Determination of Plants Diversity and Similarity in Selected three Communities of Stachytarpheta jamaicensis (L.) Vahl | Chapter 3 | Innovations in Biological Science Vol. 7

 

Aims: to know and determine plants diversity and similarity in three communities of S. jamaicensis habitat.

Study Design: Explorative and descriptive.

Place and Duration of Study: Research in Gajahrejo Village, Pasuruan, East Java Province was conducted in August 2012; in Kepala Jeri Islands Batam, Riau Island Province in October 2014; and in Bissoloro Village, South Sulawesi Province conducted in October 2016.

Methodology: The study used exploratory and descriptive methods. Sample plots of 1m x 1m were determined and put around S. jamaicensis, which was found along the edge of village roads during exploration (purposive random sampling). Vegetation analysis was carried out to determine the composition, dominance, diversity, evenness and similarity of the species in each S. jamaicensis growing place by counting the Important Value Index, Similarity Index, Shannon Diversity Index, Domination Index, and Evenness Index.

Results: Assosiant plant species found in the three sites growing area of S. jamaicensis were 84 species, 74 genera and 28 families. There were six plant species obtained in the three sites growing area of S. jamaicensis, i.e., Ageratum conyzoides L., Axonopus compressus (Swartz) Beauv., Centrosema pubescens Bth., Chromolaena odorata (L.) R.M.King & H. Rob., Cynodon dactylon (L.) Pers and Mimosa pudica L. Axonoppus compressus was the most dominance species in Kepala Jeri Island and Bissoloro Village, whereas Panicum brevifolium L. was the most dominant species in Gajahrejo Village. The difference in floristic composition between the sites may be caused by environmental factors, human activity, land use systems and disturbance on fields where S. jamaicensis obtained.

Conclusion: The plants' diversity and similarity in the three sites growing area of S. jamaicensis habitat were different. There were 84 species, 74 genera and 28 Families found in three sites growing area of S.jamaicensis. There were six plant species obtained in the three sites growing area of S. jamaicensis, i.e., Ageratum conyzoides L., Axonopus compressus (Swartz) Beauv., Centrosema pubescens Bth., Chromolaena odorata (L.) R.M.King & H. Rob., Cynodon dactylon (L.) Pers and Mimosa pudica L. Axonoppus compressus (Swartz.) Beauv. was the most dominant species in Kepala Jeri Island and Bissoloro Village, whereas Panicum brevifolium L. was the most dominant species in Gajahrejo Village.

 

Author (s) Details

Solikin
Research Center for Ecology and Ethnobiology, National Research and Innovation Agency Jl. Raya Jakarta Bogor Km 46, Cibinong, Bogor West Java 16911, Indonesia.

 

Please see the link :- https://doi.org/10.9734/bpi/ibs/v7/775

Quantitative and Qualitative Analysis of Sweat as a Biomarker | Chapter 12 | Contemporary Perspective on Science, Technology and Research Vol. 6

Sweat glands (1.6–5 million) are distributed over the whole-body surface with an average density of 200 sweat glands per square centimeter. In the present medical scenario, several invasive methods are used to analyse the biomarkers in biofluids. Like, blood, urine, saliva and sweat, which are used for clinical analyses. Blood sampling is an invasive procedure, where patients require frequent analysis, are therefore at higher risk of infection. However, Its sample preparation, storage and plasma-protein removal is difficult. On the other hand, sweat analysis is very easy as compared with other biofluids, especially blood. The objective of the study is to analyse the transparent biofluid with low tonocity and slightly acidic nature with mean pH 6.3, which is more acidic than blood. Forehead sweat contains nitrogenous compounds like amino acids, lactic acid, electrolytes, urea and protein. A double beam uv-spectrophotometer2206 was used to determine amino acid, lactic acid and protein in sweat sample. Biomarkers were analysed qualitatively and quantitatively. To determine the abnormalities in human body by using analytical methods. With these methodologies, diseases like diabetes and cancers can be analysed cost-effectively.

Author(s) Details:

Jeyadaisy I.,
Kumaraguru College of Technology, Electronics and Instrumentation Engineering, Jeyadaisy. I, Coimbatore, India.

Paarkavidevi C.,
Kumaraguru College of Technology, Electronics and Instrumentation Engineering, Jeyadaisy. I, Coimbatore, India.

Sathish Kumar B.,
Kumaraguru College of Technology, Electronics and Instrumentation Engineering, Jeyadaisy. I, Coimbatore, India.

Ammu G.,
Kumaraguru College of Technology, Electronics and Instrumentation Engineering, Jeyadaisy. I, Coimbatore, India.

Please see the link here: https://stm.bookpi.org/CPSTR-V6/article/view/13591