bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aggregate aggrephagy regulator patterns guide intercellular communication of tumor microenvironment that contribute to gastric cancer progression and immunotherapy
Authors: Wei Chen , Zhang Qi , Ma Yichao , Liu Zhu , Ji Hao , Wang Fei , Liao Yiqun , Tang Dong 
Published: September 2023

Abstract
Background
Aggregate autophagy plays a key role in cancer progression; however, how aggregate autophagy plays a role in tumor microenvironment (TME) remains to be elucidated.
Methods
30571 single cells from the single-cell RNA-seq data of 13 gastric cancer (GC) specimens were analyzed by nonnegative matrix factorization (NMF) to find out the effects of 44 aggregate autophagy-related genes on major TME cells. The prognosis and immune response of TME clusters were determined using GC and immunotherapy cohorts from the public information base.
Results
Single-cell sequencing revealed that aggregate autophagy may be closely related to the clinical and biological characteristics of GC and the pseudo-time trajectories of major TME cell types. At the same time, NMF algorithm combined with Bulk-seq analysis to cluster cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), T cells and B cells, revealing that clusters regulated by different aggregates of autophagy-related genes have significant prognostic value in metabolism, prognosis and immune response in patients with gastric cancer. Finally, CellChat analysis showed that the aggregate autophagy-related cell subtypes of TME cells showed diversified and extensive interaction with tumor epithelial cells, and ligand-receptor pairs such as SPP1-CD44,MIF-CD74-CD44,MIF-CD74-CXCR4 mediated the communication between aggregate autophagy-related subtypes of TME cells and tumor epithelial cells.
Conclusion
Our study reveals for the first time that the tumor microenvironment mediated by aggregate autophagy regulates the intercellular communication in the process of GC development and anti-tumor immunoregulation.
MHD simulation of an externally driven magnetic reconnection.
Authors: Sarkhosh Masom , Hosseinpour Mahboub , Mohammadi Mohammad Ali 
Published: September 2023

Abstract

Using two-dimensional magnetohydrodynamic (MHD) simulations, we investigate the onset and growth of an instability associated with forced magnetic reconnection phenomenon in the well-known equilibrium structure of the Harris current sheet in the presence of a plasma resistivity. To derive externally the magnetic reconnection process, we perturb the plasma velocity close to the boundaries in the form of two localized pulses. The results show that these pulses propagate towards the current sheet, where the magnetic field changes its direction, generate perturbed magnetic field consequently and trigger the magnetic reconnection phenomenon in an X-point in the center of the current sheet. We realized that increasing the amplitude of pulses results in a faster reconnection and symmetric pulses are more efficient in deriving the reconnection process. Furthermore, by imposing a transient (time-dependent) MHD wave normal to the current sheet, we found that an MHD wave with a larger period (lower frequency) affects considerably on the topology of current sheet and excites a faster reconnection. A similar conclusion was also obtained for an MHD wave with a larger wavelength (lower wavenumber). The obtained results are of interest for understanding the interaction of an MHD wave with an equilibrium current sheet in the contexts of confined fusion plasmas and also the solar corona plasmas.
Classifier Comparison for Stroke Prediction Ensembling SMOTE+ENN using Machine Learning Approach
Authors: K Poorani , M Karuppasamy , M Jansi Rani , M Prabha 
Published: September 2023

Abstract
Stroke is one of the deadliest diseases found in the world which is the second major reason for mortality rate. Early detection of stroke can reduce the mortality due to stroke. Inorder to diagnose it earlier several machine learning techniques are being utilized. This proves that machine learning can also be used for disease prediction for various diseases. Supervised machine learning algorithms has been used for stroke prediction. Important feature responsible for stroke prediction has been done. Inorder to balance the dataset hybrid sampling technique of SMOTE + ENN has been performed so that the results are much appreciable. After sampling, machine learning models has been used for stroke prediction using Logistic Regression (LR), KNearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Support Vector Classifier (SVC) and Random Forest (RF). With all the models Random Forest achieved higher performance with accuracy of 99%, recall of 100%, precision of 98% and f-score of 99%. The proposed work also extends the use of different classifiers like Voting, Bagging and Stacking methods. Among all the proposed classifiers stacking provides higher performance with all metrics. The stacking classifer achieved accuracy of 100%, recall of 100%, precision of 99% and f-score of 100%. From the results, it is known that Random forest algorithm perform better with data sampling of SMOTE + ENN than other models.
Movie-RNET: Best of Best Movie Recommendation via Deep Learning Networks from Multi-Format Data
Authors: Aramuthakannan S , Ahilan A , Jayapriya P , Rajakumar S 
Published: September 2023

Abstract

Recommendation systems are desperately needed because of the large volume of information generated by the ever-growing use of social networks and the Internet. A recommendation system is essential since exploring the large collection can be time-consuming and difficult. In this research, a novel deep learning-based MovieRNet for best of best movie recommendation system using multi-format data has been proposed. Initially, the multiformat data such as reviews, emoji and trailer are gathered and pre-processed the reviews, emojis using normalization. The trailer videos are converted into frames and pre-processed using contrast stretching adaptive Gaussian Star filter for eliminating the noise artifacts. Pre-processed text is used as an input for BiGRU, which uses reviews to categorise films as offensive, non-offensive, or offensive but non-offensive. Pre-processed images are used as an input for Yolo v7, which uses features extracted from the movie trailer to categorise films as violent and non-violent. Pre-processed text is used as an input for BiGRU, which uses reviews to categorise films as offensive, non-offensive, or offensive but non-offensive. Pre-processed images are used as an input for Yolo v7, which uses features extracted from the movie trailer to categorise films as violent and non-violent. Jelly fish optimization algorithm is used for decision making by analyzing the outputs of the two neural networks to get the optimal prediction rate for time-to-time by updating the user profile with past references of the users. Recall, accuracy, specificity, precision, and F-measure were some of the criteria used to evaluate the proposed technique. The accuracy of the proposed method is improved by 9.6%, 7.8%, 3.5%, and 2.15% better than the existing LSTM-CNN, SRDNet, VRConvMF and SDLM methods respectively.
Streptomyces chenzhouensis sp. nov., an endophytic actinobacterium from the root tissue of Cathaya argyrophylla
Authors: Mo Ping , Wu Can , Han Ruonan , Chen Zhongyuan , Luo Yushuang , Xie Peng , Li Li , Wang Yun 
Published: September 2023

Abstract
An endophytic actinobacterium, designated strain HUAS 5T, was isolated from the root tissue of Cathaya argyrophylla collected in Chenzhou city of Hunan Province, PR China. This strain produced grey aerial mycelium that differentiated into spiral spore chains with smooth-surfaced round or oval spores on Gause's synthetic No. 1 medium. Strain HUAS 5T grew well on Gause's synthetic No. 1, Reasoner'2 and ISP serial media. This strain grew at 15–40°C (optimum, 28°C), pH 6.0–9.0 (optimum, pH 7) and in presence of 0–5.0% (w/v) NaCl. The predominant cellular fatty acids of strain HUAS 5T (> 5.0%) were iso-C16:0, iso-C14:0, anteiso-C15:0, iso-C15:0, C16:0, iso H-C16:1 and Sum in Feature 3 (C16:1 ω7C/C16:1 ω6C). Sequence analysis of the 16S rRNA gene indicated that this strain belonged to the genus Streptomyces and exhibited highest sequence similarity to Streptomyces hirsutus NRRL B-2713T (97.3%), which is much less than 98.7% cut-off point of species definitions for bacteria and archaea. Phylogenetic analysis of 16S rRNA gene sequence indicated that strain HUAS 5T formed an independent lineage, which suggested that it belonged to a potential novel species. Based on the morphological, cultural, physio-biochemical properties and chemotaxonomy, strain HUAS 5T (= MCCC 1K08552T = JCM 36055T) is deemed to represent a novel Streptomyces species, for which we put forward the name Streptomyces chenzhouensis sp. nov.
Quantifying Fat Zonation in Liver Lobules: An IntegratedMultiscale In-silico Model Combining DisturbedMicroperfusion and Fat Metabolism via aContinuum-Biomechanical Bi-scale, Tri-phasic Approach
Authors: Lambers Lena , Waschinsky Navina , Schleicher Jana , König Matthias , Tautenhahn Hans-Michael , Albadry Mohamed , Dahmen Uta , Ricken Tim 
Published: September 2023

Abstract
Metabolic zonation refers to the spatial separation of metabolic functions along thesinusoidal axes of the liver. This phenomenon forms the foundation for adjusting hepaticmetabolism to physiological requirements in health and disease (e.g. metabolicdysfunction-associated steatotic liver disease/ MASLD). Zonated metabolic functions areinfluenced by zonal morphological abnormalities in the liver, such as periportal fibrosisand pericentral steatosis. We aim to analyze the interplay between microperfusion,oxygen gradient, fat metabolism and resulting zonated fat accumulation in a liver lobule.Therefore we developed a continuum-biomechanical, tri-phasic, bi-scale, and multicomponent insilicomodel, which allows to numerically simulate coupled perfusion-function-growth interactionstwo-dimensionally in liver lobules. The developed homogenized model has the following specifications:i) thermodynamically consistent, ii) tri-phase model (tissue, fat, blood), iii) penta-substances(glycogen, glucose, lactate, FFA, oxygen), and iv) bi-scale approach (lobule, cell). Our presentedin-silico model accounts for the mutual coupling between spatial and time-dependent liver perfusion,metabolic pathways and fat accumulation. The model thus allows the prediction of fatdevelopment in the liver lobule, depending on perfusion, oxygen and plasma concentration offree fatty acids (FFA), oxidative processes, the synthesis and the secretion of triglycerides (TGs).The use of a bi-scale approach allows in addition to focus on scale bridging processes. Thus,we will investigate how changes at the cellular scale affect perfusion at the lobular scale andvice versa. This allows to predict the zonation of fat distribution (periportal or pericentral)depending on initial conditions, as well as external and internal boundary value conditions.
When capital region divide comes along with country democratization: trends in French spatial inequality over the past 50 years.
Authors: Bonnet Florian 
Published: September 2023

Abstract
The 2019 widespread protests have brought back the issue of territorial inequalities in France. Following these debates, this paper analyses the evolution of income inequalities between French departments over the past 50 years. It highlights the dramatic rise in inequality within the capital region, which have tripled since the end of the 1960s and have increased ninefold since the early 1980s. This evolution contrasts with the sharp decline in regional inequality observed in the whole country (60%). Using an original dataset providing regional income distributions, this paper shows that this increase within the capital region is largely due to the polarisation of the top income households. In 2019, the income of a Top 1% household in the city of Paris equals 113 years of income of a Bottom 50% household in the suburban department of Seine-Saint-Denis. These evolutions raise questions about the national social cohesion and the likelihood of social conflicts in France.
JEL: R11 – R12.
Identification of TNFRSF21 as an inhibitory factor of osteosarcoma based on a necroptosis-related prognostic gene signature and molecular experiments
Authors: Li Xiang , Sun Zhenqian , Ma Jinlong , Yang Miaomiao , Cao Hongxin , Jiao Guangjun 
Published: September 2023

Abstract
Background
Osteosarcoma is one of the most common malignant bone tumors with bad prognosis. Necroptosis is a form of programmed cell death. Recent studies showed that targeting necroptosis was a new promising approach for tumor therapy. This study aimed to establish a necroptosis-related gene signature to evaluated prognosis and explore the relationship between necroptosis and osteosarcoma.
Methods
Data from The Cancer Genome Atlas was used for developing the signature and the derived necroptosis score (NS). Data from Gene Expression Omnibus served as validation. Principal component analysis (PCA), Cox regression, receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis were used to assess the performance of signature. The association between the NS and osteosarcoma was analyzed via gene set enrichment analysis, gene set variation analysis and Pearson test. Single-cell data was used for further exploration. Among the genes that constituted the signature, the role of TNFRSF21 in osteosarcoma was unclear. Molecular experiments were used to explore TNFRSF21 function.
Results
Our data revealed that lower NS indicated more active necroptosis in osteosarcoma. Patients with lower NS had a better prognosis. PCA and ROC curves demonstrated NS was effective to predict prognostic. NS was negatively associated with immune infiltration levels and tumor microenvironment scores and positively associated with tumor purity and stemness index. Single-cell data showed necroptosis heterogeneity in osteosarcoma. The cell communication pattern of malignant cells with high NS was positively correlation with tumor progression. The expression of TNFRSF21 was down-regulated in osteosarcoma cell lines. Overexpression of TNFRSF21 inhibited proliferation and motility of osteosarcoma cells. Mechanically, TNFRSF21 upregulated the phosphorylation levels of RIPK1, RIPK3 and MLKL to promote necroptosis in osteosarcoma.
Conclusions
The necroptosis prognostic signature and NS established in this study could be used as an independent prognostic factor. And TNFRSF21 may be a necroptosis target in osteosarcoma therapy.
Collective Behavior based Slime Mold Optimization with application to UAV Energy conversion device analysis
Authors: Verma Monika , Sreej Mini , Singh Madhusudan , NA NA , NA NA 
Published: September 2023

Abstract
As an extensively used traction component of unmanned aerial vehicles (UAV), outer-rotor-permanent- magnet-synchronous-motor with inner-periphery-inset- magnet structure (OR-PMSM-IPIM) has eminent advantages with respect to its electromagnetic performance. In order to achieve light weight, high efficiency, efficient speed-torque and low turbulence in UAV, a novel method for optimizing design parameters of OR-PMSM-IPIM is proposed in this paper. A modified slime mold inspired optimization (SMO) algorithm is designed by introducing its collective behavior and swarm intelligence in the environment. In order to avoid collapsing for local optimal spots and enhance diversity in the population, the modified framework of SMO is proposed in this work by introducing complex group interaction patterns in the traditional SMO algorithm. In order to verify the effectiveness and convergence speed of proposed methodology, few comparative experiments are administered. The application of proposed method is tested by developing design-parameter-optimization (DPO) problem for OR-PMSM-IPIM. The analysis results show that the performance of optimized model of OR-PMSM-IPIM has been improved through proposed SMO design optimization.
IoT Device Identification Based on Network Traffic
Authors: Gu Dinglin , Zhang Jian , Tang Zhangguo , Li Qizhen , Zhu Min , Yan Hao , Li Huanzhou 
Published: September 2023

Abstract
With the rapid development of Internet of Things (IoT) technology, the widespread deployment and heterogeneity of IoT devices bring new challenges to device identification. For network administrators, understanding the IoT devices connected to the network is very important. However, there are still some issues with existing IoT device identification methods, including the dependence on a large number of features, vulnerable features (such as IP addresses), and payloads. In response to these issues, an IoT device identification method based on network traffic is proposed in this paper. The method utilizes network traffic data of IoT devices over a short period of time, extracts protocol statistical features and flow-level statistical features, and thus achieves the identification of IoT device categories and device types. The proposed method was evaluated on two publicly available datasets, and the results demonstrate that the proposed method has superiority and can accurately and effectively identify devices.
Reuse of sun mushroom post-harvest areas as an alternative to maize cropping
Authors: Alves Lucas da Silva , Iossi Matheus Rodrigo , Caitano Cinthia Elen Cardoso , Júnior Wagner Gonçalves Vieira , Heinrichs Reges , Pardo-Giménez Arturo , Zied Diego Cunha 
Published: September 2023

Abstract
The reuse of sun mushroom post-harvest areas (SMS) presents an innovative alternative for smallholders seeking to move away from conventional maize cropping systems and reduce their reliance on synthetic fertilizers. This study evaluated the growth of two successive maize crops in post-harvest areas while assessing drivers of yields and soil/leaf macronutrient composition. Four management practices were carried out: SMS, without the application of synthetic fertilization; SMS + S, with the application of synthetic fertilization recommended at sowing; SMS + S + TD, with the application of synthetic fertilization at sowing and topdressing; and Control, a common area with the application of synthetic fertilization. Agronomic aspects and macronutrients soil and leaf contents were evaluated in two successive maize crops in post-harvest areas. Different management practices adopted in the sun mushroom post-harvest areas did not significantly differ between yields in the first crop. SMS provided an increase in P, Ca, and Mg in the soil for the first crop and an increase in P and Ca for the second crop, indicating mineralization of the SMS. Nevertheless, the macronutrient content was reduced in the successive crops without sowing and topdressing fertilization (SMS + S + TD), indicating a dependence on synthetic fertilizers in intensified systems. Despite a nutritional limitation of the Ca: Mg/K ratio, SMS results in a maize yield comparable to the control in the first crop. The application of N in these areas was crucial for the second crop.
Bioinformatics Prediction and Experimental Verification Confirm SCD as a Prognostic Factor in Chromophobe Renal Cell Carcinoma
Authors: Liu Shuai , Mei Jingchang , Yao Yu , Sun Lijiang , Zhang Guiming 
Published: September 2023

Abstract
Background
Stearoyl-CoA desaturase (SCD) is a key enzyme required for the biosynthesis of monounsaturated fatty acids and catalyzes the D (9)-cis desaturation of various fatty acyl-CoA substrates. In this study, we investigated the expression levels of SCD in chromophobe renal cell carcinoma (chRCC) and several types of cancers through TCGA database.
Materials and methods
Genomic data and clinical information of patients were obtained from the TCGA database and The Affiliated Hospital of Qingdao University. The relationships between clinicopathologic factors and SCD expression were analyzed with the Wilcoxon signed-rank test. Gene Set Enrichment Analysis (GSEA) was performed to explore the mechanism of SCD.
Results
We observed that SCD was highly expressed in chRCC and several types of cancers, and SCD expression was associated with poor prognosis in chRCC. Immunohistochemical analysis revealed that SCD was highly expressed in tumor tissues compared with para-cancer normal tissues. GSEA showed that asthma, graft versus host disease, allograft rejection, primary immunodeficiency, NOD-like receptor signaling pathway, cell adhesion molecules (CAMS), and cytokine-cytokine receptor interaction were enriched in the SCD low expression group.
Conclusion
Overall, SCD was a reliable prognostic biomarker for chRCC. Moreover, the signature had significant potential for assessing the biological mechanism of tumors and providing individualized treatment.
Reduction in D-dimer Levels After Treatment with Auxora in Patients with Severe Covid-19 Pneumonia Reflects Endothelial Stabilization
Authors: Hou Peter C. , Miller Joseph , Bruen Charles , Youssef Fady , Schnaus Michael J. , Brouillette Kathyrn , Mendoza-Ayala Raul , Zhang Jeffrey , Stauderman Kenneth , Hebbar Sudarshan 
Published: September 2023

Abstract
Background
Auxora, a calcium release-activated channel (CRAC) inhibitor, was demonstrated to improve recovery and decrease mortality in patients with severe COVID-19 pneumonia initially in an open-label trial and then in CARDEA, a phase 2, randomized, double-blind, placebo-controlled trial. In the open-label trial, treatment with Auxora was noted to be associated with a decrease in D-Dimer levels. To confirm these findings, blood samples were collected in CARDEA and tested for D-dimer levels. In a subset of patients, additional biomarkers were assessed to elucidate a potential mechanism of action of Auxora in decreasing D-dimer levels.

Methods
In patients enrolled in CARDEA, blood samples were collected prior to randomization and again at 72 hours after the start of the first infusion of Auxora for testing of D-dimer levels. In patients who consented for additional biomarker testing, blood samples were collected prior to randomization and again at 96 hours for testing of Angiopoietin-1, Angiopoietin-2, renin, and sCD25 levels.

Results
The baseline mean D-dimer level in the Auxora group was 2.61 mg/L and in the placebo group 2.05 mg/L. Patients treated with Auxora had a significant decrease in D-dimer levels within the first 72 hours compared to those treated with placebo. The difference was -0.92 (95% CI: -1.82, -0.02; P<0.0460). The decrease in D-dimer levels correlated with an increase in imputed PaO2/FiO2 (P/F) at 72 hours (r: -0.193; P<0.05) which in turn correlated with improved clinical outcomes at 168 hours (r: 0.218, P<0.01). Additional biomarker testing demonstrated that treatment with Auxora reduced levels of Angiopoietin-2 and sCD25 and increased Angiopoietin-1 levels at 96 hours.

Conclusion
In patients with severe COVID-19 pneumonia, Auxora reduced D-dimer levels which correlated with improved oxygenation and clinical outcomes. In addition, Auxora appears to have decreased endothelial activation through a reduction in systemic inflammation and likely had a direct effect on endothelium stabilization.
This trial is registered at ClinicalTrials.gov number, NCT04345614.