Schuurs–Hoeijmakers Syndrome (PACS1 Neurodevelopmental Disorder): Seven Novel Patients and a Review

Schuurs–Hoeijmakers syndrome (SHMS) or PACS1 Neurodevelopmental disorder is a rare disorder characterized by intellectual disability, abnormal craniofacial features and congenital malformations. SHMS is an autosomal dominant hereditary disease caused by pathogenic variants in the PACS1 gene. PACS1 is a trans-Golgi-membrane traffic regulator that directs protein cargo and several viral envelope proteins. It is upregulated during human embryonic brain development and has low expression after birth. So far, only 54 patients with SHMS have been reported. In this work, we report on seven new identified SHMS individuals with the classical c.607C > T: p.Arg206Trp PACS1 pathogenic variant and review clinical and molecular aspects of all the patients reported in the literature, providing a summary of clinical findings grouped as very frequent (≥75% of patients), frequent (50–74%), infrequent (26–49%) and rare (less than ≤25%).

Intelligent Vehicular Traffic Signal Control for Vehicular AdHoc Networks

Current traffic regulator in remote is vehicle impelled, pre-coordinated, and webster’s technique, which produce more deferral at higher traffic. The chance of sending a keen and constant versatile traffic light regulator, which gets data from vehicles, for example, the position and speed of the vehicle, and then use this information to streamline the traffic light signal at the convergence for vehicle to vehicle(V2V) and vehicle to infrastructure(V2I) communication. The traffic board framework utilizing the AdHoc Ondemand Distance Vector (AODV) convention for VANET is sufficiently used in this work. It has been seen that practically all routes demand communication arrive at the target, a couple over significant distances with center vehicle thickness fizzled. Nonetheless, the load on the association starting from the unsophisticated transmission is gigantic. Therefore, it additionally prompts rapidly developing postponements and connection disappointment. A few trajectory answers don’t come through considering the way that telecomunication is as yet going on. This is a basic issue, particularly in city regions with high vehicle thickness. Based on the information in this paper, appropriate traffic signal control is developed to minimize the congestion at the intersections

Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach

Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.

Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research.