A unified approach on Springer fibers in the hook, two-row and two-column cases

We consider the Springer fiber over a nilpotent endomorphism. Fix a Jordan basis and consider the standard torus relative to this. We deal with the problem to describe the flags fixed by the torus which belong to a given component of the Springer fiber. We solve the problem in the hook, two-row and two-column cases. We provide two main characterizations which are common to the three cases, and which involve dominance relations between Young diagrams and combinatorial algorithms. Then, for these three cases, we deduce topological properties of the components and their intersections.Comment: 42 page

Variable star classification across the Galactic bulge and disc with the VISTA Variables in the Vía Láctea survey

We present VIVACE, the VIrac VAriable Classification Ensemble, a catalogue of variable stars extracted from an automated classification pipeline for the Vista Variables in the Vía Láctea (VVV) infrared survey of the Galactic bar/bulge and southern disc. Our procedure utilises a two-stage hierarchical classifier to first isolate likely variable sources using simple variability summary statistics and training sets of non-variable sources from the Gaia early third data release, and then classify candidate variables using more detailed light curve statistics and training labels primarily from OGLE and VSX. The methodology is applied to point-spread-function photometry for ∼490 million light curves from the VIRAC v2 astrometric and photometric catalogue resulting in a catalogue of ∼1.4 million likely variable stars, of which ∼39, 000 are high-confidence (classification probability >0.9) RR Lyrae ab stars, ∼8000 RR Lyrae c/d stars, ∼187, 000 detached/semi-detached eclipsing binaries, ∼18, 000 contact eclipsing binaries, ∼1400 classical Cepheid variables and ∼2200 Type II Cepheid variables. Comparison with OGLE-4 suggests a completeness of around 90  per cent for RRab and ≲ 60 per cent for RRc/d, and a misclassification rate for known RR Lyrae stars of around 1 per cent for the high confidence sample. We close with two science demonstrations of our new VIVACE catalogue: first, a brief investigation of the spatial and kinematic properties of the RR Lyrae stars within the disc/bulge, demonstrating the spatial elongation of bar-bulge RR Lyrae stars is in the same sense as the more metal-rich red giant population whilst having a slower rotation rate of ∼40 km s−1kpc−1; and secondly, an investigation of the Gaia EDR3 parallax zeropoint using contact eclipsing binaries across the Galactic disc plane and bulge

The Multiscale Systems Immunology project: software for cell-based immunological simulation

<p>Abstract</p> <p>Background</p> <p>Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing.</p> <p>Results</p> <p>The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales.</p> <p>Conclusion</p> <p>MSI addresses the need for a flexible and high-performing agent based model of the immune system.</p

The ZOON R package for reproducible and shareable species distribution modelling

1. The rapid growth of species distribution modelling (SDM) as an ecological discipline has resulted in a large and diverse set of methods and software for constructing and evaluating SDMs. The disjointed nature of the current SDM research environment hinders evaluation of new methods, synthesis of current knowledge and the dissemination of new methods to SDM users. 2. The zoon r package aims to overcome these problems by providing a modular framework for constructing reproducible SDM workflows. zoon modules are interoperable snippets of r code, each carrying a SDM method that zoon combines into a single analysis object. 3. Rather than defining these modules, zoon draws modules from an open, version-controlled online repository. zoon makes it easy for SDM researchers to contribute modules to this repository, enabling others to rapidly deploy new methods in their own workflows or to compare alternative methods. 4. Each workflow object created by zoon is a rerunnable record of the data, code and results of an entire SDM analysis. This can then be easily shared, scrutinised, reproduced and extended by the whole SDM research community. 5. We explain how zoon works and demonstrate how it can be used to construct a completely reproducible SDM analyses, create and share a new module, and perform a methodological comparison study

Derivation and External Validation of a Prediction Rule for Five-Year Mortality in Patients With Early Diffuse Cutaneous Systemic Sclerosis

OBJECTIVE: Although diffuse cutaneous systemic sclerosis (dcSSc) is associated with a reduction in life expectancy, there are no validated prognostic models for determining 5-year mortality in patients with dcSSc. The objective of this study was to derive and validate a rule for predicting 5-year mortality in patients with early dcSSc. METHODS: We studied an inception cohort of 388 US Caucasian patients with early dcSSc (<2 years from the appearance of the first symptom). Predefined baseline variables were analyzed in a stepwise logistic regression model in order to identify factors independently associated with 5-year all-cause mortality. We rounded the beta weights to the nearest integer and summed the points assigned to each variable in order to stratify patients into low-risk (<0 points), moderate-risk (1-2 points), and high-risk (≥3 points) groups. We then applied this rule to an external validation cohort of 144 Caucasian patients with early dcSSc from the Royal Free Hospital cohort and compared stratum-specific 5-year mortality. RESULTS: Six independent predictors (rounded beta weight) comprised the model: age at first visit (points allotted: -1, 0, or 1), male sex (points allotted: 0 or 1), tendon friction rubs (points allotted: 0 or 1), gastrointestinal involvement (points allotted: 0 or 1), RNA polymerase III antibodies (points allotted: 0 or 1), and anemia (points allotted: 0 or 1). The 3-level risk stratification model performed well, with no significant differences between the US derivation cohort and the UK validation cohort. CONCLUSION: We derived and externally validated, in US and UK cohorts, an easy-to-use 6-variable prediction rule that assigns low-risk, moderate-risk, and high-risk categories for 5-year mortality in patients with early dcSSc. Only history, physical examination, and basic laboratory assessments are required

SIGNATURE: A workbench for gene expression signature analysis

<p>Abstract</p> <p>Background</p> <p>The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.</p> <p>Results</p> <p>We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access.</p> <p>Conclusions</p> <p>SIGNATURE is available for public use at <url>http://genepattern.genome.duke.edu/signature/</url>.</p