Robust Brain MRI Image Classification with SIBOW-SVM

The majority of primary Central Nervous System (CNS) tumors in the brain are among the most aggressive diseases affecting humans. Early detection of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention and treatment, ultimately improving human life expectancy. Magnetic Resonance Imaging (MRI) stands as the most effective technique to detect brain tumors by generating comprehensive brain images through scans. However, human examination can be error-prone and inefficient due to the complexity, size, and location variability of brain tumors. Recently, automated classification techniques using machine learning (ML) methods, such as Convolutional Neural Network (CNN), have demonstrated significantly higher accuracy than manual screening, while maintaining low computational costs. Nonetheless, deep learning-based image classification methods, including CNN, face challenges in estimating class probabilities without proper model calibration. In this paper, we propose a novel brain tumor image classification method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with SIFT feature extraction and weighted Support Vector Machines (wSVMs). This new approach effectively captures hidden image features, enabling the differentiation of various tumor types and accurate label predictions. Additionally, the SIBOW-SVM is able to estimate the probabilities of images belonging to each class, thereby providing high-confidence classification decisions. We have also developed scalable and parallelable algorithms to facilitate the practical implementation of SIBOW-SVM for massive images. As a benchmark, we apply the SIBOW-SVM to a public data set of brain tumor MRI images containing four classes: glioma, meningioma, pituitary, and normal. Our results show that the new method outperforms state-of-the-art methods, including CNN

Improvement on Solving the Constraint System in Automated Test Data Generation for Database Stored Procedure Testing

Abstract. Stored procedure has occurred independently in database application systems. How to test the stored procedure effectively becomes an urgent problem in automated testing. Test data generation is a vital part in automated test of stored procedure. However, the current approaches of test data generation for stored procedure needs manual intervention and the solution of the constraint system which limits test data can not effectively cover all the situations. Existing approaches can not solve constraint system with character string and strict inequality. This paper improved the approach to generate test data, perfected existing approach to solve constraint system and reduced the limitation on solving the constraint system

Evaluation of LOXL1 polymorphisms in exfoliation syndrome in a Chinese population

Purpose: To evaluate the association profiles of the lysyl oxidase-like 1 (LOXL1) gene polymorphisms with exfoliation syndrome in a Chinese population. Methods: Fifty unrelated patients with exfoliation syndrome and 125 control subjects were included. Genotypes of the three single nucleotide polymorphisms (SNPs) of LOXL1 (rs1048661, rs3825942, and rs2165241) were analyzed by direct sequencing, and a case-control association study was performed. Results: The three SNPs were significantly associated with exfoliation syndrome (XFS) and exfoliation glaucoma (XFG) individually. After controlling for rs3825942 and rs2165241, the association between rs1048661 and XFS/XFG remained significant (p=3.6x10(-7)). At this SNP, the T allele and TT genotype conferred a 7.59-(95% confidence interval [CI]: 3.87-14.89, p=6.95x10(-11)) and 8.69-(95% CI: 4.15-18.20, p<1.00x10(-7)) fold increased risk to the disease. The alleles of T at rs1048661 and C at rs2165241 were found to be risk alleles in Chinese subjects, which were opposite to Caucasian individuals. The haplotypes T-G, defined by SNPs rs1048661 and rs3825942, and T-C by SNPs rs1048661 and rs2165241, were also significantly associated with the disorder. However when the genotypic or allelic frequencies of the three SNPs were compared between XFS and XFG, no significant difference was detected. Conclusions: LOXL1 is a susceptibility gene of XFS/XFG in the Chinese population, and the association is mainly attributed to SNP rs1048661. The risk alleles of rs1048661 and rs2165241 in Chinese subjects were found to be opposite to that of Caucasians. The genotypic and allelic distributions of these SNPs are similar between XFS and XFG.Biochemistry & Molecular BiologyOphthalmologySCI(E)30ARTICLE250-522349-23571

Dynamics of Gut Microbiome in Giant Panda Cubs Reveal Transitional Microbes and Pathways in Early Life

Adult giant pandas (Ailuropoda melanoleuca) express transitional characteristics in that they consume bamboos, despite their carnivore-like digestive tracts. Their genome contains no cellulolytic enzymes; therefore, understanding the development of the giant panda gut microbiome, especially in early life, is important for decoding the rules underlying gut microbial formation, inheritance and dietary transitions. With deep metagenomic sequencing, we investigated the gut microbiomes of two newborn giant panda brothers and their parents living in Macao, China, from 2016 to 2017. Both giant panda cubs exhibited progressive increases in gut microbial richness during growth, particularly from the 6th month after birth. Enterobacteriaceae dominated the gut microbial compositions in both adult giant pandas and cubs. A total of 583 co-abundance genes (CAGs) and about 79 metagenomic species (MGS) from bacteria or viruses displayed significant changes with age. Seven genera (Shewanella, Oblitimonas, Helicobacter, Haemophilus, Aeromonas, Listeria, and Fusobacterium) showed great importance with respect to gut microbial structural determination in the nursing stage of giant panda cubs. Furthermore, 10 orthologous gene functions and 44 pathways showed significant changes with age. Of the significant pathways, 16 from Escherichia, Klebsiella, Propionibacterium, Lactobacillus, and Lactococcus displayed marked differences between parents and their cubs at birth, while 29 pathways from Escherichia, Campylobacter and Lactobacillus exhibited significant increase in cubs from 6 to 9 months of age. In addition, oxidoreductases, transferases, and hydrolases dominated the significantly changed gut microbial enzymes during the growth of giant panda cubs, while few of them were involved in cellulose degradation. The findings indicated diet-stimulated gut microbiome transitions and the important role of Enterobacteriaceae in the guts of giant panda in early life

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

A common variant near TGFBR3 is associated with primary open angle glaucoma

Primary open angle glaucoma (POAG), a major cause of blindness worldwide, is a complex disease with a significant genetic contribution. We performed Exome Array (Illumina) analysis on 3504 POAG cases and 9746 controls with replication of the most significant findings in 9173 POAG cases and 26 780 controls across 18 collections of Asian, African and European descent. Apart from confirming strong evidence of association at CDKN2B-AS1 (rs2157719 [G], odds ratio [OR] = 0.71, P = 2.81 × 10−33), we observed one SNP showing significant association to POAG (CDC7–TGFBR3 rs1192415, ORG-allele = 1.13, Pmeta = 1.60 × 10−8). This particular SNP has previously been shown to be strongly associated with optic disc area and vertical cup-to-disc ratio, which are regarded as glaucoma-related quantitative traits. Our study now extends this by directly implicating it in POAG disease pathogenesis

Downloaded from

Abstract Primary open angle glaucoma (POAG), a major cause of blindness worldwide, is a complex disease with a significant genetic contribution. We performed Exome Array ), we observed one SNP showing significant association to POAG (CDC7-TGFBR3 rs1192415, OR G-allele = 1.13, P meta = 1.60 × 10 −8 ). This particular SNP has previously been shown to be strongly associated with optic disc area and vertical cup-to-disc ratio, which are regarded as glaucoma-related quantitative traits. Our study now extends this by directly implicating it in POAG disease pathogenesis

Scalable and Sparse Class Probability Estimation with Weighted Support Vector Machines

Classification problems have broad applications in many scientific areas such as biology, engineering, finance, and medicine. Support vector machines (SVMs) are popular classification tools due to their flexibility, accuracy, and fast computation for high dimensional datasets. However, standard SVMs can not estimate the probability of a data point belonging to a given class and hence fails to provide a confidence measure of uncertainty for label prediction. This drawback can be tackled by weighted SVMs (wSVMs) (Wang et al., 2008; Wu et al., 2010; Wang et al., 2019), which estimate class probabilities by aggregating multiple classification rules trained by weighted samples. Despite their promising performance, existing wSVMs have limitations in their theory, computation, and applications. Specifically, they do not perform variable selection, so both their classification accuracy and interpretability may suffer when too many noise features are present in the data. Also, the multiclass wSVM algorithm proposed by Wang et al. (2019) has a polynomial complexity in K (the number of classes), which is suboptimal. To address these issues, my Ph.D. research work is devoted to developing new ideas to improve existing wSVMs from three different perspectives. This thesis consists of three projects under the unified theme of weighted SVMs. The first project develops more efficient and scalable algorithms to reduce the complexity of K-class wSVMs from polynomial time to linear time. The second project develops a new robust classification method, called the SIBOW-SVM, for brain MRI image analysis and shows that it can outperform the state-of-art convolutional neural networks (CNNs). The third project proposes a set of new frameworks to achieve sparse learning in binary wSVMs and perform simultaneous variable selection, classification, and probability estimation in high-dimensional data. For each project, I present new statistical methods and algorithms, as well as extensive numerical studies and real examples to demonstrate finite sample performance

Tourist Satisfaction, Willingness to Revisit and Recommend, and Mountain Kangyang Tourism Spots Sustainability: A Structural Equation Modelling Approach

The rapid development of society and economy has imposed insurmountable pressure on the urban population, and many people suffer from sub-health conditions. Kangyang tourism (KT), which combines the concepts of health preservation, ecological resources, and tourism activities, has developed rapidly in China since the concept was first introduced. Although previous studies have examined the relationship among experience, satisfaction, and intention, there is a lack of study of experience value’s impact on Mountain Kangyang Tourim Spots’ Sustainability, that is, willingness to revisit and recommend to other potential tourists. Consequently, an “experience value (functional value, contextual value, emotional value, cognitive value, economic value), satisfaction and post-trip willingness to revisit and recommend” framework is suggested to examine mountain Kangyang tourism (MKT). Data were collected from 500 tourists after visiting five well-known MKT destinations in Panzhihua city. Using the structural equation modeling (SEM) technique, the results suggest that tourist satisfaction plays an important role in experience value as well as willingness to revisit and recommend the MKT spots after their revisits. Our research offers some practical suggestions for MKT destination operators when they design and provide MKT destinations. The results would be useful for governments and non-profit organizations which attempt to promote MKT