DESIGNING A WATER QUALITY INDEX FOR KESBEWA LAKE

Water Quality Index (WQI) is a raung reflecting the composite influence on overall quality of anumber of individual quality parameters in a selected water body. The objective of this researchproject is to establish a comprehensive Water Quality Index (WQI) for the Kesbewa Lake byusing physical, chemical and microbiological water quality parameters, to identify the pollutionlevels. These results could be USI.;~( to maintain the quality of water, and conserve flora andfauna, and also to advise the individuals, organizations and funding bodies associated with thelake.The Water Quality Index is defined as, WQI =I WiQi where, WQI = Water Quality Index, anumber between 0 - 100, Qi == Quality ofthe ith parameter, Wi = The weighting factor of the ilbparameter, a number between 1 an 1'), such that, I Wi = 1 for n number of parameters, Wi = Xi IY, Xi = Points given by reference material data for ilb parameter, Y = The total points for nnumber of parameters.The raw analytical results for each parameter, having different units of measurements, aretransformed into unit less Qwalues by using the respective function of quality value of eachparameter in the Q-value graphs. These Q- value graphs are plotted with respect to the eachmeasured parameter value and their relative Q-values assigned by a points system.Based on the World Health Organization (WHO) guidelines, points system for the Q-values for aparameter is assigned and Central Environment Authority (CEA) standards were used, where theWHO guidelines are not available. The highest Q-value 100 is assigned for the best value of aparticular parameter that falls within the above guideline values. Separate ratings for Qualityvalues were given for drinking and bathing, irrigation water, and for fish and aquatic life. Thesethree Quality values were averaged to one value, which gives the Q-value for the measuredvalue of that particular parameter ill the Q-value graph. By using the functions of these graphs,the Q-value (Qi) for any measured value of ith parameter can be obtained.The weighting factor, (Wi) was determined by considering a large number of reference materialdata obtained from different water quality indices, in which the relative contribution of aparameter for the overall water quality has been weighted in different manner, according to thedifferent point of views of scientists. The Q-value (Qi) is then multiplied by the weighting factor(Wi) and resulted values of all n number of parameters are summed to yield the total value of theWQI.Twenty samples were collected from ten different locations of the lake over a period of eightmonths. According to the analyzed results, the prepared WQI for the Kesbewa lake gets ageneral ratings of 49.74, which falls, in the region of Bad in the water quality index. Incomparing the average values of measured parameters with WHO and Sri Lankan Standards, itcan be concluded that the water of Kesbewa Lake is relatively polluted and water quality shouldbe improved for aquatic life, irrigation and drinking and bathing purposes

Zooplankton Assemblage in Hambanota Port and Adjacent Coastal Waters of Sri Lanka

Present study was to investigate zooplankton assemblage in Hambantota port andadjacent coastal waters in Sri Lanka. Samples were collected from the port before thecommencement of commercial operations in order to have baseline information onzooplankton assemblage that can be used in the future to study any community change.Species composition, abundance, spatial distribution and diversity of zooplankton wereinvestigated over a period of six months from January 2011 to June 2011. Monthlysamples were collected from both inner-harbor and outer-harbor. Physico-chemicalparameters such as temperature, pH, salinity, density, conductivity, nitrate,orthophosphate, DO, BOD5 and Ch a were also measured. Zooplankton diversity, speciesrichness and evenness were calculated using Shannon-Weiner diversity index (Hˊ ),Simpson‟s index (D) and Pielou‟s evenness (E). A total of 72 zooplankton types wereidentified throughout the research project mainly Calanus sp., Paracalanus sp.,Sapphirina sp., Acartia tranteri, Barnacle nauplii, Crustacean cypris larvae, Oikopleurasp., Tunicate larvae, Brachionus calyciflorus calyciflorus, Brachionus forficula, Fishlarvae, Discorbis sp., Actinula larvae and Sagitta sp.. Copepod nauplii dominated thezooplankton. According to percentage occurrences arthropods (71%), protozoans (10%)and ichthyoplankton (9%) were abundant in the inner harbor. In the outer harbor alsoarthropods (66%), ichthyoplankton (11%) and protozoans (7%) were recorded in highernumbers. Highest species diversity (Hˊ =2.685), highest species richness (S=39) andhighest evenness (E=0.856) were recorded from outer harbor locations (HFHB1 andHPM). Several species such as Ceratium furca, Chaetoceros sp., Thalassiosira sp.,Rhizosolenia sp. and Protoperidinium sp. known to form harmful algal blooms were alsoobserved in this study.Key words: Zooplankton assemblage, Hambantota port, Ballast water, Invasive AlienSpecies (IAS

mGluR5 Regulates Glutamate-Dependent Development of the Mouse Somatosensory Cortex

We have previously reported that mGluR5 signaling via PLC-β1 regulates the development of whisker patterns within S1 (barrel) cortex of mice (Hannan et al., 2001). However, whether these defects arise from the loss of postsynaptic mGluR5 signaling, and whether the level of mGluR5 is important for barrel formation, was not examined. Furthermore, whether mGluR5 regulates other developmental processes that occur before or after barrel development is not known. We now show that mGluR5 is present postsynaptically at thalamocortical synapses during barrel formation. In addition, Mglur5(+/−) mice exhibit normal TCA patch formation but reduced cellular segregation in layer 4, indicating a dose-dependent role for mGluR5 in the regulation of pattern formation. Furthermore Mglur5(−/−) and Mglur5(+/−) mice display normal cortical arealization, layer formation, and size of PMBSF indicating the defects within S1 do not result from general abnormalities of cortical mapping during earlier stages of development. At P21 layer 4 neurons from Mglur5(−/−) and Mglur5(+/−) mice show a significant reduction in spine density but normal dendritic complexity compared with Mglur5(+/+) mice indicating a role in synaptogenesis during cortical development. Finally, mGluR5 regulates pattern formation throughout the trigeminal system of mice as the representation of the AS whiskers in the PrV, VpM, and S1 cortex was disrupted in Mglur5(−/−) mice. Together these data indicate a key role for mGluR5 at both early and late stages of neuronal development in the trigeminal system of mice

The molecular characterisation of Escherichia coli K1 isolated from neonatal nasogastric feeding tubes

Background: The most common cause of Gram-negative bacterial neonatal meningitis is E. coli K1. It has a mortality rate of 10–15%, and neurological sequelae in 30– 50% of cases. Infections can be attributable to nosocomial sources, however the pre-colonisation of enteral feeding tubes has not been considered as a specific risk factor. Methods: Thirty E. coli strains, which had been isolated in an earlier study, from the residual lumen liquid and biofilms of neonatal nasogastric feeding tubes were genotyped using pulsed-field gel electrophoresis, and 7-loci multilocus sequence typing. Potential pathogenicity and biofilm associated traits were determined using specific PCR probes, genome analysis, and in vitro tissue culture assays. Results: The E. coli strains clustered into five pulsotypes, which were genotyped as sequence types (ST) 95, 73, 127, 394 and 2076 (Achman scheme). The extra-intestinal pathogenic E. coli (ExPEC) phylogenetic group B2 ST95 serotype O1:K1:NM strains had been isolated over a 2 week period from 11 neonates who were on different feeding regimes. The E. coli K1 ST95 strains encoded for various virulence traits associated with neonatal meningitis and extracellular matrix formation. These strains attached and invaded intestinal, and both human and rat brain cell lines, and persisted for 48 h in U937 macrophages. E. coli STs 73, 394 and 2076 also persisted in macrophages and invaded Caco-2 and human brain cells, but only ST394 invaded rat brain cells. E. coli ST127 was notable as it did not invade any cell lines. Conclusions: Routes by which E. coli K1 can be disseminated within a neonatal intensive care unit are uncertain, however the colonisation of neonatal enteral feeding tubes may be one reservoir source which could constitute a serious health risk to neonates following ingestion

Characterizing the pathotype of neonatal meningitis causing <i>Escherichia coli</i> (NMEC)

Background Neonatal meningitis-causing Escherichia coli (NMEC) is the predominant Gram-negative bacterial pathogen associated with meningitis in newborn infants. High levels of heterogeneity and diversity have been observed in the repertoire of virulence traits and other characteristics among strains of NMEC making it difficult to define the NMEC pathotype. The objective of the present study was to identify genotypic and phenotypic characteristics of NMEC that can be used to distinguish them from commensal E. coli. Methods A total of 53 isolates of NMEC obtained from neonates with meningitis and 48 isolates of fecal E. coli obtained from healthy individuals (HFEC) were comparatively evaluated using five phenotypic (serotyping, serum bactericidal assay, biofilm assay, antimicorbial susceptibility testing, and in vitro cell invasion assay) and three genotypic (phylogrouping, virulence genotyping, and pulsed-field gel electrophoresis) methods. Results A majority (67.92 %) of NMEC belonged to B2 phylogenetic group whereas 59 % of HFEC belonged to groups A and D. Serotyping revealed that the most common O and H types present in NMEC tested were O1 (15 %), O8 (11.3 %), O18 (13.2 %), and H7 (25.3 %). In contrast, none of the HFEC tested belonged to O1 or O18 serogroups. The most common serogroup identified in HFEC was O8 (6.25 %). The virulence genotyping reflected that more than 70 % of NMEC carried kpsII, K1, neuC, iucC, sitA, and vat genes with only less than 27 % of HFEC possessing these genes. All NMEC and 79 % of HFEC tested were able to invade human cerebral microvascular endothelial cells. No statistically significant difference was observed in the serum resistance phenotype between NMEC and HFEC. The NMEC strains demonstrated a greater ability to form biofilms in Luria Bertani broth medium than did HFEC (79.2 % vs 39.9 %). Conclusion The results of our study demonstrated that virulence genotyping and phylogrouping may assist in defining the potential NMEC pathotype

Child with Deletion 9p Syndrome Presenting with Craniofacial Dysmorphism, Developmental Delay, and Multiple Congenital Malformations

A 4-month-old Sri Lankan male child case with a de novo terminal deletion in the p22 → pter region of chromosome 9 is described. The child presented with craniofacial dysmorphism, developmental delay, and congenital malformations in agreement with the consensus phenotype. A distinctive feature observed in this child was complete collapse of the left lung due to malformation of lung tissue. Cytogenetic studies confirmed terminal deletion of the short arm of chromosome 9 distal to band p22 [46,XY,del(9)(p22 → pter)]. This is the first reported case of a de novo deletion 9p syndrome associated with pulmonary hypoplasia. This finding contributes to the widening of the spectrum of phenotypic features associated with deletion 9p syndrome

Convergence of hippocampal pathophysiology in <i>Syngap^+/- </i>and <i>Fmr1</i>^<i>-/y</i>mice

Previous studies have hypothesized that diverse genetic causes of intellectual disability (ID) and autism spectrum disorders (ASDs) converge on common cellular pathways. Testing this hypothesis requires detailed phenotypic analyses of animal models with genetic mutations that accurately reflect those seen in the human condition (i.e., have structural validity) and which produce phenotypes that mirror ID/ASDs (i.e., have face validity). We show that SynGAP haploinsufficiency, which causes ID with co-occurring ASD in humans, mimics and occludes the synaptic pathophysiology associated with deletion of the Fmr1 gene. Syngap[superscript +/−] and Fmr1[superscript −/y] mice show increases in basal protein synthesis and metabotropic glutamate receptor (mGluR)-dependent long-term depression that, unlike in their wild-type controls, is independent of new protein synthesis. Basal levels of phosphorylated ERK1/2 are also elevated in Syngap[superscript +/−] hippocampal slices. Super-resolution microscopy reveals that Syngap[superscript +/−] and Fmr1[superscript −/y] mice show nanoscale alterations in dendritic spine morphology that predict an increase in biochemical compartmentalization. Finally, increased basal protein synthesis is rescued by negative regulators of the mGlu subtype 5 receptor and the Ras–ERK1/2 pathway, indicating that therapeutic interventions for fragile X syndrome may benefit patients with SYNGAP1 haploinsufficiency

Developing a utility index for the Aberrant Behavior Checklist (ABC-C) for fragile X syndrome

Purpose This study aimed to develop a utility index (the ABC-UI) from the Aberrant Behavior Checklist-Community (ABC-C), for use in quantifying the benefit of emerging treatments for fragile X syndrome (FXS). Methods The ABC-C is a proxy-completed assessment of behaviour and is a widely used measure in FXS. A subset of ABC-C items across seven dimensions was identified to include in health state descriptions. This item reduction process was based on item performance, factor analysis and Rasch analysis performed on an observational study dataset, and consultation with five clinical experts and a methodological expert. Dimensions were combined into health states using an orthogonal design and valued using time trade-off (TTO), with lead-time TTO methods used where TTO indicated a state valued as worse than dead. Preference weights were estimated using mean, individual level, ordinary least squares and random-effects maximum likelihood estimation [RE (MLE)] regression models. Results A representative sample of the UK general public (n = 349; mean age 35.8 years, 58.2 % female) each valued 12 health states. Mean observed values ranged from 0.92 to 0.16 for best to worst health states. The RE (MLE) model performed best based on number of significant coefficients and mean absolute error of 0.018. Mean utilities predicted by the model covered a similar range to that observed. Conclusions The ABC-UI estimates a wide range of utilities from patient-level FXS ABC-C data, allowing estimation of FXS health-related quality of life impact for economic evaluation from an established FXS clinical trial instrument