Ammonia toxicity: from head to toe?

Ammonia is diffused and transported across all plasma membranes. This entails that hyperammonemia leads to an increase in ammonia in all organs and tissues. It is known that the toxic ramifications of ammonia primarily touch the brain and cause neurological impairment. However, the deleterious effects of ammonia are not specific to the brain, as the direct effect of increased ammonia (change in pH, membrane potential, metabolism) can occur in any type of cell. Therefore, in the setting of chronic liver disease where multi-organ dysfunction is common, the role of ammonia, only as neurotoxin, is challenged. This review provides insights and evidence that increased ammonia can disturb many organ and cell types and hence lead to dysfunction

ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization

Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly from (a series of) images or by merging depth maps with optical flow (OF), research that combines absolute pose regression with OF is limited. We propose ViPR, a novel modular architecture for long-term 6DoF VO that leverages temporal information and synergies between absolute pose estimates (from PoseNet-like modules) and relative pose estimates (from FlowNet-based modules) by combining both through recurrent layers. Experiments on known datasets and on our own Industry dataset show that our modular design outperforms state of the art in long-term navigation tasks.Comment: Conf. on Computer Vision and Pattern Recognition (CVPR): Joint Workshop on Long-Term Visual Localization, Visual Odometry and Geometric and Learning-based SLAM 202

Effect of Caffeine on near Maximal Blood Pressure and Blood Pressure Recovery in Physically-Active, College-Aged Females

International Journal of Exercise Science 10(2): 266-273, 2017 The purpose of this study is to determine how caffeine affects exercise blood pressure (BP) and active and passive recovery BP after vigorous intensity exercise in physically active college-aged females. Fifteen physically active, ACSM stratified low-risk females (age (y): 23.53 ± 4.07, weight (kg): 60.34 ± 3.67, height (cm): 165.14 ± 7.20, BMI (kg/m2): 22.18 ± 1.55) participated in two Bruce protocol exercise tests. Before each test participants consumed 1) a placebo or 2) 3.3 mg·kg-1 of caffeine at least one hour before exercise in a counterbalanced double-blinded fashion. After reaching 85% of their age-predicted maximum heart rate, BP was taken and participants began an active (i.e. walking) recovery phase for 6 minutes followed by a passive (i.e. sitting) recovery phase. BP was assessed every two minutes in each phase. Recovery times were assessed until active and passive BP equaled 20 mmHg and 10 mmHg above resting, respectively. Participants completed each test 1-2 weeks a part. Maximal systolic and diastolic blood pressures were not significantly different between the two trials. Active recovery, passive recovery, and total recovery times were all significantly longer during the caffeine trial than the placebo trial. Furthermore, the time to reach age-predicted maximum heart rate was significantly shorter in the placebo trial than the caffeine trial. While caffeine consumption did not significantly affect maximal blood pressure, it did affect active and passive recovery time following vigorous intensity exercise in physically active females. Exercise endurance also improved after consuming caffeine in this population

High Rate Proton Irradiation of 15mm Muon Drifttubes

Future LHC luminosity upgrades will significantly increase the amount of background hits from photons, neutrons and protons in the detectors of the ATLAS muon spectrometer. At the proposed LHC peak luminosity of 5*10^34 1/cm^2s, background hit rates of more than 10 kHz/cm^2 are expected in the innermost forward region, leading to a loss of performance of the current tracking chambers. Based on the ATLAS Monitored Drift Tube chambers, a new high rate capable drift tube detecor using tubes with a reduced diameter of 15mm was developed. To test the response to highly ionizing particles, a prototype chamber of 46 15mm drift tubes was irradiated with a 20 MeV proton beam at the tandem accelerator at the Maier-Leibnitz Laboratory, Munich. Three tubes in a planar layer were irradiated while all other tubes were used for reconstruction of cosmic muon tracks through irradiated and non-irradiated parts of the chamber. To determine the rate capability of the 15mm drift-tubes we investigated the effect of the proton hit rate on pulse height, efficiency and spatial resolution of the cosmic muon signals

Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments

The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advances in deep learning have enabled the localization using monocular visual cameras. While structure from motion (SfM) predicts the absolute pose from a point cloud, absolute pose regression (APR) methods learn a semantic understanding of the environment through neural networks. However, both fields face challenges caused by the environment such as motion blur, lighting changes, repetitive patterns, and feature-less structures. This study aims to address these challenges by incorporating additional information and regularizing the absolute pose using relative pose regression (RPR) methods. The optical flow between consecutive images is computed using the Lucas-Kanade algorithm, and the relative pose is predicted using an auxiliary small recurrent convolutional network. The fusion of absolute and relative poses is a complex task due to the mismatch between the global and local coordinate systems. State-of-the-art methods fusing absolute and relative poses use pose graph optimization (PGO) to regularize the absolute pose predictions using relative poses. In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction. We evaluate eight different recurrent units and construct a simulation environment to pre-train the APR and RPR networks for better generalized training. Additionally, we record a large database of different scenarios in a challenging large-scale indoor environment that mimics a warehouse with transportation robots. We conduct hyperparameter searches and experiments to show the effectiveness of our recurrent fusion method compared to PGO

High-Accuracy Measurements of Total Column Water Vapor From the Orbiting Carbon Observatory-2

Accurate knowledge of the distribution of water vapor in Earth's atmosphere is of critical importance to both weather and climate studies. Here we report on measurements of total column water vapor (TCWV) from hyperspectral observations of near-infrared reflected sunlight over land and ocean surfaces from the Orbiting Carbon Observatory-2 (OCO-2). These measurements are an ancillary product of the retrieval algorithm used to measure atmospheric carbon dioxide concentrations, with information coming from three highly resolved spectral bands. Comparisons to high-accuracy validation data, including ground-based GPS and microwave radiometer data, demonstrate that OCO-2 TCWV measurements have maximum root-mean-square deviations of 0.9-1.3mm. Our results indicate that OCO-2 is the first space-based sensor to accurately and precisely measure the two most important greenhouse gases, water vapor and carbon dioxide, at high spatial resolution [1.3 x 2.3 km(exp. 2)] and that OCO-2 TCWV measurements may be useful in improving numerical weather predictions and reanalysis products

Conserved noncoding sequences highlight shared components of regulatory networks in dicotyledonous plants

Conserved noncoding sequences (CNSs) in DNA are reliable pointers to regulatory elements controlling gene expression. Using a comparative genomics approach with four dicotyledonous plant species (Arabidopsis thaliana, papaya [Carica papaya], poplar [Populus trichocarpa], and grape [Vitis vinifera]), we detected hundreds of CNSs upstream of Arabidopsis genes. Distinct positioning, length, and enrichment for transcription factor binding sites suggest these CNSs play a functional role in transcriptional regulation. The enrichment of transcription factors within the set of genes associated with CNS is consistent with the hypothesis that together they form part of a conserved transcriptional network whose function is to regulate other transcription factors and control development. We identified a set of promoters where regulatory mechanisms are likely to be shared between the model organism Arabidopsis and other dicots, providing areas of focus for further research

Long-Lived Inverse Chirp Signals from Core-Collapse in Massive Scalar-Tensor Gravity.

This Letter considers stellar core collapse in massive scalar-tensor theories of gravity. The presence of a mass term for the scalar field allows for dramatic increases in the radiated gravitational wave signal. There are several potential smoking gun signatures of a departure from general relativity associated with this process. These signatures could show up within existing LIGO-Virgo searches

Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression

Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Recent methods directly regress the pose using convolutional and spatio-temporal networks. Absolute pose regression (APR) techniques predict the absolute camera pose from an image input in a known scene. Odometry methods perform relative pose regression (RPR) that predicts the relative pose from a known object dynamic (visual or inertial inputs). The localization task can be improved by retrieving information of both data sources for a cross-modal setup, which is a challenging problem due to contradictory tasks. In this work, we conduct a benchmark to evaluate deep multimodal fusion based on PGO and attention networks. Auxiliary and Bayesian learning are integrated for the APR task. We show accuracy improvements for the RPR-aided APR task and for the RPR-RPR task for aerial vehicles and hand-held devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets, and record a novel industry dataset.Comment: Under revie