Synaptic proteome SQLite database

Genes encoding synaptic proteins are highly associated with neuronal disorders many of which show clinical co-morbidity. We integrated 58 published synaptic proteomic datasets that describe over 8,000 proteins and combined them with direct protein-protein interactions and functional metadata to build a network resource that reveals the shared and unique protein components that underpin multiple disorders. All the data are provided in a flexible and accessible format to encourage custom use.Sorokina, Oksana. (2021). Synaptic proteome SQLite database, 2000-2020 [dataset]. University of Edinburgh. School of Informatics. Systems Neuroscience group. https://doi.org/10.7488/ds/3017

BioNAR: An Integrated Biological Network Analysis Package in Bioconductor

Biological function in protein complexes emerges from more than just the sum of their parts: Molecules interact in a range of different subcomplexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone. Here we present the Bioconductor package BioNAR which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network’s vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein’s impact within multiple complexes, and enables estimation of the co-occurrence of meta-data, i.e., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms. The package is available from Bioconductor release 3.16: https://bioconductor.org/packages/release/bioc/html/BioNAR.html The dataset is also related to the paper McLean et al. (in submission)

Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras

The performance of image-based Reinforcement Learning (RL) agents can vary depending on the position of the camera used to capture the images. Training on multiple cameras simultaneously, including a first-person egocentric camera, can leverage information from different camera perspectives to improve the performance of RL. However, hardware constraints may limit the availability of multiple cameras in real-world deployment. Additionally, cameras may become damaged in the real-world preventing access to all cameras that were used during training. To overcome these hardware constraints, we propose Multi-View Disentanglement (MVD), which uses multiple cameras to learn a policy that is robust to a reduction in the number of cameras to generalise to any single camera from the training set. Our approach is a self-supervised auxiliary task for RL that learns a disentangled representation from multiple cameras, with a shared representation that is aligned across all cameras to allow generalisation to a single camera, and a private representation that is camera-specific. We show experimentally that an RL agent trained on a single third-person camera is unable to learn an optimal policy in many control tasks; but, our approach, benefiting from multiple cameras during training, is able to solve the task using only the same single third-person camera

Synaptic Proteome SQLite database V1.00.1

Genes encoding synaptic proteins are highly associated with neuronal disorders many of which show clinical co-morbidity. Previously, we integrated 58 published synaptic proteomic datasets that describe over 8,000 proteins and combined them with direct protein-protein interactions and functional metadata to build a network resource that reveals the shared and unique protein components that underpin multiple disorders. All the data are provided in a flexible and accessible format to encourage custom use and available from here: https://datashare.ed.ac.uk/handle/10283/3877?show=full. In the current update we added 6 more synaptic proteome studies, which results in total of 64 studies. We introduced Synaptic Vesicle as a separate compartment. We also added coding mutations for Autistic Spectral disorder and Epilepsy collected from publicly available databases.Sorokina, Oksana; Armstromg, J Douglas. (2022). Synaptic Proteome SQLite database V1.00.1, 2000-2022 [software]. University of Edinburgh. School of Informatics. https://doi.org/10.7488/ds/3771

THE INDO AND ROUNDO DATASETS OF VEHICLE OCCLUSIONS

The dataset contains the occlusions for the "Bendplatz", "Frankenburg" and "Heckstrasse" scenarios in the inD [1] dataset, and for "Neuweiler" in the rounD [2] dataset. The occlusions are stored from the perspective of each of the vehicles alive in each of the frames contained in the recordings in [1] and [2]. More information about the dataset, and the references to the inD and rounD datasets, can be found in the README.txt below and at https://arxiv.org/abs/2206.14163Brewitt, Cillian; Tamborski, Massimiliano; Albrecht, Stefano V. (2022). THE INDO AND ROUNDO DATASETS OF VEHICLE OCCLUSIONS, [dataset]. University of Edinburgh. School of Informatics. Institute of Perception, Action and Behaviour. https://doi.org/10.7488/ds/3498

Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition

Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-stationarity caused by mutually adapting agents. To facilitate efficient learning of complex multi-agent tasks, we propose an approach which uses an expert-provided decomposition of a task into simpler multi-agent sub-tasks. In each sub-task, a subset of the entire team is trained to acquire sub-task-specific policies. The sub-teams are then merged and transferred to the target task, where their policies are collectively fine-tuned to solve the more complex target task. We show empirically that such approaches can greatly reduce the number of timesteps required to solve a complex target task relative to training from-scratch. However, we also identify and investigate two problems with naive implementations of approaches based on sub-task decomposition, and propose a simple and scalable method to address these problems which augments existing actor-critic algorithms. We demonstrate the empirical benefits of our proposed method, enabling sub-task decomposition approaches to be deployed in diverse multi-agent tasks

Pilot study on inhaled corticosteroids in asthma patients with side effects. MS data

In this study, we sought to elucidate the microbiome-related etiologies underlying the side effects of inhaled salmeterol We collected fecal samples from 24 individuals, stratified into three cohorts: asthma patients experiencing corticosteroid-induced side effects, asthma patients devoid of such side effects, and healthy controls.Sorokin, A., Goryanin, I., & Osmonov, B. (2023). Pilot study on inhaled corticosteroids in asthma patients with side effects. MS data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1029424

[CODE] Code for Chadwick et al. (2022), Neuron.

This code finds the fixed point of the stabilised supralinear network and then computes the eigenmodes of the Jacobian. This forms the basis of the analysis of E-I network models in the paper.Chadwick, A. (2022). Code for Chadwick et al. (2022), Neuron. Zenodo. https://doi.org/10.5281/zenodo.710999

CodeDataCharSynchPatternsPBV

Code and data accompanying the manuscript: Characterizing synchrony patterns across cognitive task stages of associative recognition memory (doi: 10.1111/ejn.13817)

Executable version of Synaptic proteome database paper

This is the Executable Research Paper - the version of the paper published in Scientific Reports https://www.nature.com/articles/s41598-021-88945-7 This dataset contains the Rdata files and scripts to enable ERP, and is supplemental to the files included in DataShare item "Synaptic proteome SQLite database" (https://doi.org/10.7488/ds/3017).Finke, Bernhard; Sorokina, Oksana; Armstrong, J Douglas. (2022). Executable version of Synaptic proteome database paper, 2000-2020 [software]. University of Edinburgh. School of Informatics. https://doi.org/10.7488/ds/3516