bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer
Authors: Wang Yuzhi , Zou Yu , Zhang Yi , Li Chengwen 
Published: December 2020

Abstract
The immune system and the tumor interact closely during tumor development. Aberrantly expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. The present study aimed to build a risk scoring system to improve the prognosis of GC patients. In the present study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. About 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. About 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, ‘clusterProfiler’. The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.
Label-free, mass-sensitive single-molecule imaging using interferometric scattering microscopy
Authors: Hundt Nikolas 
Published: December 2020

Abstract
Single-molecule imaging has mostly been restricted to the use of fluorescence labelling as a contrast mechanism due to its superior ability to visualise molecules of interest on top of an overwhelming background of other molecules. Recently, interferometric scattering (iSCAT) microscopy has demonstrated the detection and imaging of single biomolecules based on light scattering without the need for fluorescent labels. Significant improvements in measurement sensitivity combined with a dependence of scattering signal on object size have led to the development of mass photometry, a technique that measures the mass of individual molecules and thereby determines mass distributions of biomolecule samples in solution. The experimental simplicity of mass photometry makes it a powerful tool to analyse biomolecular equilibria quantitatively with low sample consumption within minutes. When used for label-free imaging of reconstituted or cellular systems, the strict size-dependence of the iSCAT signal enables quantitative measurements of processes at size scales reaching from single-molecule observations during complex assembly up to mesoscopic dynamics of cellular components and extracellular protrusions. In this review, I would like to introduce the principles of this emerging imaging technology and discuss examples that show how mass-sensitive iSCAT can be used as a strong complement to other routine techniques in biochemistry.
NB‐IoT optimisation: Holistic view for smart cities applications with smart meters networks case study
Authors: Abbas Ahmed M. , Youssef Khaled Y. , Zekry Abdelhalim , Mahmoud Imbaby I. 
Published: December 2020

AbstractThe future demands for smart‐cities applications impose maximising the resources utilisation. This study achieves two main objectives to comply with fifth‐generation networks goals. First one is to enhance the Narrowband‐Internet of Things (NB‐IoT) spectral‐efficiency. The second is to alleviate the burden of two issues: The signalling during each transmission request for smart‐meters (SM) and queuing burden. First, we model the uplink scheduler for the NB‐IoT access network using state‐machine modelling methodology on Simulink environment. The simulation result is verified and validated with trusted modelling technique. Second, we optimise the NB‐IoT uplink scheduler which exploits the periodicity nature of SMs applications with putting envisage considering the emergency conditions. This is done by proposing integrated scheduling protocol which classifies the IoT traffic types and rearranges the transmission times of different SMs utilities and draws a map for the transmission schedule of them to better utilise the sparse time resources. The proposal comprises architecture, signalling, and algorithms. The optimisation is evaluated in term of numbers of SMs versus four spectral performance indicators; utilisation, efficiency, setup‐success‐percentage, and session‐drop‐rate according to a case study. The simulation results prove the ability of our proposal to increase the spectrum‐utilisation to 17.47%. This enhancement reflects on spectral‐efficiency that improved doubled.
Predictor control for non‐linear systems actuated via counter‐convecting transport PDEs
Authors: Cai Xiushan , Lin Cong , Zhan Xisheng , Liu Leibo 
Published: December 2020

AbstractThe authors investigate predictor control for the cascaded system of a non‐linear ordinary differential equation with counter‐convecting transport partial differential equations with propagation velocities dependent on partial differential equations states in this paper. Stability analysis of the closed‐loop system is shown by using the infinite‐dimensional backstepping transformations and an appropriate Lyapunov functional. A predictor control is developed such that the closed‐loop system is asymptotically stable. An example is given to illustrate the validity of the proposed method.
Improved noise‐to‐state stability criteria of random nonlinear systems with stochastic impulses
Authors: Feng Likang , Park Ju H. , Zhang Weihai 
Published: December 2020

AbstractThis paper considers noise‐to‐state stability for random non‐linear systems with stochastic impulses. The impulsive random non‐linear systems contain three random characteristics: the second‐moment processes in continuous dynamics, the sequence of random variables in discrete dynamics, and the number of stochastic impulses obeyed a renewal process. Firstly, the improved criteria of noise‐to‐state stability are established for random non‐linear systems subject to unstable stochastic impulses based on the uniformly asymptotically stable function. Secondly, improved Lyapunov approaches of NSS with unstable stochastic continuous dynamics are accomplished by the uniformly exponentially stable function. Finally, two numerical examples are used to illustrate the feasibility of the proposed methods.
Separation principle for discrete‐time quasi‐one‐sided Lipschitz nonlinear systems
Authors: Dong Wenqiang , Hu Guang‐Da , Cong Yuhao 
Published: December 2020

AbstractThis paper is concerned with output stabilisation for a class of discrete‐time quasi‐one‐sided Lipschitz nonlinear systems. Firstly, an observer is designed for estimating the state of the systems in terms of quasi‐one‐sided Lipschitz condition and quadratic inner‐boundness condition. Then, a state feedback controller is proposed to stabilize the systems. Subsequently, it is shown that the separation principle holds for stabilisation of the discrete‐time nonlinear systems based on the observer‐based controller. Under the quasi‐one‐sided Lipschitz condition, the state observer and feedback controller can be designed separately, even though the parameter of discrete‐time systems is not stabilisable and parameter is not detectable. Finally, two numerical examples are given to indicate the feasibility of the main results.
Probabilistic–proactive distribution network scheduling against a hurricane as a high impact–low probability event considering chaos theory
Authors: Nasri Amirhossein , Abdollahi Amir , Rashidinejad Masoud 
Published: December 2020

AbstractHigh impact–low probability (HILP) incidents, such as hurricanes, usually and gravely damage electric distribution networks. A resilient distribution network must have an ability to recover itself with a fast restoration methodology against the effects of HILP events. Due to improve the electric distribution network resiliency against HILP incidents, this paper suggests a probabilistic–proactive distribution network operation model based upon the chaos theory (P‐PDNOMC(. Here, a resilience index based upon operational cost and load shedding cost is employed to decrease the effects of a hurricane as an HILP incident. P‐PDNOMC is formulated as a framework that consists of a novel hurricane modelling and operation scheduling in normal and emergency conditions with uncertainties considering. It should be noted that a prediction approach based on the chaos theory and the least‐squares support vector machine (LS‐SVM) is also designed to consider the provisional and spatial behaviour of the hurricane. Furthermore, this paper proposes a novel optimization framework for damaged lines determination based upon the P‐PDNOM C (DLsP‐PDNOM C) by considering multi‐zone and multi‐period damaged equipment budget constraints. Here, the load shedding cost is applied to present the efficiency of the proposed model. The numerical results indicate the resiliency enhancement of the electric distribution network in the face of HILP incidents.
A pricing strategy based on potential game and bargaining theory in smart grid
Authors: Yang Jie , Dai Yachao , Ma Kai , Liu Hongru , Liu Zhixin 
Published: December 2020

AbstractIn this paper, a two‐stage pricing framework is proposed for the electricity market which is consisted of a generation company (GC), multiple electric utility companies (EUC) and consumers. In the electricity wholesale market, the EUCs will choose an agent to negotiate the wholesale price with GC. An appropriate wholesale price plays an important role in the stable operation of the electricity wholesale market. However, it is challenging to find the optimal wholesale price. Therefore, the Raiffa‐Kalai‐Smorodinsky bargaining solution (RBS) is applied to realize the pricing equilibrium which is . In the electricity retail market, this study designs a retail pricing strategy based on the potential game, which can optimize both social welfare and the benefit of the EUCs. Moreover, the impact of demand disturbance on the benefit of the EUCs and GC is studied in the electricity retail market. Then an iterative pricing algorithm is proposed for the two‐stage pricing model. The simulation results reveal that the demand disturbance has little effect on the benefit of the EUCs and GC, indicating the reliable/promising robustness of the algorithm.
Power transformer fault diagnosis considering data imbalance and data set fusion
Authors: Zhang Yang , Chen Hong Cai , Du Yaping , Chen Min , Liang Jie , Li Jianhong , Fan Xiqing , Yao Xin 
Published: December 2020

AbstractImproving the accuracy of transformer dissolved gas analysis is always an important demand for power companies. However, the requirement for large numbers of fault samples becomes an obstacle to this demand. This article creatively uses a large number of health data, which is much easier to obtain by power companies, to improve diagnosis accuracy. Comprehensive investigations from the view of both data set and methodology to deal with this problem are presented. A data set consists of 9595 health samples and 993 fault samples is used for analysis. The characteristics of the data set and the influence of the health data on diagnostic accuracy are discussed. The performance of many state‐of‐art algorithms that handle the imbalanced problem is evaluated. Meanwhile, an efficient fault diagnosis algorithm named self‐paced ensemble (SPE) is presented. In SPE, classification hardness is proposed to include the data characteristic in the classification. This method can guarantee the diversity of the data set and keep high performance. According to the experiment results, the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.
Micro‐mechanism study on synergistic degradation of the oil‐paper insulation with dibenzyl disulfide, hexadecyl mercaptan and benzothiophene
Authors: Cong Haoxi , Pan Hao , Hu Xuefeng , Zhang Minhao , Li Qingmin 
Published: December 2020

AbstractIn recent years, there have been many cases of transformer failures caused by corrosive sulfides. At present, research mainly focuses on the single sulfide dibenzyl disulphide, but few research on multiple sulphur or the oil‐paper hybrid insulation. In this study, three typical sulfides dibenzyl disulfide, hexadecyl mercaptan and benzothiophene were selected to form seven sulfide‐oil‐paper models. Then relaxation calculations were carried out, and through molecular dynamics simulation, the synergistic effect of different sulfides on the properties of insulating paper and insulating oil was discussed. Results show that the coexistence of the three sulfides has the most severe weakening on the mechanical properties of cellulose, and it also causes great damage to hydrogen bonds. Hexadecyl mercaptan has a weaker effect on hydrogen bond destruction, but it will greatly aggravate the cellulose chain movement. The viscosity of insulating oil is generally increased by the influence of sulfide. Hexadecyl mercaptan is the main factor affecting the viscosity. Thiophene has little effect on the viscosity, ctive protection technology on sulphur corrosion.
Classification of breast mass in two‐view mammograms via deep learning
Authors: Li Hua , Niu Jing , Li Dengao , Zhang Chen 
Published: December 2020

AbstractBreast cancer is the second deadliest cancer among women. Mammography is an important method for physicians to diagnose breast cancer. The main purpose of this study is to use deep learning to automatically classify breast masses in mammograms into benign and malignant. This study proposes a two‐view mammograms classification model consisting of convolutional neural network (CNN) and recurrent neural network (RNN), which is used to classify benign and malignant breast masses. The model is composed of two branch networks, and two modified ResNet are used to extract breast‐mass features of mammograms from craniocaudal (CC) view and mediolateral oblique (MLO) view, respectively. In order to effectively utilise the spatial relationship of the two‐view mammograms, gate recurrent unit (GRU) structures of RNN is used to fuse the features of the breast mass from the two‐view. The digital database for screening mammography (DDSM) be used for training and testing our model. The experimental results show that the classification accuracy, recall and area under curve (AUC) of our method reach 0.947, 0.941 and 0.968, respectively. Compared with previous studies, our method has significantly improved the performance of benign and malignant classification.
Nierenversagen im Rahmen von autoimmunen Systemerkrankungen
Authors: Weinmann-Menke Julia 
Published: December 2020

ZUSAMMENFASSUNGNierenfunktionsstörungen bei Patienten mit rheumatischen Erkrankungen sind keine Seltenheit und häufig Prognose-bestimmend. Einige dieser rheumatologischen Erkrankungen werden unter anderem durch ihre Nierenbeteiligung und hier insbesondere durch das Auftreten eines Nierenversagens diagnostiziert. Dies zeigt schon, dass die Nierenbeteiligung bei rheumatologischen Erkrankungen sehr variabel in der Intensität ausfallen kann und von einer leichten Nierenfunktionsstörung (z. B. bei der rheumatoiden Arthritis) bis zum Nierenversagen (z. B. bei den Kleingefäßvaskulitiden, Lupusnephritis, Goodpasture-Syndrom oder der renalen Krise bei der systemischen Sklerose) reichen kann. Wichtig ist es hier, die Differenzierung der Ursache, z. B. chronische Schäden wie Hypertonie/Diabetes versus Manifestation der rheumatologischen Grunderkrankung oder medikamentös toxischen Nebenwirkungen vorzunehmen. Hierbei nimmt die rasche und gezielte Diagnostik einen hohen Stellenwert ein, welche von der gezielten Anamnese, Urindiagnostik (Urinsediment, Proteinurie), serologischen Diagnostik, Sonografie bis hin zur Nierenbiopsie reicht. Das frühe Erkennen sowie die rasche Einleitung einer dann entsprechenden immunsuppressiven Therapie kann bei entzündlichen rheumatologischen Systemerkrankungen die renale Prognose verbessern und damit auch die Mortalität der Patienten maßgeblich beeinflussen.
Screening auf problematischen Alkoholkonsum – Erhebung zur Umsetzung der S3-Leitlinienempfehlungen in der transdisziplinären Versorgung einer Modellregion
Authors: Frischknecht Ulrich , Hoffmann Sabine , Steinhauser Alisa , Lindemann Christina , Buchholz Angela , Manthey Jakob , Schulte Bernd , Rehm Jürgen , Kraus Ludwig , Verthein Uwe , Reimer Jens , Kiefer Falk 
Published: December 2020

Zusammenfassung
Ziel Erfassung der Häufigkeit, mit der zentral in die Gesundheitsversorgung der Zielgruppe eingebundene Berufsgruppen auf problematischen Alkoholkonsum screenen. Die S3-Leitlinie „Screening, Diagnose und Behandlung alkoholbezogener Störungen“ empfiehlt „allen Patient/innen in allen Settings“ ein fragebogenbasiertes Screening anzubieten.
Methodik Querschnittsstudie zur Umsetzungshäufigkeit von Screenings auf problematischen Alkoholkonsum durch Fachärzt/innen (Allgemein- und Innere Medizin, Gynäkologie, Psychiatrie und Psychotherapie, Pädiatrie, Kinder- und Jugend- Psychiatrie), Psychotherapeut/innen, Sozialarbeiter/innen und Hebammen/Geburtshelfer in der Modellregion Bremen. Mittels logistischer Regression wurden behandler/innenseitige Einflussfaktoren auf die Umsetzung von Screenings exploriert.
Ergebnisse Mit Rücklaufquoten von etwa 20% berichteten die verschiedenen Berufsgruppen, durchschnittlich bei 6,9% aller behandelten Patient/innen innerhalb der zurückliegenden 4 Wochen ein fragebogenbasiertes Alkoholscreening durchgeführt zu haben. Meist wurden eigene Formulierungen anstatt empfohlener Instrumente (AUDIT, AUDIT-C) genutzt. Höhere Screeningraten wurden für Patient/innen mit neu diagnostiziertem Bluthochdruck (21,2%), alkoholassoziierten Folgeerkrankungen (43,3%) und psychischen Störungen (39,3%) berichtet. Die Kenntnis der S3-Leitlinie war mit einer häufigeren Umsetzung von Screenings verbunden (OR=4,67; 95% KI 1,94–11,25, p<0,001).
Schlussfolgerungen Das von der Leitlinie empfohlene flächendeckende, fragebogenbasierte Screening auf problematischen Alkoholkonsum ist von einer praktischen Umsetzung weit entfernt. Maßnahmen zu einer breiteren Bekanntmachung der S3-Leitlinie erscheinen sinnvoll, um eine häufigere Anwendung von Alkoholscreenings im Gesundheitswesen zu fördern.
Neurologische Notfälle bei rheumatologischen Erkrankungen
Authors: Wolf Joachim 
Published: December 2020

ZUSAMMENFASSUNGDas zentrale und periphere Nervensystem ist ein häufig geschädigtes Organ bei rheumatologischen Erkrankungen. Treten die neurologischen Symptome notfallmäßig auf, sind schwerwiegende und lebensbedrohliche Folgen für den Patienten meist nur durch eine rasche Diagnostik und Therapie abwendbar, weshalb ein enger Austausch zwischen Rheumatologen und Neurologen notwendig ist. Patienten mit einem systemischen Lupus erythematodes, einer rheumatoiden Arthritis oder einer systemischen Vaskulitis sind besonders gefährdet, akute Schädigungen des Nervensystems zu erleiden. Der folgende Artikel stellt relevante neurologische Notfallsyndrome bei rheumatologischen Erkrankungen dar und gibt Ihnen eine Übersicht über diagnostische und therapeutische Optionen.
Differential Uptake of Telehealth for Prenatal Care in a Large New York City Academic Obstetrical Practice during the COVID-19 Pandemic
Authors: Limaye Meghana A. , Lantigua-Martinez Meralis , Trostle Megan E. , Penfield Christina A. , Conroy Erin M. , Roman Ashley S. , Mehta-Lee Shilpi S. 
Published: December 2020

During the coronavirus disease 2019 (COVID-19) pandemic in New York City, telehealth was rapidly implemented for obstetric patients. Though telehealth for prenatal care is safe and effective, significant concerns exist regarding equity in access among low-income populations. We performed a retrospective cohort study evaluating utilization of telehealth for prenatal care in a large academic practice in New York City, comparing women with public and private insurance. We found that patients with public insurance were less likely to have at least one telehealth visit than women with private insurance (60.9 vs. 87.3%, p < 0.001). After stratifying by borough, this difference remained significant in Brooklyn, one of the boroughs hardest hit by the pandemic. As COVID-19 continues to spread around the country, obstetric providers must work to ensure that all patients, particularly those with public insurance, have equal access to telehealth.
Key Points
Consistently biased: documented consistency in self-reported holiday healthfulness behaviors and associated social desirability bias
Authors: Bir Courtney , Widmar Nicole Olynk 
Published: December 2020

AbstractHoliday healthfulness conversations are dominated by overindulgence of consumption and then, largely in reference to resolutions to do better, physical activity, and exercise aspirations. Consistency was found in self-reported agreement with a series of holiday healthfulness statements, across time, holidays (Thanksgiving versus Christmas), and samples of respondents. The largest proportion of respondents displaying social desirability bias (SDB) were found in response to two statements, namely “I will consume more alcohol during the holiday season than at other times of the year” at (63–66%) and “I make it a New Year’s Resolution to lose weight” (60–63%). Cheap talk was tested as a mechanism to reduce SDB in holiday healthfulness reporting, but showed only limited efficacy compared to the control group surveyed simultaneously. Nonetheless, the consistency across time in reporting and SDB are notable in both self-reporting of health-related data and in studying a unique consumption period around the holidays. Healthcare providers and researchers alike seek to improve the accuracy of self-reported data, making understanding of biases in reporting on sensitive topics, such as weight gain and eating over the holiday season, of particular interest.
Heritage and interculturality in EU science diplomacy
Authors: Andersen Casper , Clopot Cristina , Ifversen Jan 
Published: December 2020

AbstractIn recent years, culture and heritage have explicitly entered into science diplomacy debates and initiatives within the EU system and in EU’s foreign policy. For EU’s external relations heritage offers opportunities for developing partnerships based on shared, entangled histories but also challenges posed by dealing with difficult pasts of domination and colonialism. The paper, therefore, presents a new conceptual model for European science diplomacy that can enable more equitable ways of dealing with colonial heritage in relations between EU countries and partners outside Europe. It does so by combining recent literature on science diplomacy, heritage diplomacy, decolonial thinking, and on the concepts of interculturality. We argue that to engage successfully with colonial legacies and heritage, the concept of science diplomacy needs to be developed from a traditional “diffusionist” understanding towards a dialogical approach, which is epismologically open and acknowledges the inequalities in global knowledge production. In the second part of the paper, the practical implications of the theoretical framework are fleshed out in a discussion of three cases involving colonial heritage: The Tendaguru Fossil Collection, the Royal Museum for Central Africa in Brussels, and the work of Canadian indigenous artist Sonny Assu.