Microbial single-cell omics: the crux of the matter

Single-cell genomics and transcriptomics can provide reliable context for assembled genome fragments and gene expression activity on the level of individual prokaryotic genomes. These methods are rapidly emerging as an essential complement to cultivation-based, metagenomics, metatranscriptomics, and microbial community-focused research approaches by allowing direct access to information from individual microorganisms, even from deep-branching phylogenetic groups that currently lack cultured representatives. Their integration and binning with environmental ‘omics data already provides unprecedented insights into microbial diversity and metabolic potential, enabling us to provide information on individual organisms and the structure and dynamics of natural microbial populations in complex environments. This review highlights the pitfalls and recent advances in the field of single-cell omics and its importance in microbiological and biotechnological studies

Derivative based global sensitivity measures

The method of derivative based global sensitivity measures (DGSM) has recently become popular among practitioners. It has a strong link with the Morris screening method and Sobol' sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol' sensitivity indices. This paper presents a survey of recent advances in DGSM concerning lower and upper bounds on the values of Sobol' total sensitivity indices $S\_{i}^{tot}$. Using these bounds it is possible in most cases to get a good practical estimation of the values of $S\_{i}^{tot}$. Several examples are used to illustrate an application of DGSM

Hyperparameter Importance Across Datasets

With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond performance-optimizing hyperparameter settings. In this work, we aim to answer the following two questions: Given an algorithm, what are generally its most important hyperparameters, and what are typically good values for these? We present methodology and a framework to answer these questions based on meta-learning across many datasets. We apply this methodology using the experimental meta-data available on OpenML to determine the most important hyperparameters of support vector machines, random forests and Adaboost, and to infer priors for all their hyperparameters. The results, obtained fully automatically, provide a quantitative basis to focus efforts in both manual algorithm design and in automated hyperparameter optimization. The conducted experiments confirm that the hyperparameters selected by the proposed method are indeed the most important ones and that the obtained priors also lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Minin

Derivative based global sensitivity measures

International audienceThe method of derivative based global sensitivity measures (DGSM) has recently become popular among practitioners. It has a strong link with the Morris screening method and Sobol' sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol' sensitivity indices. This paper presents a survey of recent advances in DGSM concerning lower and upper bounds on the values of Sobol' total sensitivity indices $S_{i}^{tot}$. Using these bounds it is possible in most cases to get a good practical estimation of the values of $S_{i}^{tot}$. Several examples are used to illustrate an application of DGSM

Modeling of impact deformation processes of the ceramic container for radioactive waste storage

Results of researches in the field of designing containers for storage of radioactive materials are presented in the work. The purposes of researches include a development of an effective method of modeling static and dynamic deformation processes at shock impact on the ceramic container with radioactive materials at transportation. The next tasks have been solved: on the basis of the mathematical description of physic-mechanical processes of deformation of the complex design container has been chosen method of finite-element's for effective modeling of the stress-strain State of static and dynamic deformation processes in the containers made of ceramic elements; laws of deformation, estimations of durability and rigidity for designed containers have been received after calculations by means of the computer software; recommendations on perfection of a design of the container for maintenance of requirements to safety are given. Analysis Finite Element Method (FEM) has been conducted in the ANSYS system and results is presented

X-ray line formation in the spectrum of SS 433

The mechanisms for the formation of X-ray lines in the spectrum of SS 433 are investigated by taking into account the radiative transfer inside the jets. The results of Monte Carlo numerical simulations are presented. The effect of a decrease in line intensity due to scattering inside the jet turns out to be pronounced, but it does not exceed 60% in magnitude on the entire grid of parameters. The line broadening due to scattering, nutational motion, and the contribution of satellites can lead to overestimates of the jet opening angle $\Theta$ from the line widths in Chandra X-ray observations. The fine structure of the lines turns out to be very sensitive to the scattering effects. This makes its investigation by planned X-ray observatories equipped with high-resolution spectrometers (primarily Astro-H) a powerful tool for diagnosing the parameters of the jets in SS 433.Comment: 23 pages, 14 figures, to be published in Astronomy Letters, v. 38, n. 7, p. 443 (2012

Parental bonding and identity style as correlates of self-esteem among adult adoptees and nonadoptees

Adult adoptees (n equals 100) and non-adoptees (n equals 100) were compared with regard to selfesteem, identity processing style, and parental bonding. While some differences were found with regard to self-esteem, maternal care, and maternal overprotection, these differences were qualified by reunion status such that only reunited adoptees differed significantly from nonadoptees. Moreover, hierarchical regression analyses indicated that parental bonding and identity processing style were more important than adoptive status per se in predicting self esteem. Implications for practitioners who work with adoptees are discussed

Study of the ηπo\eta\pi^o system in the mass range up to 1200 MeV

The reaction $\pi^-p \to \eta\pi^o n$ has been studied with GAMS-2000 spectrometer in the secondary 38 GeV/c $\pi^-$-beam of the IHEP U-70 accelerator. Partial wave analysis of the reaction has been performed in the $\eta\pi^o$ mass range up to 1200 MeV. The $a_0(980)$-meson is seen as a sharp peak in S-wave. The $t$-dependence of $a_0(980)$ production cross section has been studied. Dominant production of the $a_0(980)$ at a small transfer momentum $t$ confirms the hypothesis of Achasov and Shestakov about significant contribution of the $\rho_2$ exchange ($I^GJ^{PC}=1^+2^{--}$) in the mechanism of $a_0(980)$ meson production in $t$-channel of the reaction.Comment: 4 pages, 3 figures, talk given at HADRON'9

Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation

A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from in vivo clinical measurements in a timeframe that is suitable for use within a clinic. Understanding these properties can provide insight into heart function or dysfunction and help to inform personalized medicine. However, finding a solution to the differential equations which mathematically describe the kinematics and dynamics of the myocardium through numerical integration can be computationally expensive. To circumvent this issue, we use the concept of emulation to infer the myocardial properties of a healthy volunteer in a viable clinical timeframe by using in vivo magnetic resonance image data. Emulation methods avoid computationally expensive simulations from the left ventricular model by replacing the biomechanical model, which is defined in terms of explicit partial differential equations, with a surrogate model inferred from simulations generated before the arrival of a patient, vastly improving computational efficiency at the clinic. We compare and contrast two emulation strategies: emulation of the computational model outputs and emulation of the loss between the observed patient data and the computational model outputs. These strategies are tested with two interpolation methods, as well as two loss functions. The best combination of methods is found by comparing the accuracy of parameter inference on simulated data for each combination. This combination, using the output emulation method, with local Gaussian process interpolation and the Euclidean loss function, provides accurate parameter inference in both simulated and clinical data, with a reduction in the computational cost of about three orders of magnitude compared with numerical integration of the differential equations by using finite element discretization techniques