bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer
Authors: Chen Yuhua , Zhou Hao , Wang Zhendong , Huang Zhanghao , Wang Jinjie , Zheng Miaosen , Ni Xuejun , Liu Lei 
Published: May 2021

Abstract
Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.
Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.
Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.
Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.
Maternal microvascular dysfunction during preeclamptic pregnancy
Authors: Stanhewicz Anna E. , Nuckols Virginia R. , Pierce Gary L. 
Published: May 2021

AbstractPreeclampsia is a hypertensive disorder of pregnancy effecting ∼5–8% of pregnancies in the United States, and ∼8 million pregnancies worldwide. Preeclampsia is clinically diagnosed after the 20th week of gestation and is characterized by new onset hypertension accompanied by proteinuria and/or thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, or cerebral or visual symptoms. This broad definition emphasizes the heterogeneity of the clinical presentation of preeclampsia, but also underscores the role of the microvascular beds, specifically the renal, cerebral, and hepatic circulations, in the pathophysiology of the disease. While the diagnostic criteria for preeclampsia relies on the development of de novo hypertension and accompanying clinical symptoms after 20-week gestation, it is likely that subclinical dysfunction of the maternal microvascular beds occurs in parallel and may even precede the development of overt cardiovascular symptoms in these women. However, little is known about the physiology of the non-reproductive maternal microvascular beds during preeclampsia, and the mechanism(s) mediating microvascular dysfunction during preeclamptic pregnancy are largely unexplored in humans despite their integral role in the pathophysiology of the disease. Therefore, the purpose of this review is to provide a summary of the existing literature on maternal microvascular dysfunction during preeclamptic pregnancy by reviewing the functional evidence in humans, highlighting potential mechanisms, and providing recommendations for future work in this area.
Murine endometrial-derived mesenchymal stem cells suppress experimental autoimmune encephalomyelitis depending on indoleamine-2,3-dioxygenase expression
Authors: Manganeli Polonio Carolina , Longo de Freitas Carla , Garcia de Oliveira Marília , Rossato Cristiano , Nogueira Brandão Wesley , Ghabdan Zanluqui Nágela , Gomes de Oliveira Lilian , Ayumi Nishiyama Mimura Luiza , Braga Barros Silva Maysa , Lúcia Garcia Calich Vera , Gil Nisenbaum Marcelo , Halpern Silvio , Evangelista Lucila , Maluf Mariangela , Perin Paulo , Eduardo Czeresnia Carlos , Schatzmann Peron Jean Pierre 
Published: May 2021

Abstract
Cellular therapy with mesenchymal stem cells (MSCs) is a huge challenge for scientists, as little translational relevance has been achieved. However, many studies using MSCs have proved their suppressive and regenerative capacity. Thus, there is still a need for a better understanding of MSCs biology and the establishment of newer protocols, or to test unexplored tissue sources. Here, we demonstrate that murine endometrial-derived MSCs (meMSCs) suppress Experimental Autoimmune Encephalomyelitis (EAE). MSC-treated animals had milder disease, with a significant reduction in Th1 and Th17 lymphocytes in the lymph nodes and in the central nervous system (CNS). This was associated with increased Il27 and Cyp1a1 expression, and presence of IL-10-secreting T CD4+ cells. At EAE peak, animals had reduced CNS infiltrating cells, histopathology and demyelination. qPCR analysis evidenced the down-regulation of several pro-inflammatory genes and up-regulation of indoleamine-2,3-dioxygenase (IDO). Consistently, co-culturing of WT and IDO−/− meMSCs with T CD4+ cells evidenced the necessity of IDO on the suppression of encephalitogenic lymphocytes, and IDO−/− meMSCs were not able to suppress EAE. In addition, WT meMSCs stimulated with IL-17A and IFN-γ increased IDO expression and secretion of kynurenines in vitro, indicating a negative feedback loop. Pathogenic cytokines were increased when CD4+ T cells from AhR−/− mice were co-cultured with WT meMSC. In summary, our research evidences the suppressive activity of the unexplored meMSCs population, and shows the mechanism depends on IDO-kynurenines-Aryl hydrocarbon receptor (AhR) axis. To our knowledge this is the first report evidencing that the therapeutic potential of meMSCs relying on IDO expression.
Image‐denoising algorithm based on improved K‐singular value decomposition and atom optimization
Authors: Chen Rui , Pu Dong , Tong Ying , Wu Minghu 
Published: May 2021

AbstractThe traditional K‐singular value decomposition (K‐SVD) algorithm has poor image‐denoising performance under strong noise. An image‐denoising algorithm is proposed based on improved K‐SVD and dictionary atom optimization. First, a correlation coefficient‐matching criterion is used to obtain a sparser representation of the image dictionary. The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary. Then, non‐local regularity is incorporated into the denoising model to further improve image‐denoising performance. Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.
A critical comparison on attitude estimation: From gaussian approximate filters to coordinate‐free dual optimal control
Authors: Koumpis N. P. , Panagiotou P. A. , Arvanitakis I. 
Published: May 2021

AbstractThis paper conveys attitude and rate estimation without rate sensors by performing a critical comparison, validated by extensive simulations. The two dominant approaches to facilitate attitude estimation are based on stochastic and set‐membership reasoning. The first one mostly utilizes the commonly known Gaussian‐approximate filters, namely the EKF and UKF. Although more conservative, the latter seems to be more promising as it considers the inherent geometric characteristics of the underline compact state space and accounts—from first principles—for large model errors. The set‐theoretic approach from a control point of view is addressed, and it is shown that it can overcome reported deficiencies of the Bayesian architectures related to this problem, leading to coordinate‐free optimal filters. Lastly, as an example, a modified predictive filter is derived on the tangent bundle of the special orthogonal group .
Research on the protection scheme of MMC‐MTDC based on high frequency energy
Authors: Xue Shimin , Chen Shuo , Sun Yabing , Gu Cheng , Liu Baibing 
Published: May 2021

AbstractThe development of flexible HVDC transmission technology requires the improvement of the theory of relay protections. The protection method based on the time domain has poor resistance to transition resistance and is easily affected by interference factors. The protection based on frequency domain analysis can better reflect the essential characteristics of the fault, and it is also an urgent problem for relay protections. The blocking effect of boundary elements on both sides of the line on high‐frequency components is an important entry point for current protection research, but high‐frequency protections are very sensitive to noise signals. Therefore, this paper analyses the frequency characteristics of the voltage at the measuring point from the perspective of the traveling wave transmission process, and uses the S transform to design a protection method based on high‐frequency energy to realize the identification of internal and external faults. Finally, the ability to withstand transition resistance and anti‐noise of the protection proposed here is verified by using PSCAD/EMTDC. The protection can complete fault identification within 2 ms, which means that it has both reliability and quick action.
Cloud detection of GF‐7 satellite laser footprint image
Authors: Yao Jiaqi , Tang Xinming , Li Guoyuan , Guo Jinquan , Chen Jiyi , Yang Xiongdan , Ai Bo 
Published: May 2021

AbstractIn November 2019, the GaoFen‐7(GF‐7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high‐precision long‐range three‐dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U‐Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF‐7 data processing and related research on footprint images.
Crowd activity recognition in live video streaming via 3D‐ResNet and region graph convolution network
Authors: Kang Junpeng , Zhang Jing , Li Wensheng , Zhuo Li 
Published: May 2021

AbstractSince the era of we‐media, live video industry has shown an explosive growth trend. For large‐scale live video streaming, especially those containing crowd events that may cause great social impact, how to identify and supervise the crowd activity in live video streaming effectively is of great value to push the healthy development of live video industry. The existing crowd activity recognition mainly uses visual information, rarely fully exploiting and utilizing the correlation or external knowledge between crowd content. Therefore, a crowd activity recognition method in live video streaming is proposed by 3D‐ResNet and regional graph convolution network (ReGCN). (1) After extracting deep spatiotemporal features from live video streaming with 3D‐ResNet, the region proposals are generated by region proposal network. (2) A weakly supervised ReGCN is constructed by making region proposals as graph nodes and their correlations as edges. (3) Crowd activity in live video streaming is recognised by combining the output of ReGCN, the deep spatiotemporal features and the crowd motion intensity as external knowledge. Four experiments are conducted on the public collective activity extended dataset and a real‐world dataset BJUT‐CAD. The competitive results demonstrate that our method can effectively recognise crowd activity in live video streaming.
Thigh muscle segmentation using a hybrid FRFCM‐based multi‐atlas method and morphology‐based interpolation algorithm
Authors: Molaie Malihe , Aghaeizadeh Zoroofi Reza 
Published: May 2021

AbstractThe volume of lower extremity muscles is affected by some diseases. Quantification of thigh muscles in medical images can lead to an easier investigation of these diseases. Most of the previous works in thigh muscle segmentation are based on models and atlases that require manually segmented datasets in 3D. As manual segmentation of these muscles is a time‐consuming task, in this work, only one initial slice is segmented by a new hybrid FRFCM‐based multi‐atlas method and other slices are segmented based on this slice. In the proposed method, after noise reduction, the muscle region is extracted from other tissues by the FRFCM method. Then, an initial slice of each dataset is segmented by a multi‐atlas method. The segmented muscles in the initial slice are used to segment muscles in the other slices of each dataset. The proposed method was evaluated with 20 CT datasets. The average DSC, Precision, and Sensitivity of the method for individual muscle segmentation were , , and , respectively. The quantitative and intuitive results of the proposed method show the better results of this method in comparison to other state‐of‐the‐art thigh muscle segmentation techniques.
Novel pediatric suspension of nanoparticulate zafirlukast for the treatment of asthma: Assessment and evaluation in animal model
Authors: Li Hongmei , Yao Yingying , Fu Huizhi 
Published: May 2021

AbstractThe aim of the present study was to formulate the novel pediatric suspension containing nanoparticulate Zafirlukast (ZFR) for the treatment of pediatric asthma. The ZFR loaded nanoparticles (NPs) were formulated by ionotropic external gelation method using tripolyphosphate (TPP) and Tween 80. NPs were characterized for drug loading, encapsulation efficiency, surface morphology, saturation solubility, particle size, zeta potential and polydispersity index (PDI). The optimized NP formulation was used for the development of suspension. The suspensions were characterized for pH, viscosity, sedimentation volume, dissolution and drug content. The encapsulation efficiency of NPs was found between 93.24% and 98.57% with drug loading in the range of 26.50–35.90%. All formulations were found of nanosized in nature (150–220 nm) with zeta potential (19.18–21.41 mV). PDI of all NP formulations was found less than 0.3. The NPs were spherical and smooth in nature. The saturation solubility of ZFR was enhanced nearly 16 times as compared to pure ZFR. The optimized suspension showed almost 100% ZFR release within the period of 30 min. The reduction in lung resistance (RI) was found nearly four fold as compared to the control group animals. The study supported the efficacy of suspension containing nanoparticulate ZFR in asthmatic animals.
Antenna position optimization method based on adaptive genetic algorithm with self‐supervised differential operator for distributed coherent aperture radar
Authors: Yang Xiaopeng , Li Yuqing , Liu Feifeng , Lan Tian , Teng Long , Sarkar Tapan K. 
Published: May 2021

AbstractThe performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self‐supervised differential operator. In the proposed method, the antenna positions are firstly coded as the chromosomes of the population with multiple constraints, and the reciprocal of the peak side lobe level (PSLL) of the beam pattern is calculated as the fitness function for optimization. Then, the adaptive probabilities are calculated for the crossover and mutation of chromosomes and a self‐supervised differential operator is utilized in the mutation. Finally, the optimal antenna positions for DCAR can be obtained with the lowest PSLL compared with the existing methods. The effectiveness of the proposed method is verified by linear and planar DCARs, respectively.
General design methodology of code multi‐correlator discriminator for GNSS multi‐path mitigation
Authors: Qi Yunhan , Yao Zheng , Lu Mingquan 
Published: May 2021

AbstractMulti‐path becomes a main source of positioning errors in new‐generation global navigation satellite systems (GNSS). Code multi‐correlator discriminators (MCD), represented by narrow early‐minus‐late (NEML) and double delta (DD) discriminators which are designed for binary phase offset keying (BPSK) signals, are one of the main multi‐path mitigation techniques. However, because of the complicated auto‐correlation function and intricate relationships among multiple correlators, it is hard to implement a trial and error approach, which is the conventional design method of discriminator structures, to specifically design code MCD structures for new GNSS signals which have complicated spreading waveforms. In this article, instead of a trial and error approach, a general design methodology for designing code MCD structures based on heuristic optimisation for multi‐path mitigation is proposed. The proposed method can specifically design code MCD structures for signals with various modulations and receivers with different bandwidths, indicating the adaptability of the proposed method. Multi‐path mitigation performances of such designed structures are better than those of traditional code discriminator structures with strong tracking robustness and slight degradation of thermal noise performance. Designed code MCD structures are also insensitive to multi‐path relative amplitudes and bandwidths of receivers, ensuring the practicality of the designed code MCD structures.
Coverage control of multiple robots in cluttered three‐dimensional environments
Authors: Kim Jonghoek 
Published: May 2021

AbstractDistributed network systems can be utilized to measure environmental conditions such as temperature, sound, pollution levels, humidity, wind, and so on. Multiple autonomously moving mobile sensors are used to construct distributed sensor networks in an unknown cluttered 3‐D workspace. The proposed strategy is to deploy mobile sensors (robots) sequentially and individually to extend the network until the network fully covers the entire open space. This strategy results in 3‐D sensor networks without sensing holes and does not require global localization of a robot except for a single robot called the Communication Navigation Aid. The authors believe this article is unique in constructing sensor networks in 3‐D workspaces cluttered with many obstacles. Moreover, this article presents an innovative recovery algorithm to handle sensor failures in the constructed network. Network connectivity is maintained while robots move in unknown 3‐D cluttered environments. Once a robot is deployed at its designated location, it becomes localized in global coordinate systems. Utilizing MATLAB, the effectiveness of the proposed deployment methods is demonstrated.
Analysis of hybrid polarization frequency domain spectrum characteristics of oil‐paper insulation transformers
Authors: Zou Yang , He Jin , He Qianlin , Wang Mengqi 
Published: May 2021

AbstractThis paper utilizes a hybrid Debye model to accurately simulate the relaxation response process of the oil‐paper insulation of a transformer to explore the application of frequency domain dielectric spectroscopy (FDS) in nondestructive diagnosis of the aging condition of oil‐paper insulation. Via FDS test data, the model parameters can be identified, and the influence laws of FDS can be obtained through the variation of model parameters. To explore the effects of insulating paper moisture content, insulating paper layers, and test temperature on FDS characteristics, an FDS experimental platform is established. The results show that the hybrid Debye model can effectively reflect the relaxation response process of oil‐paper insulation under different conditions, as well as that a physical relationship between the model parameters and oil‐paper dielectric polarization exists. In addition, the parameters of the hybrid Debye model can be used as the characteristic quantities for the non‐destructive evaluation of the oil‐paper insulation state, providing new ideas for the non‐destructive evaluation of the oil‐paper insulation state.
Comment on ‘Coincident ion acceleration and electron extraction for space propulsion using the self-bias formed on a set of RF biased grids bounding a plasma source’
Authors: Fu S H , Ding Z F 
Published: May 2021

Abstract
The neutralizer-free ion thruster based on radio frequency (RF) acceleration was proposed as an attractive approach. This comment presents problems arising from such RF acceleration. Two mayor limitations in developing the neutralizer-free RF ion thruster into a practical technology are found to be involved with the grounded grid: (a) the high frequency bias required by low ion impingement from the upstream; (b) serious backstreaming of electron due to the grounded grid that cannot establish a potential barrier. Limitation (a) will degrade the ion production efficiency and cause more serious thermal deformation of the biased grids, especially for large-sized RF ion thrusters. Limitation (b) is the Achilles’ heel of a neutralizer-free RF-biased ion thruster: it in fact operates from the very beginning in the failure mode occurring in the final stage of seriously-eroded DC (direct current) biased ion thruster. The backstreaming electrons will substantially reduce the lifetime of neutralizer-free RF ion thrusters. In the current stage, the RF acceleration is not a promising neutralizer-free technology.
Electronic structure of cubic boron arsenide probed by scanning tunneling spectroscopy
Authors: Lee Hwijong , Gamage Geethal Amila , Lyons John L , Tian Fei , Smith Brandon , Glaser Evan R , Ren Zhifeng , Shi Li 
Published: May 2021

Abstract
The unusually high lattice thermal conductivity of semiconducting cubic boron arsenide (BAs) has motivated studies of the bulk electronic band structure of BAs for its potential use as an active layer material in electronic devices. However, the surface electronic structure of BAs remains to be investigated. Scanning tunneling spectroscopy (STS) is employed here to probe the electronic structure of as-grown and in situ cleaved surfaces of BAs single crystals. The bandgap measured at several interior locations of the cleaved surface is about 2.1 eV, close to our calculated bulk bandgap value of 2.05 eV. In comparison, the measured bandgap within several micrometers from the two edges of the cleaved surface decreases to about 1.9 eV. This decrease is attributed to tunneling from an increased concentration of shallow acceptors. Several of the tunneling peaks observed by STS within the bandgap are close to the calculated energy levels for bulk lattice defects and substitutional impurities. In contrast to some other III–V compound semiconductors where surface relaxation prevents intrinsic surface states from appearing in the bulk bandgap, some measured tunneling peaks in the BAs bulk bandgap can contain contributions from intrinsic surface states calculated for boron dangling bonds.