Effect of culture media and tissue extracts in the mantle explant culture of abalone, Haliotis varia Linnaeus

The study is aimed at developing appropriate media for the mantle explant culture of abalone Haliotis varia. The effect of different media viz., L-15, Ham's F12, M199 was studied in combination with 10% Fetal Calf Serum (FCS) and 10% tissue extracts of gonad, mantle and whole body of abalone, H. varia to understand cell behaviour, cell yield and cell adherence in mantle explant culture of H. varia. Cultures with L-15 media gave better cell yield and M199 promoted better cell adherence. Addition of mantle extract to all media enhanced the cell yield to a maximum followed by whole body extract. Addition of whole body extract facilitated in cell adherence followed by the addition of mantle extract

Research and development in marine fisheries extension in Tamil Nadu

improvement In the standard of living of fishing households and fishing labor households and optimal utilisation of fishery resources for employment, production and export are the Important goals of marine fisheries extension service. Despite ttie increasing attention of the Government to fisheries extension work, majority of fisherfolk are caught In a vicious cycle of poverty and indebtness. The paper analyses the present strategies of fisheries extension service In Tamil Nadu, Identifies the pitfalls and lists suggestions for Improving the efficiency of fisheries extension work In the state. The extension teaching methods used for Individual contact, group contact and mass contact, the sul)Ject-matter fields covered In extension teaching with their relative Importance. The characteristics and extension personnel, research support provided, coordination among the various agencies Involved and participation of the clientele and their organisations are analysed and discussed. The analysis helped identifying the major Impediments to success of extension efforts. The analysis provided a tasks for evolving future extension strategies focussed on marine fisheries development. It provides suggestions in term of personnel requirement, effective extension methods, thrust areas among different subject matter- fields, co-ordination among organisations concerned with marine fisheries and extension research support. The recommendations will be of use to the organisations concerned with fisheries extension In their approach to tone up the service of fisheries extension

Network Traffic Behavioral Analytics for Detection of DDoS Attacks

As more organizations and businesses in different sectors are moving to a digital transformation, there is a steady increase in malware, facing data theft or service interruptions caused by cyberattacks on network or application that impact their customer experience. Bot and Distributed Denial of Service (DDoS) attacks consistently challenge every industry relying on the internet. In this paper, we focus on Machine Learning techniques to detect DDoS attack in network communication flows using continuous learning algorithm that learns the normal pattern of network traffic, behavior of the network protocols and identify a compromised network flow. Detection of DDoS attack will help the network administrators to take immediate action and mitigate the impact of such attacks. DDoS attacks are costing enterprises anywhere between $50,000 to$2.3 million per year. We performed experiments with Intrusion Detection Evaluation Dataset (CICIDS2017) available from Canadian Institute for Cybersecurity to detect anomalies in network traffic. We use flow based traffic characteristics to analyze the difference in pattern between normal vs anomaly packet.We evaluate several supervised classification algorithms using metrics like maximum detection accuracy, lowest false negatives prediction, time taken to train and run. We prove that decision tree based Random Forest is the most promising algorithm whereas Dense Neural network performs equally well on certain DDoS types but require more samples to improve the accuracy of low sampled attacks

NSAIDs for analgesia in the era of COVID-19

Globally, non-steroidal anti-inflammatory drugs (NSAIDs) are highly used to treat pain. With the rise of the COVID-19 pandemic, the safety of NSAIDs use has been called into question. These concerns are worthy of review. At present, there is no compelling data showing that NSAIDs worsen the severity of COVID-19 symptoms or increase one\u27s likelihood of contracting the illness. For patients in pain and without symptoms that could potentially be attributed to COVID-19 (cough, fevers/chills, lethargy, myalgias, anosmia and so on), NSAIDs should continue to remain a viable option to provide analgesia to patients in need

Influence of Parental Expectations on Young Children’s Approaches to Learning

This study explores the level of young children’s Approaches to Learning (ATL) in Shanxi, China, with a specific on the impact of parental expectations on ATL. 360 parents with preschool-aged children were enlisted for a comprehensive questionnaire survey. Applying descriptive statistics, inferential statistics, correlation, and regression analyses on the data gathered revealed that children's ATL was moderate, with noticeable variations based on gender and age. Importantly, parental expectations were positively correlated with children's ATL and remained a significant predictor even when accounting for age and gender, highlighting the strong relationship between parental expectations and early learning development. &nbsp

Genomic Prediction with Pedigree and Genotype x Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico

Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a subset of lines that were not observed in any of the 18 environments (CV1), and a subset of lines that were not observed in a set of the environments, but were observed in other environments (CV2). Genomic prediction models, including genotype x environment (GxE) interaction, had the highest average prediction ability under the CV1 scenario for GY (0.31), GN (0.32), GW (0.45), and TTF (0.27). For CV2, the average prediction ability of the model including the interaction terms was generally high for GY (0.38), GN (0.43), GW (0.63), and TTF (0.53). Wheat lines in siteyear combinations in Mexico and India had relatively high prediction ability for GY and GW. Results indicated that prediction ability of lines not observed in certain environments could be relatively high for genomic selection when predicting GxE interaction in multi-environment trials

Diversity, distribution and seasonal variation of seaweeds in Southwest coast of Peninsular India

Six different research locations around the southwest coasts of Tamil Nadu and Kerala were used for the current investigations on the diversity, distribution, and seasonal fluctuation of seaweeds (India). A total of 73 Chlorophyta, Ochrophyta and Rhodophyta were recorded under 23 families and 38 genera. The study site Rasthakaadu (53) was with the maximum number of seaweeds followed by Kanniyakumari (51). Out of six study sites, four study sites (Rasthakaadu, Kanniyakumari, Muttom, Kurumpanai) were dominant with Rhodophyta in contrast to the other two study sites (Vallavilai, Vizhinjam) in which Chlorophyta was dominant. Chlorophyta such as Chaetomorpha antannina, C. indica, C. media, Ulva fasciata, U. lactuca, brown seaweeds Sargassum ilicifolium and red seaweed Gracilariopsis longissima were commonly seen in the study area. Chaetomorpha indica (Chlorophyceae) was recorded as the most dominant species in season I, whereas Sargassum ilicifolium (Phaeophyaceae) was considered as the most dominant seaweed taxon in seasons II and III. The seasonal variation in physicochemical parameters of seawater had much influence on the growth of seaweeds. Comparing the eastern Coromandel Coast of peninsular India to the western Malabar Coast, it has been found from the current study that the eastern Coromandel Coast was rich in seaweed. Moreover, the study shows that the topography and seasonal change of the physicochemical characteristics of seawater at a given site were the key determinants of seaweed richness. Anthropogenic activities, like Nuclear power plants (Koodankulam), sand mining, construction works, dumping of plastics etc., also affected the potential growth of seaweeds thereby reducing the sustainability of the natural resource

Quantitative evaluation of essential oils for the identification of chemical constituents by gas chromatography/mass spectrometry

Essential oils are greatly strenuous aromatic materials having various constituents. They are used in the preparation of various precious substances like making perfumes, medicines, cleaning agent, and aromatic treatment etc. The purpose of the present investigation was to identify the major and minor chemical constituent in eighteen essential oils viz., amyris, basil, black pepper, camphor, catnip, chamomile, cinnamon, citronella, dill, frankincense, galbanum, jasmine, juniper, lavender, peppermint, rosemary, tagetes and thyme with the help of gas chromatography /mass spectrometry (GC/MS). In eighteen essential oils the identified compounds studied by GC-MS contain various types of high and low molecular weights of chemical ingredients. Therefore, GC/MS efficiently and speedily screened all the volatile elements present in the essential oils for the quantitative use of these identified chemical constituents for various reasons