Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Denoising Two-Photon Calcium Imaging Data

Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.National Institutes of Health (U.S.) (DP1 OD003646-01)National Institutes of Health (U.S.) (R01EB006385-01)National Institutes of Health (U.S.) (EY07023)National Institutes of Health (U.S.) (EY017098

Annealing study and thermal investigation on bismuth sulfide thin films prepared by chemical bath deposition in basic medium

This is a post-peer-review, pre-copyedit version of an article published in Applied Physics A 124.2 (2018): 166. The final authenticated version is available online at: http://doi.org/10.1007/s00339-018-1584-7Bismuth sulfide thin films were prepared by chemical bath deposition using thiourea as sulfide ion source in basic medium. First, the effects of both the deposition parameters on films growth as well as the annealing effect under argon and sulfur atmosphere on as-deposited thin films were studied. The parameters were found to be influential using the Doehlert matrix experimental design methodology. Ranges for a maximum surface mass of films (3 mg cm-2) were determined. A well crystallized major phase of bismuth sulfide with stoichiometric composition was achieved at 190°C for 3 hours. The prepared thin films were characterized using Grazing Incidence X-ray diffraction (GIXRD), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray analysis (EDX). Second, the band gap energy value was found to be 1.5 eV. Finally, the thermal properties have been studied for the first time by means of the electropyroelectric (EPE) technique. Indeed, the thermal conductivity varied in the range of 1.20 - 0.60 W m-1 K-1 while the thermal diffusivity values increased in terms of the annealing effect ranging from 1.8 to 3.5 10-7 m2s-1This work was financially supported by the Tunisian Ministry of Higher Education and Scientific Research and by the WINCOST (ENE2016-80788-C5-2-R) project funded by the Spanish Ministry of Economy and Competitivenes

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance.

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies

Sucrose in the concentrated solution or the supercooled “state” : a review of caramelisation reactions and physical behaviour

Sucrose is probably one of the most studied molecules by food scientists, since it plays an important role as an ingredient or preserving agent in many formulations and technological processes. When sucrose is present in a product with a concentration near or greater than the saturation point—i.e. in the supercooled state—it possesses high potentialities for the food industry in areas as different as pastry industry, dairy and frozen desserts or films and coatings production. This paper presents a review on critical issues and research on highly concentrated sucrose solutions—mainly, on sucrose thermal degradation and relaxation behaviour in such solutions. The reviewed works allow identifying several issues with great potential for contributing to significant advances in Food Science and Technology.Authors are grateful for the valuable discussions with Teresa S. Brandao and Rosiane Lopes da Cunha during this research. Author M. A. C. Quintas acknowledges the financial support of her research by FCT grant SFRH/BPD/41715/2007

Addressing the sample size problem in behavioural operational research: simulating the newsvendor problem

Laboratory-based experimental studies with human participants are beneficial for testing hypotheses in behavioural operational research. However, such experiments are not without their problems. One specific problem is obtaining a sufficient sample size, not only in terms of the number of participants but also the time they are willing to devote to an experiment. In this paper, we explore how agent-based simulation (ABS) can be used to address the sample size problem and demonstrate the approach in the newsvendor setting. The decision-making strategies of a small sample of individual decision-makers are determined through laboratory experiments. The interactions of these suppliers and retailers are then simulated using an ABS to generate a large sample set of decisions. With only a small number of participants, we demonstrate that it is possible to produce similar results to previous experimental studies that involved much larger sample sizes. We conclude that ABS provides the potential to extend the scope of experimental research in behavioural operational research

Analysis of computational approaches for motif discovery

Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contains follow-up analysis of the assessment results, and raises and discusses some important issues concerning the state of the art in motif discovery methods: 1. We categorize the objective functions used by existing tools, and design experiments to evaluate whether any of these objective functions is the right one to optimize. 2. We examine various features of the data sets that were used in the assessment, such as sequence length and motif degeneracy, and identify which features make data sets hard for current motif discovery tools. 3. We identify an important feature that has not yet been used by existing tools and propose a new objective function that incorporates this feature

Identification of stable QTLs for vegetative and reproductive traits in the microvine (Vitis vinifera L.) using the 18 K Infinium chip

UMR AGAP - équipe DAAV - Diversité, adaptation et amélioration de la vigne[b]Background[/b] [br/]The increasing temperature associated with climate change impacts grapevine phenology and development with critical effects on grape yield and composition. Plant breeding has the potential to deliver new cultivars with stable yield and quality under warmer climate conditions, but this requires the identification of stable genetic determinants. This study tested the potentialities of the microvine to boost genetics in grapevine. A mapping population of 129 microvines derived from Picovine x Ugni Blanc flb, was genotyped with the Illumina® 18 K SNP (Single Nucleotide Polymorphism) chip. Forty-three vegetative and reproductive traits were phenotyped outdoors over four cropping cycles, and a subset of 22 traits over two cropping cycles in growth rooms with two contrasted temperatures, in order to map stable QTLs (Quantitative Trait Loci). [br/][b]Results[/b] [br/]Ten stable QTLs for berry development and quality or leaf area were identified on the parental maps. A new major QTL explaining up to 44 % of total variance of berry weight was identified on chromosome 7 in Ugni Blanc flb, and co-localized with QTLs for seed number (up to 76 % total variance), major berry acids at green lag phase (up to 35 %), and other yield components (up to 25 %). In addition, a minor QTL for leaf area was found on chromosome 4 of the same parent. In contrast, only minor QTLs for berry acidity and leaf area could be found as moderately stable in Picovine. None of the transporters recently identified as mutated in low acidity apples or Cucurbits were included in the several hundreds of candidate genes underlying the above berry QTLs, which could be reduced to a few dozen candidate genes when a priori pertinent biological functions and organ specific expression were considered. [br/][b]Conclusions[/b] [br/]This study combining the use of microvine and a high throughput genotyping technology was innovative for grapevine genetics. It allowed the identification of 10 stable QTLs, including the first berry acidity QTLs reported so far in a Vitis vinifera intra-specific cross. Robustness of a set of QTLs was assessed with respect to temperature variatio

Interaction of 8-Hydroxyquinoline with Soil Environment Mediates Its Ecological Function

Background: Allelopathic functions of plant-released chemicals are often studied through growth bioassays assuming that these chemicals will directly impact plant growth. This overlooks the role of soil factors in mediating allelopathic activities of chemicals, particularly non-volatiles. Here we examined the allelopathic potential of 8-hydroxyquinoline (HQ), a chemical reported to be exuded from the roots of Centaurea diffusa. Methodology/Principal Findings: Growth bioassays and HQ recovery experiments were performed in HQ-treated soils (non-sterile, sterile, organic matter-enriched and glucose-amended) and untreated control soil. Root growth of either Brassica campestris or Phalaris minor was not affected in HQ-treated non-sterile soil. Soil modifications (organic matter and glucose amendments) could not enhance the recovery of HQ in soil, which further supports the observation that HQ is not likely to be an allelopathic compound. Hydroxyquinoline-treated soil had lower values for the CO2 release compared to untreated non-sterile soil. Soil sterilization significantly influenced the organic matter content, PO 4-P and total organic nitrogen levels. Conclusion/Significance: Here, we concluded that evaluation of the effect of a chemical on plant growth is not enough in evaluating the ecological role of a chemical in plant-plant interactions. Interaction of the chemical with soil factors largel