CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario

We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. In this paper, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. Moreover, we also build a new large-scale malware dataset, named MAL-100, to fill the gap of lacking large open-set malware benchmark dataset. Experimental results on two widely used malware datasets and our MAL-100 demonstrate the effectiveness of our model compared with other representative methods.Comment: 16 pages, 8 figure

User-Defined Privacy Location-Sharing System in Mobile Online Social Networks

With the fusion of social networks and location-based services, location sharing is one of the most important services in mobile online social networks (mOSNs). In location-sharing services, users have to provide their location information to service provider. However, location information is sensitive to users, which may cause a privacy-preserving issue needs to be solved. In the existing research, location-sharing services, such as friends’ query, does not consider the attacks from friends. In fact, a user may not trust all of his/her friends, so just a part of his/her friends will be allowed to obtain the user’s location information. In addition, users’ location privacy and social network privacy should be guaranteed. In order to solve the above problems, we propose a new architecture and a new scheme called User-Defined Privacy Location-Sharing (UDPLS) system for mOSNs. In our scheme, the query time is almost irrelevant to the number of friends. We also evaluate the performance and validate the correctness of our proposed algorithm through extensive simulations

MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from known families and detect the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this paper, we propose the Multi-modal Dual-Embedding Networks, dubbed MDENet, to take advantage of comprehensive malware features (i.e., malware images and malware sentences) from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Last, to further guarantee the open-set recognition, we dually embed the fused multi-modal representation into one primary space and an associated sub-space, i.e., discriminative and exclusive spaces, with contrastive sampling and rho-bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multi-modal characteristics and contribute an improved version dubbed MAL-100+. Experimental results on the widely used malware dataset Mailing and the proposed MAL-100+ demonstrate the effectiveness of our method.Comment: 14 pages, 7 figure

De novo transcriptome sequencing and comprehensive analysis of the drought-responsive genes in the desert plant Cynanchum komarovii

The figure of an agarose gel with PCR products. (TIFF 11899 kb

De novo sequencing and comparative transcriptome analysis of white petals and red labella in Phalaenopsis for discovery of genes related to flower color and floral differentation

Phalaenopsis is one of the world’s most popular and important epiphytic monopodial orchids. The extraordinary floral diversity of Phalaenopsis is a reflection of its evolutionary success. As a consequence of this diversity, and of the complexity of flower color development in Phalaenopsis, this species is a valuable research material for developmental biology studies. Nevertheless, research on the molecular mechanisms underlying flower color and floral organ formation in Phalaenopsis is still in the early phases. In this study, we generated large amounts of data from Phalaenopsis flowers by combining Illumina sequencing with differentially expressed gene (DEG) analysis. We obtained 37 723 and 34 020 unigenes from petals and labella, respectively. A total of 2736 DEGs were identified, and the functions of many DEGs were annotated by BLAST-searching against several public databases. We mapped 837 up-regulated DEGs (432 from petals and 405 from labella) to 102 Kyoto Encyclopedia of Genes and Genomes pathways. Almost all pathways were represented in both petals (102 pathways) and labella (99 pathways). DEGs involved in energy metabolism were significantly differentially distributed between labella and petals, and various DEGs related to flower color and floral differentiation were found in the two organs. Interestingly, we also identified genes encoding several key enzymes involved in carotenoid synthesis. These genes were differentially expressed between petals and labella, suggesting that carotenoids may influence Phalaenopsis flower color. We thus conclude that a combination of anthocyanins and/or carotenoids determine flower color formation in Phalaenopsis. These results broaden our understanding of the mechanisms controlling flower color and floral organ differentiation in Phalaenopsis and other orchids

De novo sequencing and comparative transcriptome analysis of white petals and red labella in Phalaenopsis for discovery of genes related to flower color and floral differentation

Phalaenopsis is one of the world’s most popular and important epiphytic monopodial orchids. The extraordinary floral diversity of Phalaenopsis is a reflection of its evolutionary success. As a consequence of this diversity, and of the complexity of flower color development in Phalaenopsis, this species is a valuable research material for developmental biology studies. Nevertheless, research on the molecular mechanisms underlying flower color and floral organ formation in Phalaenopsis is still in the early phases. In this study, we generated large amounts of data from Phalaenopsis flowers by combining Illumina sequencing with differentially expressed gene (DEG) analysis. We obtained 37 723 and 34 020 unigenes from petals and labella, respectively. A total of 2736 DEGs were identified, and the functions of many DEGs were annotated by BLAST-searching against several public databases. We mapped 837 up-regulated DEGs (432 from petals and 405 from labella) to 102 Kyoto Encyclopedia of Genes and Genomes pathways. Almost all pathways were represented in both petals (102 pathways) and labella (99 pathways). DEGs involved in energy metabolism were significantly differentially distributed between labella and petals, and various DEGs related to flower color and floral differentiation were found in the two organs. Interestingly, we also identified genes encoding several key enzymes involved in carotenoid synthesis. These genes were differentially expressed between petals and labella, suggesting that carotenoids may influence Phalaenopsis flower color. We thus conclude that a combination of anthocyanins and/or carotenoids determine flower color formation in Phalaenopsis. These results broaden our understanding of the mechanisms controlling flower color and floral organ differentiation in Phalaenopsis and other orchids

A Single-Cell Atlas of Bovine Skeletal Muscle Reveals Mechanisms Regulating Intramuscular Adipogenesis and Fibrogenesis

Background Intramuscular fat (IMF) and intramuscular connective tissue (IMC) are often seen in human myopathies and are central to beef quality. The mechanisms regulating their accumulation remain poorly understood. Here, we explored the possibility of using beef cattle as a novel model for mechanistic studies of intramuscular adipogenesis and fibrogenesis. Methods Skeletal muscle single-cell RNAseq was performed on three cattle breeds, including Wagyu (high IMF), Brahman (abundant IMC but scarce IMF), and Wagyu/Brahman cross. Sophisticated bioinformatics analyses, including clustering analysis, gene set enrichment analyses, gene regulatory network construction, RNA velocity, pseudotime analysis, and cell-cell communication analysis, were performed to elucidate heterogeneities and differentiation processes of individual cell types and differences between cattle breeds. Experiments were conducted to validate the function and specificity of identified key regulatory and marker genes. Integrated analysis with multiple published human and non-human primate datasets was performed to identify common mechanisms. Results A total of 32 708 cells and 21 clusters were identified, including fibro/adipogenic progenitor (FAP) and other resident and infiltrating cell types. We identified an endomysial adipogenic FAP subpopulation enriched for COL4A1 and CFD (log2FC = 3.19 and 1.92, respectively; P \u3c 0.0001) and a perimysial fibrogenic FAP subpopulation enriched for COL1A1 and POSTN (log2FC = 1.83 and 0.87, respectively; P \u3c 0.0001), both of which were likely derived from an unspecified subpopulation. Further analysis revealed more progressed adipogenic programming of Wagyu FAPs and more advanced fibrogenic programming of Brahman FAPs. Mechanistically, NAB2 drives CFD expression, which in turn promotes adipogenesis. CFD expression in FAPs of young cattle before the onset of intramuscular adipogenesis was predictive of IMF contents in adulthood (R2 = 0.885, P \u3c 0.01). Similar adipogenic and fibrogenic FAPs were identified in humans and monkeys. In aged humans with metabolic syndrome and progressed Duchenne muscular dystrophy (DMD) patients, increased CFD expression was observed (P \u3c 0.05 and P \u3c 0.0001, respectively), which was positively correlated with adipogenic marker expression, including ADIPOQ (R2 = 0.303, P \u3c 0.01; and R2 = 0.348, P \u3c 0.01, respectively). The specificity of Postn/POSTN as a fibrogenic FAP marker was validated using a lineage-tracing mouse line. POSTN expression was elevated in Brahman FAPs (P \u3c 0.0001) and DMD patients (P \u3c 0.01) but not in aged humans. Strong interactions between vascular cells and FAPs were also identified. Conclusions Our study demonstrates the feasibility of beef cattle as a model for studying IMF and IMC. We illustrate the FAP programming during intramuscular adipogenesis and fibrogenesis and reveal the reliability of CFD as a predictor and biomarker of IMF accumulation in cattle and humans