Use of Peptide Libraries for Identification and Optimization of Novel Antimicrobial Peptides.

The increasing rates of resistance among bacteria and to a lesser extent fungi have resulted in an urgent need to find new molecules that hold therapeutic promise against multidrug-resistant strains. Antimicrobial peptides have proven very effective against a variety of multidrug-resistant bacteria. Additionally, the low levels of resistance reported towards these molecules are an attractive feature for antimicrobial drug development. Here we summarise information on diverse peptide libraries used to discover or to optimize antimicrobial peptides. Chemical synthesized peptide libraries, for example split and mix method, tea bag method, multi-pin method and cellulose spot method are discussed. In addition biological peptide library screening methods are summarized, like phage display, bacterial display, mRNA-display and ribosomal display. A few examples are given for small peptide libraries, which almost exclusively follow a rational design of peptides of interest rather than a combinatorial approach

Is There a Connection Between Gut Microbiome Dysbiosis Occurring in COVID-19 Patients and Post-COVID-19 Symptoms?

According to WHO, currently 215 countries/areas/territories report a total of more than 176 million confirmed COVID-19 cases and 3.8 million deaths (June 18, 2021). SARS-CoV-2, the causative agent of COVID-19, does not impact only the respiratory system but also the various organs in the body. It can directly or indirectly affect the pulmonary system, cardiovascular system (including heart failure), renal system (including kidney failure), hepatic system (including liver failure), gastrointestinal system, nervous system, and/or various systems, leading to shock and multi-organ failure (Zaim et al., 2020). In consequence, comorbidity in these systems leads to a higher risk for a severe disease progression

Data-driven charging strategies for grid-beneficial, customer-oriented and battery-preserving electric mobility

Electric Vehicle (EV) penetration and renewable energies enables synergies between energy supply, vehicle users, and the mobility sector. However, also new issues arise for car manufacturers: During charging and discharging of EV batteries a degradation (battery aging) occurs that correlates with a value depreciation of the entire EV. As EV users' satisfaction depends on reliable and value-stable products, car manufacturers offer charging assistants for simplified and sustainable EV usage by considering individual customer needs and battery aging. Hitherto models to quantify battery aging have limited practicability due to a complex execution. Data-driven methods hold feasible alternatives for SOH estimation. However, the existing approaches barely use user-related data. By means of a linear and a neural network regression model, we first estimate the energy consumption for driving considering individual driving styles and environmental conditions. In following work, the consumption model trained on data from batteries without degradation can be used to estimate the energy consumption for EVs with aged batteries. A discrepancy between the estimation and the real consumption indicates a battery aging caused by increased internal losses. We then target to evaluate the influence of charging strategies on battery degradation

Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8 [Vorhersage der Kernhärtenvarianz von industriell einsatzgehärtetem 18CrNi8]

To explain the variance in core hardness of 18CrNi8 nozzle bodies after industrial heat treatment, several data sources, including steel melt composition, sensor process data, and measurement errors, of five years are aggregated. In order to predict hardness variations caused by alloy composition, traditional physical models by Maynier are compared with data-driven machine learning models, which show no advantage due to low data variability. Neither method can fully explain the visible drifts, which are better tracked by an alternative (i. e., filter model) that uses past measurements. Machine learning on features from heat treatment is not successful in predicting hardness change, presumably because the process is too stable. Finally, a large part of the variance is caused by the HV 1 measurement error

Automated phenotype pattern recognition of zebrafish for high-throughput screening

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79–99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented

GPU-accelerated ray-casting for 3D fiber orientation analysis

Orientation analysis of fibers is widely applied in the fields of medical, material and life sciences. The orientation information allows predicting properties and behavior of materials to validate and guide a fabrication process of materials with controlled fiber orientation. Meanwhile, development of detector systems for high-resolution non-invasive 3D imaging techniques led to a significant increase in the amount of generated data per a sample up to dozens of gigabytes. Though plenty of 3D orientation estimation algorithms were developed in recent years, neither of them can process large datasets in a reasonable amount of time. This fact complicates the further analysis and makes impossible fast feedback to adjust fabrication parameters. In this work, we present a new method for quantifying the 3D orientation of fibers. The GPU implementation of the proposed method surpasses another popular method for 3D orientation analysis regarding accuracy and speed. The validation of both methods was performed on a synthetic dataset with varying parameters of fibers. Moreover, the proposed method was applied to perform orientation analysis of scaffolds with different fibrous micro-architecture studied with the synchrotron μCT imaging setup. Each acquired dataset of size 600x600x450 voxels was analyzed in less 2 minutes using standard PC equipped with a single GPU