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To better protect the ecological environment, this study investigates the evolution of the landscape in Hubei Province and its impact on Net Primary Production (NPP). By utilizing landscape assessment tools and neighborhood proxy methods, it quantifies the changes in NPP resulting from landscape transformations over periods of 5 and 20 years. The results indicate significant NPP variations, with changes ranging from −406.199 to 507.181 g*C/m2. The geographical detector model identifies key drivers, particularly noting significant changes in landscape features at type boundaries, which affect NPP. Land cover, especially transitions between forests, shrublands, and croplands, is identified as a critical factor. This research highlights the complex relationship between landscape modifications and NPP variations, emphasizing the importance of ecological considerations in landscape management amid urbanization. It offers valuable insights for future landscape management and conservation strategies in Hubei, aiming for ecological preservation alongside development. Additionally, this work fills a theoretical gap by linking changes in landscape features to NPP, proposing new perspectives for landscape protection and ecological capacity enhancement, and providing practical guidance for improving NPP and coordinated landscape management in Hubei.

Abstract Six Transmembrane Epithelial Antigen of Prostate 2 (STEAP2) belongs to a family of metalloreductases, which indirectly aid in uptake of iron and copper ions. Its role in hepatocellular carcinoma (HCC) remains to be characterized. Here, we report that STEAP2 expression was upregulated in HCC tumors compared with paired adjacent non-tumor tissues by RNA sequencing, RT-qPCR, Western blotting, and immunostaining. Public HCC datasets demonstrated upregulated STEAP2 expression in HCC and positive association with tumor grade. Transient and stable knockdown (KD) of STEAP2 in HCC cell lines abrogated their malignant phenotypes in vitro and in vivo, while STEAP2 overexpression showed opposite effects. STEAP2 KD in HCC cells led to significant alteration of genes associated with extracellular matrix organization, cell adhesion/chemotaxis, negative enrichment of an invasiveness signature gene set, and inhibition of cell migration/invasion. STEAP2 KD reduced intracellular copper levels and activation of stress-activated MAP kinases including p38 and JNK. Treatment with copper rescued the reduced HCC cell migration due to STEAP2 KD and activated p38 and JNK. Furthermore, treatment with p38 or JNK inhibitors significantly inhibited copper-mediated cell migration. Thus, STEAP2 plays a malignant-promoting role in HCC cells by driving migration/invasion via increased copper levels and MAP kinase activities. Our study uncovered a novel molecular mechanism contributing to HCC malignancy and a potential therapeutic target for HCC treatment.

Abstract Of all gynecologic cancers, epithelial-ovarian cancer (OCa) stands out with the highest mortality rates. Despite all efforts, 90% of individuals who receive standard surgical and cytotoxic therapy experience disease recurrence. The precise mechanism by which leukemia inhibitory factor (LIF) and its receptor (LIFR) contribute to the progression of OCa remains unknown. Analysis of cancer databases revealed that elevated expression of LIF or LIFR was associated with poor progression-free survival of OCa patients and a predictor of poor response to chemotherapy. Using multiple primary and established OCa cell lines or tissues that represent five subtypes of epithelial-OCa, we demonstrated that LIF/LIFR autocrine signaling is active in OCa. Moreover, treatment with LIFR inhibitor, EC359 significantly reduced OCa cell viability and cell survival with an IC50 ranging from 5-50 nM. Furthermore, EC359 diminished the stemness of OCa cells. Mechanistic studies using RNA-seq and rescue experiments unveiled that EC359 primarily induced ferroptosis by suppressing the glutathione antioxidant defense system. Using multiple in vitro, ex vivo and in vivo models including cell-based xenografts, patient-derived explants, organoids, and xenograft tumors, we demonstrated that EC359 dramatically reduced the growth and progression of OCa. Additionally, EC359 therapy considerably improved tumor immunogenicity by robust CD45+ leukocyte tumor infiltration and polarizing tumor-associated macrophages (TAMs) toward M1 phenotype while showing no impact on normal T-, B-, and other immune cells. Collectively, our findings indicate that the LIF/LIFR autocrine loop plays an essential role in OCa progression and that EC359 could be a promising therapeutic agent for OCa.

Summary: Ewing sarcoma is a cancer of bone and soft tissue in children and young adults primarily driven by the EWS-FLI1 fusion oncoprotein, which has been undruggable. Here, we report that Ewing sarcoma depends on secreted sphingomyelin phosphodiesterase 1 (SMPD1), a ceramide-generating enzyme, and ceramide. We find that G-protein-coupled receptor 64 (GPR64)/adhesion G-protein-coupled receptor G2 (ADGRG2) responds to ceramide and mediates critical growth signaling in Ewing sarcoma. We show that ceramide induces the cleavage of the C-terminal intracellular domain of GPR64, which translocates to the nucleus and restrains the protein levels of RIF1 in a manner dependent on SPOP, a substrate adaptor of the Cullin3-RING E3 ubiquitin ligase. We demonstrate that both SMPD1 and GPR64 are transcriptional targets of EWS-FLI1, indicating that SMPD1 and GPR64 are EWS-FLI1-induced cytokine-receptor dependencies. These results reveal the SMPD1-ceramide-GPR64 pathway, which drives Ewing sarcoma growth and is amenable to therapeutic intervention.

The undulating appearance of 3D-printed concrete (3DPC) is an essential feature that impacts the integrity and stability of the multilayer structure, yet the related evaluation parameters have rarely been proposed. This study proposed a quantitative evaluation and parameters through the on-site 3D scan technology to impel a direct and precise understanding of the surface quality of 3DPC, especially concrete with coarse aggregates. Firstly, 8-layer 3DPC walls are produced at four different printer printing rates and then 3D scanned. After point cloud correction, noise reduction, and 2D processing onto acquired points cloud, the evaluation parameters for evaluating 3DPC surface characteristics are defined and calculated. Results indicate that a higher 3D printer printing rate helps reduce surface fluctuation of 3DPC multilayer structures but disfavor the vertical flatness, of which the linear relationship between the parameters and the printing speed of the printer is explored and discussed. Recommendation of 3DPC straight wall structures controlled to surface defects ratio within 2% and slope within 0.2% is suggested. These conclusions provide precise external surface quality control and treatment basements that contribute to improving the practical application of 3DPC.

Abstract To achieve the triple goals of digesting excess capacity, making effective investments, and accelerating green governance, it is important and necessary to comprehensively examine the intrinsic relationship between media reports, environmental regulation, and enterprise investment inefficiency. To this end, this study employs multiple econometric models to investigate the intrinsic relationship between them by using the data of listed companies in China’s A-share heavy-polluting industries between 2010 and 2020. The results of the study are as follows. Firstly, media reports can encourage heavy-polluting enterprises to pay attention to stakeholder demands and significantly ameliorate the enterprise investment inefficiency. In addition, environmental regulation can ameliorate the enterprise investment inefficiency through the “push-back effect” and “compensation effect”. Moreover, pollution fees can positively moderate the amelioration effect of media reports on the investment inefficiency of heavy-polluting enterprises, while it fails in terms of environmental protection subsidies. Finally, conclusions and policy implications are provided.

Abstract Animal studies have demonstrated the ability of pancreatic acinar cells to transform into pancreatic ductal adenocarcinoma (PDAC). However, the tumorigenic potential of human pancreatic acinar cells remains under debate. To address this gap in knowledge, we expand sorted human acinar cells as 3D organoids and genetically modify them through introduction of common PDAC mutations. The acinar organoids undergo dramatic transcriptional alterations but maintain a recognizable DNA methylation signature. The transcriptomes of acinar organoids are similar to those of disease-specific cell populations. Oncogenic KRAS alone do not transform acinar organoids. However, acinar organoids can form PDAC in vivo after acquiring the four most common driver mutations of this disease. Similarly, sorted ductal cells carrying these genetic mutations can also form PDAC, thus experimentally proving that PDACs can originate from both human acinar and ductal cells. RNA-seq analysis reveal the transcriptional shift from normal acinar cells towards PDACs with enhanced proliferation, metabolic rewiring, down-regulation of MHC molecules, and alterations in the coagulation and complement cascade. By comparing PDAC-like cells with normal pancreas and PDAC samples, we identify a group of genes with elevated expression during early transformation which represent potential early diagnostic biomarkers.

Outdoor 3D object detection is a hot topic in autonomous driving. The mainstream pure point cloud method is down-sampling through different task-oriented strategies to retain representative foreground points. Although such strategies are conducive to finding instances, these methods still suffer from two issues: class points imbalance during down-sampling stages, and foreground/background points imbalance in the final retained point clouds. The former imbalance results in poor precision for small objects; and the latter ignores background points, leading to a false positive phenomenon. To tackle the unbalanced phenomenon, we propose a simple yet effective balanced 3D detector, termed CB-SSD, including two balanced strategies: class balance strategy (CBS) and foreground/background balance strategy (FBBS). It is important to note that we do not alter the distribution of point clouds. Instead, we guide the model’s attention towards different classes equally. CB-SSD shows better precision on small objects, reducing false positives where foreground points and background points are similar. Considering both speed and accuracy, CB-SSD achieves state-of-the-art based on pure point clouds (single-stage) on KITTI and ONCE datasets. On KITTI, CB-SSD attains a multi-class accuracy of 72.92 mAP with 81 FPS.

Effective 3D object detection relies on strong feature representation. Both global features and representative characteristics of the objects are vital for detection. However, traditional convolution’s perception range limits large receptive fields, and current object feature representation has room for enhancement. Addressing these concerns, we introduce a 3D outdoor object detector to enhance the point clouds feature, referred to as EPDet. Specifically, for global features, we propose a BEV-Offset Transformer in the BEV (Bird’s Eye View) domain. This adaptable module enhances semantic connections among objects, suiting various 3D detection methods. In addition, to refine point cloud features, we employ Focal Conv as our 3D ( 3-dimensional) backbone, exploring multi-modal fusion effects. In the 2D (2-dimensional) backbone, our Pyramid-like Conv captures detailed contextual features. EPDet performs well in the dense object scene owing to scene-wide global features captured by BEV Offset Transformer. In the multi-class tests, EPDet excels in detecting smaller objects due to more refined features represented by Focal Conv and Pyramid-like Conv. In experiments, as a plug-and-play module, we validate the BEV Offset Transformer’s effectiveness across single-stage (SECOND), two-stage (Voxel-RCNN), and multi-stage (CasA) algorithms. Robustness is tested on KITTI, NuScenes, and ONCE datasets. The proposed EPDet, in the KITTI subset, EPDet (CasA-based) achieves an impressive 85.56% accuracy in the car category. EPDet (Voxel-RCNN-based) surpassed baseline 1.65% mAP (mean Average Precision) (moderate subsets) in multi-class detection. The precision of EPDet is on par with the SotA (State of the Art) 3D outdoor object detectors based on point clouds.

Malignant rhabdoid tumors (MRTs) are among the most aggressive and treatment-resistant malignancies affecting infants, originating in the kidney, brain, liver, and soft tissues. The 5-year event-free survival rate for these cancers is a mere 20%. In nearly all cases of MRT, the SMARCB1 gene (occasionally SMARCA4)—a pivotal component of the SWI/SNF chromatin remodeling complex—is homozygously deleted, although the precise etiology of these tumors remains unknown. While young patients with localized MRT generally show improved outcomes, especially those who are older and have early-stage disease, the overall prognosis remains poor despite optimal standard treatments. This highlights the urgent need for more effective treatment strategies. We investigated the antitumor activity of a PARP1 inhibitor (talazoparib, TLZ) combined with a DNA alkylating agent (temozolomide, TMZ) in MRT xenograft models. PARP1 is a widely targeted molecule in cancer treatment and, beyond its role in DNA repair, it participates in transcriptional regulation by recruiting chromatin remodeling complexes to modulate DNA accessibility for RNA polymerases. To widen the therapeutic window of the drug combination, we employed PEGylated TLZ (PEG~TLZ), which has been reported to reduce systemic toxicity through slow drug release. Remarkably, our findings indicate that five out of six MRT xenografts exhibited an objective response to PEG~TLZ+TMZ therapy. Significantly, the loss of SMARCB1 was found to confer a protective effect, correlating with higher expression levels of DNA damage and repair proteins in SMARCB1-deficient MRT cells. Additionally, we identified MGMT as a potential biomarker indicative of in vivo MRT response to PEG~TLZ+TMZ therapy. Moreover, our analysis revealed alterations in signaling pathways associated with the observed antitumor efficacy. This study presents a novel and efficacious therapeutic approach for MRT, along with a promising candidate biomarker for predicting tumor response.

Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning tool based on a deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and the cancer and tissue classification yields performance rates of 99.8 ± 0.2% and 95.0 ± 2.3%, respectively. A benchmark analysis suggests that the proposed model outperforms traditional generative models such as the variational autoencoder. In metastatic subtype classification, our generated samples show 97.6% predicting power compared to true metastatic samples. Additionally, we demonstrate MetGen’s interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironments, such as immune responses and the metastasis process, which can potentially foster a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer.

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards.

Background & Aims: Acinar-to-ductal metaplasia (ADM) serves as a precursor event in the development of pancreatic ductal adenocarcinoma (PDAC) upon constitutive environmental and genetical stress. While the role of ADM in PDAC progression has been established, the molecular mechanisms underlying human ADM remain elusive. We previously demonstrated the induction of ADM in human acinar cells through the transforming growth factor beta (TGFβ) signaling pathway. We aim to investigate the interaction between TGFβ and Hippo pathways in mediating ADM. Methods: RNA-sequencing was conducted on sorted normal primary human acinar, ductal, and AD (acinar cells that have undergone ADM) cells. ATAC-seq analysis was utilized to reveal the chromatin accessibility in these three cell types. ChIP-Seq of YAP1, SMAD4, and H3K27ac was performed to identify the gene targets of YAP1 and SMAD4. The role of YAP1/TAZ in ADM-driven cell proliferation, as well as in oncogenic KRAS driven proliferation, was assessed using sphere formation assay. Results: AD cells have a unique transcription profile, with upregulated genes in open chromatin states in acinar cells. YAP1 and SMAD4 co-occupy the loci of ADM-related genes, including PROM1, HES1, and MMP7, co-regulating biological functions such as cell adhesion, cell migration, and inflammation. Overexpression of YAP1/TAZ promoted acinar cell proliferation but still required the TGFβ pathway. YAP1/TAZ were also crucial for TGFβ-induced sphere formation and were necessary for KRAS-induced proliferation. Conclusions: Our study reveals the intricate transition between acinar and AD states in human pancreatic tissues. It unveils the complex interaction between the Hippo and TGF-β pathways during ADM, highlighting the pivotal role of YAP1/TAZ and SMAD4 in PDAC initiation.