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Modelling structurel interconversion within Alzheimers' amyloid beta peptide using traditional and also intrinsically unhealthy necessary protein drive fields.
It was not possible to reduce the number of strides for the MaxL or TREND. However, for the RR, DET, AVG, and EntD, it was possible to reduce the number of strides by 60% when analyzed together. The minimum sampling frequency required to extract all quantifiers simultaneously was 100 Hz. This potential reduction in the number of strides is appropriate for evaluating fast gait events, with short temporal localization in the RP, by applying the sliding window method to the recurrence plot. Many diagnostic and some therapeutic ophthalmic devices require a reliable complementing method to track the direction of gaze or just to validate fixation of the eye on a presented target. This would allow acquisition of artefact-free robust images of the fovea and the surrounding macula. So far, there have been only few attempts to provide fast and dependable fixation information to an optical imaging system in real time, to guide image acquisition. The author's lab has developed several instruments that detect the location of the fovea using retinal birefringence scanning (RBS), proven to be very effective. Here, an RBS-based fixation detection subsystem is proposed, designed to operate conjointly with a number of ophthalmic imaging technologies. Combining RBS with such technologies is not trivial, because RBS uses polarized light and polarization-sensitive optics, while most other modalities don't. click here The polarization optics was optimized by means of enhanced computer modeling. Both the electronic hardware and the software were designed for fast and reliable performance. Because many retinal imaging systems are used in pediatric settings, extensive audio-visual circuitry was employed for efficient attention/fixation attraction. The optomechanics has been optimized for robust data acquisition. This computer-aided conjoint system employs true anatomical information from the back of the eye and needs no calibration. The prototype instrument uses a decision-making logic based on four frequencies generated during scanning. The results reveal the applicability of RBS as an adjunct fixation monitoring modality, showing promise to remove the limitation imposed by eye movements upon advanced ophthalmic imaging technologies. Epilepsy involves brain abnormalities that may cause sudden seizures or other uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality of life, and its detection heavily relies on tedious and time-consuming manual curation by experienced clinicians, based on EEG signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. A new patient can only be monitored effectively after several episodes of epileptic seizures. This study investigates the patient-independent detection of seizure events using the open dataset CHB-MIT Scalp EEG. First, a novel feature extraction algorithm called MinMaxHist is proposed to measure the topological patterns of the EEG signals. Following this, MinMaxHist and several other feature extraction algorithms are applied to parameterize the EEG signals. Next, a comprehensive series of feature screening and classification optimization experiments are conducted, and finally, an optimized EEG-based seizure detection model is presented that can achieve overall values for accuracy, sensitivity, specificity, Matthews correlation coefficient, and Kappa of 0.8627, 0.8032, 0.9222, 0.7504 and 0.7254, respectively, with only 30 features. The classification accuracy of the method with MinMaxHist features was 0.0464 higher than that without MinMaxHist features. Compared with existing methods, the proposed algorithm achieved higher accuracy and sensitivity, as shown in the experimental results. Segmentation of tumors from hybrid PET/MRI scans plays an essential role in accurate diagnosis and treatment planning. However, when treating tumors, several challenges, notably heterogeneity and the problem of leaking into surrounding tissues with similar high uptake, have to be considered. To address these issues, we propose an automated method for accurate delineation of tumors in hybrid PET/MRI scans. The method is mainly based on creating intermediate images. In fact, an automatic detection technique that determines a preliminary Interesting Uptake Region (IUR) is firstly performed. To overcome the leakage problem, a separation technique is adopted to generate the final IUR. Then, smart seeds are provided for the Graph Cut (GC) technique to obtain the tumor map. To create intermediate images that tend to reduce heterogeneity faced on the original images, the tumor map gradient is combined with the gradient image. Lastly, segmentation based on the GCsummax technique is applied to the generated images. The proposed method has been validated on PET phantoms as well as on real-world PET/MRI scans of prostate, liver and pancreatic tumors. Experimental comparison revealed the superiority of the proposed method over state-of-the-art methods. This confirms the crucial role of automatically creating intermediate images in addressing the problem of wrongly estimating arc weights for heterogeneous targets. Defining important information from biological data is critical for the study of disease diagnosis, drug efficacy and individualized treatment. Hence, the feature selection technique is widely applied. Many feature selection methods measure features based on relevance, redundancy and complementarity. Feature complementarity means that two features' cooperation can provide more information than the simple summation of their individual information. In this paper, we studied the feature selection technique and proposed a new feature selection algorithm based on relevance, redundancy and complementarity (FS-RRC). On selecting the feature subset, FS-RRC not only evaluates the feature relevance with the class label and the redundancy among the features but also evaluates the feature complementarity. If complementary features exist for a selected relevant feature, FS-RRC retains the feature with the largest complementarity to the selected feature subset. To show the performance of FS-RRC, it was compared with eleven efficient feature selection methods, MIFS, mRMR, CMIM, ReliefF, FCBF, PGVNS, MCRMCR, MCRMICR, RCDFS, SAFE and SVM-RFE on two synthetic datasets and fifteen public biological datasets.
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