Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation

Due to convenience, open-source software is widely used. For beneficial reasons, open-source maintainers often fix the vulnerabilities silently, exposing their users unaware of the updates to threats. Previous works all focus on black-box binary detection of the silent dependency alerts that suffer from high false-positive rates. Open-source software users need to analyze and explain AI prediction themselves. Explainable AI becomes remarkable as a complementary of black-box AI models, providing details in various forms to explain AI decisions. Noticing there is still no technique that can discover silent dependency alert on time, in this work, we propose a framework using an encoder-decoder model with a binary detector to provide explainable silent dependency alert prediction. Our model generates 4 types of vulnerability key aspects including vulnerability type, root cause, attack vector, and impact to enhance the trustworthiness and users' acceptance to alert prediction. By experiments with several models and inputs, we confirm CodeBERT with both commit messages and code changes achieves the best results. Our user study shows that explainable alert predictions can help users find silent dependency alert more easily than black-box predictions. To the best of our knowledge, this is the first research work on the application of Explainable AI in silent dependency alert prediction, which opens the door of the related domains

Deep Sequencing Analyses of DsiRNAs Reveal the Influence of 3′ Terminal Overhangs on Dicing Polarity, Strand Selectivity, and RNA Editing of siRNAs

25/27 Base duplex RNAs that are substrates for Dicer have been demonstrated to enhance RNA interference (RNAi) potency and efficacy. Since the target sites are not always equally susceptible to suppression by small interfering RNA (siRNA), not all 27-mer duplexes that are processed into the corresponding conventional siRNAs show increased potency. Thus random designing of Dicer-substrate siRNAs (DsiRNAs) may generate siRNAs with poor RNAi due to unpredictable Dicer processing. Previous studies have demonstrated that the 3′-overhang affects dicing cleavage site and the orientation of Dicer entry. Moreover, an asymmetric 27-mer duplex having a 3′ two-nucleotide overhang and 3′-DNA residues on the blunt end has been rationally designed to obtain greater efficacy. This asymmetric structure directs dicing to predictably yield a single primary cleavage product. In the present study, we analyzed the in vitro and intracellular dicing patterns of chemically synthesized duplex RNAs with different 3′-overhangs. Consistent with previous studies, we observed that Dicer preferentially processes these RNAs at a site 21–22 nucleotide (nt) from the two-base 3′-overhangs. We also observed that the direction and ability of human Dicer to generate siRNAs can be partially or completely blocked by DNA residues at the 3′-termimi. To examine the effects of various 3′-end modifications on Dicer processing in cells, we employed Illumina Deep sequencing analyses to unravel the fates of the asymmetric 27-mer duplexes. To validate the strand selection process and knockdown capabilities we also conducted dual-luciferase psiCHECK reporter assays to monitor the RNAi potencies of both the “sense” (S) and “antisense” (AS) strands derived from these DsiRNAs. Consistent with our in vitro Dicer assays, the asymmetric duplexes were predictably processed into desired primary cleavage products of 21–22-mers in cells. We also observed the trimming of the 3′ end, especially when DNA residues were incorporated into the overhangs and this trimming ultimately influenced the Dicer-cleavage site and RNAi potency. Moreover, the observation that the most efficacious strand was the most abundant revealed that the relative frequencies of each “S” or “AS” strand are highly correlated with the silencing activity and strand selectivity. Collectively, our data demonstrate that even though the only differences between a family of DsiRNAs was the 3′ two-nuclotide overhang, dicing polarity and strand selectivity are distinct depending upon the sequence and chemical nature of this overhang. Thus, it is possible to predictably control dicing polarity and strand selectivity via simply changing the 3′-end overhangs without altering the original duplex sequence. These optimal design features of 3′-overhangs might provide a facile approach for rationally designing highly potent 25/27-mer DsiRNAs

Crop Area Estimation from UAV Transect and MSR Image Data Using Spatial Sampling Method

AbstractUsing remote sensing data to estimate crop area is efficient to a wide range of end-users, including government agencies, farmers and researchers. Moderate spatial resolution (MSR) image data are widely used to estimate crop area. But its accuracy can’t meet the demands of precision. Spatial sampling techniques integrated the strengths of remote sensing and sampling survey are being widely used. This method need large sample size which is cannot be guaranteed by remote sensing due to weather. The Unmanned Aerial Vehicle (UAV) can be used as an effective way to guarantee enough sample size. This paper proposed a spatial sampling method using MSR image classification results and UAV transects, a stratified random sampling method was proposed, area-scale (from MSR image classification) was used as auxiliary variable to guide the distribution of UAV transects, which had proved that 2% sampling ratio can make the crop area estimation accuracy more than 95% with a 95% confidence interval

Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference

APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitations, such as limited API text corpus and affected by the characteristics of the input text.To address these limitations, we propose utilizing large language models (LLMs) (e.g., GPT-3.5) as a neural knowledge base for API relation inference. This approach leverages the entire Web used to pre-train LLMs as a knowledge base and is insensitive to the context and complexity of input texts. To ensure accurate inference, we design our analytic flow as an AI Chain with three AI modules: API FQN Parser, API Knowledge Extractor, and API Relation Decider. The accuracy of the API FQN parser and API Relation Decider module are 0.81 and 0.83, respectively. Using the generative capacity of the LLM and our approach's inference capability, we achieve an average F1 value of 0.76 under the three datasets, significantly higher than the state-of-the-art method's average F1 value of 0.40. Compared to CoT-based method, our AI Chain design improves the inference reliability by 67%, and the AI-crowd-intelligence strategy enhances the robustness of our approach by 26%

Numerical simulation study on the effects of liquid water atomization on the flow field and performance of aluminum-based water ramjet engines

In order to investigate the effects of different water inlet droplet diameters on the performance of aluminum-based water ramjet engines, the internal flow field of the engine was analyzed through numerical simulation. The results showed that by selecting a suitable water droplet diameter at the water inlet and controlling the time required for water droplet evaporation and heat absorption, the working range of aluminum-water combustion reaction can be expanded and the specific impulse of the engine can be increased. In engine design and practical application, the design of the water injection nozzle upstream of the engine is critical, and the droplet diameter at the water inlet should be controlled within a suitable range. A diameter that is too large will reduce the evaporation efficiency and hinder the further diffusion of combustion reaction. Droplet sizes that are too small will rapidly evaporate, causing the temperature in the flow field to decrease rapidly, leading to a large range of low-temperature regions in the main reaction zone of the combustion chamber, thereby reducing the overall aluminum-water reaction rate of the engine. In addition, the variation of droplet diameter in the downstream water atomization nozzle has little effect on the aluminum-water reaction in the main combustion zone. However, reducing the droplet diameter can facilitate the downstream diffusion of the combustion reaction, further expanding the combustion range and increasing the specific impulse. Furthermore, it can also reduce the temperature near the wall, which is beneficial for reducing the overall thermal protection requirements of the engine

Retropharyngeal Lymph Node Metastasis Diagnosed by Magnetic Resonance Imaging in Hypopharyngeal Carcinoma: A Retrospective Analysis From Chinese Multi-Center Data

BackgroundTo assess the prevalence, risk factors and prognostic significance of retropharyngeal lymph node (RPLN) metastasis diagnosed by magnetic resonance imaging (MRI) in patients with hypopharyngeal squamous cell carcinoma (HPSCC).Methods259 patients from three cancer institutions in China from Jan 2010 to Dec 2018 were analyzed, retrospectively. All the patients had been given pre-treatment magnetic resonance imaging (MRI) of head and neck and were then treated with definitive radiotherapy with or without chemotherapy. Pretreatment diagnostic MRIs were reviewed by a dedicated head and neck radiologist, for the presence or absence of radiographically positive RPLN, cervical LN and tumor invasion.Demographic variables were analysed by descriptive statistics using SPSS 20.0. Predictors of the presence of RPLN and its prognostic significance were examined.ResultsRPLN metastasis was discovered in 44 patients (17%). Logistic analysis showed that posterior pharyngeal wall (PPW) primary tumor; PPW invasion; N2-3; multiple cervical lymph node (LN) involvement (>2 LNs) were associated with RPLN metastasis, with metastasis rates 37%, 30%, 31% and 33% respectively. Patients with RPLN metastasis had a significantly reduced 5-year overall survival (OS) and disease-free survival (DFS) compared to the non-RPLN metastasis group (OS 28% vs. 48%, p=0.001; DFS 25% vs. 41%, p=0.040).ConclusionsRPLN metastasis was not uncommon in HPSCC patients. Risk factors were: PPW primary tumor, PPW invasion and cervical LN status. RPLN metastasis is a poor prognosticator for survival

Downregulation of TLX induces TET3 expression and inhibits glioblastoma stem cell self-renewal and tumorigenesis

International audienceGlioblastomas have been proposed to be maintained by highly tumorigenic glioblastoma stem cells (GSCs) that are resistant to current therapy. Therefore, targeting GSCs is critical for developing effective therapies for glioblastoma. In this study, we identify the regulatory cascade of the nuclear receptor TLX and the DNA hydroxylase Ten eleven translocation 3 (TET3) as a target for human GSCs. We show that knockdown of TLX expression inhibits human GSC tumorigenicity in mice. Treatment of human GSC-grafted mice with viral vector-delivered TLX shRNA or nanovector-delivered TLX siRNA inhibits tumour development and prolongs survival. Moreover, we identify TET3 as a potent tumour suppressor downstream of TLX to regulate the growth and self-renewal in GSCs. This study identifies the TLX-TET3 axis as a potential therapeutic target for glioblastoma