Inherently UV Photodegradable Poly(methacrylate) Gels

Organogels (hydrophobic polymer gels) are soft materials based on polymeric networks swollen in organic solvents. They are hydrophobic and possess a high content of solvent and low surface adhesion, rendering them interesting in applications such as encapsulants, drug delivery, actuators, slippery surfaces (self-cleaning, anti-waxing, anti-bacterial), or for oil-water separation. To design functional organogels, strategies to control their shape and surface structure are required. Herein, the inherent UV photodegradability of poly(methacrylate) organogels is reported. No additional photosensitizers are required to efficiently degrade organogels (d ≈ 1 mm) on the minute scale. A low UV absorbance and a high swelling ability of the solvent infusing the organogel are found to be beneficial for fast photodegradation, which is expected to be transferrable to other gel photochemistry. Organogel arrays, films, and structured organogel surfaces are prepared, and their extraction ability and slippery properties are examined. Films of inherently photodegradable organogels on copper circuit boards serve as the first ever positive gel photoresist. Spatially photodegraded organogel films protect or reveal copper surfaces against an etchant (FeCl3 aq.)

Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks

Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device

Space Information Technology in the Arctic Policy of the European Union

The article describes the status of the most important part of the Arctic information policy of the European Union — space of information security. The data on the number and types of space systems of Earth remote sensing used by the European Union over the past fifteen years and analyzes the role of space information technology in achieving the main goals of the Arctic policy of the European Union

Covalent cucurbit[7]uril-dye conjugates for sensing in aqueous saline media and biofluids

Non-covalent chemosensing ensembles of cucurbit[n]urils (CBn) have been widely used in proof-of-concept sensing applications, but they are prone to disintegrate in saline media, e.g. biological fluids. We show here that covalent cucurbit[7]uril–indicator dye conjugates are buffer- (10× PBS buffer) and saline-stable (up to 1.4 M NaCl) and allow for selective sensing of Parkinson\u27s drug amantadine in human urine and saliva, where the analogous non-covalent CB7⊃dye complex is dysfunctional. The in-depth analysis of the covalent host–dye conjugates in the gas-phase, and deionized versus saline aqueous media revealed interesting structural, thermodynamic and kinetic effects that are of general interest for the design of CBn-based supramolecular chemosensors and systems. This work also introduces a novel high-affinity indicator dye for CB7 through which fundamental limitations of indicator displacement assays (IDA) were exposed, namely an impractical slow equilibration time. Unlike non-covalent CBn⊃dye reporter pairs, the conjugate chemosensors can also operate through a S$_{N}$2-type guest–dye exchange mechanism, which shortens assay times and opens new avenues for tailoring analyte-selectivity

High-throughput screening of multifunctional nanocoatings based on combinations of polyphenols and catecholamines

Biomimetic surface coatings based on plant polyphenols and catecholamines have been used broadly in a variety of applications. However, the lack of a rational cost-effective platform for screening these coatings and their properties limits the true potential of these functional materials to be unleashed. Here, we investigated the oxidation behavior and coating formation ability of a library consisting of 45 phenolic compounds and catecholamines. UV–vis spectroscopy demonstrated significant acceleration of oxidation and polymerization under UV irradiation. We discovered that several binary mixtures resulted in non-additive behavior (synergistic or antagonistic effect) yielding much thicker or thinner coatings than individual compounds measured by ellipsometry. To investigate the properties of coatings derived from new combinations, we used a miniaturized high-throughput strategy to screen 2,532 spots coated with single, binary, and ternary combinations of coating precursors in one run. We evaluated the use of machine learning models to learn the relation between the chemical structure of the precursors and the thickness of the nanocoatings. Formation and stability of nanocoatings were investigated in a high-throughput manner via discontinuous dewetting. 30 stable combinations (hits) were used to tune the surface wettability and to form water droplet microarray and spot size gradients of water droplets on the coated surface. No toxicity was observed against eukaryotic HeLa cells and Pseudomonas aeruginosa (strain PA30) bacteria after 24 h incubation at 37 °C. The strategy introduced here for high-throughput screening of nanocoatings derived from combinations of coating precursors enables the discovery of new functional materials for various applications in science and technology in a cost-effective miniaturized manner

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure

Deep neural networks have the capability to solve complex tasks through accurate function approximation. The process from submitting domain data and defining process requirements to analyzed data consists of multiple steps, disallowing a simplistic straightforward procedure. It follows that one of the core questions is: how does an application development process facilitating interaction between data scientists and domain experts look like? Practically, two connected challenges need to be addressed. Firstly, it requires a solution for handling large amounts of domain-specific data. Secondly, when dealing with complex deep neural networks, it is essential to find a concept of how model training can be designed in an computationally efficient manner. While tailored solutions for addressing these challenges in interdisciplinary deep learning projects exist, a comprehensive and structured approach is missing. Hence, we present a computational workflow to enhance these kinds of projects concerning data handling, integration of cluster computing resources such as bwHPC infrastructure, and development processes. We exemplify our proposal by means of a biomedical image analysis project