bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation
Authors: Wang Xiaolong , Dang Jianwu , Zhao Shuxu , Zhang Zhanping , Wang Yangping , Hao Zhanjun 
Published: October 2021

Abstract
The emergence of multi-access edge computing (MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale IoT services are rapidly growing, a single edge infrastructure provider (EIP) may not be sufficient to handle the data traffic generated by these services. The coalition method has been used in MEC for resource optimization, latency, energy consumption reduction, computation offloading, etc. However, the majority of research does not consider the price of computing resources corresponded to a container. Moreover, each SP does not choose EIP with the highest cost-performance to sign a medium/long-term computing resource purchase or lease contract. In this work, we consider a scenario with a collection of SPs with different budgets and several EIPs distributed in geographical locations. During the first phase, we get the market equilibrium price and select the optimal EIPs to make a deal by solving the Eisenberg-Gale convex program. In the second stage, using a mathematical model, we maximize EIP's profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that the effectiveness of our method is significantly better than the existing model.
Untangling competition between epitaxial strain and growth stress through examination of variations in local oxidation
Authors: Yankova Maria , Garner Alistair , Baxter Felicity , Armson Samual , Race Christopher , Preuss Michael , Frankel Philipp 
Published: October 2021

Abstract
Understanding oxide formation during corrosion of high-performance alloys in harsh environments is of great fundamental and industrial interest and provides a potential route for reducing the significant annual cost of corrosion globally. However, corrosion mechanisms involve multiple length scales, requiring a multitude of advanced experimental procedures. Here, we use correlated high resolution electron microscopy techniques over a range of length scales, combined with crystallographic modelling to show that there is a clear competition between epitaxial strain and growth stress during oxidation. The degree to which these competing mechanisms operate is shown to depend on the orientation of the substrate grains leading to significant local variations in oxide microstructure and thus protectiveness, even across a single sample. This leads to the possibility of tailoring substrate crystallographic textures in order to promote gradual phase transformation and the development of stress driven, well-oriented protective oxides, and so to improving overall corrosion performance.
Deep Ensemble Learning for Automatic Modulation Classification
Authors: Nie Jiali , Tan Wenke , Zhang Houmei 
Published: October 2021

Abstract
Automatic modulation classification (AMC) plays an increasingly vital role in cognitive radio (CR), cognitive electronic warfare, and other areas. It aims at classifying the modulated modes of the received signals accurately and provides a guarantee for the subsequent detailed parameter identification. Deep learning (DL) methods allow the computer to automatically learn the pattern features and integrate features into the process of building the model, thereby reducing the incompleteness caused by artificial design features. At the same time, the DL methods have been applied in the AMC field as its powerful ability to process complex data and have achieved excellent performance in recent years. In this paper, we propose a deep ensemble learning AMC network, which uses a multi-model ensemble method to fuse multiple DL features. Specifically, different DL models are integrated by ensemble learning, which enhances the learning ability of the single model. With the proposed ensemble model trained on a measured wireless signal dataset, we conclude that the ensemble structure of Inception and CLDNN can fuse spatial features and temporal features, and achieve state-of-the-art performance in AMC tasks. Besides, the impact of the inphase/quadrature (I/Q) sample-length on wireless signals is further investigated, and find that the classification accuracy of the deep ensemble model is improved by 0.7% to 10% compared to the single model under various sample-length. Simultaneously, we visualize convergence clustering with t-distributed stochastic neighbor embedding (t-SNE), and the visualization results prove that the deep ensemble model has a stronger clustering ability than a single model.
Comorbidities in Unclassifiable Interstitial Lung Disease
Authors: Prior Thomas Skovhus , Hyldgaard Charlotte , Torrisi Sebastiano Emanuele , Kronborg-White Sissel , Ganter Claudia , Bendstrup Elisabeth , Kreuter Michael 
Published: October 2021

Abstract
BackgroundComorbidities are common in interstitial lung diseases (ILD) and have an important association with survival, but the frequency and prognostic impact of comorbidities in unclassifiable interstitial lung disease (uILD) remains elusive. We aimed to describe the prevalence of comorbidities and assess the impact on survival in patients with uILD. Furthermore, we aimed to identify and characterize potential phenotypes based on clusters of comorbidities and examine their association with disease progression and survival.MethodsIncident patients diagnosed with uILD were identified at two ILD referral centers in Denmark and Germany from 2003 to 2018. The diagnosis uILD was based on multidisciplinary team meetings. Clinical characteristics and comorbidities were extracted from ILD registries and patient case files. Survival analyses were performed using Cox regression analyses, disease progression was analyzed by linear mixed effects models, and clusters of comorbidities were analyzed using self-organizing maps.ResultsA total of 249 patients with uILD were identified. The cohort was dominated by males (60%), former (49%) or current (15%) smokers, median age was 70 years, mean FVC was 75.9% predicted, and mean DLCO was 49.9% predicted. One-year survival was 89% and three-year survival was 73%. Eighty-five percent of the patients had ≥1 comorbidities, 33% had ≥3 comorbidities and 9% had ≥5 comorbidities. The only comorbidity associated with excess mortality was dyslipidemia. No association between survival and number of comorbidities or the Charlson comorbidity index was observed. Three clusters with different comorbidities profiles and clinical characteristics were identified. A significant annual decline in FVC and DLCO % predicted was observed in cluster 1 and 2, but not in cluster 3. No difference in mortality was observed between the clusters.ConclusionsThe comorbidity burden in uILD is lower than in other types of ILD and the impact of comorbidities on mortality is less clear. Three clusters with distinct comorbidity profiles were identified and could represent specific phenotypes. No difference in mortality was observed between clusters, but slower disease progression was observed in cluster 3. Better understanding of disease behavior and mortality will require further studies of subgroups of uILD with longer observation time.
Decreased Physical Activity With Subjective Pleasure Is Associated With Rumination and Avoidance Behaviors
Authors: Kagawa Fumi , Yokoyama Satoshi , Takamura Masahiro , Takagaki Koki , Mitsuyama Yuki , Shimizu Ayaka , Jinnin Ran , Ihara Hirotaka , Kurata Akiko , Okada Go , Okamoto Yasumasa 
Published: October 2021

Abstract
The main hypothesis for the relation between physical activity and mental health is that autonomous motivation, such as subjective pleasure for the activity, plays an important role. However, no report has described empirical research designed to examine the role of subjective pleasure in the relation between objectively measured physical activity and psychological indexes. We used accelerometers to collect data indicating participants' physical activity intensity during a week. Participants recorded their subjective pleasure of activity per hour. In 69% of them, the individual correlation coefficients between physical activity and pleasure in an hour (an index of Physical Activity-Pleasure; PA-PL) were positive (r=0.22, 95%Cl=[0.11 0.38]), indicating that pleasant sensations increased concomitantly with increasing physical activity. Conversely, 31% participants exhibited negative values of PA-PL, which means that the increase in physical activity had the opposite effect, decreasing pleasure. Multiple linear regression analysis showed that avoidance/rumination behaviors decreased significantly with increased PA-PL (β=-6.82, 95%CI: [-13.27 -0.38], p<.05). These results indicate that subjective pleasure attached to the PA is more important than the PA amount for reducing depressive behavior.
Overexpression of FTO Protects Human Granulosa Cells From Cisplatin-Induced Injury
Authors: Wang Rongli , Yang Xinyuan 
Published: October 2021

Abstract
Background: Premature ovarian failure (POF) is a serious problem for young women who received chemotherapy, and its pathophysiological basis is the dysfunction of granulosa cells. The RNA methylation on the sixth Natom of adenylate (m6A) plays an important role in epigenetic regulation, and previous studies have demonstrated that the fat mass- and obesity-associated (FTO) was decreased in POF and may be a biomarker for the occurrence of POF. Furthermore, to preserve the fertility of these young females, various approaches have been used to prevent chemotherapy-induced ovarian failure, and menstrual-derived stem cells (MenSCs) have been considered as a promising treatment strategy. Here, we aimed to explore the role of FTO in the MenSCs recovering the function of injured granulosa cells.Method: First, the cisplatin was used to make a granulosa cell injury model. Then, the menstrual-derived stem cells (MenSCs)-injured granulosa cells co-culture model and POF mouse model were established in this study to explore the role of FTO. Human ovarian granulosa cell lines (KGN) were used to explore the effect of FTO on cell proliferation and apoptosis. Furthermore, gain- and loss-of-function studies, small interfering RNA transfection, and meclofenamic acid (MA), a highly selective inhibitor of FTO, were also conducted to clarified the regulation mechanism of FTO in granulosa.Results: MenSCs co-culture could improve the function of injured granulosa cells by increasing the expression of FTO. MenSCs transplantation could restore the expression of FTO in the ovary of POF mice. Overexpression of FTO could restore the injured cells’ proliferation and decrease its apoptosis through regulating the expression of BNIP3. Down-regulation of FTO got the opposite results.Conclusion: FTO has a protective effect, which could improve the viability of granulosa cells after cisplatin treatment by decreasing the expression of BNIP3, and it may provide new insight into the therapeutic targets for the cisplatin-induced POF.
Mutations of PI3K-AKT-mTOR Pathway as Predictors for Immune Cell Infiltration and Immunotherapy Efficacy in dMMR/MSI-H Gastric Adenocarcinoma
Authors: wang zhenghang , Wang Xinyu , Xu Yu , Li Jian , Zhang Xiaotian , Peng Zhi , Hu Yajie , Zhao Xinya , Dong Kun , Zhang Bei , Gao Chan , Zhao Xiaochen , Chen Hui , Cai Jinping , Bai Yuezong , Sun Yu , Shen Lin 
Published: October 2021

Abstract
Background: A significant subset of mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) gastric adenocarcinomas (GAC) are resistant to immune checkpoint inhibitors (ICIs), yet the underlying mechanism remains largely unknown. We sought to investigate the genomic correlates of the density of tumor-infiltrating immune cells (DTICs) and primary resistance to ICI treatment.Methods: Four independent cohorts of MSI-H GAC were included: (i) the surgery cohort (n=175) with genomic and DTIC data, (ii) the 3DMed cohort (n=32) with genomic and PD-L1 data, (iii) the Cancer Genome Atlas (TCGA) cohort (n=73) with genomic, transcriptomic, and survival data and (iv) the ICI treatment cohort (n=36) with pre-treatment genomic profile and ICI efficacy data.Results: In the dMMR/MSI-H GAC, the number of mutated genes in the PI3K-AKT-mTOR pathway (NMP) was positively correlated with tumor mutational burden (P<0.001) and sensitivity to PI3K-AKT-mTOR inhibitors, and negatively correlated with CD3+ (P<0.001), CD4+ (P=0.065), CD8+ (P=0.004), and FOXP3+ cells (P=0.033) in the central-tumor rather than invasive-margin area, and the transcription of immune-related genes. Compared to the NMP-low (NMP=0/1) patients, the NMP-high (NMP≥2) patients exhibited a poorer objective response rate (29.4% vs. 85.7%, P<0.001), progression-free survival (HR=3.40, P=0.019), and overall survival (HR=3.59, P=0.048) upon ICI treatment.Conclusion: Higher NMP was identified as a potential predictor of lower DTICs and primary resistance to ICIs in the dMMR/MSI-H GAC. Our results highlight the possibility of using mutational data to estimate DTICs and administering the PI3K-AKT-mTOR inhibitor as an immunotherapeutic adjuvant in NMP-high subpopulation to overcome the resistance to ICIs.
Structural evolution and synthesis mechanism of ytterbium disilicate powders prepared by cocurrent chemical coprecipitation method
Authors: Wu Nannan , Wang Yalei , Liu Rutie , Liu Huaifei , Xiong Xiang 
Published: October 2021

Abstract
Ytterbium disilicate powders were synthesized by cocurrent chemical coprecipitation method. The influence of Si/Yb molar ratio and calcination temperature on compositions and structures of Yb2Si2O7 products were investigated. The formation mechanism and thermal behavior of precursor as well as the phase evolution of Yb2Si2O7 were also discussed in depth. Results show that pure β-Yb2Si2O7 powders with nanoscale size can be obtained from the precursor with Si/Yb molar ratio of 1.1 after being calcinated at temperatures above 1200 ℃. The Yb2Si2O7 precursor is an amorphous polymer cross-linked with -[Si-O-Yb]- chain segments which are formed though Yb atoms embedding in the -[Si-O-Si]- network. After a continuous dihydroxylation and structural ordering, the amorphous precursor transformed to α-Yb2Si2O7 crystals by atomic rearrangement. Elevated calcination temperature can induce to the coordination structures and environment evolutions of structural units and then converted to stable (Si2O7) groups and (YbO6) polyhedrons, which results in the formation of β-Yb2Si2O7.
Genetic Architecture of End-use Quality Traits in Soft White Winter Wheat
Authors: Aoun Meriem , Carter Arron H. , Morris Craig F. , Kiszonas Alecia M. 
Published: October 2021

Abstract
Background:Genetic improvement of end-use quality is an important objective in wheat breeding programs to meet the requirements of grain markets, millers, and bakers. However, end-use quality phenotyping is expensive and laborious thus, testing is often delayed until advanced generations. To better understand the underlying genetic architecture of end-use quality traits, we investigated the phenotypic and genotypic structure of 14 end-use quality traits in 672 advanced soft white winter wheat breeding lines and cultivars adapted to the Pacific Northwest region of the United States.Results:This collection of germplasm had continuous distributions for the 14 end-use quality traits with industrially significant differences for all traits. The breeding lines and cultivars were genotyped using genotyping-by-sequencing and 40,518 SNP markers were used for association mapping (GWAS). The GWAS identified 178 marker-trait associations (MTAs) distributed across all wheat chromosomes. A total of 40 MTAs were positioned within genomic regions of previously discovered end-use quality genes/QTL. Among the identified MTAs, 12 markers had large effects and thus could be considered in the larger scheme of selecting and fixing favorable alleles in breeding for end-use quality in soft white wheat germplasm. We also identified 15 loci (two of them with large effects) that can be used for simultaneous breeding of more than a single end-use quality trait. The results highlight the complex nature of the genetic architecture of end‑use quality, and the challenges of simultaneously selecting favorable genotypes for a large number of traits. This study also illustrates that some end-use quality traits were mainly controlled by a larger number of small-effect loci and may be more amenable to alternate selection strategies such as genomic selection.Conclusions:In conclusion, a breeder may be faced with the dilemma of balancing genotypic selection in early generation(s) versus costly phenotyping later on.
Selective Catalytic Conversion of Acetylene to Ethylene Powered by Water and Visible Light
Authors: Arcudi Francesca , Dordevik Luka , Schweitzer Neil , Stupp Samuel , Weiss Emily 
Published: October 2021

Abstract
The production of polymer-grade ethylene requires the purification of ethylene feed from acetylene contaminant. Accomplishing this task by state-of-the-art thermal hydrogenation requires high temperature, external feed of H2 gas, and noble metal catalysts, and is not only expensive and energy-intensive but also prone to overhydrogenate to ethane. We report a photocatalytic system to reduce acetylene to ethylene with >99% selectivity for ethylene under both non-competitive (no ethylene co-feed) and competitive (ethylene co-feed) conditions, and near 100% conversion under the latter industrially relevant condition. Our system uses a molecular catalyst based on earth-abundant cobalt operating under ambient conditions and sensitized by either [Ru(bpy)3]2+ or an inexpensive organic semiconductor (mpg-CN) under visible light. These features and the use of water as a proton source offer substantial advantages over current hydrogenation technologies with respect to selectivity and sustainability.
Prognostic Nomogram in Children and Adolescents with Central Nervous System Germ Cell Tumors
Authors: Jia Zetian , Yan Yaqi , Wang Jiuxin , Yang He , Liu Shuo , Han Chengxi , Hu Yuhua 
Published: October 2021

Abstract
Background: The prognostic risk factors for primary central nervous system (CNS) germ cell tumors (GCTs) in children and adolescents remain controversial. This study aimed to develop and validate a nomogram that predicts 5-and 10-year survival rates for CNS GCTs in children and adolescents.Methods: Pediatric with intracranial GCTs in the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2018 were analyzed. All GCTs children were randomly divided into the development group (70%) and the validation group (30%). The least absolute shrinkage and selection operator (LASSO) was used to screen the characteristic variables, and the 5- and 10-year survival probability nomogram was constructed. The accuracy of the nomogram was evaluated by consistency index (C-index), calibration plot, receiver operating characteristic curve (ROC), decision curve analysis (DCA) and survival curve.Results: We identified 819 cases of CNS GCTs in children. Three variables (histology, site and size) were selected by lasso regression to construct the nomogram. The C-index of the development group and verification group is 0.744 and 0.759. According to the time points of 5 and 10 years, the area under the curve of the development group is 0.753 and 0.696, and the verification set is 0.787 and 0.718. The results show that the prognostic nomogram developed by us has good accuracy.Conclusions: This is the first prognostic nomogram of CNS GCTs in children and adolescents. It can effectively predict the 5- and 10-year survival probability of children. It provides a useful prediction tool for clinicians.
ELECTRA-DTA: A New Compound-protein Binding Affinity Prediction Model based on the Contextualized Sequence Encoding
Authors: wang junjie , Wen NaiFeng , Wang Chunyu , Zhao Lingling , Cheng Liang 
Published: October 2021

Abstract
Motivation: Drug-target binding affinity (DTA) reflects the strength of the drug-target interaction; therefore, predicting the DTA can considerably benefit drug discovery by narrowing the search space and pruning drug-target (DT) pairs with low binding affinity scores. Representation learning using deep neural networks has achieved promising performance compared with traditional machine learning methods; hence, extensive research efforts have been made in learning the feature representation of proteins and compounds. However, such feature representation learning relies on a large-scale labelled dataset, which is not always available.Results: We present an end-to-end deep learning framework, ELECTRA-DTA, to predict the binding affinity of drug-target pairs. This framework incorporates an unsupervised learning mechanism to train two ELECTRA-based contextual embedding models, one for protein amino acids and the other for compound SMILES string encoding. In addition, ELECTRA-DTA leverages a squeeze-and-excitation (SE) convolutional neural network block stacked over three fully connected layers to further capture the sequential and spatial features of the protein sequence and SMILES for the DTA regression task. Experimental evaluations show that ELECTRA-DTA outperforms various state-of-the-art DTA prediction models, especially with the challenging, interaction-sparse BindingDB dataset. In target selection and drug repurposing for COVID-19, ELECTRA-DTA also offers competitive performance, suggesting its potential in speeding drug discovery and generalizability for other compound- or protein-related computational tasks.
MCM6 Versus Ki-67 in Diagnosis of Luminal Molecular Subtypes of Breast Cancers
Authors: Sadeghian Dorsay , Saffar Hana , Sharif Pouya Mahdavi , Soleimani Vahid , Jahanbin Behnaz 
Published: October 2021

Abstract
Background: Currently, breast cancers are divided into four major molecular subtypes. The distinction between the luminal A and luminal B subtypes is mainly based on the cellular proliferation indices and is assessed by the Ki-67 scoring. Due to the limitations in the assessment and expression of Ki-67, we hypothesized that minichromosome maintenance protein 6 (MCM6) can be taken as a surrogate marker to differentiate molecular subtypes and aid in more precise grading of tumors. Methods: We performed a retrospective, cross-sectional study on 124 samples of breast cancer and 40 samples of normal breast tissue. Relevant clinical information was retrieved from the relevant Cancer Institute database.Results: MCM6 could discriminate between different histologic grades. The luminal B subtype exhibited a higher MCM6 score in comparison to luminal A (P=0.01). There were significantly higher MCM6 scores in the hormone receptor (HR) negative, in comparison to luminal breast cancers (P<0.001). MCM6 score had a significant correlation with the mitotic count (P<0.001).Conclusion: MCM6 can reliably differentiate luminal A and luminal B subtypes and was correlated with the mitotic counts. More studies are needed to standardize its assessment methods, determine more robust cut-off values, and evaluate its associations with prognostic features of breast cancer.