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With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users' devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious applications that exploit unknown vulnerabilities) classification problem. In this article, we introduce an innovative meta-learning approach for multi-family Android malware classification named Meta-MAMC, which uses meta-learning technology to learn meta-knowledge (i.e., the similarities and differences among different malware families) of few-family samples and combines new sampling algorithms to solve the above challenges. Meta-MAMC integrates (i) the meta-knowledge contained within the dataset to guide models in learning to identify unknown malware; and (ii) more accurate and diverse tasks based on novel sampling strategies, as well as directly adapting meta-learning to a new few-sample and zero-sample task to classify families. We have evaluated Meta-MAMC on two popular datasets and a corpus of real-world Android applications. The results demonstrate its efficacy in accurately classifying malicious applications belonging to certain malware families, even achieving 100% classification in some families. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer. Methods: The Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed. Results: We identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A's interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels. Conclusions: This study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A. [ABSTRACT FROM AUTHOR]

Code search, which refers to the process of identifying the most relevant code snippets for a given natural language query, plays a crucial role in software maintenance. However, current approaches heavily rely on labeled data for training, which results in performance decreases when confronted with cross-domain scenarios including domain- or project-specific situations. This decline can be attributed to their limited ability to effectively capture the semantics associated with such scenarios. To tackle the aforementioned problem, we propose a zeRo-shot domAin adaPtion with pre-traIned moDels framework for code search named RAPID. The framework first generates synthetic data by pseudo labeling, then trains the CodeBERT with sampled synthetic data. To avoid the influence of noisy synthetic data and enhance the model performance, we propose a mixture sampling strategy to obtain hard negative samples during training. Specifically, the mixture sampling strategy considers both relevancy and diversity to select the data that are hard to be distinguished by the models. To validate the effectiveness of our approach in zero-shot settings, we conduct extensive experiments and find that RAPID outperforms the CoCoSoDa and UniXcoder model by an average of 15.7% and 10%, respectively, as measured by the MRR metric. When trained on full data, our approach results in an average improvement of 7.5% under the MRR metric using CodeBERT. We observe that as the model's performance in zero-shot tasks improves, the impact of hard negatives diminishes. Our observation also indicates that fine-tuning CodeT5 for generating pseudo labels can enhance the performance of the code search model, and using only 100-shot samples can yield comparable results to the supervised baseline. Furthermore, we evaluate the effectiveness of RAPID in real-world code search tasks in three GitHub projects through both human and automated assessments. Our findings reveal RAPID exhibits superior performance, e.g., an average improvement of 18% under the MRR metric over the top-performing model. [ABSTRACT FROM AUTHOR]

Recent evidence has shed light on the significant role of FANCD2 in cancer initiation, development, and progression. However, a comprehensive pan-cancer analysis of FANCD2 has been lacking. In this study, we have conducted a thorough investigation into the expression profiles and prognostic significance of FANCD2, as well as its correlation with clinicopathological parameters and immune cell infiltration, using advanced bioinformatic techniques. The results demonstrate that FANCD2 is significantly upregulated in various common cancers and is associated with prognosis. Notably, higher expression levels of FANCD2 are linked to poor overall survival, as indicated by Cox regression and Kaplan-Meier analyses. Additionally, we have observed a decrease in the methylation of FANCD2 DNA in some cancers, and this decrease is inversely correlated with FANCD2 expression. Genetic alterations in FANCD2 predominantly manifest as mutations, which are associated with overall survival, disease-specific survival, disease-free survival, and progression-free survival in certain tumor types. Moreover, FANCD2 exhibits a strong correlation with infiltrating cell levels, immune checkpoint genes, tumor mutation burden (TMB), and microsatellite instability (MSI). Enrichment analysis further highlights the potential impact of FANCD2 on Fanconi anemia (FA) pathway and cell cycle regulation. Through this comprehensive pan-cancer analysis, we have gained a deeper understanding of the functions of FANCD2 in oncogenesis and metastasis across different types of cancer. [ABSTRACT FROM AUTHOR]

Technical Q&A sites, such as Stack Overflow and Ask Ubuntu, have been widely utilized by software engineers to seek support for development challenges. However, not all the raised questions get instant feedback, and the retrieved answers can vary in quality. The users can hardly avoid spending much time before solving their problems. Prior studies propose approaches to automatically recommend answers for the question posts on technical Q&A sites. However, the lengthiness and the lack of background knowledge issues limit the performance of answer recommendation on these sites. The irrelevant sentences in the posts may introduce noise to the semantics learning and prevent neural models from capturing the gist of texts. The lexical gap between question and answer posts further misleads current models to make failure recommendations. From this end, we propose a novel neural network named TopicAns for answer selection on technical Q&A sites. TopicAns aims at learning high-quality representations for the posts in Q&A sites with a neural topic model and a pre-trained model. This involves three main steps: (1) generating topic-aware representations of Q&A posts with the neural topic model, (2) incorporating the corpus-level knowledge from the neural topic model to enhance the deep representations generated by the pre-trained language model, and (3) determining the most suitable answer for a given query based on the topic-aware representation and the deep representation. Moreover, we propose a two-stage training technique to improve the stability of our model. We conduct comprehensive experiments on four benchmark datasets to verify our proposed TopicAns's effectiveness. Experiment results suggest that TopicAns consistently outperforms state-of-the-art techniques by over 30% in terms of Precision@1. [ABSTRACT FROM AUTHOR]

In massive and rapid graph streams, a useful and important task is to summarize the structure of graph streams in order to enable efficient and effective graph query processing. Although this task has been extensively studied in the literature, we observe that the existing solutions for graph sketches can only answer queries about the current status of the graph stream. In this paper, we aim at designing persistent graph sketches to support graph queries in any given time range in the past. To this end, we first introduce a baseline method by extending an existing graph summarization method. However, our empirical study suggests that the accuracy performance of the baseline method is unsatisfactory, especially when the query time interval is large. To tackle this issue, we propose a new method PGSS-BDH, which stores the streaming edges using a set of hierarchically organized hashmaps. When a query arrives, we divide the query time interval into a set of disjoint sub-intervals and return the sum of query results on all sub-intervals as the overall query answer. Observing that PGSS-BDH bears a linear space cost to the graph stream size, we further propose an advance method PGSS-MDC by using a set of fixed-size hierarchical counters to store the weight of edges, where the query processing is similar to PGSS-BDH. We theoretically analyze the accuracy performance of PGSS-BDH and PGSS-MDC. The experiment results on real-life datasets demonstrate that PGSS-MDC can return much more accurate answers than the competitors by consuming comparable query time and much less memory. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese medicine (TCM) has been extensively used for neoplasm treatment and has provided many promising therapeutic candidates. We previously found that Centipeda minima (C. minima), a Chinese medicinal herb, showed anti-cancer effects in lung cancer. However, the active components and underlying mechanisms remain unclear. In this study, we used network pharmacology to evaluate C. minima active compounds and molecular mechanisms in lung cancer. Methods: We screened the TCMSP database for bioactive compounds and their corresponding potential targets. Lung cancer-associated targets were collected from Genecards, OMIM, and Drugbank databases. We then established a drug-ingredients-gene symbols-disease (D-I-G-D) network and a protein–protein interaction (PPI) network using Cytoscape software, and we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses using R software. To verify the network pharmacology results, we then performed survival analysis, molecular docking analysis, as well as in vitro and in vivo experiments. Results: We identified a total of 21 C. minima bioactive compounds and 179 corresponding targets. We screened 804 targets related to lung cancer, 60 of which overlapped with C. minima. The top three candidate ingredients identified by D-I-G-D network analysis were quercetin, nobiletin, and beta-sitosterol. PPI network and core target analyses suggested that TP53, AKT1, and MYC are potential therapeutic targets. Moreover, molecular docking analysis confirmed that quercetin, nobiletin, and beta-sitosterol, combined well with TP53, AKT1, and MYC respectively. In vitro experiments verified that quercetin induced non-small cell lung cancer (NSCLC) cell death in a dose-dependent manner. GO and KEGG analyses found 1771 enriched GO terms and 144 enriched KEGG pathways, including a variety of cancer related pathways, the IL-17 signaling pathway, the platinum drug resistance pathway, and apoptosis pathways. Our in vivo experimental results confirmed that a C. minima ethanol extract (ECM) enhanced cisplatin (CDDP) induced cell apoptosis in NSCLC xenografts. Conclusions: This study revealed the key C. minima active ingredients and molecular mechanisms in the treatment of lung cancer, providing a molecular basis for further C. minima therapeutic investigation. [ABSTRACT FROM AUTHOR]

Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user experience during app development and maintenance to improve the rating of their apps on app platforms and attract more user downloads. Previous studies indicated that responding to users' reviews tends to change their attitude towards the apps positively. Users who have been replied are more likely to update the given ratings. However, reading and responding to every user review is not an easy task for developers since it is common for popular apps to receive tons of reviews every day. Thus, automation tools for review replying are needed. To address the need above, the paper introduces a Transformer‐based approach, named TRRGen, to automatically generate responses to given user reviews. TRRGen extracts apps' categories, rating, and review text as the input features. By adapting a Transformer‐based model, TRRGen can generate appropriate replies for new reviews. Comprehensive experiments and analysis on the real‐world datasets indicate that the proposed approach can generate high‐quality replies for users' reviews and significantly outperform current state‐of‐art approaches on the task. The manual validation results on the generated replies further demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Water quality testing is of great significance for daily water use or water use in high precision manufacturing. The use of single-photon detection technology to detect water particles is one of the important methods of physical water quality detection. However, the current water quality particle detection technology needs to be improved in the range and accuracy of turbidity detection. The paper aims to use single-photon detection technology to design a fiber optic turbidity detection system to solve the current problems of small turbidity detection range and low accuracy in a large range. In response to this, this paper has made an in-depth understanding of single-photon detection technology and designed two sets of detection schemes using the performance of photons. The first set is the transmission optical fiber turbidity detection, which can effectively detect the ultralow turbidity range and a wide range of water quality particles. The second set is a scattering optical fiber turbidity detection system, which can effectively detect water particles in the low and medium turbidity range. In this paper, the two systems were integrated into a whole system, so the results of the two detection methods can also be compared, and the detected turbidity can be obtained more accurately. The experimental results showed that for the transmission optical fiber turbidity detection system, the detection effect of the system was the best under the incident light intensity of 11 wm, and the fitting value was 0.99; for the scattering optical fiber turbidity detection system, the detection effect of the system was the best under the incident light intensity of 4 wm, and the fitting value was 0.99. [ABSTRACT FROM AUTHOR]

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The objective of this study is to explore the value of fecal calprotectin (FC) for early screening of the abdominal type of Henoch-Schonlein purpura (AHSP) in children. The study cohort included 40 children with AHSP treated at Shengjing Hospital of China Medical University from November 2014 to November 2015, and 40 children hospitalized in the Division of Pediatric Orthopedics in the corresponding period were selected as a control group. Fresh fecal samples were collected in the acute phase of the first visit (FC1), 3 days after treatment (FC2), and 7 days after treatment (FC3) from the AHSP group and the control group. Calprotectin levels in the fecal samples were measured using an enzyme-linked immunosorbent assay. At the same time, gastrointestinal performance and the laboratory examination indicators white blood cell (WBC) count and C-reactive protein (CRP) level were recorded. The median levels of FC1 (3053 μg/g) and FC2 (2778.3 μg/g) were higher than in the control group (102.5 μg/g), with significant differences among the three groups (p < 0.001). FC levels gradually decreased in remission, and the level of FC3 on day 7 was close to that of the control samples (p > 0.05). When the optimal cut-off was 264.5 μg/g, the area under the receiver operating characteristic (ROC) curve of FC for diagnosis of AHSP was 0.961 with a corresponding sensitivity and specificity of 93.1 and 87.5%, respectively. The levels of FC in children with AHSP were positively correlated with WBC count (rs = 0.688) and CRP value (rs = 0.513). The area under the ROC curve of WBC count for screening AHSP was 0.785 when the optimal cut-off value was 11.1 × 109/L with a corresponding sensitivity and specificity of 81.5 and 62.5%, respectively. The area under the ROC curve of CRP was 0.963 when the optimal cut-off value was 5.72 mg/dL with a corresponding sensitivity and specificity of 88.9 and 100%, respectively. Comparisons of FC, WBC count, and CRP level as diagnostic indicators of AHSP showed that the sensitivity of FC was higher than that of the WBC count and CRP level, and its diagnostic value was better than that of the WBC count. The levels of FC began to increase in the early stages of AHSP, showing a decreasing tendency in remission and tending to be within a normal range after a week or so. For the early diagnosis of AHSP, FC with a cut-off level of 264.5 μg/g has good sensitivity and specificity. The sensitivity of FC is better than that of the traditional inflammation indicators CRP and WBC count, and its diagnostic performance is better than WBC count; FC can be suitable as a new marker for the early diagnosis of AHSP. [ABSTRACT FROM AUTHOR]

This article takes advantage of the pilot Emissions Trading Scheme (ETS) project to estimate the causal impact of the ETS on CO2 abatement in China. The CO2 emissions and CO2 intensities of each province are calculated by using the fossil fuel data of 30 provincial administration regions from 2006 to 2016. Then difference in difference (DiD) models and propensity score matching (PSM) with panel data are applied to estimate the causal impact of the pilot ETS project. Results show that the pilot regions reduce their CO2 emissions and intensities more than the non-pilot regions under the pilot ETS project. The pilot ETS project significantly induced 12% decreases in the nominal CO2 intensity and 7.6% decrease in the real CO2 intensity, after controlling for regional heterogeneity, but its reduction effects on CO2 emissions are insignificant. Decreasing the proportion of coal to total energy consumption may be the main channel of the pilot ETS project inducing CO2 abatement. The estimated results for control variables indicate that upgrading industrial structures, attracting FDI, and purifying the export structure have significant effects on CO2 abatement. [ABSTRACT FROM AUTHOR]