bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elevated whole blood viscosity is associated with an impaired insulin-stimulated myocardial glucose metabolism
Authors: Succurro Elena , Vizza Patrizia , Cicone Francesco , Rubino Mariangela , Fiorentino Teresa Vanessa , Perticone Maria , Mannino Gaia Chiara , Sciacqua Angela , Guzzi Pietro Hiram , Veltri Pierangelo , Cascini Giuseppe Lucio , Andreozzi Francesco , Sesti Giorgio 
Published: November 2024

Abstract

Background: Increased whole blood viscosity (WBV) was associated with peripheral insulin resistance, type 2 diabetes, and cardiovascular disease (CVD). Impaired myocardial glucose metabolism is a risk factor for CVD. Whether an increased WBV is associated with myocardial insulin resistance is still undefined.
Methods: To elucidate this issue, we evaluated the association between WBV and myocardial glucose metabolic rate (MRGlu) in 57 individuals with different glucose tolerance status. Myocardial MRGlu was assessed using dynamic cardiac 18F-FDG PET combined with euglycemic hyperinsulinemic clamp. WBV was calculated using a validated equation including hematocrit and plasma proteins: WBV = [0.12 x h] + [0.17 x (p-2.07)], where h is the hematocrit (%) and p the plasma proteins (g/dl).
Results: As compared with individuals in the highest myocardial MrGlu tertile, those in the lowest tertile showed an age-adjusted increase in WBV (5.54 ± 0.3 cP vs 6.13 ± 0.4 cP respectively; P=0.001), hematocrit (39.1 ± 3.1% vs 43.2 ± 3.7% respectively; P=0.004), and total proteins (7.06 ± 0.3 g/l vs 7.60 ± 0.3 g/l respectively; P<0.0001). WBV was negatively correlated with myocardial MRGlu (r= -0.416, P=0.001). In a stepwise multivariate regression analysis, including several cardiovascular risk factors, the only variables significantly associated with myocardial MrGlu were WBV (b -0.505; P<0.0001), fasting insulin (b -0.346; P=0.004), fasting plasma glucose (b -0.287; P=0.01), and sex (b 0.280; P=0.003) explaining the 69.6% of its variation.
Conclusions: To the best of our knowledge, the current study was the first to show an association between WBV and myocardial glucose metabolism in individuals with a broad spectrum of glucose tolerance.
Computational Elucidation of Hub Genes and Pathways Correlated with the Development of 5-Fluorouracil Resistance in HCT 116 Colorectal Carcinoma Cell Line
Authors: Tan Chun Hoe , Lim Siew Huah , Sim Kae Shin 
Published: November 2024

Abstract

Colorectal cancer (CRC) is the third most deadly cancer diagnosed in both men and women. 5-Fluorouracil (5-FU) treatment frequently causes the CRC cells to become chemoresistance, which has a negative impact on prognosis. Using bioinformatic techniques, this work describes important genes and biological pathways linked to 5-FU resistance in CRC cells. In our studies, a 5-FU-resistant HCT 116 cell line exhibiting elevated TYMS was created and validated using various tests. Bioinformatic studies were conducted to determine which differentially expressed genes (DEGs) were responsible for the establishment of 5-FU resistance in the same cell line. After screening 3949 DEGs from the two public datasets (GSE196900 and GSE153412), 471 overlapping DEGs in 5-FU-resistant HCT 116 cells were chosen. These overlapping DEGs were used to build the PPI network, and a major cluster module containing 21 genes was found. Subsequently, using three topological analysis algorithms, 10 hub genes were identified, which included HLA-DRA, HLA-DRB1, CXCR4, MMP9, CDH1, SMAD3, VIM, SYK, ZEB1, and SELL. Their roles were ascertained by utilizing Gene Ontology keywords and pathway enrichment studies. Our results also demonstrated that the miRNA and TF that had the strongest connection with the hub genes were hsa-mir-26a-5p, hsa-mir-30a-5p, RELA, and NFKB1. Ultimately, 84 FDA-approved drugs that target those hub genes were found to potentially treat 5-FU resistance CRC. Our research's findings increase our understanding of the fundamental factors that contribute to the prevalence of 5-FU resistance CRC, which will ultimately assist in the identification of valuable malignancy biomarkers and targeted treatment approaches based on key regulatory pathways.
Using VIS-NIR hyperspectral imaging and machine learning for non-destructive detection of nutrient contents in sorghum
Authors: Wu Kai , Zhang Zilin , He Xiuhan , Li Gangao , Zheng Decong , Li Zhiwei 
Published: November 2024

Abstract

Non-destructive, rapid, and accurate detection of the nutritional compositions in sorghum is of great significance to the application of sorghum in agricultural production and food industry. In the process of sorghum nutrition detection, it can obtain good effect by extracting the corresponding characteristic wavelengths and selecting the suitable detection model for different nutrients. In this study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the research object. Firstly, the visible near-infrared(Vis-NIR) hyperspectral curves of sorghum were measured by the Starter Kit indoor mobile scanning platform (Starter Kit, Headwall Photonics, USA). Secondly, the nutritional components were determined using chemical methods in order to analyze the differences in nutritional composition among different varieties. Thirdly, the original spectral curves were de-noised by Standard normal variate(SNV), Detrending, and Multiplicative Scatter Correction (MSC) algorithms, and the Competitive adaptive reweighted sampling (CARS) and Bootstrapping soft shrinkage (BOSS) algorithms were used to coarse extract the characteristic variables, then Iteratively retains informative variables (IRIV) was used to judge the importance of the characteristic variables, and the optimal wavelength sets of crude protein, tannin and crude fat were obtained respectively. Finally, Partial least squares(PLS), Back propagation(BP) and Extreme learning machine(ELM) were used to establish the non-destructive detection models of crude protein, tannin and crude fat content respectively. The results showed the following: (1) The optimal variable sets of crude protein, tannin and crude fat contain 41, 38 and 22 wavelength variables, respectively. (2) The CARS-IRIV-PLS model was suitable for detecting crude protein, the prediction set exhibits R2, RMSE and RPD values of 0.6913, 0.7996% and 1.7998. The BOSS-IRIV-PLS model achieved good results in tannin detection, the prediction set exhibits R2, RMSE and RPD values of 0.8760, 0.2169% and 2.8398. The BOSS-IRIV-ELM model achieved the best results in crude fat detection, the prediction set exhibits R2, RMSE and RPD values of 0.6145, 0.3208% and 1.6106. (3) Linear PLS model is suitable for crude protein and tannin detection, and nonlinear ELM model is suitable for crude fat detection. These detection models can be used for the effective estimation of the nutritional compositions in sorghum with Vis-NIR spectral data, and can provide an important basis for the application of food nutrition assessment.
Tyro3 and Gas6 are associated with white matter and myelin integrity in multiple sclerosis
Authors: Rosenstein Igal , Novakova Lenka , Kvartsberg Hlin , Nordin Anna , Rasch Sofia , Rembeza Elzbieta , Sandgren Sofia , Malmeström Clas , Fruhwürth Stefanie , Axelsson Markus , Blennow Kaj , Zetterberg Henrik , Lycke Jan 
Published: November 2024

Abstract

Background
The Gas6/TAM (Tyro3, Axl, and Mer) receptor system has been implicated in demyelination and delayed remyelination in experimental animal models, but data in humans are scarce. We aimed to investigate the role of Gas6/TAM in neurodegenerative processes in multiple sclerosis (MS).
Methods
From a prospective 5-year follow-up study, soluble Gas6/TAM biomarkers were analyzed in cerebrospinal fluid (CSF) by enzyme-linked immunosorbent assay (ELISA) at baseline in patients with relapsing-remitting MS (RRMS) (n = 40), progressive MS (PMS) (n = 20), and healthy controls (HC) (n = 25). Brain volumes, including myelin content (MyC) and white matter (WM) were measured by synthetic magnetic resonance imaging at baseline, 12 months, and 60-month follow-up. Associations with brain volume changes were investigated in multivariable linear regression models. Gas6/TAM concentrations were also determined at 12 month follow-up in RRMS to assess treatment response.
Results
Baseline concentrations of Tyro3, Axl, and Gas6 were significantly higher in PMS vs. RRMS and HC. Mer was significantly higher in PMS vs. HC. Tyro3 and Gas6 were associated with reduced WM (β = 25.5, 95% confidence interval [CI] [6.11–44.96, p = 0.012; β = 11.4, 95%CI [0.42–22.4], p = 0.042, respectively) and MyC (β = 7.95, 95%CI [1.84–14.07], p = 0.012; β = 4.4, 95%CI [1.04–7.75], p = 0.012 respectively) at 60 months. Patients with evidence of remyelination at last follow-up had significantly lower baseline soluble Tyro3 (p = 0.033) and Gas6 (p = 0.014). Except Mer, Gas6/TAM concentrations did not change with treatment in RRMS.
Discussion
Our data indicate a potential role for the Gas6/TAM receptor system in neurodegenerative processes influencing demyelination and ineffective remyelination.
Generating Reconstructable Collaborative Virtual Environments via Graph Matching for Mixed Reality Remote Collaboration
Authors: Lu Yaguang , Hu Yong , Feng Huiyan , Duan PengShuai , Shen Xukun 
Published: November 2024

Abstract

Generating Reconstructable Collaborative Virtual Environments (RCVEs) addresses the spatial heterogeneity challenge in Mixed Reality (MR) remote collaboration. Existing methods partition remote spaces into discrete areas, aligning them with local spaces. However, disparities in structural and semantic configurations hinder continuous avatar movement. This paper proposes a Graph Matching-based approach to constrain the spatial topological relationships among discrete areas during RCVEs alignment. An Artificial Potential Fields-based avatar mapping method, named APFBAM, is introduced to rapidly capture and redirect avatars in heterogeneous spaces. Experiments using reconstructed virtual scenes from real-world data demonstrate the effectiveness of these methods in MR remote collaboration. The results indicate that users perceive minimal impact on their experience despite spatial structural differences, validating the approach's suitability for enabling flexible and scalable MR remote collaboration.
Improving diffusion-based protein backbone generation with global-geometry-aware latent encoding
Authors: Gong Haipeng , Zhang Yuyang , Liu Yuhang , Ma Zinnia , Li Min , Xu Chunfu 
Published: November 2024

Abstract

Recent breakthroughs in diffusion-based generative models have prompted de novo protein design, notably in generating diverse and realistic structures. Nevertheless, while existing models either excel at unconditional generation or employ residue-wise conditioning for topological control, explorations on a holistic, top-down approach to control the overall topological arrangements is still limited. In response, we introduce TopoDiff, a diffusion-based framework augmented by a structure encoder and a latent sampler. Our model can unsupervisedly learn a compact latent representation of protein global geometry, while simultaneously integrating a diffusion module to leverage this information for controlled structure generation. In benchmark against existing models, TopoDiff demonstrates comparable performance on established metrics and exhibits an improved coverage over the fold modes of natural proteins. Moreover, our method enables versatile control at the global-geometry level for structural generation, under the assistance of which we derived a number of novel folds of mainly-beta proteins with comprehensive experimental validation.
Using a Dynamic Spatial Difference-in-Differences estimator to evaluate the effect of High speed rail and tourist transit service on Tourism Demand
Authors: Liu Tzu-Ming 
Published: November 2024

Abstract

This study uses the Dynamic Spatial Difference-in-Differences model (Dynamic SDID) to analyze the impact of the Taiwan High-Speed Rail (THSR) on Taiwan's tourism demand. To control for spillover effects, the model incorporates the Taiwan Tourist Shuttle service (TSHU) as an alternative transportation option, the interactive effects between TSHU and THSR, and the spatial autocorrelation between TSHU and THSR. The analysis results indicate that controlling for spillover effects is crucial for analyzing the impact of the High-Speed Rail and tourist transit service on Tourism Demand, and the Dynamic SDID is a better analytical model for this purpose. The THSR has a significant positive impact on tourism demand, while its spatial autocorrelation effect is significantly negative. This suggests that the increase in tourist traffic brought about by THSR mainly comes from existing tourists in the surrounding areas rather than generating new tourism demand. The TSHU, on the other hand, has a negative but insignificant impact on tourism demand, but its interaction with THSR has a significant positive effect, indicating that the two services complement each other. Therefore, to enhance Taiwan's tourism demand, the focus should still be on improving the attractiveness of tourist destinations rather than solely relying on the construction of the High-Speed Rail. Additionally, while the TSHU does not contribute significantly to the development of specific individual tourist destinations, it does facilitate regional tourism development. Therefore, selecting TSHU routes based on actual market conditions can promote the growth of the tourism industry.
Multilingual Competency and Academic Performance: A Machine Learning-Based Analysis of the 2022/2023 Somaliland National Primary Exam Data
Authors: Ali Jibril Abdikadir , Abdi Mustafe Khadar , Ali Tawakal Abdi , Muse Abdisalan Hassan , Omar Mukhtar Abdi , Cumar Mukhtaar Axmed 
Published: November 2024

Abstract

This study assesses the influence of language proficiency on academic performance in primary education within Somaliland, utilizing data from the 2022/2023 National Primary Exams. Employing a dataset of 20,638 students and applying ten machine learning regression models, the research investigates the impact of Somali, Arabic, and English language skills on overall academic outcomes. The findings reveal that proficiency in these languages significantly contributes to overall performance, with English showing the strongest positive association, followed by Arabic and Somali. Additionally, the study highlights minimal gender disparities in academic performance, aligning with previous research from the region. However, the urban-rural divide in educational outcomes remains substantial, with urban students outperforming their rural counterparts. Machine learning models, particularly Polynomial Regression, outperformed traditional methods in predicting student success, showcasing the utility of advanced analytics in educational research. These findings offer critical insights for policymakers aiming to improve language education, reduce regional disparities, and promote equitable access to quality education across Somaliland.
LiDAR SLAM method in outdoor environment based on global descriptor loop detection
Authors: Huang Chao , Ren Weiwei , Ran Dong , Zhang Yi 
Published: November 2024

Abstract

In outdoor environments, the odometer based on lidar sensors has accumulated errors, which may lead to map drift and map construction failure. This paper proposes a SLAM method based on global descriptor loop detection. This method uses stable triangle (STD) descriptors for loop closure detection to reduce cumulative errors. At the same time, this paper also proposes a candidate loopback frame search algorithm based on inverse document frequency (IDF) to ensure that high query efficiency can be maintained when the number of descriptors increases. In addition, the iterative closest point method (ICP) weighted by multi-scale residual terms is used to obtain the pose transformation between matching loop closure frames, thereby effectively improving the accuracy and speed of loop closure detection. To verify the performance of the proposed algorithm, we conducted experiments on the public dataset KITTI. Experimental results show that compared with existing algorithms such as LeGO-LOAM and Scan-Context, the algorithm proposed in this article has better performance in outdoor scenes and can improve the accuracy of pose estimation and mapping accuracy.
Sarcomatous Transformation of IDH-mutant Astrocytoma Matching to Methylation Class Oligosarcoma Following Embolization, a Case Report
Authors: Landvater Ryan , Tripathy Arushi , Nieblas-Bedolla Edwin , Shao Lina , Conway Kyle , Al-Holou Wajd , Ferris Sean 
Published: November 2024

Abstract

The mesenchymal transformations of infiltrating gliomas are uncommon and prognostically relevant events. This is particularly true of IDH-mutant astrocytomas and oligodendrogliomas, in which mesenchymal transformation is exceedingly rare. Oligosarcoma is a newly recognized methylation class (MC) that represents transformed 1p/19q co-deleted oligodendrogliomas, but recent studies indicate it may be non-specific. Herein we report the diffuse sarcomatous transformation of a multifocal recurrent astrocytoma from a precursor IDH-mutant astrocytoma, CNS WHO grade 3, in a young patient following embolization therapy and matching to MC oligosarcoma. The sarcomatous recurrence and original tumor showed identical 17q breakpoints with loss of heterozygosity of TP53. Both lack the defining 1p/19q co-deletion or copy-neutral heterozygosity of an oligodendroglioma and oligosarcoma. The findings in this case report both contribute to the apparent heterogeneity of the novel MC oligosarcoma and describe a second reported mesenchymal transformation of an IDH-mutant astrocytoma.
Identification of crucial genes through WGCNA in the progression of clear cell renal cell carcinoma
Authors: Li Ge , wang Jie-qiong , Liu Qin-wen , Kong Xiang-jun , Yang Bo 
Published: November 2024

Abstract

Background
Due to the limited clinical treatment options for clear cell renal cell carcinoma (ccRCC), this study aimed to explore the molecular mechanisms underlying ccRCC and identify potential therapeutic targets.
Methods
A series of bioinformatics techniques were utilized. Differentially expressed genes were identified from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was employed to isolate relevant gene modules. Least absolute shrinkage and selection operator regression was applied to determine potential target genes, which were subsequently validated in the The Cancer Genome Atlas Program (TCGA) dataset. Multivariate Cox proportional hazards model analysis was conducted. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on intersection genes. The relationship between genes and immune cells was explored. Dual verification using GEO and TCGA data was carried out to screen genes.
Results
WGCNA was utilized to identify relevant gene modules. This led to the discovery of 236 differentially expressed genes, 193 candidate hub intersection genes, and 12 potential target genes. The AIF1L gene showed statistical differences, with higher expression in some samples. Enrichment analysis revealed these genes' implications in renal tumors. Twelve ccRCC-related genes were identified, having diagnostic value and correlations with immune cells. Through dual verification, five genes were screened out and AIF1L in ccRCC had unique characteristics. Clinical correlation analysis suggested it might act as a suppressor gene. Differences in the tumor microenvironment were observed between AIF1L high- and low-expression groups.
Conclusion
This study on ccRCC presented notable findings. The combination of data from different datasets offered a comprehensive understanding of ccRCC-related genes. AIF1L showed promise. this finding provides a foundation and direction for future research on ccRCC's molecular mechanisms and potential therapeutic strategies.
A Correction Method Based on Experiment Cutting Process for Grinder Profile of Screw Rotor Compressor
Authors: Tran Quyet Van , Hoang Thuan Minh 
Published: November 2024

Abstract

The accuracy of the grinding wheel (GW) body is known as a critical factor impacting the rotor profile error of the screw compressor. However, GW profile errors typically arise during the lathe process as a result of the comprehensive errors in the machining tools. Therefore, this research proposes a correction method for enhancing the profile accuracy of the GW body based on the normal deviation of the manufactured GW body compared to design profiles. The mathematical model is established for generating the GW according to the design rotor profile of the screw compressor. The CMM machine measures the actual profile of the GW body after manufacturing it using a lathe process. The closed-loop method reduces the manufactured profile error of the GW body based on the profile error of the actual profile compared with the design profiles of the GW body. In the experiment result, the maximum normal deviation of the GW body was significantly reduced by about 82.56%. The interface software is constructed based on the proposed method and C# language to be easily applied in the industrial setting.
Serotransferrin enhances transferrin receptor-mediated brain uptake of antibodies
Authors: Morrison Jamie I. , Metzendorf Nicole G. , Liu Jielu , Hultqvist Greta 
Published: November 2024

Abstract

The propensity of antibody-based therapies to systemically enter the brain interstitium and ameliorate pathology associated with numerous neurological maladies is precluded by the presence of the blood-brain barrier (BBB). Through distinct mechanisms, the BBB has evolved to regulate transport of essential ions, minerals, certain peptides and cells between the blood and the brain, but very restrictive otherwise. Hijacking receptor-mediated transport pathways of the BBB has proved fruitful in developing “Trojan Horse” therapeutic approaches to deliver antibody-based therapies to the brain milieu. The transferrin receptor (TfR)-mediated transcytosis pathway (RMT) is one such example where large recombinant molecules have been designed to bind to the TfR, which in turn activates the RMT pathway, resulting in delivery across the BBB into the brain milieu. Based on these findings, we here investigated whether the addition of serotransferrin could trigger the endogenous TfR-mediated RMT pathway and hence be used to enhance the uptake of TfR binding antibodies. By using an in vitro model of a mouse BBB we could test whether co-administration of mouse serotransferrin with mouse and human-based monoclonal antibodies enhanced brain uptake. In all cases tested, no matter if the monoclonal antibodies were designed to bind the TfR in a monovalent, partially monovalent/bivalent or entirely bivalent fashion, with high or low affinity or avidity, the addition of mouse serotransferrin significantly improved transport across the artificial BBB. This was also true for TfR binding antibodies that on their own passes the BBB poorly. These results were subsequently confirmed using a human in vitro BBB model, along with human serotransferrin and human TfR-binding antibody. To corroborate the in vitro results further, we conducted an in vivo brain uptake study in wildtype mice, intravenously co-administering a monoclonal TfR-binding antibody in the presence or absence of mouse serotransferrin. In a similar outcome to the in vitro studies, we observed a significant almost two fold increase in brain uptake of two different TfR binding antibodies when it was co-administered with mouse serotransferrin. These findings show for the first time that serotransferrin supplementation can significantly improve the ability of TfR-binding antibodies to traverse the BBB, which provides a realistic therapeutic opportunity for improving the delivery of therapeutic antibodies to the brain.