bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolomics analysis of salt tolerance of Zygosaccharomyces rouxii and guided exogenous fatty acid addition for improved salt tolerance
Authors: Wang Dingkang , Mi Ting , Huang Jun , Zhou Rongqing , Jin Yao , Wu Chongde 
Published: May 2022

AbstractBACKGROUNDZygosaccharomyces rouxii plays an irreplaceable role in the manufacture of traditional fermented foods, which are produced in a high‐salt environment. However, there is little research on strategies for improving salt tolerance of Z. rouxii.RESULTSIn this study, metabolomics was used to reveal the changes in intracellular metabolites under salt stress, and the results show that most of the carbohydrate contents decreased, the contents of xanthohumol and glycerol increased (fold change 4.07 and 5.35, respectively), while the contents of galactinol, xylitol and d‐threitol decreased (fold change −9.43, −5.83 and −3.59, respectively). In addition, the content of four amino acids and six organic acids decreased, while that of the ten nucleotides increased. Notably, except for stearic acid (C18:0), all fatty acid contents increased. Guided by the metabolomics results, the effect of addition of seven exogenous fatty acids (C12:0, C14:0, C16:0, C18:0, C16:1, C18:1, and C18:2) on the salt tolerance of Z. rouxii was analyzed, and the results suggested that four exogenous fatty acids (C12:0, C16:0, C16:1, and C18:1) can increase the biomass yield and maximum growth rate. Physiological analyses demonstrated that exogenous fatty acids could regulate the distribution of fatty acids in the cell membrane, increase the degree of unsaturation, improve membrane fluidity, and maintain cell integrity, morphology and surface roughness.CONCLUSIONThese results are applicable to revealing the metabolic mechanisms of Z. rouxii under salt stress and screening potential protective agents to improve stress resistance by adding exogenous fatty acids. © 2022 Society of Chemical Industry.
Development of high‐internal‐phase emulsions stabilized by soy protein isolate–dextran complex for the delivery of quercetin
Authors: Du Xiaoqian , Hu Miao , Liu Guannan , Yan Shizhang , Qi Baokun , Zhang Shuang , Huang Yuyang , Li Yang , Chen Hao , Zhu Xiuqing 
Published: May 2022

AbstractBackgroundProtein–polysaccharide complexes have been widely used to stabilize high‐internal‐phase emulsion (HIPEs). However, it is still unknown whether soy protein isolate–dextran (SPI–Dex) complexes can stabilize HIPEs or what is the effect of Dex concentration on the HIPEs. Furthermore, the non‐covalent interaction mechanism between SPI and Dex is also unclear. Therefore, we fabricated SPI–Dex complexes and used them to stabilize HIPEs‐loaded quercetin and explore the interaction mechanism between SPI and Dex, as well as the effect of Dex concentration on the particle size, ζ‐potential, microstructure, rheology, quercetin encapsulation efficiency, and gastrointestinal fate of the HIPEs.ResultsSpectral analysis (fourier transform infrared spectroscopy, ultraviolet spectroscopy, and fluorescence spectroscopy) results identified the formation of SPI–Dex complexes, and indicated that the addition of Dex changed the spatial structure of SPI, whereas thermodynamic analysis (ΔH > 0, ΔS > 0) showed that hydrophobic interactions were the main driving forces in the formation of SPI–Dex complexes. Compared with HIPEs stabilized by SPI, the SPI–Dex complex‐stabilized HIPEs had smaller particles (3000.33 ± 201.22 nm), as well as higher ζ‐potential (−21.73 ± 1.10 mV), apparent viscosities, modulus, and quercetin encapsulation efficiency (98.19 ± 0.14%). In addition, in vitro digestion revealed that SPI–Dex complex‐stabilized HIPEs significantly reduced the release of free fatty acid and improved quercetin bioaccessibility.ConclusionHIPEs stabilized by SPI–Dex complexes delayed the release of free fat acid and improved the bioaccessibility of quercetin, and may be help in designing delivery systems for bioactive substances with specific properties. © 2022 Society of Chemical Industry.
Helix Induction and Inversion of Polymeric Foldamer Regulated by the Single Enantiomers
Authors: Cao Shuang , Sun Chenchen , Wang Jing , Jiang Qian , Qiu Yuan , Wang Hong , Liao Yonggui , Xie Xiaolin 
Published: May 2022

AbstractGenerally, a single enantiomer can induce a foldamer into a preferred‐handed helix, while another condition is required for the helical inversion. Herein, it is found that the helix induction and subsequent inversion of poly(m‐phenylene diethynylene)‐based foldamer bearing aza‐18‐crown‐6 pendants (Poly‐1) can be realized by increasing the concentration of sterically hindered l‐amino acid perchlorate salts. When the amount of chiral enantiomers is small, one enantiomer tends to complex with two non‐adjacent aza‐18‐crown‐6 rings via three N+H···O hydrogen bonds in a sandwich mode. Notably, the transition dipole moment is perpendicular to the aza‐18‐crown‐6 ring, so that the induced helical chirality in Poly‐1 backbone is opposite to the chirality of enantiomers. When the amount of chiral enantiomers is large enough, each aza‐18‐crown‐6 is occupied by one enantiomer, which causes the transition dipole moment in a parallel direction to aza‐18‐crown‐6 ring. In this case, the increased steric hindrance can facilitate the inversion of screw sense of Poly‐1 backbone, which is directed by chiral center of enantiomers. As a result, a helix inversion has been achieved successfully. This work not only provides a novel strategy for regulating the two‐stage folded helical conformations by the single enantiomers, but opens a window to develop chiral recognition materials.
Grain growth behavior of niobium microalloyed 20MnCr5 steel and the effect of boron
Authors: He G. , Zhu X. , Jiang B. , Wang Z. , Liu Y. , Wu C. , Wei W. 
Published: May 2022

AbstractGrain growth behaviors of niobium free, 0.021 wt. % niobium microalloyed and 0.024 wt. % niobium‐0.0022 wt. % boron microalloyed 20MnCr5 steels are investigated in this paper. Optical microscopy and field‐emission scanning electron microscopy were used to analyze the prior austenite grain. Transmission electron microscopy and energy dispersive spectroscopy were used to investigate the morphologies and compositions of the precipitates, respectively. The results show that adding niobium reduces the size of prior austenite grain. The addition of boron to niobium microalloyed steel, however, causes grain coarsening. Thermodynamic and kinetic calculations were performed to explain the mechanism of grain growth behavior. The results indicate that the addition of niobium and boron causes the difference in solution temperatures of aluminum nitride and niobium carbonitride precipitates. The addition of boron inhibits the precipitation of niobium carbonitride and aluminum nitride and causes precipitates coarsening. The precipitation observation backs up the calculation results.
Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8
Authors: Lingelbach Y. , Waldenmaier T. , Hagymasi L. , Mikut R. , Schulze V. 
Published: May 2022

AbstractTo 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.
Peculiarities of chemical interaction of some carbon nanoreinforcements with aluminum matrix in metal matrix composite (MMC)
Authors: Popov V. , Borunova A. , Shelekhov E. , Khodos I. , Senatilin B. , Matveev D. , Versinina E. 
Published: May 2022

AbstractThe possibility of applying globular carbon for reinforcing the aluminum matrix when using mechanical alloying to produce composites was shown by investigating specific properties of chemical interaction of nanocarbon with the aluminum matrix. The structure of the initial particles of globular carbon and composite granules was studied. Mechanical alloying can significantly crush the initial particles of globular carbon. The use of the method of differential scanning calorimetry allowed the temperature of the start of the reaction between globular carbon nanoparticles and aluminum matrix with the formation of aluminum carbide to be determine as 320 °C–350 °C. Aluminum matrix composites with reinforcing particles of globular carbon can be used up to these temperatures without risk of the formation of aluminum carbide.
Dental composite resin: a review of major mechanical properties, measurements and its influencing factors
Authors: Suryawanshi A. , Behera N. 
Published: May 2022

AbstractNowadays, there is a large variety of dental restorative composite materials on the market, making it difficult to select the proper one. The mechanical properties of various dental materials determine how long they endure when used in the mouth. On the other hand, the therapeutic efficiency of these restorative materials cannot be predicted solely based on their mechanical properties. These features, on the other hand, help with the examination of the impact of changes in their composition. As a consequence, these tests can help the clinician choose the right material. The various mechanical properties can be investigated using a variety of laboratory experiments. The common laboratory tests used to evaluate the mechanical characteristics, composition, and classification of dental composite materials, as well as the factors that influence mechanical qualities, are discussed in this study.
An open workflow to gain insights about low‐likelihood high‐impact weather events from initialized predictions
Authors: Kelder T. , Marjoribanks T. I. , Slater L. J. , Prudhomme C. , Wilby R. L. , Wagemann J. , Dunstone N. 
Published: May 2022

AbstractLow‐likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high‐impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low‐likelihood high‐impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium‐Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record‐shattering August 2020 California‐Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low‐likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low‐likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science‐policy interface to manage high impacts.
Development of an abdominal phantom for the validation of an oligometastatic disease diagnosis workflow
Authors: Bauer Dominik F. , Rosenkranz Julian , Golla Alena‐Kathrin , Tönnes Christian , Hermann Ingo , Russ Tom , Kabelitz Gordian , Rothfuss Andreas J. , Schad Lothar R. , Stallkamp Jan L. , Zöllner Frank G. 
Published: May 2022

AbstractPurposeThe liver is a common site for metastatic disease, which is a challenging and life‐threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)‐guided biopsy for subsequent molecular subtyping. By combining morphological, molecular, and functional information about the tumor, a patient‐specific treatment planning is possible. We designed and manufactured an abdominal liver phantom that we used to demonstrate multimodal image acquisition, image processing, and biopsy of the OMD diagnosis workflow.MethodsThe anthropomorphic abdominal phantom contains a rib cage, a portal vein, lungs, a liver with six lesions, and a hepatic vessel tree. This phantom incorporates three different lesion types with varying visibility under computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography CT (PET‐CT), which reflects clinical reality. The phantom is puncturable and the size of the corpus and the organs is comparable to those of a real human abdomen. By using several modern additive manufacturing techniques, the manufacturing process is reproducible and allows to incorporate patient‐specific anatomies. As a first step of the OMD diagnosis workflow, a preinterventional CT, MRI, and PET‐CT data set of the phantom was acquired. The image information was fused using image registration and organ information was extracted via image segmentation. A CBCT‐guided needle puncture experiment was performed, where all six liver lesions were punctured with coaxial biopsy needles.ResultsQualitative observation of the image data and quantitative evaluation using contrast‐to‐noise ratio (CNR) confirms that one lesion type is visible only in MRI and not CT. The other two lesion types are visible in CT and MRI. The CBCT‐guided needle placement was performed for all six lesions, including those visible only in MRI and not CBCT. This was possible by successfully merging multimodal preinterventional image data. Lungs, bones, and liver vessels serve as realistic inhibitions during needle path planning.ConclusionsWe have developed a reusable abdominal phantom that has been used to validate a standardized OMD diagnosis workflow. Utilizing the phantom, we have been able to show that a multimodal imaging pipeline is advantageous for a comprehensive detection of liver lesions. In a CBCT‐guided needle placement experiment we have punctured lesions that are invisible in CBCT using registered preinterventional MRI scans for needle path planning.
Prediction of the post‐failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing
Authors: Yousefpour Negin , Pouragha Mehdi 
Published: May 2022

AbstractAcoustic emission (AE) reading is among the most common methods for monitoring the behavior of brittle materials such as rock and concrete. This study uses discrete element method (DEM) simulations to explore the correlations between the pre‐failure AE readings with the post‐failure behavior and residual strength of rock masses. The deep learning (DL) method based on long short‐term memory (LSTM) algorithms has been applied to generate predictive models based on the data from DEM simulations of biaxial compression. The dataset has been populated by varying interparticle friction while keeping bond cohesion constant. Various configurations of the LSTM algorithm were evaluated considering different scenarios for input features (strain, stress, and AE energy records) and a range of values for the key hyperparameters. The prime AI models show promising accuracy in predicting residual strength decay with strain based on pre‐failure patterns in AE readings. The results indicate that the pre‐failure AE indeed encapsulates information about the developing failure mechanisms and the post‐failure response in rocks, which can be captured through artificial intelligence.
Observer‐based control of Takagi–Sugeno descriptor system with time‐varying delay: Free‐weighting matrix
Authors: El Aiss Hicham , Barbosa Karina. A. 
Published: May 2022

AbstractThis article investigates the stability/stabilization problem for Takagi–Sugeno (T–S) descriptor systems with time‐varying delay. A transformation‐based model has been employed through a two‐term approximation, and the input‐output approach has been investigated using the small gain Theorem. A novel free‐weighting matrix method, in combination with a new construct Lyapunov–Krasovskii functional (LKF), has been developed. Delay‐dependent conditions have been obtained in a set of linear matrix inequalities (LMIs). To estimate the system states T–S fuzzy descriptor observer‐based controller has been introduced. The gains have been designed using a new design strategy. Finally, three examples have been borrowed to test the merit and the validity of the proposed method.
The effect of tannic acid functional multi‐walled carbon nanotubes on the properties of nitrile rubber/ethylene propylene diene monomer composites
Authors: Song Kaiwen , Wang Jikui 
Published: May 2022

AbstractMulti‐walled carbon nanotubes (MWCNT) were first non‐covalently functioned by tannic acid (TA), then blended with nitrile rubber (NBR) and ethylene propylene diene monomer (EPDM) with NBR:EPDM:TA‐MWCNT = 70:30:5. TA‐MWCNT and the composites had been characterized by various techniques, including Fourier transform infrared spectroscopy (FT‐IR), Raman analysis, transmission electron microscopy (TEM), mechanical properties testing, thermal‐oxidative aging testing, differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM). Results indicated that TA molecules were successfully absorbed to the surface of MWCNT. And compared with NBR/EPDM/MWCNT composites, the compatibility between NBR and EPDM had been significantly improved. The NBR/EPDM/TA‐MWCNT exhibited excellent mechanical properties and thermal‐oxidative aging properties due to the phenolic hydroxyl groups on the surface of TA‐MWCNT, which had high chemical reactivity and physical adhesion. These phenolic hydroxyl groups gave TA‐MWCNT better dispersion in the rubber matrixes and can participate in the vulcanization process, creating the firm interface between the rubber matrixes and the surface of MWCNT.
Multi‐state modeling of G‐protein coupled receptors at experimental accuracy
Authors: Heo Lim , Feig Michael 
Published: May 2022

AbstractThe family of G‐protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high‐accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi‐state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state‐annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 Å and 1.41 Å for the active and inactive state models, respectively. The models were more suitable for protein‐ligand docking than the original AlphaFold2 models and template‐based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR‐peptide complex structures in GPCR Dock 2021, a blind GPCR‐ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high‐accuracy via machine‐learning methods.
Impact of COVID‐19 lockdown on exercise capacity in PAH patients
Authors: Baratto Claudia , Dewachter Céline , Caravita Sergio , Zambon Antonella , Bondue Antoine , Parati Gianfranco , Vachiéry Jean‐Luc 
Published: May 2022

AbstractThe outbreak of novel coronavirus‐19 disease (COVID‐19) was classified as a global pandemic thanks to the rapid viral spread, and restrictive policy measures of infection containment, including “lockdown” periods and self‐isolation, were first instituted in Belgium from March to June 2020. The consequent reduction in physical activity could have a negative impact on exercise capacity, especially in frail patients with pre‐existing chronic diseases, such as pulmonary arterial hypertension (PAH). With the aim to define the impact of COVID‐19 lockdown on functional status, we included in our observational analysis clinically stable PAH patients, who had performed at least four consecutive 6‐min walking tests (6MWT) during 2019–2020, to compare their exercise performance before and after the lockdown. In the 63 patients included, a comparison between the distance covered at 6MWT after the lockdown period and the pooled mean of the previous three 6MWTs showed a mean reduction of 14 m after the lockdown (p = 0.004). Moreover, the mean distance covered at 6MWT went from 447 m in March 2020 to 434 m in June 2020, with a significant average loss of 13 m (p = 0.024). Our results showed that PAH patients were less performing at 6MWT after 3 months of reduced physical activity, despite constant clinical stability and the absence of signs of disease progression, suggesting that this confounding factor should be kept in mind when evaluating changes in 6MWT during or after COVID‐19 pandemic.
How dynamic capabilities change ordinary capabilities: Reconnecting attention control and problem‐solving
Authors: Schulze Anja , Brusoni Stefano 
Published: May 2022

AbstractResearch SummaryBuilding on the attention‐based view of the firm, we elaborate the concept of dynamic capabilities and identify two constitutive elements: attention control and problem‐solving. We show empirically that the control element of dynamic capabilities regulates how organizations (dis‐)engage attention on operational versus change‐oriented tasks. On this basis, we develop a process model of how control and problem‐solving interact to reconfigure resources and thus modify ordinary capabilities. We study the adoption of lean management in the R&D unit of a large U.S. corporation. Our longitudinal case study identifies obstacles that organizations have to overcome to establish effective dynamic capabilities that enable their adaptation to changing environmental circumstances.Managerial Summary“The vast majority of all change initiatives fail”: We hear this statement a lot in our interactions with practitioners. In this article, we suggest an explanation of why achieving persistent, behavioral change is hard: attention to change processes is difficult to maintain over an extended period of time. Initiatives start, then fade away. By studying the interplay of control mechanisms (that keep organizational attention on the long‐term goals) and problem‐solving tools (that identify what and how to change in the short term), we provide a framework that can generate actionable implications for executives. In particular, we focus on the decisive and yet underestimated role played by key performance indicators in sustaining attention on change initiatives.