Intermittent Cold-Induced Hippocampal Oxidative Stress is Associated with Changes in the Plasma Lipid Composition and is Modifiable by Vitamins C and E in Old Rats

This study primarily investigated the effects of intermittent cold exposure (ICE) on oxidative stress (OS) in the hippocampus(HC) and plasma lipid profile of old male rats. Secondly, it evaluated structural changes in the hippocampus region of the rat’s brain. Thirdly, it attempted an evaluation of the effectiveness of the combined supplement of vitamins C and E in alleviating cold stress in terms of these biochemical parameters. Thirty male rats aged 24 months were divided into groups of five each: control (CON), cold-exposed at 10 °C (C10), cold-exposed at 5 °C (C5), supplemented control (CON+S), and supplemented cold-exposed at either 5 °C (C5+S) or 10 °C (C10+S). The rats were on a daily supplement of vitamin C and vitamin E. Cold exposure lasted 2 h/day for 4 weeks. Rats showed increased levels of hydrogen peroxide (H2O2), and thiobarbituric acid reactive substances (TBARS) in the HC at 10 °C with further increase at 5 °C. Cold also induced neuronal loss in the hippocampus with concomitant elevations in total cholesterol (TCH), triglycerides (TG) and low-density lipoproteins (LDL-C) levels, and a depletion in high-density lipoprotein (HDL-C). A notable feature was the hyperglycaemic effects of ICE and depleted levels of vitamins C and E in the hippocampus and plasma while supplementation increased their levels. More importantly, a positive correlation was observed between plasmatic LDL-C, TCH and TG and hippocampal TBARS and H2O2 levels. Further, intensity of cold emerged as a significant factor impacting the responses to vitamin C and E supplementation. These results suggest that cold-induced changes in the plasma lipid profile correlate with OS in the hippocampus, and that vitamin C and E together are effective in protecting from metabolic and possible cognitive consequences in the old under cold exposures

Interestingness measure on privacy preserved data with horizontal partitioning

Association rule mining is a process of finding the frequent item sets based on the interestingness measure. The major challenge exists when performing the association of the data where privacy preservation is emphasized. The actual transaction data provides the evident to calculate the parameters for defining the association rules. In this paper, a solution is proposed to find one such parameter i.e. support count for item sets on the non transparent data, in other words the transaction data is not disclosed. The privacy preservation is ensured by transferring the x-anonymous records for every transaction record. All the anonymous set of actual transaction record perceives high generalized values. The clients process the anonymous set of every transaction record to arrive at high abstract values and these generalized values are used for support calculation. More the number of anonymous records, more the privacy of data is amplified. In experimental results it is shown that privacy is ensured with more number of formatted transactions

Semi-Supervised Domain Adaptation and Collaborative Deep Learning for Dual Sentiment Analysis

Sentiment classification is a much needed topic that has grabbed the interest of many researchers. Especially, classification of data from customer reviews on various commercial products has been an important source of research. A model called supervised dual sentiment analysis is used to handle the polarity shift problem that occurs in sentiment classification. Labeling the reviews is a tedious and time consuming process. Even, a classifier trained on one domain may not perform well on the other domain. To overcome these limitations, in this paper we propose semi-supervised domain adaptive dual sentiment analysis that train a domain independent classifier with few labeled data. Reviews are of varying length and hence, classification is more accurate if long term dependency between the words is considered. We propose a collaborative deep learning approach to the dual sentiment analysis. Long short term memory (LSTM) recurrent neural network is used to handle sequence prediction to classify the reviews more accurately. LSTM takes more time to extract features from the reviews. Convolution neural network is used before LSTM layers to extract features resulting in the reduction of training time compared to LSTM alone

Grape seed proanthocyanidin lowers brain oxidative stress in adult and middle-aged rats

There is growing concern over the increasing instances of decline in cognitive abilities with aging in humans. The present study evaluated the benefits of the natural antioxidant, grape seed proanthocyanidin extract (GSPE) in treating the effects of age-related oxidative stress (OS) and accumulation of lipofuscin (LF) on the cognitive ability in rats. Female Wistar rats of 3- and 12-months of age received a daily oral supplement of GSPE until they attained 6- and 15-months of age. During this period, rats were tested for their cognitive ability. At the end of this period, blood glucose and markers of OS were assessed in the hippocampus. GSPE lowered blood glucose, lipid peroxidation, hydrogen peroxide level, and increased protein sulphydryl (P-SH) content in the hippocampus. In addition, GSPE significantly improved cognitive performance in the two age groups. These results demonstrate that the extent of OS-related LF accumulation is reducible by GSPE. They also suggest a critical role for GSPE as a neuroprotectant in the hippocampus and in preventing cognitive loss with aging. © 2011 Elsevier Inc

TKP: Three level key pre-distribution with mobile sinks for wireless sensor networks

Wireless Sensor Networks are by its nature prone to various forms of security attacks. Authentication and secure communication have become the need of the day. Due to single point failure of a sink node or base station, mobile sinks are better in many wireless sensor networks applications for efficient data collection or aggregation, localized sensor reprogramming and for revoking compromised sensors. The existing sytems that make use of key predistribution schemes for pairwise key establishment between sensor nodes and mobile sinks, deploying mobile sinks for data collection has drawbacks. Here, an attacker can easily obtain many keys by capturing a few nodes and can gain control of the network by deploying a node preloaded with some compromised keys that will be the replica of compromised mobile sink. We propose an efficient three level key predistribution framework that uses any pairwise key predistribution in different levels. The new framework has two set of key pools one set of keys for the mobile sink nodes to access the sensor network and other set of keys for secure communication among the sensor nodes. It reduces the damage caused by mobile sink replication attack and stationary access node replication attack. To further reduce the communication time it uses a shortest distance to make pair between the nodes for comunication. Through results, we show that our security framework has a higher network resilience to a mobile sink replication attack as compared to the polynomial pool-based scheme with less communication tim

A Progressive Approach to Enhance Lifetime for Barrier Coverage in Wireless Sensor Network

Wireless sensor networks have their applications deployed in all the fields of area of research beyond the visualization of smart sensors. The sensors installed may experience many coverage related faults e.g., Barrier coverage problem. This problem affects the random deployment in sensor network to conserve energy and therefore has to be rectified, confined and approved. The protocol CSP andVSP defined extends the advantageoreducingtheenergyconsumption and increases the lifetime of sensor nodes with the intrusion detection model over heterogeneous deployment. Inspite of low connectivity and multihop signal paths, the protocols is entirely scalabl e in terms of computational control and communication bandwidth. Two diverse cases are employed between th e nodes with the protocols: position to position connectivity and load balancing. The former produces better results with a linear increase in network lifetime whereas through latter achieves 40 percent of energy utilization. Simul ation results are provide d to display the efficiency of the protocol designed

TIME OPTIMIZATION FOR AUTHENTIC AND ANONYMOUS GROUP SHARING IN CLOUD STORAGE

The Cloud computing is a rising technique which offers information sharing are more, and efficient effective economical approaches between group members. To create an authentic and anonymous information sharing, IDentity based Ring Signature (ID-) is one of the promising method between the groups. Ring signature RS conspire grants the chief or data owner to authenticate into the framework in an anonymous way. In conventional Public Key Infrastructure (PKI) information sharing plan contains certificate authentication process, which is a bottleneck due to its high cost for consumption of more time to signature. To maintain a strategic distance from this issue, we proposed Cost Optimized Identity-based Ring Signature with forward secrecy COIRS () scheme. This plan evacuates the traditional certificate verification process. Just once the client should be confirmed by the chief giving his public details. The time required for this procedure is relatively not as much as customary public key framework. If the secret key holder has been compromised, all early generated signatures remain valid (Forward Secrecy). This paper examines how to optimize the time when sharing the documents to the cloud. We provide a protection from collision attack, which means revoked users will not get the original documents. In general better efficiency and secrecy can be provided for group sharing by applying approaches

Trust aware system for social networks: A comprehensive survey

Social networks are the platform for the users to get connected with other social network users based on their interest and life styles. Existing social networks have millions of users and the data generated by them are huge and it is difficult to differentiate the real users and the fake users. Hence a trust worthy system is recommended for differentiating the real and fake users. Social networking enables users to send friend requests, upload photos and tag their friends and even suggest them the web links based on the interest of the users. The friends recommended, the photos tagged and web links suggested may be a malware or an untrusted activity. Users on social networks are authorised by providing the personal data. This personal raw data is available to all other users online and there is no protection or methods to secure this data from unknown users. Hence to provide a trustworthy system and to enable real users activities a review on different methods to achieve trustworthy social networking systems are examined in this paper

Mathematical model of semantic look-an efficient context driven search engine

The WorldWideWeb (WWW) is a huge conservatory of web pages. Search Engines are key applications that fetch web pages for the user query. In the current generation web architecture, search engines treat keywords provided by the user as isolated keywords without considering the context of the user query. This results in a lot of unrelated pages or links being displayed to the user. Semantic Web is based on the current web with a revised framework to display a more precise result set as response to a user query. The current web pages need to be annotated by finding relevant meta data to be added to each of them, so that they become useful to Semantic Web search engines. Semantic Look explores the context of user query by processing the Semantic information recorded in the web pages. It is compared with an existing algorithm called OntoLook and it is shown that Semantic Look is a better optimized search engine by being more than twice as fast as OntoLook

Sentiment analysis and opinion mining from social media: A review

Ubiquitous presence of internet, advent of web 2.0 has made social media tools like blogs, Facebook, Twitter very popular and effective. People interact with each other, share their ideas, opinions, interests and personal information. These user comments are used for finding the sentiments and also add financial, commercial and social values. However, due to the enormous amount of user generated data, it is an expensive process to analyze the data manually. Increase in activity of opinion mining and sentiment analysis, challenges are getting added every day. There is a need for automated analysis techniques to extract sentiments and opinions conveyed in the user-comments. Sentiment analysis, also known as opinion mining is the computational study of sentiments and opinions conveyed in natural language for the purpose of decision making. Preprocessing data play a vital role in getting accurate sentiment analysis results. Extracting opinion target words provide fine-grained analysis on the customer reviews. The labeled data required for training a classifier is expensive and hence to over come, Domain Adaptation technique is used. In this technique, Single classifier is designed to classify homogeneous and heterogeneous input from di_erent domain. Sentiment Dictionary used to find the opinion about a word need to be consistent and a number of techniques are used to check the consistency of the dictionaries. This paper focuses on the survey of the existing methods of Sentiment analysis and Opinion mining techniques from social media