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

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

Natural history of Arabidopsis thaliana and oomycete symbioses

Molecular ecology of plant–microbe interactions has immediate significance for filling a gap in knowledge between the laboratory discipline of molecular biology and the largely theoretical discipline of evolutionary ecology. Somewhere in between lies conservation biology, aimed at protection of habitats and the diversity of species housed within them. A seemingly insignificant wildflower called Arabidopsis thaliana has an important contribution to make in this endeavour. It has already transformed botanical research with deepening understanding of molecular processes within the species and across the Plant Kingdom; and has begun to revolutionize plant breeding by providing an invaluable catalogue of gene sequences that can be used to design the most precise molecular markers attainable for marker-assisted selection of valued traits. This review describes how A. thaliana and two of its natural biotrophic parasites could be seminal as a model for exploring the biogeography and molecular ecology of plant–microbe interactions, and specifically, for testing hypotheses proposed from the geographic mosaic theory of co-evolution

Design principles for riboswitch function

Scientific and technological advances that enable the tuning of integrated regulatory components to match network and system requirements are critical to reliably control the function of biological systems. RNA provides a promising building block for the construction of tunable regulatory components based on its rich regulatory capacity and our current understanding of the sequence–function relationship. One prominent example of RNA-based regulatory components is riboswitches, genetic elements that mediate ligand control of gene expression through diverse regulatory mechanisms. While characterization of natural and synthetic riboswitches has revealed that riboswitch function can be modulated through sequence alteration, no quantitative frameworks exist to investigate or guide riboswitch tuning. Here, we combined mathematical modeling and experimental approaches to investigate the relationship between riboswitch function and performance. Model results demonstrated that the competition between reversible and irreversible rate constants dictates performance for different regulatory mechanisms. We also found that practical system restrictions, such as an upper limit on ligand concentration, can significantly alter the requirements for riboswitch performance, necessitating alternative tuning strategies. Previous experimental data for natural and synthetic riboswitches as well as experiments conducted in this work support model predictions. From our results, we developed a set of general design principles for synthetic riboswitches. Our results also provide a foundation from which to investigate how natural riboswitches are tuned to meet systems-level regulatory demands