Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors

Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years. However, the shroud of opacity surrounding the decision-making process of these detectors makes it difficult for security analysts to comprehend. To address this, various explanation approaches have been proposed to explain the predictions by highlighting important features, which have been demonstrated effective in other domains such as computer vision and natural language processing. Unfortunately, an in-depth evaluation of vulnerability-critical features, such as fine-grained vulnerability-related code lines, learned and understood by these explanation approaches remains lacking. In this study, we first evaluate the performance of ten explanation approaches for vulnerability detectors based on graph and sequence representations, measured by two quantitative metrics including fidelity and vulnerability line coverage rate. Our results show that fidelity alone is not sufficient for evaluating these approaches, as fidelity incurs significant fluctuations across different datasets and detectors. We subsequently check the precision of the vulnerability-related code lines reported by the explanation approaches, and find poor accuracy in this task among all of them. This can be attributed to the inefficiency of explainers in selecting important features and the presence of irrelevant artifacts learned by DL-based detectors.Comment: Accepted by Tose

Comparative metatranscriptomic profiling and microRNA sequencing to reveal active metabolic pathways associated with a dinoflagellate bloom.

Harmful algal blooms (HABs) have increased as a result of global climate and environmental changes, exerting increasing impacts on the aquatic ecosystem, coastal economy, and human health. Despite great research efforts, our understanding on the drivers of HABs is still limited in part because HAB species’ physiology is difficult to probe in situ. Here, we used molecular ecological analyses to characterize a dinoflagellate bloom at Xiamen Harbor, China. Prorocentrum donghaiense was identified as the culprit, which nutrient bioassays showed were not nutrient-limited. Metatranscriptome profiling revealed that P. donghaiense highly expressed genes related to N- and P-nutrient uptake, phagotrophy, energy metabolism (photosynthesis, oxidative phophorylation, and rhodopsin) and carbohydrate metabolism (glycolysis/gluconeogenesis, TCA cycle and pentose phosphate) during the bloom. Many genes in P. donghaiense were up-regulated at night, including phagotrophy and environmental communication genes, and showed active expression in mitosis. Eight microbial defense genes were up-regulated in the bloom compared with previously analyzed laboratory cultures. Furthermore, 76 P. donghaiense microRNA were identified from the bloom, and their target genes exhibited marked differences in amino acid metabolism between the bloom and cultures and the potential of up-regulated antibiotic and cell communication capabilities. These findings, consistent with and complementary to recent reports, reveal major metabolic processes in P. donghaiense potentially important for bloom formation and provide a gene repertoire for developing bloom markers in future research

Comparative metatranscriptomic profiling and microRNA sequencing to reveal active metabolic pathways associated with a dinoflagellate bloom

Abstract(#br)Harmful algal blooms (HABs) have increased as a result of global climate and environmental changes, exerting increasing impacts on the aquatic ecosystem, coastal economy, and human health. Despite great research efforts, our understanding on the drivers of HABs is still limited in part because HAB species’ physiology is difficult to probe in situ . Here, we used molecular ecological analyses to characterize a dinoflagellate bloom at Xiamen Harbor, China. Prorocentrum donghaiense was identified as the culprit, which nutrient bioassays showed were not nutrient-limited. Metatranscriptome profiling revealed that P. donghaiense highly expressed genes related to N- and P-nutrient uptake, phagotrophy, energy metabolism (photosynthesis, oxidative phophorylation, and rhodopsin) and carbohydrate metabolism (glycolysis/gluconeogenesis, TCA cycle and pentose phosphate) during the bloom. Many genes in P. donghaiense were up-regulated at night, including phagotrophy and environmental communication genes, and showed active expression in mitosis. Eight microbial defense genes were up-regulated in the bloom compared with previously analyzed laboratory cultures. Furthermore, 76 P. donghaiense microRNA were identified from the bloom, and their target genes exhibited marked differences in amino acid metabolism between the bloom and cultures and the potential of up-regulated antibiotic and cell communication capabilities. These findings, consistent with and complementary to recent reports, reveal major metabolic processes in P. donghaiense potentially important for bloom formation and provide a gene repertoire for developing bloom markers in future research

Association of lipoprotein lipase (LPL) gene variants with hyperlipidemic acute pancreatitis in southeastern Chinese population

ABSTRACT Objective: The study aims to explore the relationship between lipoprotein lipase (LPL) variants and hyperlipidemic acute pancreatitis (HLAP) in the southeastern Chinese population. Subjects and methods: In total, 80 participants were involved in this study (54 patients with HLAP and 26 controls). All coding regions and intron-exon boundaries of the LPL gene were sequenced. The correlations between variants and phenotypes were also analysed. Results: The rate of rare LPL variants in the HLAP group is 14.81% (8 of 54), higher than in controls. Among the detected four variants (rs3735959, rs371282890, rs761886494 and rs761265900), the most common variant was rs371282890. Further analysis demonstrated that subjects with rs371282890 "GC" genotype had a 2.843-fold higher risk for HLAP (odds ratio [OR]: 2.843, 95% confidence interval [CI]: 1.119-7.225, p = 0.028) than subjects with the "CC" genotype. After adjusting for sex, the association remained significant (adjusted OR: 3.083, 95% CI: 1.208-7.869, p = 0.018). Subjects with rs371282890 "GC" genotype also exhibited significantly elevated total cholesterol, triglyceride and non-high-density lipoprotein cholesterol levels in all the participants and the HLAP group (p < 0.05). Conclusion: Detecting rare variants in LPL might be valuable for identifying higher-risk patients with HLAP and guiding future individualised therapeutic strategies

Generation of ESTs for Flowering Gene Discovery and SSR Marker Development in Upland Cotton

BACKGROUND: Upland cotton, Gossypium hirsutum L., is one of the world's most important economic crops. In the absence of the entire genomic sequence, a large number of expressed sequence tag (EST) resources of upland cotton have been generated and used in several studies. However, information about the flower development of this species is rare. METHODOLOGY/PRINCIPAL FINDINGS: To clarify the molecular mechanism of flower development in upland cotton, 22,915 high-quality ESTs were generated and assembled into 14,373 unique sequences consisting of 4,563 contigs and 9,810 singletons from a normalized and full-length cDNA library constructed from pooled RNA isolated from shoot apexes, squares, and flowers. Comparative analysis indicated that 5,352 unique sequences had no high-degree matches to the cotton public database. Functional annotation showed that several upland cotton homologs with flowering-related genes were identified in our library. The majority of these genes were specifically expressed in flowering-related tissues. Three GhSEP (G. hirsutum L. SEPALLATA) genes determining floral organ development were cloned, and quantitative real-time PCR (qRT-PCR) revealed that these genes were expressed preferentially in squares or flowers. Furthermore, 670 new putative microsatellites with flanking sequences sufficient for primer design were identified from the 645 unigenes. Twenty-five EST-simple sequence repeats were randomly selected for validation and transferability testing in 17 Gossypium species. Of these, 23 were identified as true-to-type simple sequence repeat loci and were highly transferable among Gossypium species. CONCLUSIONS/SIGNIFICANCE: A high-quality, normalized, full-length cDNA library with a total of 14,373 unique ESTs was generated to provide sequence information for gene discovery and marker development related to upland cotton flower development. These EST resources form a valuable foundation for gene expression profiling analysis, functional analysis of newly discovered genes, genetic linkage, and quantitative trait loci analysis

Cassava genome from a wild ancestor to cultivated varieties

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology