Deducing water parameters in rivers via statistical modelling

Advanced monitoring of water quality in order to perform a real-time hazard analysis prior to Water Treatment Works (WTW) is more of a necessity nowadays, both to give warning of any contamination and also to avoid downtime of the WTW. Downtimes could be a major contributor to risk. Any serious accident will cause a significant loss in customer and investor confidence. This has challenged the industry to become more efficient, integrated and attractive, with benefits for its workforce and society as a whole. The reality is that water companies are not yet prepared to invest heavily in trials, before another company announces its success in implementing a new monitoring strategy. This has slowed down the development of the water industry. This research has taken the theoretical idea that the use of advanced online monitoring technique in the water industry would be beneficial and a step further; demonstrating by means of a state-of-the-art assessment, usability trials, case studies and demonstration that the barriers to mainstream adoption can be overcome. The findings of this work have been presented in four peer-reviewed papers. The research undertaken has shown that Turbidity levels in rivers can be measured from the rivers’ mean flow rate, using either Doppler Ultrasound device for real-time readings or based on past performance history. In both cases, the Turbidity level can also help estimate both the Colour and Conductivity levels of the subject river. Recalibration of the equations used is a prerequisite as each individual river has its own unique “finger print”

The impact of bad sensors on the water industry and possible alternatives

Advanced monitoring of water quality in order to perform a real-time hazard analysis prior to Water Treatment Works (WTW) is more important nowadays, both to give warning of contamination and also to avoid downtime of the WTW. Downtimes could be a major contributor to risk. Any serious accident will cause a significant loss in customer and investor confidence. In this paper, two treatment plants (case studies) were examined. One was a groundwater WTW and the other a river WTW. The results showed that good correlations existed between the controlling parameters measured at the river WTW, but not at the Groundwater Treatment Works (GWTW), where there was a lack of good correlation between warning parameters. Results emphasised the value of backup monitoring and self-adjusting automation processes that are needed to counteract the rise in power costs. The study showed that a relationship between the different types of sensors and/or measured parameters can be deduced in order to cross-check the sensors performance and be used as a guide to when maintenance is really needed. Operating hierarchal procedures within the WTWs could also be used to cut costs, by improving condition monitoring. Both of the case studies highlighted the need for new non-invasive/remote sensors and some new investment in information technology infrastructure

PhyloSort: a user-friendly phylogenetic sorting tool and its application to estimating the cyanobacterial contribution to the nuclear genome of -1

<p><b>Copyright information:</b></p><p>Taken from "PhyloSort: a user-friendly phylogenetic sorting tool and its application to estimating the cyanobacterial contribution to the nuclear genome of "</p><p>http://www.biomedcentral.com/1471-2148/8/6</p><p>BMC Evolutionary Biology 2008;8():6-6.</p><p>Published online 15 Jan 2008</p><p>PMCID:PMC2254586.</p><p></p> are performed: I. The paths from to , , and are (→ → → ), (→ y → → ), and (→ → ) respectively. II. The longest shared segment among the three paths is (→ ). III. The LCA of , , and is . IV. The subtree rooted by contains only , , and . V. , , and are monophyletic in the clade rooted by

PhyloSort: a user-friendly phylogenetic sorting tool and its application to estimating the cyanobacterial contribution to the nuclear genome of -0

<p><b>Copyright information:</b></p><p>Taken from "PhyloSort: a user-friendly phylogenetic sorting tool and its application to estimating the cyanobacterial contribution to the nuclear genome of "</p><p>http://www.biomedcentral.com/1471-2148/8/6</p><p>BMC Evolutionary Biology 2008;8():6-6.</p><p>Published online 15 Jan 2008</p><p>PMCID:PMC2254586.</p><p></p> are performed: I. The paths from to , , and are (→ → → ), (→ y → → ), and (→ → ) respectively. II. The longest shared segment among the three paths is (→ ). III. The LCA of , , and is . IV. The subtree rooted by contains only , , and . V. , , and are monophyletic in the clade rooted by

The blood DNA virome in 8,000 humans

<div><p>The characterization of the blood virome is important for the safety of blood-derived transfusion products, and for the identification of emerging pathogens. We explored non-human sequence data from whole-genome sequencing of blood from 8,240 individuals, none of whom were ascertained for any infectious disease. Viral sequences were extracted from the pool of sequence reads that did not map to the human reference genome. Analyses sifted through close to 1 Petabyte of sequence data and performed 0.5 trillion similarity searches. With a lower bound for identification of 2 viral genomes/100,000 cells, we mapped sequences to 94 different viruses, including sequences from 19 human DNA viruses, proviruses and RNA viruses (herpesviruses, anelloviruses, papillomaviruses, three polyomaviruses, adenovirus, HIV, HTLV, hepatitis B, hepatitis C, parvovirus B19, and influenza virus) in 42% of the study participants. Of possible relevance to transfusion medicine, we identified Merkel cell polyomavirus in 49 individuals, papillomavirus in blood of 13 individuals, parvovirus B19 in 6 individuals, and the presence of herpesvirus 8 in 3 individuals. The presence of DNA sequences from two RNA viruses was unexpected: Hepatitis C virus is revealing of an integration event, while the influenza virus sequence resulted from immunization with a DNA vaccine. Age, sex and ancestry contributed significantly to the prevalence of infection. The remaining 75 viruses mostly reflect extensive contamination of commercial reagents and from the environment. These technical problems represent a major challenge for the identification of novel human pathogens. Increasing availability of human whole-genome sequences will contribute substantial amounts of data on the composition of the normal and pathogenic human blood virome. Distinguishing contaminants from real human viruses is challenging.</p></div

Viral content.

<p>The heatmap shows the presence of reads of viral nature in sequencing reactions of blood from 8,240 individuals. Extensive phage and other viral DNA is found in sequencing reactions, but it is almost universally associated to including phiX174 phage spike-in in the reaction (used in 60% of samples). For reference, we include the ubiquitous identification of human endogenous retrovirus (HERVs) sequences in the pool of unmapped reads.</p

Study design.

<p>The flowchart summarizes the steps followed to identify viral content in the human blood DNA from whole-genome sequencing reads.</p

Detected human viruses in blood DNA of 8,240 individuals.

<p>Detected human viruses in blood DNA of 8,240 individuals.</p

Integration of human herpesvirus 6.

<p>The two populations of HHV6A andHHV6B are present in a bimodal distribution. The frequency of integrated viruses, at approximately 0.5 per cell corresponds to the haploid nature of the integration in the case of inherited, vertical transmission—from one of the parents. The identification of chimeric reads, or paired human-virus reads is shown for a substantial proportion of integrated HHV6 (green dots). The bar represents the median.</p

Relative proportion and viral load in the context of age, sex and ancestry.

<p>The relative proportion, normalized to 100% for visualization purposes (A, C and E) and distribution of observed viral loads (B, D and F) are depicted for the 8 viruses that have the largest prevalence in the study. Among the 4,505 with demographic information, the ancestries were: EUR, European = 3,048; AFR, African = 665; MDE, Middle Eastern = 94; EAS, East Asian = 91; CSA, Central South Asian = 54; AMR, Admixed American = 8; Multi-Racial and Others = 545.</p