bluuebunny/crossref_metadata_2025_split huggingface dataset:
CircRNF121 facilitates ovarian cancer progression through modulating microRNA-153/IGF2BP2/B3GNT6 axis
Authors: Wang Zhifeng , Sun Hao , Hu Dian , Liu Xiaojun 
Published: August 2023

Abstract
Background
Increasing studies have evidenced that circular RNAs (circRNAs), a new class of non-coding RNAs, exert a vital role in controlling the progression of multiple human cancers.
Methods
The functional roles of circRNF121 in the initiation and progression of ovarian cancer (OC) warrant further investigation. In our study, RT-qPCR was utilized to determine the expression of circRNF121 in OC cancerous tissues and cells. Also, CCK-8 analysis was performed to detect the viability of CaSki and MS751 cells. The effects of circRNF121 on OC cells were explored using in vitro migration assay and in vivo subcutaneous xenograft tumor model. RT-qPCR and western blotting were used to determine the expression of a panel of genes relative to the EMT process. Luciferase reporter assay were conducted to confirm the specific interaction between circRNF121 and miRNA-153 or between miRNA-153 and IGF2BP2. RNA-Immunoprecipitation (RIP) assay confirmed the binding efficacy of IGF2BP2 and B3GNT6.
Results
In our study, we firstly demonstrated circRNF121 was upregulated in OC cancerous tissues and cells, and the expression of circRNF121 was negatively correlated with patient’s survival. Functionally, circRNF121 can function as an oncogene to promote OC progression both in vitro and in vivo. Mechanistically, circRNF121 worked as a sponge for miRNA-153 to regulate IGF2BP2 expression. Besides, RIP and qPCR analysis suggested that IGF2BP2 can promote B3GNT6 expression to drive OC progression.
Conclusions
CircRNF121 promotes OC progression through modulating the miR-153/ IGF2BP2/B3GNT6 axis, thereby providing a promising therapeutic target for OC treatment.
Mini-abstract:
CircRNF121 promotes OC progression through modulating the miR-153/ IGF2BP2/B3GNT6 axis.
Multi-scale Adaptive Atrous Graph Convolution for Point Cloud Analysis
Authors: Wang Xiaohong , Zhao Xu , Xu Kun , Xu Shihao 
Published: August 2023

Abstract
Despite extensive research, the representation and learning of local characteristics for sparse and dense points in 3D point clouds could have been better. Traditional convolutional neural networks (CNNs) have limitations due to fixed convolutional kernels with narrow receptive fields, making them inefficient in recognizing correlation properties between local points and processing 3D point clouds. To address this, we propose a novel multi-level feature pyramid graph neural convolutional network (MSAGCNet) that incorporates Multi-scale Adaptive Atrous Graph Convolution (MSAGConv) and Learnable Graph Pooling (LGP) techniques. By adaptive atrous kernels and multi-scale architecture, MSAGConv can deeply learn the features of points, enhance the flexibility of point convolution kernels, expand the perception field, and capture rich semantic information and local correlation features between sparse points at various scales. Meanwhile, LGP aims to eliminate the interference of redundant graph nodes (dense points), aggregating fine-grained and coarse-grained point cloud features. Our methods outperform advanced shape classification and part segmentation techniques on the ModelNet40, ScanObjectNN, and ShapeNetPart datasets in a thorough qualitative and quantitative evaluation.
TKG: Telecom Knowledge Governance Framework for LLM Application
Authors: Cai Haoran , Wu Sijie 
Published: August 2023

Abstract
The emergence of ChatGPT and the competition among various Large Language Models have drawn significant attention from society. The success of Natural Language Processing (NLP) in the general field has captured the interest of researchers and engineers from academia and industry. In the telecom industry, Large Language Models (LLMs) have found extensive use in intelligent customer service and data search, contributing to the improvement of work efficiency in product delivery and operation. Nevertheless, it is important to note that while generic large models are versatile, their application to specific domains is hindered by the lack of high-quality domain-specific data. This often results in LLMs being unable to provide high-quality answers to specific problems.
In this paper, we aim to investigate knowledge data governance in the telecom field, build high-quality domain-specific corpus, and utilize the dataset to fine-tune the large language model, which can enhance its ability to solve downstream practical problems such as Q\&A, search, and text generation. To address the challenge of model hallucination, we propose a data solution, namely Telecom Knowledge Governance (TKG), for intelligent application scenarios. TKG constructs a large-scale telecom domain corpus that ensures the accessed data is up-to-date, comprehensive, and of the highest quality. Moreover, we recruit five technical experts in the telecom field and develop automated generation tools to create a high-quality telecom Q\&A dataset that includes over 100,000 domain-specific questions and answers. Finally, we use this dataset to fine-tune the model, allowing it to learn how to apply telecom knowledge to specific scenarios more effectively. The results show that our knowledge data can greatly improve the model performance.
Secret Information Sharing using Multipartite Entanglement in Monty Hall Game
Authors: Kaur Hargeet , Shah Kavit , Swain Debabrata , Shah Kaushal 
Published: August 2023

Abstract

Quantum computing has emerged as a useful resource for winning random games. Monty Hall problem is one such game where the powers of quantum mechanics can be utilized for winning the prize. Our paper depicts this problem as a two-player game, where the two players, i.e. the host and the participant share an entangled quantum state. The entangled state is closely knit with the position of the prize containing door, and the decision of the player i.e. either switch or stay from his initial choice of door. The two players are part of one team as they share a quantum communication channel among them by sharing the entangled state as a quantum resource. This enables them to share share secret information about the position of the price as well as the strategy to opt for winning the game. The article shows a unique way of utilizing three different type of entangled states, 3-qubit symmetric W state, 3-qubit X state, and 5-qubit highly entangled Brown state. Further, the effectiveness of each quantum state as a source of communication in disguise is also compared. All the entangled states under study assure winning the price by the participant. However, each quantum state has different entanglement degree and offer varied merits as per the game setting.
ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences
Authors: Pei Yao , Shum Marcus Ho-Hin , Liao Yunshi , Leung Vivian Wingsee-Wai , Gong Yu-Nong , Smith David , Yin Xiao-Le , Guan Yi , Luo Rui-Bang , Zhang Tong , Lam Tommy Tsan-Yuk 
Published: August 2023

Abstract

Background
Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.
Results
In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs, and a multiclass classification convolutional neural network to classify ARGs that does not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30–50 aa; 100-150nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistancy with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.
Conclusions
ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk.
Solution of Multi-objective Fractional Solid Transportation Problem (MFSTP) some aggregation operators
Authors: KARA NURDAN , TİRYAKİ FATMA 
Published: August 2023

Abstract
This paper investigates a multi-objective fractional solid transportation problem (MFSTP) with several fractional criteria such as cost/profit, etc., and its three items are origin, destination, and product type or transportation mode. This paper deals with the problem's solution and analyzes the effect of several important fuzzy aggregation operators on the problem's solution. The most commonly used aggregation operators are investigated for this problem in this context. To explain the solution approach, a numerical example from the literature is given and solved. Moreover, by comparing the generated solutions, a set of solutions is presented to the decision-maker to choose the appropriate operator for our problem.
Partially substituted La for Sm in Sm 0.8 Sr 0.2 NiO 3 : An efficient electrocatalyst towards alkaline water splitting
Authors: Parihar Reena , Mishra Prakhar , Singh Narendra Kumar 
Published: August 2023

Abstract
Perovskite-type oxides having composition LaxSm0.8-xSr0.2NiO3 (0 ≤ x ≤ 0.6) were explored for their ability to act as facilitators for oxygen evolution reaction in alkaline media. Synthesis of the materials was conducted through sol-gel procedure via malic acid. The materials underwent advanced physicochemical characterization utilizing cutting-edge techniques, namely Scanning Electron Microscope (SEM) and X-Ray Diffraction (XRD). These analyses provided crucial details about the material’s surface morphology and crystalline structure, opening up exciting possibilities for their diverse applications. Cyclic voltammetry and Tafel Plot were observed for the electrochemical analysis in 1M KOH (25℃). An investigation on the anodic polarization of oxides revealed an increased electrocatalytic activity when La was partially substituted for Sm in Sm0.8Sr0.2NiO3. La0.6Sm0.2Sr0.2NiO3 was found to be most active at 800 mV with current density, j = 184.1 mA/cm2. Tafel experiment was conducted in 1M KOH at varying temperatures to determine thermodynamic properties like standard electrochemical energy of activation (ΔH˚#el), standard enthalpy of activation (ΔH˚#) and standard entropy of activation (ΔS˚#).
Short-term panel data analysis of the effect of flood risk communication on individual evacuation decisions
Authors: Ikegai Mirai , Hidaka Ken , Masuda Satoki 
Published: August 2023

Abstract
People-centered risk communication is important to mitigate flood damage caused by the recent increase in heavy rainfall. Longitudinal studies are particularly important for evaluating the effectiveness of different risk communication methods. However, there is a lack of sufficient research. We conducted a longitudinal study, specifically through four panel surveys conducted over a short period of time, to investigate the effects of various risk communication methods, such as a simulated evacuation experience with learning about flood damage, flood-related information distribution including information about the evacuation behavior of others, and hazard maps. The results of a fixed effects analysis of the panel data suggest that the manner in which risk communication is affected varies depending on the initial evacuation attitude. In particular, we found that the distribution of hazard maps had a negative effect on the evacuation behavior of those who initially responded that they would evacuate. This suggests that residents in non-flood-prone areas may have acquired correct hazard perceptions from the hazard maps. However, for those who initially chose not to evacuate, confirmation of the delivered content had a positive effect on their evacuation behavior 12 hours before the typhoon hit. This suggests that those who initially chose not to evacuate may have reconsidered their decision. The risk communication presented in this study may help future risk communication by reducing congestion at evacuation sites due to excessive evacuation, while increasing the evacuation rate of those who should evacuate.
Changes in Vaginal Cytokines Concentrations during Artificial Insemination and Natural Service in Beef Heifers
Authors: McLean Kyle J. , Ault-Seay Taylor B. , Myer Phillip R. 
Published: August 2023

Abstract

Heifer development is crucial for the optimization of reproductive efficiency in beef production. Heifer development is largely influenced by nutrition, however, nutritional status can influence immunological responses crucial for reproduction. Commercial Angus heifers (n = 9) were utilized to elucidate the effects of sampling time, pregnancy status, and type of mating on individual cytokine concentrations and cytokine profiles in the vagina following breeding. A vaginal flush was collected by injecting 20 mL of sterile saline into the vagina via insertion of a Foley catheter and was collected on days 0, 7, 14, 21, 28, and 35. Cytokine profiles were analyzed using MetaboAnalyst 5.0 and one-way ANOVAs were performed in R Studio to identify differences in individual cytokines based on sampling time, pregnancy status, and type of mating. Cytokine profiles were different (P = 0.05) 7 days after either mating type. Cytokines IL-1β, IL-17a, MCP-1, and TNFα were different based on the mating type and pregnancy status. Multiple cytokines, IL-1α, IL-1β, IL-6, IL-8, IL-10, IL-17a, VEGFa, and MIP1α, were different between days regardless of pregnancy status. In conclusion, vaginal cytokines differ based on pregnancy status, type of mating, and time which may be indicative of vital pathways that need to be activated for pregnancy.
Comparison of Immediate Intensive Care Outcomes of Patent Ductus Arteriosus Stenting Versus Modified Blalock-Taussig-Thomas Shunt in Infants With Ductal-Dependent Pulmonary Circulation
Authors: Sirisani Joyce Darshinee , Haranal Maruti , Soo Kok Wai , Sivalingam Sivakumar , Khalid Khairul Faizah Mohd 
Published: August 2023

Abstract

Background
Patent Ductus Arteriosus (PDA) stenting is an alternative to Modified Blalock-Taussig Thomas Shunt (BT)shunt to increase pulmonary blood flow in ductal-dependant pulmonary circulation. The objective of this study is to compare the immediate Intensive Care Unit (ICU) outcomes of PDA stent versus BT shunt in infants with ductal-dependent pulmonary circulation.
Method
This is a single centre, retrospective study in infants less than 6 months with ductal- dependant pulmonary circulation palliated with either PDA stenting or BT shunt from January 2015 till October 2019. Data was reviewed from paediatric ICU database. Demographics details, ICU parameters, mortality and morbidity were compared. The propensity score with inverse probability weighting was used to control the effect of covariates.
Result
A total 183 patients with PDA stent and 80 patients with BT shunt were included. Median age for PDA stent were 5.7 (3.5–9.8) weeks versus 5.9 (3.5–9.8) weeks for BT shunt (P = 0.651). The PDA stent group has shorter ICU stay, 3 (1.0–4.0) days versus 8 (4.0–13.0) days (p < 0.001), shorter length of hospital stay, 9 (6.0–14.0) days versus 16 (10.3–21.0) days (p < 0.001), shorter ventilation days, 2 (1.0–3.0) days versus 3 (2.0-6.5) days (p = 0.002), shorter inotrope days, 3 (1–4) days versus 4 (2–7) days (p < 0.011) and lower mortality, 3 (1.6%) versus 9 (11.2%) (p = 0.001). At 3 months follow-up, the freedom from mortality was not significant (p = 0.831)
Conclusion
PDA stent in infants with ductal-dependant pulmonary blood flow has a more favourable intensive care outcomes with shorter ICU and hospital stay. PDA stent has a better survival outcome.
Validation of the Novel GLAS Algorithm for Early Detection of Liver Fibrosis and Cirrhosis Based on GP73, LG2m, Age, and Sex
Authors: Hemken Philip M. , Qin Xuzhen , Sokoll Lori J. , Jackson Laurel , Feng Fan , Li Peng , Gawel Susan H. , Tu Bailin , Lin Zhihong , Hartnett James , Hawksworth David , Tieman Bryan C. , Yoshimura Toru , Kinukawa Hideki , Ning Shaohua , Liu Enfu , Meng Fanju , Chen Fei , Miao Juru , Mi Xuan , Tong Xin , Chan Daniel W. , Davis Gerard J. 
Published: August 2023

Abstract

Background Diagnosis of liver disease at earlier stages can improve outcomes and reduce the risk of progression to malignancy. Liver biopsy is the gold standard for diagnosis of liver disease, but it is invasive and sample acquisition errors are common. Serum biomarkers for liver function and fibrosis, combined with patient factors, may allow for noninvasive detection of liver disease. We tested and validated the performance of an algorithm that combines GP73 and LG2m serum biomarkers with age and sex (GLAS) to differentiate between patients with early-stage liver disease and healthy individuals in two independent cohorts. Methods To develop the algorithm, prototype immunoassays were used to measure GP73 and LG2m in residual serum samples collected between 2003 and 2016 from patients with staged fibrosis and cirrhosis of viral or non-viral etiology (n = 260) and healthy subjects (n = 133). The performance of five predictive models using combinations of age, sex, GP73, and/or LG2m from the development cohort were tested. Residual samples from a separate cohort with liver disease (fibrosis, cirrhosis, or chronic liver disease; n = 395) and healthy subjects (n = 106) were used to validate the best performing model. Results GP73 and LG2M concentrations were higher in patients with liver disease than healthy controls and higher in those with cirrhosis than fibrosis in both the development and validation cohorts. The best performing model included both GP73 and LG2m plus age and sex (GLAS algorithm), which had an AUC of 0.92 (95% CI: 0.90–0.95), a sensitivity of 88.8%, and a specificity of 75.9%. In the validation cohort, the GLAS algorithm had an estimated an AUC of 0.93 (95% CI: 0.90–0.95), a sensitivity of 91.1%, and a specificity of 80.2%. In both cohorts, the GLAS algorithm had high predictive probability for distinguishing between patients with liver disease versus healthy controls. Conclusions GP73 and LG2m serum biomarkers, when combined with age and sex (GLAS algorithm), showed high sensitivity and specificity for early detection of liver fibrosis and cirrhosis in two independent cohorts. The GLAS algorithm will need to be validated and refined in larger cohorts and tested in longitudinal studies for differentiating between stable versus advancing liver disease over time.
Sister haplotypes and recombination disequilibrium: A new Approach to identify associations of haplotypes with complicated diseases
Authors: Liao Shun-Yao , Tan Yuande 
Published: August 2023

Abstract
Haplotype-based association analysis has several advantages over single-SNP association analysis. However, to date all haplotype-disease associations have not excluded recombination interference among multiple loci and hence some results might be confounded by recombination interference. A ssociation of sister haplotypes with a complicated disease, based on recombination disequilibrium (RD) was presented. We convert DNA base language to genotype language to determine four pairs of sister haplotypes. After doing RD tests in control and case cohorts, one can construct a two-by-two contingent table with sister-haplotypes and a disease and perform Chi-square test for association between haplotypes and disease. Applying this method to a haplotype dataset of Alzheimer disease (AD), association of sister-haplotypes containing ApoE3/4 with risk for AD was identified under no RD. Haplotypes within genes IL-13 were not associated with risk for breast cancer in the case of no RD and no association of haplotypes in IL-17A gene with risk for coronary artery disease were detected without RD. The previously reported associations of haplotypes within these genes with risk for these diseases might be due to strong RD and/or inappropriate haplotype pairs.
An experimental review of the ensemble-based data stream classification algorithms in non-stationary environments
Authors: Khezri Shirin , Tanha Jafar , Samadi Negin 
Published: August 2023

Abstract

Data streams are sequences of fast-growing and high-speed data points that typically suffer from the infinite length, large volume, and specifically unstable data distribution. These potential issues of data streams bold the necessity of data stream mining tasks. Ensemble learning as a prevalent classification approach is widely used in data stream mining studies. Besides the impressive performance of ensemble learning algorithms in providing a collection of diverse and accurate classifiers, they are specifically efficient in handling non-stationary data streams. Due to the component-based nature and the chance of dynamic updates for the components of the ensemble, this category is appropriate for dynamically learning the changing concepts of the data. This paper aims to provide a thorough review of the most significant ensemble-based data stream classification approaches, along with a discussion about the potential issues of the non-stationary data streams. Furthermore, comprehensive experimental analysis is performed to compare the classification performance of the well-known state-of-the-art ensemble-based data stream classification approaches on 24 synthetic non-stationary data streams. The superiority of the approaches is proved by conducting various statistical tests.