Evaluation of the passage of Lactobacillus gasseri K7 and bifidobacteria from the stomach to intestines using a single reactor model

<p>Abstract</p> <p>Background</p> <p>Probiotic bacteria are thought to play an important role in the digestive system and therefore have to survive the passage from stomach to intestines. Recently, a novel approach to simulate the passage from stomach to intestines in a single bioreactor was developed. The advantage of this automated one reactor system was the ability to test the influence of acid, bile salts and pancreatin.</p> <p><it>Lactobacillus gasseri </it>K7 is a strain isolated from infant faeces with properties making the strain interesting for cheese production. In this study, a single reactor system was used to evaluate the survival of <it>L. gasseri </it>K7 and selected bifidobacteria from our collection through the stomach-intestine passage.</p> <p>Results</p> <p>Initial screening for acid resistance in acidified culture media showed a low tolerance of <it>Bifidobacterium dentium </it>for this condition indicating low survival in the passage. Similar results were achieved with <it>B. longum </it>subsp. <it>infantis </it>whereas <it>B. animalis </it>subsp. <it>lactis </it>had a high survival.</p> <p>These initial results were confirmed in the bioreactor model of the stomach-intestine passage. <it>B. animalis </it>subsp. <it>lactis </it>had the highest survival rate (10%) attaining approximately 5 × 10⁶cfu ml^-1compared to the other tested bifidobacteria strains which were reduced by a factor of up to 10⁶. <it>Lactobacillus gasseri </it>K7 was less resistant than <it>B. animalis </it>subsp. <it>lactis </it>but survived at cell concentrations approximately 1000 times higher than other bifidobacteria.</p> <p>Conclusion</p> <p>In this study, we were able to show that <it>L. gasseri </it>K7 had a high survival rate in the stomach-intestine passage. By comparing the results with a previous study in piglets we could confirm the reliability of our simulation. Of the tested bifidobacteria strains, only <it>B. animalis </it>subsp. <it>lactis </it>showed acceptable survival for a successful passage in the simulation system.</p

Weather Influence and Classification with Automotive Lidar Sensors

Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted in-depth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.Comment: 8 pages, will be published in the IEEE IV 2019 Proceeding

Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs

Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. Convolutional neural networks (CNNs), in contrast, have been specifically designed for highly heterogeneous data, such as natural images, by sliding convolutional filters over different positions in an image. Here, we suggest a new CNN architecture that combines the idea of hierarchical abstraction in neural networks with a prior on the spatial homogeneity of neuroimaging data: Whereas early layers are trained globally using standard convolutional layers, we introduce for higher, more abstract layers patch individual filters (PIF). By learning filters in individual image regions (patches) without sharing weights, PIF layers can learn abstract features faster and with fewer samples. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer's disease detection on ADNI data and multiple sclerosis detection on private hospital data. We demonstrate that CNNs using PIF layers result in higher accuracies, especially in low sample size settings, and need fewer training epochs for convergence. To the best of our knowledge, this is the first study which introduces a prior on brain MRI for CNN learning

Geometrical frustration and incommensurate magnetic order in Na3RuO4 with two triangular motifs

Incommensurate magnetic order in the spin-3/2 antiferromagnet Na3RuO4 is uncovered by neutron diffraction combined with ab initio calculations. The crystal structure of Na3RuO4 contains two triangular motifs on different length scales. The magnetic Ru5+ ions form a lozenge (diamond) configuration, with tetramers composed of two isosceles triangles. These tetramers are further arranged in layers, such that an effective triangular lattice is formed. The tetramers are nearly antiferromagnetic but frustration between them leads to an incommensurately modulated magnetic structure described by the propagation vector →k=(0.242(1),0,0.313(1)). We show that the long-range Ru-O-O-Ru couplings between the tetramers play a major role in Na3RuO4 and suggest an effective description in terms of the spatially anisotropic triangular lattice if the tetramers are treated as single sites