Abstract: Methamphetamine (MA) is a synthetic psychoactive drug which is consumed both licitly and illicitly. In some countries it is prescribed for attention-deficit and hyperactivity disorder, and short-term treatment of obesity. More often though, it is abused for its psychostimulant properties. Unfortunately, the spread and abuse of this synthetic drug have increased globally, being reported as the most widely consumed synthetic psychoactive drug in the world in 2019. Attempting to overcome the shortcomings of the currently used on-site methods for MA detection in suspected cargos, the present study explores the potential of electrochemical identification of MA by means of square wave voltammetry on disposable graphite screen-printed electrodes. Hence, the analytical characterization of the method was evaluated under optimal conditions exhibiting a linear range between 50 mu M and 2.5 mM MA, a LOD of 16.7 mu M, a LOQ of 50.0 mu M and a sensitivity of 5.3 mu A mM-1. Interestingly, two zones in the potential window were identified for the detection of MA, depending on its concentration in solution. Furthermore, the oxidative pathway of MA was elucidated employing liquid chromatography – mass spectrometry to understand the change in the electrochemical profile. Thereafter, the selectivity of the method towards MA in mixtures with other drugs of abuse as well as common adulterants/cutting agents was evaluated. Finally, the described method was employed for the analysis of MA in confiscated samples and compared with forensic methods, displaying its potential as a fast and easy-to-use method for on-site analysis.

Abstract: This study compared two anammox sequencing batch reactors (SBR) for one year. SBRconstantT was kept at 30 °C while temperature in SBRloweringT was decreased step-wise from 30 °C to 20 °C and 15 °C followed by over 140 days at 12.5 °C and 10 °C. High retention of anammox bacteria (AnAOB) and minimization of competition with AnAOB were key. 5-L anoxic reactors with the same inoculum were fed synthetic influent containing 25.9 mg NH4+-N/L and 34.1 mg NO2−-N/L (no COD). Specific ammonium removal rates continuously increased in SBRconstantT, reaching 785 mg NH4+-N/gVSS/d, and were maintained in SBRloweringT, reaching 82.2 and 91.8 mg NH4+-N/gVSS/d at 12.5 and 10 °C respectively. AnAOB enrichment (increasing hzsA and 16S rDNA gene concentrations) and adaptation (shift from Ca. Brocadia to Ca. Kuenenia in SBRloweringT) contributed to these high rates. Rapidly settling granules developed, with average diameters of 1.2 (SBRconstantT) and 1.6 mm (SBRloweringT). Results reinforce the potential of anammox for mainstream applications.

Abstract: Single nucleotide point mutations in the KRAS oncogene occur frequently in human cancers, rendering them intriguing targets for diagnosis, early detection and personalized treatment. Current detection methods are based on polymerase chain reaction, sometimes combined with next-generation sequencing, which can be expensive, complex and have limited availability. Here, we propose a novel singlet oxygen (1O2)-based photoelectrochemical detection methodology for single-point mutations, using KRAS mutations as a case study. This detection method combines the use of a sandwich assay, magnetic beads and robust chemical photosensitizers, that need only air and light to produce 1O2, to ensure high specificity and sensitivity. We demonstrate that hybridization of the sandwich hybrid at high temperatures enables discrimination between mutated and wild-type sequences with a detection rate of up to 93.9%. Additionally, the presence of background DNA sequences derived from human cell-line DNA, not containing the mutation of interest, did not result in a signal, highlighting the specificity of the methodology. A limit of detection as low as 112 pM (1.25 ng/mL) was achieved without employing any amplification techniques. The developed 1O2-based photoelectrochemical methodology exhibits unique features, including rapidity, ease of use, and affordability, highlighting its immense potential in the field of nucleic acid-based diagnostics.

Abstract: The correct prediction of air pollutants dispersed in urban areas is of paramount importance to safety, public health and a sustainable environment. Vehicular traffic is one of the main sources of nitrogen oxides (NO ) and particulate matter (PM), strongly related to human morbidity and mortality. In this study, the pollutant level and distribution in a section of one of the main road arteries of Antwerp (Belgium, Europe) are analyzed. The assessment is performed through computational fluid dynamics (CFD), acknowledged as a powerful tool to predict and study dispersion phenomena in complex atmospheric environments. The two main traffic lanes are modeled as emitting sources and the surrounding area is explicitly depicted. A Reynolds-averaged Navier–Stokes (RANS) approach specific for Atmospheric Boundary Layer (ABL) simulations is employed. After a validation on a wind tunnel urban canyon test case, the dispersion within the canopy of two relevant urban pollutants, nitrogen dioxide (NO) and particulate matter with an aerodynamic diameter smaller than 10 m (PM10), is studied. An experimental field campaign led to the availability of wind velocity and direction data, as well as PM10 concentrations in some key locations within the urban canyon. To accurately predict the concentration field, a relevant dispersion parameter, the turbulent Schmidt number, , is prescribed as a locally variable quantity. The pollutant distributions in the area of interest – exhibiting strong heterogeneity – are finally demonstrated, considering one of the most frequent and concerning wind directions. Possible local remedial measures are conceptualized, investigated and implemented and their outcomes are directly compared. A major goal is, by realistically reproducing the district of interest, to identify the locations inside this intricate urban canyon where the pollutants are stagnating and to analyze which solution acts as best mitigation measure. It is demonstrated that removal by electrostatic precipitation (ESP), an active measure, and by enhancing the dilution process through wind catchers, a passive measure, are effective for local pollutant removal in a realistic urban canyon. It is also demonstrated that the applied ABL methodology resolves some well known problems in ABL dispersion modeling.

Abstract: The present study aimed at: (1) determining the effect of sulfur addition on biomass growth and (2) assessing the effect of sulfur, phosphorus and nitrogen limitation on lipid accumulation by C. vulgaris SAG 211-11b. The sulfur cellular content was more than two-fold higher under nitrogen and phosphorus limitation (0.52% and 0.54% w w(-1), respectively) compared to sulfur requirements (0.20% w w(-1)) under sulfur limiting conditions. The nitrogen needs are significantly lower (2.81-3.35% w w(-1)) when compared to other microalgae and become 23% lower under nitrogen or phosphorus limitation. The microalga exhibited substrate inhibition above 30 g L-1 initial glucose concentration. Sulfur limitation had the most significant effect on lipid accumulation, resulting in maximum total lipid content of 53.43 +/- 3.93% g g(DW)(1). In addition to enhancing lipid productivity, adopting the optimal nutrient limitation strategy can result in cost savings by avoiding unnecessary nutrient additions and eliminate the environmental burden due to wasted resources. (C) 2017 Elsevier Ltd. All rights reserved.

Abstract: Below and aboveground vegetation dynamics are crucial in understanding how climate warming may affect terrestrial ecosystem carbon cycling. In contrast to aboveground biomass, the response of belowground biomass to long-term warming has been poorly studied. Here, we characterized the impacts of decadal geothermal warming at two levels (on average +3.3 degrees C and +7.9 degrees C) on below and aboveground plant biomass stocks and production in a subarctic grassland. Soil warming did not change standing root biomass and even decreased fine root production and reduced aboveground biomass and production. Decadal soil warming also did not significantly alter the root-shoot ratio. The linear stepwise regression model suggested that following 10 yr of soil warming, temperature was no longer the direct driver of these responses, but losses of soil N were. Soil N losses, due to warming-induced decreases in organic matter and water retention capacity, were identified as key driver of the decreased above and belowground production. The reduction in fine root production was accompanied by thinner roots with increased specific root area. These results indicate that after a decade of soil warming, plant productivity in the studied subarctic grassland was affected by soil warming mainly by the reduction in soil N.

Abstract: Self-healing strategies aim at avoiding part repair or even replacement, which is time consuming, expen-sive and generates waste. However, strategies for metallic systems are still under-developed and solid-state solutions for room temperature service are limited to nano-scale damage repair. Here we propose a new healing strategy of micron-sized damage requiring only short and low temperature heating. This new strategy is based on damage localization particles, which can be healed by fast diffusing atoms of the matrix activated during heat treatment. The healing concept was successfully validated with a com-mercial aluminum alloy and manufactured by Friction Stir Processing (FSP). Damage was demonstrated to initiate on particles that were added to the matrix during material processing. In situ 2D and 3D nano -imaging confirmed healing of the damaged material and showed that heating this material for 10 min at 400 degrees C is sufficient to heal incipient damage with complete filling of 70% of all damage (and up to 90% when their initial size is below 0.2 mu m). Furthermore, strength is retained and the work of fracture of the alloy is improved by about 40% after healing. The proposed Programmed Damage and Repair healing strategy could be extended to other metal based systems presenting precipitation. (C) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Abstract: Green walls (GW) can diminish building's surface temperature through shading, insulation, and evapotranspiration mechanisms. These can be analyzed by computer models that account for heat and mass transfer phenomena. However, most previous models were one-dimensional thermal simulations in which boundary conditions (BC), like convective moisture transport, were not or only partly considered. The present work proposes a more comprehensive way to predict GW's hygrothermal behavior by integrating a 3D multiphysics model that couples heat and moisture transport in Comsol Multiphysics®. The air cavity that usually separates the GW from the building was also considered. Heat sink terms were added to represent plants' transpiration and substrates' evaporation, considering the leaf area density (LAD) and substrate's water saturation (Sr). The model was validated against experiments where four green wall-test panels (GW-TPs) were evaluated in a climate chamber under steady-state conditions. This provides a much sounder approach for validation than what currently exists (r = 0.97; RMSE = 0.33 °C). The four GW-TPs decreased the masonry's surface temperature in the range of 0.89–1.14 °C (0.97 ± 0.11 SD °C). The average contribution of the evapotranspiration effect was 30%, whereas the contribution of the air cavity was 60.7 ± 0.09%. The temperature at the substrate's rear was reduced on average by 0.57 ± 0.15 SD °C. When solar radiation was considered as a BC, the GW-TPs decreased the building's surface temperature by 10 °C. Lastly, high values of LAD and Sr translated into increased temperature reduction values.

Abstract: Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.

Abstract: The simulation of transmission electron microscopy (TEM) images or diffraction patterns is often required to interpret their contrast and extract specimen features. This is especially true for high-resolution phase-contrast imaging of materials, but electron scattering simulations based on atomistic models are widely used in materials science and structural biology. Since electron scattering is dominated by the nuclear cores, the scattering potential is typically described by the widely applied independent atom model. This approximation is fast and fairly accurate, especially for scanning TEM (STEM) annular dark-field contrast, but it completely neglects valence bonding and its effect on the transmitting electrons. However, an emerging trend in electron microscopy is to use new instrumentation and methods to extract the maximum amount of information from each electron. This is evident in the increasing popularity of techniques such as 4D-STEM combined with ptychography in materials science, and cryogenic microcrystal electron diffraction in structural biology, where subtle differences in the scattering potential may be both measurable and contain additional insights. Thus, there is increasing interest in electron scattering simulations based on electrostatic potentials obtained from first principles, mainly via density functional theory, which was previously mainly required for holography. In this Review, we discuss the motivation and basis for these developments, survey the pioneering work that has been published thus far, and give our outlook for the future. We argue that a physically better justified ab initio description of the scattering potential is both useful and viable for an increasing number of systems, and we expect such simulations to steadily gain in popularity and importance.

Abstract: Gielis transformations, with their origin in botany, are used to define square waves and trigonometric functions of higher order. They are rewritten in terms of Chebyshev polynomials. The origin of both, a uniform descriptor and the origin of orthogonal polynomials, can be traced back to a letter of Guido Grandi to Leibniz in 1713 on the mathematical description of the shape of flowers. In this way geometrical description and analytical tools are seamlessly combined.

Abstract: The range of applications for aptamers, small oligonucleotide-based receptors binding to their targets with high specificity and affinity, has been steadily expanding. Our understanding of the mechanisms governing aptamer-ligand recognition and binding is however lagging, stymieing the progress in the rational design of new aptamers and optimization of the known ones. Here we demonstrate the capabilities and limitations of native ion mobility-mass spectrometry for the analysis of their higher-order structure and non-covalent interactions. A set of related cocaine-binding aptamers, displaying a range of folding properties and ligand binding affinities, was used as a case study in both positive and negative electrospray ionization modes. Using carefully controlled experimental conditions, we probed their conformational behavior and interactions with the high-affinity ligand quinine as a surrogate for cocaine. The ratios of bound and unbound aptamers in the mass spectra were used to rank them according to their apparent quinine-binding affinity, qualitatively matching the published ranking order. The arrival time differences between the free aptamer and aptamer-quinine complexes were consistent with a small ligand-induced conformational change, and found to inversely correlate with the affinity of binding. This mass spectrometry-based approach provides a fast and convenient way to study the molecular basis of aptamer-ligand recognition.