Borehole depth and regolith aquifer hydraulic characteristics of bedrock types in Kano area, Northern Nigeria

In this study, the performance of regolith aquifers derived from the different bedrock types wasexamined using information on depth of borehole, depth to the static water level, yield of borehole and drawdown in 259 boreholes covering the different bedrock types. Results show that mean depth of wells varies from about 37 to 48 m in rocks of the Younger Granites and in the migmatite-gneiss complex and schists, respectively. Static water level ranges from about 9 to 18 m in regolith of the Younger Granites and that of the migmatite-gneiss complex and schists, respectively. Yield varies from about 28 to 44 L/min in the migmatite-gniess complex and schist and in the porphyritic granite, respectively. Drawdown varies from about 6 to 9 m in the regolith of the Younger Granite and in that of the migmatite-gneiss complex and schist, respectively. Mean values of specific capacity varies from about 9 to 11 m²/d while transmissivity ranges from about 16 to 20 m²/d. The least values are found with the regolith of the migmatite-gneiss complex and schists while the highest values are found in aquifers of the porphyritic granite. Results further indicate that aquifers derived from rocks of granitic composition tend to exhibit similar hydraulic characteristics. But the poor performance of regolith of the migmatite-gneiss complex and schists (Younger Metasediments) is ascribed to their texture and structure.Key words: Kano, regolith aquifer, Younger Metasediments, groundwater, yield, transmissivity, basement complex

MicroRNA-155 Deficiency Attenuates Liver Steatosis and Fibrosis without Reducing Inflammation in a Mouse Model of Steatohepatitis

BACKGROUND & AIM: MicroRNAs (miRs) regulate hepatic steatosis, inflammation and fibrosis. Fibrosis is the consequence of chronic tissue damage and inflammation. We hypothesized that deficiency of miR-155, a master regulator of inflammation, attenuates steatohepatitis and fibrosis. METHODS: Wild type (WT) and miR-155-deficient (KO) mice were fed methionine-choline-deficient (MCD) or -supplemented (MCS) control diet for 5 weeks. Liver injury, inflammation, steatosis and fibrosis were assessed. RESULTS: MCD diet resulted in steatohepatitis and increased miR-155 expression in total liver, hepatocytes and Kupffer cells. Steatosis and expression of genes involved in fatty acid metabolism were attenuated in miR-155 KO mice after MCD feeding. In contrast, miR-155 deficiency failed to attenuate inflammatory cell infiltration, nuclear factor kappa beta (NF-kappaB) activation and enhanced the expression of the pro-inflammatory cytokines tumor necrosis factor alpha (TNFalpha) and monocyte chemoattractant protein-1 (MCP1) in MCD diet-fed mice. We found a significant attenuation of apoptosis (cleaved caspase-3) and reduction in collagen and alpha smooth muscle actin (alphaSMA) levels in miR-155 KO mice compared to WTs on MCD diet. In addition, we found attenuation of platelet derived growth factor (PDGF), a pro-fibrotic cytokine; SMAD family member 3 (Smad3), a protein involved in transforming growth factor-beta (TGFbeta) signal transduction and vimentin, a mesenchymal marker and indirect indicator of epithelial-to-mesenchymal transition (EMT) in miR-155 KO mice. Nuclear binding of CCAAT enhancer binding protein beta (C/EBPbeta) a miR-155 target involved in EMT was significantly increased in miR-155 KO compared to WT mice. CONCLUSIONS: Our novel data demonstrate that miR-155 deficiency can reduce steatosis and fibrosis without decreasing inflammation in steatohepatitis

World society of emergency surgery study group initiative on Timing of Acute Care Surgery classification (TACS).

Timing of surgical intervention is critical for outcomes of patients diagnosed with surgical emergencies. Facing the challenge of multiple patients requiring emergency surgery, or of limited resource availability, the acute care surgeon must triage patients according to their disease process and physiological state. Emergency operations from all surgical disciplines should be scheduled by an agreed time frame that is based on accumulated data of outcomes related to time elapsed from diagnosis to surgery. Although literature exists regarding the optimal timing of various surgical interventions, implementation of protocols for triage of surgical emergencies is lacking. For institutions of a repetitive triage mechanism, further discussion on optimal timing of surgery in diverse surgical emergencies should be encouraged. Standardizing timing of interventions in surgical emergencies will promote clinical investigation as well as a commitment by administrative authorities to proper operating theater provision for acute care surgery

Structure Discovery in Mixed Order Hyper Networks

Background  Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human readability that networks with hidden units lack. They can be used for regression, classification or as content addressable memories and have been shown to be useful as fitness function models in constraint satisfaction tasks. They are fast to train and, when their structure is fixed, do not suffer from local minima in the cost function during training. However, their main drawback is that the correct structure (which neurons to connect with weights) must be discovered from data and an exhaustive search is not possible for networks of over around 30 inputs.  Results  This paper presents an algorithm designed to discover a set of weights that satisfy the joint constraints of low training error and a parsimonious model. The combined structure discovery and weight learning process was found to be faster, more accurate and have less variance than training an MLP.  Conclusions  There are a number of advantages to using higher order weights rather than hidden units in a neural network but discovering the correct structure for those weights can be challenging. With the method proposed in this paper, the use of high order networks becomes tractable

Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

hSAGEing: An Improved SAGE-Based Software for Identification of Human Tissue-Specific or Common Tumor Markers and Suppressors

SAGE (serial analysis of gene expression) is a powerful method of analyzing gene expression for the entire transcriptome. There are currently many well-developed SAGE tools. However, the cross-comparison of different tissues is seldom addressed, thus limiting the identification of common- and tissue-specific tumor markers.To improve the SAGE mining methods, we propose a novel function for cross-tissue comparison of SAGE data by combining the mathematical set theory and logic with a unique “multi-pool method” that analyzes multiple pools of pair-wise case controls individually. When all the settings are in “inclusion”, the common SAGE tag sequences are mined. When one tissue type is in “inclusion” and the other types of tissues are not in “inclusion”, the selected tissue-specific SAGE tag sequences are generated. They are displayed in tags-per-million (TPM) and fold values, as well as visually displayed in four kinds of scales in a color gradient pattern. In the fold visualization display, the top scores of the SAGE tag sequences are provided, along with cluster plots. A user-defined matrix file is designed for cross-tissue comparison by selecting libraries from publically available databases or user-defined libraries

Targeted Deletion of Neuropeptide Y (NPY) Modulates Experimental Colitis

Neurogenic inflammation plays a major role in the pathogenesis of inflammatory bowel disease (IBD). We examined the role of neuropeptide Y (NPY) and neuronal nitric oxide synthase (nNOS) in modulating colitis.Colitis was induced by administration of dextran sodium sulphate (3% DSS) or streptomycin pre-treated Salmonella typhimurium (S.T.) in wild type (WT) and NPY (NPY(-/-)) knockout mice. Colitis was assessed by clinical score, histological score and myeloperoxidase activity. NPY and nNOS expression was assessed by immunostaining. Oxidative stress was assessed by measuring catalase activity, glutathione and nitrite levels. Colonic motility was assessed by isometric muscle recording in WT and DSS-treated mice.DSS/S.T. induced an increase in enteric neuronal NPY and nNOS expression in WT mice. WT mice were more susceptible to inflammation compared to NPY(-/-) as indicated by higher clinical & histological scores, and myeloperoxidase (MPO) activity (p<0.01). DSS-WT mice had increased nitrite, decreased glutathione (GSH) levels and increased catalase activity indicating more oxidative stress. The lower histological scores, MPO and chemokine KC in S.T.-treated nNOS(-/-) and NPY(-/-)/nNOS(-/-) mice supported the finding that loss of NPY-induced nNOS attenuated inflammation. The inflammation resulted in chronic impairment of colonic motility in DSS-WT mice. NPY -treated rat enteric neurons in vitro exhibited increased nitrite and TNF-alpha production.NPY mediated increase in nNOS is a determinant of oxidative stress and subsequent inflammation. Our study highlights the role of neuronal NPY and nNOS as mediators of inflammatory processes in IBD

A resampling-based meta-analysis for detection of differential gene expression in breast cancer

<p>Abstract</p> <p>Background</p> <p>Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures.</p> <p>Methods</p> <p>A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis.</p> <p>Results</p> <p>The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results.</p> <p>Conclusion</p> <p>The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development.</p

Rare Variant Analysis of Human and Rodent Obesity Genes in Individuals with Severe Childhood Obesity

Obesity is a genetically heterogeneous disorder. Using targeted and whole-exome sequencing, we studied 32 human and 87 rodent obesity genes in 2,548 severely obese children and 1,117 controls. We identified 52 variants contributing to obesity in 2% of cases including multiple novel variants in GNAS, which were sometimes found with accelerated growth rather than short stature as described previously. Nominally significant associations were found for rare functional variants in BBS1, BBS9, GNAS, MKKS, CLOCK and ANGPTL6. The p.S284X variant in ANGPTL6 drives the association signal (rs201622589, MAF∼0.1%, odds ratio = 10.13, p-value = 0.042) and results in complete loss of secretion in cells. Further analysis including additional case-control studies and population controls (N = 260,642) did not support association of this variant with obesity (odds ratio = 2.34, p-value = 2.59 × 10 -3 ), highlighting the challenges of testing rare variant associations and the need for very large sample sizes. Further validation in cohorts with severe obesity and engineering the variants in model organisms will be needed to explore whether human variants in ANGPTL6 and other genes that lead to obesity when deleted in mice, do contribute to obesity. Such studies may yield druggable targets for weight loss therapies