bluuebunny/crossref_metadata_2025_split huggingface dataset:
Catalase biosensor based on the PAni/cMWCNT support for peroxide sensing
Authors: Domínguez-Aragón Angélica , Dominguez Rocio B. , Peralta-Pérez María del Rosario , Armando Zaragoza-Contreras Erasto 
Published: July 2021

Abstract
Polymeric-based composites can contribute to enhancing the detection, stability, and performance of enzymatic biosensors, due to their high structural stability, conductivity, and biocompatibility. This work presents the fabrication of a nanocomposite of polyaniline (PAni)/gold nanoparticles (AuNP)/carboxylated multiwalled carbon nanotubes (cMWCNT) as functional support for covalently linked catalase (CAT) enzyme. PAni was electropolymerized on a screen-printed carbon electrode (SPCE) and decorated with AuNP to improve charge transfer properties. CAT was bonded through amide formation using the carboxylic groups of cMWCNT, resulting in PAni/AuNP/cMWCNT/CAT biosensor. The structural and electroactive characteristics of the nanocomposite were studied by SEM, FT-IR, and cyclic voltammetry. The optimal performance was achieved after CAT immobilization over PAni/AuNP/cMWCNT/nanocomposite, showing improved analytical features such as a fast amperometric response of 1.28 s, a wide detection range from 0.01 to 6.8 mM, a correlation coefficient (R
2) of 0.9921, a low detection limit of 2.34 µM, and an average recovery rate of 99.6% when evaluated in milk samples. Additionally, the bioelectrode showed excellent selectivity and retained bioactivity after 30 days of storage. Such remarkable performance proved the synergistic effects of both the high surface area of the cMWCNT and AuNP and the inherent PAni electroactivity, yielding direct electron transfer from CAT.
Performance Impacts of Innovation Outcomes in Entrepreneurial New Ventures
Authors: Manolopoulos Dimitris , Söderquist Klas Eric , Mamakou Xenia J. 
Published: July 2021

Abstract
Entrepreneurial innovation and its link to performance are attracting increasing attention among academics, entrepreneurs, and innovators alike. Our purpose is to prioritise and investigate the relative impact of distinct categories and types of innovation on multidimensional performance areas of new ventures. Accordingly, we relate the recently developed 10 Types of Innovation model to a comprehensive performance measurement framework specific to entrepreneurial new ventures and conduct empirical validation of the model for performance impacts. The 10 Types model shows reasonable psychometric properties and appears to effectively portray the innovation outcome conundrum. Our empirical evidence shows that the influence of innovation categories – configuration, offering, and experience – is substantively different across various performance criteria. Conversely, the effects of innovation types on different performance areas (financial, customer, process, and learning) do not present important variations, and thus do not seem to be contingent upon the nature of the performance measures considered.
The Mechanism of Entrepreneurs’ Social Networks on Innovative Startups’ Innovation Performance Considering the Moderating Effect of the Entrepreneurial Competence and Motivation
Authors: Gao Junguang , Cheng Yuan , He Hui , Gu Fuzhen 
Published: July 2021

Abstract
Innovative startups can bring many benefits to society. Drawing on the social network theory (SNT) and resource-based view (RBV), with mix methods approach, this paper argues that as the primary path for startups acquiring external resources, social networks are beneficial to improving innovative startups’ innovation performance. Using a large amount of data from GEM (Global Entrepreneurship Monitor), this paper first runs correlation analysis and regression analysis to empirically analyze entrepreneurs’ social networks’ impact on China’s innovative startups’ innovation performance. The results show that both formal and informal social networks are positively correlated with innovative startups’ innovation performance. Then we consider the moderating effect of entrepreneurial competence and motivation. And the results show both entrepreneurial competence and motivation positively moderate the correlation above. Second, to explore the above correlation’s internal mechanism, we conduct semi-structured interviews with 14 entrepreneurs. Drawing on the resource management theory (RMT) and the process of cross-border knowledge search and assimilation, the mechanism model of entrepreneurs’ social networks on startups’ innovation performance is proposed through content analysis.
Research on the electromagnetic scattering from foam sea based on SMCG
Authors: Huang Weifan 
Published: July 2021

Abstract
The electromagnetic scattering characteristics of foam in Marine environment are studied in this paper. The Elfouhaily sea spectrum model is established, and the dielectric constants of seawater/ocean foam are calculated based on the double Debye dielectric constant model, furthermore, the formula relation between them is given. As an important factor affecting ocean electromagnetic scattering, the thickness of foam is proportional to the wind speed above the sea surface. In this paper, the SMCG method based on the surface current equation is mainly used to calculate the electromagnetic scattering coefficient of the environment, and the correctness of the SMCG algorithm is verified by comparing the calculated results with the actual measured data. Based on the computer simulation, the influence of the existence of ocean foam on the environmental scattering is discussed, and the Marine electromagnetic scattering coefficients under different wind speeds, different scrape angles and different radar bands are calculated. Through the analysis of the calculation results, the influences of wind speed, grazing angle and frequency on the electromagnetic scattering characteristics of the Marine environment containing foam are studied, and the general variation rule is obtained: the intensity of electromagnetic scattering increases with the increase of wind speed, the decrease of incident Angle and the increase of frequency. The results of this paper can be used to guide ocean navigation and fishery, and have reference significance in the military field.
Green synthesis of ZnS nanoparticles and fabrication of ZnS–chitosan nanocomposites for the removal of Cr(vi) ion from wastewater
Authors: Xaba Thokozani 
Published: July 2021

Abstract
A modified homogeneous precipitation method has been used to synthesize ZnS nanoparticles. Starch and polyvinyl alcohol (PVA) were utilized as capping molecules, and later, the ZnS–PVA-capped nanoparticles were then incorporated with chitosan to form ZnS–chitosan nanocomposites for the removal of Cr(vi) ion from wastewater. The optical measurements of the synthesized ZnS nanoparticles showed the band gap which was blue-shifted when compared with the bulk ZnS material. The crystalline structures were determined by X-ray diffraction, and the crystalline sizes were estimated from the Scherer formula. XRD spectra confirmed the formation of hexagonal phase for the uncapped ZnS nanoparticles with an average crystalline size of 3.71 nm whereas the starch- and PVA-capped ZnS nanoparticles showed the formation of cubic phase structures with crystalline sizes of 3.26 and 2.88 nm. The TEM image showed spherical particles with regular morphologies and significantly narrow size distributions. The calculated average particle diameters were in good agreement with the estimated XRD result. The removal of Cr(vi) ion from wastewater was studied through the adsorption process. The effect of pH, dosage, and contact time was investigated. More than 95% of the metal ion recovery was achieved through using ZnS–chitosan nanocomposites.
Machine learning based disease prediction from genotype data
Authors: Katsaouni Nikoletta , Tashkandi Araek , Wiese Lena , Schulz Marcel H. 
Published: July 2021

Abstract
Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.
The relevance of adhesion G protein-coupled receptors in metabolic functions
Authors: Kaczmarek Isabell , Suchý Tomáš , Prömel Simone , Schöneberg Torsten , Liebscher Ines , Thor Doreen 
Published: July 2021

Abstract
G protein-coupled receptors (GPCRs) modulate a variety of physiological functions and have been proven to be outstanding drug targets. However, approximately one-third of all non-olfactory GPCRs are still orphans in respect to their signal transduction and physiological functions. Receptors of the class of Adhesion GPCRs (aGPCRs) are among these orphan receptors. They are characterized by unique features in their structure and tissue-specific expression, which yields them interesting candidates for deorphanization and testing as potential therapeutic targets. Capable of G-protein coupling and non-G protein-mediated function, aGPCRs may extend our repertoire of influencing physiological function. Besides their described significance in the immune and central nervous systems, growing evidence indicates a high importance of these receptors in metabolic tissue. RNAseq analyses revealed high expression of several aGPCRs in pancreatic islets, adipose tissue, liver, and intestine but also in neurons governing food intake. In this review, we focus on aGPCRs and their function in regulating metabolic pathways. Based on current knowledge, this receptor class represents high potential for future pharmacological approaches addressing obesity and other metabolic diseases.
Tuning the network charge of biohybrid hydrogel matrices to modulate the release of SDF-1
Authors: Kühn Sebastian , Freyse Joanna , Atallah Passant , Rademann Jörg , Freudenberg Uwe , Werner Carsten 
Published: July 2021

AbstractThe delivery of chemotactic signaling molecules via customized biomaterials can effectively guide the migration of cells to improve the regeneration of damaged or diseased tissues. Here, we present a novel biohybrid hydrogel system containing two different sulfated glycosaminoglycans (sGAG)/sGAG derivatives, namely either a mixture of short heparin polymers (Hep-Mal) or structurally defined nona-sulfated tetrahyaluronans (9s-HA4-SH), to precisely control the release of charged signaling molecules. The polymer networks are described in terms of their negative charge, i.e. the anionic sulfate groups on the saccharides, using two parameters, the integral density of negative charge and the local charge distribution (clustering) within the network. The modulation of both parameters was shown to govern the release characteristics of the chemotactic signaling molecule SDF-1 and allows for seamless transitions between burst and sustained release conditions as well as the precise control over the total amount of delivered protein. The obtained hydrogels with well-adjusted release profiles effectively promote MSC migrationin vitroand emerge as promising candidates for new treatment modalities in the context of bone repair and wound healing.
Collagen/glycosaminoglycan-based matrices for controlling skin cell responses
Authors: Anderegg Ulf , Halfter Norbert , Schnabelrauch Matthias , Hintze Vera 
Published: July 2021

Abstract
Wound healing and tissue regeneration are orchestrated by the cellular microenvironment, e.g. the extracellular matrix (ECM). Including ECM components in biomaterials is a promising approach for improving regenerative processes, e.g. wound healing in skin. This review addresses recent findings for enhanced epidermal-dermal regenerative processes on collagen (coll)/glycosaminoglycan (GAG)-based matrices containing sulfated GAG (sGAG) in simple and complex in vitro models. These matrices comprise 2D-coatings, electrospun nanofibrous scaffolds, and photo-crosslinked acrylated hyaluronan (HA-AC)/coll-based hydrogels. They demonstrated to regulate keratinocyte and fibroblast migration and growth, to stimulate melanogenesis in melanocytes from the outer root sheath (ORS) of hair follicles and to enhance the epithelial differentiation of human mesenchymal stem cells (hMSC). The matrices’ suitability for delivery of relevant growth factors, like heparin-binding epidermal growth factor like growth factor (HB-EGF), further highlights their potential as bioinspired, functional microenvironments for enhancing skin regeneration.
iMLP, a predictor for internal matrix targeting-like sequences in mitochondrial proteins
Authors: Schneider Kevin , Zimmer David , Nielsen Henrik , Herrmann Johannes M. , Mühlhaus Timo 
Published: July 2021

Abstract
Matrix targeting sequences (MTSs) direct proteins from the cytosol into mitochondria. Efficient targeting often relies on internal matrix targeting-like sequences (iMTS-Ls) which share structural features with MTSs. Predicting iMTS-Ls was tedious and required multiple tools and webservices. We present iMLP, a deep learning approach for the prediction of iMTS-Ls in protein sequences. A recurrent neural network has been trained to predict iMTS-L propensity profiles for protein sequences of interest. The iMLP predictor considerably exceeds the speed of existing approaches. Expanding on our previous work on iMTS-L prediction, we now serve an intuitive iMLP webservice available at http://iMLP.bio.uni-kl.de and a stand-alone command line tool for power user in addition.
Biophysical applications in structural and molecular biology
Authors: Tsegaye Solomon , Dedefo Gobena , Mehdi Mohammed 
Published: July 2021

Abstract
The main objective of structural biology is to model proteins and other biological macromolecules and link the structural information to function and dynamics. The biological functions of protein molecules and nucleic acids are inherently dependent on their conformational dynamics. Imaging of individual molecules and their dynamic characteristics is an ample source of knowledge that brings new insights about mechanisms of action. The atomic-resolution structural information on most of the biomolecules has been solved by biophysical techniques; either by X-ray diffraction in single crystals or by nuclear magnetic resonance (NMR) spectroscopy in solution. Cryo-electron microscopy (cryo-EM) is emerging as a new tool for analysis of a larger macromolecule that couldn’t be solved by X-ray crystallography or NMR. Now a day’s low-resolution Cryo-EM is used in combination with either X-ray crystallography or NMR. The present review intends to provide updated information on applications like X-ray crystallography, cryo-EM and NMR which can be used independently and/or together in solving structures of biological macromolecules for our full comprehension of their biological mechanisms.
Effect of K and Na on reduction swelling performance of oxidized roasted briquettes
Authors: Song Wei , Luo Guo-Ping , Sun Chen-Chen , Zhang Jing , Zhu Jian-Guo 
Published: July 2021

Abstract
The presence of potassium oxide (K2O) and sodium oxide (Na2O) causes high reduction swelling of pellets of Bayan Obo iron concentrate during reduction and thus affects the permeability of blast gases during blast furnace operations. The influencing mechanism of K2O and Na2O on the swelling behavior of reduction reactions (1) Fe2O3 → Fe3O4, (2) Fe3O4 → Fe
x
O, and (3) Fe
x
O → Fe was researched by adding (K2O + Na2O) to Australian fine ore briquettes. The mineral composition and structure of the briquettes, as well as the reduction swelling after the three reactions coupled with the morphology and lattice parameters of the reduced products, were studied. From the results, the swelling index with 0.6% (K2O + Na2O) added was 8.52%, 7.91%, and 33.81%, respectively, and without were 12.36%, 3.27%, and 12.61%, respectively, for the three reactions. The swelling index of the first reaction (Fe2O3 → Fe3O4) is reduced because alkali metal suppresses crystal cracking. The swelling mainly occurs at the third stage (Fe
x
O → Fe), because K2O and Na2O enhance the oriented growth of iron whiskers, as well as make them smaller. Crystal transformation does not occur at the second stage (Fe3O4 → Fe
x
O) and the reduction swelling is small, but the swelling index of the briquettes with added with K2O and Na2O increases (7.91% compared to 3.27%). The main reason is that the alkali metal reduces the melting point of the slag phase and promotes the cascade crystallization of FeO. Therefore, the abnormal swelling of briquettes caused by K and Na is mainly caused by the growth of iron whiskers at the third stage.
Residence time distribution studies on recycle reactor with recirculation
Authors: Datta Arghya , Bhunia Haripada , Gupta Raj Kumar 
Published: July 2021

Abstract
Residence time distribution (RTD) experiments provide very important information about the performance of reactors. In the present work, RTD experiments were performed with varying recycle and recirculation rates to see their effect on mean residence time (MRT), flow bypassing and stagnant volume in the reactor. A computer program was developed to solve the model equations using fourth-order Runge–Kutta method. A low bypass flow (<5%) was observed from the experimental RTD curves obtained at different operating conditions. A change in the MRT from 1.2 to 1.8 h was observed at different recycle and recirculation rates. At maximum recycle and maximum recirculation, in the study ranges, a 37% stagnant volume (with exchange) was predicted. In the absence of recycle and recirculation, a 53% stagnant volume (with exchange) was predicted corresponding to the best fit of the experimental RTD data.
Focus on form in task repetition through oral and written task modeling
Authors: Khezrlou Sima 
Published: July 2021

Abstract
This study expands upon research into task repetition effects by exploring the effect of task modeling between the performances of the same oral narrative task and its extension to a new task. Seventy-one advanced beginner English as a foreign language (EFL) learners were divided into three groups: task repetition with oral modeling (TR + OM), task repetition with written modeling (TR + WM), and task repetition with no modeling (TR). All groups enacted another oral narrative task (a new task of the same type), three days apart. Participants’ oral narrative task performances were analyzed in terms of complexity, accuracy and fluency. Results revealed that TR + WM was more effective than the TR + OM, and both were significantly better than the control group in leading to subordination complexity in the repeated task and the new task. Whereas the percentage of error-free clauses remained unchanged over time, the accurate verb forms increased in TR + WM’s repeated task, but declined in the new task. Fluency in terms of articulation speed and mid-clause silent pauses was improved and maintained in the new task in all groups, with both the experimental groups particularly the TR + OM outperforming the TR regarding the significant reduction of repair in the repeated and new task performances. In conclusion, these significant developments induced by the modeling conditions speak to the strength of models in providing and extending linguistic features beyond learners’ current repertoires. Pedagogical implications of these findings are discussed.
Mixed-hybrid and mixed-discontinuous Galerkin methods for linear dynamical elastic–viscoelastic composite structures
Authors: Márquez Antonio , Meddahi Salim 
Published: July 2021

Abstract
We introduce and analyze a stress-based formulation for Zener’s model in linear viscoelasticity. The method is aimed to tackle efficiently heterogeneous materials that admit purely elastic and viscoelastic parts in their composition.We write the mixed variational formulation of the problem in terms of a class of tensorial wave equation and obtain an energy estimate that guaranties the well-posedness of the problem through a standard Galerkin procedure. We propose and analyze mixed continuous and discontinuous Galerkin space discretizations of the problem and derive optimal error bounds for each semidiscrete solution in the corresponding energy norm. Finally, we discuss full discretization strategies for both Galerkin methods.
Mild phenotype in two siblings with a missense GHR variant
Authors: Sarıkaya Özdemir Behiye , Çetinkaya Semra , Güleray Lafcı Naz , Şakar Merve , Karacan Küçükali Gülin , Elmaoğullari Selin , Savaş Erdeve Şenay 
Published: July 2021

Abstract

Objectives
Laron syndrome (LS) is a disease caused by growth hormone receptor (GHR) defects. It is characterized by severe postnatal growth retardation and distinctive facial features.

Case presentation
In this case report, we describe the clinical and biochemical characteristics of two siblings with LS, a sister and a brother, and identify a homozygous c.344A> C (p.Asn115Thr) variant in GHR. The sister was 11 years 9 months old with a height of 127.5 cm (-3.86 SDS), and the brother was 14 years 10 months old with a height of 139 cm (-4.27 SDS). Their phenotype did not have features suggesting classical LS.

Conclusion
In the current literature, there are three cases with the same missense variant. Our cases differ from them in clinical (higher height SDS, mild dysmorphism including a broad forehead, malar hypoplasia, prominent columella and chin, thick lips) and biochemical characteristics. Here, we present the variable expressivity in the two siblings.
Some estimates for the commutators of multilinear maximal function on Morrey-type space
Authors: Yu Xiao , Zhang Pu , Li Hongliang 
Published: July 2021

Abstract
In this paper, we study the equivalent conditions for the boundedness of the commutators generated by the multilinear maximal function and the bounded mean oscillation (BMO) function on Morrey space. Moreover, the endpoint estimate for such operators on generalized Morrey spaces is also given.
Share Contract Revisited: A New Transaction Cost Approach
Authors: Ding Jian , Zhou Yixiao 
Published: July 2021

Abstract
The purpose of this paper is to explore how sharecropping contracts are chosen over fixed-rent contracts. There are two concerning issues. First, theoretical explanation has been criticized for not providing a satisfactory answer to the question as to why share contracts are chosen. Second, among the existing empirical studies, there are great controversies about the impact of variance of output. Inspired by the latest insights from (Cheung, S. N. S. 2014. Economic Explanation. Hong Kong: Arcadia Press.), this paper not only provides an explanation for the choice of share contract that is suitable for empirical testing, but also solves the puzzle over variance of output.
On the Observability Requirement in Economics as an Axiomatic Science
Authors: Cheung Steven N.S. 
Published: July 2021

Abstract
This paper points out as the only social science that is axiomatic, economics has the power of predicting beyond interpreting. It then explains that in hypothesis testing, the law of demand is the only indispensable axiom in economics, and that quantity demanded is the only non-observable variable that must be retained. After reviewing his disagreements with his peers and colleagues, and his success in predicting the transformation of China, the author argues that the profession has gone astray with the surge in the use of non-observables.