Dual Arginine Recognition of LRRK2 phosphorylated Rab GTPases

Parkinson’s-disease-associated LRRK2 is a multidomain Ser/Thr kinase that phosphorylates a subset of Rab GTPases to control their effector functions. Rab GTPases are the prime regulators of membrane trafficking in eukaryotic cells. Rabs exert their biological effects by recruitment of effector proteins to subcellular compartments via their Rab-binding domain (RBD). Effectors are modular and typically contain additional domains that regulate various aspects of vesicle formation, trafficking, fusion, and organelle dynamics. The RBD of effectors is typically an α-helical coiled coil that recognizes the GTP conformation of the switch 1 and switch 2 motifs of Rabs. LRRK2 phosphorylates Rab8a at T72 (pT72) of its switch 2 α-helix. This post-translational modification enables recruitment of RILPL2, an effector that regulates ciliogenesis in model cell lines. A newly identified RBD motif of RILPL2, termed the X-cap, has been shown to recognize the phosphate via direct interactions between an arginine residue (R132) and pT72 of Rab8a. Here, we show that a second distal arginine (R130) is also essential for phospho-Rab binding by RILPL2. Through structural, biophysical, and cellular studies, we find that R130 stabilizes the primary R132:pT72 salt bridge through favorable enthalpic contributions to the binding affinity. These findings may have implications for the mechanism by which LRRK2 activation leads to assembly of phospho-Rab complexes and subsequent control of their membrane trafficking functions in cells

Investigation of noise and disturbance from vehicles crossing cattle grids and examination of options for mitigation

YesCattle grids are used on roads and tracks to prevent grazing animals from leaving an open space without fencing onto a more controlled area where access to the road from surrounded land is more limited. They are widely used in the UK at the entrances to common and moorland areas where animals are free to roam, but also on private drive entrances. Typically, they consist of a series of metal bars across the road that are spaced so that an animal’s legs would fall through the gaps if it attempted to cross. Below the grid is a shallow pit that is intended to further deter livestock from using that particular crossing point. The sound produced as vehicles cross these devices is a characteristic low frequency “brrrr” where the dominant frequencies relates to the bar passage frequency under the tyres. The sound can be disturbing to riders and their horses and walkers and residents living close by as evidenced by press reports and the need to consider noise aspects in planning for new installations. For this reason and due to the lack of available information on the size and nature of the problem measurements and recordings have been made at a number of sites in Yorkshire in the UK. In addition, questionnaire surveys of residents living close by and façade measurements have also been used to gauge impact. Results show that there is a wide variation in the maximum noise level produced by cattle grids of apparently similar design. This can be related to impact noise produced by the movement of all or part of the grid as the frame comes under impulsive loading as the vehicle crosses. It was further established that some residents living close to the cattle grids were disturbed by the noise, and in some cases vibration, and wanted them removed or suitably modified. Means of reducing the problem are proposed

Recurrent neural network and reinforcement learning model for COVID-19 prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate

Development of a New Concept for Fire Fighting Robot Propulsion System

An additional cost to human loss and property destruction during fire disaster is fire fighters injuries and death. The recent statistics of 63,350 fire fighters injuries that occurred during the year 2014 confirms that firefighting still presents great risks of personal injury to the fire fighters [1]. The lack of details on information about the victims trapped in fire and situation in the fire zone increase the risk to fire fighters [2, 3]. To reduce these fatalities fire fighting robots (FFRs) emerged as possible solutions therefore they are developed and researched on. The FFRs are designed for either prevention or emergency (same as intervention) tasks of fire and are applied indoor or outdoor. However, the prime movers of the majority of the FFRs are electrically powered [4] which made them to be suitable for preventive task alone and inappropriate for the emergency task. Their inappropriateness is due to the vulnerability in high temperature environment that characterised fire emergency. Thus, alternative propulsion systems for the mobility of fire fighting robots in emergency setting are evolving. Furthermore, literature survey reveals that water powered hydraulic propulsion system has been the only alternative to the drawbacks of dc motors in the hot environment. The mechanism was implemented on snake fire fighting robot for tunnel fire application [5]. In the mechanism, hydraulic motor was used to actuate the snake joints for mobility while water provides power for the hydraulic motors. However, the snake robot was designed for outdoor application. Consequently, the need for an autonomous fire fighting robot with a novel propulsion system becomes imminent

The activation cycle of Rab GTPase Ypt32 reveals structural determinants of effector recruitment and GDI binding

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116366/1/feb2s0014579311007538-sup-m0005.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116366/2/feb2s0014579311007538.pd

CLIMATE CHANGE IMPACT ON MOUNTAIN BIODIVERSITY: A SPECIAL REFERENCE TO GILGIT-BALTISTAN OF PAKISTAN

Climate Change is not a stationary phenomenon; it moves from time to time, it represents a major threat to mountainous biodiversity and to ecosystem integrity. The present study is an attempt to identify the current knowledge gap and the effects of climate change on mountainous biodiversity, a special reference to the Gilgit-Baltistan is briefly reviewed. Measuring the impact of climate change on mountain biodiversity is quite challenging, because climate change interacts with every phenomenon of ecosystem. The scale of this change is so large and very adverse so strongly connected to ecosystem services, and all communities who use natural resources. This study aims to provide the evidences on the basis of previous literature, in particular context to mountain biodiversity of Gilgit-Baltistan (GB). Mountains of Gilgit-Baltistan have most fragile ecosystem and are more vulnerable to climate change. These mountains host variety of wild fauna and flora, with many endangered species of the world. There are still many gaps in our knowledge of literature we studied because very little research has been conducted in Gilgit-Baltistan about climate change particular to biodiversity. Recommendations are made for increased research efforts in future this including jointly monitoring programs, climate change models and ecological research. Understanding the impact of climate change particular to biodiversity of GB is very important for sustainable management of these natural resources. The Government organizations, NGOs and the research agencies must fill the knowledge gap, so that it will help them for policy making, which will be based on scientific findings and research based