Neural connectivity predicts spreading of alpha-synuclein pathology in fibril-injected mouse models: Involvement of retrograde and anterograde axonal propagation.

In Parkinson's disease, some of the first alpha-synuclein aggregates appear in the olfactory system and the dorsal motor nucleus of the vagus nerve before spreading to connected brain regions. We previously demonstrated that injection of alpha-synuclein fibrils unilaterally into the olfactory bulb of wild type mice leads to widespread synucleinopathy in brain regions directly and indirectly connected to the injection site, consistently, over the course of periods longer than 6 months. Our previously reported observations support the idea that alpha-synuclein inclusions propagates between brain region through neuronal networks. In the present study, we further defined the pattern of propagation of alpha-synuclein inclusions and developed a mathematical model based on known mouse brain connectivity. Using this model, we first predicted the pattern of alpha-synuclein inclusions propagation following an injection of fibrils into the olfactory bulb. We then analyzed the fitting of these predictions to our published histological data. Our results demonstrate that the pattern of propagation we observed in vivo is consistent with axonal transport of alpha-synuclein aggregate seeds, followed by transsynaptic transmission. By contrast, simple diffusion of alpha-synuclein fits very poorly our in vivo data. We also found that the spread of alpha-synuclein inclusions appeared to primarily follow neural connections retrogradely until 9 months after injection into the olfactory bulb. Thereafter, the pattern of spreading was consistent with anterograde propagation mathematical models. Finally, we applied our mathematical model to a different, previously published, dataset involving alpha-synuclein fibril injections into the striatum, instead of the olfactory bulb. We found that the mathematical model accurately predicts the reported progressive increase in alpha-synuclein neuropathology also in that paradigm. In conclusion, our findings support that the progressive spread of alpha-synuclein inclusions after injection of protein fibrils follows neural networks in the mouse connectome

Statistics of Weighted Brain Networks Reveal Hierarchical Organization and Gaussian Degree Distribution

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable

Prevalence of Refractive Error and Associated Risk Factors in School-Age Children in Nepal: A Cross-sectional Study

Introduction: The most common visual disorder in school age children is refractive error globally. The present study aimed to know the prevalence of refractive errors and explore the factors associated with the refractive error in school-age children in Palpa district of western part of Nepal. Methods: All the school children were selected between age groups 5 to 18 years from four schools of Palpa by multistage sampling method. After the preliminary examination on visual acuity, the children were referred to the Department of Ophthalmology, Lumbini Medical College, Palpa for confirmation of the refractive errors. Results: In school-age children the prevalence of refractive error was 9% of which myopia was the most common (4.05%). Females (about 12%) were more likely to have refractive errors than males (about 7%). The refractive error of males was 0.106 (right eye) and 0.564 (left eye) times more likely than females. The refractive errors were statistically found more common in Dalit students (14.6%) than Brahmin/Chhetri (about 12%) and Janajati (7.6%). The prevalence of refractive errors among students using smart phone/ laptop (about 12%) was higher than those not using (8.36%). Conclusion: Sex, ethnicity, and near-work activity like using the smart device were the covariates of developing refractive error on the eye. Myopia was more among those students who were using smartphones/laptops. Near activities stress on eyes of the children and might be one of the causes of developing myopia

Characterization and Prevalence of Clindamycin Resistance Staphylococcus aureus from Clinical samples of National Medical College and Teaching Hospital, Nepal

Objective: Clindamycin is the drug of choice for the treatment of severe form of skin, soft tissue, and blood infections caused by resistant Staphylococcus aureus in the form of methicillin-resistant S. aureus (MRSA) and erythromycin-resistant S. aureus. In this research, we determine the susceptibility pattern of isolated S. aureus strains against antibiotics and the prevalence of resistant S. aureus in the form of MRSA, inducible clindamycin-resistant S. aureus (inducible macrolide-lincosamide-streptogramin B [iMLSB]) and constitutive clindamycin-resistant S. aureus (cMLSB). Methods: A total of 310 isolated S. aureus among 2000 different clinical samples were subjected to oxacillin (1 μg) as per the Kirby-Bauer disk diffusion method for MRSA. Clindamycin-resistant either in the form of iMLSB or cMLSB was determined through double disk diffusion method or D-test by use erythromycin (2 μg) and clindamycin (15 μg) as per the CLSI guidelines. Results: Out of total S. aureus, MRSA and methicillin-sensitive S. aureus (MSSA) were 78.06% and 20.64%, respectively. This study showed that iMLSB and cMLSB were 34.19% and 23.22%. Both iMLSB and cMLSB were found more among MRSA than MSSA (43.80%, 26.85% and 40.62%, 10.93%), respectively. Conclusion: This study helps for the characterization of different resistant strains of S. aureus along with the determination of the prevalence rate of these mutant forms causing nosocomial infections

Generalized Gradient Flows with Provable Fixed-Time Convergence and Fast Evasion of Non-Degenerate Saddle Points

Gradient-based first-order convex optimization algorithms find widespread applicability in a variety of domains, including machine learning tasks. Motivated by the recent advances in fixed-time stability theory of continuous-time dynamical systems, we introduce a generalized framework for designing accelerated optimization algorithms with strongest convergence guarantees that further extend to a subclass of non-convex functions. In particular, we introduce the GenFlow algorithm and its momentum variant that provably converge to the optimal solution of objective functions satisfying the Polyak-{\L}ojasiewicz (PL) inequality in a fixed time. Moreover, for functions that admit non-degenerate saddle-points, we show that for the proposed GenFlow algorithm, the time required to evade these saddle-points is uniformly bounded for all initial conditions. Finally, for strongly convex-strongly concave minimax problems whose optimal solution is a saddle point, a similar scheme is shown to arrive at the optimal solution again in a fixed time. The superior convergence properties of our algorithm are validated experimentally on a variety of benchmark datasets.Comment: Accepted to Transactions on Automatic Control (TAC

Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language using CNN

Sign Language Recognition is a device or program to help deaf and mute people. However, communication has always been difficult for a person with verbal and physical disabilities. Sign language recognition communication between the average person and the disabled using this device easily communicates with people who cannot communicate with the average person, this program reduces the communication gap between people. In total, the world has a population of about 15 -20% of the deaf and mute population which is a clear indication of the need for a Sign Language Awareness Program. Different methods are used to identify sign language but they are not effective due to the economic and commercial situation so we use this cheap and affordable method for people. Therefore, sign language recognition systems based on image processing and sensory networks are preferred over gadget programs as they are more accurate and easier to implement. This paper aims to create an easy-to-use and accurate sign language recognition system trained in the neural network thus producing text and speech input