive tumour growth and give rise to differentiated progeny. Yet, unbiased evidence for CSCs in solid human malignancies remains elusive. Here we profile 4,347 single cells from six IDH1 or IDH2 mutant human oligodendrogliomas by RNA sequencing (RNA-seq) and reconstruct their developmental programs from genome-wide expression signatures. We infer that most cancer cells are differentiated along two specialized glial programs, whereas a rare subpopulation of cells is undifferentiated and associated with a neural stem cell expression program. Cells with expression signatures for proliferation are highly enriched in this rare subpopulation, consistent with a model in which CSCs are primarily responsible for fuelling the growth of oligodendroglioma in humans. Analysis of copy number variation (CNV) shows that distinct CNV sub-clones within tumours display similar cellular hierarchies, suggesting that the architecture of oligodendroglioma is primarily dictated by developmental programs. Subclonal point mutation analysis supports a similar model, although a full phylogenetic tree would be required to definitively determine the effect of genetic evolution on the inferred hierarchies. Our single-cell analyses provide insight into the cellular architecture of oligodendrogliomas at single-cell resolution and support the cancer stem cell model, with substantial implications for disease management.

Computational genetics analysis of grey matter density in Alzheimer's disease.

BACKGROUND: Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships. RESULTS: We found a statistically significant synergistic interaction among two SNPs located in the intergenic region of an olfactory gene cluster. This model did not replicate in an independent dataset. However, genes in this region have high-confidence biological relationships and are consistent with previous findings implicating sensory processes in Alzheimer's disease. CONCLUSIONS: Previous genetic studies of Alzheimer's disease have revealed only a small portion of the overall variability due to DNA sequence differences. Some of this missing heritability is likely due to complex gene-gene and gene-environment interactions. We have introduced here a novel bioinformatics analysis pipeline that embraces the complexity of the genetic architecture of Alzheimer's disease while at the same time harnessing the power of functional genomics. These findings represent novel hypotheses about the genetic basis of this complex disease and provide open-access methods that others can use in their own studies.

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.

BACKGROUND: Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. RESULTS: We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. CONCLUSIONS: GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection.

BACKGROUND: Algorithms designed to detect complex genetic disease associations are initially evaluated using simulated datasets. Typical evaluations vary constraints that influence the correct detection of underlying models (i.e. number of loci, heritability, and minor allele frequency). Such studies neglect to account for model architecture (i.e. the unique specification and arrangement of penetrance values comprising the genetic model), which alone can influence the detectability of a model. In order to design a simulation study which efficiently takes architecture into account, a reliable metric is needed for model selection. RESULTS: We evaluate three metrics as predictors of relative model detection difficulty derived from previous works: (1) Penetrance table variance (PTV), (2) customized odds ratio (COR), and (3) our own Ease of Detection Measure (EDM), calculated from the penetrance values and respective genotype frequencies of each simulated genetic model. We evaluate the reliability of these metrics across three very different data search algorithms, each with the capacity to detect epistatic interactions. We find that a model's EDM and COR are each stronger predictors of model detection success than heritability. CONCLUSIONS: This study formally identifies and evaluates metrics which quantify model detection difficulty. We utilize these metrics to intelligently select models from a population of potential architectures. This allows for an improved simulation study design which accounts for differences in detection difficulty attributed to model architecture. We implement the calculation and utilization of EDM and COR into GAMETES, an algorithm which rapidly and precisely generates pure, strict, n-locus epistatic models.