Breast cancer data analysis for survivability studies and prediction

© 2017 Elsevier B.V. Background Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. Objective The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Methods Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. Results SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. Conclusions A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and survivability. The survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into survivability of a particular subgroup of breast cancer patients

Applying a novel combination of techniques to develop a predictive model for diabetes complications

© 2015 Sangi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models

Association between antimicrobial stewardship programs and antibiotic use globally: a systematic review and meta-analysis

IMPORTANCE: Antimicrobial resistance continues to spread rapidly at a global scale. Little evidence exists on the association of antimicrobial stewardship programs (ASPs) with the consumption of antibiotics across health care and income settings. OBJECTIVE: To synthesize current evidence regarding the association between antimicrobial stewardship programs and the consumption of antibiotics globally. DATA SOURCES: PubMed, Web of Science, and Scopus databases were searched from August 1, 2010, to Aug 1, 2020. Additional studies from the bibliography sections of previous systematic reviews were included. STUDY SELECTION: Original studies of the association of ASPs with antimicrobial consumption across health care and income settings. Animal and environmental studies were excluded. DATA EXTRACTION AND SYNTHESIS: Following the Preferred Reporting Items in Systematic Reviews and Meta-Analyses guideline, the pooled association of targeted ASPs with antimicrobial consumption was measured using multilevel random-effects models. The Effective Public Health Practice Project quality assessment tool was used to assess study quality. MAIN OUTCOMES AND MEASURES: The main outcome measures were proportion of patients receiving an antibiotic prescription and defined daily doses per 100 patient-days. RESULTS: Overall, 52 studies (with 1 794 889 participants) measured the association between ASPs and antimicrobial consumption and were included, with 40 studies conducted in high-income countries and 12 in low- and middle-income countries (LMICs). ASPs were associated with a 10% (95% CI, 4%-15%) reduction in antibiotic prescriptions and a 28% reduction in antibiotic consumption (rate ratio, 0.72; 95% CI, 0.56-0.92). ASPs were also associated with a 21% (95% CI, 5%-36%) reduction in antibiotic consumption in pediatric hospitals and a 28% reduction in World Health Organization watch groups antibiotics (rate ratio, 0.72; 95% CI, 0.56-0.92). CONCLUSIONS AND RELEVANCE: In this systematic review and meta-analysis, ASPs appeared to be effective in reducing antibiotic consumption in both hospital and nonhospital settings. Impact assessment of ASPs in resource-limited settings remains scarce; further research is needed on how to best achieve reductions in antibiotic use in LMICs

The genome sequence and effector complement of the flax rust pathogen Melampsora lini

Rust fungi cause serious yield reductions on crops, including wheat, barley, soybean, coffee, and represent real threats to global food security. Of these fungi, the flax rust pathogen Melampsora lini has been developed most extensively over the past 80 years as a model to understand the molecular mechanisms that underpin pathogenesis. During infection, M. lini secretes virulence effectors to promote disease. The number of these effectors, their function and their degree of conservation across rust fungal species is unknown. To assess this, we sequenced and assembled de novo the genome of M. lini isolate CH5 into 21,130 scaffolds spanning 189 Mbp (scaffold N50 of 31 kbp). Global analysis of the DNA sequence revealed that repetitive elements, primarily retrotransposons, make up at least 45% of the genome. Using ab initio predictions, transcriptome data and homology searches, we identified 16,271 putative protein-coding genes. An analysis pipeline was then implemented to predict the effector complement of M. lini and compare it to that of the poplar rust, wheat stem rust and wheat stripe rust pathogens to identify conserved and species-specific effector candidates. Previous knowledge of four cloned M. lini avirulence effector proteins and two basidiomycete effectors was used to optimize parameters of the effector prediction pipeline. Markov clustering based on sequence similarity was performed to group effector candidates from all four rust pathogens. Clusters containing at least one member from M. lini were further analyzed and prioritized based on features including expression in isolated haustoria and infected leaf tissue and conservation across rust species. Herein, we describe 200 of 940 clusters that ranked highest on our priority list, representing 725 flax rust candidate effectors. Our findings on this important model rust species provide insight into how effectors of rust fungi are conserved across species and how they may act to promote infection on their hosts.This work was funded by a grant from the CSIRO Transformational Biology Capability Platform to Adnane Nemri. Claire Anderson was supported by an ARC Discovery Grant (DP120104044) awarded to David A. Jones and Peter N. Dodds

The complete conformal spectrum of a sl(2∣1)sl(2|1) invariant network model and logarithmic corrections

We investigate the low temperature asymptotics and the finite size spectrum of a class of Temperley-Lieb models. As reference system we use the spin-1/2 Heisenberg chain with anisotropy parameter $\Delta$ and twisted boundary conditions. Special emphasis is placed on the study of logarithmic corrections appearing in the case of $\Delta=1/2$ in the bulk susceptibility data and in the low-energy spectrum yielding the conformal dimensions. For the $sl(2|1)$ invariant 3-state representation of the Temperley-Lieb algebra with $\Delta=1/2$ we give the complete set of scaling dimensions which show huge degeneracies.Comment: 18 pages, 5 figure

Risk factor-based screening compared to universal screening for gestational diabetes mellitus in marginalized Burman and Karen populations on the Thailand-Myanmar border: an observational cohort

Background: Gestational diabetes mellitus (GDM) contributes significantly to maternal and neonatal morbidity, but data from marginalized populations remains scarce. This study aims to compare risk-factor-based screening to universal testing for GDM among migrants along the Thailand-Myanmar border. Methods: From the prospective cohort (September 2016, February 2019), 374 healthy pregnant women completed a 75g oral glucose tolerance test (OGTT) at 24-32 weeks gestation. Fasting, one hour and two hour cut-offs were based on Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO trial) criteria and cases were treated. The sensitivity and specificity of risk-factor-based screening criteria was calculated using OGTT as the gold standard. Risk factors included at least one positive finding among 10 criteria, e.g., obesity (body mass index (BMI) >/=27.5kg/m (2)), 1 (st) degree relative with diabetes etc. Adverse maternal and neonatal outcomes were compared by GDM status, and risk factors for GDM were explored. Results: GDM prevalence was 13.4% (50/374) (95% CI: 10.3-17.2). Risk-factors alone correctly identified 74.0% (37/50) OGTT positive cases: sensitivity 74.0% (59.7-85.4) and specificity 27.8% (3.0-33.0). Burman women accounted for 29.1% of the cohort population, but 38.0% of GDM cases. Percentiles for birthweight (p=0.004), head circumference (p=0.005), and weight-length ratio (p=0.010) were higher in newborns of GDM mothers compared with non-GDM, yet 21.7% (75/346) of newborns in the cohort were small-for-gestational age. In Burman women, overweight/obese BMI was associated with a significantly increased adjusted odds ratio 5.03 (95% CI: 1.43-17.64) for GDM compared to normal weight, whereas underweight and overweight/obese in Karen women were both associated with similarly elevated adjusted odds, approximately 2.4-fold (non-significant) for GDM. GDM diagnosis by OGTT was highest prior to peak rainfall. Conclusions: Risk-factor-based screening was not sufficiently sensitive or specific to be useful to diagnose GDM in this setting among a cohort of low-risk pregnant women. A two-step universal screening program has thus been implemented

Metastable chaos in the ammonia ring laser

We report experimental studies of metastable chaos in the far-infrared ammonia ring: laser. When the laser pump power is switched from above chaos threshold to slightly below, chaotic intensity pulsations continue for a varying time afterward before decaying to either periodic or cw emission. The behavior is in good qualitative agreement with that predicted by the Lorenz equations, previously used to describe this laser. The statistical distribution of the duration of the chaotic transient is measured and shown to be in excellent agreement with the Lorenz equations in showing a modified exponential distribution. We also give a brief numerical analysis and graphical visualization of the Lorenz equations in phase space illustrating the boundary between the metastable chaotic and the stable fixed point basins of attraction. This provides an intuitive understanding of the metastable dynamics of the Lorenz equations and the experimental system

Experimental evidence of frequency entrainment between coupled chaotic oscillations

The nonlinear response of a chaotic system to a chaotic variation in a system parameter is investigated experimentally. Clear experimental evidence of frequency entrainment of the chaotic oscillations is observed. We show that analogous to the frequency locking between coupled periodic oscillations, this effect is generic for coupled chaotic systems.Tang, D. Dykstra, R. ; Hamilton, M. ; Heckenberg, N

The genome sequence and effector complement of the flax rust pathogen Melampsora lini

Rust fungi cause serious yield reductions on crops, including wheat, barley, soybean, coffee, and represent real threats to global food security. Of these fungi, the flax rust pathogen Melampsora lini has been developed most extensively over the past 80 years as a model to understand the molecular mechanisms that underpin pathogenesis. During infection, M. lini secretes virulence effectors to promote disease. The number of these effectors, their function and their degree of conservation across rust fungal species is unknown. To assess this, we sequenced and assembled de novo the genome of M. lini isolate CH5 into 21, 130 scaffolds spanning 189 Mbp (scaffold N50 of 31 kbp). Global analysis of the DNA sequence revealed that repetitive elements, primarily retrotransposons, make up at least 45% of the genome. Using ab initio predictions, transcriptome data and homology searches, we identified 16, 271 putative protein-coding genes. An analysis pipeline was then implemented to predict the effector complement of M. lini and compare it to that of the poplar rust, wheat stem rust and wheat stripe rust pathogens to identify conserved and species-specific effector candidates. Previous knowledge of four cloned M. lini avirulence effector proteins and two basidiomycete effectors was used to optimize parameters of the effector prediction pipeline. Markov clustering based on sequence similarity was performed to group effector candidates from all four rust pathogens. Clusters containing at least one member from M. lini were further analyzed and prioritized based on features including expression in isolated haustoria and infected leaf tissue and conservation across rust species. Herein, we describe 200 of 940 clusters that ranked highest on our priority list, representing 725 flax rust candidate effectors. Our findings on this important model rust species provide insight into how effectors of rust fungi are conserved across species and how they may act to promote infection on their hosts