bluuebunny/crossref_metadata_2025_split huggingface dataset:
Barrier effect and wound healing activity of the medical device REF-FTP78 in the treatment of gastroesophageal reflux disease
Authors: Pecora Tiziana M. G. , Parisi Ortensia Ilaria , Bertin Walter , Ragazzo Barbara , Dattilo Marco , Scigliano Norma , Malivindi Rocco , Amone Fabio , Puoci Francesco 
Published: April 2022

AbstractREF-FTP78 is a class IIb medical device present on the market with different trade names and developed for the treatment of gastroesophageal reflux disease (GERD). This medical device is based on polysaccharides from Aloe Barbadensis and fucoidans from brown seaweeds, such as Undaria pinnatifida and Fucus vesiculosus, and aims to exert a protective effect on the esophageal mucosa against the noxious components of refluxate. The present study reports on the efficacy of REF-FTP78 devoting a particular attention to the barrier effect and wound healing properties, combined with antioxidant and anti-inflammatory activities. Film-forming properties and barrier effect were investigated on in vitro reconstructed human esophageal epithelium, through TEER measurement and evaluation of caffeine and Lucifer yellow permeability, and in an ex vivo swine model of esophageal mucosa damage. Antioxidant and anti-inflammatory properties were evaluated in terms of scavenging activity towards DPPH, ABTS and NO radicals and a wound healing assay was carried out to study the influence of the product on cell migration. The obtained results highlighted a significant barrier effect, with a reduction in caffeine penetration equal to 65.3%, the ability to both repair and prevent the damage caused by an acid insult, confirmed by a good transepithelial resistance for the tissue treated with the tested item, and the capacity to promote wound healing. Furthermore, the tested product showed good antioxidant and anti-inflammatory properties in the performed radical scavenging assays. These findings support the use of REF-FTP78 in the treatment of GERD.
A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data
Authors: Wang Caihua , Li Yuanzhong , Tsuboshita Yukihiro , Sakurai Takuya , Goto Tsubasa , Yamaguchi Hiroyuki , Yamashita Yuichi , Sekiguchi Atsushi , Tachimori Hisateru ,   for the Alzheimer’s Disease Neuroimaging Initiative, Wang Caihua , Li Yuanzhong , Goto Tsubasa 
Published: April 2022

AbstractAlzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.
Machine learning for medical imaging: methodological failures and recommendations for the future
Authors: Varoquaux Gaël , Cheplygina Veronika 
Published: April 2022

AbstractResearch in computer analysis of medical images bears many promises to improve patients’ health. However, a number of systematic challenges are slowing down the progress of the field, from limitations of the data, such as biases, to research incentives, such as optimizing for publication. In this paper we review roadblocks to developing and assessing methods. Building our analysis on evidence from the literature and data challenges, we show that at every step, potential biases can creep in. On a positive note, we also discuss on-going efforts to counteract these problems. Finally we provide recommendations on how to further address these problems in the future.
Cooperation by necessity: condition- and density-dependent reproductive tactics of female house mice
Authors: Ferrari Manuela , Lindholm Anna K. , Ozgul Arpat , Oli Madan K. , König Barbara 
Published: April 2022

AbstractOptimal reproductive strategies evolve from the interplay between an individual’s intrinsic state and extrinsic environment, both factors that are rarely fixed over its lifetime. Conditional breeding tactics might be one evolutionary trajectory allowing individuals to maximize fitness. We apply multi-state capture-mark-recapture analysis to a detailed 8-year data set of free-ranging house mice in a growing population to discern causes and fitness consequences of two alternative reproductive tactics in females, communal and solitary breeding. This allows us to integrate natural variation in life-history traits when analysing the expression of two alternative reproductive tactics in females. We find that communal breeding reduces average population fitness, but nevertheless increases over our 8-year study period. The tactic proves to be expressed conditionally dependent on both population density and female body mass – allowing females to breed under subpar conditions, i.e. at high density or when of low body mass. Our results contradict previous laboratory studies and emphasize the importance of studying cooperation under natural conditions, including natural variation in state-dependent survival and breeding probabilities.
The intra-mitochondrial O-GlcNAcylation system rapidly modulates OXPHOS function and ROS release in the heart
Authors: Dontaine Justine , Bouali Asma , Daussin Frederic , Bultot Laurent , Vertommen Didier , Martin Manon , Rathagirishnan Raahulan , Cuillerier Alexanne , Horman Sandrine , Beauloye Christophe , Gatto Laurent , Lauzier Benjamin , Bertrand Luc , Burelle Yan 
Published: April 2022

AbstractProtein O-GlcNAcylation is increasingly recognized as an important cellular regulatory mechanism, in multiple organs including the heart. However, the mechanisms leading to O-GlcNAcylation in mitochondria and the consequences on their function remain poorly understood. In this study, we use an in vitro reconstitution assay to characterize the intra-mitochondrial O-GlcNAc system without potential cytoplasmic confounding effects. We compare the O-GlcNAcylome of isolated cardiac mitochondria with that of mitochondria acutely exposed to NButGT, a specific inhibitor of glycoside hydrolase. Amongst the 409 O-GlcNAcylated mitochondrial proteins identified, 191 display increased O-GlcNAcylation in response to NButGT. This is associated with enhanced Complex I (CI) activity, increased maximal respiration in presence of pyruvate-malate, and a striking reduction of mitochondrial ROS release, which could be related to O-GlcNAcylation of specific subunits of ETC complexes (CI, CIII) and TCA cycle enzymes. In conclusion, our work underlines the existence of a dynamic mitochondrial O-GlcNAcylation system capable of rapidly modifying mitochondrial function.
Case study: Biochemistry without borders: a case study utilising infographics
Authors: Sahai Michelle A. , Ivanova Anna 
Published: April 2022

Abstract
The present paper addresses a case study on the implementation of an online learning exercise utilising infographics in undergraduate Biochemistry and General Chemistry courses at the University of Roehampton (UoR) and Hostos Community College (HCC) of the City University of New York (CUNY). Students at UoR were asked to create infographics on topics related to the four major classes of biomolecules: carbohydrates, lipids, proteins and nucleic acids, and these infographics were shared with HCC students in an active learning exercise which incorporated peer evaluation and feedback. We highlight the various teaching and learning strategies, as well as the challenges related to the implementation of digital tools, in the educational process during the COVID-19 pandemic to maintain student engagement and active learning. Student feedback revealed positive learning gains on biochemistry concepts related to the four biomolecules. The exercise was viewed favourably by students, with learners indicating the acquisition of digital skills to effectively represent and visualise their understanding of biochemical concepts and explain these processes to peers.
Number of targets detection method with FCM‐based granulation–degranulation mechanism
Authors: Sha Minghui , Xu Kaijie , Mao Erke , Xing Mengdao 
Published: April 2022

AbstractIn this letter, the authors elaborate on a novel method to detect the number of targets in low SNR and small snapshots through using the Fuzzy C‐Means clustering based granulation‐degranulation mechanism. In the developed scheme, the eigenvalues of the array output correlation matrix are regarded as a time series, and granulated into a pair of ordered information granules. The information about the number of targets is included in the information granules. Subsequently, the degranulation mechanism is used to modify the cost function of the granulation mechanism to improve the quality of the information granules, which can increase the detection accuracy of the number of targets. Finally, the number of targets is determined by the prototype matrix and partition matrix generated by the enhanced version of the granulation. Explicit analysis and derivation of the proposed scheme are presented.
Attack and defence methods in cyber‐physical power system
Authors: Yang Ting , Liu Yuzhe , Li Wei 
Published: April 2022

AbstractWith the development of digitalisation and intelligence, the power system has been upgraded from the traditional single energy transmission and conversion system to a complex cyber‐physical power system (CPPS) with tightly coupled energy and information flows. The cyber‐physical power system achieves the controllability and observability of the power system through ubiquitous sensing technology, advanced measurement technology, and powerful information processing technology. However, the large number of intelligent electronic device accesses and frequent information interactions in CPPS make it more vulnerable and susceptible to be attacked than any previous single structured system. Viruses and intrusions can attack the CPPS through the cyber subsystem, which in turn can deal a fatal blow to the energy supply physical system. Because of the above problems, malicious attacks against CPPS have been occurring in recent years and have generated a great deal of scholarly attention. This paper precisely focusses on this problem, by profiling the structure of CPPS and the potential threats, conducting an in‐depth analysis of CPPS attack modes from the cyber and physical subsystems, and summarising the three‐level security defence methods for CPPS in detail. Finally, the future technological development prospects of CPPS security research are explicitly addressed, which will provide technical support for building reliable, safe, and robust energy systems.
A deep grouping fusion neural network for multimedia content understanding
Authors: Song Lingyun , Yu Mengzhen , Shang Xuequn , Lu Yu , Liu Jun , Zhang Ying , Li Zhanhuai 
Published: April 2022

AbstractHow Deep Neural Networks (DNNs) best cope with the understanding of multimedia contents still remains an open problem, mainly due to two factors. First, conventional DNNs cannot effectively learn the representations of the images with sparse visual information. For example, the images describing knowledge concepts in textbooks. Second, existing DNNs cannot effectively capture the fine‐grained interactions between the images and text descriptions. To address these issues, we propose a deep Cross‐Media Grouping Fusion Network (CMGFN), which mainly has two distinctive properties: 1) CMGFN can effectively learn visual features from the images with sparse visual information. This is achieved by first progressively adjusting the attention of convolution filters to valuable visual regions, and then enhancing the use of key visual information in feature construction. 2) By a cross‐media grouping co‐attention mechanism, CMGFN can effectively use the interactions between visual features of different semantics and textual descriptions, to learn cross‐media features representing different fine‐grained semantics in different groups. Empirical studies demonstrate that CMGFN not only achieves state‐of‐the‐art performance on the multimedia documents containing sparse visual information, but also shows superior general applicability on other multimedia data, e.g., the multimedia fake news.
Quantifying the performance and optimizing the placement of roadside sensors for cooperative vehicle‐infrastructure systems
Authors: Du Yuchuan , Wang Fengqi , Zhao Cong , Zhu Yifan , Ji Yuxiong 
Published: April 2022

AbstractA cooperative vehicle‐infrastructure system (CVIS) can provide perception information beyond the visual range for autonomous vehicles via roadside directional sensors, such as cameras, millimeter‐wave radar, and lidar. The performance of roadside perception strongly depends on sensor placement, where physical occlusion between vehicles is inevitable. This paper proposes an occlusion degree model (ODM) to describe the dynamic occlusion between sensors, obstacles, and targets in a three‐dimensional space. The ODM is then integrated with microscopic traffic simulation to study the impacts of traffic density, vehicle model composition, and sensor configurations on vehicle detection and tracking performances. Based on the simulation, a multifactor regression model with a logistic growth curve is established to quantify the performance of roadside sensors with different factors, where the evaluation metrics are rigorously designed. Finally, an optimization model of roadside sensor placement with non‐linear constraints is formulated to reduce the construction cost under a coverage constraint and improve the perception accuracy within budget limitations. A case study of a highway scenario shows that the performance of the proposed approach improves by 5.63%, 6.55%, and 8.01%, respectively, under the worst possible condition with accuracy requirements of 0.97, 0.96, and 0.95 compared with the conventional placement scheme. The study provides a map of the performance of roadside perception with different influencing factors and guides the optimal roadside sensor placement for a CVIS.
Review of the designs in low inductance SiC half‐bridge packaging
Authors: Ma Haohao , Yang Yuan , Wu Lei , Wen Yang , Li Qiang 
Published: April 2022

AbstractSilicon carbide (SiC) device has become the primary choice for high‐efficiency power electronic equipment due to its excellent performance. However, its higher switching frequency and faster switching speed have also incurred new challenges, such as low parasitic inductance packaging. Despite a significant amount of literature to overview the low parasitic inductance packages, most of them focus on total parasitic inductance from different packaging technology. Here, from the viewpoint of partial inductance which includes gate loop, power loop and common source, the designs for different inductances are surveyed. The power loop is further discussed from power terminals, bonding wires and direct bonded copper (DBC) conductor traces. The effects and sources of these inductances are analyzed. Furthermore, the typical design solutions to reduce the inductances are summarized. Finally, the challenges of SiC packages are presented.
Low elevation and range joint estimation method with metre wave frequency diverse array‐multiple input multiple output radar based on sparse array
Authors: Wang Hongzhen , Zheng Guimei , Liu Yibin , Song Yuwei 
Published: April 2022

AbstractFrequency diverse array‐multiple input multiple output (FDA‐MIMO) radar not only has the advantage of waveform diversity but also can realise azimuth‐range correlation map, so it can realise high precision azimuth‐range joint estimation and has the ability to distinguish different range unit targets in the same azimuth. However, the existing research mainly adopts uniform linear array (ULA) model and mainly focusses on incoherent targets. At present, the elevation‐range joint estimation algorithm of metre wave FDA‐MIMO radar based on ULA has the problems of sharp decline in angle‐range measurement accuracy of ultra‐low altitude targets and large fluctuation of elevation‐range measurement accuracy with changing elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, this paper innovatively combines sparse array with FDA‐MIMO radar, establishes the low elevation‐range joint estimation model of metre wave FDA‐MIMO radar based on sparse array, and puts forward the low elevation‐range joint estimation method suitable for this model, and it fills the gap of low elevation‐range measurement method based on sparse array metre wave FDA‐MIMO radar. Simulation experiments compare two typical sparse arrays with ULA of equal array elements, which verify the superiority of low elevation‐range measurement performance of monostatic metre wave FDA‐MIMO radar based on sparse array and the effectiveness of the proposed method.
A spectroscopic multiplicity survey of Galactic Wolf-Rayet stars
Authors: Dsilva K. , Shenar T. , Sana H. , Marchant P. 
Published: April 2022

Context. Most massive stars reside in multiple systems that will interact over the course of their lifetime. This has important consequences on their future evolution and their end-of-life products. Classical Wolf-Rayet (WR) stars represent the final end stages of stellar evolution at the upper-mass end. While their observed multiplicity fraction is reported to be ∼0.4 in the Galaxy, their intrinsic multiplicity properties and the distributions of their orbital parameters remain insufficiently constrained to provide a reliable anchor to compare to evolutionary predictions.
Aims. As part of a homogeneous, magnitude-limited (V ≤ 12) spectroscopic survey of northern Galactic WR stars, this paper aims to establish the observed and intrinsic multiplicity properties of the early-type nitrogen-rich WR population (WNE), including estimates of the multiplicity fraction and the shape of their orbital period distribution. Additionally, we compare these with the properties of the carbon-rich WR population (WC) stars obtained in the first paper of this series.
Methods. We obtained high-resolution spectroscopic time series of the complete magnitude-limited sample of 16 WNE stars observable with the 1.2 m Mercator telescope at La Palma, typically providing a time base of about two to eight years. We measured relative radial velocities (RVs) using cross-correlation and used RV variations to flag binary candidates. Using an updated Monte Carlo method with a Bayesian framework, we calculated the three-dimensional likelihood for the intrinsic binary fraction (fintWNE), the maximum period (log Pmax), and the power-law index for the period distribution (π) for the WNE population with Pmin fixed at 1 d. We also used this updated method to re-derive multiplicity parameters for the Galactic WC population.
Results. Adopting a peak-to-peak RV variability threshold of 50 km s−1 as a criterion, we classify 7 of the 16 targets as binaries. This results in an observed multiplicity fraction (fobsWNE) of 0.44 ± 0.12. Assuming flat priors, we derive the best-fit multiplicity properties fintWNE = 0.56−0.15+0.20, log Pmax = 4.60−0.77+0.40, and π = −0.30−0.53+0.55 for the parent WNE population. We explored different mass-ratio distributions and note that they did not change our results significantly. For the Galactic WC population from Paper I, we re-derive fintWC = 0.96−0.22+0.04, log Pmin = 0.75−0.60+0.26, log Pmax = 4.00−0.34+0.42, and π = 1.90−1.25+1.26.
Conclusions. The derived multiplicity parameters for the WNE population are quite similar to those derived for main-sequence O binaries but differ from those of the WC population. The significant shift in the WC period distribution towards longer periods is too large to be explained via expansion of the orbit due to stellar winds, and we discuss possible implications of our results. Analysis of the WNL population and further investigation of various evolutionary scenarios is required to connect the different evolutionary phases of stars at the upper-mass end.
Neue Therapien akuter und chronischer Beckenvenenobstruktionen
Authors: Lichtenberg Michael 
Published: April 2022

ZusammenfassungDie Inzidenz der tiefen Venenthrombose (TVT) beträgt in Westeuropa 1/1000 Einwohner pro Jahr. Je nach Lokalisation und Ausdehnung der TVT entwickeln 20–83% der Patienten ein
postthrombotisches Syndrom trotz einer optimalen Antikoagulation und Kompressionstherapie. Endovaskuläre Thrombektomieverfahren mit Wiedereröffnung der iliofemoralen venösen Strombahn haben
das Ziel, mittel- und langfristig ein postthrombotisches Syndrom zu vermeiden. Bei Patienten mit chronischen V.-cava- und Beckenvenenverschlüssen stehen heutzutage spezielle
Rekanalisationstechniken mit Einsatz von speziellen venösen Stents zur Verfügung.
Endovaskuläre Behandlung der Unterschenkel-PAVK bei kritischer
Ischämie und diabetischem Fußsyndrom
Authors: Schmidt Andrej , Scheinert Dierk 
Published: April 2022

ZusammenfassungDie kritische Ischämie der unteren Extremität aufgrund einer PAVK, bei der häufig
eine Mitbeteiligung der Unterschenkelarterien besteht, ist ohne
Revaskularisation mit einer schlechten Prognose vergesellschaftet. Für Patienten
mit diabetischem Fußsyndrom trifft dies in besonderem Maße zu. Auch finden sich
bei diesen Patienten nicht selten PAVK-Läsionen ausschließlich infrapopliteal.
Die endovaskuläre Rekanalisation wird in diesem Bereich zunehmend als Therapie
der ersten Wahl betrachtet. Der retrograde Zugang, spezielle Ballontechniken
sowie Atherektomiesysteme haben die technischen Erfolgsraten verbessern können.
Drug-eluting Stents und neue Drug-coated Ballons führen zu einer Verbesserung
der Offenheitsrate. Bei schwerer distaler PAVK ist allerdings eine
Revaskularisation häufig nicht möglich. Bei diesen sogenannten
„no-option“-Patienten könnte eventuell die Arterialisierung des tiefen
Venensystems in der Zukunft eine Rolle spielen.
Bedürfnisse onkologisch erkrankter Kinder: Psychometrische Überprüfung des Erhebungsinstruments „KiWuF-PädOnk“ und erste Ergebnisse
Authors: Böcherer Sarah Magdalena , Burger Thorsten , Schepper Florian , Farin-Glattacker Erik 
Published: April 2022

ZusammenfassungIn der vorliegenden Querschnittstudie wurde ein neu konzipierter Bedürfnisfragebogen für 8- bis 13-jährige Krebspatienten psychometrisch überprüft (N=117). Auf der Grundlage einer explorativen Faktorenanalyse wurden 4 Skalen mit insgesamt 19 Items gebildet. Die interne Konsistenz des Fragebogens ist zufriedenstellend. Die Teilnehmenden äußerten ein starkes Bedürfnis nach sozialer Teilhabe, Sicherheit und Kontrollerleben sowie nach Normalität. Der KiWuF-PädOnk liefert Informationen zu Bedürfnissen aus Patientensicht und fördert somit eine individuumszentrierte psychosoziale Versorgung.