Retinal diseases classification based on hybrid ensemble deep learning and optical coherence tomography images

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications

The Cellular Phenotype of the Neurodegenerative Disease Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay.

PhDAutosomal recessive spastic ataxia of Charlevoix Saguenay (ARSACS) is an early onset neurodegenerative disorder resulting from mutations in the SACS gene that encodes the protein sacsin. Sacsin is a 520kDa multi-domain protein localised at the cytosolic face of the outer mitochondrial membrane with suggested roles in proteostasis and most recently in the regulation of mitochondrial morphology. An excessively interconnected mitochondrial network was observed as a consequence of reduced levels of sacsin protein following SACS knockdown in neuroblastoma cells as well as in an ARSACS patient carrying the common Quebec homozygous SACS mutation 8844delT. Moreover, it was suggested that sacsin has a role in mitochondrial fission as it was found to interact with mitochondrial fission protein Dynamin related protein 1 (Drp1). The aim of this thesis was to explore sacsin’s role in the regulation of mitochondrial morphology and dynamics in non-Quebec ARSACS patients and sacsin knockdown fibroblasts. This study shows that loss of sacsin function promotes a more interconnected mitochondrial network in non-Quebec ARSACS patients and in sacsin knockdown fibroblasts. Moreover, recruitment of the essential mitochondrial fission protein Drp1 to the mitochondria was significantly reduced in ARSACS patient cells and in sacsin knockdown fibroblasts. This reduced recruitment of Drp1 to mitochondria also occurred when cells were treated to induce mitochondrial fission. Furthermore, both the size and intensity of Drp1 foci localised to the mitochondria were significantly reduced in both sacsin knockdown and patient fibroblasts. Finally, reduced ATP production, decreased respiratory capacity of mitochondria and an increase in mitochondrial reactive oxygen species demonstrated impaired mitochondrial function in ARSACS patient and sacsin knockdown fibroblasts. These results suggest a role for sacsin in the stabilisation or recruitment of cytoplasmic Drp1 to prospective sites of mitochondrial fission similar to that observed by other mitochondrial fission accessory proteins.Ataxia Charlevoix-Saguenay Foundation; Queen Mary, University of Londo

Abstracts from the 50th European Society of Human Genetics Conference: Posters

International audienc

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems