How rapidly do neutron stars spin at birth? Constaints from archival X-ray observations of extragalactic supernovae

Traditionally, studies aimed at inferring the distribution of birth periods of neutron stars are based on radio surveys. Here we propose an independent method to constrain the pulsar spin periods at birth based on their X-ray luminosities. In particular, the observed luminosity distribution of supernovae (SNe) poses a constraint on the initial rotational energy of the embedded pulsars, via the correlation found for radio pulsars, and under the assumption that this relation continues to hold beyond the observed range. We have extracted X-ray luminosities (or limits) for a large sample of historical SNe observed with Chandra, XMM and Swift, which have been firmly classified as core-collapse SNe. We have then compared these observational limits with the results of Monte Carlo simulations of the pulsar X-ray luminosity distribution for a range of values of the birth parameters. We find that a pulsar population dominated by millisecond periods at birth is ruled out by the data

Validation of a quantifier-based fuzzy classification system for breast cancer patients on external independent cohorts

Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide linguistic based classification rules to ease those from consensus clustering. The objective of this study is to present the validation of a recently developed extension of a fuzzy quantification subsethood-based algorithm on three sets of newly available breast cancer data. Results show that our algorithm is able to reproduce the seven biological classes previously identified, preserving their characterisation in terms of marker distributions and therefore their clinical meaning. Moreover, because our algorithm constitutes the fundamental basis of the newly developed Nottingham Prognostic Index Plus (NPI+), our findings demonstrate that this new medical decision making tool can help moving towards a more tailored care in breast cancer

Cancer subtype identification pipeline: a classifusion approach

Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes - leading to improved treatment options. However, the current literature on the use of CI to achieve this is fragmented, making further advances challenging. This paper captures developments in this area so far, with the goal to establish a clear, step-by-step pipeline for cancer subtype identification. Based on establishing the pipeline, the paper identifies key potential advances in CI at the individual steps, thus establishing a roadmap for future research. As such, it is the aim of the paper to engage the CI community to address the research challenges and leverage the strong potential of CI in this important area. Finally, we present a small set of recent findings on the Nottingham Tenovus Primary Breast Carcinoma Series enabling the classification of a higher number of patients into one of the identified breast cancer groups, and introduce Classifusion: a combination of results of multiple classifiers

Sex and Age-Related Differences in Neuroticism and Allostatic Load Index in Urban Patients with General Anxiety Disorder Treated with Alprazolam

Introduction: Allostatic Load (AL) index proposes indicators for the functioningof the main potentially stress-affected systems. Sex differences instress response and stress-related diseases susceptibility have been describedfor the general population. In this observational study we describe the effectsof sex and age on allostatic load variables, in a cohort of patients with generalanxiety disorders and neuroticism treated with alprazolam during 12 weeks,before and after treatment. Methods: Patients with general (DSM IV) anxietydisorders with >6 in Hamilton scale, Allostatic load (>1 Crimmins and SeemanAL modified criteria) and neuroticism >18 (NEO-FFI inventory), wereincluded. All patients completed psychiatric assessment, allostatic load indexdetermination before (−1 week) and after 12 weeks of treatment with alprazolam(0.25 - 1 mg/t.i.d). Allostatic load parameters comprised cardiovascular,metabolic and inflammatory variables. Univariate analysis (two-wayANOVA), Student?s t-test (related variables) and Pearson correlations weredetermined. Results: Fifty-four patients, 35 females (48.6 ± 11.7 years) and 19males (44.2 ± 12.8 years) with general anxiety disorder were included; 28 patientswith <50 years (60.7% females), and 26 with ≥50 years (69.2% females).Younger patients (<50 years) (two-way ANOVA, p = 0.02) were significantlyassociated with lower allostatic load index after treatment. However, womenshowed higher anxiety levels in both, before (Two-way ANOVA, p = 0.059)and after treatment (two-way ANOVA, p = 0.005), with a significantly betterprofile than men in many individual AL variables, particularly cardiovascular(systolic blood pressure), obesity (body mass index), and lipids (higher HDLlevels). After treatment a higher reduction of fibrinogen levels was found inmen (two-way ANOVA, p = 0.02). Conclusions: In this preliminary analysiswe described sex and age differences in psychiatry aspects and allostatic loadindexes in patients with general anxiety disorders in the short-term treatmentwith alprazolam. These considerations remark the need of pondering sex andage differences during the use of drugs for protracted periods.Fil: D`Alessio, Luciana. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos A.. Henri Laborit Institute Of Biosciences; ArgentinaFil: Remedi, Carolina. Henri Laborit Institute Of Biosciences; Argentina; ArgentinaFil: Roldán, Emilio J. A.. Instituto de Neurobiología IDNEU; Argentin

Study of Underexpanded Supersonic Jets with Optical Techniques

An experimental investigation of underexpanded axissymmetric supersonic jets is presented. Particle Image Velocimetry is used to obtain quantitative measurements of the velocity field, while a high framerate shadowgraph technique is used to assess shock position and stability. The PIV technique demonstrates the ability to consistently resolve the instantaneous velocity field, with major flow characteristics such as shock structures clearly evident. The shadowgraph images show that at lower pressures the shock structures are highly unstable, demonstrating periodic oscillation in angle and position, while in the highly underexpanded condition the location of the Mach disk is stable. A discussion of limitation due to optical resolution and particle fidelity is presented, concluding that the system is more limited by inadequate particle fidelity post-shock than sensor limitations

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

BackgroundTesting a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.MethodsThe PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.ResultsThe search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.ConclusionES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research

Fuzzy Integral Driven Ensemble Classification using A Priori Fuzzy Measures

Aggregation operators are mathematical functions that enable the fusion of information from multiple sources. Fuzzy Integrals (FIs) are widely used aggregation operators, which combine information in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. However, FIs suffer from the potential drawback of not fusing information according to the intuitively interpretable FM, leading to non-intuitive results. The latter is particularly relevant when a FM has been defined using external information (e.g. experts). In order to address this and provide an alternative to the FI, the Recursive Average (RAV) aggregation operator was recently proposed which enables intuitive data fusion in respect to a given FM. With an alternative fusion operator in place, in this paper, we define the concept of ‘A Priori’ FMs which are generated based on external information (e.g. classification accuracy) and thus provide an alternative to the traditional approaches of learning or manually specifying FMs. We proceed to develop one specific instance of such an a priori FM to support the decision level fusion step in ensemble classification. We evaluate the resulting approach by contrasting the performance of the ensemble classifiers for different FMs, including the recently introduced Uriz and the Sugeno lambda-measure; as well as by employing both the Choquet FI and the RAV as possible fusion operators. Results are presented for 20 datasets from machine learning repositories and contextualised to the wider literature by comparing them to state-of-the-art ensemble classifiers such as Adaboost, Bagging, Random Forest and Majority Voting

The ultraluminous X-ray source NGC 1313 X-2 - Its optical counterpart and environment

NGC 1313 X-2 is one of the brightest ultraluminous X-ray sources in the sky, at both X-ray and optical wavelengths; therefore, quite a few studies of available ESO VLT and HST data have appeared in the literature. Here, we present our analysis of VLT/FORS1 and HST/ACS photometric data, confirming the identification of the B ~ 23 mag blue optical counterpart. We show that the system is part of a poor cluster with an age of 20 Myr, leading to an upper mass limit of some 12 M_sun for the mass donor. We attribute the different results with respect to earlier studies to the use of isochrones in the F435W and F555W HST/ACS photometric system that appear to be incompatible with the corresponding Johnson B and V isochrones. The counterpart exhibits significant photometric variability of about 0.2 mag amplitude, both between the two HST observations and during the one month of monitoring with the VLT. This includes variability within one night and suggests that the light is dominated by the accretion disk in the system and not by the mass donor.Comment: 13 pages, 11 figures. Accepted for publication in Astronomy & Astrophysic