Hybrid Multipixel Array X-Ray Detectors for Real-Time Direct Detection of Hard X-Rays

X-ray detectors currently employed in dosimetry suffer from a number of drawbacks including the inability to conform to curved surfaces and being limited to smaller dimensions due to available crystal sizes. In this study, a hybrid X-ray detector (HXD) has been developed which offers real-time response with added advantages of being highly sensitive over a broad energy range, mechanically flexible, relatively inexpensive, and able to be fabricated over large areas on the desired surface. The detector comprises an organic matrix embedded with high-atomic-number inorganic nanoparticles which increase the radiation attenuation and within the device allows for simultaneous transfer of electrons and holes. The HXD delivers a peak response of 14 nA cm −2 , which corresponds to a sensitivity of 30.8 μC Gy −1 cm −2 , under the exposure of 6-MV hard X-rays generated by a medical linear accelerator. The angular dependence of the HXD has been studied, which offers a maximum variation of 26% in the posterior versus lateral beam directions. The flexible HXD can be conformed to the human body shape and is expected to eliminate variations due to source-to-skin distance with reduced physical evaluation complexities

Tin(iv) dopant removal through anti-solvent engineering enabling tin based perovskite solar cells with high charge carrier mobilities

We report the need for careful selection of anti-solvents for Sn-based perovskite solar cells fabricated through the commonly used anti-solvent method, compared to their Pb-based counterparts.</p

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

The Airway Microbiota in Cystic Fibrosis: A Complex Fungal and Bacterial Community—Implications for Therapeutic Management

International audienceBackground Given the polymicrobial nature of pulmonary infections in patients with cystic fibrosis (CF), it is essential to enhance our knowledge on the composition of the microbial community to improve patient management. In this study, we developed a pyrosequencing approach to extensively explore the diversity and dynamics of fungal and prokaryotic populations in CF lower airways. Methodology and Principal Findings Fungi and bacteria diversity in eight sputum samples collected from four adult CF patients was investigated using conventional microbiological culturing and high-throughput pyrosequencing approach targeting the ITS2 locus and the 16S rDNA gene. The unveiled microbial community structure was compared to the clinical profile of the CF patients. Pyrosequencing confirmed recently reported bacterial diversity and observed complex fungal communities, in which more than 60% of the species or genera were not detected by cultures. Strikingly, the diversity and species richness of fungal and bacterial communities was significantly lower in patients with decreased lung function and poor clinical status. Values of Chao1 richness estimator were statistically correlated with values of the Shwachman-Kulczycki score, body mass index, forced vital capacity, and forced expiratory volume in 1 s (p = 0.046, 0.047, 0.004, and 0.001, respectively for fungal Chao1 indices, and p = 0.010, 0.047, 0.002, and 0.0003, respectively for bacterial Chao1 values). Phylogenetic analysis showed high molecular diversities at the sub-species level for the main fungal and bacterial taxa identified in the present study. Anaerobes were isolated with Pseudomonas aeruginosa, which was more likely to be observed in association with Candida albicans than with Aspergillus fumigatus

Candida glabrata : a review of its features and resistance

Candida species belong to the normal microbiota of the oral cavity and gastrointestinal and vaginal tracts, and are responsible for several clinical manifestations, from mucocutaneous overgrowth to bloodstream infections. Once believed to be non-pathogenic, Candida glabrata was rapidly blamable for many human diseases. Year after year, these pathological circumstances are more recurrent and problematic to treat, especially when patients reveal any level of immunosuppression. These difficulties arise from the capacity of C. glabrata to form biofilms and also from its high resistance to traditional antifungal therapies. Thus, this review intends to present an excerpt of the biology, epidemiology, and pathology of C. glabrata, and detail an approach to its resistance mechanisms based on studies carried out up to the present.The authors are grateful to strategic project PTDC/SAU-MIC/119069/2010 for the financial support to the research center and for Celia F. Rodrigues' grant

Probiotic lactobacilli inhibit early stages of Candida albicans biofilm development by reducing their growth, cell adhesion, and filamentation

We evaluated the inhibitory effects of the probiotic Lactobacillus species on different phases of Candida albicans biofilm development. Quantification of biofilm growth and ultrastructural analyses were performed on C. albicans biofilms treated with Lactobacillus rhamnosus, Lactobacillus casei, and Lactobacillus acidophilus planktonic cell suspensions as well as their supernatants. Planktonic lactobacilli induced a significant reduction (p\ua0\ua00.05), but significantly reduced the early stages of Candida biofilm formation (p\ua

MERCon 2020 - 6th International Multidisciplinary Moratuwa Engineering Research Conference, Proceedings

Numerical Weather Models (NWMs) utilize data from diverse sources such as automated weather stations, radars, and satellite images. Such multimodal data need to be transcoded into a NWM compatible format before use. Moreover, the data integration system's response time needs to be relatively low to reduce the time to forecast weather events like hurricanes and flash floods. Furthermore, the resulting data need to be accessed by many researchers and third-party applications. Existing weather data integration systems are based on monolithic or client-server architectures, and are proprietary or closed source. Hence, they are not only expensive to operate in an era of cloud computing, but also challenging to customize for regions with different weather patterns. In this paper, we present a Weather Data Integration and Assimilation System (WDIAS) that uses microservices architecture and container orchestration to achieve high scalability, availability, and low-cost operation. WDIAS provides a modular architecture to integrate data from different sources, enforce data quality controls, export data into different formats, and extend the functionality by adding new modules. Using a synthetic workload and an experimental setup on a public cloud, we demonstrate that WDIAS can handle 300 RPS with relatively low latency

Tin(iv) dopant removal through anti-solvent engineering enabling tin based perovskite solar cells with high charge carrier mobilities

We report the need for careful selection of anti-solvents for Sn-based perovskite solar cells fabricated through the commonly used anti-solvent method, compared to their Pb-based counterparts.</p