Browsing by Author "Alam, Munirul"
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Item Drug resistance of clinical and environmental isolates of vibrio cholera 01(1991) Alam, Munirul; Khan, S.I.; Huq, AnwarulItem Occurrence and expression of luminescence in Vibrio cholerae(American Society for Microbiology, 2008-02) Grim, Christopher J.; Taviani, Elisa; Alam, Munirul; Huq, Anwar; Sack, R. Bradley; Colwell, Rita R.Item Occurrence of resistance to vibriostatic compound 0/129 in Vibrio cholerae 01 isolated from clinical and environmental samples in Bangladesh(1992) Huq, Anwarul; Alam, Munirul; Parveen, Salina; Colwell, R.R.Item Prevalence of fecal coliform in isolated ponds in a village-setting[short comminication](1991-12) Alam, Munirul; Khan, S.I.; Huq, AnwarulItem Psychosocial factors mediating the effect of the CHoBI7 intervention on handwashing with soap : a randomized controlled trial(2017) George, Christine Marie; Biswas, Shwapon; Jung, Danielle; Perin, Jamie; Parvin, Tahmina; Monira, Shirajum; Saif-Ur-Rahman, K.M.; Rashid, Mahamud-ur; Bhuyian, Sazzadul Islam; Thomas, Elizabeth D; Dreibelbis, Robert; Begum, Farzana; Zohura, Fatema; Zhang, Xiaotong; Sack, David A; Alam, Munirul; Sack, R. Bradley; Leontsini, Elli; Winch, Peter JItem Statistical Modeling for Genome Data Analysis to Detect Agricultural Biomarkers(University of Rajshahi, Rajshahi, 2019) Akond, Zobaer; Mollah, Md. Nurul Haque; Alam, MunirulThe focuses of this study were to evaluate the performance of different statistical methods from the perspective of various genomic data such as phenotypic-genotypic data, gene expression (microarray/RNA-Seq) data, SNP data and meta-genomic data collected from different environmental samples. We also performed some in silico analysis of RNA silencing machinery genes in wheat (Triticum aestivum) based on the RNAi genes of arabidopsis thaliana and expression profile analysis of seven TaDCL genes in leaves and roots as well as against drought stress using qRT-PCR. In Chapter Two, we explored better QTL mapping approach by comparative study. We found that Composite Interval Mapping (CIM) performs significantly better than the other four Simple Interval Mapping (SIM) methods in detecting QTL positions in backcross technique both on simulated data and on real rice genome dataset. In the case of real rice genome data analysis for backcross population, the CIM identified some vital positions that were not detected by the traditional SIM approaches.-----Item TnphoA mutants of Providencia alcalifaciens with altered invasiveness of HEp-2 cells(2002) Rahman, Motiur; Monira, Shirajum; Nahar, Shamsun; Ansaruzzaman, Mohammad; Alam, Khorshed; Alam, Munirul; Albert, M. John