COMPARATIVE ANALYSIS OF SENSILLA ON ANTENNAE AND MOUTHPARTS OF LARVAE IN TWO SPECIES OF DITRYSIAN MOTHS (LEPIDOPTERA)

Systematics encompasses two more narrowly defined but highly interdependent fields. The first is taxonomy and the second field is phylogenetics. In modern systematics, taxonomy aims to reflect evolutionary history. The morphological traits of immature stages remain largely unresolved for a vast majority of the lepidopteran species worldwide, although they have potential to be applied in lepidopteran classification and systematic studies. The larval instars in Lepidoptera are signature examples of agricultural pests. The present study deals with SEM investigation of ultrastructure of different instars of two lepidopteran pest species i.e., Somena scintillans Walker 1856 and Trabala vishnou Lefebvre 1827. These findings will not only help in enriching taxonomic database but will also act as an aid for future studies aimed at devising pest control methods

A comparative study to assess the safety and efficacy of etoricoxib versus aceclofenac in osteoarthritis

Background: Osteoarthritis (OA) is most common form of arthritis; also referred as degenerative joint disease or “wear and tear” arthritis. Cyclooxygenase-2 (COX-2) inhibitors are effective for pain and inflammation in OA and gained importance over conventional non-steroidal anti-inflammatory agents (NSAIDs), as causes significantly less toxicity, particularly, in gastrointestinal tract. The objective of the present research was to study the short-term comparative clinical efficacy of aceclofenac and etoricoxib in patients with osteoarthritis and to compare the safety profile of the two drugs i.e. aceclofenac and etoricoxib.Methods: The present study was a prospective, open label, parallel, intention to treat 80 patients out of 102 screened for osteoarthritis in the Department of Orthopaedics, Guru Nanak Dev Hospital attached to the Government Medical College, Amritsar. Patients were randomly divided in two groups with 40 patients each. Group A patients received Tab etoricoxib 60mg once daily and Group B patients received Tab. Aceclofenac 100mg twice daily. Patients were followed up after three weeks and at six weeks for clinical efficacy and safety.Results: Both the groups found to have significant improvement in signs and symptoms of osteoarthritis. However, aceclofenac was superior to etoricoxib in terms of change in visual analogue scale score, osteoarthritic severity index, patients’ and physicians’ global assessment while, etoricoxib was superior in terms of WOMAC osteoarthritic index and safety parameters in terms of ADR.Conclusions: Etoricoxib was better than conventional NSAIDs for the symptomatic management of osteoarthritis in terms of safety profile and clinical efficacy

A Hybrid Machine Learning Technique For Feature Optimization In Object-Based Classification of Debris-Covered Glaciers

Object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier’s performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a time consuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier’s complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy

Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: A case study

AbstractSeizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT) and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic–ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT–EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT–EEG has the potential to provide improved detection of neonatal seizures

The brain's response to pleasant touch: an EEG investigation of tactile caressing

Somatosensation as a proximal sense can have a strong impact on our attitude toward physical objects and other human beings. However, relatively little is known about how hedonic valence of touch is processed at the cortical level. Here we investigated the electrophysiological correlates of affective tactile sensation during caressing of the right forearm with pleasant and unpleasant textile fabrics. We show dissociation between more physically driven differential brain responses to the different fabrics in early somatosensory cortex - the well-known mu-suppression (10-20 Hz) - and a beta-band response (25-30 Hz) in presumably higher-order somatosensory areas in the right hemisphere that correlated well with the subjective valence of tactile caressing. Importantly, when using single trial classification techniques, beta-power significantly distinguished between pleasant and unpleasant stimulation on a single trial basis with high accuracy. Our results therefore suggest a dissociation of the sensory and affective aspects of touch in the somatosensory system and may provide features that may be used for single trial decoding of affective mental states from simple electroencephalographic measurements

Development of EEG based BCI approaches for detection of awareness in human disorders of consciousness

Electroencephalogram (EEG) based Brain Computer Interfaces (BCIs) have been successfully developed to help patients with motor disabilities but with retained cognitive abilities. In this thesis, the BCI techniques are developed for patients with severe brain injuries such as those in minimally conscious states (MCS) and vegetative states (VS). In 2006, neuroimaging based volitional imagery paradigms akin to the ones used for the development of motor imagery based BCIs revealed that a VS patient could produce neural responses indistinguishable from those produced by a healthy subject. The work presented in the thesis is inspired by this revelation and presents first attempts to develop electrophysiology based objective bedside methods to detect awareness in disorders of consciousness. The benefit of electrophysiology based methods is that they are able to register the response from the brain immediately and provide far better time resolution than imaging. As many patients either cannot undergo a fMRI scan or do not have access to it, it is believed that long term benefits to quality of life for this patient group can be better achieved at the bedside by an electrophysiological solution. In order to achieve the objectives, EEG data is collected using two BCI approaches: volitional imagery and event related potentials (ERPs) through rare/odd presentation of a target stimulus amongst a sequence of stimuli which produces high amplitude EEG wave after 300ms of its occurrence, this is called P300. Four different variants of volitional paradigms of 'imagine playing tennis' and 'spatial navigation' are used to collect data from 19 healthy subjects and the P300 speller is used to collect data from 5 healthy subjects, two MCS and two VS patients. In the case of imagery data, a channel selection scheme based on classifier performance, which also evaluates the contribution of each channel to the classification process, is used. This scheme is developed from the offline analysis of a benchmark dataset from the BCI competition III. The comparative results of algorithms for BCI imagery data analysis (time domain parameters (TDP), adaptive autoregressive (AAR) and bandpower (BP) for feature extraction and linear discriminant analysis (LDA), support vector machines (SVM) for classification) is presented to determine the feasibility of using these paradigms with patients. Consistent performance accuracy Figures for classification, in the range of 80-90%, are achieved showing that volitional tasks are distinguishable through EEG. A combination of AAR and LDA outperformed the other combinations of algorithms. The actively contributing channels, in achieving these classification results, are used to create EEG signatures for the volitional tasks. The EEG signatures indirectly signify the areas of brain activation for each of the volitional tasks and are found to be comparable to those obtained from neuroimaging. The validation of techniques is performed using a two class, 64-channel electrocorticogram (ECoG) dataset and initial data exploration was performed using principal component analysis (PCA). The derivative of the linear least fit polynomial was used as features and 64% classification was achieved on the unlabelled test data with multi layer perceptron (MLP) as the benchmark mechanism. Ten channels which actively contributed to the classification process were selected using genetic algorithms (GAs), thereby reducing the dimensionality, an important benefit when analysing multichannel, multi-trial datasets. Feature extraction techniques, which can combine spatial and temporal information such as common spatial patterns (CSP), were evaluated and 86% trials were classified correctly using MLP classification. The validation of classifier performance based channel selection produced six channels of interest, the bipolar combinations of which produced a best accuracy of 86% classification with AAR features and LDA classifier and also with TDP features and SVM classification. The P300 data recorded from the patients was investigated for a reproducible P300 response to the target letters. This is achieved by signal averaging and the analysis of square of Pearson‘s correlation coefficient (r-square). Clearly identifiable differential responses to the target letters were observed for three patients. It is believed that with auditory addition to the stimulus presentation in the stimulation procedure, training and consistency of responses, a tool for an objective method of diagnosis and assistive communication could be developed for this patient group. The BCI technology had not been used for the cognitively impaired patient groups such as MCS and VS, hence, the results of this work are new and contribute to bridging the gap between the core BCI research and its applications for patients. The objective measures of awareness developed through EEG based BCI methods will help to reduce the misdiagnosis rate, which is 43% for this patient group. The findings of the work presented in this thesis can be used to further develop an assistive communication tool for patients in this group

Using EV charging control to provide building load flexibility.

Funder: Natural Resources Canada; doi: http://dx.doi.org/10.13039/501100000159Funder: Natural Sciences and Engineering Research Council of Canada; doi: http://dx.doi.org/10.13039/501100000038Buildings are responsible for a significant fraction of the overall electrical load. Given the increasing penetration of renewables into the generation mix, it is important to make building loads flexible, to better match the variability in generation. Of course, building loads can be made arbitrarily flexible using sufficient stationary storage, but this comes at considerable cost. In this paper, we investigate how to reduce this cost by exploiting electric vehicle (EV) charging control for unidirectional and bidirectional charging. Specifically, we design a model-predictive control algorithm to reshape building load to match a specified load shape. In realistic settings and for two use cases, we investigate the degree to which the amount of stationary storage is reduced using EV charging control. In both cases, we find that our controller reduces the need for stationary storage compared to existing solutions. Moreover, bidirectional EV charging control substantially reduces the required amount of stationary storage

Using EV charging control to provide building load flexibility

Funder: Natural Resources Canada; doi: http://dx.doi.org/10.13039/501100000159Funder: Natural Sciences and Engineering Research Council of Canada; doi: http://dx.doi.org/10.13039/501100000038AbstractBuildings are responsible for a significant fraction of the overall electrical load. Given the increasing penetration of renewables into the generation mix, it is important to make building loads flexible, to better match the variability in generation. Of course, building loads can be made arbitrarily flexible using sufficient stationary storage, but this comes at considerable cost. In this paper, we investigate how to reduce this cost by exploiting electric vehicle (EV) charging control for unidirectional and bidirectional charging. Specifically, we design a model-predictive control algorithm to reshape building load to match a specified load shape. In realistic settings and for two use cases, we investigate the degree to which the amount of stationary storage is reduced using EV charging control. In both cases, we find that our controller reduces the need for stationary storage compared to existing solutions. Moreover, bidirectional EV charging control substantially reduces the required amount of stationary storage.</jats:p

Using EV charging control to provide building load flexibility

Funder: Natural Resources Canada; doi: http://dx.doi.org/10.13039/501100000159Funder: Natural Sciences and Engineering Research Council of Canada; doi: http://dx.doi.org/10.13039/501100000038Buildings are responsible for a significant fraction of the overall electrical load. Given the increasing penetration of renewables into the generation mix, it is important to make building loads flexible, to better match the variability in generation. Of course, building loads can be made arbitrarily flexible using sufficient stationary storage, but this comes at considerable cost. In this paper, we investigate how to reduce this cost by exploiting electric vehicle (EV) charging control for unidirectional and bidirectional charging. Specifically, we design a model-predictive control algorithm to reshape building load to match a specified load shape. In realistic settings and for two use cases, we investigate the degree to which the amount of stationary storage is reduced using EV charging control. In both cases, we find that our controller reduces the need for stationary storage compared to existing solutions. Moreover, bidirectional EV charging control substantially reduces the required amount of stationary storage