Animal Allergy

There is a growing appreciation of the importance of hypersensitiveness in animals. Compared to the incidence of allergies in human beings, animal allergy is relatively common

Changes in production of organic nitrogen and carboxylates (C-A) in young sugar-beet plants grown in nutrient solutions of different nitrogen composition.

Sugar-beet seedlings grown in controlled environment for 19 days in culture solution containing nitrate were then grown with nitrate, ammonium or no N. Carboxylate levels (in meq.) were approximately equal to organic N levels (in mM) in plants given nitrate, were lower in plants grown with ammonium and were higher in plants grown without further N; this was accompanied by a slight rise in culture solution pH where nitrate was supplied and a sharp drop in pH in the other treatments.-R.B. (Abstract retrieved from CAB Abstracts by CABI’s permission

An Elementary Rigorous Introduction to Exact Sampling

We introduce coupling from the past, a recently developed method for exact sampling from a given distribution. Focus is on rigour and thorough proofs. We stay on an elementary level which requires little or no prior knowledge from probability theory. This should fill an obvious gap between innumerable intuitive and incomplete reviews, and few precise derivations on an abstract level

Deep domain adaptation by weighted entropy minimization for the classification of aerial images

Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs. © 2020 Copernicus GmbH. All rights reserved

Assessing the semantic similarity of images of silk fabrics using convolutional neural networks

This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (http://silknow.eu/), two variants of training were developed, one based on a Siamese CNN and one based on a triplet architecture. We propose different definitions of similarity and different loss functions for both training strategies, some of them also allowing the use of incomplete information about the training data. We assess the quality of the trained model by using the learned image features in a k-NN classification. We achieve overall accuracies of 93-95% and average F1-scores of 87-92%. © 2020 Copernicus GmbH. All rights reserved

A multimodal imaging workflow for monitoring CAR T cell therapy against solid tumor from whole-body to single-cell level

CAR T cell research in solid tumors often lacks spatiotemporal information and therefore, there is a need for a molecular tomography to facilitate high-throughput preclinical monitoring of CAR T cells. Furthermore, a gap exists between macro- and microlevel imaging data to better assess intratumor infiltration of therapeutic cells. We addressed this challenge by combining 3D µComputer tomography bioluminescence tomography (µCT/BLT), light-sheet fluorescence microscopy (LSFM) and cyclic immunofluorescence (IF) staining. Methods: NSG mice with subcutaneous AsPC1 xenograft tumors were treated with EGFR CAR T cell (± IL-2) or control BDCA-2 CAR T cell (± IL-2) (n = 7 each). Therapeutic T cells were genetically modified to co-express the CAR of interest and the luciferase CBR2opt. IL-2 was administered s.c. under the xenograft tumor on days 1, 3, 5 and 7 post-therapy-initiation at a dose of 25,000 IU/mouse. CAR T cell distribution was measured in 2D BLI and 3D µCT/BLT every 3-4 days. On day 6, 4 tumors were excised for cyclic IF where tumor sections were stained with a panel of 25 antibodies. On day 6 and 13, 8 tumors were excised from rhodamine lectin-preinjected mice, permeabilized, stained for CD3 and imaged by LSFM. Results: 3D µCT/BLT revealed that CAR T cells pharmacokinetics is affected by antigen recognition, where CAR T cell tumor accumulation based on target-dependent infiltration was significantly increased in comparison to target-independent infiltration, and spleen accumulation was delayed. LSFM supported these findings and revealed higher T cell accumulation in target-positive groups at day 6, which also infiltrated the tumor deeper. Interestingly, LSFM showed that most CAR T cells accumulate at the tumor periphery and around vessels. Surprisingly, LSFM and cyclic IF revealed that local IL-2 application resulted in early-phase increased proliferation, but long-term overstimulation of CAR T cells, which halted the early added therapeutic effect. Conclusion: Overall, we demonstrated that 3D µCT/BLT is a valuable non-isotope-based technology for whole-body cell therapy monitoring and investigating CAR T cell pharmacokinetics. We also presented combining LSFM and MICS for ex vivo 3D- and 2D-microscopy tissue analysis to assess intratumoral therapeutic cell distribution and status

Semantic segmentation of Brazilian Savanna vegetation using high spatial resolution satellite data and U-net

Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome. © Authors 2020. All rights reserved

IMPROVED CLASSIFICATION OF SATELLITE IMAGERY USING SPATIAL FEATURE MAPS EXTRACTED FROM SOCIAL MEDIA

In this work, we consider the exploitation of social media data in the context of Remote Sensing and Spatial Information Sciences. To this end, we explore a way of augmenting and integrating information represented by geo-located feature vectors into a system for the classification of satellite images. For that purpose, we present a quite general data fusion framework based on Convolutional Neural Network (CNN) and an initial examination of our approach on features from geo-located social media postings on the Twitter and Sentinel images. For this examination, we selected six simple Twitter features derived from the metadata, which we believe could contain information for the spatial context. We present initial experiments using geotagged Twitter data from Washington DC and Sentinel images showing this area. The goal of classification is to determine local climate zones (LCZ). First, we test whether our selected feature maps are not correlated with the LCZ classification at the geo-tag position. We apply a simple boost tree classifier on this data. The result turns out not to be a mere random classifier. Therefore, this data can be correlated with LCZ. To show the improvement by our method, we compare classification with and without the Twitter feature maps. In our experiments, we apply a standard pixel-based CNN classification of the Sentinel data and use it as a baseline model. After that, we expand the input augmenting additional Twitter feature maps within the CNN and assess the contribution of these additional features to the overall F1-score of the classification, which we determine from spatial cross-validation