A RETROSPECTIVE STUDY ON THE INCIDENCE OF FISH DISEASES AND USE OF THERAPEUTANTS IN AQUACULTURE FARMS OF MOYNA, THE ‘FISHERIES HUB’ OF WEST BENGAL, INDIA

The present study depicts the disease incidences in the aqua farms of Moyna, West Bengal, India, during 2018- 2020 and aqua-drugs used by farmers to combat diseases. A total of 132 fish disease incidences were reported, among which parasitic diseases were the most common (53.03%) followed by bacterial diseases (27.27%), diseases due to poor nutrition and environmental fluctuations (10.61%), and fungal diseases (9.09%), respectively. Out of the 53.03% parasitic disease incidences reported, Argulus (22.86%), Dactylogyrus (17.14%), Gyrodactylus (10.00%), Myxospores (10.00%), Lernaea (8.57%), Ichthyophthirius (5.71%) and Trichodina (4.29%) were the major disease-causing parasites. Among bacteria, Pseudomonas spp. and Aeromonas spp. were the most dominant genera encountered in diseased fish. Labeo catla was the most susceptible fish species followed by Labeo rohita, and Cirrhinus mrigala. Seasonal influence in disease occurrence was noticed. Monsoon and winter were favorable seasons for disease outbreaks. The influence of water quality parameters like hardness, pH, ammonia, total dissolved solids of water, and total organic carbon of sediment had a significant correlation with parasite abundance. Farmers of Moyna were observed to use a wide range of chemicals and aqua-drugs to control diseases and related problems. The majority of the fish farms of Moyna were found using feed additives and supplements (32.00%) followed by sanitizers and disinfectants (24.00%), probiotics (17.00%), anti-parasitic drugs (11.00%), antibiotics (8.00%), and other chemicals (20.00%). Lime (calcium carbonate) and zeolite along with sodium chloride, potassium permanganate, formalin, and calcium hypochlorite were extensively used as disinfectants in Moyna. Farmers being unaware of the adverse consequences of using chemicals and aqua-medicines are fully dependent on private aquaculture consultants for time-to-time advice, which may have augmented their indiscriminate use. Initiative for the implementation of better management practices by creating awareness among farmers and adopting strict aquaculture policy guidelines might improve the scenario

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This is an institute funded project reportLatin hypercube designs is a popular choice of experimental design when computer simulation is used for studying a physical process as the design points of a Latin hypercube are equally spaced in the design region when projected onto univariate margins. In computer experiments, changing the levels of variables is only a matter of setting different numbers for the input, whereas in physical experiments, taking more levels of variables often requires an additional cost of making prototypes and a more elaborate and time-consuming implementation of the experiment. Therefore, the differences between computer experiments and traditional physical experiments call for different considerations in design and analysis methods for computer experiments. To handle such type of experimental situations Orthogonal Latin Hypercube design was introduced. Orthogonal Array (OA) designs are used extensively for planning experiments and their success is due to the uniformity properties but when a large number of factors are to be studied in an experiment and only a few of them are virtually effective, OA designs projected onto the subspace spanned by the effective factors can result in repetition of points on effective part only which is undesirable for physical experiments in which the bias of the proposed model is more serious than the variance. In this case LHD may be preferred. But the projection of such design points onto even bivariate margins cannot be guaranteed to be uniformly scattered. Thus to handle this situation Orthogonal arrays based Latin hypercube has been proposed which generally have better space filling properties than random Latin hypercube designs. In some experiments large and expensive computer code can be executed at various degrees of fidelity, and result in computer experiments with multiple levels of cost and accuracy. Efficient data collection from these experiments is critical. Nested designs are useful for designing such experiments. The main drawback of this approach to use LHD is that it requires imputation of some responses of the high-accuracy and low-accuracy experiments when the two sources are aligned together. To mitigate this difficulty, Nested orthogonal LHD has been defined. A general method of construction of Orthogonal Latin Hypercube Designs has been describe. The methods of construction deal with both first order and second order orthogonal Latin hypercube designs. Orthogonal and nearly orthogonal space filling Latin Hypercube Designs has been constructed by modifying the OLH designs. A construction methods of Nested Orthogonal Latin Hypercube (NOLH) Designs has been described. Two general methods of constructing nested orthogonal Latin hypercube designs have been developed. First method deals with 2 layers of NOLH and the second methods deals with three or more layers of NOLH. The methods give many new nested orthogonal Latin hypercube designs with fewer number of runs as compared to existing nested orthogonal Latin hypercube designs.Not Availabl

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Not AvailableRow-column designs are useful for the experimental situations in which there are two cross classified sources of heterogeneity in the experimental material. Often it is desired to compare two or more factors in row-column set up where only two units can be accommodated in a single column. In this article, a general method of construction has been developed to generate row-column designs for factorial experiments with two rows which permit orthogonal estimation of all main effects and specific two factor interactions as per the choice of experimenters.Not Availabl

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (<i>Glycine max</i> L.)

<div><p>Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case <i>vs</i>. control study. Based on this proposed approach, an R package, i.e., dhga (<a href="https://cran.r-project.org/web/packages/dhga" target="_blank">https://cran.r-project.org/web/packages/dhga</a>) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.</p></div

Decision matrix for differential hub gene analysis.

<p>Decision matrix for differential hub gene analysis.</p

Distribution of WGS in complete networks under stress and control conditions.

<p>The distributions of WGS of genes in GCNs for Al stress (A) and control (B) conditions in soybean are shown. The distributions of WGS of genes in GCNs for salinity stress (C) and control (D) conditions in rice are shown. For all these cases, the distributions are heavy tailed.</p

Clustering dendrogram of selected genes and gene modules under Al stress and control condition.

<p>The correspondence between Consensus Modules (CM) with modules under Stress (SM) (A) and control (NM) (B) conditions is represented.</p

Comparison of Boot-SVM-RFE with other competitive algorithms for different sliding window sizes.

<p>Comparison of Boot-SVM-RFE with other competitive algorithms for different sliding window sizes.</p

Functional enrichment analysis of selected genes and hub genes under Al stress.

<p>The GO term enrichment analysis of 981 selected informative genes (A) and hub genes (B) for Al stress condition using <i>Agrig</i>o is shown for different gene ontology categories (CC, MF and BP). For (A), the GO terms are chosen whose p-values < 0.008 and FDR values (false discovery rate) < 0.6. For (B), the GO terms are chosen whose p-values < 0.1 and FDR values < 0.8.</p

Comparison of proposed and existing approach in terms of predicted hub genes.

<p>Comparison of proposed and existing approach in terms of predicted hub genes.</p