 intervention so that children with diarrhea that might have the tendency of leading to dehydration will not die.
“This also applies to many other diseases and it is a great initiative,” he said.
Dr Ezenyimulu said that the visit was to inform the commissioner about the flag-off of the CHIPS Programme in the state.
She explained that the programme was initiated by the National Healthcare Development Agency (NPHCDA) to address the identified gaps and inefficiencies observed in primary healthcare services at community levels.
She noted that the programme was premised on the assertion; `An Effective Primary Health Care System with a strong community level component is a Foundation for Achieving Universal Health Coverage.
“The programme will undertake the following, promote community participation, demand generation and conduct behavior change communication.
According to her, the CHIPS programme will be first implemented across seven local government areas namely; Aguata, Anambra East, Awka South, Anaocha, Ekwusigo, Ihiala and Ogbaru and 35 wards across some other selected LGAs in the state.
How's My Waterway
How's My Waterway provides information about the conditions of local streams, lakes and other waters anywhere in the U.S.
How's My Waterway provides useful public information based on data that states have provided to EPA on a two-year cycle. More recent or more detailed water information may exist that is not yet available through EPA databases or other sources.
Technical users may want to obtain the national or statewide mapped data, upon which How's My Waterway is based.
How's My Waterway developed mainly as a quick-reference non-technical service. The following downloadable documents may also be educational and informative:
- How's My Waterway Fact Sheet (PDF) (1 pg, 550 K, About PDF).
- Pollution Categories Summary Document (PDF) (20 pp, 557 K).
- How's My Waterway Questions and Answers (PDF) (2 pp, 274 K).
- How's My Waterway User Help (PDF) (4 pp, 453 K).Once I was a member of the Order of Dragonslayer, like Galanoth. It shouldn't matter if you're protecting the helpless from these creatures or hunting them for sport as long as you get the job done, but the Order disagrees. I was… asked… to leave but I use what they taught me to track and kill these creatures and I'm BEST at what I do.
Well done! I didn't think you had it in you, but the savage heart of a true Dragonhunter beats in your chest. I've used the remains of those animals to make you some trophies based on their own designs. It should let any other dragon know that you have claimed your first prize and you are looking for your next one! HAHA!
I knew you didn't have it in you to be a real Dragonhunter. Get out of here. Go talk to scaly friends inside the lair. I don't have anything else to say to you.
Note: Also see Duncan (Monster).
A proteome view of structural, functional, and taxonomic characteristics of major protein domain clusters

Proteome-scale bioinformatics research is increasingly conducted as the number of completely sequenced genomes increases, but analysis of protein domains (PDs) usually relies on similarity in their amino acid sequences and/or three-dimensional structures. Here, we present results from a bi-clustering analysis on presence/absence data for 6,580 unique PDs in 2,134 species with a sequenced genome, thus covering a complete set of proteins, for the three superkingdoms of life, Bacteria, Archaea, and Eukarya. Our analysis revealed eight distinctive PD clusters, which, following an analysis of enrichment of Gene Ontology functions and CATH classification of protein structures, were shown to exhibit structural and functional properties that are taxa-characteristic. For examples, the largest cluster is ubiquitous in all three superkingdoms, constituting a set of 1,472 persistent domains created early in evolution and retained in living organisms and characterized by basic cellular functions and ancient structural architectures, while an Archaea and Eukarya bi-superkingdom cluster suggests its PDs may have existed in the ancestor of the two superkingdoms, and others are single superkingdom- or taxa (e.g. Fungi)-specific. These results contribute to increase our appreciation of PD diversity and our knowledge of how PDs are used in species, yielding implications on species evolution.

Unlike the studies reported by Caetano-Anolles and colleagues 9 in which the abundance of PDs in species was coded into 21 alphabets and a parsimonious path was used to define the evolutionary history of PDs, the aim of the present work was to identify PD clusters that were defined by similarity of species usage and characterize these in terms of species-related protein function and structure. Previously, PDs have been clustered according to similarity in their three-dimensional (3D) structure in several structure classification databases, such as SCOP 18 and CATH 19, which have proven to be very useful for structural bioinformatics research. In contrast, we used the 3D classifications of CATH and the functional annotations of Gene Ontology (GO) 20 to characterize PD clusters that were defined by similarities of their species usage. Our results revealed several prominent PD clusters associated with specific classes of organisms, as well as characteristic 3D architectural design and molecular and cellular functions. Our results therefore provide new perspectives on the relationship between species usage and the structure and function of proteins.

Methods
Data. The taxonomical classifications of 2,134 species, each with a fully sequenced genome, were retrieved from UniProt 21. Note that these species did not include viruses. For these species, 6,580 unique PDs and the three categories of Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) of their functional annotation in Gene Ontolog (GO) 20 and the four levels of Class (C), Architecture (A), Topology (T), and Homologous superfamily (H) of their 3D structure classification by CATH 19 were downloaded from InterPro 22. Note that InterPro -an expert curated PD database that provides comprehensive and integrative annotations for PDs -was used here to avoid potentially conflicting PD definitions. No other criteria were used to select these species (genomes) and all the protein domains classified as "Domain" in InterPro v42.0 (2014 version) were used, as we aimed to include as much data as possible in our analysis.
Bi-clustering analysis. The information about the presence or absence of each of the 6,580 PDs in each of the 2,134 species yielded a 2,134 × 6,580 2D matrix. In this matrix, each of the 2,134 species rows is a horizontal vector of 6,580 elements, each of the 6,580 PD columns is a vertical vector of 2,134 elements, and every element in the matrix is either 1 or 0, representing, respectively, the presence or absence of a specific PD in a specific species.
Bi-clustering analysis (also known as co-clustering analysis) was then performed on the binary matrix using Generalized Association Plots (GAP), a tool for matrix visualization and clustering analysis 23. The Jaccard similarity coefficient 24 is used to compute the distance (i.e. similarity) between any two matrix rows (i.e. species) or between any two matrix columns (PDs), since this coefficient is especially suitable for measuring the similarity between sparse binary sets, which is the case in the PD-species matrix constructed here. During the clustering, rows of the matrix were moved up or down and columns moved left and right, such that similar rows (i.e. species with similar usage of PDs) and similar columns (PDs with similar patterns of species usage) would be clustered together as much as possible. This was achieved by transforming the PD-species matrix into a symmetric Robinson correlation matrix 25 and optimizing it using an Elliptical seriation scheme 23, which is very effective in identifying global and transitional clustering patterns 26. Using a cutoff for the Jaccard similarity coefficient of 0.5, optimization resulted in eight PD clusters, which covered 4,019 PDs (61%), leaving 2,561 PDs (39%) scattered around the matrix that could not be assigned to any of the eight clusters without lowering the cutoff. The cutoff of 0.5 was chosen such that one of the 8 resultant PD clusters would cover as many as possible of the PDs encoded in a set of persistent genes reported by Acevedo-Rocha et al. 27.
GO and CATH analysis. An enrichment test based on the hyper-geometric distribution of GO terms 28 was carried out for each of the eight PD clusters derived from the bi-clustering analysis. The enriched GO terms (p value < 0.05) in a studied PD cluster were then fed into the REViGO program 29 to retrieve a set of representative GO terms that could be visualized using GO TermLogo (http://www.wordle.net/) as well as in a concentric GO pie showing their composition of the three GO categories (BP, CC, and MF). For the CATH structural analysis, we drew a CATH pie 19 to display, within a given PD cluster, the percentage of PDs with a given structural category on each of the four levels of the CATH classification.

Results
Overview of the bi-clustering analysis results. Figure 1 is a standard GAP output of the bi-clustering analysis results. The bottom left panel shows the final PD-species presence/absence matrix after rearranging the rows and columns of the matrix to maximize the optimization score, the top panel is a heat map of the resulting PD-