Antagonistic Autoantibodies to Insulin-Like Growth Factor-1 Receptor Associate with Poor Physical Strength

Natural autoantibodies to the IGF1 receptor (IGF1R-aAb) have been described in relation to Graves' ophthalmopathy. Other physiological roles of natural IGF1R-aAb are not known. We hypothesized that IGF1R-aAb may be related to muscle development. Serum samples (n = 408) from young overweight subjects (n = 143) were collected during a lifestyle intervention study. Anthropometric parameters, along with leptin, IGF1 and IGF1R-aAb concentrations, were analyzed, and the subjects were categorized into positive or negative for IGF1R-aAb. Eleven out of 143 subjects (7.7%) were positive for IGF1R-aAb. Identified IGF1R-aAb were molecularly characterized and showed antagonistic activity in vitro impairing IGF1-mediated IGF1R activation. Mean body weight, height or age were similar between IGF1R-aAb-positive and -negative subjects, but IGF1 concentrations differed. Jumping ability, as well as right and left handgrip strengths, were lower in the IGF1R-aAb-positive as compared to the IGF1R-aAb-negative subjects. We conclude that natural IGF1R-aAb are detectable in apparently healthy subjects and are capable of antagonizing IGF1-dependent IGF1R activation. Moreover, the presence of IGF1R-aAb is associated with poor physical strength. Although the causality of this association is unclear, the data imply a potential influence of IGF1R autoimmunity on muscle development

Performance of a TthPrimPol-based whole genome amplification kit for copy number alteration detection using massively parallel sequencing

Starting from only a few cells, current whole genome amplification (WGA) methods provide enough DNA to perform massively parallel sequencing (MPS). Unfortunately, all current WGA methods introduce representation bias which limits detection of copy number aberrations (CNAs) smaller than 3 Mb. A recent WGA method, called TruePrime single cell WGA, uses a recently discovered DNA primase, TthPrimPol, instead of artificial primers to initiate DNA amplification. This method could lead to a lower representation bias, and consequently to a better detection of CNAs. The enzyme requires no complementarity and thus should generate random primers, equally distributed across the genome. The performance of TruePrime WGA was assessed for aneuploidy screening and CNA analysis after MPS, starting from 1, 3 or 5 cells. Although the method looks promising, the single cell TruePrime WGA kit v1 is not suited for high resolution CNA detection after MPS because too much representation bias is introduced

The Poisson-Boltzmann model for implicit solvation of electrolyte solutions: Quantum chemical implementation and assessment via Sechenov coefficients.

We present the theory and implementation of a Poisson-Boltzmann implicit solvation model for electrolyte solutions. This model can be combined with arbitrary electronic structure methods that provide an accurate charge density of the solute. A hierarchy of approximations for this model includes a linear approximation for weak electrostatic potentials, finite size of the mobile electrolyte ions, and a Stern-layer correction. Recasting the Poisson-Boltzmann equations into Euler-Lagrange equations then significantly simplifies the derivation of the free energy of solvation for these approximate models. The parameters of the model are either fit directly to experimental observables-e.g., the finite ion size-or optimized for agreement with experimental results. Experimental data for this optimization are available in the form of Sechenov coefficients that describe the linear dependence of the salting-out effect of solutes with respect to the electrolyte concentration. In the final part, we rationalize the qualitative disagreement of the finite ion size modification to the Poisson-Boltzmann model with experimental observations by taking into account the electrolyte concentration dependence of the Stern layer. A route toward a revised model that captures the experimental observations while including the finite ion size effects is then outlined. This implementation paves the way for the study of electrochemical and electrocatalytic processes of molecules and cluster models with accurate electronic structure methods

Genomic introgression mapping of field-derived multiple-anthelmintic resistance in Teladorsagia circumcincta

Preventive chemotherapy has long been practiced against nematode parasites of livestock, leading to widespread drug resistance, and is increasingly being adopted for eradication of human parasitic nematodes even though it is similarly likely to lead to drug resistance. Given that the genetic architecture of resistance is poorly understood for any nematode, we have analyzed multidrug resistant Teladorsagia circumcincta, a major parasite of sheep, as a model for analysis of resistance selection. We introgressed a field-derived multiresistant genotype into a partially inbred susceptible genetic background (through repeated backcrossing and drug selection) and performed genome-wide scans in the backcross progeny and drug-selected F2 populations to identify the major genes responsible for the multidrug resistance. We identified variation linking candidate resistance genes to each drug class. Putative mechanisms included target site polymorphism, changes in likely regulatory regions and copy number variation in efflux transporters. This work elucidates the genetic architecture of multiple anthelmintic resistance in a parasitic nematode for the first time and establishes a framework for future studies of anthelmintic resistance in nematode parasites of humans

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Automated novelty detection in the WISE survey with one-class support vector machines

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources whose existence and properties cannot be easily predicted from earlier observations: novelties or even anomalies. Such objects can be efficiently sought for with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. OCSVM detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but most importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues.Comment: 14 pages, 15 figure