Determining the role of statins in Parkinson's disease risk reduction and disease modification: A comprehensive meta-analysis of 4 million participants' data.

BACKGROUND: Many observational studies have examined the association between statins and the incidence of Parkinson's disease (PD) in high-risk populations. On the other hand, clinical trials as well as other observational studies investigated the safety and efficacy of statins in slowing disease progression in PD patients. However, the evidence has been inconclusive in both questions. To that end, we conducted this systematic review and meta-analysis to synthesize evidence on the role of statins in decreasing the risk of PD among high-risk populations and as a possible disease-modifying agent for patients with PD. METHODS: A comprehensive literature search of electronic databases including PubMed, Scopus, Cochrane, and Web of Science has been performed. Relevant studies were chosen and data were extracted and analyzed using RevMan software version 5.4.1. RESULTS: Twenty-five studies (14 cohort, 9 case-control, and 2 randomized controlled trials) have been included in the present systematic review. Of them, 21 studies reported the association between statins and PD risk. Statins were found to significantly reduce the risk of developing PD (pooled RR 0.86, 95% CI [0.77-0.95], p < 0.005). Four studies investigated statins as a disease-modifying agent. The pooled mean difference (MD) in the UPDRS-III from baseline to endpoint did not differ significantly between the statin and control groups (MD -1.34 points, 95% CI [-3.81 to 1.14], p = 0.29). CONCLUSION: Although epidemiological observational studies showed that statin use was associated with a reduced risk of PD, current evidence is insufficient to support the role of statins in slowing the progression of PD. These findings are limited by the fact that most of the included studies are observational studies which carry a high risk of confounding bias which highlights the need for future well-designed RCTs.

Leveraging cross-view geo-localization with ensemble learning and temporal awareness.

The Global Navigation Satellite System (GNSS) is unreliable in some situations. To mend the poor GNSS signal, an autonomous vehicle can self-localize by matching a ground image against a database of geotagged aerial images. However, this approach has challenges because of the dramatic differences in the viewpoint between aerial and ground views, harsh weather and lighting conditions, and the lack of orientation information in training and deployment environments. In this paper, it is shown that previous models in this area are complementary, not competitive, and that each model solves a different aspect of the problem. There was a need for a holistic approach. An ensemble model is proposed to aggregate the predictions of multiple independently trained state-of-the-art models. Previous state-of-the-art (SOTA) temporal-aware models used heavy-weight network to fuse the temporal information into the query process. The effect of making the query process temporal-aware is explored and exploited by an efficient meta block: naive history. But none of the existing benchmark datasets was suitable for extensive temporal awareness experiments, a new derivative dataset based on the BDD100K dataset is generated. The proposed ensemble model achieves a recall accuracy R@1 (Recall@1: the top most prediction) of 97.74% on the CVUSA dataset and 91.43% on the CVACT dataset (surpassing the current SOTA). The temporal awareness algorithm converges to R@1 of 100% by looking at a few steps back in the trip history.

Impact of COVID-19 on youth's mental health in Egypt.

Background: Youth represent 21% of the Egyptian population; such proportion can create a leading demographic power for economic development and transition. However, with the current COVID-19 pandemic, everyone is exposed to more than usual stressors, adding a burden to their mental health and well-being. Aim: This study aims to understand the pandemic's effect on youth's mental health in Egypt to strengthen the intervention areas needed to tackle such issues. Methods: This observational, analytical, cross-sectional study employed internet platforms of Facebook & WhatsApp groups for a web-based survey that included 412 respondents between 15 and 30 years old. RESULTS The median age of the respondents was 22 years. At least 30% reported increased violence in the street and/or household, and 27.4% of the respondents have considered visiting a psychiatrist during the last period. Conclusion: It is evident that the current situation is unprecedented and challenging for everyone; however, some populations are more vulnerable than others. Thus, it's important to support young people to ensure that the whole community can withstand the pandemic. The governments should support and mitigate some of the stresses that can be directly amended, like the education and job security concerns.

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning.

In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck's capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning.

Association of folic acid, vitamin B12, and intelligence scores in epileptic children.

Epilepsy is a serious childhood disease associated with cognitive impairment. Our aim was to investigate the possible association of serum folic acid, vitamin B12, and intelligence scores in epileptic children. A group of 30 children with established diagnosis of idiopathic epilepsy for at least one year as well as another group of 30 nonepileptic healthy children as the control group were recruited for analysis. Cognitive performance was assessed by a battery of psychological tests that covers verbal and nonverbal intelligence. Serum B12 level was significantly lower in patients than the control group (264.17 ± 58.07, 450.55 ± 134.9, respectively). No significant difference was detected between patients and the control group regarding serum folic acid level. Verbal, performance, and total IQ were significantly lower in patients than the control group (83.2 ± 3.08 vs. 95.8 ± 6.22, 78.4 ± 10.68 vs. 91.3 ± 2.45, and 180.6 ± 6.58 vs. 93.5 ± 3.02, respectively). However, no significant correlation was detected in folic acid, vitamin B 12, and cognitive scores. Epileptic children were five times more at risk of having low IQ (verbal, performance, and total) < 85 than the control group (OR = 4.754, 95% CI 13.047-1031.316, p = .000). In conclusion, children with epilepsy might be at higher risk for cognitive dysfunction than normal children. No significant association was detected between cognitive performance and either folic acid or vitamin B12 in epileptic children receiving sodium valproate. Supplementation of those vitamins should be restricted to those with documented deficiency.

Hybrid pointer networks for traveling salesman problems optimization.

In this work, we proposed a hybrid pointer network (HPN), an end-to-end deep reinforcement learning architecture is provided to tackle the travelling salesman problem (TSP). HPN builds upon graph pointer networks, an extension of pointer networks with an additional graph embedding layer. HPN combines the graph embedding layer with the transformer's encoder to produce multiple embeddings for the feature context. We conducted extensive experimental work to compare HPN and Graph pointer network (GPN). For the sack of fairness, we used the same setting as proposed in GPN paper. The experimental results show that our network significantly outperforms the original graph pointer network for small and large-scale problems. For example, it reduced the cost for travelling salesman problems with 50 cities/nodes (TSP50) from 5.959 to 5.706 without utilizing 2opt. Moreover, we solved benchmark instances of variable sizes using HPN and GPN. The cost of the solutions and the testing times are compared using Linear mixed effect models. We found that our model yields statistically significant better solutions in terms of the total trip cost. We make our data, models, and code publicly available https://github.com/AhmedStohy/Hybrid-Pointer-Networks.

Crash severity analysis of vulnerable road users using machine learning.

Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users-pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups-for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.

A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems.

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries.

Advancements in data collection and processing methods have produced large databases containing high quality vehicular data. Despite this, conventional vehicle-vehicle collisions remain difficult to identify due to their rarity. Therefore, there is a need to identify potential collisions given the introduction of these new data collection methods. Surrogate indicators are a popular methods utilised to identify such events, however, the type of surrogate that can be used depends heavily on the type of data collection method. Though most surrogate indicators are used at different road geometries, there is evidence to suggest that some surrogate indicators may perform better than others at a given geometry. This review provides two key contributions to the body of literature. Firstly, a review of kinematic surrogates is put forward, along with a discussion on the whether these surrogates can be contextualised at different road geometries. Secondly, an extensive analysis and discussion of observer-based and video processed surrogate indicators, the collision types they aim to identify and the geometries they have been used at previously were analysed and advantages and disadvantages of the surrogates have been presented for future use. To do this, intersections, highways and roundabouts were selected and divided into geometry subtypes (i.e. three-legged and four-legged intersection) and segments (i.e. approaches to intersections and internal to the intersection) based on the likelihood of crash types and pre-crash manoeuvres occurring in that segment. Due to the lack of research around the use of kinematic triggers at road geometries, it is difficult to advocate for the use of any given trigger over another at a given geometry. Furthermore, it was found that kinematic triggers cannot accurately identify conflicts from naturalistic driving data and require the use of advanced statistical techniques such as machine learning to increase accuracy. A brief analysis of threshold identification techniques was also performed. Several future works have been put forward including the introduction of surrogates which capture conflict severity and the role of surrogate indicators in connected and automated vehicle environments.

Correction: A framework for testing independence between lane change and cooperative intelligent transportation system.

[This corrects the article DOI: 10.1371/journal.pone.0229289.].

Circulating glial fibrillary acidic protein and ubiquitin carboxy-terminal hydrolase-L1 as markers of neuronal damage in children with epileptic seizures.

BACKGROUND: Epilepsy is a common neurological disease that has a negative impact on physical, social, and cognitive function. Seizure-induced neuronal injury is one of the suggested mechanisms of epilepsy complications. We aimed to evaluate the circulating level of glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1) as markers of neuronal damage in children with epilepsy and its relation to epilepsy characteristics. STUDY DESIGN: METHODS: This case control study included 30 children with epilepsy and 30 healthy children as a control group. Seizure severity was determined based on Chalfont score. Serum level of GFAP and UCH-L1were measured, and their associations with epilepsy characteristics were investigated. RESULTS: Circulating levels of GFAP and UCH-L1 were significantly higher in children with epilepsy than in controls (17.440 ± 6.74 and 5.700 ± 1.64 vs 7.06 ± 3.30 and 1.81 ± 0.23, respectively) especially in those with generalized and active seizures. GFAP and UCH-L1 were significantly correlated to the severity of seizures in the previous 6 months. Elevated GFAP level was a predictor for active seizures (OR 1.841, 95%CI 1.043-3.250, P = 0.035). CONCLUSION: Circulating GFAP and UCH-L1 expression is increased in children with epilepsy especially those with active seizures. SIGNIFICANCE: GFAP and UCH-L 1may serve as peripheral biomarkers for neuronal damage in children with epilepsy that can be used to monitor disease progression and severity for early identification of those with drug-resistant epilepsy and those who are in need for epilepsy surgery.

A framework for testing independence between lane change and cooperative intelligent transportation system.

Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.

Experimental dataset for optimising the freight rail operations.

The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E. Kozan, G. Kent, Liu, Shi Qiang, 2016b) [1]). Producing an efficient daily schedule to optimise the rail operations requires integration of the main elements of harvesting, transporting and milling in the value chain of the Australian agriculture industry. The data utilised in this research involve four main tables: sidings, harvesters, sectional rail network and trains. The utilised data were collected from Australian sugar mills as a real application. Operations Research techniques such as metaheuristic and constraint programming are used to produce the optimised solutions in an analytical way.