BACKGROUND AND OBJECTIVE: Early-onset asthma (EOA) significantly increases the risk of chronic obstructive pulmonary disease (COPD), yet the causal mechanisms and molecular mediators underlying this progression remain poorly understood. Multi-omics integration provides a powerful framework for prioritizing potential mediating proteins and disease-specific therapeutic candidates. METHODS: This study integrated large-scale genetic and proteomic data using Mendelian randomization (MR) approaches to investigate the progression from EOA to COPD. Proteome-wide MR evaluated protein quantitative trait loci (pQTLs) in relation to EOA and COPD risk, with mediation analysis evaluating their roles and single-cell transcriptomics defining the cell-type-specific expression of the mediating proteins. Finally, colocalization, multi-tissue expression quantitative trait loci (eQTLs), and druggability assessment were used to prioritize potential disease-specific therapeutic targets. RESULTS: Evidence from genetic instruments supports a causal relationship between EOA and COPD. Proteome-wide analyses of 7847 pQTLs identified 339 proteins with potential effects on EOA and 389 on COPD. Six proteins, KREMEN1, BLMH, CNTN5, IL1RN, MIA, and PILRA, showed statistically significant mediation effects in the EOA-to-COPD pathway. PILRA strongly colocalized at shared genetic loci between the two diseases and was significantly downregulated in macrophages from COPD patients. For disease-specific targets, immune-tissue eQTL validation supported ITPKA in EOA. Integration of druggability assessment with multi-tissue eQTL analyses prioritized FES, CCN3, NMI, and NMT1 as promising therapeutic candidates for COPD. CONCLUSION: These findings provide genetic evidence supporting a causal relationship between EOA and COPD, reveal putative mediating proteins, and prioritize therapeutic candidates with translational potential, offering new insights into pathogenesis, prevention, and intervention.

Semantic segmentation is crucial in autonomous driving, offering exceptional scene understanding to tackle challenges like small object edges and blurred textures in complex traffic environments. By performing pixel-level classification, it provides vehicles with comprehensive environmental information, ensuring safe navigation. However, when dealing with fine, fuzzy-bordered objects in complex scenes, the existing techniques face the issue of low segmentation accuracy due to their insufficient feature extraction capability. To address this problem, this study proposes a semantic segmentation network that combines Gaussian perception with iterative multi-scale attention. The method integrates Gaussian perception with local–global channel attention, accurately models pixel associations, and dynamically focuses on features to address the issue of edge blurring in complex scene segmentation. At the same time, the method employs the difference module to enhance the low-frequency features, and integrates the iterative multi-scale attention mechanism to achieve deep integration of low-frequency and high-frequency information. This enhances the fine capture of features and mitigates the edge discontinuity issue in segmentation caused by the masking of boundary information. In addition, the method combines channel and spatial attention to optimize feature extraction, enhance the sensory field, and improve detail, context, and boundary recognition abilities. This significantly improves the feature expression ability and reduces the probability of background mis-segmentation. The experimental results show that the proposed method achieves 79.34% mIoU on the Cityscapes validation set (a 1.32% improvement over PIDNet-S) and 81.48% mIoU on the CamVid test set (a 1.05% improvement over PIDNet-S). These results demonstrate significant improvements over existing state-of-the-art methods, confirming the effectiveness of this approach in semantic segmentation of complex urban scenes. The source code has been made publicly available on GitHub: https://github.com/wgsheng897/GMSANet.git

Web Application Firewalls (WAFs) are a crucial line of defense against web-based attacks. However, an emerging threat comes from protocol-level evasion vulnerabilities, in which adversaries exploit parsing discrepancies between the WAF HTTP parser and those of web applications to circumvent WAFs. Currently, uncovering these vulnerabilities still depends on manual, ad hoc methods. In this paper, we propose WAF Manis, a novel testing methodology to automatically discover protocol-level evasion vulnerabilities in WAFs. We evaluated WAF Manis against 14 popular WAFs including Cloudflare and ModSecurity and 20 popular web frameworks including Laravel and Spring. In total, we discovered 311 protocol-level evasion cases affecting all tested WAFs and applications. Due to the generic nature of protocol-level evasions, these evasion vulnerabilities do not hinge on specific payload patterns and can transmit any malicious payloads - for instance, SQL injection, XSS, or Log4jShell - to the target websites. We further analyzed these vulnerabilities and identified three primary reasons contributing to WAF evasions. We have reported those identified vulnerabilities to the affected providers and received acknowledgments and bug bounty rewards from Cloudflare WAF, Fortinet WAF, Alibaba Cloud WAF, Huawei Cloud WAF, ModSecurity, Go security Team, and the PHP security team.

Content Delivery Networks (CDNs) aim to improve network performance and protect against web attack traffic for their hosting websites. And the HTTP range request mechanism is majorly designed to reduce unnecessary network transmission. However, we find the specifications failed to consider the security risks introduced when CDNs meet range requests. In this study, we present a novel class of HTTP amplification attack, Range-based Amplification (RangeAmp) Attacks. It allows attackers to massively exhaust not only the outgoing bandwidth of the origin servers deployed behind CDNs but also the bandwidth of CDN surrogate nodes. We examined the RangeAmp attacks on 13 popular CDNs to evaluate the feasibility and real-world impacts. Our experiment results show that all these CDNs are affected by the RangeAmp attacks. We also disclosed all security issues to affected CDN vendors and already received positive feedback from 12 vendors.

Content Delivery Networks (CDNs) are critical Internet infrastructure. Besides high availability and high performance, CDNs also provide security services such as anti-DoS and Web Application Firewalls to CDN-powered websites. However, the massive resources of CDNs may also be leveraged by attackers exploiting their architectural, implementation, or operational weaknesses. In this paper, we show that today's CDN operation is overly loose in customer-controlled forwarding policy and the lack of origin validation leads to a wide range of abuse cases such as DoS attack and stealthy port scan. We systematically study these abuse cases and demonstrate their feasibility in popular CDNs. Further, we evaluate the impact of these abuses by discovering that there are millions of CDN edge servers, and a substantial fraction of them can be abused. Lastly, we propose mitigation solutions against such abuses and discuss their feasibility.