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The operational status of photovoltaic modules directly impacts power generation efficiency, making rapid and precise fault detection crucial for intelligent operation and maintenance of Photovoltaic (PV) power plants. Addressing the perceptual limitations of single-modal images in complex environments, this study constructs an RGBIRPV multimodal dataset tailored for centralized PV power plants and proposes an RFE-YOLO model. This model enhances detection performance through three core mechanisms: The RC module employs a CBAM-based attention mechanism for multi-parameter feature extraction, utilizing heterogeneous RC_V and RC_I architectures to achieve differentiated feature enhancement for visible and infrared modalities. The lightweight adaptive fusion FA module introduces learnable modality balance and attention cascading mechanisms to optimize multimodal information fusion. Concurrently, the multi-scale enhanced EVG module based on GSConv achieves synergistic representation of shallow details and deep semantics with low computational overhead. The experiment employed an 8:1:1 data partitioning scheme. Compared to the YOLOv11n model employing feature-level mid-fusion, the model proposed in this study achieves improvements of 2.9%, 1.8%, and 1.5% in precision, mAP@50, and F1 score, respectively. It effectively meets the demand for rapid and accurate detection of PV module failures in real power plant environments, providing an effective technical solution for intelligent operation and maintenance of photovoltaic power plants.

The electrocatalytic reduction of nitrogenous waste offers a sustainable approach to producing nitrogen-containing chemicals. The selective synthesis of high-value hydroxylamine (NH 2 OH) is challenging due to the instability of NH 2 OH as an intermediate. Here, we present a rational electrocatalyst design strategy for promoting NH 2 OH electrosynthesis by suppressing the competing pathways of further reduction. We screen zinc phthalocyanines (ZnPc) with a high energy barrier for NH 2 OH reduction by regulating their intrinsic activity. Additionally, we discover that carbon nanotube substrates exhibit significant NH 3 -producing activity, which can be effectively inhibited by the high coverage of ZnPc molecules. In-situ characterizations reveal that NH 2 OH and HNO are generated as intermediates in nitrate reduction to NH 3 , and NH 2 OH can be enriched in the ZnPc electrode. In the H-cell, the optimized ZnPc catalyst demonstrates a Faradaic efficiency (FE) of 53 ± 1.7% for NH 2 OH with a partial current density exceeding 270 mA cm −2 and a turnover frequency of 7.5 ± 0.2 s −1 . It also enables the rapid electrosynthesis of cyclohexanone oxime from nitrite with a FE of 64 ± 1.0%. Electroreduction of nitrate or nitrite offers an appealing route for hydroxylamine synthesis but remains challenging due to its intermediate nature. Here, authors present a selective and fast electrosynthesis of hydroxylamine by tuning metal phthalocyanine activity and removing side reaction sites.

Photovoltaic (PV) module faults, such as hotspots, diode short circuits, occlusions, and shadows, degrade power generation efficiency and safety. Existing manual inspection and single-modality methods show limited robustness under complex conditions, especially with illumination variations and weak thermal responses, while most deep learning approaches fail to exploit the complementarity of visible and infrared modalities. To address this issue, a dual-modality visible-infrared fusion framework based on YOLO11 is proposed, integrating a multi-scale pyramid pooling and dilated convolution module (MSPPD), a gradient-aware fusion module (GAFusion), and a dynamic convolution and element-wise scaling detection head (Detect-DEhead). GAFusion enhances cross-modal structural consistency and reduces feature misalignment and information loss during fusion by introducing gradient-aware feature interaction. Shape-IoU loss is employed to improve localization accuracy. The proposed method improves mean average precision (mAP)@0.5 from 86.7% to 88.1%, while reducing parameters, computational cost, and model size from 4.3 M to 3.7 M, 11.42 GFLOPs to 9.37 GFLOPs, and 9.1 MB to 7.9 MB, respectively. With Shape-IoU, mAP@0.5 reaches 88.4%, and recall increases from 78.5% to 84.9%. Experiments on the FLIR Thermal dataset achieve gains of 2.2%, 1.6%, and 2.7% in precision, recall, and mAP@0.5. The method achieves an effective trade-off between accuracy and efficiency for intelligent PV module inspection.

The electron transport layer (ETL) is a key component of regular perovskite solar cells to promote the overall charge extraction efficiency and tune the crystallinity of the perovskite layer for better device performance. The authors present a novel protocol of ETL engineering by incorporating a composition of the perovskite precursor, methylammonium chloride (MACl), or formamidine chloride (FACl), into SnO2 layers, which are then converted into the crystal nuclei of perovskites by reaction with PbI2. The SnO2‐embedded nuclei remarkably improve the morphology and crystallinity of the optically active perovskite layers. The improved ETL‐to‐perovskite electrical contact and dense packing of large‐grained perovskites enhance the carrier mobility and suppress charge recombination. The power conversion efficiency increases from 20.12% (blank device) to 21.87% (21.72%) for devices with MACl (FACl) as an ETL dopant. Moreover, all the precursor‐engineered cells exhibit a record‐high fill factor (82%). Methylammonium chloride (MACl) or formamidine chloride (FACl) of perovskite precursor is added into SnO2 electron transport layer (ETL) and reacts with PbI2 to form perovskite crystal nucleus, which improves the contact between SnO2 ETL and perovskite, as well as the morphology and crystallinity of perovskite layer, and greatly improves the fill factor (FF) of perovskite solar cells (PSCs).

Purpose Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. Methods We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. Results We developed a six-gene prognostic model ( MYO1E , FEN1 , NMI , ZNF506 , ALDOA , and MLLT6 ) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts ( N  = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. Conclusion We have identified malignant cell-associated ligand–receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.

Atherosclerosis (AS) and Non-alcoholic fatty liver disease (NAFLD) are chronic metabolic disorders with high prevalence and significant health impacts. Both conditions share common pathophysiological pathways including abnormal lipid metabolism and inflammation. Berberine (BBR), an isoquinoline alkaloid, is known for its beneficial effects on various metabolic and cardiovascular disorders. This study investigates BBR’s impact on AS and NAFLD through bioinformatics analysis and experimental models. This study utilized various bioinformatics methods, including transcriptome analysis, weighted gene co-expression network analysis (WGCNA), machine learning, and molecular docking, to identify key genes and pathways involved in AS and NAFLD. Subsequently an animal model of AS combined with NAFLD was established using ApoE -/- mice fed a high-fat diet. The efficacy and mechanism of action of BBR were verified using methods such as hematoxylin and eosin (HE) staining, Oil Red O staining, and real-time quantitative PCR (RTqPCR). Through transcriptome analysis, WGCNA, and machine learning, this study identified 48 key genes involved in both AS and NAFLD. Function analysis revealed that the implicated genes were significantly involved in pathways like cytokine-cytokine receptor interaction, chemokine signaling, and IL-17 signaling pathway, suggesting their role in inflammation and immune responses. Single cell validation identified six key genes: dual specificity phosphatase 6 (DUSP6), chemokine ligand 3 (CCL3), complement component 5a receptor 1 (C5AR1), formyl peptide receptor 1 (FPR1), myeloid nuclear differentiation antigen (MNDA), and proviral integration site of murine 2(PIM2). Finally, molecular docking and animal experiments showed that BBR significantly reduced lipid deposits and inflammatory markers in liver and aortic tissues. In conclusion, BBR can improve AS combined with NAFLD by regulating genes like MNDA, PIM2, DUSP6, CCL3, C5AR1, and FPR1, with the mechanism related to inflammation control. The findings suggest potential clinical benefits of BBR in reducing the progression of both AS and NAFLD, warranting further investigation.

Lymph nodes are crucial for perioperative immunotherapy but have to be completely resected in surgery. Trials evaluating the safety and necessity of omitting systemic mediastinal lymph node (mLN) dissection in non-small cell lung cancer (NSCLC) are still absent. cN0/N1 NSCLC patients who received neoadjuvant immunotherapy and radical surgery were retrospectively collected from three institutions. Restricted cubic spline regression and receiver operating characteristic curve were used to analyze the association between mLN dissection number and survival outcomes. Confounding factors between selective and systemic mLN dissection groups were adjusted by inverse probability of treatment weighting (IPTW). The characteristics of memory CD8 T cells in immunotherapy-treated mLN were identified by single-cell RNA and T-cell receptor sequencing (scRNA/TCR-seq) data retrieved from GSE185206. From 2019 to 2021, 131 neoadjuvant-treated cN0/N1 NSCLC patients were collected. The mLN clearance rate was 98.5% in the whole cohort and 100% in patients with radiologically confirmed complete response. Resected lymph node counts were irrelevant with local recurrence, distant metastasis, or death. Compared with selective mLN dissection, systemic mLN dissection did not show any survival benefit but showed slightly higher postoperative recurrence risk in both unadjusted and IPTW-adjusted cohorts. scRNA/TCR-seq showed that stem-like exhausted CD8 memory T cells were the progenitors of tumor-specific CD8 T lymphocytes in primary tumors and were abundantly enriched in resected mLN. Omitting systemic mLN dissection was safe in cN0/N1 NSCLC patients who received neoadjuvant immunotherapy. Excessive mLN dissection may disrupt the repertoire of stem-like exhausted CD8 memory T cells and consequently impair the efficacy of adjuvant immunotherapy.

Immunotherapy has emerged as a promising treatment for patients with pulmonary lymphoepithelioma carcinoma (PLEC). This study aimed to evaluate the efficacy and safety of neoadjuvant immunochemotherapy in patients with PLEC. Clinical stage IB-III patients with PLEC who underwent neoadjuvant immunochemotherapy followed by radical surgery were retrospectively collected from two centers. For comparative analysis, 83 lung squamous cell carcinoma (LUSC) and 82 lung adenocarcinoma (LUAD) patients receiving the same neoadjuvant regimen were retrospectively enrolled. Pathological response and survival outcomes were compared between PLEC and the other cohorts. To minimize selection bias and potential confounding factors, the data were analyzed using inverse probability of treatment weighting (IPTW). Among 40 patients with PLEC, 23 (57.5%) were female, with a mean age of 50.2 years. Major pathological response (MPR) was identified in 18 patients (45.0%) and pathological complete response (pCR) was achieved in 8 patients (20.0%). The median event-free survival (EFS) was 24.8 months, with 1-year and 2-year EFS rates of 92.3% and 76.6%. Following IPTW adjustment, both pathological response and survival outcomes were comparable among the three cohorts. No statistically significant differences were observed between PLEC and LUSC patients in terms of MPR (45.0% vs. 66.3%, p = 0.306), pCR (20.0% vs. 47.0%, p = 0.228), or EFS (median: 24.8 vs. 28.5 months, p = 0.983). Similarly, PLEC demonstrated comparable MPR, pCR, and EFS to the LUAD cohort (MPR: 45.0% vs. 41.5%, p = 0.646; pCR: 20.0% vs. 25.6%, p = 0.978; median EFS: 24.8 vs. 22.5 months, p = 0.623). Neoadjuvant immunochemotherapy demonstrates favorable efficacy and safety in patients with PLEC in this retrospective two-center cohort. In adjusted analyses, EFS did not significantly differ from that observed in matched LUSC or LUAD patients, suggesting that this approach may be a reasonable option for selected PLEC patients.

The role of long noncoding RNA (LncRNA) ADAMTS9 antisense RNA 2 (ADAMTS9-AS2) is unclear in lung adenocarcinoma (LUAD). The aim of this study was to explore the relationship between ADAMTS9-AS2 and LUAD, based on The Cancer Genome Atlas (TCGA) database and bioinformatics analysis. Various statistical methods, Kaplan-Meier method, Cox regression analysis, GSEA, and immune infiltration analysis were used to evaluate the relationship between clinical features and ADAMTS9-AS2 expression, prognostic factors, and the significant involvement of ADAMTS9-AS2 in function. In LUAD patients, low expression of ADAMTS9-AS2 was associated with N stage (P=0.011), gender (P=0.002), number of packs smoked (P=0.024) and smoker (P<0.001). Low ADAMTS9-AS2 expression predicted a poorer overall survival (OS) (HR: 0.68; 95% CI: 0.51-0.91; P=0.01). And ADAMTS9-AS2 expression (HR: 0.626; 95% CI: 0.397-0.986; P=0.043) was independently correlated with OS in LUAD patients. Unwinding of DNA, extrinsic pathway, polo-like kinase-mediated events, cori cycle, MCM pathway, proteasome pathway, lagging strand synthesis and PCNA-dependent long patch base excision repair were differentially enriched in ADAMTS9-AS2 high expression phenotype. ADAMTS9-AS2 expression was correlated with certain immune infiltrating cells. In LUAD patients, ADAMTS9-AS2 expression was significantly associated with poor survival and immune infiltration. ADAMTS9-AS2 may be a promising biomarker of prognosis and response to immunotherapy for LUAD.

Background Phospholipid phosphatase 4 (PPAPDC1A or PLPP4) has been demonstrated to be involved in the malignant process of many cancers. The purpose of this study was to investigate the clinical significance and biological roles of PLPP4 in lung carcinoma. Methods PLPP4 expression was examined in 8 paired lung carcinoma tissues by real-time PCR and in 265 lung carcinoma tissues by immunohistochemistry (IHC). Statistical analysis was performed to evaluate the clinical correlation between PLPP4 expression and clinicopathological features and survival in lung carcinoma patients. In vitro and in vivo assays were performed to assess the biological roles of PLPP4 in lung carcinoma. Fluorescence-activated cell sorting, Western blotting and luciferase assays were used to identify the underlying pathway through which PLPP4 silencing mediates biological roles in lung carcinoma. Results PLPP4 is differentially elevated in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQC) tissues. Statistical analysis demonstrated that high expression of PLPP4 significantly and positively correlated with clinicopathological features, including pathological grade, T category and stage, and poor overall and progression-free survival in lung carcinoma patients. Silencing PLPP4 inhibits proliferation and cell cycle progression in vitro and tumorigenesis in vivo in lung carcinoma cells. Our results further reveal that PLPP4 silencing inhibits Ca 2+ -permeable cationic channel, suggesting that downregulation of PLPP4 inhibits proliferation and tumorigenesis in lung carcinoma cells via reducing the influx of intracellular Ca 2+ . Conclusion Our results indicate that PLPP4 may hold promise as a novel marker for the diagnosis of lung carcinoma and as a potential therapeutic target to facilitate the development of novel treatment for lung carcinoma.