Evaluation of in vitro antibacterial property of seaweeds of southeast coast of India

The in vitro antibacterial activities of seaweeds belong to Chlorophyceae (Caulerpa racemosa and Ulva lactuca), Rhodophyceae (Gracillaria folifera and Hypneme muciformis) and Phaeophyceae (Sargassum myricocystum, Sargassum tenneerimum and Padina tetrastomatica) were studied against both Gramnegative and Gram-positive pathogenic bacteria. Methanolic extracts of all seaweed extracts tested in the present study exhibited broad spectrum of antibacterial activity. Chlorophyceae members showedhigh antibacterial activity than other members of the algae tested in the present investigation. Escherchia coli alone resistant to all the seaweed extracts except S. teneerimum. Results of the present study confirmed the potential use of seaweed extracts as a source of antibacterial compounds

Wiener filtering with a seismic underground array at the Sanford Underground Research Facility

A seismic array has been deployed at the Sanford Underground Research Facility in the former Homestake mine, South Dakota, to study the underground seismic environment. This includes exploring the advantages of constructing a third-generation gravitational-wave detector underground. A major noise source for these detectors would be Newtonian noise, which is induced by fluctuations in the local gravitational field. The hope is that a combination of a low-noise seismic environment and coherent noise subtraction using seismometers in the vicinity of the detector could suppress the Newtonian noise to below the projected noise floor for future gravitational-wave detectors. In this paper, we use Wiener filtering techniques to subtract coherent noise in a seismic array in the frequency band 0.05 -- 1\,Hz. This achieves more than an order of magnitude noise cancellation over a majority of this band. We show how this subtraction would benefit proposed future low-frequency gravitational wave detectors. The variation in the Wiener filter coefficients over the course of the day, including how local activities impact the filter, is analyzed. We also study the variation in coefficients over the course of a month, showing the stability of the filter with time. How varying the filter order affects the subtraction performance is also explored. It is shown that optimizing filter order can significantly improve subtraction of seismic noise, which gives hope for future gravitational-wave detectors to address Newtonian noise

Performance Analysis of Classifier Models to Predict Diabetes Mellitus

AbstractDiabetes is one of the common and growing diseases in several countries and all of them are working to prevent this disease at early stage by predicting the symptoms of diabetes using several methods. The main aim of this study is to compare the performance of algorithms those are used to predict diabetes using data mining techniques. In this paper we compare machine learning classifiers (J48 Decision Tree, K-Nearest Neighbors, and Random Forest, Support Vector Machines) to classify patients with diabetes mellitus. These approaches have been tested with data samples downloaded from UCI machine learning data repository. The performances of the algorithms have been measured in both the cases i.e dataset with noisy data (before pre-processing) and dataset set without noisy data (after pre-processing) and compared in terms of Accuracy, Sensitivity, and Specificity

Insight into the reproductive biology of euryhaline cyclopoid copepods Apocyclops dengizicus and Apocyclops royi

The present research work aimed to investigate the unexplored information about mating strategies and reproductive potential of two euryhaline cyclopoid species Apocyclops dengizicus and A. royi. Laboratory experiments on mating strategy and reproductive aspects such as the efficacy of females under different mating conditions, the reproductive potential of males, mating frequency, and the effect of starvation on survival and reproductive potential, were conducted and documented. The once-mated female and females pairing continuously with males favored higher reproductive potential. In the present study, the mate-pursuing males prefers to copulate with mature virgin females. The older female was unable to produce eggs even after the sperm discharge. The lifespan of both the Apocyclops species under different states of mating and unmated conditions showed considerable variation. Apocyclops dengizicus produced 7.92±0.57 pairs of spermatophores, while A. royi extruded 13.32±0.99 pairs of spermatophores during its lifespan. The results were analyzed and inferred; thus, high throughput was applied to reveal the understudied topics of these Apocyclops species to add considerable knowledge about its reproductive biology

A Review on Analytical Methods for Honey Classification, Identification and Authentication

Authentication of food products is of great concern in the context of food safety and quality. In recent years, interest in honey authenticity in relation with botanical or geographical origin and adulteration has increased. Honey is a ready-to-eat natural food with high nutritional content and gives many health benefits. Authentication of honey has primary importance for both industries and consumers in combatting common honey frauds in the form of mislabeling of honey origin and adulteration with sugar or syrups. Various analytical methods are used for detecting original honey. With a diverse range of equipment and techniques, the conventional analytical methods are still being used in association with advanced techniques as they are part of preliminary screening, processing and product standards. Most of the analytical methods provide indications of pollen distribution, physicochemical parameters and profile analysis of phenolic, flavonoid, carbohydrate, amino acids, aroma and individual marker components. This review provides an overview and summary of instrumental and analytical methods available for honey authentication from conventional to recent molecular techniques. It is useful as a guide to choosing appropriate method for analysis, classification and authentication of honey

INFLUENCE OF HARVESTING TIME ON CHEMICAL COMPOSITION OF THE ESSENTIAL OIL OF ABIES KOREANA TWIGS

Objective: In the present study, the influence of harvesting time (April, June, August and October 2015) on the essential oil composition of Abies koreana twigs from Korea was investigated.Methods: The essential oil from the twigs of A. koreana was isolated by steam distillation and its chemical composition was determined by gas chromatography-mass spectrometry (GC-MS).Results: The essential oil yield was found to vary from 0.76 to 1.20% depending on the month of harvesting. The GC-MS analysis revealed the identification of 26 different essential oil components from the twigs harvested in the months of April, June, August and October, which were mostly monoterpene hydrocarbons (57.63–72.38%) followed by oxygenated monoterpenes (18.82–25.96%). Harvesting time mainly influenced on the concentration of the major components of the essential oil from the twigs of A. koreana. Limonene (17.38–31.13%), bornyl acetate (13.22–21.17%), camphene (12.56–13.26%), α-pinene (11.05–13.02%), β-pinene (4.55–5.70%), 3-carene (5.21–6.43%) and β-eudesmol (1.49–8.24%) were detected as the major components in the essential oil.Conclusion: The main differences between the essential oil compositions of four different months can be referred to limonene and bornyl acetate. The results showed considerable variations in the composition of essential oil, particularly quantitative variation during different harvesting months

A Novel Approach to Extract High Utility Itemsets from Distributed Databases

Traditional approaches in data mining focus on support and confidence measures which are just statistics based. Support and confidence measures which are based on the frequency count of the items enable us to derive the frequent itemsets. The frequency of the items as a single factor does not represent the interestingness of the items. To enhance the process of data mining tasks based on the value of the product, several researches were conducted. It resulted in utility mining which is an emerging field of research in data mining. In the recent years various data mining approaches have been implemented in order to find the high utility itemsets. The main objective of utility mining is to identify the itemsets with highest utilities, by considering the subjectively defined utility values, as set by the user. Existing methods based on utility mining concept focus on centralized systems where the data and associated processing is pertained to a particular location. As a further step ahead we try to implement the utility mining concept in a distributed environment. In this approach we use a sophisticated way of mining high utility itemsets using a Fast Utility Mining (FUM) algorithm

Mosquito survey and larvicidal activity of actinobacterial isolates against Culex larvae (Diptera: Culicidae)

AbstractThe present study aims to survey and identify the mosquito vector distribution throughout the Bharathidasan University Campus, Tiruchirappalli district, Tamil Nadu, India. Mosquito larvae were collected during October 2012–December 2012 from different breeding habitats. The survey revealed the presence of Aedes sp., Anopheles sp. and Culex sp. mosquito larvae. Among them Culex is dominant, in particularly Culex pipiens followed by Culex quinquifasciatus. Totally 41 actinobacterial isolates were isolated from soil and screened for the production of larvicidal metabolites against the 3rd instar Culex larvae. The obtained data exhibited that the isolated metabolites have lethal effects. Five isolates have shown a most significant mortality rate of the Culex mosquito larvae. In the biolarvicidal assay 1000ppm concentration of the isolate KA13-3 showed 100% mortality and KA25-A showed 90% mortality after 24h of incubation