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BACKGROUND
Various neutral and alkaline peptidases are commercially available for use in protein hydrolysis under neutral to alkaline conditions. However, the hydrolysis of proteins under acidic conditions by applying fungal aspartic peptidases (FAPs) has not been investigated in depth so far. The aim of this study, thus, was to purify a FAP from the commercial enzyme preparation, ROHALASE® BXL, determine its biochemical characteristics, and investigate its application for the hydrolysis of food and animal feed proteins under acidic conditions.
RESULTS
A Trichoderma reesei derived FAP, with an apparent molecular mass of 45.8 kDa (sodium dodecyl sulfate–polyacrylamide gel electrophoresis; SDS-PAGE) was purified 13.8-fold with a yield of 37% from ROHALASE® BXL. The FAP was identified as an aspartate protease (UniProt ID: G0R8T0) by inhibition and nano-LC-ESI-MS/MS studies. The FAP showed the highest activity at 50°C and pH 4.0. Monovalent cations, organic solvents, and reducing agents were tolerated well by the FAP. The FAP underwent an apparent competitive product inhibition by soy protein hydrolysate and whey protein hydrolysate with apparent Ki-values of 1.75 and 30.2 mg*mL−1, respectively. The FAP showed promising results in food (soy protein isolate and whey protein isolate) and animal feed protein hydrolyses. For the latter, an increase in the soluble protein content of 109% was noted after 30 min.
CONCLUSION
Our results demonstrate the applicability of fungal aspartic endopeptidases in the food and animal feed industry. Efficient protein hydrolysis of industrially relevant substrates such as acidic whey or animal feed proteins could be conducted by applying fungal aspartic peptidases. © 2022 Society of Chemical Industry.
This review provides an overview on the production and analysis techniques of antioxidative peptides from food proteins. Regarding the production of antioxidative peptides, interlinked factors must be considered. Depending on the protein substrate, different peptidases or peptidase systems containing multiple enzymes as well as a specific production process must be chosen. The antioxidative peptides might be produced in a batch process including multiple pre- and post-treatments, besides the hydrolyses with peptidases itself. As an alternative, the potential of continuous production systems is discussed in this review. Furthermore, robust analyses tools are needed to gain control of the process and final product properties. With no standardized methodology available for antioxidative peptide evaluation, pros and cons of various strategies for peptide separation and antioxidative measurement are discussed in this review. Therefore, this review provides a roadmap for antioxidative peptide generation from various sources for research and development as well as for potential industrial use.
Gas Analysis and Optimization of Debinding and Sintering Processes for Metallic Binder-Based AM*
(2022)
Binder-based additive manufacturing processes for metallic
AM components in a wide range of applications usually use
organic binders and process-related additives that must be
thermally removed before sintering. Debinding processes are
typically parameterized empirically and thus far from the optimum.
Since debinding based on thermal decomposition processes
of organic components and the subsequent thermochemical
reactions between process atmosphere and metal
powder materials make uncomplicated parameterization difficult,
in-situ instrumentation was introduced at Fraunhofer
IFAM. This measurement method relies on infrared spectroscopy
and mass spectrometry in various furnace concepts to
understand the gas processes of decomposition of organic
components and the subsequent thermochemical reactions
between the carrier gas atmosphere and the metal part, as well
as their kinetics. This method enables an efficient optimization
of the temperature-time profiles and the required atmosphere
composition to realize dense AM components with low contamination.
In the paper, the optimization strategy is presented,
and the achievable properties are illustrated using a fused
filament fabrication (FFF) component example made of 316L
stainless steel.
In the literature, many studies have described the 3D printing of ceramic-based scaffolds (e.g., printing with calcium phosphate cement) in the form of linear structures with layer rotations of 90°, although no right angles can be found in the human body. Therefore, this work focuses on the adaptation of biological shapes, including a layer rotation of only 1°. Sample shapes were printed with calcium phosphate cement using a 3D Bioplotter from EnvisionTec. Both straight and wavy spokes were printed in a round structure with 12 layers. Depending on the strand diameter (200 and 250 µm needle inner diameter) and strand arrangement, maximum failure loads of 444.86 ± 169.39 N for samples without subsequent setting in PBS up to 1280.88 ± 538.66 N after setting in PBS could be achieved.
Titanium and stainless steel are commonly known as osteosynthesis materials with high strength and good biocompatibility. However, they have the big disadvantage that a second operation for hardware removal is necessary. Although resorbable systems made of polymers or magnesium are increasingly used, they show some severe adverse foreign body reactions or unsatisfying degradation behavior. Therefore, we started to investigate molybdenum as a potential new biodegradable material for osteosynthesis in craniomaxillofacial surgery. To characterize molybdenum as a biocompatible material, we performed in vitro assays in accordance with ISO Norm 10993-5. In four different experimental setups, we showed that pure molybdenum and molybdenum rhenium alloys do not lead to cytotoxicity in human and mouse fibroblasts. We also examined the degradation behavior of molybdenum by carrying out long-term immersion tests (up to 6 months) with molybdenum sheet metal. We showed that molybdenum has sufficient mechanical stability over at least 6 months for implants on the one hand and is subject to very uniform degradation on the other. The results of our experiments are very promising for the development of new resorbable osteosynthesis materials for craniomaxillofacial surgery based on molybdenum.
Acoustic waves are investigated which are guided at the edge (apex line) of a wedge-shaped elastic body or at the edge of an elastic plate. The edges contain a periodic sequence of modifications, consisting either of indentations or inclusions with a different elastic material which gives rise to high acoustic mismatch. Dispersion relations are computed with the help of the finite element method. They exhibit zero-group velocity points on the dispersion branches of edge-localized acoustic modes. These special points also occur at Bloch-Floquet wavenumbers away from the Brillouin zone boundary. Deep indentations lead to flat branches corresponding to largely non-interacting, Einstein-oscillator like vibrations of the tongues between the grooves of the periodic structure. Due to the nonlinearity of the elastic media, quantified by their third-order elastic constants, an acoustic mode localized at a periodically modified edge generates a second harmonic which partly consists of surface and plate modes propagating into the elastic medium in the direction vertical to the edge. This acoustic radiation at the second-harmonic frequency is investigated for an elastic plate and a truncated sharp-angle wedge with periodic inclusions at their edges. Unlike nonlinear bulk wave generation by surface acoustic waves in an interdigital structure, surface and plate mode radiation by edge-localized modes can be visualized directly in laser-ultrasound experiments.
The impact of the circular economy on sustainable development: A European panel data approach
(2022)
The circular economy (CE) has attracted considerable attention because of its potential to help achieve sustainable development (SD). This paper presents a comprehensive analysis of the effect of the CE on the three dimensions of SD at the country level. We analysed the impact of each CE source of value (renewable energy, reuse, repair, recycling) and the influence of an overall factor-analysis-derived measure of the CE on the economic, environmental and social dimensions of SD. The aim was to compare the individual impacts and outcomes of the CE and its sources of value in a single study. Panel data analysis was performed using a sample of 25 European countries for the period 2010 to 2019. The findings show a major impact of the CE on achieving SD, which has positive
effects on the economy, environment and society. However, the results show that the impact of each CE value source on the three SD dimensions varies. While renewable energies and reuse reduce the impact on the environment, recycling has no effect, and repair increases GHG emissions. However, repair is the only CE source with a positive economic impact at the country level. Finally, renewable energy, repair and recycling reduce unemployment. Decision makers should conduct impact analysis to design suitable, efficient and targeted measures depending on each country's specific objectives.
Surface treatment intensity monitoring is still an open and challenging nondestructive testing problem. For the estimation of residual stress with ultrasonic measurements, local linear and nonlinear elastic constants are needed as input. In this paper, nonlinear elastic-wave interactions (also called wave mixing or scattering) — namely, the generation of secondary ultrasonic waves in a nonlinear medium — are considered as a prospective means for near-surface nonlinear elastic parameter evaluation. The allowed interactions between bulk and surface waves, as well as the dependence of the scattering efficiency on the frequency and angle between source waves, were investigated through an analytical model, then compared with FEM simulations and experimental results. Finally, possible future steps for the development of the applied methods for the determination of near-surface higher-order elastic constants are discussed. In addition, several problem-relevant data processing procedures are presented.
The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements.
A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time‑variant Multi‑objective Particle Swarm Optimization Algorithm (AT‑MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time‑variant weights for the velocity of the particle swarm optimization and the non‑dominated sorting and mutation schemes from NSGA‑III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA‑II), and the Pareto Envelope‑based Selection Algorithm with region‑based selection (PESA‑II), and NSGA‑III. The proposed AT‑MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT‑MOPSO achieved 52% energy efficiency compared to NSGA‑III.
To show how this algorithm can be applied to a real‑world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT‑MOPSO can be used with existing Blockchain systems and the benefits it provides.
Research is often conducted to investigate footwear mechanical properties and their effects on running biomechanics, but little is known about their influence on runner satisfaction, or how well the shoe is perceived. A tool to predict runner satisfaction in a shoe from its mechanical properties would be advantageous for footwear companies. Data in this study were from a database (n = 615 subject-shoe pairings) of satisfaction ratings (gathered after participants ran on a treadmill), and mechanical testing data for 87 unique subjects across 61 unique shoes. Random forest and elastic net logistic regression models were built to test if footwear mechanical properties and subject characteristics could predict runner satisfaction in 3 ways: degree-of-satisfaction on a 7-point Likert scale, overall satisfaction on a 3-point Likert scale, and willingness-to-purchase the shoe (yes/no response). Data were divided into training and validation sets, using an 80–20 split, to build the models and test their accuracy, respectively. Model accuracies were compared against the no-information rate (i.e. proportion of data belonging to the largest class). The models were not able to predict degree-of-satisfaction or overall satisfaction from footwear mechanical properties but could predict runner’s willingness to purchase with 68–75% accuracy. Midsole Gmax at the heel and forefoot appeared in the top five of variable importance rankings across both willingness-to-purchase models, suggesting its role as a major factor in purchase decisions. The negative regression coefficient for both heel and forefoot Gmax indicated that softer midsoles increase the likelihood of a shoe purchase. Future models to predict satisfaction may improve accuracy with the addition of more subject-specific parameters, such as running goals or foot proportions.
Background: Running overuse injuries (ROIs) occur within a complex, partly injury-specific interplay between training loads and extrinsic and intrinsic risk factors. Biomechanical risk factors (BRFs) are related to the individual running style. While BRFs have been reviewed regarding general ROI risk, no systematic review has addressed BRFs for specific ROIs using a standardized methodology.
Objective: To identify and evaluate the evidence for the most relevant BRFs for ROIs determined during running and to
suggest future research directions.
Design: Systematic review considering prospective and retrospective studies. (PROSPERO_ID: 236,832).
Data Sources: PubMed. Connected Papers. The search was performed in February 2021.
Eligibility Criteria: English language. Studies on participants whose primary sport is running addressing the risk for the seven most common ROIs and at least one kinematic, kinetic (including pressure measurements), or electromyographic BRF. A BRF needed to be identified in at least one prospective or two independent retrospective studies. BRFs needed to be determined during running.
Results: Sixty-six articles fulfilled our eligibility criteria. Levels of evidence for specific ROIs ranged from conflicting to moderate evidence. Running populations and methods applied varied considerably between studies. While some BRFs appeared for several ROIs, most BRFs were specific for a particular ROI. Most BRFs derived from lower-extremity joint kinematics and kinetics were located in the frontal and transverse planes of motion. Further, plantar pressure, vertical ground reaction force loading rate and free moment-related parameters were identified as kinetic BRFs.
Conclusion: This study offers a comprehensive overview of BRFs for the most common ROIs, which might serve as a starting point to develop ROI-specific risk profiles of individual runners. We identified limited evidence for most ROI-specific risk factors, highlighting the need for performing further high-quality studies in the future. However, consensus on data collection standards (including the quantification of workload and stress tolerance variables and the reporting of injuries) is warranted.
Background: Many countries have restricted public life in order to contain the spread of the novel coronavirus (SARS-CoV2). As a side effect of related measures, physical activity (PA) levels may have decreased.
Objective: We aimed (1) to quantify changes in PA and (2) to identify variables potentially predicting PA reductions.
Methods: A systematic review with random-effects multilevel meta-analysis was performed, pooling the standardized mean differences in PA measures before and during public life restrictions.
Results: A total of 173 trials with moderate methodological quality (modified Downs and Black checklist) were identified. Compared to pre-pandemic, total PA (SMD − 0.65, 95% CI − 1.10 to − 0.21) and walking (SMD − 0.52, 95% CI − 0.29 to − 0.76) decreased while sedentary behavior increased (SMD 0.91, 95% CI: 0.17 to 1.65). Reductions in PA affected all intensities (light: SMD − 0.35, 95% CI − 0.09 to − 0.61, p = .013; moderate: SMD − 0.33, 95% CI − 0.02 to − 0.6; vigorous: SMD − 0.33, − 0.08 to − 0.58, 95% CI − 0.08 to − 0.58) to a similar degree. Moderator analyses revealed no influence of variables such as sex, age, body mass index, or health status. However, the only continent without a PA reduction was Australia and cross-sectional trials yielded higher effect sizes (p < .05).
Conclusion: Public life restrictions associated with the COVID-19 pandemic resulted in moderate reductions in PA levels and large increases in sedentary behavior. Health professionals and policy makers should therefore join forces to develop strategies counteracting the adverse effects of inactivity.
Running footwear is continuously being modified and improved; however, running-related overuse injury rates remain high. Nevertheless, novel manufacturing processes enable the production of individualized running shoes that can fit the individual needs of runners, with the potential to reduce injury risk. For this reason, it is essential to investigate functional groups of runners, a collective of runners who respond similarly to a footwear intervention. Therefore, the objective of this study was to develop a framework to identify functional groups based on their individual footwear response regarding injury-specific running-related risk factors for Achilles tendinopathy, Tibial stress fractures, Medial tibial stress syndrome, and Patellofemoral pain syndrome. In this work, we quantified the footwear response patterns of 73 female and male participants when running in three different footwear conditions using unsupervised learning (k-means clustering). For each functional group, we identified the footwear conditions minimizing the injury-specific risk factors. We described differences in the functional groups regarding their running style, anthropometric, footwear perception, and demographics. The results implied that most functional groups showed a tendency for a single footwear condition to reduce most biomechanical risk factors for a specific overuse injury. Functional groups often differed in their hip and pelvis kinematics as well as their subjective rating of the footwear conditions. The footwear intervention only partially affected biomechanical risk factors attributed to more proximal joints. Due to its adaptive nature, the framework could be applied to other footwear interventions or performance-related biomechanical variables.
High-performance thin-layer chromatography (HPTLC), as the modern form of TLC (thin-layer chromatography), is suitable for detecting pharmaceutically active compounds over a wide polarity range using the gradient multiple development (GMD) technique. Diode-array detection (DAD) in conjunction with HPTLC can simultaneously acquire ultraviolet‒visible (UV‒VIS) and fluorescence spectra directly from the plate. Visualization as a contour plot helps to identify separated zones. An orange peel extract is used as an example to show how GMD‒DAD‒HPTLC in seven different developments with seven different solvents can provide an overview of the entire sample. More than 50 compounds in the extract can be separated on a 6-cm HPTLC plate. Such separations take place in the biologically inert stationary phase of HPTLC, making it a suitable method for effect-directed analysis (EDA). HPTLC‒EDA can even be performed with living organism, as confirmed by the use of Aliivibrio fischeri bacteria to detect bioluminescence as a measure of toxicity. The combining of gradient multiple development planar chromatography with diode-array detection and effect-directed analysis (GMD‒DAD‒HPTLC‒EDA) in conjunction with specific staining methods and time-of-flight mass spectrometry (TOF‒MS) will be the method of choice to find new chemical structures from plant extracts that can serve as the basic structure for new pharmaceutically active compounds.
Two solvent mixtures for high-performance thin-layer chromatographic (HPTLC) separation of some compounds showing estrogenic activity in the yeast estrogen screen (YES) assay are presented. The new method, planar yeast estrogen screen (pYES) combines the YES assay and a chromatographic separation on silica gel HPTLC plates with the performance of the YES assay. For separation, the analytes were applied bandwise to HPTLC plates (10 × 20 cm) with fluorescent dye (Merck, Germany). The plates were developed in a vertical developing chamber after 30 min of chamber saturation over a separation distance of 70 mm, using cyclohexane‒methyl-ethyl ketone (2:1, V/V) or cyclohexane‒CPME (3:2, V/V) as solvents. Both solvents allow separation of estriol, daidzein, genistein, 17β-estradiol, 17α-ethinyl estradiol, estrone, 4-nonylphenol and bis(2-ethylhexyl) phthalate.
During the coronavirus crisis, labs had to be offered in digital form in mechanical engineering at short notice. For this purpose, digital twins of more complex test benches in the field of fluid energy machines were used in the mechanical engineering course, with which the students were able to interact remotely to obtain measurement data. The concept of the respective lab was revised with regard to its implementation as a remote laboratory. Fortunately, real-world labs were able to be fully replaced by remote labs. Student perceptions of remote labs were mostly positive. This paper explains the concept and design of the digital twins and the lab as well as the layout, procedure, and finally the results of the accompanying evaluation. However, the implementation of the digital twins to date does not yet include features which address the tactile experience of working in real-world labs.
This paper aims to draw attention to an urgent need for reform of the regulatory framework of the broader export credit system to ensure a new and comprehensive "safe haven" for officially supported export credits. The purpose is to analyse the complex debate on disciplines of the World Trade Organization (WTO) and the Organisation for Economic Co-operation and Development (OECD), creating a point of reference for future analysis of and debates around the "carve-out clause" of the Agreement on Subsidies and Countervailing Measures (ASCM) and a "safe haven" in a broader sense.
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input–output pairs to train a neural network (NN) on it. We are able to learn the variables’ tendencies achieving an average R² score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
This paper has the objective of creating a framework for a different cultural dimension of corporate entrepreneurship leading to corporate entrepreneurial culture (CEC). The analysis of CEC is based on a review of existing concepts of organisational culture and entrepreneurship. They are combined to create a framework of CEC, including macro- and microlevels and examples of subcultures. Core ideas of the framework are validated by qualitative interviews with ten experts. The identified organisational category of the CEC framework is defined by the levels of micro-cultures or subcultures and includes the upper levels of the hierarchy, including the industry level. Geographic categories such as regional or national culture are also part of the system. The individual category of the CEC framework is characterised by competencies (including aspects such as motivation, creativity, mobilising others, coping with uncertainty, teamwork and social competencies) and entrepreneurial personalities. The results of the interviews show the importance of these individual competencies for a lively CEC. The different levels, such as national and professional cultures, as a dimension of the organisational category of the framework are also confirmed by the interviews. The findings indicate that the individual category of CEC could be used for job satisfaction or engagement and the degree of CEC of an organisation could be defined and developed by the organisational category. The identified framework contributes to an understanding of this complex topic and supports companies in the implementation of entrepreneurial ideas in different organisational contexts.