Coefficient of thermal expansion of nanostructured tungsten based coatings assessed by thermally induced substrate curvature method

The in plane coefficient of thermal expansion (CTE) and the residual stress of nanostructured W based coatings are extensively investigated. The CTE and the residual stresses are derived by means of an optimized ad-hoc developed experimental setup based on the detection of the substrate curvature by a laser system. The nanostructured coatings are deposited by Pulsed Laser Deposition. Thanks to its versatility, nanocrystalline W metallic coatings, ultra-nano-crystalline pure W and W-Tantalum coatings and amorphous-like W coatings are obtained. The correlation between the nanostructure, the residual stress and the CTE of the coatings are thus elucidated. We find that all the samples show a compressive state of stress that decreases as the structure goes from columnar nanocrystalline to amorphous-like. The CTE of all the coatings is higher than the one of the corresponding bulk W form. In particular, as the grain size shrinks, the CTE increases from 5.1 10$^{-6}$ K$^{-1}$ for nanocrystalline W to 6.6 10$^{-6}$ K$^{-1}$ in the ultra-nano-crystalline region. When dealing with amorphous W, the further increase of the CTE is attributed to a higher porosity degree of the samples. The CTE trend is also investigated as function of materials stiffness. In this case, as W coatings become softer, the easier they thermally expand.Comment: The research leading to these results has also received funding from the European Research Council Consolidator Grant ENSURE (ERC-2014-CoG No. 647554

Clinical and operational value of the extensively drug-resistant tuberculosis definition.

Currently, no information is available on the effect of resistance/susceptibility to first-line drugs different from isoniazid and rifampicin in determining the outcome of extensively drug-resistant tuberculosis (XDR-TB) patients, and whether being XDR-TB is a more accurate indicator of poor clinical outcome than being resistant to all first-line anti-tuberculosis (TB) drugs. To investigate this issue, a large series of multidrug-resistant TB (MDR-TB) and XDR-TB cases diagnosed in Estonia, Germany, Italy and the Russian Federation during the period 1999-2006 were analysed. Drug-susceptibility testing for first- and second-line anti-TB drugs, quality assurance and treatment delivery was performed according to World Health Organization recommendations in all study sites. Out of 4,583 culture-positive TB cases analysed, 361 (7.9%) were MDR and 64 (1.4%) were XDR. XDR-TB cases had a relative risk (RR) of 1.58 to have an unfavourable outcome compared with MDR-TB cases resistant to all first-line drugs (isoniazid, rifampicin ethambutol, streptomycin and, when tested, pyrazinamide), and an RR of 2.61 compared with "other" MDR-TB cases (those susceptible to at least one first-line anti-TB drug among ethambutol, pyrazinamide and streptomycin, regardless of resistance to the second-line drugs not defining XDR-TB). The emergence of extensively drug-resistant tuberculosis confirms that problems in tuberculosis management are still present in Europe. While waiting for new tools which will facilitate management of extensively drug-resistant tuberculosis, accessibility to quality diagnostic and treatment services should be urgently ensured and adequate public health policies should be rapidly implemented to prevent further development of drug resistance

Drip and Mate Operations Acting in Test Tube Systems and Tissue-like P systems

The operations drip and mate considered in (mem)brane computing resemble the operations cut and recombination well known from DNA computing. We here consider sets of vesicles with multisets of objects on their outside membrane interacting by drip and mate in two different setups: in test tube systems, the vesicles may pass from one tube to another one provided they fulfill specific constraints; in tissue-like P systems, the vesicles are immediately passed to specified cells after having undergone a drip or mate operation. In both variants, computational completeness can be obtained, yet with different constraints for the drip and mate operations

Analysis of single-cell RNA sequencing data based on autoencoders

Background: Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches. Results: Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of scRNA-Seq data, so that the deployment of AE-based tools might represent the way forward in this context. We introduce here scAEspy, a unifying tool that embodies: (1) four of the most advanced AEs, (2) two novel AEs that we developed on purpose, (3) different loss functions. We show that scAEspy can be coupled with various batch-effect removal tools to integrate data by different scRNA-Seq platforms, in order to better identify the cell-types. We benchmarked scAEspy against the most used batch-effect removal tools, showing that our AE-based strategies outperform the existing solutions. Conclusions: scAEspy is a user-friendly tool that enables using the most recent and promising AEs to analyse scRNA-Seq data by only setting up two user-defined parameters. Thanks to its modularity, scAEspy can be easily extended to accommodate new AEs to further improve the downstream analysis of scRNA-Seq data. Considering the relevant results we achieved, scAEspy can be considered as a starting point to build a more comprehensive toolkit designed to integrate multi single-cell omics

The Italian registry of pulmonary non-tuberculous mycobacteria - IRENE:The study protocol

Background: A substantial increase in pulmonary and extra-pulmonary diseases due to non-tuberculous mycobacteria (NTM) has been documented worldwide, especially among subjects suffering from chronic respiratory diseases and immunocompromised patients. Many questions remain regarding the epidemiology of pulmonary disease due to NTM (NTM-PD) mainly because reporting of NTM-PD to health authorities is not mandated in several countries, including Italy. This manuscript describes the protocol of the first Italian registry of adult patients with respiratory infections caused by NTM (IRENE). Methods: IRENE is an observational, multicenter, prospective, cohort study enrolling consecutive adult patients with either a NTM respiratory isolate or those with NTM-PD. A total of 41 centers, including mainly pulmonary and infectious disease departments, joined the registry so far. Adult patients with all of the following are included in the registry: 1) at least one positive culture for any NTM species from any respiratory sample; 2) at least one positive culture for NTM isolated in the year prior the enrolment and/or prescribed NTM treatment in the year prior the enrolment; 3) given consent to inclusion in the study. No exclusion criteria are applied to the study. Patients are managed according to standard operating procedures implemented in each IRENE clinical center. An online case report form has been developed to collect patients' demographics, comorbidities, microbiological, laboratory, functional, radiological, clinical, treatment and outcome data at baseline and on an annual basis. An IRENE biobank has also been developed within the network and linked to the clinical data of the registry. Conclusions: IRENE has been developed to inform the clinical and scientific community on the current management of adult patients with NTM respiratory infections in Italy and acts as a national network to increase the disease's awareness

A Multiscale Modeling Framework Based on P Systems

Cellular systems present a highly complex organization at different scales including the molecular, cellular and colony levels. The complexity at each one of these levels is tightly interrelated. Integrative systems biology aims to obtain a deeper understanding of cellular systems by focusing on the systemic and systematic integration of the different levels of organization in cellular systems. The different approaches in cellular modeling within systems biology have been classified into mathematical and computational frameworks. Specifically, the methodology to develop computational models has been recently called executable biology since it produces executable algorithms whose computations resemble the evolution of cellular systems. In this work we present P systems as a multiscale modeling framework within executable biology. P system models explicitly specify the molecular, cellular and colony levels in cellular systems in a relevant and understandable manner. Molecular species and their structure are represented by objects or strings, compartmentalization is described using membrane structures and finally cellular colonies and tissues are modeled as a collection of interacting individual P systems. The interactions between the components of cellular systems are described using rewriting rules. These rules can in turn be grouped together into modules to characterize specific cellular processes. One of our current research lines focuses on the design of cell systems biology models exhibiting a prefixed behavior through the automatic assembly of these cellular modules. Our approach is equally applicable to synthetic as well as systems biology.Kingdom's Engineering and Physical Sciences Research Council EP/ E017215/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/F01855X/1Biotechnology and Biological Sciences Research Council/United Kingdom BB/D019613/

Biochemical parameter estimation vs. benchmark functions: A comparative study of optimization performance and representation design

© 2019 Elsevier B.V. Computational Intelligence methods, which include Evolutionary Computation and Swarm Intelligence, can efficiently and effectively identify optimal solutions to complex optimization problems by exploiting the cooperative and competitive interplay among their individuals. The exploration and exploitation capabilities of these meta-heuristics are typically assessed by considering well-known suites of benchmark functions, specifically designed for numerical global optimization purposes. However, their performances could drastically change in the case of real-world optimization problems. In this paper, we investigate this issue by considering the Parameter Estimation (PE) of biochemical systems, a common computational problem in the field of Systems Biology. In order to evaluate the effectiveness of various meta-heuristics in solving the PE problem, we compare their performance by considering a set of benchmark functions and a set of synthetic biochemical models characterized by a search space with an increasing number of dimensions. Our results show that some state-of-the-art optimization methods – able to largely outperform the other meta-heuristics on benchmark functions – are characterized by considerably poor performances when applied to the PE problem. We also show that a limiting factor of these optimization methods concerns the representation of the solutions: indeed, by means of a simple semantic transformation, it is possible to turn these algorithms into competitive alternatives. We corroborate this finding by performing the PE of a model of metabolic pathways in red blood cells. Overall, in this work we state that classic benchmark functions cannot be fully representative of all the features that make real-world optimization problems hard to solve. This is the case, in particular, of the PE of biochemical systems. We also show that optimization problems must be carefully analyzed to select an appropriate representation, in order to actually obtain the performance promised by benchmark results

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements

Host range of mammalian orthoreovirus type 3 widening to alpine chamois

Mammalian orthoreoviruses (MRV) type 3 have been recently identified in human and several animal hosts, highlighting the apparent lack of species barriers. Here we report the identification and genetic characterization of MRVs strains in alpine chamois, one of the most abundant wild ungulate in the Alps. Serological survey was also performed by MRV neutralization test in chamois population during five consecutive years (2008-2012). Three novel MRVs were isolated on cell culture from chamois lung tissues. No respiratory or other clinical symptoms neither lung macroscopic lesions were observed in the chamois population. MRV strains were classified as MRV-3 within the lineage III, based on S1 phylogeny, and were closely related to Italian strains identified in dog, bat and diarrheic pig. The full genome sequence was obtained by next-generation sequencing and phylogenetic analyses showed that other segments were more similar to MRVs of different geographic locations, serotypes and hosts, including human, highlighting genome reassortment and lack of host specific barriers. By using serum neutralization test, a high prevalence of MRV-3 antibodies was observed in chamois population throughout the monitored period, showing an endemic level of infection and suggesting a self-maintenance of MRV and/or a continuous spill-over of infection from other animal species