Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet made from reviewer comment

Estimation of a Noise Level Using Coarse-Grained Entropy of Experimental Time Series of Internal Pressure in a Combustion Engine

We report our results on non-periodic experimental time series of pressure in a single cylinder spark ignition engine. The experiments were performed for different levels of loading. We estimate the noise level in internal pressure calculating the coarse-grained entropy from variations of maximal pressures in successive cycles. The results show that the dynamics of the combustion is a nonlinear multidimensional process mediated by noise. Our results show that so defined level of noise in internal pressure is not monotonous function of loading.Comment: 12 pages, 6 figure

Morphological studies in modern teratological investigations

Despite the variety of modern molecular techniques available, examination of foetal anatomy is still a fundamental part of teratological studies in evaluating the developmental toxicity of xenobiotics or other non-chemical factors. The article presents contemporary methods of embryotoxicity and foetotoxicity assessment. A single alizarin red S and double alcian blue followed by alizarin red S staining, as well as various methods of soft tissue examination are discussed

Solar photovoltaic power output forecasting using machine learning technique

Photovoltaic (PV) systems are used around the world to generate solar power. Solar power sources are irregular in nature due to the output power of PV systems being intermittent and depending greatly on environmental factors. These factors include, but are not limited to, irradiance, humidity, PV surface temperature, speed of the wind. Due to uncertainties in the photovoltaic generation, it is critical to precisely envisage the solar power generation. Solar power forecasting is necessary for supply and demand planning in an electric grid. This prediction is highly complex and challenging as solar power generation is weather-dependent and uncontrollable. This paper describes the effects of various environmental parameters on the PV system output. Prediction models based on Artificial Neural Networks (ANN) and regression models are evaluated for selective factors. The selection is done by using the correlation-based feature selection (CSF) and ReliefF techniques. The ANN model outperforms all other techniques that were discussed

Prominent members of the human gut microbiota express endo-acting O-glycanases to initiate mucin breakdown

Epithelial cells that line the gut secrete complex glycoproteins that form a mucus layer to protect the gut wall from enteric pathogens. Here, the authors provide a comprehensive characterisation of endo-acting glycoside hydrolases expressed by mucin-degrading members of the microbiome that are able to cleave the O-glycan chains of a range of different animal and human mucins

Impacts of caring for a child with the CDKL5 disorder on parental wellbeing and family quality of life

Background: Although research in this area remains sparse, raising a child with some genetic disorders has been shown to adversely impact maternal health and family quality of life. The aim of this study was to investigate such impacts in families with a child with the CDKL5 disorder, a newly recognised genetic disorder causing severe neurodevelopmental impairments and refractory epilepsy. Methods: Data were sourced from the International CDKL5 Disorder Database to which 192 families with a child with a pathogenic CDKL5 mutation had provided data by January 2016. The Short Form 12 Health Survey Version 2, yielding a Physical Component Summary and a Mental Component Summary score, was used to measure primary caregiver's wellbeing. The Beach Center Family Quality of Life Scale was used to measure family quality of life. Linear regression analyses were used to investigate relationships between child and family factors and the various subscale scores. Results: The median (range) age of the primary caregivers was 37.0 (24.6-63.7) years and of the children was 5.2 (0.2-34.1) years. The mean (SD) physical and mental component scores were 53.7 (8.6) and 41.9 (11.6), respectively. In mothers aged 25-54 years the mean mental but not the physical component score was lower than population norms. After covariate adjustment, caregivers with a tube-fed child had lower mean physical but higher mean mental component scores than those whose child fed orally (coefficient = -4.80 and 6.79; p = 0.009 and 0.012, respectively). Child sleep disturbances and financial hardship were negatively associated with the mental component score. The mean (SD) Beach Center Family Quality of Life score was 4.06 (0.66) and those who had used respite services had lower scores than those who had not across the subscales. Conclusions: Emotional wellbeing was considerably impaired in this caregiver population, and was particularly associated with increased severity of child sleep problems and family financial difficulties. Family quality of life was generally rated lowest in those using respite care extensively, suggesting that these families may be more burdened by daily caregiving

A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

Polymer microarrays rapidly identify competitive adsorbents of virus-like particles (VLPs)

The emergence of SARS-CoV-2 highlights the global need for platform technologies to enable rapid development of diagnostics, vaccines, treatments, and personal protective equipment (PPE). However, many current technologies require the detailed mechanistic knowledge of specific material-virion interactions before they can be employed, for example to aid in the purification of vaccine components, or in design of more effective PPE. Here we show that an adaption of polymer micro array method for screening bacterial-surface interactions allows for screening of polymers for desirable material-viron interactions. Non-pathogenic virus like particles including fluorophores are exposed to the arrays in aqueous buffer as a simple model of virons carried to the surface in saliva/sputum. Competitive binding of Lassa and Rubella particles is measured to probe the relative binding properties of a selection of copolymers. This provides the first step in the development of a method for discovery of novel materials with promise for viral binding, with the next being development of this method to assess absolute viral adsorption and assessment of the attenuation of the activity of live virus which we propose would be part of a material scale up step carried out in biological laboratory safety level 4 facilities and the use of more complex media to represent biological fluids

NIST interlaboratory study on glycosylation analysis of monoclonal antibodies : comparison of results from diverse analytical methods

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals since it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy‑six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submitted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation  analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide community-derived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type.. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods