Mouse ultrasonic vocalizations (USVs) contain predictable sequential structures like bird songs and speech. Neural representation of USVs in the mouse primary auditory cortex (Au1) and its plasticity ...with experience has been largely studied with single-syllables or dyads, without using the predictability in USV sequences. Studies using playback of USV sequences have used randomly selected sequences from numerous possibilities. The current study uses mutual information to obtain context-specific natural sequences (NSeqs) of USV syllables capturing the observed predictability in male USVs in different contexts of social interaction with females. Behavioral and physiological significance of NSeqs over random sequences (RSeqs) lacking predictability were examined. Female mice, never having the social experience of being exposed to males, showed higher selectivity for NSeqs behaviorally and at cellular levels probed by expression of immediate early gene c-
in Au1. The Au1 supragranular single units also showed higher selectivity to NSeqs over RSeqs. Social-experience-driven plasticity in encoding NSeqs and RSeqs in adult females was probed by examining neural selectivities to the same sequences before and after the above social experience. Single units showed enhanced selectivity for NSeqs over RSeqs after the social experience. Further, using two-photon Ca
imaging, we observed social experience-dependent changes in the selectivity of sequences of excitatory and somatostatin-positive inhibitory neurons but not parvalbumin-positive inhibitory neurons of Au1. Using optogenetics, somatostatin-positive neurons were identified as a possible mediator of the observed social-experience-driven plasticity. Our study uncovers the importance of predictive sequences and introduces mouse USVs as a promising model to study context-dependent speech like communications.
Humans need to detect patterns in the sensory world. For instance, speech is meaningful sequences of acoustic tokens easily differentiated from random ordered tokens. The structure derives from the predictability of the tokens. Similarly, mouse vocalization sequences have predictability and undergo context-dependent modulation. Our work investigated whether mice differentiate such informative predictable sequences (NSeqs) of communicative significance from RSeqs at the behavioral, molecular, and neuronal levels. Following a social experience in which NSeqs occur as a crucial component, mouse auditory cortical neurons become more sensitive to differences between NSeqs and RSeqs, although preference for individual tokens is unchanged. Thus, speech-like communication and its dysfunction may be studied in circuit, cellular, and molecular levels in mice.
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•Electrospun nanohybrid new generation scaffolds was fabricated from synthesized biomolecule tethered polyurethane-urea.•Functionalized CNT doped hybrid nanomaterials (CCNTH) improved ...physico-mechanical properties of nanohybrid scaffolds.•A minute amount of functionalized CNT drastically improved cell viability and upregulated osteogenic gene expression.•In vivo study in rat Tibia and Skull indicates accelerated bone regeneration efficacy of nanohybrid scaffold.•Histopathological study indicates no toxic effect of incorporated functionalized CNT.
Complete natural healing of skeletal tissues may take several months or even years, depending on the type and size of defects owing to slow auto-osteogenesis. Numerous techniques have been explored to gear-up the bone-healing process, as bone tissue has complex nanohybrid hierarchical meshwork containing nano apatite layers. Healing of such tissues associates with risks of pathogenic infection and ectopic bone formation. Therefore, most of the approaches fail to meet the requisite criteria to adopt in clinical practice. Carbonaceous bone substitutes may trigger healing, but an excess of it may render many adverse effects. In the current study, nanohydroxyapatite (nHA) was prepared, doped with a minute amount (0.15%) of carboxyl functionalized multiwall carbon nanotube (CCNT). The hybrid nanomaterial (CCNTH) was introduced within the synthesized biomolecule tethered segmented polyurethane-urea (SP) through in-situ technique and scaffolds were fabricated by electrospinning technique. CCNT doping improves the tensile strength and the hardness of the nano-scaffold by 94.5% and 173.6%, respectively. In vitro MTT assay, FESEM and protein adsorption study indicated the excellent cytocompatibility of the nanohybrid scaffolds. The qRT-PCR study indicates the significant expression of the osteogenic bone marker, osteocalcin (OCN) and the alkaline phosphatase (ALP) of the CCNTH incorporated nanohybrid scaffolds compared to the SP scaffold. Furthermore, the in vivo rat tibia and skull model exhibit excellent bone regeneration efficacy compared to the control without showing any sign of organ toxicity. Thus, a minute amount of CCNT doped nHA incorporated SP based micro-porous nanohybrid scaffold can be used as an alternate suitable biomaterial for osteoinduction application.
Functional near infrared spectroscopy (fNIRS) devices capture the variability in oxygenated and deoxygenated blood flow in the cortical layers of the brain during execution of cognitive tasks. The ...present letter employs fNIRS device to study the cognitive lagging in working memory (WM) performance based on visuo-spatial forward span number search. The unsupervised learning-based clustering yields three distinct clusters of hemodynamic loads during the task performance. From these three clusters, we estimate cognitive lagging by computing the performance score per unit time and assigned three cognitive load classes: low, moderate, and high. Moving toward the classification approach of these three cognitive load classes, ensemble learning classifier produces higher classification accuracy, which reaches a maximum of 91.66%. The trend of shifting cognitive load from low toward high with performance score is observed from estimated Pearson's cross-correlation using the medoid points of cognitive load clusters and associated performance scores. The visualization of dynamic changes in cognitive load (low, moderate, and high) in temporal span of WM performance is obtained from the voxel plot approach, which advocates that regional deactivation of orbitofrontal cortex and augmented hemodynamic load in the dorsolateral prefrontal cortex has a possible relation with this cognitive lagging. In the view of experimental outcomes, the fNIRS-sensor-based measuring of cognitive load could be a future assessment tool for cognitive failure in higher task demand.
During the past two decade researchers have been exploring the mechanism of object shape and depth perception using EEG and fMRI. However, the underlying cortical process of perceiving different ...object sizes from a constant visual distance has never been explored. This paper provides a novel understanding of relative object size classification based on direct measure of parieto-occipital hemodynamics using functional near infrared spectroscopy (fNIRS). The cortical response is recorded from subjects engaged in visual perception task of relative object size. The signal is preprocessed (artifact removal) for construction of 176 features, which are thus reduced to 22 features using particle swarm optimization (PSO) technique. The reduced features are subsequently fed into an interval type -2 fuzzy set to classify the perceived objects (based on the underlying hemodynamic data) into three different classes: LARGE, MEDIUM and SMALL. Experimental analysis shows that the proposed feature-selection and classification framework attain higher classification accuracy which reaches over 87% in the classification of large objects. Analysis, further undertaken to know the underlying neurovascular mechanisms, reveals a distinct dorso-ventral shift (shall-medium-to-large) in parieto-occipital hemodynamic load which can be observed from the topographic brain activation. The average activation shifts are measured as 73.35 degrees in the right hemisphere compared to 93.71 degrees in the left hemisphere. The experimental outcomes could provide a novel measure in cortical hemodynamic features based perception of object size. In future, it could provide justification towards the visually challenged persons with perceptual difficulties.
Although there exist recent works on fMRI based cognitive learning, there is a dearth of literature on fNIRs based studies on learning and memory. This paper provides a novel study on the cognitive ...load detection of subjects engaged in symbol-meaning associative learning tasks from the direct measurement of the hemodynamic response of the brain. The hemodynamic response collected during symbol-meaning associative learning tasks by subjects are pre-processed (filtered from artifacts) for extraction of 112-dimensional features, which are reduced to 20 dimensions by a meta-heuristic optimization algorithm for subsequent transfer to a interval type-2 fuzzy classifier to classify three levels of cognitive loads (High, Low and Moderate) borne by the subjects at different time slots of the learning task. Analysis undertaken reveals that the type-2 fuzzy classifier with the proposed feature selection mechanism has a high performance in classification of the cognitive loads over 89%. Experimental analysis further reveals that the transfer of brain activation from orbitofrontal to ventrolateral prefrontal cortex takes place during transition of cognitive load from high to low. In addition, the activation of dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. These findings would offer justification of inability to handle high cognitive loads by people with under-developed/damaged orbitofrontal and dorsolateral prefrontal cortex.
During past few decades cancer has remained as the largest cause of mortality worldwide and number of patients suffering from cancer has been increasing at a fast rate. Hence medical research during ...the last few decades has been concentrating on identification and characterization of new synthetic pharmacological compounds to overcome this enormous problem. Leaf extracts of coniferous plant Cryptomeria japonica being known for their strong antibacterial and antifungal functions were selected to determine their antitumor/anticancer potentialities.Methanolic extract of leaves were tested to determine its antitumor action in standard murine model of Ehrlich Ascites Carcinoma (EAC). Graded doses of the extract were given intraperitoneally to batches of mice, who received EAC challenge after 3hr. Treatment with same amounts of extract was continued for 9 consecutive days. Protective capacity of the leaf extract was evaluated in animals.Statistically significant protection was observed with respect to different parameters including tumor volume , tumor cell count , viable tumor cell count, non- viable tumor cell count , mean survival time and increase in life span. Simultaneously hematological parameters were restored in treated mice vis-à-vis untreated control animals. Furthermore, the extract revealed distinct cytotoxic property, which may be the relevant reason of its anticancer/antitumor function.This study shows efficacy of methanolic extract of leaves of Cryptomeria japonica as a probable antitumor/anticancer agent. Phytochemical analysis of the extract showed presence of flavonoids, which are known to possess significant anticancer activity. Thus there is a definite possibility of developing novel anticancer drugs from such plant products
Schizophrenia is a mental disorder which has underlying neurological deficits. Schizophrenic patients have low social acceptance as well as a higher death rate. Initiation of schizophrenic symptoms ...are associated with a wide range of cognitive deficits. Impaired working memory performance is a basic characteristics of understanding schizophrenia. The performance impairment increases significantly with increased cognitive demand. Impaired verbal working memory is also associated with schizophrenic condition which can be measuredby analyzing prefrontal hemodynamics during various task difficulties. The brain regions associated with working memory performance is related to the prefrontal cortex. Therefore, near infrared spectroscopy becomes a significant tool for recording hemodynamic changes during working memory tasks. Our goal in this experiment is to classify schizophrenic and normal participants using three different task difficulties of verbal working memory and recall using differentconventional classification algorithms (LSVM, LDA and kNN). A principal component analysis induced feature selector-classifier based approach is undertaken to improve classification accuracy. LSVM provides the maximum classification accuracy of 85.12% for backward span verbal memory tasks. It is followed by LDA and KNN. The classifiers' performances are correlated with the performance accuracy data obtained from recall tasks. Among three different verbal working memory tasks, backward span recall is found to be the most appropriate in classifying schizophrenic from normal population. This paper reports a novel verbal working memory task performance based approach to diagnose schizophrenia. The experimental outcome supports the above mentioned possibility by providing a reliable classification accuracy in detecting schizophrenia from the normal population.
Frontal lobe epilepsy is the second most common form of epilepsy which initiates during sleep and causes death. Early detection is the solitary measure to control seizure. Electroencephalography ...(EEG)is the only confirmatory test for seizure. However, epileptic research still depends on studies based on animal models. In this research, our main objective is to study the significance of low frequency brainwaves (which is dominant during sleep) in the prognosis of seizure from WAG/Rij rat data. Wavelet based decomposition coefficients and power spectral density (PSD) are selected as features to take care ofnon-stationary nature of brain waves. A comparative study of classifiers' performance is formulated between low frequency wave range (0.5-13 Hz) and the total frequency range (0.5-40 Hz) where RBF-SVM provides the maximum classification accuracy. The average classification accuracy of RBF-SVM for low frequency wave range is found to be 92.50%, which lies within a range of <;2%, compared to the total frequency wave range as 94.03%. QDA has the second highest classification accuracy. Both RBF-SVM and QDA perform better for total frequency wave range compared to low frequency wave range. However, LSVM and LDA produce a different pattern - higher classification accuracies for low frequency wave range EEG data. The novelty of this paper lies to the fact that selection of low frequency brain wave is more significant in the prognosis of frontal lobe epilepsy that is not only useful detecting spikes during sleep but also removes environmental and physiological artifacts with higher frequencies.
This paper demonstrates a near infrared spectroscopic study of understanding effect of disturbance on working memory performance. Embedded prefrontal hemodynamic characters are obtained from recorded ...data which is filtered and preprocessed by adopting a mean normalization technique. Furthermore, a ReleifF algorithm based feature selection is adopted to sort out the most prominent feature set. A comparative study is drawn to demonstrate the effectivity of the proposed framework by comparing classifiers accuracy which shows that our proposed framework outperforms other conventional principal component analysis (PCA) based feature selection and classification techniques. In addition, correlations among different learning conditions, as well as recall conditions are calculated to demonstrate how diverse conditions (without and with disturbances) alter prefrontal hemodynamics. In addition, a voxel plot approach shows the changing oxyhemoglobin concentrations during different recall tasks (with and without disturbance during learnings). The novelty of this paper lies in understanding the role of disturbance during encoding of information which affects recall performance in working memory tasks.
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