Experimental evaluation of an advanced Space Shuttle main engine hot-gas manifold design concept

This study, using an extensively modified, full-scale space shuttle main engine (SSME) hot-gas manifold (HGM), established a detailed aerodynamic data base to support development of an advanced, three-dimensional, fluid-dynamic analysis computer model. In addition, the advanced SSME hot-gas manifold design used in this study demonstrated improved flow environment (uniformity) in the fuel side turbine exit and transfer duct exit regions. Major modifications were incorporated in the full-scale HGM flow test article model using two large transfer ducts on the fuel turbine side of the HGM in place of the three small transfer ducts in the present design. Other model features included an increases in the flow areas downstream of the 180-degree turn and in the fishbowl regions

The survey of Italian springs by the National Hydrographic Service, a forgotten database. Structuring and analysis of a dataset of Campania springs (southern Italy)|Il censimento delle sorgenti italiane del Servizio Idrografico, un database dimenticatoStrutturazione e analisi del dataset delle sorgenti della Campania (Italia meridionale)

The analysis of groundwater resources is a particularly significant aspect of the economic, social and environmental development of the national territory. This is particularly relevant for the Campania region which, although characterized by the most significant aquifer systems of southern Italy, suffers from critical issues related to the progressive increase in demand and climatic variability at different time scales. In this framework, the lack of data concerning the survey of springs, including the minor ones, and of historical discharge easurements represents the main limitation to a more comprehensive regional hydrogeological characterization. The only source of historical data regarding the systematic and comprehensive survey of springs and discharge measurement is the Publication No. 14 of the National Hydrographic Service of the Ministry of Public Works “The Italian springs. List and description” reporting measures made between the 1920s and 1940s which was published in distinct volumes for each compartment. Despite its potential relevance, this source has so far been little used in regional hydrogeological studies. In this paper, a comparative analysis among data of springs derived from the Publication No. 14 and from measurement campaigns made by the Cassa per il Mezzogiorno (Special Project 26), between the 1960s and 1980s for main springs, was carried out for the Campania region. The information available from each source was validated through a cross-check, by means of a comparison of coordinates and a statistical analysis of the characterizing parameters. The new dataset allowed to expand the hydrogeological regional characterization with a higher number of springs, including the minor ones. The results obtained recognize the Publication No. 14 of the National Hydrographic Service as an important source of data to not be overlooked, especially in a condition of historical data shortage, by which can be both carried out regional hydrogeological and temporal analyses as well as identified integrative groundwater resources

An acetylcholinesterase biosensor for determination of low concentrations of Paraoxon and Dichlorvos

The characterization of an economic and ease-to-use carbon paste acetylcholinesterase (AChE) based biosensor to determine the concentration of pesticides Paraoxon and Dichlorvos is discussed. AChE hydrolyses acetylthiocholine (ATCh) in thiocoline (TC) and acetic acid (AA). When AChE is immobilized into a paste carbon working electrode kept at +410mV vs. Ag/AgCl electrode, the enzyme reaction rate using acetylthiocholine chloride (ATCl) as substrate is monitored as a current intensity. Because Paraoxon and Dichlorvos inhibit the AChE reaction, the decrease of the current intensity, at fixed ATCl concentration, is a measure of their concentration. Linear calibration curves for Paraoxon and Dichlorvos determination have been obtained. The detection limits resulted to be 0.86ppb and 4.2ppb for Paraoxon and Dichlorvos, respectively, while the extension of the linear range was up 23ppb for the former pesticide and up to 33ppb for the latter. Because the inhibited enzyme can be reactivated when immediately treated with an oxime, the biosensor reactivation has been studied when 1,1'-trimethylene bis 4-formylpyridinium bromide dioxime (TMB-4) and pyridine 2-aldoxime methiodide (2-PAM) were used. TMB-4 resulted more effective. The comparison with the behavior of similar AChE based biosensors is also presented

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration