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Conservation – A Strategy to Overcome Shortages of Ayurveda Herbs
Published in D. Suresh Kumar, Ayurveda in the New Millennium, 2020
S. Noorunnisa Begum, K. Ravikumar
The forestry and ecology division of Indian Institute of Remote Sensing, Dehradun, developed a methodology for rapid assessment of biodiversity, encompassing large natural vegetation areas in India using a three-pronged approach (Singh and Kushwaha 2008). The technique makes use of satellite imagery to generate homogeneous vegetation strata and landscape analysis. Landscape parameters like fragmentation, patchiness (Romme 1982), porosity (Forman and Godron 1986), interspersion (Lyon 1983), juxtaposition (Lyon 1983) and proximity of the vegetation patch to biotic disturbance features, such as roads, railways and settlements, are then considered to derive the disturbance index. This is followed by field assessment by the Shannon-Wiener index of diversity in different vegetation strata and evaluation of the vegetation community for its uniqueness and determination of its biodiversity value following Belal and Springuel (1996).
Response of Benthic Biodiversity to Climate-Sensitive Regional and Local Conditions in a Complex Estuarine System
Published in Vyacheslav Lyubchich, Yulia R. Gel, K. Halimeda Kilbourne, Thomas J. Miller, Nathaniel K. Newlands, Adam B. Smith, Evaluating Climate Change Impacts, 2020
Ryan J. Woodland, Jeremy M. Testa
The three biodiversity response metrics we considered were total species richness, and two biodiversity indices (Shannon–Wiener index, Simpson's Index of Diversity). Species richness (S) is the total number of unique species encountered per site. The Shannon–Wiener index (H′) is calculated as: where pi is the proportional abundance of species i in a given sample (Pielou, 1974). Simpson's Index of Diversity (D) is calculated as , where p and i are defined as for H′ (Pielou, 1974). As formulated, each of these indices will increase as the number of species encountered increases and, in the case of H′ and D, as the relative abundances of species in a sample become more even. Readers are referred to any basic ecology text for additional information on these and other biodiversity indices (e.g., Pielou, 1974; Begon et al, 2006).
Endophytic Fungal Diversity in Selected Medicinal Plants of Western Ghats of India
Published in Jayanta Kumar Patra, Gitishree Das, Sanjeet Kumar, Hrudayanath Thatoi, Ethnopharmacology and Biodiversity of Medicinal Plants, 2019
Fazilath Uzma, Srinivas Chowdappa
Out of the 80 segments incubated, we obtained 20 endophytic fungal isolates from T. cordifolia (5 from leaves, 3 from petiole, 6 from stem and 6 from roots) were clustered into 9 genera, of which Aspergillus sp., Cladosporium sp., Penicillium sp. and Trichoderma sp. occurred with high frequency (2.29%), followed by Alternaria sp., Curvularia sp., and Fusarium sp. (1.52%) which had low frequency of occurrence and found in only one tissue type. The colonization rate is higher in stem and root tissues (30%) than in the other tissues (Table 6.1). The species richness of the fungal isolates was greater in stem and roots (6) compared to the leaf and petiole segments produced five and three endophytic species respectively (Table 6.2). The diversity of the endophytic isolates associated with different tissue segments of T. cordifolia was compared using diversity indices. Simpson’s diversity indices and Shannon-Wiener index was higher in stem and root tissues. The species evenness revealed minute differences amongst the different tissue types studied (Table 6.3).
Effects of garlic polysaccharide on alcoholic liver fibrosis and intestinal microflora in mice
Published in Pharmaceutical Biology, 2018
Yuchuan Wang, Min Guan, Xin Zhao, Xinli Li
The statistical software SPSS version 17.0 (SPSS Inc., Chicago, IL) was used for analysis. p Values were determined using the Student’s t-test, p value < 0.01 was considered significant. DGGE and Western blotting gels were analysed by using Quantity One 4.6.2 gel analysis software (Bio-Rad, Hercules, CA). Similarities were displayed graphically as a dendrogram. The clustering algorithms used to calculate the dendrograms were an unweighted pair group method with arithmetic average (UPGMA). The Shannon-Wiener index of diversity (H′) was used to determine the diversity of the bacterial community. The evenness (E) which reflected uniformity of bacterial species distribution was also computed.
Time-spatial analysis of T cell receptor repertoire in esophageal squamous cell carcinoma patients treated with combined radiotherapy and PD-1 blockade
Published in OncoImmunology, 2022
Cihui Yan, Xiaoxue Ma, Zhoubo Guo, Xiaoying Wei, Dong Han, Tian Zhang, Xi Chen, Fuliang Cao, Jie Dong, Gang Zhao, Xuan Gao, Tao Wang, Yao Jiang, Ping Wang, Qingsong Pang, Wencheng Zhang
Diversity, clonality, top frequency, and overlap index were used to characterize the immune repertoire. The diversity of the TCR repertoire is calculated with the Shannon–Wiener index (Shannon index), which is the function of both the relative number of clonotypes present and the relative abundance or distribution of each clonotype.23 The Shannon index is calculated as follows, where ni is the clonal size of the ith clonotype (that is, the number of copies of a specific clonotype), S is the number of different clonotypes, and N is the total number of TCR/BCR sequences analyzed:
The application of molecular topology for ulcerative colitis drug discovery
Published in Expert Opinion on Drug Discovery, 2018
Carolina L. Bellera, Mauricio E. Di Ianni, Alan Talevi
At the core of the chemical graph theory lies the adjacency matrix. From a hydrogen-suppressed molecular representation, a N × N symmetric matrix can be obtained whose elements Aij equal 1 if vertices i and j are directly connected through a covalent chemical bond, and 0 otherwise. The sum of all entries in the ith row or the jth column provides the degree or topological valence δ of vertex i or j, respectively. Professor Milan Randić defined the first connectivity index (now known as Randić index) back in 1975 [49]. It was defined as the sum of the degrees of the two vertices adjacent to each edge, extended to all edges of the graph. Another very relevant topological matrix is the distance matrix, whose elements Dij equal the number of edges joining two vertices i and j by the shortest path, provided that i and j are different, or 0 otherwise. The first topological index ever defined, the Wiener index, equals one half of the sum of all entries in the distance matrix [50]. These two examples (the simpler at hand, illustrated in Figure 3) depict the general procedure to obtain topological descriptors. It is interesting to note that the numbering of the vertices of the graph does not influence the value of the graph-derived descriptors: they are graph invariants. In principle, the molecular graph is not influenced by any deformation introduced to the molecule: topological indices are conformation-independent unless deliberately pursued otherwise. Reproducibility and ease of calculation are thus two of the important (and interrelated) virtues of topological descriptors. No conformational analysis, no geometry optimization, no orientation, or conformation-related decisions are required to compute topological descriptor. The modeler is released from the burden of answering the difficult question ‘What conformation should be used to compute a molecular descriptor?’, and from the noise that defining a conformer could introduce to the QSAR model [51,52]. Note that the former question is particularly difficult to answer if, in the frame of a VS campaign, one pretends to apply a QSAR model to a large chemical database. At the other side of the coin, the values of the topological descriptors are usually insensitive to space or even geometry isomers, with some very specific exceptions (see, for instance, [53]).