CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning

Cycling is a promising sustainable mode for commuting and leisure in cities. However, the perception of cycling as a risky activity reduces its wide expansion as a commuting mode. A novel method called CyclingNet has been introduced here for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built environment, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on a convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road users, the built and the natural environments

Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe

Human monoclonal islet specific autoantibodies share features of islet cell and 64 kDa antibodies

The first human monoclonal islet cell antibodies of the IgG class (MICA 1-6) obtained from an individual with Type 1 (insulin-dependent) diabetes mellitus were cytoplasmic islet cell antibodies selected by the indirect immunofluorescence test on pancreas sections. Surprisingly, they all recognized the 64 kDa autoantigen glutamate decarboxylase. In this study we investigated which typical features of cytoplasmic islet cell antibodies are represented by these monoclonals. We show by double immunofluorescence testing that MICA 1-6 stain pancreatic beta cells which is in agreement with the beta-cell specific expression of glutamate decarboxylase. In contrast an islet-reactive IgM monoclonal antibody obtained from a pre-diabetic individual stained all islet cells but lacked the tissue specificity of MICA 1-6 and must therefore be considered as a polyreactive IgM-antibody. We further demonstrate that MICA 1-6 revealed typical features of epitope sensitivity to biochemical treatment of the target tissue which has been demonstrated for islet cell antibodies, and which has been used to argue for a lipid rather than a protein nature of target antigens. Our results provide direct evidence that the epitopes recognized by the MICA are destroyed by methanol/chloroform treatment but reveal a high stability to Pronase digestion compared to proinsulin epitopes. Conformational protein epitopes in glutamate decarboxylase therefore show a sensitivity to biochemical treatment of sections such as ganglioside epitopes. MICA 1-6 share typical features of islet cell and 64 kDa antibodies and reveal that glutamate decarboxylase-reactive islet cell antibodies represent a subgroup of islet cell antibodies present in islet cell antibody-positive sera

Antibodies to the Mr 64,000 (64K) protein in islet cell antibody positive non-diabetic individuals indicate high risk for impaired Beta-cell function

A prospective study of a normal childhood population identified 44 islet cell antibody positive individuals. These subjects were typed for HLA DR and DQ alleles and investigated for the presence of antibodies to the Mr 64,000 (64K) islet cell antigen, complement-fixing islet cell antibodies and radiobinding insulin autoantibodies to determine their potency in detecting subjects with impaired Beta-cell function. At initial testing 64K antibodies were found in six of 44 islet cell antibody positive subjects (13.6%). The same sera were also positive for complement-fixing islet cell antibodies and five of them had insulin autoantibodies. During the follow-up at 18 months, islet cell antibodies remained detectable in 50% of the subjects studied. In all six cases who were originally positive, 64K antibodies were persistently detectable, whereas complement-fixing islet cell antibodies became negative in two of six and insulin autoantibodies in one of five individuals. HLA DR4 (p < 0.005) and absence of asparic acid (Asp) at position 57 of the HLA DQ chain (p < 0.05) were significantly increased in subjects with 64K antibodies compared with control subjects. Of 40 individuals tested in the intravenous glucose tolerance test, three had a first phase insulin response below the first percentile of normal control subjects. Two children developed Type 1 (insulin-dependent) diabetes mellitus after 18 and 26 months, respectively. Each of these subjects was non-Asp homozygous and had persistent islet cell and 64K antibodies. We conclude that 64K antibodies, complement-fixing islet cell antibodies and insulin autoantibodies represent sensitive serological markers in assessing high risk for a progression to Type 1 diabetes in islet cell antibody positive non-diabetic individuals

Transcriptomic identification of starfish neuropeptide precursors yields new insights into neuropeptide evolution

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.This work was supported by a PhD studentship funded by QMUL and awarded to D.C.S. and a Leverhulme Trust grant (RPG- 2013-351) awarded to M.R.E. Sequencing of the A. rubens neural transcriptome was funded by an EPSRC grant (EP/J501360/1

Biohydrogenation of 22:6n-3 by Butyrivibrio proteoclasticus P18

Background: Rumen microbes metabolize 22:6n-3. However, pathways of 22:6n-3 biohydrogenation and ruminal microbes involved in this process are not known. In this study, we examine the ability of the well-known rumen biohydrogenating bacteria, Butyrivibrio fibrisolvens D1 and Butyrivibrio proteoclasticus P18, to hydrogenate 22:6n-3. Results: Butyrivibrio fibrisolvens D1 failed to hydrogenate 22:6n-3 (0.5 to 32 mu g/mL) in growth medium containing autoclaved ruminal fluid that either had or had not been centrifuged. Growth of B. fibrisolvens was delayed at the higher 22:6n-3 concentrations; however, total volatile fatty acid production was not affected. Butyrivibrio proteoclasticus P18 hydrogenated 22:6n-3 in growth medium containing autoclaved ruminal fluid that either had or had not been centrifuged. Biohydrogenation only started when volatile fatty acid production or growth of B. proteoclasticus P18 had been initiated, which might suggest that growth or metabolic activity is a prerequisite for the metabolism of 22:6n-3. The amount of 22:6n-3 hydrogenated was quantitatively recovered in several intermediate products eluting on the gas chromatogram between 22:6n-3 and 22:0. Formation of neither 22:0 nor 22:6 conjugated fatty acids was observed during 22:6n-3 metabolism. Extensive metabolism was observed at lower initial concentrations of 22:6n-3 (5, 10 and 20 mu g/mL) whereas increasing concentrations of 22:6n-3 (40 and 80 mu g/mL) inhibited its metabolism. Stearic acid formation (18:0) from 18:2n-6 by B. proteoclasticus P18 was retarded, but not completely inhibited, in the presence of 22:6n-3 and this effect was dependent on 22:6n-3 concentration. Conclusions: For the first time, our study identified ruminal bacteria with the ability to hydrogenate 22:6n-3. The gradual appearance of intermediates indicates that biohydrogenation of 22:6n-3 by B. proteoclasticus P18 occurs by pathways of isomerization and hydrogenation resulting in a variety of unsaturated 22 carbon fatty acids. During the simultaneous presence of 18:2n-6 and 22:6n-3, B. proteoclasticus P18 initiated 22:6n-3 metabolism before converting 18:1 isomers into 18:0

Gad65 is recognized by t-cells, but not by antibodies from nod-mice

Since the 64kDa-protein glutamic acid decarboxylase (GAD) is one of the major autoantigens in T-cell mediated Type 1 diabetes, its relevance as a T-cell antigen needs to be clarified. After isolation of splenic T-cells from non-obese diabetic (NOD) mice, a useful model for human Type 1 diabetes, we found that these T-cells proliferate spontaneously when incubated with human GAD65, but only marginally after incubation with GAD67, both recombinated in the baculovirus system. No effect was observed with non-diabetic NOD mice or with T-cells from H-2 identical NON-NOD-H-2g7 control mice. It has been published previously that NOD mice develop autoantibodies against a 64kDa protein detected with mouse beta cells. In immunoprecipitation experiments with sera from the same NOD mice and 33S-methionine-labelled GAD, no autoantibody binding could be detected. We conclude firstly that GAD65 is an important T-cell antigen which is relevant early in the development of Type 1 diabetes and secondly that there is an antigenic epitope in the human GAD65 molecule recognized by NOD T-cells, but not by NOD autoantibodies precipitating conformational epitopes. Our results therefore provide further evidence that GAD65 is a T-cell antigen in NOD mice, being possibly also involved in very early processes leading to the development of human Type 1 diabetes

Larval Connectivity in an Effective Network of Marine Protected Areas

Acceptance of marine protected areas (MPAs) as fishery and conservation tools has been hampered by lack of direct evidence that MPAs successfully seed unprotected areas with larvae of targeted species. For the first time, we present direct evidence of large-scale population connectivity within an existing and effective network of MPAs. A new parentage analysis identified four parent-offspring pairs from a large, exploited population of the coral-reef fish Zebrasoma flavescens in Hawai'i, revealing larval dispersal distances ranging from 15 to 184 km. In two cases, successful dispersal was from an MPA to unprotected sites. Given high adult abundances, the documentation of any parent-offspring pairs demonstrates that ecologically-relevant larval connectivity between reefs is substantial. All offspring settled at sites to the north of where they were spawned. Satellite altimetry and oceanographic models from relevant time periods indicated a cyclonic eddy that created prevailing northward currents between sites where parents and offspring were found. These findings empirically demonstrate the effectiveness of MPAs as useful conservation and management tools and further highlight the importance of coupling oceanographic, genetic, and ecological data to predict, validate and quantify larval connectivity among marine populations

Endogenous cholinergic inputs and local circuit mechanisms govern the phasic mesolimbic dopamine response to nicotine

Nicotine exerts its reinforcing action by stimulating nicotinic acetylcholine receptors (nAChRs) and boosting dopamine (DA) output from the ventral tegmental area (VTA). Recent data have led to a debate about the principal pathway of nicotine action: direct stimulation of the DAergic cells through nAChR activation, or disinhibition mediated through desensitization of nAChRs on GABAergic interneurons. We use a computational model of the VTA circuitry and nAChR function to shed light on this issue. Our model illustrates that the α4β2-containing nAChRs either on DA or GABA cells can mediate the acute effects of nicotine. We account for in vitro as well as in vivo data, and predict the conditions necessary for either direct stimulation or disinhibition to be at the origin of DA activity increases. We propose key experiments to disentangle the contribution of both mechanisms. We show that the rate of endogenous acetylcholine input crucially determines the evoked DA response for both mechanisms. Together our results delineate the mechanisms by which the VTA mediates the acute rewarding properties of nicotine and suggest an acetylcholine dependence hypothesis for nicotine reinforcement.Peer reviewe

Flow chemistry for process optimisation using design of experiments

Implementing statistical training into undergraduate or postgraduate chemistry courses can provide high-impact learning experiences for students. However, the opportunity to reinforce this training with a combined laboratory practical can significantly enhance learning outcomes by providing a practical bolstering of the concepts. This paper outlines a flow chemistry laboratory practical for integrating design of experiments optimisation techniques into an organic chemistry laboratory session in which students construct a simple flow reactor and perform a structured series of experiments followed by computational processing and analysis of the results