Neuroscience informatics最新文献

In the rapidly growing world of technology and evolution, the outbreak and emergences diseases have become a critical issue. Precaution, prevention and controlling the diseases by technology has become the major challenge for healthcare professionals and health care industries. Maintaining a healthy lifestyle has become impossible in the busy work schedules. Smart health monitoring system is the solution to the above poses challenges. The recent revolution of industry 5.0 and 5G has led to development of smart cum cost effective sensors which help in real time health monitoring or individuals. The SHM has led to fast, cost effective, and reliable health monitoring services from remote locations which was not possible with traditional health care systems. The integration of blockchain framework improved data security and data privacy of confidential data of patient to prevent the data misuse against patients. Involvement of Deep Learning and Machine learning to analyze health data to achieve multiple targets has helped attain preventive healthcare and fatality management in patients. This has helped in the early detection of chronic diseases which was not possible recently. To make the services more cost effective and real time, the integration of cloud computing and cloud storage has been implemented. The work presents the systematic review of SHM along with recent advancements in SHM with existing challenges.

Text devices are effectively and heavily used for interactions these days. Emotion extraction from the text has derived huge importance and is upcoming area of research in Natural Language Processing. Recognition of emotions from text has high practical utilities for quality improvement like in Human-Computer Interaction, recommendation systems, online education, data mining and so on. However, there are the issues of irrelevant feature extraction during emotion extraction from text. It causes mis-prediction of emotion. To overcome such challenges, this paper proposes a Leaky Relu activated Deep Neural Network (LRA-DNN). The proposed model comes under four categories, such as pre-processing, feature extraction, ranking and classification. The collected data from the dataset are pre-processed for data cleansing, appropriate features are extracted from the pre-processed data, relevant ranks are assigned for each extracted feature in the ranking phase and finally, the data are classified and accurate output is obtained from the classification phase. Publically available datasets are used in this research to compare the results obtained by the proposed LRA-DNN with the previous state-of-art algorithms. The outcomes indicated that the proposed LRA-DNN obtains the highest accuracy, sensitivity, and specificity at the rate of 94.77%, 92.23%, and 95.91% respectively which is promising compared to the existing ANN, DNN and CNN methods. It also efficiently reduces the mis-prediction and misclassification error.

Fetal Alcohol Spectrum Disorder (FASD) comprises the phenotypes induced by prenatal alcohol exposure. Understanding the molecular mechanisms of FASD is needed since it is a public health problem. This study aimed to evaluate the impact of ethanol in the differential gene expression (DGE) of embryonic cells and fetal tissues by performing a transcriptome meta-analysis in microarrays datasets publicly available. The datasets were obtained in the GEO database and DGE was evaluated, followed by meta-analysis. DGE was also analyzed in a RNA-Seq dataset, although it was not included in the meta-analysis. To filter the main candidate genes, a database and literature review was performed, followed by ontologies enrichment analyses. In the meta-analysis, 1,938 genes were deregulated and 487 were perturbed in the RNA-Seq. Calcium homeostasis and neuroinflammation genes were overrepresented in the meta-analysis and RNA-Seq, respectively. After the database and literature review, DOCK8, FOXG1, IL1RN, and PRKN genes were proposed as new candidates for FASD; they are associated with neurodevelopment and neuroinflammation. BDNF and SLC2A1, previously associated to FASD, were also suggested in meta-analysis as candidate genes. It is known neuroinflammation reduction might help to minimize the alcohol damage. Hence, there is an urgent need to understand FASD molecular mechanisms to help in strategies aimed at preventing ethanol-induced neurologic damage.

The visual evoked potential as an electrophysiological signal is mainly used in the neurophysiological exploration of the optic nerves. Traditionally, medical doctors base their diagnosis of specific pathologies related to the time delay of the nerve flow on the time scale. In this context, the VEP latency P100 that reflects a temporal notion is considered the main characteristic on which human interpretation is based. However, its value is influenced by different factors and remains a limited method. This insufficiency triggers our interest instead in deep learning architectures, taking into consideration and adapting to the specificity of each particularity related to the laboratory of the neurophysiological exploration unit in the hospital. The comparison between the results obtained from Matlab by the application of the CNN as well as the RNN, based on the evaluation parameters calculated after k-fold cross-validation, confirms that the CNN-1D architecture can be considered powerful in terms of reliability of classification between signals that are related to pathological subjects and normal ones, which privileges the use of this architecture compared with recurrent neural networks that are less reliable and require more time for execution, subsequently the use of the CNN will allow us to avoid even the extraction of attributes for the discrimination between the two classes object of classification, with the possibility to progressively improve the performance of the solution over time based on the new signals acquired in the VEP analysis laboratory.

Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) applications. Electroencephalography (EEG) signals, which record brain activity, can be used to analyze BCI-based tasks utilizing Machine Learning (ML) methods. The current paper considers decoding IS brain waves using the fusion of classical signal processing, Graph Signal Processing (GSP), and Graph Learning (GL) based features. The proposed fusion method, named GraphIS (short for a Graph-based Imagined Speech BCI decoder), is applied to the four-class classification (three classes of the imagined words, in addition to the rest state) on EEG recordings of fifteen subjects. Results show that GSP and GL-based features can highly improve the performance of classification outcomes compared to using only classical signal processing features and over the state-of-the-art Common Spatial Pattern (CSP) feature extractor by considering the spatial information of the signals as well as interactions between channels in regions of interest. The proposed GraphIS method leads to a mean accuracy of 50.10% in the studied four-class IS classification task, compared to using only one feature set with an accuracy of 47.86% and 46.10%, and also the state-of-the-art CSP with an accuracy of 47.10%. Additionally, using an EEG connectivity map of the electrode signals obtained from GL methods, we also found a strong connection in the right frontal region as well as in the left frontal regions during IS, which had not been focused on in the previous IS papers.

Increased population has led to create the environmental related issues. Zinc Oxide has great attention due to its application in versatile smart and functional material. In the recent paper, we have observed the variation in shape and size for different precursor (0.45 M - Zn acetate dihydrate, Zn nitrate hexahydrate) with aloe vera extract ZnO nanoparticles, data analytics have been prepared with annealing at 650 ^∘C. The prepared solution was analyzed by using simple solution method. Structural, morphological, optical and electrical properties defined certain nanomaterials. XRD spectra showed polycrystalline in nature. In the case of Zn nitrate, more instance peaks are found and SEM reveals the particle size drop into a range of 50-90 nm. Analysis of FTIR was conducted to classify the mineral constituents. The further capacitance levels are measured at a low scale. The resistivity spectrum of ZnO nanoparticles ranged from $3\times {10}^{-2}$ to $5\times {10}^{-2}$ (in cm)⁻¹. Optical band gap of the synthesized particles lies in the range of 3.10-3.20 eV, which confirmed that nanoparticles are suitable for gas sensor and solar cell applications. These synthesized nanoparticles can further be used for the neuroscience application such as fabrication of medical instruments and for medical purpose too. In turn, these materials contribute to novel diagnostic and therapeutic strategies, including drug delivery, neuroprotection, neural regeneration, neuroimaging and neurosurgery.

Background: Prophylactic treatment for recurrence of febrile seizure generally consists of intermittent administration of diazepam or clobazam, or long-term treatment with valproic acid or phenobarbital. However, the adverse effects outweigh the benefits. A newer, effective, more tolerable drug treatment is warranted.

Objective: To study melatonin efficacy in prevention of recurrence of one or more episodes of either simple or complex febrile seizure compared to a control group.

Methods: A quasi-experimental study in children who were diagnosed with febrile seizure in Bhumibol Adulyadej Hospital, between 6 months to 5 years old, divided into two groups, melatonin group and control group, depending upon parental convenience. Melatonin was given 0.3 mg/kg/dose every 8 hours for 48 to 72 hours during febrile illness to melatonin group if body temperature was more than 37.5 °C. Control group had no medicine. Patients were followed at 3 and 6 months.

Results: The study included 23 patients in the melatonin group and 41 in the control group. Mean age of diagnosed of febrile seizure onset was 17.3 and 21.6 months, respectively. In the melatonin group, 8.7% of patients had recurrent febrile seizure compared to 36.6% in the control group, which is statistically significant (P-value 0.015, RD −0.28(95%CI: −0.46 to −0.09)). There was no statistically significant difference in adverse effects between the two groups.

Conclusion: This study demonstrates the efficacy and safety of short-term melatonin use to prevent the recurrence of one or more episodes of either simple or complex-febrile seizure in children.

Devanagari script is one of the bases of various language scripts in India. With the growth of computing and technology, manual systems are replaced by automated one. The purpose of this research is to automate the existing manual system for digitization of Devanagari script with the use of an automated approach so that it saves time, antique data. The prescriptions given by the expert doctors and the treatments which are present in ancient Vedic literature are useful for handling patients with serious diseases. Digitization helps in easy access, manipulation, and longer storage of this data. Unlike Western languages such as English, Devanagari, is a famous script in India which does not have formal digitization tools. This work employs the best suited techniques that are useful to enhance the recognition rate and configures a Convolutional Neural Network (CNN) for effective Devanagari handwritten text recognition (DHTR). This approach uses Devanagari handwritten character dataset (DHCD) which is a vigorous open dataset with 46 classes of Devanagari characters and each of this class has two thousand different images. After recognition, conflict resolution is subtle for effective recognition therefore, this approach provides an arrangement to the user to handle the conflicts. This approach obtains promising results in terms of accuracy and training time.

Forex is an important currency indicator. The index is a major factor in the development of the country. This look examines the effects of currency trading on the Random stroll version, Exponential Smoothing One, Double Exponential Smoothing and Holt-wintry weather models and the performance of the fashion forecast were judged using the accuracy level of each symmetric loss factor and asymmetric used rectangular (MSE) errors, mean Total Deviations (MAD) and mean Total percentage errors (MAPE). From a precision rating, a double slider version of the interpreter can be used to anticipate and smooth out a series of currency exchange rates with three different versions. In an effort to test several models of the Akaike information Criterion (AIC) small currency, we have examined the Autoregressive version that incorporates conventional change (ARIMA) that can be used to anticipate the change in funding for the South Asian Local Cooperation (SAARC). This research allows for the discovery of different strategies and reduces the type of human intelligence which ultimately leads to good health.

Background and Purpose

MRI features of tumor progression and pseudoprogression may be indistinguishable especially without enhancing portion of the diffuse gliomas. Our aim is to discriminate these two conditions using radiomics and machine learning algorithm and to compare them with human observations.

Materials and Methods

Three consecutive MRI studies before a definitive biopsy in 43 diffuse glioma patients (7 pseudoprogression and 36 true progression cases) who underwent treatment were evaluated. Two neuroradiologists reviewed pre- and post-contrast T1, T2, FLAIR, ADC, rCBV, rCBF, K2, and MTT maps. Patterns of enhancement, ADC maps, rCBV, rCBF, MTT, K2 values, and perilesional FLAIR signal intensity changes were recorded. Odds ratios (OR) for each descriptor, raters' success in predicting true and pseudoprogression, and inter-observer reliability were calculated using the R statistics software. Unpaired Student's t-test and receiver operating characteristic (ROC) analysis were applied to compare the texture parameters and histogram analysis of pseudo- and true progression groups. All first-order and second-order image texture features and shape features were used to train and test the Random Forest classifier (RFC). Observers' success and RFC were compared.

Results

Observers could not identify true progression in the first visit. However, accuracy of the RFC model was 81%. For the second and third visits, the rater's success of prediction was between 62% and 72%. The accuracy for the second and last visit with RFC was 75% and 81%.

Conclusions

Random Forest classifier was more successful than human observations in predicting pseudoprogression using MRI.