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In recent years, with the continuous evolution of the global economy and the adjustment of industrial structures, the understanding of the role played by human capital in the process of economic development has become particularly important. However, existing research on the impact of human capital on economic growth often adopts traditional regression methods, failing to comprehensively consider the heterogeneity and nonlinear relationships in the data. Therefore, to more accurately understand the influence of human capital on economic growth at different stages, this study employs Bayesian quantile regression method (BQRM). By incorporating BQRM, a better capture of the dynamic effects of human capital in the process of industrial structure upgrading is achieved, offering policymakers more targeted and effective policy recommendations to drive the economy towards a more sustainable direction. Additionally, the experiment also examines the impact of other key factors such as technological progress, capital investment, and labor market conditions on economic growth. These factors, combined with human capital, collectively promote the upgrading of industrial structure and the sustainable development of the economy. This study, by introducing BQRM, aims to fill the research gap regarding the impact of human capital on economic development during the industrial structural upgrading process. In the backdrop of the ongoing evolution of the global economy and adjustments in industrial structure, understanding the role of human capital in economic development becomes particularly crucial. To better comprehend the direct impact of human capital, the experiment collected macroeconomic data, including GDP, industrial structure, labor skills, and human capital, from different regions over the past 20 years. By establishing a dynamic panel data model, this study delves into the trends in the impact of human capital at various stages of industrial structure upgrading. The research findings indicate that during the high-speed growth phase, the contribution of human capital to GDP growth is 15.2% ± 2.1%, rising to 23.8% ± 3.4% during the period of industrial structure adjustment. Technological progress, capital investment, and labor market conditions also significantly influence economic growth at different stages. In terms of innovation improvement, this study pioneers the use of BQRM to gain a deeper understanding of the role of human capital in economic development, providing more targeted and effective policy recommendations. Ultimately, to promote sustainable economic development, the experiment proposes concrete and targeted policy recommendations, emphasizing government support in training and skill development. This study not only fills a research gap in the relevant field but also provides substantive references for decision-makers, driving the economy towards a more sustainable direction.

This paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to oblique imaging. The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. Additionally, we construct a dedicated dataset for urban vehicle detection that is characterized by high-resolution images, a large sample size, and low training resource requirements. Experimental results show that the proposed algorithm achieves gains of 1.9% in precision, 6.0% in recall, 4.2% in mAP@0.5, and 3.3% in mAP@0.5:0.95 compared to the baseline network. The improved model also achieves the highest F1-score, indicating an optimal balance between precision and recall.

Vehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robustness against environmental noise. A Restormer module is incorporated into the backbone to model long-range dependencies via self-attention, enabling better handling of multi-scale features and complex scenes. A dedicated detection head is introduced for small objects, focusing on critical channels while suppressing irrelevant information. Additionally, the original CIoU loss is replaced with WIoU, which dynamically reweights predicted boxes based on their quality, enhancing localization accuracy and stability. Experimental results on the DJCAR dataset show mAP@0.5 and mAP@0.5:0.95 improvements of 5.4% and 6.2%, respectively, and corresponding gains of 4.3% and 2.6% on the VisDrone dataset. These results demonstrate that RSW-YOLO offers a robust and accurate solution for UAV-based vehicle detection, particularly in urban scenes with dense or small targets.

Ectopic expression in fibroblasts of synapsin 1 and synaptophysin is sufficient to generate condensates of vesicles highly reminiscent of synaptic vesicle (SV) clusters and with liquid-like properties. Here we show that unlike synaptophysin, other major integral SV membrane proteins fail to form condensates with synapsin, but co-assemble into the clusters formed by synaptophysin and synapsin in this ectopic expression system. Another vesicle membrane protein, ATG9A, undergoes activity-dependent exo-endocytosis at synapses, raising questions about the relation of ATG9A traffic to the traffic of SVs. We find that both in fibroblasts and in nerve terminals ATG9A does not co-assemble into synaptophysin-positive vesicle condensates but localizes on a distinct class of vesicles that also assembles with synapsin but into a distinct phase. Our findings suggest that ATG9A undergoes differential sorting relative to SV proteins and also point to a dual role of synapsin in controlling clustering at synapses of SVs and ATG9A vesicles. ATG9 is the only transmembrane protein of the core autophagy machinery known to be present at presynapses. Here, the authors show that both synaptophysin and ATG9A vesicles assemble into condensates with synapsin but remain segregated from each other.

Tumor-associated neutrophils (TANs) can promote tumor progression. This study aimed to investigate the molecular signature that predict the prognosis and immune response of breast cancer (BRCA) based on TAN-related gene (TANRG) expression data. The RNA-seq data of BRCA were gathered from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) datasets. Univariate Cox regression analysis and the least absolute shrinkage and selection operator for selecting prognostic genes. A neo-TAN-related risk signature was constructed by multivariate Cox regression analysis. Time-dependent receiver operating characteristic (ROC) curve analyses and Kaplan–Meier analyses were performed to validate the signature in GEO cohorts and the triple-negative breast cancer (TNBC) subtype. We constructed an independent prognostic factor model with 11 TANRGs. The areas under the ROC curve (AUCs) of the TCGA training cohorts for 3-, 5-, and 7-year overall survival were 0.72, 0.73, and 0.73, respectively. The AUCs of the GEO test cohorts for 3-, 5-, and 7-year overall survival were 0.83, 0.89, and 0.94 (GSE25066) and 0.67, 0.69, and 0.73 (GSE58812), respectively. The proportion of immune subtypes differed among the different risk groups. The IC50 values differed significantly between risk groups and can be used as a guide for systemic therapy. The prognostic model developed by TANRGs has excellent predictive performance in BRCA patients. In addition, this feature is closely related to the prediction of survival, immune activity and treatment response in BRCA patients.

Injecting spins from ferromagnetic metals into semiconductors efficiently is a crucial step towards the seamless integration of charge- and spin-information processing in a single device1,2. However, efficient spin injection into semiconductors has remained an elusive challenge even after almost three decades of major scientific effort3–5, due to, for example, the extremely low injection efficiencies originating from impedance mismatch1,2,5,6, or technological challenges originating from stability and the costs of the approaches7–12. We show here that, by utilizing the strongly out-of-equilibrium nature of subpicosecond spin-current pulses, we can obtain a massive spin transfer even across a bare ferromagnet/semiconductor interface. We demonstrate this by producing ultrashort spin-polarized current pulses in Co and injecting them into monolayer MoS2, a two-dimensional semiconductor. The MoS2 layer acts both as the receiver of the spin injection and as a selective converter of the spin current into a charge current, whose terahertz emission is then measured. Strikingly, we measure a giant spin current, orders of magnitude larger than typical injected spin-current densities using currently available techniques. Our result demonstrates that technologically relevant spin currents do not require the very strong excitations typically associated with femtosecond lasers. Rather, they can be driven by ultralow-intensity laser pulses, finally enabling ultrashort spin-current pulses to be a technologically viable information carrier for terahertz spintronics.Efficient spin injection across ferromagnet/semiconductor interfaces is a major goal for future spintronic approaches. Ultrafast spectroscopy now reveals strong spin currents to be inducible in monolayer MoS2 by ultralow-intensity laser pulses.

Mechanistic target of rapamycin mTOR complex 1 (mTORC1) plays a key role in the integration of various environmental signals to regulate cell growth and metabolism. mTORC1 is recruited to the lysosome where it is activated by its interaction with GTP-bound Rheb GTPase. However, the regulatory mechanism of Rheb activity remains largely unknown. Here, we show that ubiquitination governs the nucleotide-bound status of Rheb. Lysosome-anchored E3 ligase RNF152 catalyzes Rheb ubiquitination and promotes its binding to the TSC complex. EGF enhances the deubiquitination of Rheb through AKT-dependent USP4 phosphorylation, leading to the release of Rheb from the TSC complex. Functionally, ubiquitination of Rheb is linked to mTORC1-mediated signaling and consequently regulates tumor growth. Thus, we propose a mechanistic model whereby Rheb–mediated mTORC1 activation is dictated by a dynamic opposing act between Rheb ubiquitination and deubiquitination that are catalyzed by RNF152 and USP4 respectively.

Background Undifferentiated carcinoma with osteoclast-like giant cells (OGCs) of pancreas (UCOGCP) is a rare subtype of pancreatic ductal adenocarcinoma (PDAC), which had poorly described histopathological and clinical features. Methods In this study, single-cell RNA sequencing (scRNA-seq) was used to profile the distinct tumor microenvironment of UCOGCP using samples obtained from one UCOGCP patient and three PDAC patients. Bioinformatic analysis was carried out and immunohistochemical (IHC) staining was used to support the findings of bioinformatic analysis. After quality control of the raw data, a total of 18,376 cells were obtained from these four samples for subsequent analysis. These cells were divided into ten main cell types following the Seurat analysis pipeline. Among them, the UCOGCP sample displayed distinct distribution patterns from the rest samples in the epithelial cell, myeloid cell, fibroblast, and endothelial cell clusters. Further analysis supported that the OGCs were generated from stem-cell-like mesenchymal epithelial cells (SMECs). Results Functional analysis showed that the OGCs cluster was enriched in antigen presentation, immune response, and stem cell differentiation. Gene markers such as LOX, SPERINE1, CD44, and TGFBI were highly expressed in this SMECs cluster which signified poor prognosis. Interestingly, in myeloid cell, fibroblasts, and endothelial cell clusters, UCOGCP contained higher percentage of these cells and unique subclusters, compared with the rest of PDAC samples. Conclusions Analysis of cell communication depicted that CD74 plays important roles in the formation of the microenvironment of UCOGCP. Our findings illustrated the genesis and function of OGCs, and the tumor microenvironment (TME) of UCOGCP, providing insights for prognosis and treatment strategy for this rare type of pancreatic cancer.

Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality.

Ferroptosis is an iron-dependent form of programmed cell death, which plays crucial roles in tumorigenesis, ischemia–reperfusion injury and various human degenerative diseases. Ferroptosis is characterized by aberrant iron and lipid metabolisms. Mechanistically, excess of catalytic iron is capable of triggering lipid peroxidation followed by Fenton reaction to induce ferroptosis. The induction of ferroptosis can be inhibited by sufficient glutathione (GSH) synthesis via system Xc – transporter-mediated cystine uptake. Therefore, induction of ferroptosis by inhibition of cystine uptake or dampening of GSH synthesis has been considered as a novel strategy for cancer therapy, while reversal of ferroptotic effect is able to delay progression of diverse disorders, such as cardiopathy, steatohepatitis, and acute kidney injury. The ubiquitin (Ub)–proteasome pathway (UPP) dominates the majority of intracellular protein degradation by coupling Ub molecules to the lysine residues of protein substrate, which is subsequently recognized by the 26S proteasome for degradation. Ubiquitination is crucially involved in a variety of physiological and pathological processes. Modulation of ubiquitination system has been exhibited to be a potential strategy for cancer treatment. Currently, more and more emerged evidence has demonstrated that ubiquitous modification is involved in ferroptosis and dominates the vulnerability to ferroptosis in multiple types of cancer. In this review, we will summarize the current findings of ferroptosis surrounding the viewpoint of ubiquitination regulation. Furthermore, we also highlight the potential effect of ubiquitination modulation on the perspective of ferroptosis-targeted cancer therapy.