Description: Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

Description: The growing production and use of plastics are becoming a serious progressive issue and people pay increasing attention to the effects of plastics on ecosystems and human health. The availability of hyperspectral data from space sensors inspired us to study the feasibility to detect and identify different types of plastics in aircraft -, Goafen-5 (GF-5) and PRISMA satellite data by means of deep -, and machine learning models trained with spectral signatures. In this context, various inhouse and public spectral libraries are used to create a comprehensive database with mixed pixels of different plastic and non-plastic materials. The endmembers of plastic types involved in this study are polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET) and polystyrene (PS), covering 95% of the global production. Additionally, some important varieties of industrial plastics types such as acrylonitrile butadiene styrene (ABS), ethylene vinyl acetate (EVA), polyamide (PA), polycarbonate (PC), and polymethyl methacrylate (PMMA) were included in the investigations. Different samples with varying optical properties (color, brightness, transmissivity) have been selected for each plastic type. As non-plastic materials we have chosen spectra of vegetation, rocks, soils and minerals contained in the public US libraries (ECOSTRESS and USGS). The number of spectra for the training of the deep learning and machine learning models was enlarged by a random linear mixing method and the resulting database was separated into a training and a test group for subsequent multi-label classification. Algorithms selected are a convolutional neural network (CNN), random forest (RF) and support vector machine (SVM). To investigate the transferability to any hyperspectral image data obtained by air-, and spacecraft sensors, we opted for a unification of the spectral response functions (SRF) and the spectral sampling intervals of all data. Validation is accomplished based on the test group of the spectral database, and tested by controlled laboratory and aircraft experiments recorded over surfaces with varying background materials. Results are further analyzed for the influence of different noise quantities and abundance levels. The performance of the three models is roughly balanced for the validation of the spectral data with an overall accuracy of 97%, 96%, and 95% for the CNN, RF, and SVM, models respectively. In the controlled lab experiments, various accuracy indicators, such as the recall rates and the comprehensive metrics F1-score, OA, and Kappa suggest the RF classifier as the most robust one, followed by the SVM and CNN models. As for the evaluation of the aircraft data from controlled experiments, the RF further outperforms the other two models, behaving most robustly and reliably against conditions with unknown plastics and unknown background surfaces. Thus, the RF was used to classify the ten types of plastics mentioned above in one GF-5 and two PRISMA satellite recordings of the same area. In comparison of both sensor systems, the RF produced high quality and transferable results for detecting plastic mainly related to greenhouses, sport fields, photovoltaic constructions and industrial sites that are discussed in detail in this paper.

Description: Plastic pollution is becoming an increasingly prominent problem and the function of plastics determines whether they need to be recycled or not. In order to explore the possibility of using satellite imagery to classify the functionality of plastics, this study proposes a two-stage workflow: firstly, a classification map is obtained based on hyperspectral satellite imagery to generate plastic types, and then using these identified plastic coverage areas, a deep learning algorithm is used to assign functionality to these classified plastic areas based on sentinel-2 imagery. By comparing five leading-edge image classification models, classification accuracies of up to 74% were achieved, demonstrating the feasibility of using deep learning models trained on satellite images to identify plastic features.

Description: The purpose of the presented study is to evaluate the comprehensive impact of different land cover types on the temperature development in the entire Shandong Province by a 20-year-long (MODIS) LST time series from 2003 to 2022. To find out the primary influencing factors, methods such as the Pearson correlation, stepwise analysis, and best-subset selection were applied. The results revealed that the average temperatures had been rising in summer during day- and night-time by 2.32 °C and 1.22 °C, respectively and in winter during day- and night-time by 3.25 °C and 1.33 °C, whereby a significant contribution can be attributed to the period 2014-2022. Substantial variations in LSTs emerge between built-up and vegetated areas and landlocked and coastal regions. Moreover, we could identify a contribution of 0.089 °C, caused merely by the extension of built-up areas of 1.65% in the entire Shandong Province. Modeling the combined effects of further relevant variables/factors, the percentage of cropland area (crop-per) and the number of landscape patches (NPl) indicate considerable influence on the daytime temperature in the temporal domain.