Identification of density and breeding places of Aedes mosquito and prevalence of dengue in Rajshahi city corporation of Bangladesh

Background: There are various varieties of habitats that have specific characteristics of water for the breeding of mosquito. A house-to-house cross-sectional entomological survey used to be carried out at per domestic area to become aware of larval breeding sites. Aedes aeygypti used to be primary vector and Aedes albopictus used to be predominant species in container-breeding habitats. Most breeding habitats have been category into excessive stage of larval density. Turbidity, pH, TOC, magnesium, calcium and sodium is amongst the characteristics that indicates a significant difference with larval density and species composition respectively. This study personal based entomological research and funding carried out by corresponding author. Students of zoology department of Rajshahi university involved in this research. Students were working as a research assistant for this study. Aim of this study was to assess determination of prevalence, density and breeding place of Aedes mosquito in Rajshahi city corporation. Methods: This observational study carried out 30 wards in Rajshahi city corporation areas have been surveyed in department of communicable diseases control, director general of health services, Dhaka, Bangladesh. Duration of study 3 years. Total 3 surveys were conducted in each year; pre monsoon, monsoon and post monsoon total 9 surveys conducted by this 3 years survey period. Data entered in MS excel and statistical analysis done by SPSS trial version. Results: This study shows that according to breeding area of 2020-2022. Here, total surveyed household were 8100. Total positive Wet container were 474 and positive place were 473 in these three years survey. Conclusions: Aedes aegypti and Aedes albopictus are properly established inside urban places. Meteorological variables additionally affected mosquito populations. Characteristics of mosquito breeding area can affect larval density and give impact quality of life

Enhancement of androgenesis by abiotic stress and other pretreatments in major crop species

Rapid production of doubled haploids (DHs) through androgenesis is an important and promising method for genetic improvement of crop plants. Through androgenesis complete homozygous plants can be produced within a year compared to long inbreeding methods that may take several years and costly. Significant advantage of androgenesis is that it not only speeds up the process to achieve homozygosity, but also increases the selection efficiency. Though success in androgenesis has been achieved in many crop plants, yet there are certain limitations especially, low frequency of embryogenesis and regeneration in few species. In fact in many cereals, induction of embryos and regeneration of green plants is still a hurdle that one needs to overcome to improve the efficiency of androgenesis. Efficient androgenesis is usually induced by the successful application of different stress pretreatment. Since so many stress factors can trigger the reprogramming of microspores and that have been co-related to change the ultrastuctural changes of cells to embryos and finally haploid plants. It has been shown that certain pretreatment such as (i) physical stresses as cold, heat shock, starvation, drought stress, osmotic pressure, gamma irradiation, oxidative stress, reduced atmospheric pressure, and (ii) chemical treatments such as colchicine, heavy metal, ABA, CGA, AEC, Azetidine, 2-NHA, either individual or combined effect of more than one stress factors may positively influence androgenetic efficiency. This review highlights the recent and past work on uses of various abiotic stresses and pretreatments and their impact on enhancing the efficiency of androgenesis on some major crop species for the development of doubled haploid plants

Application of Three Compounds Extracted from <i>Cynodon dactylon</i> against <i>Streptococcus mutans</i> Biofilm Formation to Prevent Oral Diseases

Streptococcus mutans bacteria form a biofilm called plaque that causes oral diseases, including tooth decay. Therefore, inhibition of biofilm formation is essential to maintaining good oral health. The health and nutritional benefits of Cynodon dactylon are well documented, but very little is known about its use to treat against oral diseases. The aim of this study was to detect the adhesion strength of the S. mutans bacterial biofilm in 100 cases in the Rajshahi region and evaluate the inhibitory activity of different compound extracts of C. dactylon on the S. mutans bacterial biofilm by determining the composition of isolated compounds using phytochemical analysis. Nuclear magnetic resonance (NMR) spectroscopy confirmed that three specific compounds from C. dactylon were discovered in this study: 3,7,11,15 tetramethyl hexadec-2-4dien 1-o1, compound 3,7,11,15 tetramethylhexadec-2-en-1-o1 from phytol derivatives, and stigmasterol. Results indicated that the compound of 3,7,11,15-tetramethyl-hexadec-2-en-1-ol exhibited higher antibiofilm activities on S. mutans than those of the other compound extracts. A lower level of minimum inhibitory concentration was exposed by 3, 7, 11,15 tetramethyl hexadeca-2-en-1-o1 (T2) on S. mutans at 12.5 mL. In this case, the compound of 3,7,11,15 tetramethyl hexadec 2en-1-o1 was used, and patients showed a mean value and standard error reduced from 3.42 ± 0.21 to 0.33 ± 0.06 nm. The maximum inhibition was (80.10%) in the case of patient no. 17, with a value of p S. mutans to which 12.5 μL/mL ethyl acetate extract was applied. From these findings, it may be concluded that C. dactylon extracts can be incorporated into various oral preparations to prevent tooth decay

<span style="font-size:11.0pt;font-family:"Times New Roman";mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:Mangal;mso-ansi-language:EN-US; mso-fareast-language:EN-US;mso-bidi-language:HI" lang="EN-US">Effect of cold pretreatment and different media in improving anther culture response in rice (<i>Oryza sativa </i>L.) in Bangladesh</span>

458-463In vitro production of doubled haploid (DH) plants through anther-culture provides an efficient and convenient system for rapid production of homozygous lines. Various pretreatments have been reported to influence callus induction and plantlet regeneration efficiency. In the present study, cold pretreated anthers <span style="mso-bidi-font-weight: bold">influenced the embryoid formation in five different induction media where hormonal combination was modified. Regeneration potential of 25 rice cultivars was assessed on the basis of anther response, embryo induction, plantlet regeneration and production of green and albino plantlets. Of 20 rice cultivars, embryoids were obtained from only five cultivars on media containing specific amino acids and different combination of phytohormones. IR43 produced maximum embryos (16.13%) and green plantlets (11.88%), followed by BRRI dhan33, IR54, Jaya and BR3 in SK3 medium. All the responding genotypes produced albinos in addition to the green plantlets. Cold pretreatment at 4°C for 3-7 d generated highest frequency of embryos and green plantlets. However, all the responding genotypes showed better induction from cold pretreated anthers in comparison to the control. 5-d (T3) cold pretreatment was found to be the most effective compared to other treatments on SK3 medium. </span

A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs

Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p-values or fold change (FC) values to detect the DE genes. However, sometimes, p-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values computed by SAM statistic using minimum &beta;-divergence method, which is designed for paired samples. Materials and Methods: The proposed method introduces a weight function known as &beta;-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The &beta;-weight function plays the significant role on the performance of the proposed method. The proposed method uses &beta;-weight function as a measure of outlier detection by setting &beta; = 0.2. We unify both classical and robust estimates using &beta;-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum &beta;-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan&ndash;Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations. Conclusions: Using pathway analysis, disease association study, protein&ndash;protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer

Metabolomic Biomarker Identification in Presence of Outliers and Missing Values

Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease

Robust Significance Analysis of Microarrays by Minimum β-Divergence Method

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition