Neuroscience informatics最新文献

The well-known medical participation of AZO dye industry derivatives in the use of vital brilliant red dye acts as an anticonvulsant. The AZO dyes permeability through the blood–brain barrier was found to be a factor in the development of a vast variety of chronic neurological diseases. Because of the potential influence on the environment and human health, the presence of AZO dyes in textile effluents is a major concern. Under visible light, we analyze the photocatalytic degradation of AZO dyes, which are widely utilized in the textile sector. In the present analysis, the properties of combustion-formed mixed sulfide solids were investigated in solutions which are supersaturated simultaneously with dysprosium and erbium. The Sr_1-xCu_xSO₄ nanoparticles arrangement acts as a step for dysprosium and erbium co-doping based on the interactions between thiourea. The phase structure and sample states obtained of the stimulant components be analyzed by Field emission scanning electron microscopy (FESEM), High-resolution transmission electron microscopy (HR-TEM) and photosynthetic studies with X-ray powder diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR). Additional data analysis by Rietveld refinement allowed the exposure of a smaller lattice parameter and volume increase related to the absorption of stimulant components. Integrated Sr_1-xCu_xSO₄ co-doped Dy³⁺, Er⁴⁺ showed effective photosynthetic presentation at some stage in decomposition of organic dyes (Acid Black 1, direct blue 15), and hydrogen production from water under ultraviolet light. In addition, Dy, Er co-doped was also deposited and their photosynthetic activities were examined. The consequences and impact on neurology are also examined in this article.

Extreme Learning Machine (ELM) is widely known to train feed forward network with high speed and good generalization performance. The only problem associated with ELM is required higher number of hidden neurons due to random selection. In this paper we proposed a new model Cuckoo Search with Invasive weed optimization based Extreme Learning Machine (CSIWO-ELM) to optimize input weight and hidden neurons. This model provides the optimize input to the feedforward network to improve the ELM. The developed model is experimented on three medical datasets to see the data classification. Also, the developed model is compared with different optimize algorithm. The experimental result proves the excellent working of CSIWO-ELM model for classification problem.

Stroke is one of the lethal diseases that has significant negative impact on an individual's life. To diagnose stroke, MRI images play an important role. A large number of images are being produced day by day such as MRI (Medical Resonance Imaging), CT (Computed Tomography) X-Ray images and many more. Machine Learning algorithms are less efficient and time-consuming in localization of such medical images. Object detection using deep learning can reduce the efforts and time required in screening and evaluation of these images. In the proposed paper, several approaches such as RCNN (Region-based Convolutional Neural-Network), Fast R-CNN (Fast Region-based Convolutional Neural Network), Faster R-CNN (Faster Region-based Convolutional Neural Network with Region proposal Network), YOLO (You Only Look Once), SSD (Single-Shot Multibox Detector) and Efficient-Det are listed which can be used for stroke localization and classification. Comparison of RCNN, Fast R-CNN, Faster R-CNN, YOLO, SSD and Efficient-Det with accuracy are also present in this paper. A Chart of the Data Set available for object detection is also considered in this paper. By The maP (Mean-Average Precision) and the accuracy of every single method, it is identified that the speed and accuracy need to poise.

Objective

Subjective cognitive decline (SCD) is the first clinical manifestation of the Alzheimer's disease (AD) continuum. Hemodynamic response function (HRF) carries information related to brain pathology and function. The shape of the HRF can be described by three parameters: response height (RH), time-to-peak (TTP), and full-width at half-max (FWHM). We proposed and explored our two hypotheses. Hypothesis 1: HRF was pathologically related to SCD: compared with healthy controls (HC), patients with SCD show HRF aberrations. Hypothesis 2: HRF could be employed as a novel marker of brain imaging for the classification of SCD.

Methods

We used resting-state functional magnetic resonance imaging (fMRI) data and performed deconvolution to investigate the HRF parameters in 54 individuals with SCD and 64 HC. Statistical two-sample t tests were performed to investigate between-group differences in HRF parameters. Finally, we used logistic regression to construct a binary classification of SCD and HC.

Results

We found altered HRF parameters in the SCD group compared to HC. In the brain regions with altered HRF, we found that RH and FWHM decreased in the SCD group compared to HC, while TTP increased in the SCD group. From the binary logistic regression, we found that the classification accuracy of SCD and HC was 94.07%.

Conclusion

The study demonstrated altered HRF parameters in patients with SCD, which could be used as a novel marker of brain function for the classification of SCD.

Background: Nowadays, there is a lot more emphasis on promoting health, wellbeing, and self-care including stress management strategies. Health is regarded as a natural extension of a wellness-oriented lifestyle. The objectives are to measure knowledge, evaluate the efficacy of a video education program, and examine the relationship between before and after-existing knowledge measurement and specified socio factors on Urinary tract infections (UTI) and neurological stress in teenage girls.

Materials and methods: This study employed an uncontrolled experimental study design. Initially, the mean and standard deviation of before and after-existing knowledge were determined. The ‘t’ test was applied to compare the variance between the before-existing and after-existing knowledge measurements of teenage girls on UTI and neurological stress, to find the efficacy of a video education program on eliminating urinary tract infection and neurological stress in teenage girls. Finally, the Chi-square model is used to measure the relationship between before-existing knowledge measurements and social characteristics.

Results and interpretation: The analyzed data found that the teenage girls' mean after-existing knowledge measurement was 33.46% times greater than their mean before-existing knowledge measurement of 24.6%. According to the findings, there is no strong relationship between teenage girls before-existing knowledge measurement and selected socio-demographic factors.

Conclusion: According to the study's findings, there is a critical need for healthcare providers to educate teenage girls about the interception of UTI prevalence and neurological stress management strategies inorder to avoid UTI among teenage girls.

On an MRI scan of the brain, the boundary between endocrine tissues is highly convoluted and irregular. Outdated segmentation algorithms face a severe test. Machine learning as a new sort of learning Here, researchers categorize normal and abnormal tissue using the fuzzy min-max neural network approach, which helps classify normal and abnormal tissues such as GM, CSF, WM, OCS, and OSS. This classification helps to explain the fuzzy min-max neural network method. Osseous Spongy Substance, SCALP, and Osseous Compact Substance are all MRI-classified as aberrant tissue in these tissues. Denoising and improving images can be accomplished using the Gabor filtering technique. Using the filtering method, the tumour component will be accurately identified during the segmentation operation. A dynamically changed region growing approach may be applied to a picture by modifying the Modified Region Growing method's two thresholds. This helps to raise Modified Region Growing's upper and lower bounds. Once the Region Growth is accomplished, the edges may be observed using the Modified Region Growing segmented image's Edge Detection approach. After removing the texture, an entropy-based method may be used to abstract the colour information. After the Dynamic Modified Region Growing phase findings have been merged with those from the texture feature generation phase, a distance comparison within regions is performed to combine comparable areas in the region merging phase. After tissues have been identified, a Fuzzy Min-Max Neural Network may be utilised to categorise them.

Human cognitive load level assessment is a challenging issue in the field of functional brain imaging. This work aims to study different cognitive load levels statistically from brain hemodynamics. Since the functional brain activities can be evaluated by functional near-infrared spectroscopy (fNIRS), a renowned fNIRS dataset is considered for this work. The dataset contains fNIRS data of three types of n-back tasks (0-back, 2-back, and 3-back) of twenty-six healthy volunteers. The fNIRS signals were pre-processed and separated according to the tasks and trials. The mean changes of oxygenated hemoglobin (HbO₂) and deoxygenated hemoglobin (dHb) are calculated from each trial corresponding to the tasks and tested for significant inference among three levels utilizing analysis of variance (ANOVA). From the outcomes of the ANOVA ($p<0.005$), two significant channels (AF7 (frontal) and C3h (motor)) were figured out. The significance of these two channels was further justified using the property consistency test by three different time intervals of hemodynamics inside the total task period. The latter result also explored the functional pattern of the hemodynamics of AF7 and C3h positions. Moreover, two-level cognitive load (due to easy i.e., 0-back test and hard i.e., 2-back and 3-back task) is classified using support vector machine and found classification accuracy in average 73.40%±0.076 for HbO₂ data and 71.48%±0.061 for dHb data. The study signposts the collective role played by both fNIRS signals and statistical valuation of functioning cognitive load efficacy to use fNIRS as a cognitive load assessment biomarker.

An accurate, fully automatic detection and segmentation technique for brain tumors in magnetic resonance images (MRI) is introduced. The approach basically combines geometric active contours segmentation with a deep learning-based initialization. As a pre-processing step, an anisotropic filter is used to smooth the image; afterwards, the segmentation process takes place in two phases: the first one is based on the concept of transfer learning, where a pre-trained convolutional neural network coupled with a detector is fine-tuned using a training set of 388 T1-weighted contrast enhanced MRI images that contain a brain tumor (Meningioma); this trained network is able to automatically detect the location of the tumor by generating a bounding box with certain coordinates. The second phase takes place by using the coordinates of the bounding box to initialize the geometric active contour that iteratively evolves towards the tumor's boundaries. While most of the ingredients of this processing chain are more or less well known, the main contribution of this work is in integrating the various techniques in a novel and hopefully clever form, which could take the best of both geometric segmentation algorithms and neural networks, with a relatively light training phase. The performance of such a processing network is evaluated using a separate testing set of 97 MRI images containing the same type of brain tumor. The technique proves to be remarkably effective, with a precision of 97.92%, recall of 96.91%, F-measure of 97.41% and an average Dice similarity coefficient (DSC) for segmented images above 0.95.

COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.