Probabilistic Approach to Tank Design in Rainwater Harvesting Systems

Storage tanks from rainwater harvesting systems (RWHs) are designed to provide flow equalization between rainfall and water demand. The minimum storage capacity required to take into account the maximum variations of stored water volumes, i.e., the active storage, depends basically on the magnitude and the variability of rainfall profiles and the size of the demand. Given the random nature of the variables involved in the hydrological process, probability theory is a suitable technique for active storage estimation. This research proposes a probabilistic approach to determine an analytical expression for the cumulative distribution function (CDF) of the active storage as a function of rainfall moments, water demand and the mean number of consecutive storm events in a deficit sub-period. The equation can be used by developers to decide on the storage capacity required at a desired non-exceedance probability and under a preset water demand. The model is validated through a continuous simulation of the tank behavior using rainfall time series from Milan (Northern Italy)

Costs-benefit Analysis for the use of Shallow Groundwater as non-conventional Water Resource

Encouraging the implementation of non-conventional water resources (NCWR) is a fundamental strategy to face the future challenges due to urban population growth and resource scarcity. The implementation of a systematic process of Cost Benefit Analysis (CBA) offers reliable economic indicators to support decision makers in taking actions shifting towards NCWR. While infrastructure costs are directly estimated, while the benefits depend upon the considered stakeholders and require a tough estimation of the achieved ecosystem services. This research provides a framework for CBA analysis adopting NCWR at municipal level. The framework has been then applied to two case studies in Milan focused on the exploitation of shallow groundwater, where the obtained economic indicators has stressed out the importance of considering a complete benefits analysis that could support incentive policies on shifting part of the financial benefits to direct users leading to benefits for the whole community

Permeable Pavements Efficiency Under Clogging and Pollutants Load Removal

Permeable pavement is used to reduce stormwater volume, peak flow and promote pollutant load removal (Scholz and Grabowiecki, 2007, Brunetti et al., 2016, Marchioni and Becciu, 2014). The volume reduction depends on the base depth, while peak flow and loads removal are dependent on the surface layer, that must present a hydraulic conductivity capable of significantly limiting runoff; while the pore structure acts as a filter retaining particulate matter (PM), deriving from erosive phenomena and anthropogenic activities. The infiltration capacity tends to decrease over time due to the PM accumulation. An adequate maintenance program guarantees that hydraulic conductivity remains above a threshold. The infiltration capacity is a function of the characteristics of the material used in the surface layer, normally permeable concrete (PC), porous asphalt (PA) or interlocking concrete blocks. For this research a series of a rainfall simulation tests were used to analyze the hydrological and load removal response of permeable pavement surface under clogging. Results confirm the permeable efficiency under clogging on stormwater runoff reduction and pollutants removal

Likelihood Ratio Testing for Admixture Models with Application to Genetic Linkage Analysis

We consider likelihood ratio tests (LRT) and their modifications for homogeneity in admixture models. The admixture model is a special case of two component mixture model, where one component is indexed by an unknown parameter while the parameter value for the other component is known. It has been widely used in genetic linkage analysis under heterogeneity, in which the kernel distribution is binomial. For such models, it is long recognized that testing for homogeneity is nonstandard and the LRT statistic does not converge to a conventional 2 distribution. In this paper, we investigate the asymptotic behavior of the LRT for general admixture models and show that its limiting distribution is equivalent to the supremum of a squared Gaussian process. We also provide insights on the connection and comparison between LRT and alternative approaches in the literature, mostly modifications of LRT and score tests, including the modified or penalized LRT (Fu et al., 2006). The LRT is an omnibus test that is powerful against general alternative hypothesis. In contrast, alternative approaches may be slightly more powerful against certain type of alternatives, but much less powerful for other types. Our results are illustrated by simulation studies and an application to a genetic linkage study of schizophrenia

Olea europea L. Leaves and Hibiscus sabdariffa L. Petals Extracts: Herbal Mix from Cardiovascular Network Target to Gut Motility Dysfunction Application

It is well known that diet and nutrition play a critical role in the etiopathogenesis of many disorders. On the other hand, nutrients or bioactive compounds can specifically target and control various aspects of the mechanism underlying the pathology itself, and, in this context, diseases related to intestinal motility disorders stand out. The Herbal Mix (HM) consisting of Olea europea L. leaf (OEE) and Hibiscus sabdariffa L. (HSE) extracts (13:2) has been proven to be a promising nutraceutical option for many diseases, but its potential in inflammatory-driven gastrointestinal disorders is still unexplored. In this study, HM effects on guinea-pig ileum and colon contractility (induced or spontaneous) and on human iNOS activity, as well as on human colorectal adenocarcinoma Caco-2 cells, were studied. Results showed that the HM can control the ileum and colon contractility without blocking the progression of the food bolus, can selectively inhibit iNOS and possesses a strong pro-apoptotic activity towards Caco-2 cells. In conclusion, the present results suggest that, in some diseases, such as those related to motility disorders, an appropriate nutritional approach can be accompanied by a correct use of nutraceuticals that could help not only in ameliorating the symptoms but also in preventing more severe, cancer-related conditions

'Sifting the significance from the data' - the impact of high-throughput genomic technologies on human genetics and health care.

This report is of a round-table discussion held in Cardiff in September 2009 for Cesagen, a research centre within the Genomics Network of the UK's Economic and Social Research Council. The meeting was arranged to explore ideas as to the likely future course of human genomics. The achievements of genomics research were reviewed, and the likely constraints on the pace of future progress were explored. New knowledge is transforming biology and our understanding of evolution and human disease. The difficulties we face now concern the interpretation rather than the generation of new sequence data. Our understanding of gene-environment interaction is held back by our current primitive tools for measuring environmental factors, and in addition, there may be fundamental constraints on what can be known about these complex interactions.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Severe acute respiratory syndrome coronavirus 2 may exploit human transcription factors involved in retinoic acid and interferon-mediated response: a hypothesis supported by an in silico analysis

The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19), resulting in acute respiratory disease, is a worldwide emergency. Because recently it has been found that SARS-CoV is dependent on host transcription factors (TF) to express the viral genes, efforts are required to understand the molecular interplay between virus and host response. By bioinformatic analysis, we investigated human TF that can bind the SARS-CoV-2 sequence and can be involved in viral transcription. In particular, we analysed the key role of TF involved in interferon (IFN) response. We found that several TF could be induced by the IFN antiviral response, specifically some induced by IFN-stimulated gene factor 3 (ISGF3) and by unphosphorylated ISGF3, which were found to promote the transcription of several viral open reading frame. Moreover, we found 22 TF binding sites present only in the sequence of virus infecting humans but not bat coronavirus RaTG13. The 22 TF are involved in IFN, retinoic acid signalling and regulation of transcription by RNA polymerase II, thus facilitating its own replication cycle. This mechanism, by competition, may steal the human TF involved in these processes, explaining SARS-CoV-2's disruption of IFN-I signalling in host cells and the mechanism of the SARS retinoic acid depletion syndrome leading to the cytokine storm. We identified three TF binding sites present exclusively in the Brazilian SARS-CoV-2 P.1 variant that may explain the higher severity of the respiratory syndrome. These data shed light on SARS-CoV-2 dependence from the host transcription machinery associated with IFN response and strengthen our knowledge of the virus's transcription and replicative activity, thus paving the way for new targets for drug design and therapeutic approaches

Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design

As part of the Genetic Epidemiology Network of Arteriopathy study, hypertensive non-Hispanic White sibships were screened using 471 single nucleotide polymorphisms (SNPs) to identify genes influencing coronary artery calcification (CAC) measured by computed tomography. Individuals with detectable CAC and CAC quantity ≥70th age- and sex-specific percentile were classified as having a high CAC burden and compared to individuals with CAC quantity <70th percentile. Two sibs from each sibship were randomly chosen and divided into two data sets, each with 360 unrelated individuals. Within each data set, we applied two machine learning algorithms, Random Forests and RuleFit, to identify the best predictors of having high CAC burden among 17 risk factors and 471 SNPs. Using five-fold cross-validation, both methods had ∼70% sensitivity and ∼60% specificity. Prediction accuracies were significantly different from random predictions ( P -value<0.001) based on 1,000 permutation tests. Predictability of using 287 tagSNPs was as good as using all 471 SNPs. For Random Forests, among the top 50 predictors, the same eight tagSNPs and 15 risk factors were found in both data sets while eight tagSNPs and 12 risk factors were found in both data sets for RuleFit. Replicable effects of two tagSNPs (in genes GPR35 and NOS3 ) and 12 risk factors (age, body mass index, sex, serum glucose, high-density lipoprotein cholesterol, systolic blood pressure, cholesterol, homocysteine, triglycerides, fibrinogen, Lp(a) and low-density lipoprotein particle size) were identified by both methods. This study illustrates how machine learning methods can be used in sibships to identify important, replicable predictors of subclinical coronary atherosclerosis. Genet. Epidemiol . 2008. © 2008 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58570/1/20309_ftp.pd

SimHap GUI: An intuitive graphical user interface for genetic association analysis

<p>Abstract</p> <p>Background</p> <p>Researchers wishing to conduct genetic association analysis involving single nucleotide polymorphisms (SNPs) or haplotypes are often confronted with the lack of user-friendly graphical analysis tools, requiring sophisticated statistical and informatics expertise to perform relatively straightforward tasks. Tools, such as the <it>SimHap </it>package for the R statistics language, provide the necessary statistical operations to conduct sophisticated genetic analysis, but lacks a graphical user interface that allows anyone but a professional statistician to effectively utilise the tool.</p> <p>Results</p> <p>We have developed SimHap GUI, a cross-platform integrated graphical analysis tool for conducting epidemiological, single SNP and haplotype-based association analysis. SimHap GUI features a novel workflow interface that guides the user through each logical step of the analysis process, making it accessible to both novice and advanced users. This tool provides a seamless interface to the <it>SimHap </it>R package, while providing enhanced functionality such as sophisticated data checking, automated data conversion, and real-time estimations of haplotype simulation progress.</p> <p>Conclusion</p> <p>SimHap GUI provides a novel, easy-to-use, cross-platform solution for conducting a range of genetic and non-genetic association analyses. This provides a free alternative to commercial statistics packages that is specifically designed for genetic association analysis.</p