bluuebunny/crossref_metadata_2025_split huggingface dataset:
Allogeneic Mesenchymal Stromal Cells for Cartilage Regeneration: A Review of in Vitro Evaluation, Clinical Experience, and Translational Opportunities
Authors: Aldrich Ellison D. , Cui Xiaolin , Murphy Caroline A. , Lim Khoon S. , Hooper Gary J. , McIlwraith C. Wayne , Woodfield Tim B.F. 
Published: August 2021

Abstract
The paracrine signaling, immunogenic properties and possible applications of mesenchymal stromal cells (MSCs) for cartilage tissue engineering and regenerative medicine therapies have been investigated through numerous in vitro, animal model and clinical studies. The emerging knowledge largely supports the concept of MSCs as signaling and modulatory cells, exerting their influence through trophic and immune mediation rather than as a cell replacement therapy. The virtues of allogeneic cells as a ready-to-use product with well-defined characteristics of cell surface marker expression, proliferative ability, and differentiation capacity are well established. With clinical applications in mind, a greater focus on allogeneic cell sources is evident, and this review summarizes the latest published and upcoming clinical trials focused on cartilage regeneration adopting allogeneic and autologous cell sources. Moreover, we review the current understanding of immune modulatory mechanisms and the role of trophic factors in articular chondrocyte-MSC interactions that offer feasible targets for evaluating MSC activity in vivo within the intra-articular environment. Furthermore, bringing labeling and tracking techniques to the clinical setting, while inherently challenging, will be extremely informative as clinicians and researchers seek to bolster the case for the safety and efficacy of allogeneic MSCs. We therefore review multiple promising approaches for cell tracking and labeling, including both chimerism studies and imaging-based techniques, that have been widely explored in vitro and in animal models. Understanding the distribution and persistence of transplanted MSCs is necessary to fully realize their potential in cartilage regeneration techniques and tissue engineering applications.
High‐Temperature Skin Softening Materials Overcoming the Trade‐Off between Thermal Conductivity and Thermal Contact Resistance
Authors: Kim Taehun , Kim Seongkyun , Kim Eungchul , Kim Taesung , Cho Jungwan , Song Changsik , Baik Seunghyun 
Published: August 2021

AbstractThe trade‐off between thermal conductivity (κ) and thermal contact resistance (Rc) is regarded as a hurdle to develop superior interface materials for thermal management. Here a high‐temperature skin softening material to overcome the trade‐off relationship, realizing a record‐high total thermal conductance (254.92 mW mm−2K−1) for isotropic pad‐type interface materials is introduced. A highly conductive hard core is constructed by incorporating Ag flakes and silver nanoparticle‐decorated multiwalled carbon nanotubes in thermosetting epoxy (EP). The thin soft skin is composed of filler‐embedded thermoplastic poly(ethylene‐co‐vinyl acetate) (PEVA). The κ (82.8 W m−1K−1) of the PEVA‐EP‐PEVA interface material is only slightly compromised, compared with that (106.5 W m−1K−1) of the EP core (386 µm). However, the elastic modulus (E = 2.10 GPa) at the skin is significantly smaller than the EP (26.28 GPa), enhancing conformality and decreasing Rc from 108.41 to 78.73 mm2 K W−1. The thermoplastic skin is further softened at an elevated temperature (100 °C), dramatically decreasing E (0.19 GPa) and Rc (0.17 mm2 K W−1) with little change in κ, overcoming the trade‐off relationship and enhancing the total thermal conductance by 2030%. The successful heat dissipation and applicability to the continuous manufacturing process demonstrate excellent feasibility as future thermal management materials.
MOF‐Derived Fe7S8 Nanoparticles/N‐Doped Carbon Nanofibers as an Ultra‐Stable Anode for Sodium‐Ion Batteries
Authors: Wang Yu , Wen Zi , Wang Chang Chun , Yang Chun Cheng , Jiang Qing 
Published: August 2021

AbstractSodium‐ion batteries (SIBs) have aroused wide concern due to their potential applications in large‐scale energy‐storage systems. In this work, a hybrid of Fe7S8 nanoparticles/N‐doped carbon nanofibers (Fe7S8/N‐CNFs) is designed and synthesized via electrospinning. As an anode for SIBs, Fe7S8/N‐CNFs exhibit a high reversible capacity of 649.9 mAh g−1 at 0.2 A g−1 after 100 cycles, and superior cycling stability for 2000 cycles at 1 A g−1 with only 0.00302% capacity decay per cycle. Such excellent performance originates from: i) Fe7S8 nanoparticles (average diameter of 17 nm), which shorten the Na+ diffusion distance; ii) the unique 3D N‐CNFs, which enhance the conductivity, alleviate the self‐agglomeration and large volume change of Fe7S8 nanoparticles, and offer numerous active sites for Na+ adsorption and paths for electrolyte diffusion. The fascinating structure and superior electrochemical properties of Fe7S8/N‐CNFs shed light on developing high‐performance SIBs anode materials.
Comparison of intravenous regional anaesthesia with lidocaine and ropivacaine in dogs
Authors: Imani Rastabi Hadi , Mirzajani Roya , Givi Masoumeh Ezzati , Mohammadpoor Marzieh 
Published: August 2021

AbstractThe present study was designed to compare the effects of lidocaine and ropivacaine in intravenous regional anaesthesia (IVRA) in dogs. Twelve adult male dogs were used. Under isoflurane anaesthesia, exsanguination was performed in the target forelimb. Then, a blood pressure cuff was encircled around the limb proximal to the elbow joint with a pressure of approximately 150 mmHg above the mean arterial blood pressure. The animals then received one of the two treatments of lidocaine (3 mg/kg) or ropivacaine (1.5 mg/kg) with a final volume of 0.6 mL/kg into the cephalic vein. After 60 min, the anaesthesia was disrupted and the tourniquet was removed using intermittent opening (30 s) and closing (5 min) manner for three times. The results revealed that at 20 and 30 min after the initiation of IVRA, the dogs in ROP showed higher analgesia than LID. A leakage under the tourniquet during IVRA was detected. Tremor and hypersalivation were observed after tourniquet removal in some dogs. It was concluded that ropivacaine might provide a higher quality of anaesthesia than lidocaine in IVRA in dogs. The development of local anaesthetic toxicity is a major concern and should be considered at the time of tourniquet removal.
An integrative multiscale view of early cardiac looping
Authors: Ebrahimi Nazanin , Bradley Christopher , Hunter Peter 
Published: August 2021

AbstractThe heart is the first organ to form and function during the development of an embryo. Heart development consists of a series of events believed to be highly conserved in vertebrates. Development of heart begins with the formation of the cardiac fields followed by a linear heart tube formation. The straight heart tube then undergoes a ventral bending prior to further bending and helical torsion to form a looped heart. The looping phase is then followed by ballooning, septation, and valve formation giving rise to a four‐chambered heart in avians and mammals. The looping phase plays a central role in heart development. Successful looping is essential for proper alignment of the future cardiac chambers and tracts. As aberrant looping results in various congenital heart diseases, the mechanisms of cardiac looping have been studied for several decades by various disciplines. Many groups have studied anatomy, biology, genetics, and mechanical processes during heart looping, and have proposed multiple mechanisms. Computational modeling approaches have been utilized to examine the proposed mechanisms of the looping process. Still, the exact underlying mechanism(s) controlling the looping phase remain poorly understood. Although further experimental measurements are obviously still required, the need for more integrative computational modeling approaches is also apparent in order to make sense of the vast amount of experimental data and the complexity of multiscale developmental systems. Indeed, there needs to be an iterative interaction between experimentation and modeling in order to properly find the gap in the existing data and to validate proposed hypotheses.This article is categorized under:
Cardiovascular Diseases > Genetics/Genomics/Epigenetics
Cardiovascular Diseases > Computational Models
Cardiovascular Diseases > Molecular and Cellular Physiology
Big Data Technologies in DataBio
Authors: Södergård Caj , Mildorf Tomas , Berre Arne J. , Tsalgatidou Aphrodite , Charvát Karel 
Published: August 2021

AbstractIn this introductory chapter, we present the technological background needed for understanding the work in DataBio. We start with basic concepts of Big Data including the main characteristics volume, velocity and variety. Thereafter, we discuss data pipelines and the Big Data Value (BDV) Reference Model that is referred to repeatedly in the book. The layered reference model ranges from data acquisition from sensors up to visualization and user interaction. We then discuss the differences between open and closed data. These differences are important for farmers, foresters and fishermen to understand, when they are considering sharing their professional data. Data sharing is significantly easier, if the data management conforms to the FAIR principles. We end the chapter by describing our DataBio platform that is a software development platform. It is an environment in which a piece of software is developed and improved in an iterative process providing a toolset for services in agriculture, forestry and fishery. The DataBio assets are gathered on the DataBio Hub that links to content both on the DataBio website and to Docker software repositories on clouds.
Big Data Visualisation
Authors: Esbrí Miguel Ángel , Klien Eva , Charvát Karel , Zinke-Wehlmann Christian , Hitado Javier , Södergård Caj 
Published: August 2021

AbstractIn this chapter, we introduce the topic of big data visualization with a focus on the challenges related to geospatial data. We present several efficient techniques to address these challenges. We then provide examples from the DataBio project of visualisation solutions. These examples show that there are many technologies and software components available for big data visualisation, but they also point to limitations and the need for further research and development.
Genomic Prediction and Selection in Support of Sorghum Value Chains
Authors: Habyarimana Ephrem , Michailidou Sofia 
Published: August 2021

AbstractGenomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regression, genomic best linear unbiased predictions, Bayesian least absolute shrinkage and selection operator, and BayesB algorithms. The performance of the models was evaluated using Monte Carlo cross-validation with 70% and 30%, respectively, as training and validation sets. Our results show that genomic models perform comparably with traditional methods under single environments. Under multiple environments, predicting non-field evaluated lines benefits from borrowing information from lines that were evaluated in other environments. Accounting for environmental noise and other factors, also this model gave comparable accuracy with traditional methods, but higher compared to the single environment model. The GS accuracy was comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower for the dry mass fraction of the fresh weight. The genomic selection model performances obtained in our pilots are high enough to sustain sorghum breeding for several traits including antioxidants production and allow important genetic gains per unit of time and cost.
Monitoring of Bark Beetle Forest Damages
Authors: Lukeš Petr 
Published: August 2021

AbstractIn this chapter, we present a multi-source remote sensing approach for country-wise monitoring of bark beetle calamity to support government decision making processes. In the first part, we describe the forest health monitoring system, which is based on the analysis of satellite big data–Sentinel-2 observations collected every five days. We propose an automated processing chain for high-quality cloud-free image synthesis for user-defined acquisition periods. Such a processing chain is applied to yield yearly cloud-free images of the entire Czech Republic from 2015 onwards. Based on this data, we assess forest health trends using Sentinel-2 derived vegetation indices and in situ data of forest status. Finally, we demonstrate the benefits of multi-source remote sensing for timely and objective mapping of bark beetle spread by combining several data sources, including planet high-resolution satellite data, Sentinel-2 forest health maps and other maps of forest conditions. Detected bark beetle sanitary logging and dead standing wood polygons are used by the Ministry of Agriculture of Czech Republic in their decision processes regarding the management of affected forest areas.
The Potential of Big Data for Improving Pelagic Fisheries Sustainability
Authors: Reite Karl-Johan , Fernandes Jose A. , Uriondo Zigor , Quincoces Iñaki 
Published: August 2021

AbstractThe use of big data methods and tools are expected to have a profound effect on the pelagic fisheries sustainability and value creation. The potential impact on fuel consumption, planning and fish stock assessments is demonstrated in six different pilot cases. These cases cover the Spanish tropical tuna fisheries in Indian Ocean and the Norwegian small pelagic fisheries in the North Atlantic Ocean. The areas encompassed by these pilots have an annual capture production above 13 million tonnes.
Sensor Data
Authors: Rogotis Savvas , Fournier Fabiana , Charvát Karel , Kepka Michal 
Published: August 2021

AbstractThe chapter describes the key role that sensor data play in the DataBio project. It introduces the concept of sensing devices and their contribution in the evolution of the Internet of Things (IoT). The chapter outlines how IoT technologies have affected bioeconomy sectors over the years. The last part outlines key examples of sensing devices and IoT data that are exploited in the context of the DataBio project.
Remote Sensing
Authors: Esbrí Miguel Ángel 
Published: August 2021

AbstractIn this chapter we present the concepts of remote sensing and Earth Observation and, explain why several of their characteristics (volume, variety and velocity) make us consider Earth Observation as Big Data. Thereafter, we discuss the most commonly open data formats used to store and share the data. The main sources of Earth Observation data are also described, with particular focus on the constellation of Sentinel satellites, Copernicus Hub and its six thematic services, as well as other private initiatives like the five Copernicus-related Data and Information Access Services and Sentinel Hub. Next, we present an overview of representative software technologies for efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot.
Linked Data Usages in DataBio
Authors: Palma Raul , Brahma Soumya , Zinke-Wehlmann Christian , Kirschenbaum Amit , Charvát Karel , Charvat Karel , Reznik Tomas 
Published: August 2021

AbstractOne of the main goals of DataBio was the provision of solutions for big data management enabling, among others, the harmonisation and integration of a large variety of data generated and collected through various applications, services and devices. The DataBio approach to deliver such capabilities was based on the use of Linked Data as a federated layer to provide an integrated view over (initially) disconnected and heterogeneous datasets. The large amount of data sources, ranging from mostly static to highly dynamic, led to the design and implementation of Linked Data Pipelines. The goal of these pipelines is to automate as much as possible the process to transform and publish different input datasets as Linked Data. In this chapter, we describe these pipelines and how they were applied to support different uses cases in the project, including the tools and methods used to implement them.
Continuity of eigenvalues and shape optimisation for Laplace and Steklov problems
Authors: Girouard Alexandre , Karpukhin Mikhail , Lagacé Jean 
Published: August 2021

AbstractWe associate a sequence of variational eigenvalues to any Radon measure on a compact Riemannian manifold. For particular choices of measures, we recover the Laplace, Steklov and other classical eigenvalue problems. In the first part of the paper we study the properties of variational eigenvalues and establish a general continuity result, which shows for a sequence of measures converging in the dual of an appropriate Sobolev space, that the associated eigenvalues converge as well. The second part of the paper is devoted to various applications to shape optimization. The main theme is studying sharp isoperimetric inequalities for Steklov eigenvalues without any assumption on the number of connected components of the boundary. In particular, we solve the isoperimetric problem for each Steklov eigenvalue of planar domains: the best upper bound for thek-th perimeter-normalized Steklov eigenvalue is$$8\pi k$$8πk, which is the best upper bound for the$$k^{\text {th}}$$ktharea-normalised eigenvalue of the Laplacian on the sphere. The proof involves realizing a weighted Neumann problem as a limit of Steklov problems on perforated domains. For$$k=1$$k=1, the number of connected boundary components of a maximizing sequence must tend to infinity, and we provide a quantitative lower bound on the number of connected components. A surprising consequence of our analysis is that any maximizing sequence of planar domains with fixed perimeter must collapse to a point.
Lärmbelästigung in der deutschen Allgemeinbevölkerung
Authors: Hahad Omar , Beutel Manfred , Michal Matthias , Schulz Andreas , Pfeiffer Norbert , Gianicolo Emilio , Lackner Karl , Wild Philipp , Daiber Andreas , Münzel Thomas 
Published: August 2021

Zusammenfassung
Hintergrund
Lärmbelästigung, insbesondere durch Verkehrslärm, stellt ein massives Problem in der Bevölkerung dar und ist mit gesundheitlichen Einschränkungen assoziiert.

Ziel der Arbeit
Anhand von Daten der bevölkerungsrepräsentativen Gutenberg-Gesundheitsstudie (GHS) werden die Prävalenz der Lärmbelästigung durch verschiedene Quellen sowie relevante Determinanten bestimmt.

Material und Methoden
Die GHS ist eine populationsbasierte, prospektive Kohortenstudie in Deutschland, die Personen im Alter von 35 bis 74 Jahren einbezieht. 15.010 Probanden aus der Stadt Mainz und dem Landkreis Mainz-Bingen wurden von 2007 bis 2012 befragt, inwiefern sie sich in letzter Zeit durch Flug‑, Straßen‑, Schienen‑, Industrie- und Nachbarschaftslärm belästigt gefühlt haben (Angaben von „überhaupt nicht“ bis „äußerst“). Es wurde jeweils zwischen der Lärmbelästigung am Tag sowie während des Schlafens differenziert. Um die Beziehungen zwischen soziodemographischen Variablen, kardiovaskulären Risikofaktoren sowie Erkrankungen und Lärmbelästigung zu untersuchen, wurden multivariable logistische Regressionsmodelle verwendet.

Ergebnisse
Etwa 80 % der Probanden fühlten sich durch Lärm belästigt. Fluglärmbelästigung am Tag stellte die vorherrschende Lärmbelästigungsquelle mit der höchsten Prävalenz stark (9,6 %) und äußerst lärmbelästigter Probanden dar (5,4 %), gefolgt von Straßenverkehrs- (stark: 4,0 %; äußerst: 1,6 %) und Nachbarschaftslärmbelästigung (stark: 3,5 %; äußerst: 1,3 %). Die Lärmbelästigung nahm eher mit zunehmender Altersdekade ab. Relevante Determinanten der Lärmbelästigung umfassten mitunter Geschlecht, Alter, sozioökonomischen Status, Depression, Angststörung, Schlafstörung und Vorhofflimmern.

Diskussion
Lärmbelästigung betrifft einen Großteil der Bevölkerung und ist assoziiert mit soziodemographischen Variablen und kardiovaskulären Risikofaktoren sowie Erkrankungen.
The association between income inequality and adult mental health at the subnational level—a systematic review
Authors: Tibber Marc S. , Walji Fahreen , Kirkbride James B. , Huddy Vyv 
Published: August 2021

AbstractPurposeA systematic review was undertaken to determine whether research supports: (i) an association between income inequality and adult mental health when measured at the subnational level, and if so, (ii) in a way that supports the Income Inequality Hypothesis (i.e. betweenhigherinequality andpoorermental health) or the Mixed Neighbourhood Hypothesis (higherinequality andbettermental health).MethodsSystematic searches of PsycINFO, Medline and Web of Science databases were undertaken from database inception to September 2020. Included studies appeared in English-language, peer-reviewed journals and incorporated measure/s ofobjectiveincome inequality and adult mental illness. Papers were excluded if they focused onhighlyspecialised population samples. Study quality was assessed using a custom-developed tool and data synthesised using the vote-count method.ResultsForty-two studies met criteria for inclusion representing nearly eight million participants and more than 110,000 geographical units. Of these, 54.76% supported the Income Inequality Hypothesis and 11.9% supported the Mixed Neighbourhood Hypothesis. This held for highest quality studies and after controlling for absolute deprivation. The results were consistent across mental health conditions, size of geographical units, and held for low/middle and high income countries.ConclusionsA number of limitations in the literature were identified, including a lack of appropriate (multi-level) analyses and modelling of relevant confounders (deprivation) in many studies. Nonetheless, the findings suggest that area-level income inequality is associated with poorer mental health, and provides support for the introduction of social, economic and public health policies that ameliorate the deleterious effects of income inequality.Clinical registration numberPROSPERO 2020 CRD42020181507.
Sensor-integrated tap holder for process uncertainty detection based on tool vibration and axial length compensation sensors
Authors: Öztürk Tuğrul , Sarıkaya Erkut , Weigold Matthias 
Published: August 2021

AbstractThe tapping process is one of the most widespread manufacturing processes for internal threads, usually carried out at the end of the value chain. Any non-compliance with required quality standards or even the destruction of the thread due to process uncertainty in the tapping process is therefore subjected to high rework costs. Possible process uncertainties in the tapping process can be triggered by synchronization errors between feed rate and spindle speed, axis offset, faulty core holes and wear of the tapping tool. In order to detect process uncertainties during tapping and thus provide a basis for initiating countermeasures, a sensor-integrated tap holder was developed. This paper presents the realized concept of a rotating telemetry unit for signal processing, data acquisition and wireless data transmitting via WiFi standard on basis of low-cost embedded systems. Furthermore, two unique sensor concepts for measuring close-to-tool vibrations and the axial length compensation of the tapping tool are shown. Based on the sensor data in combination with feature engineering methods, process uncertainty during tapping are detected.