The extremal genus embedding of graphs

Let Wn be a wheel graph with n spokes. How does the genus change if adding a degree-3 vertex v, which is not in V (Wn), to the graph Wn? In this paper, through the joint-tree model we obtain that the genus of Wn+v equals 0 if the three neighbors of v are in the same face boundary of P(Wn); otherwise, {\deg}(Wn + v) = 1, where P(Wn) is the unique planar embedding of Wn. In addition, via the independent set, we provide a lower bound on the maximum genus of graphs, which may be better than both the result of D. Li & Y. Liu and the result of Z. Ouyang etc: in Europ. J. Combinatorics. Furthermore, we obtain a relation between the independence number and the maximum genus of graphs, and provide an algorithm to obtain the lower bound on the number of the distinct maximum genus embedding of the complete graph Km, which, in some sense, improves the result of Y. Caro and S. Stahl respectively

Pioglitazone Attenuates Vascular Fibrosis in Spontaneously Hypertensive Rats

Objective. We sought to investigate whether the peroxisome proliferator-activated receptor-γ (PPAR-γ) ligand pioglitazone can attenuate vascular fibrosis in spontaneously hypertensive rats (SHRs) and explore the possible molecular mechanisms. Methods. SHRs (8-week-old males) were randomly divided into 3 groups (n = 8 each) for treatment: pioglitazone (10 mg/kg/day), hydralazine (25 mg/kg/day), or saline. Normal male Wistar Kyoto (WKY) rats (n = 8) served as normal controls. Twelve weeks later, we evaluated the effect of pioglitazone on vascular fibrosis by Masson's trichrome and immunohistochemical staining of collagen III and real-time RT-PCR analysis of collagen I, III and fibronectin mRNA.Vascular expression of PPAR-γ and connective tissue growth factor (CTGF) and transforming growth factor-β (TGF-β) expression were evaluated by immunohistochemical staining, western blot analysis, and real-time RT-PCR. Results. Pioglitazone and hydralazine treatment significantly decreased systolic blood pressure in SHRs. Masson's trichrome staining for collagen III and real-time RT-PCR analysis of collagen I, III and fibronectin mRNA indicated that pioglitazone significantly inhibited extracellular matrix production in the aorta. Compared with Wistar Kyoto rats, SHRs showed significantly increased vascular CTGF expression. Pioglitazone treatment significantly increased PPAR-γ expression and inhibited CTGF expression but had no effect on TGF-β expression. Conclusions. The results indicate that pioglitazone attenuated vascular fibrosis in SHRs by inhibiting CTGF expression in a TGF-β-independent mechanism

EEG-based fatigue driving detection using correlation dimension

Driver fatigue is an important cause of traffic accidents and the detection of fatigue driving has been a hot issue in automobile active safety during the past decades. The purpose of this study is to develop a novel method to detect fatigue driving based on electroencephalogram (EEG). The volunteer is asked to perform simulated driving tasks under different mental state while EEG signals are acquired simultaneously from six electrodes at central, parietal and occipital lobe, including C3, C4, P3, P4, O1 and O2. Due to the non-linearity of human brain responses, correlation dimension is estimated with G-P algorithm to quantify the collected EEGs. Statistical analysis reveals significant decreases from awake to fatigue state of the correlation dimension for all the channels across 5 subjects (awake state: 3.87±0.13; fatigue state: 2.76±0.34; p< 0.05, paired t-test), which indicates that the correlation dimension is a promising parameter in detecting fatigue driving with EEGs

Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image

Domain adaptation (DA) has been widely applied in the diabetic retinopathy (DR) grading of unannotated ultra-wide-field (UWF) fundus images, which can transfer annotated knowledge from labeled color fundus images. However, suffering from huge domain gaps and complex real-world scenarios, the DR grading performance of most mainstream DA is far from that of clinical diagnosis. To tackle this, we propose a novel source-free active domain adaptation (SFADA) in this paper. Specifically, we focus on DR grading problem itself and propose to generate features of color fundus images with continuously evolving relationships of DRs, actively select a few valuable UWF fundus images for labeling with local representation matching, and adapt model on UWF fundus images with DR lesion prototypes. Notably, the SFADA also takes data privacy and computational efficiency into consideration. Extensive experimental results demonstrate that our proposed SFADA achieves state-of-the-art DR grading performance, increasing accuracy by 20.9% and quadratic weighted kappa by 18.63% compared with baseline and reaching 85.36% and 92.38% respectively. These investigations show that the potential of our approach for real clinical practice is promising

MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection

The ease of spreading false information online enables individuals with malicious intent to manipulate public opinion and destabilize social stability. Recently, fake news detection based on evidence retrieval has gained popularity in an effort to identify fake news reliably and reduce its impact. Evidence retrieval-based methods can improve the reliability of fake news detection by computing the textual consistency between the evidence and the claim in the news. In this paper, we propose a framework for fake news detection based on MUlti-Step Evidence Retrieval enhancement (MUSER), which simulates the steps of human beings in the process of reading news, summarizing, consulting materials, and inferring whether the news is true or fake. Our model can explicitly model dependencies among multiple pieces of evidence, and perform multi-step associations for the evidence required for news verification through multi-step retrieval. In addition, our model is able to automatically collect existing evidence through paragraph retrieval and key evidence selection, which can save the tedious process of manual evidence collection. We conducted extensive experiments on real-world datasets in different languages, and the results demonstrate that our proposed model outperforms state-of-the-art baseline methods for detecting fake news by at least 3% in F1-Macro and 4% in F1-Micro. Furthermore, it provides interpretable evidence for end users.Comment: 12 pages, 5 figures, accepted by KDD '23, ADS trac

Design and characterization of protein-quercetin bioactive nanoparticles

<p>Abstract</p> <p>Background</p> <p>The synthesis of bioactive nanoparticles with precise molecular level control is a major challenge in bionanotechnology. Understanding the nature of the interactions between the active components and transport biomaterials is thus essential for the rational formulation of bio-nanocarriers. The current study presents a single molecule of bovine serum albumin (BSA), lysozyme (Lys), or myoglobin (Mb) used to load hydrophobic drugs such as quercetin (Q) and other flavonoids.</p> <p>Results</p> <p>Induced by dimethyl sulfoxide (DMSO), BSA, Lys, and Mb formed spherical nanocarriers with sizes less than 70 nm. After loading Q, the size was further reduced by 30%. The adsorption of Q on protein is mainly hydrophobic, and is related to the synergy of Trp residues with the molecular environment of the proteins. Seven Q molecules could be entrapped by one Lys molecule, 9 by one Mb, and 11 by one BSA. The controlled releasing measurements indicate that these bioactive nanoparticles have long-term antioxidant protection effects on the activity of Q in both acidic and neutral conditions. The antioxidant activity evaluation indicates that the activity of Q is not hindered by the formation of protein nanoparticles. Other flavonoids, such as kaempferol and rutin, were also investigated.</p> <p>Conclusions</p> <p>BSA exhibits the most remarkable abilities of loading, controlled release, and antioxidant protection of active drugs, indicating that such type of bionanoparticles is very promising in the field of bionanotechnology.</p

Decreased BECN1 mRNA Expression in Human Breast Cancer is Associated With Estrogen Receptor-Negative Subtypes and Poor Prognosis

AbstractBoth BRCA1 and Beclin 1 (BECN1) are tumor suppressor genes, which are in close proximity on the human chromosome 17q21 breast cancer tumor susceptibility locus and are often concurrently deleted. However, their importance in sporadic human breast cancer is not known. To interrogate the effects of BECN1 and BRCA1 in breast cancer, we studied their mRNA expression patterns in breast cancer patients from two large datasets: The Cancer Genome Atlas (TCGA) (n=1067) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n=1992). In both datasets, low expression of BECN1 was more common in HER2-enriched and basal-like (mostly triple-negative) breast cancers compared to luminal A/B intrinsic tumor subtypes, and was also strongly associated with TP53 mutations and advanced tumor grade. In contrast, there was no significant association between low BRCA1 expression and HER2-enriched or basal-like subtypes, TP53 mutations or tumor grade. In addition, low expression of BECN1 (but not low BRCA1) was associated with poor prognosis, and BECN1 (but not BRCA1) expression was an independent predictor of survival. These findings suggest that decreased mRNA expression of the autophagy gene BECN1 may contribute to the pathogenesis and progression of HER2-enriched, basal-like, and TP53 mutant breast cancers

Validation of the Oxford classification of IgA nephropathy for pediatric patients from China

BACKGROUND: The Oxford classification of IgA nephropathy (IgAN) provides a useful tool for prediction of renal prognosis. However, the application of this classification in children with IgAN needs validation in different patient populations. METHODS: A total of 218 children with IgAN from 7 renal centers in China were enrolled. The inclusion criteria was similar to the original Oxford study. RESULTS: There were 98 patients (45%) with mesangial proliferation (M1), 51 patients (23%) with endocapillary proliferation (E1), 136 patients (62%) with segmental sclerosis/adhesion lesion (S1), 13 patients (6%) with moderate tubulointerstitial fibrosis (T1 26-50% of cortex scarred), and only 2 patients (1%) with severe tubulointerstitial fibrosis (T2, >50% of cortex scarred). During a median follow-up duration of 56 months, 24 children (12.4%) developed ESRD or 50% decline in renal function. In univariate COX analysis, we found that tubular atrophy/interstitial fibrosis (HR 4.3, 95%CI 1.8-10.5, P < 0.001) and segmental glomerulosclerosis (HR 9.2 1.2-68.6, P = 0.03) were significant predictors of renal outcome. However, mesangial hypercellularity, endocapillary proliferation, crescents, and necrosis were not associated with renal prognosis. In the multivariate COX regression model, none of these pathologic lesions were shown to be independent risk factors of unfavorable renal outcome except for tubular atrophy/interstitial fibrosis (HR 2.9, 95%CI 1.0-7.9 P = 0.04). CONCLUSIONS: We confirmed tubular atrophy/interstitial fibrosis was the only feature independently associated with renal outcomes in Chinese children with IgAN

Enhanced identification and biological validation of differential gene expression via Illumina whole-genome expression arrays through the use of the model-based background correction methodology

Despite the tremendous growth of microarray usage in scientific studies, there is a lack of standards for background correction methodologies, especially in single-color microarray platforms. Traditional background subtraction methods often generate negative signals and thus cause large amounts of data loss. Hence, some researchers prefer to avoid background corrections, which typically result in the underestimation of differential expression. Here, by utilizing nonspecific negative control features integrated into Illumina whole genome expression arrays, we have developed a method of model-based background correction for BeadArrays (MBCB). We compared the MBCB with a method adapted from the Affymetrix robust multi-array analysis algorithm and with no background subtraction, using a mouse acute myeloid leukemia (AML) dataset. We demonstrated that differential expression ratios obtained by using the MBCB had the best correlation with quantitative RT–PCR. MBCB also achieved better sensitivity in detecting differentially expressed genes with biological significance. For example, we demonstrated that the differential regulation of Tnfr2, Ikk and NF-kappaB, the death receptor pathway, in the AML samples, could only be detected by using data after MBCB implementation. We conclude that MBCB is a robust background correction method that will lead to more precise determination of gene expression and better biological interpretation of Illumina BeadArray data

Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy

In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky–Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula