Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction

Role of lactobacillus in female infertility via modulating sperm agglutination and immobilization

Infertility has become a common problem in recent decades. The pathogenesis of infertility is variable, but microbiological factors account for a large proportion of it. Dysbiosis of vaginal microbiota is reportedly associated with female infertility, but the influence of normal vaginal microbiota on infertility is unclear. In this review, we summarize the physiological characteristics of the vaginal tract and vaginal microbiota communities. We mainly focus on the bacterial adherence of vaginal Lactobacillus species. Given that the adherent effect plays a crucial role in the colonization of bacteria, we hypothesize that the adherent effect of vaginal Lactobacillus may also influence the fertility of the host. We also analyze the agglutination and immobilization effects of other bacteria, especially Escherichia coli, on ejaculated spermatozoa, and speculate on the possible effects of normal vaginal microbiota on female fertility

SimiSketch: Efficiently Estimating Similarity of streaming Multisets

The challenge of estimating similarity between sets has been a significant concern in data science, finding diverse applications across various domains. However, previous approaches, such as MinHash, have predominantly centered around hashing techniques, which are well-suited for sets but less naturally adaptable to multisets, a common occurrence in scenarios like network streams and text data. Moreover, with the increasing prevalence of data arriving in streaming patterns, many existing methods struggle to handle cases where set items are presented in a continuous stream. Consequently, our focus in this paper is on the challenging scenario of multisets with item streams. To address this, we propose SimiSketch, a sketching algorithm designed to tackle this specific problem. The paper begins by presenting two simpler versions that employ intuitive sketches for similarity estimation. Subsequently, we formally introduce SimiSketch and leverage SALSA to enhance accuracy. To validate our algorithms, we conduct extensive testing on synthetic datasets, real-world network traffic, and text articles. Our experiment shows that compared with the state-of-the-art, SimiSketch can improve the accuracy by up to 42 times, and increase the throughput by up to 360 times. The complete source code is open-sourced and available on GitHub for reference

Identification of a major QTL and candidate genes analysis for branch angle in rapeseed (Brassica napus L.) using QTL-seq and RNA-seq

IntroductionBranching angle is an essential trait in determining the planting density of rapeseed (Brassica napus L.) and hence the yield per unit area. However, the mechanism of branching angle formation in rapeseed is not well understood.MethodsIn this study, two rapeseed germplasm with extreme branching angles were used to construct an F2 segregating population; then bulked segregant analysis sequencing (BSA-seq) and quantitative trait loci (QTL) mapping were utilized to localize branching anglerelated loci and combined with transcriptome sequencing (RNA-seq) and quantitative real-time PCR (qPCR) for candidate gene miningResults and discussionA branching angle-associated quantitative trait loci (QTL) was mapped on chromosome C3 (C3: 1.54-2.65 Mb) by combining BSA-seq as well as traditional QTL mapping. A total of 54 genes had SNP/Indel variants within the QTL interval were identified. Further, RNA-seq of the two parents revealed that 12 of the 54 genes were differentially expressed between the two parents. Finally, we further validated the differentially expressed genes using qPCR and found that six of them presented consistent differential expression in all small branching angle samples and large branching angles, and thus were considered as candidate genes related to branching angles in rapeseed. Our results introduce new candidate genes for the regulation of branching angle formation in rapeseed, and provide an important reference for the subsequent exploration of its formation mechanism

A Survey of Foundation Models for Music Understanding

Music is essential in daily life, fulfilling emotional and entertainment needs, and connecting us personally, socially, and culturally. A better understanding of music can enhance our emotions, cognitive skills, and cultural connections. The rapid advancement of artificial intelligence (AI) has introduced new ways to analyze music, aiming to replicate human understanding of music and provide related services. While the traditional models focused on audio features and simple tasks, the recent development of large language models (LLMs) and foundation models (FMs), which excel in various fields by integrating semantic information and demonstrating strong reasoning abilities, could capture complex musical features and patterns, integrate music with language and incorporate rich musical, emotional and psychological knowledge. Therefore, they have the potential in handling complex music understanding tasks from a semantic perspective, producing outputs closer to human perception. This work, to our best knowledge, is one of the early reviews of the intersection of AI techniques and music understanding. We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities. We also discussed their limitations and proposed possible future directions, offering insights for researchers in this field.20 pages, 2 figure

A Systematic Analysis on DNA Methylation and the Expression of Both mRNA and microRNA in Bladder Cancer

Background: DNA methylation aberration and microRNA (miRNA) deregulation have been observed in many types of cancers. A systematic study of methylome and transcriptome in bladder urothelial carcinoma has never been reported. Methodology/Principal Findings: The DNA methylation was profiled by modified methylation-specific digital karyotyping (MMSDK) and the expression of mRNAs and miRNAs was analyzed by digital gene expression (DGE) sequencing in tumors and matched normal adjacent tissues obtained from 9 bladder urothelial carcinoma patients. We found that a set of significantly enriched pathways disrupted in bladder urothelial carcinoma primarily related to "neurogenesis" and "cell differentiation" by integrated analysis of -omics data. Furthermore, we identified an intriguing collection of cancer-related genes that were deregulated at the levels of DNA methylation and mRNA expression, and we validated several of these genes (HIC1, SLIT2, RASAL1, and KRT17) by Bisulfite Sequencing PCR and Reverse Transcription qPCR in a panel of 33 bladder cancer samples. Conclusions/Significance: We characterized the profiles between methylome and transcriptome in bladder urothelial carcinoma, identified a set of significantly enriched key pathways, and screened four aberrantly methylated and expressed genes. Conclusively, our findings shed light on a new avenue for basic bladder cancer research

Role of Lactobacillus in Female Infertility Via Modulating Sperm Agglutination and Immobilization

Infertility has become a common problem in recent decades. The pathogenesis of infertility is variable, but microbiological factors account for a large proportion of it. Dysbiosis of vaginal microbiota is reportedly associated with female infertility, but the influence of normal vaginal microbiota on infertility is unclear. In this review, we summarize the physiological characteristics of the vaginal tract and vaginal microbiota communities. We mainly focus on the bacterial adherence of vaginal Lactobacillus species. Given that the adherent effect plays a crucial role in the colonization of bacteria, we hypothesize that the adherent effect of vaginal Lactobacillus may also influence the fertility of the host. We also analyze the agglutination and immobilization effects of other bacteria, especially Escherichia coli, on ejaculated spermatozoa, and speculate on the possible effects of normal vaginal microbiota on female fertility.</jats:p

MSPR-Net: A Multi-Scale Features Based Point Cloud Registration Network

Point-cloud registration is a fundamental task in computer vision. However, most point clouds are partially overlapping, corrupted by noise and comprised of indistinguishable surfaces, especially for complexly distributed outdoor LiDAR point clouds, which makes registration challenging. In this paper, we propose a multi-scale features-based point cloud registration network named MSPR-Net for large-scale outdoor LiDAR point cloud registration. The main motivation of the proposed MSPR-Net is that the features of two keypoints from a true correspondence must match in different scales. From this point of view, we first utilize a multi-scale backbone to extract the multi-scale features of the keypoints. Next, we propose a bilateral outlier removal strategy to remove the potential outliers in the keypoints based on the multi-scale features. Finally, a coarse-to-fine registration way is applied to exploit the information both in feature and spatial space. Extensive experiments conducted on two large-scale outdoor LiDAR point cloud datasets demonstrate that MSPR-Net achieves state-of-the-art performance