Functional MRI Data Analysis Techniques and Strategies to Map the Olfactory System of a Rat Brain.

Understanding mysteries of a brain represents one of the great challenges for modern science. Functional magnetic resonance imaging (fMRI) has two features that make it unique amongst other imaging modalities used in behavioral neuroscience. First, it can be entirely non-invasive and second, fMRI has the spatial and temporal resolution to resolve patterns of neuronal activity across the entire brain in less than a minute. fMRI indirectly detects neural activity in different parts of the brain by comparing contrast in MR signal intensity prior to and following stimulation. Areas of the brain with increased synaptic and neuronal activity require increased levels of oxygen to sustain this activity. Enhanced brain activity is accompanied by an increase in metabolism followed by increases in blood flow and blood volume. The enhanced blood flow usually exceeds the metabolic demand exposing the active brain area to high level of oxygenated hemoglobin. Oxygenated hemoglobin increases the MR signal intensity that can be detected in MR scanner. This relatively straight forward scenario is, unfortunately, oversimplified. The fMRI signal change to noise ratio is extremely small. In this work a quantitative analysis strategy to analyze fMRI data was successfully developed, implemented and optimized for the rat brain. Therein, each subject is registered or aligned to a complete volume-segmented rat atlas. The matrices that transformed the subject\u27s anatomy to the atlas space are used to embed each slice within the atlas. All transformed pixel locations of the anatomy images are tagged with the segmented atlas major and minor regions creating a fully segmented representation of each subject. This task required the development of a full 3D surface atlas based upon 2D non-uniformly spaced 2D slices from an existing atlas. A multiple materials marching cube (M3C) algorithm was used to generate these 1277 subvolumes. After this process, they were coalesced into a dozen major zones of the brain (amygdaloid complex, cerebrum, cerebellum, hypothalamus, etc.). Each major brain category was subdivided into approximately 10 sub-major zones. Many scientists are interested in behavior and reactions to pain, pleasure, smell, for example. Consequently, the 3D volume atlas was segmented into functional zones as well as the anatomical regions. A utility (program) called Tree Browser was developed to interactively display and choose different anatomical and/or functional areas. Statistical t-tests are performed to determine activation on each subject within their original coordinate system. Due to the multiple t-test analyses performed, a false-positive detection controlling mechanism was introduced. A statistical composite of five components was created for each group. The individual analyses were summed within groups. The strategy developed in this work is unique as it registers segments and analyzes multiple subjects and presents a composite response of the whole group. This strategy is robust, incredibly fast and statistically powerful. The power of this system was demonstrated by mapping the olfactory system of a rat brain. Synchronized changes in neuronal activity across multiple subjects and brain areas can be viewed as functional neuro-anatomical circuits coordinating the thoughts, memories and emotions for particular behaviors using this fMRI module

Reverse Auction in Pricing Model

Dynamic price discrimination adjusts prices based on the option value of future sales, which varies with time and units available. This paper surveys the theoretical literature on dynamic price discrimination, and confronts the theories with new data from airline pricing behavior, Consider a multiple booking class airline-seat inventory control problem that relates to either a single flight leg or to multiple flight legs. During the time before the flight, the airline may face the problems of (1) what are the suitable prices for the opened booking classes, and (2) when to close those opened booking classes. This work deals with these two problems by only using the pricing policy. In this paper, a dynamic pricing model is developed in which the demand for tickets is modeled as a discrete time stochastic process. An important result of this work is that the strategy for the ticket booking policy can be reduced to sets of critical decision periods, which eliminates the need for large amounts of data storage

Hybrid multi-layer Deep CNN/Aggregator feature for image classification

Deep Convolutional Neural Networks (DCNN) have established a remarkable performance benchmark in the field of image classification, displacing classical approaches based on hand-tailored aggregations of local descriptors. Yet DCNNs impose high computational burdens both at training and at testing time, and training them requires collecting and annotating large amounts of training data. Supervised adaptation methods have been proposed in the literature that partially re-learn a transferred DCNN structure from a new target dataset. Yet these require expensive bounding-box annotations and are still computationally expensive to learn. In this paper, we address these shortcomings of DCNN adaptation schemes by proposing a hybrid approach that combines conventional, unsupervised aggregators such as Bag-of-Words (BoW), with the DCNN pipeline by treating the output of intermediate layers as densely extracted local descriptors. We test a variant of our approach that uses only intermediate DCNN layers on the standard PASCAL VOC 2007 dataset and show performance significantly higher than the standard BoW model and comparable to Fisher vector aggregation but with a feature that is 150 times smaller. A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.Comment: Accepted in ICASSP 2015 conference, 5 pages including reference, 4 figures and 2 table

Analysis on techniques used to recognize and identifying the Human emotions

Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwin’s work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress of research in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify the proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on the various recognition techniques used to identify the complexity in recognizing the facial expression is presented. This work will also help researchers and scholars to ease out the problem in choosing the techniques used in the identification of the facial expression domain

Health care seeking behaviour and expenditure pattern among Scrub Typhus patients attending a tertiary care hospital in Mysore city

Background: Scrub typhus is one among the re-emerging infectious diseases throughout the world. Various studies conducted across India reveals that its public health importance is increasing. This study was conducted 1) To describe the socio-demographic and epidemiological profile of patients admitted with scrub typhus. 2) To assess the health care seeking behaviour of these patients. 3) To estimate the cost factors incurred in the current episode of illness.  Methods: This prospective study was conducted from January to December 2013 among all lab confirmed cases of scrub typhus admitted to department of medicine and pediatrics of JSS Hospital, Mysore. The study subjects were interviewed with a pre-tested and structured questionnaire. Data regarding socio-demographic profile, epidemiological profile, disease outcome, health care seeking behaviour and cost factors incurred with current episode of illness were collected. Data entry and analysis were done with SPSS.v.22.0 using descriptive statistics like mean, standard deviations and inferential statistics like chi-square test.Results: Among 192 patients tested positive by Weil-Felix test and/or Immuno-Chromatographic Test (ICT) for scrub typhus majority 105 (54.7%) were males and were predominantly 135 (70.3 %) from rural areas. Mostly 172(89.6%) were unaware of any mite bite in the past. Majority 167 (87.0%) of them had visited atleast three Health Care Facilities (HCF) for treatment. The mean ± SD total duration of illness was 15.6 ± 4.1 days. Most 104 (54.2 %) of them had suffered from illness for 11-15 days. Majority 175 (91.1%) of them had recovered while 3 (1.6%) of them had succumbed to the condition. The median Total direct cost, total indirect cost and overall total cost were Rs. 7500 (7000-9500), Rs. 3000 (2500-3500) and Rs. 10500 (10000-13000) respectively. Most 104 (54.2%) of them spent from money borrowed from others, followed by 78 (40.6%) spent Out Of Pocket (OOP).Conclusion: People from rural areas, unskilled workers and children were affected predominantly. With timely diagnosis and appropriate treatment, significant morbidity and mortality could be prevented. Promotion of various public and private health insurance schemes among public would minimise the OOP expenditure and prevents debts.

A double blind randomized study to assess the addition of clonidine to ropivacaine in supraclavicular brachial plexus block

Background: The supraclavicular brachial plexus block provides anesthesia of the entire upper extremity in consistent and time-efficient manner. Ropivacaine is an amide, local anaesthetic agent, eliciting nerve block in brachial plexus. Clonidine as an adjuvant to ropivacaine enhances the quality and duration of analgesia when given epidurally or intrathecally. The aim of the present study was to assess the effect of adding clonidine to ropivacaine in supraclavicular brachial plexus block.Methods: Sixty patients were randomly divided into two groups, Group C and R. Group C received 0.5% of ropivacaine with 1 ml normal saline while Group R received same amount of ropivacaine with 1 ml (equivalent to 100μg) of clonidine for supraclavicular brachial plexus block. The groups were compared regarding quality of sensory and motor blockade, duration of post-operative analgesia, intra and post-operative hemodynamic changes and sedation scores.Results: There was a significant increase in duration of sensory and motor block and duration of analgesia in Group C as compared to Group R (P0.05).Conclusions: Clonidine 100µg added to 0.5% ropivacaine for supraclavicular brachial plexus block, does not shorten the onset of sensory and motor blockade but the combination produced prolonged sensory and motor blockade, improved and prolonged duration of analgesia, thereby decreasing the need for systemic analgesics without any hemodynamic changes

In Silico Prediction of Evolutionarily Conserved GC-Rich Elements Associated with Antigenic Proteins of Plasmodium falciparum

The Plasmodium falciparum genome being AT-rich, the presence of GC-rich regions suggests functional significance. Evolution imposes selection pressure to retain functionally important coding and regulatory elements. Hence searching for evolutionarily conserved GC-rich, intergenic regions in an AT-rich genome will help in discovering new coding regions and regulatory elements. We have used elevated GC content in intergenic regions coupled with sequence conservation against P. reichenowi, which is evolutionarily closely related to P. falciparum to identify potential sequences of functional importance. Interestingly, ~30% of the GC-rich, conserved sequences were associated with antigenic proteins encoded by var and rifin genes. The majority of sequences identified in the 5′ UTR of var genes are represented by short expressed sequence tags (ESTs) in cDNA libraries signifying that they are transcribed in the parasite. Additionally, 19 sequences were located in the 3′ UTR of rifins and 4 also have overlapping ESTs. Further analysis showed that several sequences associated with var genes have the capacity to encode small peptides. A previous report has shown that upstream peptides can regulate the expression of var genes hence we propose that these conserved GC-rich sequences may play roles in regulation of gene expression

Institutional tensions, corporate social responsibility and district-level governance of tobacco industry interference:Analysing challenges in local implementation of Article 5.3 measures in Karnataka, India

INTRODUCTION: Accelerating progress on tobacco control will require Article 5.3 of the WHO Framework Convention on Tobacco Control to be systematically integrated into policies and practices of sectors beyond health at diverse government levels. However, no study has explored implementation challenges of Article 5.3 within multilevel systems such as India, where political decisions on tobacco control occur at diverse government levels, which may constrain action at local level. METHODS: Based on 33 semi-structured interviews with diverse government and civil society stakeholders across four districts in Karnataka, India (Mysore, Mangalore, Bengaluru (rural) and Udupi), this study examines challenges to implement Article 5.3 arising from competing agendas and policies of different actors at multiple levels. RESULTS: Our analysis reveals generally low levels of awareness of Article 5.3 and its guideline recommendations, even among those directly involved in tobacco control at district level. Efforts to implement Article 5.3 were also challenged by competing views on the appropriate terms of engagement with industry actors. Scope to reconcile tensions across competing health, agriculture and commercial agendas was further constrained by the policies and practices of the national Tobacco Board, thereby undermining local implementation of Article 5.3. The most challenging aspect of Article 5.3 implementation was the difficulties in restricting engagement by government officials and departments with tobacco industry corporate social responsibility initiatives given national requirements for such activities among major corporations. CONCLUSIONS: Promoting effective implementation of Article 5.3 in Karnataka will require policymakers to work across policy silos and reconcile tensions across India’s national health and economic priorities

Order vs. Chaos: A Language Model Approach for Side-channel Attacks

We introduce the Order vs. Chaos (OvC) classifier, a novel language-model approach for side-channel attacks combining the strengths of multitask learning (via the use of a language model), multimodal learning, and deep metric learning. Our methodology offers a viable substitute for the multitask classifiers used for learning multiple targets, as put forward by Masure et al. We highlight some well-known issues with multitask classifiers, like scalability, balancing multiple tasks, slow learning, large model sizes, and the need for complex hyperparameter tuning. Thus, we advocate language models in side-channel attacks. We demonstrate improvements in results on different variants of ASCAD-V1 and ASCAD-V2 datasets compared to the existing state-of-the-art results. Additionally, we delve deeper with experiments on protected simulated datasets, allowing us to control noise levels and simulate specific leakage models. This exploration facilitates an understanding of the ramifications when the protective scheme\u27s masks do not leak and allows us to further compare our approach with other approaches. Furthermore, with the help of unprotected simulated datasets, we demonstrate that the OvC classifier, uninformed of the leakage model, can parallelize the proficiency of a conventional multi-class classifier that is leakage model-aware. This finding implies that our methodology sidesteps the need for predetermined a leakage model in side-channel attacks