The Spinocerebellar Ataxia 34-Causing W246G ELOVL4 Mutation Does Not Alter Cerebellar Neuron Populations in a Rat Model.

Spinocerebellar ataxia 34 (SCA34) is an autosomal dominant disease that arises from point mutations in the fatty acid elongase, Elongation of Very Long Chain Fatty Acids 4 (ELOVL4), which is essential for the synthesis of Very Long Chain-Saturated Fatty Acids (VLC-SFA) and Very Long Chain-Polyunsaturated Fatty Acids (VLC-PUFA) (28-34 carbons long). SCA34 is considered a neurodegenerative disease. However, a novel rat model of SCA34 (SCA34-KI rat) with knock-in of the W246G ELOVL4 mutation that causes human SCA34 shows early motor impairment and aberrant synaptic transmission and plasticity without overt neurodegeneration. ELOVL4 is expressed in neurogenic regions of the developing brain, is implicated in cell cycle regulation, and ELOVL4 mutations that cause neuroichthyosis lead to developmental brain malformation, suggesting that aberrant neuron generation due to ELOVL4 mutations might contribute to SCA34. To test whether W246G ELOVL4 altered neuronal generation or survival in the cerebellum, we compared the numbers of Purkinje cells, unipolar brush cells, molecular layer interneurons, granule and displaced granule cells in the cerebellum of wildtype, heterozygous, and homozygous SCA34-KI rats at four months of age, when motor impairment is already present. An unbiased, semi-automated method based on Cellpose 2.0 and ImageJ was used to quantify neuronal populations in cerebellar sections immunolabeled for known neuron-specific markers. Neuronal populations and cortical structure were unaffected by the W246G ELOVL4 mutation by four months of age, a time when synaptic and motor dysfunction are already present, suggesting that SCA34 pathology originates from synaptic dysfunction due to VLC-SFA deficiency, rather than aberrant neuronal production or neurodegeneration.

Conditional Knockout of Neurexins Alters the Contribution of Calcium Channel Subtypes to Presynaptic Ca2+ Influx.

Presynaptic Ca2+ influx through voltage-gated Ca2+ channels (VGCCs) is a key signal for synaptic vesicle release. Synaptic neurexins can partially determine the strength of transmission by regulating VGCCs. However, it is unknown whether neurexins modulate Ca2+ influx via all VGCC subtypes similarly. Here, we performed live cell imaging of synaptic boutons from primary hippocampal neurons with a Ca2+ indicator. We used the expression of inactive and active Cre recombinase to compare control to conditional knockout neurons lacking either all or selected neurexin variants. We found that reduced total presynaptic Ca2+ transients caused by the deletion of all neurexins were primarily due to the reduced contribution of P/Q-type VGCCs. The deletion of neurexin1α alone also reduced the total presynaptic Ca2+ influx but increased Ca2+ influx via N-type VGCCs. Moreover, we tested whether the decrease in Ca2+ influx induced by activation of cannabinoid receptor 1 (CB1-receptor) is modulated by neurexins. Unlike earlier observations emphasizing a role for ß-neurexins, we found that the decrease in presynaptic Ca2+ transients induced by CB1-receptor activation depended more strongly on the presence of α-neurexins in hippocampal neurons. Together, our results suggest that neurexins have unique roles in the modulation of presynaptic Ca2+ influx through VGCC subtypes and that different neurexin variants may affect specific VGCCs.

Robust GRK2/3/6-dependent desensitization of oxytocin receptor in neurons.

Oxytocin plays critical roles in the brain as a neuromodulator, regulating social and other affective behavior. However, the regulatory mechanisms controlling oxytocin receptor (OXTR) signaling in neurons remain unexplored. In this study, we have identified robust and rapid-onset desensitization of OXTR response in multiple regions of the mouse brain. Both cell autonomous spiking response and presynaptic activation undergo similar agonist-induced desensitization. G-protein-coupled receptor kinases (GRK) GRK2, GRK3, and GRK6 are recruited to the activated OXTR in neurons, followed by recruitment of ß-arrestin-1 and -2. Neuronal OXTR desensitization was impaired by suppression of GRK2/3/6 kinase activity but remained unaltered with double knockout of ß-arrestin-1 and -2. Additionally, we observed robust agonist-induced internalization of neuronal OXTR and its Rab5-dependent recruitment to early endosomes, which was impaired by GRK2/3/6 inhibition. This work defines distinctive aspects of the mechanisms governing OXTR desensitization and internalization in neurons compared to prior studies in heterologous cells.

Investigation of the neural correlation with task performance and its effect on cognitive load level classification.

Electroencephalogram (EEG)-based cognitive load assessment is now an important assignment in psychological research. This type of research work is conducted by providing some mental task to the participants and their responses are counted through their EEG signal. In general assumption, it is considered that during different tasks, the cognitive workload is increased. This paper has investigated this specific idea and showed that the conventional hypothesis is not correct always. This paper showed that cognitive load can be varied according to the performance of the participants. In this paper, EEG data of 36 participants are taken against their resting and task (mental arithmetic) conditions. The features of the signal were extracted using the empirical mode decomposition (EMD) method and classified using the support vector machine (SVM) model. Based on the classification accuracy, some hypotheses are built upon the impact of subjects' performance on cognitive load. Based on some statistical consideration and graphical justification, it has been shown how the hypotheses are valid. This result will help to construct the machine learning-based model in predicting the cognitive load assessment more appropriately in a subject-independent approach.

Novel Approaches for Elongation of Fish Oils into Very-Long-Chain Polyunsaturated Fatty Acids and Their Enzymatic Interesterification into Glycerolipids.

Elongation of the Very-Long-Chain Fatty Acids-4 (ELOVL4) enzyme that is expressed in neuronal tissues, sperm, and testes mediates biosynthesis of very-long-chain polyunsaturated fatty acids (VLC-PUFAs) from dietary long chain PUFAs (LC-PUFAs). The VLC-PUFAs are critical for neuronal and reproductive function. Therefore, mutations in ELOVL4 that affect VLC-PUFA biosynthesis contribute to retinal degenerative diseases including Autosomal Dominant Stargardt-like Macular Dystrophy (STGD3). Recent studies have also shown not only a depletion of retinal VLC-PUFAs with normal aging but also a more significant loss of VLC-PUFAs in donor eyes of patients with age-related macular degeneration (AMD). However, currently, there are no natural sources of VLC-PUFAs to be evaluated as dietary supplements for the attenuation of retinal degeneration in animal models of STGD3. Here, we report the development of a novel chemical approach for elongation of eicosapentaenoic (C20:5 n-3) and docosahexaenoic (C22:6 n-3) acids from fish oils by 6 carbon atoms to make a unique group of VLC-PUFAs, namely all-cis-hexacosa-11,14,17,20,23-pentaenoic acids (C26:5 n-3) and all-cis-octacosa-10,13,16,19,22,25-hexaenoic acids (C28:6 n-3). The three-step elongation approach that we report herein resulted in a good overall yield of up to 20.2%. This more sustainable approach also resulted in improved functional group compatibility and minimal impact on the geometrical integrity of the all-cis double bond system of the VLC-PUFAs. In addition, we also successfully used commercial deep-sea fish oil concentrate as an inexpensive material for the C6 elongation of fish oil LC-PUFAs into VLC-PUFAs, which resulted in the making of gram scales of VLC-PUFAs with an even higher isolation yield of 31.0%. The quality of fish oils and the content of oxidized lipids were key since both strongly affected the activity of the PEPPSI-IPr catalyst and ultimately the yield of coupling reactions. Downstream enzymatic interesterification was used for the first time to prepare structured glycerolipids enriched with VLC-PUFAs that could be evaluated in vivo to determine absorption and transport to target tissues relative to those of the free fatty acid forms. It turned out that in the synthesis of structured triacylglycerols and glycerophospholipids with VLC-PUFAs, the polarity of the immobilized lipase carrier and its humidity were essential.

A role for the cystathionine-ß-synthase /H2S axis in astrocyte dysfunction in the aging brain.

Astrocytic dysfunction is central to age-related neurodegenerative diseases. However, the mechanisms leading to astrocytic dysfunction are not well understood. We identify that among the diverse cellular constituents of the brain, murine and human astrocytes are enriched in the expression of CBS. Depleting CBS in astrocytes causes mitochondrial dysfunction, increases the production of reactive oxygen species (ROS) and decreases cellular bioenergetics that can be partially rescued by exogenous H2S supplementation or by re-expressing CBS. Conversely, the CBS/H2S axis, associated protein persulfidation and proliferation are decreased in astrocytes upon oxidative stress which can be rescued by exogenous H2S supplementation. Here we reveal that in the aging brain, the CBS/H2S axis is downregulated leading to decreased protein persulfidation, together augmenting oxidative stress. Our findings uncover an important protective role of the CBS/H2S axis in astrocytes that may be disrupted in the aged brain.

Synapse-Specific Defects in Synaptic Transmission in the Cerebellum of W246G Mutant ELOVL4 Rats-a Model of Human SCA34.

Elongation of very long fatty acids-4 (ELOVL4) mediates biosynthesis of very long chain-fatty acids (VLC-FA; ≥28 carbons). Various mutations in this enzyme result in spinocerebellar ataxia-34 (SCA34). We generated a rat model of human SCA34 by knock-in of a naturally occurring c.736T>G, p.W246G mutation in the Elovl4 gene. Our previous analysis of homozygous W246G mutant ELOVL4 rats (MUT) revealed early-onset gait disturbance and impaired synaptic transmission and plasticity at parallel fiber-Purkinje cell (PF-PC) and climbing fiber-Purkinje cell (CF-PC) synapses. However, the underlying mechanisms that caused these defects remained unknown. Here, we report detailed patch-clamp recordings from Purkinje cells that identify impaired synaptic mechanisms. Our results show that miniature EPSC (mEPSC) frequency is reduced in MUT rats with no change in mEPSC amplitude, suggesting a presynaptic defect of excitatory synaptic transmission on Purkinje cells. We also find alterations in inhibitory synaptic transmission as miniature IPSC (mIPSC) frequency and amplitude are increased in MUT Purkinje cells. Paired-pulse ratio is reduced at PF-PC synapses but increased at CF-PC synapses in MUT rats, which along with results from high-frequency stimulation suggest opposite changes in the release probability at these two synapses. In contrast, we identify exaggerated persistence of EPSC amplitude at CF-PC and PF-PC synapses in MUT cerebellum, suggesting a larger readily releasable pool (RRP) at both synapses. Furthermore, the dendritic spine density is reduced in MUT Purkinje cells. Thus, our results uncover novel mechanisms of action of VLC-FA at cerebellar synapses, and elucidate the synaptic dysfunction underlying SCA34 pathology.SIGNIFICANCE STATEMENT Very long chain-fatty acids (VLC-FA) are an understudied class of fatty acids that are present in the brain. They are critical for brain function as their deficiency caused by mutations in elongation of very long fatty acids-4 (ELOVL4), the enzyme that mediates their biosynthesis, results in neurologic diseases including spinocerebellar ataxia-34 (SCA34), neuroichthyosis, and Stargardt-like macular dystrophy. In this study, we investigated the synaptic defects present in a rat model of SCA34 and identified defects in presynaptic neurotransmitter release and dendritic spine density at synapses in the cerebellum, a brain region involved in motor coordination. These results advance our understanding of the synaptic mechanisms regulated by VLC-FA and describe the synaptic dysfunction that leads to motor incoordination in SCA34.

Automated Diagnosis of Diabetic Retinopathy Using Deep Learning: On the Search of Segmented Retinal Blood Vessel Images for Better Performance.

Diabetic retinopathy is one of the most significant retinal diseases that can lead to blindness. As a result, it is critical to receive a prompt diagnosis of the disease. Manual screening can result in misdiagnosis due to human error and limited human capability. In such cases, using a deep learning-based automated diagnosis of the disease could aid in early detection and treatment. In deep learning-based analysis, the original and segmented blood vessels are typically used for diagnosis. However, it is still unclear which approach is superior. In this study, a comparison of two deep learning approaches (Inception v3 and DenseNet-121) was performed on two different datasets of colored images and segmented images. The study's findings revealed that the accuracy for original images on both Inception v3 and DenseNet-121 equaled 0.8 or higher, whereas the segmented retinal blood vessels under both approaches provided an accuracy of just greater than 0.6, demonstrating that the segmented vessels do not add much utility to the deep learning-based analysis. The study's findings show that the original-colored images are more significant in diagnosing retinopathy than the extracted retinal blood vessels.

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.

Selective Ablation of Sod2 in Astrocytes Induces Sex-Specific Effects on Cognitive Function, d-Serine Availability, and Astrogliosis.

Cognitive decline is a debilitating aspect of aging and neurodegenerative diseases such as Alzheimer's disease are closely associated with mitochondrial dysfunction, increased reactive oxygen species, neuroinflammation, and astrogliosis. This study investigated the effects of decreased mitochondrial antioxidant response specifically in astrocytes on cognitive performance and neuronal function in C57BL/6J mice using a tamoxifen-inducible astrocyte-specific knockout of manganese superoxide dismutase (aSOD2-KO), a mitochondrial matrix antioxidant that detoxifies superoxide generated during mitochondrial respiration. We reduced astrocyte SOD2 levels in male and female mice at 11-12 months of age and tested in an automated home cage (PhenoTyper) apparatus for diurnal patterns, spatial learning, and memory function at 15 months of age. aSOD2-KO impaired hippocampal-dependent spatial working memory and decreased cognitive flexibility in the reversal phase of the testing paradigm in males. Female aSOD2-KO showed no learning and memory deficits compared with age-matched controls despite significant reduction in hippocampal SOD2 expression. aSOD2-KO males further showed decreased hippocampal long-term potentiation, but paired-pulse facilitation was unaffected. Levels of d-serine, an NMDA receptor coagonist, were also reduced in aSOD2-KO mice, but female knockouts showed a compensatory increase in serine racemase expression. Furthermore, aSOD2-KO mice demonstrated increased density of astrocytes, indicative of astrogliosis, in the hippocampus compared with age-matched controls. These data demonstrate that reduction in mitochondrial antioxidant stress response in astrocytes recapitulates age-related deficits in cognitive function, d-serine availability, and astrogliosis. Therefore, improving astrocyte mitochondrial homeostasis may provide a therapeutic target for intervention for cognitive impairment in aging.SIGNIFICANCE STATEMENT Diminished antioxidant response is associated with increased astrogliosis in aging and in Alzheimer's disease. Manganese superoxide dismutase (SOD2) is an antioxidant in the mitochondrial matrix that detoxifies superoxide and maintains mitochondrial homeostasis. We show that astrocytic ablation of SOD2 impairs hippocampal-dependent plasticity in spatial working memory, reduces long-term potentiation of hippocampal neurons and levels of the neuromodulator d-serine, and increases astrogliosis, consistent with defects in advanced aging and Alzheimer's disease. Our data provide strong evidence for sex-specific effects of astrocytic SOD2 functions in age-related cognitive dysfunction.

Interaction and Subcellular Association of PRRT1/SynDIG4 With AMPA Receptors.

AMPA receptors (AMPAR) are organized into supramolecular complexes in association with other membrane proteins that provide exquisite regulation of their biophysical properties and subcellular trafficking. Proline-rich transmembrane protein 1 (PRRT1), also named as SynDIG4, is a component of native AMPAR complexes in multiple brain regions. Deletion of PRRT1 leads to altered surface levels and phosphorylation status of AMPARs, as well as impaired forms of synaptic plasticity. Here, we have investigated the mechanisms underlying the observed regulation of AMPARs by investigating the interaction properties and subcellular localization of PRRT1. Our results show that PRRT1 can interact physically with all AMPAR subunits GluA1-GluA4. We decipher the membrane topology of PRRT1 to find that contrary to the predicted dual membrane pass, only the second hydrophobic segment spans the membrane completely, and is involved in mediating the interaction with AMPARs. We also report a physical interaction of PRRT1 with phosphatase PP2B that dephosphorylates AMPARs during synaptic plasticity. Our co-localization analysis in primary neuronal cultures identifies that PRRT1 associates with AMPARs extrasynaptically where it localizes to early and recycling endosomes as well as to the plasma membrane. These findings advance the understanding of the mechanisms by which PRRT1 regulates AMPARs under basal conditions and during synaptic plasticity.

EEG Channel Correlation Based Model for Emotion Recognition.

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images of channel correlation of EEG sub-bands. Then the images were fed into the CNN model to recognize emotion. Two protocols were conducted, namely, protocol-1 to identify two levels and protocol-2 to recognize three levels of valence and arousal that demonstrate emotion. We investigated that only the upper triangular portion of the PCC featured images reduced the computational complexity and size of memory without hampering the model accuracy. The maximum accuracy of 78.22% on valence and 74.92% on arousal were obtained using the internationally authorized DEAP dataset.

W246G Mutant ELOVL4 Impairs Synaptic Plasticity in Parallel and Climbing Fibers and Causes Motor Defects in a Rat Model of SCA34.

Spinocerebellar ataxia (SCA) is a neurodegenerative disorder characterized by ataxia and cerebellar atrophy. A number of different mutations gives rise to different types of SCA with characteristic ages of onset, symptomatology, and rates of progression. SCA type 34 (SCA34) is caused by mutations in ELOVL4 (ELOngation of Very Long-chain fatty acids 4), a fatty acid elongase essential for biosynthesis of Very Long Chain Saturated and Polyunsaturated Fatty Acids (VLC-SFA and VLC-PUFA, resp., ≥28 carbons), which have important functions in the brain, skin, retina, Meibomian glands, testes, and sperm. We generated a rat model of SCA34 by knock-in of the SCA34-causing 736T>G (p.W246G) ELOVL4 mutation. Rats carrying the mutation developed impaired motor deficits by 2 months of age. To understand the mechanism of these motor deficits, we performed electrophysiological studies using cerebellar slices from rats homozygous for W246G mutant ELOVL4 and found marked reduction of long-term potentiation at parallel fiber synapses and long-term depression at climbing fiber synapses onto Purkinje cells. Neuroanatomical analysis of the cerebellum showed normal cytoarchitectural organization with no evidence of degeneration out to 6 months of age. These results point to ELOVL4 as essential for motor function and cerebellar synaptic plasticity. The results further suggest that ataxia in SCA34 patients may arise from a primary impairment of synaptic plasticity and cerebellar network desynchronization before onset of neurodegeneration and progression of the disease at a later age.

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias.

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

A Narrative Review on Clinical Applications of fNIRS.

Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolving for its real-time monitoring, relatively low-cost, radiation-less environment, portability, patient-friendliness, etc. Including brain-computer interface and functional neuroimaging research, this technique has some important application of clinical perspectives such as Alzheimer's disease, schizophrenia, dyslexia, Parkinson's disease, childhood disorders, post-neurosurgery dysfunction, attention, functional connectivity, and many more can be diagnosed as well as in some form of assistive modality in clinical approaches. Regarding the issue, this review article presents the current scopes of fNIRS in medical assistance, clinical decision making, and future perspectives. This article also covers a short history of fNIRS, fundamental theories, and significant outcomes reported by a number of scholarly articles. Since this review article is hopefully the first one that comprehensively explores the potential scopes of the fNIRS in a clinical perspective, we hope it will be helpful for the researchers, physicians, practitioners, current students of the functional neuroimaging field, and the related personnel for their further studies and applications.

Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation.

This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain-computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection from the WPT features. In conventional approaches, Shannon entropy and mutual information methods are often used to select the features. In this work, we have shown that our proposed Rényi min-entropy-based approach outperforms the conventional methods for multiple EEG signal classification. The dataset of BCI competition-IV (contains 4-class motor imagery EEG signal) is used for this experiment. The data are preprocessed and separated as the classes and used for the feature extraction using WPT. Then, for feature selection Shannon entropy, mutual information, and Rényi min-entropy methods are applied. With the selected features, four-class motor imagery EEG signals are classified using several machine learning algorithms. The results suggest that the proposed method is better than the conventional approaches for multiple-class BCI.

Mathematical modeling of clinical engineering approach to evaluate the quality of patient care.

At present, the patient care delivery system (PCDS) in a hospital/medical institute/clinic is absolutely medical technology-dependent and this tendency is found to increase day by day. To ensure the quality of patient care (QPC) appropriate implementation of the patient care technology management system (PCTMS) is necessary. Unfortunately, it is found to be absent in the healthcare delivery system in most of the countries in the world. The situation is very much severe, particularly, in medium- and low-income countries like Malaysia, India, Sri Lanka, Bangladesh, Pakistan, etc. The opposite scenario is found in high-income countries, specifically, in Japan where QPC has been improved significantly by adopting the clinical engineering approach (CEA) in their PCDS. Up to now, QPC is determined based on prediction as there are no mathematical ways to evaluate it properly. In this study, we for the first time, propose a mathematical model to evaluate the QPC quantitatively based on feedback control analogy taking into account of CEA in PCTMS, particularly, for clinical and surgical equipment. The model consists of three subsections: the clinical engineering department (CED), PCTMS, and health care engineering directorate (HCED). The correlation among the subsections and their performance parameters are defined and standardized. Multiple linear regression method is applied to derive the least square normal equations for each of the subsections and then the regression coefficients are solved by the standard data taken from 1000 beds hospitals of different countries. The model is applied to reveal the present status of QPC for 18 different countries including high-, middle-, and low-income countries of the world. The results obtained from the model demonstrate that the present status of QPC in Japan is 84.69% and in Pakistan, it is only 0.20%. This huge discrepancy is identified to be caused by the inclusion of CEA in PCDS of Japan. The proposed model can be applied to evaluate the QPC of a hospital/in a country and hence to take necessary steps accordingly for establishing the proposed research methodology. It is to be mentioned here that the proposed model cannot be applied to evaluate the QPC in some countries like Bangladesh, Bhutan, Nepal, etc. due to the unavailability of data related to the model parameters.

Modeling and classification of voluntary and imagery movements for brain-computer interface from fNIR and EEG signals through convolutional neural network.

Practical brain-computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating the neuronal electric potential regarding the psychophysiological activity. Since the fNIR modality has an issue of temporal resolution, fNIR alone is not enough to achieve satisfactory classification accuracy as multiple neural stimuli are produced by voluntary and imagery movements. This leads us to make a combination of fNIR and EEG with a view to developing a BCI model for the classification of the brain signals of the voluntary and imagery movements. This work proposes a novel approach to prepare functional neuroimages from the fNIR and EEG using eight different movement-related stimuli. The neuroimages are used to train a convolutional neural network (CNN) to formulate a predictive model for classifying the combined fNIR-EEG data. The results reveal that the combined fNIR-EEG modality approach along with a CNN provides improved classification accuracy compared to a single modality and conventional classifiers. So, the outcomes of the proposed research work will be very helpful in the implementation of the finer BCI system.

PRRT1 regulates basal and plasticity-induced AMPA receptor trafficking.

AMPA-type glutamate receptors (AMPAR) are one of the principal mediators of fast excitatory synaptic transmission in the brain. These receptors associate with multiple integral membrane proteins which influence their trafficking and channel properties. Proline-rich transmembrane protein 1 (PRRT1) is a membrane protein and an understudied component of native AMPAR complexes. In order to understand the regulation of AMPARs by PRRT1, we have performed electrophysiological and biochemical investigations on acute hippocampal slices derived from PRRT1 knockout mice. Our results show that PRRT1 controls the levels of AMPARs at the cell surface, though it is dispensable for synaptic transmission. PRRT1 has differential effects on the stability of AMPAR GluA1 subunit phosphorylated at S845 and at S831, two residues at which the phosphorylation status has major influences on receptor trafficking. Furthermore, PRRT1 is required for NMDA receptor-dependent long-term depression (LTD) and proper NMDA-induced AMPAR trafficking. These findings position PRRT1 as an important regulator of AMPAR stabilization and trafficking in different subcellular pools under basal conditions and during synaptic plasticity.

Neuroligin-2 Determines Inhibitory Synaptic Transmission in the Lateral Septum to Optimize Stress-Induced Neuronal Activation and Avoidance Behavior.

BACKGROUND: Investigations in the neocortex have revealed that the balance of excitatory and inhibitory synaptic transmission (E/I ratio) is important for proper information processing. The disturbance of this balance underlies many neuropsychiatric illnesses, including autism spectrum disorder and schizophrenia. However, little is known about the contribution of E/I balance to the functioning of subcortical brain regions, such as the lateral septum (LS), a structure that plays important roles in regulating anxiety-related behavior. METHODS: We manipulated E/I balance in the mouse LS by localized conditional deletion of neuroligin-2, a postsynaptic cell adhesion protein located at gamma-aminobutyric acidergic synapses and important for inhibitory synaptic transmission. We then performed analyses of synaptic transmission in the LS, stress-induced expression of immediate early gene c-fos, and anxiety-related and depression-related behavior. RESULTS: The absence of neuroligin-2 in the LS in the mature mouse brain resulted in postsynaptic impairment of inhibitory synaptic transmission. Importantly, the reduced inhibition and resulting E/I imbalance decreased the responsiveness of LS neurons to stress. Furthermore, this E/I imbalance in the LS was associated with impaired stress-induced activation of downstream hypothalamic nuclei and reduced avoidance behavior of the animals in the elevated plus maze. CONCLUSIONS: Our results described the synaptic function of neuroligin-2 in the LS, uncovered a positive association between c-Fos-expressing neurons in the LS and downstream hypothalamic areas and avoidance behavior, and demonstrated that intact inhibitory synaptic transmission and proper E/I balance are required for the optimal functioning of this subcortical circuit.