Sylvestthuesen6397

We propose to use a natural data-augmentation method, which identifies the region of interest on foot images and focuses on finding the salient features existing in this area. Finally, we evaluate the performance of our proposed techniques on binary classification, i.e. ischaemia versus non-ischaemia and infection versus non-infection. Overall, our method performed better in the classification of ischaemia than infection. We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification. OBJECTIVES Develop an effective and intuitive Graphical User Interface (GUI) for a Brain-Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands. METHODS A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a 'drone prototype' capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right. RESULTS From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3 ± 4.8% and an ITR of 2.2 ± 1.1 commands/minute (12.2 ± 6.0 bits/minute) was achieved. CONCLUSION The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI. SIGNIFICANCE This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems. In this paper, a numerical investigation is carried out to provide insights into the fate of inhaled aerosols after their deposition on the lung lining fluid in both healthy and diseased states. Pulmonary drug delivery is a well-known non-invasive route of administration compared to intravenous delivery. Aerosol particles are formulated and used as drug carriers, which are then sent to the airways using aerosol drug delivery devices. This approach is useful for site-specific treatment of lung diseases, treatment of central nervous system (CNS) disorders and a variety of other diseases. Bioavailability of the inhaled therapeutic particles after landing on the airway lining fluid can be significantly altered by the lung muco-ciliary clearance, a process through which hairlike structures known as cilia beat in a harmonised manner and induce the mucus in the proximal direction, leading to an effective clearance of the foreign inhaled particles entrapped by this sticky layer from the airways. Here, we set up a 3D computational model of ciliary arrays interacting with periciliary liquid film (i.e. confined between the epithelium and mucus layer) and a detailed analysis is conducted to better understand the fate of drug nanoparticles that are able to penetrate the mucus. Consistent with clinical findings, we find that the actions of cilia result in a low rate of drug retention and absorption by the pulmonary tissues in healthy lungs. However, under conditions associated with abnormal ciliary beats, the retention time of particles is notably increased at the site of release. Nonetheless, the results associated with some of the ciliary impairments reveal that deposition of drug aerosols on the ciliated cells may still be a significant challenge. These findings have potentially important implications on the modification of therapeutic drug particles to achieve a higher absorption rate. OBJECTIVE Differential diagnosis of mild cognitive impairment MCI and temporal lobe epilepsy TLE is a debated issue, specifically because these conditions may coincide in the elderly population. We evaluate automated differential diagnosis based on characteristics derived from structural brain MRI of different brain regions. METHODS In 22 healthy controls, 19 patients with MCI, and 17 patients with TLE we used scale invariant feature transform (SIFT), local binary patterns (LBP), and wavelet-based features and investigate their predictive performance for MCI and TLE. RESULTS The classification based on SIFT features resulted in an accuracy of 81% of MCI vs. TLE and reasonable generalizability. Local binary patterns yielded satisfactory diagnostic performance with up to 94.74% sensitivity and 88.24% specificity in the right Thalamus for the distinction of MCI vs. TLE, but with limited generalizable. Wavelet features yielded similar results as LPB with 94.74% sensitivity and 82.35% specificity but generalize better. SIGNIFICANCE Features beyond volume analysis are a valid approach when applied to specific regions of the brain. Most significant information could be extracted from the thalamus, frontal gyri, and temporal regions, among others. These results suggest that analysis of changes of the central nervous system should not be limited to the most typical regions of interest such as the hippocampus and parahippocampal areas. Region-independent approaches can add considerable information for diagnosis. We emphasize the need to characterize generalizability in future studies, as our results demonstrate that not doing so can lead to overestimation of classification results. LIMITATIONS The data used within this study allows for separation of MCI and TLE subjects using a simple age threshold. TTK21 While we present a strong indication that the presented method is age-invariant and therefore agnostic to this situation, new data would be needed for a rigorous empirical assessment of this findings.