Wastewater treatment plants play an important role in the emergence of antibiotic resistance. They provide a hot spot for exchange of resistance within and between species. Here, we analyse and quantify the genomic diversity of the indicator Escherichia coli in a German wastewater treatment plant and we relate it to isolates’ antibiotic resistance. Our results show a surprisingly large pan-genome, which mirrors how rich an environment a treatment plant is. We link the genomic analysis to a phenotypic resistance screen and pinpoint genomic hot spots, which correlate with a resistance phenotype. Besides well-known resistance genes, this forward genomics approach generates many novel genes, which correlated with resistance and which are partly completely unknown. A surprising overall finding of our analyses is that we do not see any difference in resistance and pan genome size between isolates taken from the inflow of the treatment plant and from the outflow. This means that while treatment plants reduce the amount of bacteria released into the environment, they do not reduce the potential for antibiotic resistance of these bacteria.

The generation and application of human stem-cell-derived functional neural circuits promises novel insights into neurodegenerative diseases. These networks are often studied using stem-cell derived random neural networks in vitro, with electrical stimulation and recording using multielectrode arrays. However, the impulse response function of networks is best obtained with spatiotemporally well-defined stimuli, which electrical stimulation does not provide. Optogenetics allows for the functional control of genetically altered cells with light stimuli at high spatiotemporal resolution. Current optogenetic investigations of neural networks are often conducted using full field illumination, potentially masking important functional information. This can be avoided using holographically shaped illumination. In this article, we present a digital holographic illumination setup with a spatial resolution of about 8 µm, which suffices for the stimulation of single neurons, and offers a temporal resolution of less than 0.6 ms. With this setup, we present preliminary single-cell stimulation recording of stem-cell derived induced human neurons in a random neural network. This will offer the opportunity for further studies on connectivity in such networks.

Carbon concrete polyacrylonitrile (PAN)/lignin-based carbon fiber (CF) composites are a new promising material class for the building industry. The replacement of the traditional heavy and corroding steel reinforcement by carbon fiber (CF)-based reinforcements offers many significant advantages: a higher protection of environmental resources because of lower CO2 consumption during cement production, a longer lifecycle and thus, much less damage to structural components and a higher degree of design freedom because lightweight solutions can be realized. However, due to cost pressure in civil engineering, completely new process chains are required to manufacture CF-based reinforcement structures for concrete. This article describes the necessary process steps in order to develop CF reinforcement: (1) the production of cost-effective CF using novel carbon fiber lines, and (2) the fabrication of CF rebars with different geometry profiles. It was found that PAN/lignin-based CF is currently the promising material with the most promise to meet future market demands. However, significant research needs to be undertaken in order to improve the properties of lignin-based and PAN/lignin-based CF, respectively. The CF can be manufactured to CF-based rebars using different manufacturing technologies which are developed at a prototype level in this study.

In the title monohydrate compound, 1a, and the methanol solvate compound, 1b, the tri­ethyl­benzene derivative, C35H51N5O, has three functionalized side arms and three ethyl groups, the former being located on one side of the central benzene ring, while the latter are directed to the opposite side. Both the crystals are constructed of structurally similar dimers of 1:1 host–guest complexes held together by N—H...O and O—H...N hydrogen bonds, and in 1a additionally by O—H...O hydrogen bonds. The structure of 1b contains additional highly disordered solvent mol­ecules. Thus, the SQUEEZE routine [Spek (2015). Acta Cryst. C71, 9–18] in PLATON was used to generate a modified data set, in which the contribution of the disordered mol­ecules to the structure amplitudes is eliminated. These solvent mol­ecules are not considered in the reported chemical formula.

Di-tert-butyl N,N′-{[13,15,28,30,31,33-hexa­ethyl-3,10,18,25,32,34-hexa­aza­penta­cyclo­[25.3.1.15,8.112,16.120,23]tetra­triaconta-1(31),3,5,7,9,12(33),13,15,18,20,22,24,27,29-tetra­deca­ene-14,29-di­yl]bis­(methyl­ene)}dicarbamate methanol disolvate, C52H72N8O4·2CH3OH, was found to crystallize in the space group P21/c with one half of the macrocycle (host) and one mol­ecule of solvent (guest) in the asymmetric unit of the cell, i.e. the host mol­ecule is located on a crystallographic symmetry center. Within the 1:2 host–guest complex, the solvent mol­ecules are accommodated in the host cavity and held in their positions by O—H⋯N and N—H⋯O bonds, thus forming ring synthons of graph set R22(7). The connection of the 1:2 host-guest complexes is accomplished by C—H⋯O, C—H⋯N and C—H⋯π inter­actions, which create a three-dimensional supra­molecular network.

Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features extracted from spectra are crucial to correlating with these properties. We adopt a novel methodology for feature extraction of soil spectroscopy based on fractal geometry. The spectrum can be divided into multiple segments with different step–window pairs. For each segmented spectral curve, the fractal dimension value was calculated using variation estimators with power indices 0.5, 1.0 and 2.0. Thus, the fractal feature can be generated by multiplying the fractal dimension value with spectral energy. To assess and compare the performance of new generated features, we took advantage of organic soil samples from the large-scale European Land Use/Land Cover Area Frame Survey (LUCAS). Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents. Features generated by a variogram estimator performed better than two other estimators and the principal component analysis (PCA). The estimation results for SOC were coefficient of determination (R2) = 0.85, root mean square error (RMSE) = 56.7 g/kg, the ratio of percent deviation (RPD) = 2.59; for pH: R2 = 0.82, RMSE = 0.49 g/kg, RPD = 2.31; and for N: R2 = 0.77, RMSE = 3.01 g/kg, RPD = 2.09. Even better results could be achieved when fractal features were combined with PCA components. Fractal features generated by the proposed method can improve estimation accuracies of soil properties and simultaneously maintain the original spectral curve shape.

Managed aquifer recharge (MAR) is the purposeful recharge of an aquifer for later recovery or environmental benefits and represents a valuable method for sustainable water resources management. Models can be helpful tools for the assessment of MAR systems. This review encompasses a survey and an analysis of case studies which apply flow and transport models to evaluate MAR. The observed modeling objectives include the planning or optimization of MAR schemes as well as the identification and quantification of geochemical processes during injection, storage and recovery. The water recovery efficiency and the impact of the injected water on the ambient groundwater are further objectives investigated in the reviewed studies. These objectives are mainly solved by using groundwater flow models. Unsaturated flow models, solute transport models, reactive geochemical models as well as water balance models are also frequently applied and often coupled. As each planning step to setup a new MAR facility requires cost and time investment, modeling is used to minimize hazard risks and assess possible constraints of the system such as low recovery efficiency, clogging and geochemical processes.

Particle-based films are today an important part of various designs and they are implemented in structures as conductive parts, i.e., conductive paste printing in the manufacture of Li-ion batteries, solar cells or resistive paste printing in IC. Recently, particle based films were also implemented in the 3D printing technique, and are particularly important for use in aircraft, wind power, and the automotive industry when incorporated onto the surface of composite structures for protection against damages caused by a lightning strike. A crucial issue for the lightning protection area is to realize films with high homogeneity of electrical resistance where an in-situ noninvasive method has to be elaborated for quality monitoring to avoid undesirable financial and time costs. In this work the drying process of particle based films was investigated by high-frequency eddy current (HFEC) spectroscopy in order to work out an automated in-situ quality monitoring method with a focus on the electrical resistance of the films. Different types of particle based films deposited on dielectric and carbon fiber reinforced plastic substrates were investigated in the present study and results show that the HFEC method offers a good opportunity to monitor the overall drying process of particle based films. Based on that, an algorithm was developed, allowing prediction of the final electrical resistance of the particle based films throughout the drying process, and was successfully implemented in a prototype system based on the EddyCus® HFEC device platform presented in this work. This prototype is the first solution for a portable system allowing HFEC measurement on huge and uneven surfaces.

The availability of several methods to unambiguously mark individual cells has strongly fostered the understanding of clonal developments in hematopoiesis and other stem cell driven regenerative tissues. While cellular barcoding is the method of choice for experimental studies, patients that underwent gene therapy carry a unique insertional mark within the transplanted cells originating from the integration of the retroviral vector. Close monitoring of such patients allows accessing their clonal dynamics, however, the early detection of events that predict monoclonal conversion and potentially the onset of leukemia are beneficial for treatment. We developed a simple mathematical model of a self-stabilizing hematopoietic stem cell population to generate a wide range of possible clonal developments, reproducing typical, experimentally and clinically observed scenarios. We use the resulting model scenarios to suggest and test a set of statistical measures that should allow for an interpretation and classification of relevant clonal dynamics. Apart from the assessment of several established diversity indices we suggest a measure that quantifies the extension to which the increase in the size of one clone is attributed to the total loss in the size of all other clones. By evaluating the change in relative clone sizes between consecutive measurements, the suggested measure, referred to as maximum relative clonal expansion (mRCE), proves to be highly sensitive in the detection of rapidly expanding cell clones prior to their dominant manifestation. This predictive potential places the mRCE as a suitable means for the early recognition of leukemogenesis especially in gene therapy patients that are closely monitored. Our model based approach illustrates how simulation studies can actively support the design and evaluation of preclinical strategies for the analysis and risk evaluation of clonal developments.

NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.