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Research on road traffic condition prediction of smart city based on spatio-temporal multi-source information fusion

Characteristics of refractive errors in children with intermittent exotropia compared with the general population

Thrombin is increased in diabetic retinal pathology in the STZ mice model, and its attenuation by a specific inhibitor, PARIN5, is associated with preserved function

White-box modeling of asphaltene precipitation during natural depletion of oil reservoirs

Did a boy’s life-saving gene therapy cause his brain tumour?

Polyclonal antibodies and probiotics improve immune dysfunction in a mouse model of inflammatory bowel disease

Superhydrophobic PTFE/MWCNT coatings fabricated by supercritical fluid processing with controlled wettability

Persona-based stratification and XGBoost modeling for multi-dimensional vehicle sound quality evaluation

Inhibition of autophagy increases head and neck cancer sensitivity to cetuximab and radiation therapy

Abstract The five-year survival rate of head and neck squamous cell carcinoma (HNSCC) remains around 50% despite advances in treatments over the last 20 years. Cetuximab, a monoclonal antibody targeting the epidermal growth factor receptor (EGFR), demonstrates limited efficacy both alone and in combination with chemotherapy, with response rates below 20% and 35%, respectively. This underscores the critical need for novel therapeutic strategies and a better understanding of treatment resistance mechanisms in HNSCC. Autophagy, a cell survival process essential to cellular stress for both normal and cancer cells, may contribute to therapeutic resistance in HNSCC. We hypothesized that current treatments might inadvertently activate autophagy, enabling tumor cells to evade cell death. We demonstrated that basal autophagy is elevated in cetuximab-resistant cells using an isogenic pair of cetuximab-sensitive and -resistant cell lines. Furthermore, combining cetuximab or radiation therapy with SAR405, an autophagy inhibitor, significantly enhanced growth inhibition both in vitro and in vivo compared to either treatment alone. Knockdown of LAPTM4B or EGFR significantly reduced cetuximab-induced autophagy. In contrast, radiation-induced autophagy is primarily driven by mitochondrial autophagy (mitophagy). Diminished radiation-induced autophagy was observed by the knockdown of PINK1, a key regulator of mitophagy. These findings highlight that inhibition of autophagy can improve the efficacy of HNSCC treatments and suggest that targeting specific autophagy subtypes may be crucial for developing personalized treatment combinations for HNSCC patients.

Physics-informed machine learning for cross-study prediction of oil–water membrane fouling

Urinary biomarkers of tubular injury and inflammation in ADPKD patients under tolvaptan therapy

Six-month outcomes in a French cohort of patients receiving a safety planning type intervention after suicide attempt

A star gone rogue tears through the Galaxy

Development of VP1 based indirect ELISAs for BK and JC polyomaviruses with seroprevalence assessment and cross reactivity evaluation

Biological activity of Pleurotus eryngii based on antioxidant, anticholinesterase and antiproliferative effects using optimized extraction methods

Gamification elements and student engagement in higher education using fuzzy DEMATEL analysis

Development of an Oxford nanopore sequencing technology-based whole genome sequencing method for Plasmodium falciparum to support malaria molecular surveillance

Advancing solar and wind penetration in China through energy complementarity

Loganin alleviates oxidative stress-induced apoptosis by modulating mitochondrial function and STAT3 signaling in DSS-induced colitis and H2O2-injured Caco-2 cells

Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture

Abstract This paper proposes a Vehicle-Road-Cloud-Chain (VRCC) four-layer collaborative framework to address the issues of lack of interpretability, reputation evaluation failure, and architecture centralization vulnerability faced by federated learning in Internet of Vehicles (IoV) under Differential Privacy (DP), Non-Independent and Identically Distributed (Non-IID) data, and Byzantine attacks. The framework achieves explicit decoupling between low-latency data sharing and latency-tolerant collaborative training. Firstly, construct an asynchronous blockchain consensus layer based on Directed Acyclic Graph (DAG), which supports low confirmation latency model interaction record storage in high-concurrency vehicle scenarios; And design a three-layer interpretable reputation evaluation mechanism, integrating historical task performance, Maximum Mean Discrepancy (MMD) Bayesian inference, and task completion contribution, to achieve causal decoupling between “honest high loss” and “malicious low loss reporting” under Differential Privacy noise, and jointly sign and upload it to the chain through the regulatory committee and Roadside Units (RSUs), making reputation judgments auditable and transparent; Further propose a participant selection algorithm based on Deep Deterministic Policy Gradient (DDPG) and reputation partitioning, which synchronously optimizes communication overhead, computation delay, and redundant filtering in dynamic traffic flow, while utilizing local DAG weight-biased random walks to achieve lightweight asynchronous model quality verification. The experiment shows that the cumulative reward of the proposed method can quickly converge and remain stable, verifying its system-level superiority in interpretable robust aggregation and high-concurrency scalability.