General Applicability of High-Resolution Continuum-Source Graphite Furnace Molecular Absorption Spectrometry to the Quantification of Oligopeptides Using the Example of Glutathione

This communication introduces the first-time application of high-resolution continuum-source molecular absorption spectrometry (HR CS MAS) for the quantification of a peptide. The graphite furnace technique was employed and the tripeptide glutathione (GSH) served as a model compound. Based on measuring sulfur in terms of carbon monosulfide (CS), a method was elaborated to analyze aqueous solutions of GSH. The most prominent wavelength of the CS molecule occurred at 258.0560 nm and was adduced for monitoring. The methodological development covered the optimization of the pyrolysis and vaporization temperatures. These were found optimally to be 250 °C and 2250 °C, respectively. Moreover, the effect of modifiers (zirconium, calcium, magnesium, palladium) on the absorption signals was investigated. The best results were obtained after permanent coating of the graphite tube with zirconium (total amount of 400 μg) and adding a combination of palladium (10 µL, 10 g L−1) and calcium (2 µL, 1 g L−1) as a chemical modifier to the probes (10 µL). Aqueous standard samples of GSH were used for the calibration. It showed a linear range of 2.5–100 µg mL−1 sulfur contained in GSH with a correlation coefficient R2 > 0.997. The developed method exhibited a limit of detection (LOD) and quantification (LOQ) of 2.1 µg mL−1 and 4.3 µg mL−1 sulfur, respectively. The characteristic mass accounted for 5.9 ng sulfur. The method confirmed the general suitability of MAS for the analysis of an oligopeptide. Thus, this study serves as groundwork for further development in order to extend the application of classical atomic absorption spectrometry (AAS).

Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

Wireless Local Area Network Technologies as Communication Solutions for Unmanned Surface Vehicles

As the number of research activities and practical deployments of unmanned vehicles has shown a rapid growth, topics related to their communication with operator and external infrastructure became of high importance. As a result a trend of employing IP communication for this purpose is emerging and can be expected to bring significant advantages. However, its employment can be expected to be most effective using broadband communication technologies such as Wireless Local Area Networks (WLANs). To verify the effectiveness of such an approach in a specific case of surface unmanned vehicles, the paper includes an overview of IP-based MAVLink communication advantages and requirements, followed by a laboratory and field-experiment study of selected WLAN technologies, compared to popular narrowband communication solutions. The conclusions confirm the general applicability of IP/WLAN communication for surface unmanned vehicles, providing an overview of their advantages and pointing out deployment requirements.

Use of Pyriproxyfen to Induce Oogenesis in Diapausing Megacopta cribraria (Heteroptera: Plataspidae), and Evaluation of Pyriproxyfen-Induced Eggs for Rearing the Parasitoid Paratelenomus saccharalis (Hymenoptera: Scelionidae)

The mass rearing of hymenopteran egg parasitoids requires an abundant supply of host eggs. The onset of reproductive diapause and subsequent decline in egg production poses a challenge for parasitoid rearing when using host colonies augmented by field-collected insects. We investigated the application of pyriproxyfen, a juvenile hormone analog, to induce oviposition in diapausing adult kudzu bugs, Megacopta cribraria (Fabricius) (Heteroptera: Plataspidae), and the use of eggs produced by pyriproxyfen-treated kudzu bugs to rear the egg parasitoid, Paratelenomus saccharalis (Dodd) (Hymenoptera: Scelionidae). The effects of pyriproxyfen and photoperiod treatments on host mortality, egg production, and rates of parasitoid eclosion from the eggs were used to calculate the parasitoid yield for the different treatment regimes. A combination of pyriproxyfen and a long-day photoperiod increased the parasitoid yield by 87% compared to acetone and a long-day photoperiod. The general applicability of JH-analog mediated egg production for parasitoid rearing is discussed.

Kernel Regression Coefficients for Practical Significance

Quantitative researchers often use Student’s t-test (and its p-values) to claim that a particular regressor is important (statistically significantly) for explaining the variation in a response variable. A study is subject to the p-hacking problem when its author relies too much on formal statistical significance while ignoring the size of what is at stake. We suggest reporting estimates using nonlinear kernel regressions and the standardization of all variables to avoid p-hacking. We are filling an essential gap in the literature because p-hacking-related papers do not even mention kernel regressions or standardization. Although our methods have general applicability in all sciences, our illustrations refer to risk management for a cross-section of firms and financial management in macroeconomic time series. We estimate nonlinear, nonparametric kernel regressions for both examples to illustrate the computation of scale-free generalized partial correlation coefficients (GPCCs). We suggest supplementing the usual p-values by “practical significance” revealed by scale-free GPCCs. We show that GPCCs also yield new pseudo regression coefficients to measure each regressor’s relative (nonlinear) contribution in a kernel regression.

Activity-Based Protein Profiling for the Identification of Novel Carbohydrate-Active Enzymes Involved in Xylan Degradation in the Hyperthermophilic Euryarchaeon Thermococcus sp. Strain 2319x1E

Activity-based protein profiling (ABPP) has so far scarcely been applied in Archaea in general and, especially, in extremophilic organisms. We herein isolated a novel Thermococcus strain designated sp. strain 2319x1E derived from the same enrichment culture as the recently reported Thermococcus sp. strain 2319x1. Both strains are able to grow with xylan as the sole carbon and energy source, and for Thermococcus sp. strain 2319x1E (optimal growth at 85°C, pH 6–7), the induction of xylanolytic activity in the presence of xylan was demonstrated. Since the solely sequence-based identification of xylanolytic enzymes is hardly possible, we established a complementary approach by conducting comparative full proteome analysis in combination with ABPP using α- or β-glycosidase selective probes and subsequent mass spectrometry (MS)-based analysis. This complementary proteomics approach in combination with recombinant protein expression and classical enzyme characterization enabled the identification of a novel bifunctional maltose-forming α-amylase and deacetylase (EGDIFPOO_00674) belonging to the GH57 family and a promiscuous β-glycosidase (EGIDFPOO_00532) with β-xylosidase activity. We thereby further substantiated the general applicability of ABPP in archaea and expanded the ABPP repertoire for the identification of glycoside hydrolases in hyperthermophiles.

Palladium-catalyzed semihydrogenation of alkynes with EtOH: highly stereoselective synthesis of E- and Z-alkenes

Herein, a palladium-catalyzed semi-hydrogenation of alkynes to E- and Z-alkenes employing EtOH as hydrogenating agents was reported. The selectivity of the reaction system was effectively controlled by ligand/additive and solvent regulation. The use of sodium acetate/triethanolamine (NaOAc/TEOA), THF and (1R,2R)-bis[(2-methoxypheny)phenyl- phosphino]ethane ((R,R)-DIPAMP), CH3CN was critical for the stereoselective semihydrogenation of alkynes. The general applicability of this procedure was highlighted by the synthesis of more than 36 alkenes, with good yields and high stereoselectivities.

Symbolic and Structural Model-Checking

Brute-force model-checking consists in exhaustive exploration of the state-space of a Petri net, and meets the dreaded state-space explosion problem. In contrast, this paper shows how to solve model-checking problems using a combination of techniques that stay in complexity proportional to the size of the net structure rather than to the state-space size. We combine an SMT based over-approximation to prove that some behaviors are unfeasible, an under-approximation using memory-less sampling of runs to find witness traces or counter-examples, and a set of structural reduction rules that can simplify both the system and the property. This approach was able to win by a clear margin the model-checking contest 2020 for reachability queries as well as deadlock detection, thus demonstrating the practical effectiveness and general applicability of the system of rules presented in this paper.

Performance recovery of plasma actuators in wet conditions

Plasma actuators have been extensively studied for flow control applications. While these studies have been traditionally focused on characterizing their performances as flow control devices, the performance of plasma actuators under adverse conditions like light rain remains to be less explored. This paper seeks to study the effects of water adhesion from droplets directly sprayed on to a plasma actuator using thrust recovery as the performance metric. It was found in all tests that wet actuators quickly recover plasma glow, before gradually regaining performance comparable to the dry actuator. The measured thrust for the wet actuator after 5 seconds of operation recovered by 46% and 42% of the thrust of the dry actuator for 50.0-62.5 g/m2 and 125-150 g/m2 of sprayed water droplets, respectively. At 22.5 kVpp and 14 kHz, the highest thrust recovery was recorded at 84% of that of the dry actuator after 80 seconds of operation. For 17.5 kVpp and 14 kHz the wet thrust recovered by 79%, while for 22.5 kVpp and 10 kHz the wet thrust recovered by 68% of their dry counterpart in 80 seconds. For 17.5 kVpp and 14 kHz, the thrust almost fully recovered in comparison to the dry actuator after about 290 seconds of operation. These results indicate that both applied voltage and operating frequency plays a critical role in the performance recovery while the latter may have a stronger influence. Performance recovery for a wet serpentine shaped plasma actuator is also included for general applicability. The power data in all cases show that wet actuators consume more power which with time gradually approach the dry actuator power data. This because during the initial stages of operation, the rolling mean current of the wet actuator is higher than the dry actuator even though the ionization spikes of dry actuator is stronger.

A spectral demixing method for high-precision multi-color localization microscopy

Single molecule localization microscopy (SMLM) with a dichroic image splitter can provide invaluable multi-color information regarding colocalization of individual molecules, but it often suffers from technical limitations. So far, demixing algorithms give suboptimal results in terms of localization precision and correction of chromatic aberrations. Here we present an image splitter based multi-color SMLM method (splitSMLM) that offers much improved localization precision & drift correction, compensation of chromatic aberrations, and optimized performance of fluorophores in a specific buffer to equalize their reactivation rates for simultaneous imaging. A novel spectral demixing algorithm, SplitViSu, fully preserves localization precision with essentially no data loss and corrects chromatic aberrations at the nanometer scale. Multi-color performance is further improved by using optimized fluorophore and filter combinations. Applied to three-color imaging of the nuclear pore complex (NPC), this method provides a refined positioning of the individual NPC proteins and reveals that Pom121 clusters act as NPC deposition loci, hence illustrating strength and general applicability of the method.