bluuebunny/crossref_metadata_2025_split huggingface dataset:
The role of KiSS1 gene on the tumor growth and migration of prostate cancer and the underlying molecular mechanisms
Authors: Kim Cho-Won , Lee Hong Kyu , Nam Min-Woo , Lee Gabsang , Choi Kyung-Chul 
Published: June 2022

Abstract
Background Prostate cancers have a very high mortality rate, with no improvement in the survival rate being achieved over the past few decades. KiSS1 was originally identified as the metastasis suppressor gene in metastatic melanoma breast cancer, but its role in prostate cancer has been contradictory. This study was therefore undertaken to investigate the effects of KiSS1 overexpression on the growth and migration of human metastatic prostate cancer cells. Methods DU145 human metastatic prostate cancer cells were infected with the culture medium of 293T cells, which produce lentivirus particles containing KiSS1. The tumor spheroid formation assay, qPCR and immunoblotting analysis, migration and invasion assays were performed in this study following KiSS1 introduction. A xenograft mouse model was employed to investigate the overexpression of KiSS1 in prostate cancer in vivo. Results A significant increase in proliferation of KiSS1-overexpressing cancer cells was observed, and these cells formed tumor spheroids larger than the vector control group. qPCR and immunoblotting revealed the association between increased cell growth and regulation of the PI3K/Akt and cell cycle genes, and also that increases in β-catenin and CD133 contribute to tumor aggregation. KiSS1 overexpression resulted in upregulation of the β-arrestin1/2 and Raf-MEK-ERK-NF-κB pathways via KISS1R. Moreover, the migration and invasion of KiSS1-overexpressing cells were determined to be faster than the control group, along with increased metastatic colonization of the KiSS1-overexpressing cancer cells. These were associated to the regulation of EMT gene expressions, such as E-cadherin and N-cadherin, and the upregulation of MMP9. In the xenograft mouse model, KiSS1 significantly increased the tumor growth, with upregulation of PCNA and Ki-67 in the tumor tissues. In addition, KiSS1 increased the angiogenic capacity by upregulating VEGF-A and CD31, both in vitro and in vivo. Conclusion These results indicate that KiSS1 not only induces prostate cancer proliferation, but also promotes metastasis by increasing the migration, invasion, and angiogenesis of malignant cells.
Comparative transcriptomic analysis transcription factors and hormones during the flower bud differentiation in ‘Red Globe’ grape under red-blue combined light
Authors: Liu Xin , Yuan Miao , Dang Shizhuo , zhou Juan , Zhang Yahong 
Published: June 2022

Abstract
Grape is a globally significant fruit in the world. Flower bud differentiation of grape is closely related to light quality. In order to understand the regulation mechanism of grape flower bud differentiation under red-blue combined light, the transcriptome and hormone content were determined in four stages of flower bud differentiation. The contents of IAA and ABA in grape buds at all stages of flower bud differentiation under red-blue combined light were higher than those in control. However, the contents of CKs and GAs fluctuated continuously during the process. Moreover, it was found that many differential expression genes (DEGs) were involved in auxin, cytokinin, gibberellin, and the abscisic acid signal transduction pathway. There were significant differences in the genes of AUX/IAA, SAUR, A-RR, and ABF between the red-blue combination light treatment and the control buds, especially the ABF genes were utterly different. The expression of GBF4 and AI5L2 in control were always low, while the expression under the red-blue combined light increased. AI5L7 and AI5L5 showed an upward trend in control and gradually decreased under the red-blue combined light. Through weighted gene co-expression network analysis (WGCNA), transcription factors WRKY family WRK48, ERF family EF110, ABR1, CAMTA family CAMTA3, HSF family HSFA3 may be involved in the regulation of the GBF4 gene. This study will lay a foundation for further analysis of grape flower bud differentiation regulation under red-blue combined light.
Automatic Modulation Classification in Optical Wireless Communication Systems based on Cancellable Biometrics
Authors: Mohamed Safie-eldin Nasr , Abuel-hassan bidaa Mortada , El-Shafai Walid , Khalaf Ashraf A. M. , zahran osama , Dessouky Moawad I. , El-Rabaie El-Sayed M. , El-Samie Fathi E. Abd 
Published: June 2022

Abstract
Recently, automatic modulation classification (AMC) has acquired a lot of interest in the optical communication community. Most optical wireless communication systems are intended to transmit multimedia content, especially video and speech signals. The optical wireless communication channel has variable characteristics. Hence, there is a need for an adaptive modulation scheme to cope with the varying channel characteristics. Adaptive modulation requires the implementation of adaptive modulation classification at the receiver end. Instead of using complex classification with deep learning techniques, a simple proposed scheme for AMC is introduced in this paper. This proposed scheme is based on a chaotic Baker map (CBM), wavelet image fusion, and autocorrelation estimation. It depends on constellation diagrams for eight modulation formats, including (B/Q/8/16 PSK), (8/16/32/64 QAM). The constellation diagrams are acquired and utilized through the CBM, and they are merged using the wavelet image fusion and stored as reference templates in the system database. After that, the classification of each modulation format depends on estimated correlation scores and a thresholding strategy. Simulation results prove good classification and recognition for all studied modulation formats.
Facilitating the Development of Green Agriculture for Carbon Neutrality: A Study based on Evolutionary Game Theory
Authors: Luo Jianli , Huang Minmin , Bai Yanhu 
Published: June 2022

Abstract
Facilitating the development of green agriculture is an effective way to reduce carbon emissions and achieve carbon neutrality. However, researches on the development of green agriculture are still on the way. Despite the fact that many researchers have explored and analyzed this issue, there is still a gap between stakeholders of green agricultural development combined with low-carbon policies and green preferences that has been overlooked, resulting in theory and practice being imperfect. To bridge this gap, a three-party evolutionary game model based on the government’s low-carbon policies and stakeholders’ green preferences is developed. Numerical simulations are applied to verify real-world tripartite participation. The six key parameters that influence strategic behaviour are thoroughly discussed. The findings show that: (1) appropriate subsidies policy and relatively high carbon prices are conducive to promoting the development of green agriculture; (2) The improvement of three parties’ green preference enhances people’s low-carbon awareness in the market; (3) Different levels of green preference lead to government’s uneven strategies, which depend on the relative level of its green preference and regulate cost. Finally, this study addresses how to promote green agricultural development and offers several management recommendations.
Full-Duplex MIMO Relay-assisted Interference Alignment Algorithm in K-user Interference Channels
Authors: Bai Lijun , Hao Li , Jia Kejun 
Published: June 2022

Abstract
In this paper, we investigate the transceiver design schemes for the full-duplex (FD) multiple-input multipleoutput (MIMO) relay-assisted K -user single-input multipleoutput (SIMO) interference channels (ICs). Firstly, we propose an iterative optimized reference vector for IA (IORV-IA) algorithm in the perfect channel state information (CSI) scenario. The proposed IORV-IA algorithm not only achieves the alignment of interference signals at each receiver, but also iteratively optimizes the IA reference vector by orthogonalizing the directions of the interference signals and the desired signal. With the optimized IA reference vector, the relay processing matrix and the receiving filter vectors are designed to further improve the system performance. Considering that the relay cannot obtain perfect CSIs in practice due to many factors, and the performance of the IA scheme is very sensitive to this error. Furthermore, we propose a robust transceiver design scheme based on mean square error (MSE) in the imperfect CSI scenario, which minimizes the sum of MSEs in the worst case through iteration. The proposed algorithms are evaluated in terms of the average sum rate and bit error rate (BER) performance and the simulation results show the advantages of the proposed algorithms over existing centralized IA (CIA) and centralized zero-forcing (CZF) algorithms.
Comparative genome-wide analysis of hub lncRNAs and mRNAs in brain metastatic small cell lung cancer – An in-Silico Approach
Authors: Namini Arsham Mikaeili , Jahangir Motahareh , Mohseni Maryam , Kolahi Ali Asghar , Hassanian-Moghaddam Hossein , Mazloumi Zeinab , Motallebi Marzieh , Sheikhpour Mojgan , Movafagh Abolfazl 
Published: June 2022

Abstract
Lung cancer is one of the most commonly prevalent globally. Metastatic lung tumours lead to most deaths in lung cancer. Predicting and preventing tumour metastasis is crucially Important for patient survivability. Hence, in the current study, we focused on a comprehensive analysis of lung cancer patients' differentially expressed genes (DEGs) on brain metastasis cell lines. DEGs are analysed through KEGG and GO databases for the most critical biological processes and pathways for enriched DEGs. Additionally, we performed PPI, GeneMANIA and Kaplan-Meier survival analyses on our DEGs. This article focused on mRNA and lncRNA DEGs for LC patients with brain metastasis and underlying molecular mechanisms. The expression data was gathered from the Gene Expression Omnibus database (GSE161968). We demonstrate that 30 distinct genes are up expressed in brain metastasis SCLC patients, and 31 genes are down-expressed. All the analyses we performed show these genes are Involve In metastatic SCLC. PPI analysis revealed two hub genes (CAT and APP). The results of this article present three lncRNAs, Including XLOC_l2_000941, LOC100507481, and XLOC_l2_007062, also notable mRNAs, have a close relation with brain metastasis in lung cancer and may have a role in the epithelial-mesenchymal transition (EMT) in tumour cells.
Experimental study of EDM characteristics using a 5-DOF controllable magnetic levitation actuator
Authors: Luan Boran , Zhang Xiaoyou , Xu Fangchao , Yang Guang , Jin Junjie , Xu Chengcheng , Feng Sun 
Published: June 2022

Abstract
The efficiency and accuracy of conventional electrical discharge machining (EDM) is limited by the stability of the voltage between the poles. To improve the efficiency of EDM, this paper proposes a machining method that combines a self-developed 5-degree-of-freedom (5-DOF) controllable magnetic levitation actuator with a conventional EDM machine tool. The stability of the inter-pole voltage is improved by the actuator micro-adjustment the electrodes of the EDM machine tool. Firstly, an EDM control system with local current feedback and decoupling control elements is designed based on the EDM servo drive principle to improve the response speed and positioning accuracy of the actuator. Secondly, the actuator was connected to the spindle of a conventional EDM machine tool, and machining experiments were carried out. The experimental results showed that the EDM machine tool connected to the actuator could control the electrode position more quickly, adjust the discharge state quickly, and increase the number of discharges per unit time. The average machining speed increased from 1.108µm/s to 3.925µm/s, which is 3.54 times faster than conventional EDM. Finally, complex shape machining experiments were carried out and the machining results showed that by adjusting the target value of the radial direction of the actuator, the various trajectories of the electrode could be controlled to depict arbitrary shapes.
Estimation of Surface Roughness in Selective Laser Sintering Using Computational Models
Authors: Koç Ebubekir , Zeybek Sultan , Kısasöz Burçin Özbay , Çalışkan Cemal İrfan , Bulduk M. Enes 
Published: June 2022

Abstract
In this study, a novel classification model is proposed to estimate surface roughness for the parts produced in an industrial additive manufacturing technology. The proposed model focused on selective laser sintering (SLS) technology based on polyamide 12 powder applications. A comprehensive dataset is designed to simulate the production parts and manufactured at an industrial SLS machine based on a proposed positioning strategy and random positions to test the robustness of the dataset and the model. The proposed classification model is based on Deep Neural Networks (DNN) with hyperparameters designed for the problem. Benchmark results show that the model outperforms other machine learning methods on classifying the surface roughness successfully on the test dataset. The dataset and the model provide a new user interface to estimate the surface roughness depending on the coordinates of a given product surface in a SLS production chamber and the production parameters employed in the production planning phase.
Genetic diversity and structure analysis of melon accessions using URP, SRAP, and CDDP markers
Authors: Aziz Rebwar Rafat , Tahir Nawroz 
Published: June 2022

Abstract
Melon (Cucumis melo L.) is an ancient and indigenous crop that grows in Iraq's Kurdistan region, and research into its genetic diversity is needed to improve this important culinary plant while also promoting a healthy diet. This study assessed the genetic diversity of 57 melon accessions using three types of molecular markers, namely universal rice primer (URP), sequence-related amplified polymorphism (SRAP), and conserved DNA derived polymorphism (CDDP) markers. The URAP + IRAP, SRAP, and CDDP primers yielded 143, 125, and 131 polymorphic alleles, respectively. The average polymorphic information content (PIC) was 0.29, 0.26, and 0.32 for URAP + IRAP, SRAP, and CDDP, respectively. Based on the average values of polymorphic bands per primer and PIC of three types of markers, the order of the power of discrimination of melon accession was as follows: URP + IRAP > CDDP > SRAP. The URP-4R, Me1-Em2, and MYB-1 primers had the highest Shannon information index values. Based on the Ward method-based cluster and STRUCTURE analysis, URAP + IRAP and SRAP markers grouped 57 accessions into two primary clusters with various sub-clusters, whereas CDDP markers divided 57 accessions into three groups. The genetic distances between accessions ranged from 0.32 to 0.84 for URP + IRAP, 0.20 to 0.78 for SRAP, and 0.25 to 0.73 for CDDP. The analysis of molecular variance revealed an increase in genetic variation within groups, as well as significant gene exchange between populations. In SRAP analysis, substantial genetic distinctiveness was identified among populations. However, in CDDP analysis, high Shannon's information index and predicted heterozygosity were observed among populations. The outcomes of this study indicated a high level of variability in Iraqi melon germplasm, which must be preserved and included in improvement programs for this ancient crop.
Adaptive trajectory tracking control and parameter identification for autonomous underwater vehicles based on contraction theory
Authors: Ma Caipeng , Tang Yu , Wang Jiaxi , Yan Jiajie , Liao Yuchen , Jiang Dapeng 
Published: June 2022

Abstract
In this paper, contraction theory is applied to design the trajectory tracking controller for a fully-actuated 6-degree-of-freedom (6-DOF) autonomous underwater vehicle (AUV). First, assuming that all system parameters are known, an ideal controller is given. Then, to deal with the parameter uncertainties, an adaptive controller is proposed. Combined with the adaptive law, the estimated values of the parameters converge to their real values without requiring the persistent excitation (PE) condition to be satisfied, that is, the parameter identification is realized. Exponential convergence of the system is analyzed in the framework of contraction theory. The concepts of partial contraction, virtual system and modular properties reduce the difficulty of system design and analysis. The numerical simulation results show the effectiveness of the proposed method.
An effective methodology for software defects prediction using Principal Component-based Multi-layer Perceptron (PC-MLP): A Hybrid Technique
Authors: Mustaqeem Mohd. , Siddiqui Tamanna 
Published: June 2022

Abstract
Nowadays, the software is used almost in every field. The software industry is evolving rapidly, and developing quality software is challenging for developers. Defects in the software are the primary reason for degrading the quality. Software defects prediction is a difficult task. Though researchers have developed several methods to overcome this issue that support the problem, there is room for improvement in software defects prediction. The curse of dimensionality occurs when research is undertaken without feature reduction. Therefore, after consulting several other works of a similar caliber, a hybrid machine learning approach that combines Principal Component Analysis (PCA) for feature optimization with Multi-layer perceptrons for classification has been designed to overcome the hurdle. The proposed model reduces the time complexity of the traditional methods by reducing the number of attributes used for classification. We have used the PROMISE dataset CM1 & KC1 repository from the NASA directory (CM1 344 observations & KC1 2109 observations) to perform our research work. We have divided the datasets into training (240 from CM1 observations & 1476 from KC1) and testing (104 from CM1 observations & 633 from KC1). We have employed precision, recall, F-measure, and accuracy in evaluating the model. Using the proposed model, we reduced the time complexity and increased the accuracy by using the CM1 & KC1 datasets then the previously developed models. In addition, the proposed model receives a greater score on additional criteria. The primary limitation with Multi-layer perceptrons is that it requires a linearly separable dataset, which can make classifications relatively inflexible. Soon, several new strategies may be introduced to overcome this limitation and maintain a soft-based categorization margin.
Visceral adipose tissue secretome from early and late-stage oesophageal cancer patients differentially affects effector and regulatory T cells.
Authors: Davern Maria , Bracken-Clarke Dara , Donlon Noel E , Sheppard Andrew D , connell Fiona O , Heeran Aisling B , Majcher Klaudia , Conroy Melissa J , Mylod Eimear , Butler Christine , Donohoe Claire , Donnell Dearbhaile O' , Lowry Maevle , Bhardwaj Anshul , Ravi Narayanasamy , Melo Ashanty A , Sullivan Jacintha O' , Reynolds John V , Lysaght Joanne 
Published: June 2022

Abstract
Visceral obesity is a key risk factor in the development of oesophagogastric junctional adenocarcinoma (OGJ), predominantly via generation of systemic low grade inflammation. Obesity-induced inflammation promotes resistance to current standards of care, enhancing tumour cell growth and survival. This study investigates the effect of the visceral adicpose tissue secretome from OGJ patients with early versus advanced tumours on T-cell immunity and the role of immune checkpoint blockade in enhancing anti-tumour immunity. Visceral adipose conditioned media (ACM) from both early and late-stage OGJ patients significantly altered T cell activation status, upregulating co-stimulatory marker CD27 on T cells. ACM from both early and late-stage OGJ patients significantly altered immune checkpoint expression profiles downregulating immune checkpoints (ICs) on the surface of dual Th1/17-like and Th17-like cells and upregulating ICs on the surface of Th1-like cells and Treg cells. ACM derived from early-stage OGJ patients but not late-stage OGJ patients increased IFN-γ production by T cells. The addition of immune checkpoint blockers (ICBs) did not increase IFN-γ production by T cells in the presence of late-stage ACM, collectively highlighting the dichotomous immunostimulatory effect of early-stage ACM and immune-inhibitory effect of late-stage ACM. Interestingly, ACM from early-stage OGJ patients was more pro-inflammatory than ACM from late-stage patients, reflected by decreased levels of IL-17A/F, TNF-α, IL-1RA and IL-5. The ACM-induced upregulation of ICs on T cells highlights a therapeutic vulnerability that could be exploited by ICBs to harness anti-cancer immunity and improve clinical outcomes for OGJ patients.
Manufacturing agglomeration, urban form and haze pollution
Authors: Wei Jishi , Ye Yunling , Yu Haichao 
Published: June 2022

Abstract
Manufacturing agglomeration promotes rapid economic development while also causing severe environmental pollution. This paper investigates the impact and mechanism of manufacturing agglomeration on haze pollution from the Chinese city level. Furthermore, we discuss the moderating effect and threshold effect of the three urban forms of urban external shape complexity, urban compactness, and urban fragmentation on the relationship between the two. The result shows: (1) The aggregation of the manufacturing industry presents an inverted U-shaped characteristic of promoting first and then inhibiting haze pollution in China's overall, eastern and central regions. (2) The complexity of the city's external shape and the city's fragmentation has a positive moderating effect on the relationship between manufacturing agglomeration and haze pollution. And urban fragmentation shows a negative moderating effect on the relationship between the two when the level of manufacturing agglomeration is on the right side of the inverted U-shaped curve. (3) The urban form shows a significant double threshold characteristic for haze pollution, increasing the complexity of the city's external shape and the city's fragmentation. The agglomeration of manufacturing shows the characteristics of first inhibiting and then promoting haze pollution. As urban compactness increases, the inhibitory effect of manufacturing agglomeration on haze pollution increases.