Tailor-made composite functions as tools in model choice: the case of sigmoidal vs bi-linear growth profiles

BACKGROUND: Roots are the classical model system to study the organization and dynamics of organ growth zones. Profiles of the velocity of root elements relative to the apex have generally been considered to be sigmoidal. However, recent high-resolution measurements have yielded bi-linear profiles, suggesting that sigmoidal profiles may be artifacts caused by insufficient spatio-temporal resolution. The decision whether an empirical velocity profile follows a sigmoidal or bi-linear distribution has consequences for the interpretation of the underlying biological processes. However, distinguishing between sigmoidal and bi-linear curves is notoriously problematic. A mathematical function that can describe both types of curve equally well would allow them to be distinguished by automated curve-fitting. RESULTS: On the basis of the mathematical requirements defined, we created a composite function and tested it by fitting it to sigmoidal and bi-linear models with different noise levels (Monte-Carlo datasets) and to three experimental datasets from roots of Gypsophila elegans, Aurinia saxatilis, and Arabidopsis thaliana. Fits of the function proved robust with respect to noise and yielded statistically sound results if care was taken to identify reasonable initial coefficient values to start the automated fitting procedure. Descriptions of experimental datasets were significantly better than those provided by the Richards function, the most flexible of the classical growth equations, even in cases in which the data followed a smooth sigmoidal distribution. CONCLUSION: Fits of the composite function introduced here provide an independent criterion for distinguishing sigmoidal and bi-linear growth profiles, but without forcing a dichotomous decision, as intermediate solutions are possible. Our function thus facilitates an unbiased, multiple-working hypothesis approach. While our discussion focusses on kinematic growth analysis, this and similar tailor-made functions will be useful tools wherever models of steadily or abruptly changing dependencies between empirical parameters are to be compared

Density of States and Conductivity of Granular Metal or Array of Quantum Dots

The conductivity of a granular metal or an array of quantum dots usually has the temperature dependence associated with variable range hopping within the soft Coulomb gap of density of states. This is difficult to explain because neutral dots have a hard charging gap at the Fermi level. We show that uncontrolled or intentional doping of the insulator around dots by donors leads to random charging of dots and finite bare density of states at the Fermi level. Then Coulomb interactions between electrons of distant dots results in the a soft Coulomb gap. We show that in a sparse array of dots the bare density of states oscillates as a function of concentration of donors and causes periodic changes in the temperature dependence of conductivity. In a dense array of dots the bare density of states is totally smeared if there are several donors per dot in the insulator.Comment: 13 pages, 15 figures. Some misprints are fixed. Some figures are dropped. Some small changes are given to improve the organizatio

On the effect of far impurities on the density of states of two-dimensional electron gas in a strong magnetic field

The effect of impurities situated at different distances from a two-dimensional electron gas on the density of states in a strong magnetic field is analyzed. Based on the exact result of Brezin, Gross, and Itzykson, we calculate the density of states in the whole energy range, assuming the Poisson distribution of impurities in the bulk. It is shown that in the case of small impurity concentration the density of states is qualitatively different from the model case when all impurities are located in the plane of the two-dimensional electron gas.Comment: 6 pages, 1 figure, submitted to JETP Letter

Photocurrent in nanostructures with asymmetric antidots

The steady current induced by electromagnetic field in a 2D system with asymmetric scatterers is studied. The scatterers are assumed to be oriented cuts with one diffusive and another specular sides. Besides, the existence of isotropic impurity scatterers is assumed. This simple model simulates the lattice of half-disk which have been studied numerically recently. The model allows the exact solution in the framework of the kinetic equation. The static current response in the second order of electric field is obtained. The photogalvanic tensor contains both responses to linear and circular polarization of electromagnetic field. The model possesses non-analyticity with regards to the rate of impurity scattering.Comment: 9 pages, 6 figure

Theory of one-dimensional double-barrier quantum pump in two-frequency signal regime

A one-dimensional system with two $\delta$-like barriers or wells bi-chromaticaly oscillating at frequencies $\omega$ and $2\omega$ is considered. The alternating signal leads to the direct current across the structure (even in a symmetric system). The properties of this quantum pump are studied in a wide range of the system parameters.Comment: 4 pages, 5 figure

A renaissance of neural networks in drug discovery

© 2016 Informa UK Limited, trading as Taylor & Francis Group.Introduction: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. Areas covered: In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Expert opinion: Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It’s anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine

Non-Markovian spin relaxation in two-dimensional electron gas

We analyze by Monte-Carlo simulations and analytically spin dynamics of two-dimensional electron gas (2DEG) interacting with short-range scatterers in nonquantizing magnetic fields. It is shown that the spin dynamics is non-Markovian with the exponential spin relaxation followed by the oscillating tail due to the electrons residing on the closed trajectories. The tail relaxes on a long time scale due to an additional smooth random potential and inelastic processes. The developed analytical theory and Monte-Carlo simulations are in the quantitative agreement with each other.Comment: 6 pages, 3 figure

Artificial intelligence in synthetic chemistry: Achievements and prospects

The review is devoted to the achievements in analysis of information on chemical reactions using machine learning methods. Four large areas that actively use these methods are outlined: computer-assisted planning of synthesis, analysis and visualization of chemical reaction data, prediction of the quantitative characteristics of reactions and computer-aided design of catalysts

Conductivity of 2D many-component electron gas, partially-quantized by magnetic field

The 2D semimetal consisting of heavy holes and light electrons is studied. The consideration is based on assumption that electrons are quantized by magnetic field while holes remain classical. We assume also that the interaction between components is weak and the conversion between components is absent. The kinetic equation for holes colliding with quantized electrons is utilized. It has been stated that the inter-component friction and corresponding correction to the dissipative conductivity $\sigma_{xx}$ {\it do not vanish at zero temperature} due to degeneracy of the Landau levels. This correction arises when the Fermi level crosses the Landau level. The limits of kinetic equation applicability were found. We also study the situation of kinetic memory when particles repeatedly return to the points of their meeting.Comment: 13 pages, 1 figur