Anatomical Differences of Corneal Surface Parameters

Purpose: The aim of this study was to analyze possible anatomical differences of corneal surface parameters in the sample of population and between the genders. Methods: This study is presenting the sample of population which has consisted of 1354 subjects, 794 female and 560 male eyes. Subjects were chosen randomly. To determine and evaluate all values of corneal surface parameters, auto refractor keratometer with Placido disc (KR 8100P, Topcon, Japan) and the program Software Corneal Analyzer, Version 3.0 were used. The results were registered and then processed statistically. Results: From a large amounts of data, tere are chosen only the results in this study that showed statistically significant (p<0. 05) differences between right and left eye and between the genders. Found variations are in: the steepest meridian, the axis of the steepest meridian, corneal astigmatism, astigmatic difference and corneal diameter (HVID). Conclusion: The study shows that in optometric practice is also important to pay attention not only to functional but also individual anatomical parameters of corneal surface

e-Bug implementation in the Czech Republic

The Czech Republic joined the e-Bug Project in 2006, and participated in the evaluation of the e-Bug pilot version together with the United Kingdom and France in 2007. The final version of the educational material was prepared in the UK centre in 2008. These were distributed to all elementary schools in the Czech Republic at the beginning of 2010. This was accompanied by a publicity campaign. The characteristics of the Czech population and its hygiene habits, the Czech system of education, and the development of antibiotic policies are also briefly described

Sustainable Route to Inorganic Porous Hollow Fibers with Superior Properties

This research article presents a method for the fabrication of inorganic porous hollow fibers, using ecologically benign feed materials instead of organic solvents and harmful additives. Our method is based on ionic cross-linking of an aqueous mixture of sodium alginate, inorganic particles, and a carbonate. The mixture is spun into an acidic coagulation bath, where the low pH triggers the dissociation of the carbonate into multivalent cations and carbon dioxide. The multivalent cations cross-link the alginate, thereby consolidating the 3D structure and arresting the inorganic particles. In a subsequent thermal treatment, the polymer is removed, and the particles are sintered together. Adequate gelation requires a sufficiently low pH of the acid bath and a sufficing buffering capacity of the acid. In addition, to facilitate thermal treatment, it appears to be crucial that the acid has a conjugated base with limited propensity for complexing cations. The environmentally safe and sustainable lactic acid and acetic acid are shown to be convenient acids. The fibers prepared via our method have outstanding properties, such as high mechanical strength, homogeneous morphology, and sharp distribution of small pores. In addition, they are prepared using sustainable chemicals such as lactic acid and calcium carbonate

Dynamic response of ultrathin highly dense ZIF-8 nanofilms

Ultrathin ZIF-8 nanofilms are prepared by facile step-by-step dip coating. A critical withdrawal speed allows for films with a very uniform minimum thickness. The high refractive index of the films denotes the absence of mesopores. The dynamic response of the films to CO2 exposure resembles behaviour observed for nonequilibrium organic polymers

Highly permeable and mechanically robust silicon carbide hollow fiber membranes

Silicon carbide (SiC) membranes have shown large potential for applications in water treatment. Being able to make these membranes in a hollow fiber geometry allows for higher surface-to-volume ratios. In this study, we present a thermal treatment procedure that is tuned to produce porous silicon carbide hollow fiber membranes with sufficient mechanical strength. Thermal treatments up to 1500 °C in either nitrogen or argon resulted in relatively strong fibers, that were still contaminated with residual carbon from the polymer binder. After treatment at a higher temperature of 1790 °C, the mechanical strength had decreased as a result of carbon removal, but after treatments at even higher temperature of 2075 °C the SiC-particles sinter together, resulting in fibers with mechanical strengths of 30–40 MPa and exceptionally high water permeabilities of 50,000 L m−2 h−1 bar−1. Combined with the unique chemical and thermal resistance of silicon carbide, these properties make the fibers suitable microfiltration membranes or as a membrane support for application under demanding condition

gga-miRNOME, a microRNA-sequencing dataset from chick embryonic tissues

MicroRNAs (miRNAs) are small non-coding RNA molecules, with sizes ranging from 18 to 25 nucleotides, which are key players in gene expression regulation. These molecules play an important role in fine-tuning early vertebrate embryo development. However, there are scarce publicly available miRNA datasets from non-mammal embryos, such as the chicken (Gallus gallus), which is a classical model system to study vertebrate embryogenesis. Here, we performed microRNA-sequencing to characterize the early stages of trunk and limb development in the chick embryo. For this, we profiled three chick embryonic tissues, namely, Undetermined Presomitic Mesoderm (PSM_U), Determined Presomitic Mesoderm (PSM_D) and Forelimb Distal Cyclic Domain (DCD). We identified 926 known miRNAs, and 1,141 novel candidate miRNAs, which nearly duplicates the number of Gallus gallus entries in the miRBase database. These data will greatly benefit the avian research community, particularly by highlighting new miRNAs potentially involved in the regulation of early vertebrate embryo development, that can be prioritized for further experimental testing.info:eu-repo/semantics/publishedVersio

A comprehensive thermodynamic study of the U-Am-O ternary system: multiple experimental investigations and Calphad modelling

International audienc

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity