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Differences in paths of high were thus mined in an unbiased fashion by identifying shortest paths with least path costs. Ten condition-specific networks were constructed, representative of five TB and five healthy conditions. In each of these networks, the set of paths representing highest levels of activities were first identified, which were then used to find active paths of highest difference in TB compared to their corresponding controls. A total of 94,, all-vs. The path cost formulation was devised such that paths with the least cost were considered to be most active, expected to contain highly expressed genes.

A percentile-based ranking was adopted to rank paths, with lower cost paths attaining higher ranks. Two thresholds were considered to represent tiers of activity—paths in the While paths below this threshold could still be significant and their exclusion may result in the elimination of important genes, for purposes of identifying the most significant responses, we lay emphasis on the high activity paths alone, thereby erring on the side of caution. These unique highest activity paths in TB are now representative of the most active difference responses occurring in patients.

Interestingly, we observe that the Tier-1 response networks for each TB dataset comprises a well-connected subnetwork of the hPPiN, implying that the differences observed are interrelated in some sense and possibly lead to a concerted set of variations as a collective response to disease. Pooling the individual TB response networks by taking a union of the Tier-1 activities for individual datasets reveals the nature of the overlap among different datasets.

The network topology is largely dependent on the degree or connectivity of each node, and nodes with a higher degree will be situated towards the centre of the network. As observed in Fig. The nodes that are present in all five response networks red are seen to be localised at the centre of the cumulative network, forming interconnected edges.

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The topological architecture of the pooled network thus points to a central core subnetwork of genes that is likely to drive the host responses in active tuberculosis, independent of the population cohort analysed. It is thus apparent that the DEGs in different datasets, although not identical, belong to common neighbourhoods in interaction networks. Analysis of such a subnetwork would thus lead to deeper insights into the primary processes that are regulated in the host in TB.

To identify the individual interactions driving such similar processes across datasets, the computed shortest paths for individual tier-1 TB response networks were compared, and a set of common pathways was identified.

These paths constitute an interaction network comprising nodes and edges, which we refer to as the common core. The common core is seen to be largely interconnected with only a few sparse edges, implying extensive cross-talk across multiple nodes that contribute to the molecular response in TB.

To assess the probability of emergence of the common core out of chance, a permutation test was carried out as described in the Methods. As seen in Fig. Response networks can thus capture common regulatory mechanisms across datasets encompassing multiple populations.

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The most enriched processes in the common core have been shortlisted in Fig. Subsequently, the activation of anti-inflammatory processes mediated by cytokines IL-4 and TFG-beta is also observed.

Complement and coagulation cascades are seen to be at play along with other signalling processes such as Kit receptor signalling and Notch Signalling pathways, as well as natural killer cells-mediated cytotoxicity, characteristic of tubercular infection. Cytoskeletal remodelling is actively observed, and can be attributed to structural changes in the cell during phagocytosis of Mtb along with leucocyte endothelial migration involved in the activation of T cell responses by chemokines secreted from macrophages and dendritic cells towards lymph nodes.

While signalling processes are highly activated, host lipid signalling and metabolic processes are conspicuously absent in the common core. DAZAP2 is known to be influential in mediating Wnt signalling, and also participates in multiple signalling pathways 38 including interactions with TGF-beta, a cytokine known to play a central role in curtailing inflammatory responses in TB.

A C1QB-centred subnetwork highlights the activation of complement signalling. While the common core consists of only genes, these genes show a similar functional enrichment to that of the individual response networks containing significantly larger number of genes, implying that it is this common core that predominantly drives the relevant processes in the host in TB.

To identify additional processes in the host, the tier-2 paths were compared across individual response networks, revealing an overlap of paths. These paths constituted a network of genes and edges Fig. The topmost enriched KEGG pathways in each subnetwork around these hub nodes are also illustrated.

The STAT-1 centric responses are retained at Tier 2 and the emergence of other well connected hubs such as MAPK1 and SP1 is also observed, encompassing myriad signalling processes and their crosstalk across multiple cell and tissue types, captured in the whole blood milieu.

Genes reported to have SNPs in different studies ascribing susceptibility to tuberculosis are marked in red in this network Full size image While transcriptomic changes provide insights into variations in gene expression, inspecting genetic polymorphisms reported by single nucleotide polymorphism SNP studies and genome-wide association studies in multiple populations in the context of response networks could depict how changes at the gene level are carried forward to result in variations in expression.

Genes with genetic polymorphisms implicated with increased susceptibility to tuberculosis reported in literature 12 and from the Online Mendelian Inheritance in Man database 39 were enlisted, and 30 of these genes were seen to occur in the common core.

The common core can sufficiently distinguish between diseased and healthy samples To investigate if the common core was sufficiently characteristic of TB relative to healthy controls, we carried out a principle component analysis using the expression values of the genes constituting the common core from individual samples across all five datasets chosen for the meta-analysis. This analysis serves to demonstrate that the common core can discriminate between the two conditions, laying further emphasis on its specificity to active disease.

Response networks were constructed from whole blood samples capturing expression profiles from patients subjected to standard anti-tubercular therapy. Analysis of these response networks revealed a change in the network topology within 2 weeks of treatment Fig. Instead, IL2-mediated responses emerge as a significant hub, and the type 1 interferon responses are retained.

The hub nodes occurring at different time points are highlighted b Subnetwork of the common core lost gradually over 6 months of treatment c Subnetwork emerging post 6 months of treatment indicating possible end points of therapy Full size image A common repressed network highlights processes that are downregulated in disease To determine the network of processes that are downregulated in TB, top-repressed networks were also constructed by considering all those paths that were downregulated in disease as compared to the controls, and a common repressed network was subsequently identified.

This common repressed network is constituted by genes, with interactions between them. Contrary to the common core, this downregulated network is largely disconnected, with distinct modules forming around certain hub genes, as depicted in Supplementary Fig.

The genes PAK6 and CRIP2, which are present in this repressed network are also seen to emerge in the active response networks post anti-tubercular treatment, further strengthening the finding that these are indeed infection-induced downregulations which respond to treatment. Biological processes affected by these genes include Wnt signalling, Hedgehog signalling, signalling mediated by G-protein coupled receptors, cAMP signalling pathway, regulation of lipolysis, fatty acid omega oxidation, PPAR signalling pathway and tryptophan metabolism in the host, among others, as depicted in Fig.

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Validation of the common core In order to validate the common core, we performed the following analyses— a analysis of transcriptome data from a fresh Indian cohort of TB patients and healthy controls to test whether the common core is consistent in these samples and b assessing the specificity of the core compared to other pathologically similar diseases. Comparison with a fresh cohort To validate the significance and reproducibility of the common core, we sampled microarray data on an independent dataset from the Indian population.

Whole blood samples were taken from five TB patients and two healthy controls, meeting the inclusion and exclusion criteria as described in the methods. Response networks were constructed and the subsequent high activity shortest paths and networks were analysed at Tier-1 and Tier2 to assess the extent of overlap of the common core in this dataset.

Analysis of the shortest paths at Tier-1 revealed an overlap of paths with the Tier-1 paths constituting the common core, and a significant subnetwork of genes out of the genes in the common core was reproduced in this response network, and was seen to adopt a similar network topology as that of the common core, centred around STAT1.

Relaxing the threshold to tier-2 also showed an overlap of paths out of the tier-2 common paths generated by the meta-analysis. Such similarities and reproducibility of the core serve to further strengthen the significance of the approach.

Supplementary Fig. S2 highlights the overlap between the genes in the common core and the corresponding tier-1 response network generated for this dataset. Specificity of the core Several inflammatory diseases report a phenotype similar to tuberculosis with a marked inflammatory response, further impeding diagnosis of TB.

Tier 1 comparisons of the OD response networks with the paths constituting the common core show little to no overlap, indicating that the common core is a largely specific TB response.

Interestingly, while STAT1 also emerges in the Tier-1 network in pneumonia, it makes a different set of interactions in pneumonia as compared to tuberculosis.

Since it is the set of specific routes that constitute the top network for each condition, we focused on the similarities in these networks generated by common paths instead of the common nodes among the conditions. Further, additional similarities such as between the core and OD networks may emerge at lower thresholds, for purposes of analysis only the Tier-1 comparisons were considered, indicating specificity in the processes of highest activity in TB.

Discussion For several infectious diseases including TB there now exists extensive transcriptome data generated from individuals suffering from the same condition in geographically distant locations or in diverse cohorts and settings. A DEG-centric approach alone may not necessarily be suggestive of disease pathology nor can it reveal general patterns of variation in the system.

A network approach is useful to probe if the different sets of DEGs ultimately culminate in the modulation of the same functional modules. Monitoring the variations in the interacting partners of DEGs in the context of their interaction networks would facilitate such an analysis. Large scale networks typically used for studying biological systems can be broadly classified into two types—gene expression correlation networks and protein—protein interaction networks.

The former, which is more frequently represented in current literature, reflect associations between gene-pairs whose expression patterns are correlated. The latter, on the other hand capture interactions between proteins that can lead to deciphering flows, of which biochemical or signalling pathways form classic examples.

Approaches such as Weighted Gene Co-expression Network Analysis 43 show the commonalities in modules between the different datasets but rely on networks based on co-expression patterns. Thanx immensely for posting them all in a single page. Thanks for uploading this collection. I love to read detective and horror books.

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Thanks guys… This is a wonderful post… Thanks a lot again… I just love to have it and love you guys…. Volume 7 of Sharadindu Omnibus is corrupt. The pdf can not be read beyond page Please re-upload the fixed pdf. Aapnader ei website ti khubi bhalo. Aapander book list er kono catalogue ache? Tahole kindly pathiye din.

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Aar ebhabei kaj chaliye jan. Your email address will not be published. Notify me of follow-up comments by email. Notify me of new posts by email.Guidelines for determining the hand-carried transmitters. Validation of the common core In order to validate the common core, we performed the following analyses— a analysis of transcriptome data from a fresh Indian cohort of TB patients and healthy controls to test whether the common core is consistent in these samples and b assessing the specificity of the core compared to other pathologically similar diseases.

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Amal Paul Dr. Gargi Dutta Dr. While signalling processes are highly activated, host lipid signalling and metabolic processes are conspicuously absent in the common core. We focus on the tetraloop motif, and demonstrate increased frequencies, new contexts, unexpected lengths and novel topologies.

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