A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Implications of Storing Urinary DNA from Different Populations for Molecular Analyses

Molecular diagnosis using urine is established for many sexually transmitted diseases and is increasingly used to diagnose tumours and other infectious diseases. Storage of urine prior to analysis, whether due to home collection or bio-banking, is increasingly advocated yet no best practice has emerged. Here, we examined the stability of DNA in stored urine in two populations over 28 days.Urine from 40 (20 male) healthy volunteers from two populations, Italy and Zambia, was stored at four different temperatures (RT, 4 degrees C, -20 degrees C & -80 degrees C) with and without EDTA preservative solution. Urines were extracted at days 0, 1, 3, 7 and 28 after storage. Human DNA content was measured using multi-copy (ALU J) and single copy (TLR2) targets by quantitative real-time PCR. Zambian and Italian samples contained comparable DNA quantity at time zero. Generally, two trends were observed during storage; no degradation, or rapid degradation from days 0 to 7 followed by little further degradation to 28 days. The biphasic degradation was always observed in Zambia regardless of storage conditions, but only twice in Italy.Site-specific differences in urine composition significantly affect the stability of DNA during storage. Assessing the quality of stored urine for molecular analysis, by using the type of strategy described here, is paramount before these samples are used for molecular prognostic monitoring, genetic analyses and disease diagnosis

Pharmacological adjuncts to stop bleeding: options and effectiveness

Severe trauma and massive haemorrhage represent the leading cause of death and disability in patients under the age of 45 years in the developed world. Even though much advancement has been made in our understanding of the pathophysiology and management of trauma, outcomes from massive haemorrhage remain poor. This can be partially explained by the development of coagulopathy, acidosis and hypothermia, a pathological process collectively known as the “lethal triad” of trauma. A number of pharmacological adjuncts have been utilised to stop bleeding, with a wide variation in the safety and efficacy profiles. Antifibrinolytic agents in particular, act by inhibiting the conversion of plasminogen to plasmin, therefore decreasing the degree of fibrinolysis. Tranexamic acid, the most commonly used antifibrinolytic agent, has been successfully incorporated into most trauma management protocols effectively reducing mortality and morbidity following trauma. In this review, we discuss the current literature with regard to the management of haemorrhage following trauma, with a special reference to the use of pharmacological adjuncts. Novel insights, concepts and treatment modalities are also discussed

Vampires in the village Žrnovo on the island of Korčula: following an archival document from the 18th century

Središnja tema rada usmjerena je na raščlambu spisa pohranjenog u Državnom arhivu u Mlecima (fond: Capi del Consiglio de’ Dieci: Lettere di Rettori e di altre cariche) koji se odnosi na događaj iz 1748. godine u korčulanskom selu Žrnovo, kada su mještani – vjerujući da su se pojavili vampiri – oskvrnuli nekoliko mjesnih grobova. U radu se podrobno iznose osnovni podaci iz spisa te rečeni događaj analizira u širem društvenom kontekstu i prate se lokalna vjerovanja.The main interest of this essay is the analysis of the document from the State Archive in Venice (file: Capi del Consiglio de’ Dieci: Lettere di Rettori e di altre cariche) which is connected with the episode from 1748 when the inhabitants of the village Žrnove on the island of Korčula in Croatia opened tombs on the local cemetery in the fear of the vampires treating. This essay try to show some social circumstances connected with this event as well as a local vernacular tradition concerning superstitions

Research Ethics in Sport and Exercise Science.

This chapter covers common research ethics issues within research proposals in sport and exercise science. Identifying the reason or reasons for the study at the outset is the first and most important part of the research ethics process. A thorough and scientific analysis of previous findings helps the researcher identify strategies to extend current knowledge and practice within sport and exercise science settings. Understanding and application of professional body guidelines for good practice will enhance both the submission for research ethics review and the underlying ethical value providing enhanced confidence in the reliability of the research study. Obvious and overlooked researcher competencies are outlined, identifying a range of opportunities for skills training. Spanning a range of disciplines, sport and exercise science research addresses sensitive topics, issues of disclosure, and physical measurement procedures. This chapter highlights considerations that ensure the research process does not harm participants in the quest to further knowledge and practice. Finally, some solutions to actual and perceived barriers are proposed to help refine and develop the research ethics review process in sport and exercise science