Significance: The nanoscale organization of ligands and receptors is critical for cellular communication yet inherently challenging to investigate. We have here devised a DNA origami-based biointerface which allows the experimenter to adjust protein distances with nanometer precision as a means to enhance or disturb signaling while being responsive to large-scale reorganization processes during cell activation. Applying this biointerface to study the spatial requirements of T cell activation, we find that the smallest signaling-competent receptor unit consists of two stably ligated T cell receptors (TCRs) within a distance of 20 nanometers. Spatial organization of the physiological ligand pMHC, however, is not a relevant parameter of antigen-mediated T cell activation, as single, well-isolated pMHC molecules efficiently stimulate T cells.

Smart farming (SF) applications rely on robust and accurate computer vision systems. An important computer vision task in agriculture is semantic segmentation, which aims to classify each pixel of an image and can be used for selective weed removal. State-of-the-art implementations use convolutional neural networks (CNN) that are trained on large image datasets. In agriculture, publicly available RGB image datasets are scarce and often lack detailed ground-truth information. In contrast to agriculture, other research areas feature RGB-D datasets that combine color (RGB) with additional distance (D) information. Such results show that including distance as an additional modality can improve model performance further. Therefore, we introduce WE3DS as the first RGB-D image dataset for multi-class plant species semantic segmentation in crop farming. It contains 2568 RGB-D images (color image and distance map) and corresponding hand-annotated ground-truth masks. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup. Further, we provide a benchmark for RGB-D semantic segmentation on the WE3DS dataset and compare it with a solely RGB-based model. Our trained models achieve up to 70.7% mean Intersection over Union (mIoU) for discriminating between soil, seven crop species, and ten weed species. Finally, our work confirms the finding that additional distance information improves segmentation quality.

A complete understanding of signaling processes at the plasma membrane depends on a quantitative characterization of the interactions of the involved proteins. Fluorescence recovery after photobleaching (FRAP) is a widely used and convenient technique to obtain kinetic parameters on protein interactions in living cells. FRAP experiments to determine unbinding time constants for proteins at the plasma membrane, however, are often hampered by non-specific contributions to the fluorescence recovery signal. On the example of the interaction between the T cell receptor (TCR) and the Syk kinase ZAP70, we present here an approach based on protein micropatterning that allows the elimination of such non-specific contributions and considerably simplifies analysis of FRAP data. Specifically, detection and reference areas are created within single cells, each being either enriched or depleted in TCR, which permits the isolation of ZAP70-TCR binding in a straight-forward manner. We demonstrate the applicability of our method by comparing it to a conventional FRAP approach.

Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved site-specific management and livestock feeding. A systematic approach was developed to analyze vegetation of managed permanent grassland using hyperspectral imaging in a laboratory setting. In the first step, hyperspectral images of typical grassland plants were recorded, annotated, and classified according to species group and plant parts, that is, flowers, leaves, and stems. In the second step, three different machine learning model types—multilayer perceptron (MLP), random forest (RF), and partial least squares discriminant analysis (PLS-DA)—were trained with pixel-wise spectral information to discriminate different species groups and plant parts in individual models. The influence of radiometric data calibration and specific data preprocessing steps on the overall model performance was also investigated. While the influence of proper radiometric calibration was negligible in our setting, specific preprocessing variants, including smoothening and derivation of the spectrum, were found to be beneficial for classification accuracy. Compared to extensively preprocessed data, raw spectral data yielded no statistically decreased performance in most cases. Overall, the MLP models outperformed the PLS-DA and RF models and reached cross-validation accuracies of 96.8% for species group and 88.6% for plant part classification. The obtained insights provide an essential basis for future data acquisition and data analysis of grassland vegetation.

Modern precision agriculture applications increasingly rely on stable computer vision outputs. An important computer vision task is to discriminate between soil and plant pixels, which is called plant segmentation. For this task, supervised learning techniques, such as decision tree classifiers (DTC), support vector machines (SVM), or artificial neural networks (ANN) are increasing in popularity. The selection of training data is of utmost importance in these approaches as it influences the quality of the resulting models. We investigated the influence of three modeling parameters, namely proportion of plant pixels (plant cover), criteria on what pixel to choose (pixel selection), and number/type of features (input features) on the segmentation quality using DTCs. Our findings show that plant cover and, to a minor degree, input features have a significant impact on segmentation quality. We can state that the overperformance of multi-feature input decision tree classifiers over threshold-based color index methods can be explained to a high degree by the more balanced training data. Single-feature input decision tree classifiers can compete with state-of-the-art models when the same training data are provided. This study is the first step in a systematic analysis of influence parameters of such plant segmentation models.