Search Results (56)

Micro-pulse photon-counting LiDAR has difficulty accurately extracting geophysical information in strong-noise environments, with solar noise interference being a key limiting factor. This study proposes a hierarchical coarse-to-fine denoising framework, combining grid-based pre-filtering with an optimized horizontal and vertical recursive division method using Otsu’s method to achieve high time efficiency and denoising accuracy. First, an adaptive meshing strategy is employed to remove most of the noise in the data while retaining more than 99.1% of the signal. Subsequently, an alternating horizontal and vertical recursive division algorithm with automatically selected parameters is applied for denoising; the method was validated on ICESat-2 ATL03 data, GlobeLand30 V2020 data, and USGS 3DEP airborne radar data, where the method achieved a classification accuracy of more than 91.2%, with a several-fold reduction in runtime compared to traditional clustering methods. The framework demonstrates high efficiency, robustness, and computational scalability across diverse terrains, including polar, forest, and plains. It can contribute to geographic mapping, environmental protection, and ecological monitoring. Full article

Protein Disulfide Isomerases (PDIs) are emerging targets in anticancer therapy, with several PDI inhibitors demonstrating anticancer efficacy in preclinical models. Research has largely focused on “canonical” PDIs, such as PDIA1, which contain CXXC active site motifs where C represents Cysteine. Canonical PDIs have well-studied, critical roles in forming, breaking, and exchanging/scrambling disulfide bonds during protein folding. In contrast, non-canonical PDIs, which harbor CXXS active site motifs, remain less well-studied despite their role as sensors or effectors of protein folding quality control during protein trafficking in the secretory pathway. Here, we provide a review of the literature relating to the non-canonical PDIs ERp44, AGR2, and AGR3, which have been identified as strong dependencies in specific cancer subtypes according to the DepMap database. The biological and biochemical functions of ERp44, AGR2, and AGR3 are discussed, highlighting the role of ERp44 in two mechanisms of protein folding quality control, AGR2 as a selective sensor of mucin protein misfolding, and a unique role for AGR3 in cilia. Finally, we discuss recent efforts to develop small molecule inhibitors of ERp44, AGR2, and AGR3 as tool compounds and experimental therapeutics. Full article

This study assesses the accuracy of flood extent predictions in five U.S. watersheds. We generated flood maps for four return periods using various digital elevation models (DEMs)—FABDEM, SRTM, ALOS, and USGS 3DEP—and two versions of the GEOGLOWS River Forecast System (RFS) hydrography. These comparisons are notable because they build on operational global hydrology models so subsequent work can develop global modeled flood products. Models were made using the Automated Rating Curve (ARC) and Curve2Flood tools. Accuracy was measured against USGS reference maps using the F-statistic. Our results show that flood map accuracy generally increased with higher return periods. The most consistent and reliable improvements in accuracy occurred when both the DEM and hydrography datasets were upgraded to higher-resolution sources. While DEM improvements generally had a greater impact, hydrography refinements were more important for lower return periods when flood extents were the smallest. Generally, DEM resolution improved accuracy metrics more as the return period increased and hydrography and bare earth DEMs mattered more as the return period decreased. There was a 38.9% increase in the mean F-statistic between the two principal pairings of interest (FABDEM-RFS2 and SRTM 30 m DEM-RFS1). FABDEM’s bare-earth representation combined with RFS2 sometimes outperformed higher-resolution non-bare-earth DEMs, suggesting that there remains a need for site-specific investigation. Using ARC and Curve2Flood with FABDEM and RFS2 is a suitable baseline combination for general flood extent application. Full article

During carcinogenesis, cells must acquire a telomere maintenance mechanism in order to avoid telomere shortening-induced replicative senescence. While most tumors activate telomerase, a minority of them employ a recombinational mechanism called Alternative Lengthening of Telomeres (ALT). One of the most investigated features is the association between ALT and ATRX mutations, since this has been shown to be the gene with the highest rate of mutations among ALT tumors. However, most of these studies, and in particular, mechanistic studies in vitro, have been carried out on mesenchymal tumors (sarcomas). In the present study, using genomic and expression data from the DepMap portal, we identified several non-mesenchymal ALT cell lines, and we compared the incidence of ATRX and other gene mutations between ALT cell lines of different origins (mesenchymal, neural, epithelial, hematopoietic). We confirmed that ATRX is frequently mutated in mesenchymal and neural ALT cell lines but not in epithelial ones. Our results showed that mutations of ATRX or other proteins involved in the maintenance of telomere integrity are needed for ALT activation in all cell types, and ATRX is preferentially mutated in mesenchymal ALT cells. Besides a more precise interpretation of the role of ATRX loss in ALT establishment, we proposed a model in which mutation of this gene impairs differentiation in mesenchymal and neural cells (but not in epithelial ones). Therefore, we explained the high incidence of ATRX mutations in mesenchymal and neural tumors with the fact that they both trigger ALT and impair differentiation, thus promoting two steps at once in the process of carcinogenesis. Full article

Pleurotus ostreatus, classified under the phylum Basidiomycota, order Agaricales, and family Pleurotaceae, is a prevalent gray edible fungus. Its physical damage not only compromises quality and appearance but also significantly diminishes market value. This study proposed an enhanced method for detecting Pleurotus ostreatus damage based on an improved YOLOv8n model, aiming to advance the accessibility of damage recognition technology, enhance automation in Pleurotus cultivation, and reduce labor dependency. This approach holds critical implications for agricultural modernization and serves as a pivotal step in advancing China’s agricultural modernization, while providing valuable references for subsequent research. Utilizing a self-collected, self-organized, and self-constructed dataset, we modified the feature extraction module of the original YOLOv8n by integrating a lightweight GhostHGNetv2 backbone network. During the feature fusion stage, the original YOLOv8 components were replaced with a lightweight SlimNeck network, and an Attentional Scale Sequence Fusion (ASF) mechanism was incorporated into the feature fusion architecture, resulting in the proposed OMB-YOLO model. This model achieves a remarkable balance between parameter efficiency and detection accuracy, attaining a parameter of 2.24 M and a mAP@0.5 of 90.11% on the test set. To further optimize model lightweighting, the DepGraph method was applied for pruning the OMB-YOLO model, yielding the OMB-YOLO-tiny variant. Experimental evaluations on the damaged Pleurotus dataset demonstrate that the OMB-YOLO-tiny model outperforms mainstream models in both accuracy and inference speed while reducing parameters by nearly half. With a parameter of 1.72 M and mAP@0.5 of 90.14%, the OMB-YOLO-tiny model emerges as an optimal solution for detecting Pleurotus ostreatus damage. These results validate its efficacy and practical applicability in agricultural quality control systems. Full article

Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. Recently, the ICESat-2 and GEDI spaceborne LiDAR missions have significantly advanced global canopy height mapping. However, due to inherent sensor limitations, their footprint-level estimates often show systematic bias. Tall forests tend to be underestimated, while short forests are often overestimated. To address this issue, we used coincident G-LiHT airborne LiDAR measurements to correct footprint-level canopy heights from both ICESat-2 and GEDI, aiming to improve the canopy height retrieval accuracy across Puerto Rico’s tropical forests. The bias-corrected LiDAR dataset was then combined with multi-source predictors derived from Sentinel-1/2 and the 3DEP DEM. Using these inputs, we trained a canopy height inversion model based on the AutoGluon stacking ensemble method. Accuracy assessments show that, compared to models trained on uncorrected single-source LiDAR data, the new model built on the bias-corrected ICESat-2–GEDI fusion outperformed in both overall accuracy and consistency across canopy height gradients. The final model achieved a correlation coefficient (R) of 0.80, with a root mean square error (RMSE) of 3.72 m and a relative RMSE of 0.22. The proposed approach offers a robust and transferable approach for high-resolution canopy structure mapping and provides valuable support for carbon accounting and tropical forest management. Full article

The present study investigated the host cell response to EHV-8 infection in rabbit kidney (RK-13) cells through transcriptomic and proteomic approaches. At 24 h post-infection, a total of 2118 differentially expressed genes (DEGs) were identified, with 1338 upregulated and 780 downregulated. At 48 h, 7388 DEGs were detected, with 4342 upregulated and 3046 downregulated genes. Proteomic analysis revealed 932 differentially expressed proteins (DEPs) at 24 h (364 upregulated and 568 downregulated) and 3866 DEPs at 48 h (2285 upregulated and 1581 downregulated). Of these, 237 upregulated and 336 downregulated proteins were common across both time points. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the majority of DEGs and DEPs were enriched in key inflammation-related pathways, notably the TNF and NF-κB signaling pathways. Validation of the transcriptomic and proteomic data was performed using RT-PCR and parallel reaction monitoring (PRM), respectively, and confirmed consistent trends for TNFR1, NF-κB p65, and MAP3K8, as reported in the transcriptomic and proteomic screening. These findings suggest that EHV-8 infection may modulate host immune responses by activating the TNF signaling pathway. However, given that RK-13 cells may not fully replicate viral–host interactions in equine species, further in vivo studies in horses and donkeys are required to provide a more comprehensive understanding of the viral pathogenesis in these animals. Full article

Placental development plays a pivotal role in ensuring successful pregnancy outcomes, yet its molecular regulatory mechanisms in sheep remain poorly characterized. This study aimed to systematically investigate stage-specific proteomic dynamics and functional adaptations in ovine placental tissues across gestation to elucidate molecular drivers of placental maturation. Using data-independent acquisition proteomics, we identified 7774 proteins in Hu sheep placental tissues at gestational days 50, 80, and 120. Comparative analysis revealed 1450, 1026, and 1964 differentially expressed proteins (DEPs) in the 50 d vs. 80 d, 80 d vs. 120 d, and 50 d vs. 120 d comparisons, respectively. DEPs were functionally enriched in biological processes including cell proliferation, apoptosis, angiogenesis, nutrient transport, and steroid synthesis, with prominent involvement of the PI3K-Akt, MAPK, and estrogen signaling pathways. Protein interaction networks identified SRC, MAP3K1, KRAS, and TJP1 as central regulators exhibiting dynamic expression patterns across gestation. Temporal expression trends showed progressive upregulation of tight junction, immune response, and glucose metabolism proteins, contrasting with downregulation of endoplasmic reticulum protein processing and proteasome components. Validation experiments confirmed elevated proliferation/transport gene expression at 80 d versus 50 d, followed by increased apoptosis/transport genes and decreased proliferation markers at 120 d. This comprehensive proteomic profiling reveals stage-specific regulatory networks governing placental development in sheep, highlighting coordinated shifts in proliferative, metabolic, and structural remodeling processes. These findings advance our understanding of placental adaptation mechanisms and provide valuable insights for improving reproductive management in livestock species. Full article

Background/Objectives: MIDN (midnolin) is newly discovered method for critically regulating a ubiquitin-independent proteasomal degradation pathway. This study aims to examine the expression, prognostic value, genomic changes, interacting proteins, methylation status, and correlations with the tumor immune microenvironment of MIDN in various cancers. Methods: The GTEx, Depmap, GEPIA2, and Kaplan–Meier Plotter databases are applied to evaluate the MIDN level in tumor and normal tissues and the MIDN prognostic value in cancers. The genetic alterations of MIDN in cancers are investigated using the cBioPortal database. The STRING, GeneMANIA, DAVID, and Human Protein Atlas are harnessed to identify and analyze MIDN-interacted proteins. The Sangerbox 3.0 platform (a pan-cancer analysis module) is used to measure the correlations between the MIDN level and the tumor immune microenvironment, stemness, immune cell infiltration, tumor mutational burden, immune checkpoint genes, and RNA modification genes. Immunofluorescence, qRT-PCR, and Western blotting assays were used to evaluate the biological roles of MIDN in breast and gastric cancer cells. Results: MIDN expression was dysregulated in many cancers and associated with prognosis in several cancers, such as esophageal cancer. MIDN was mutated in 1.7% of cancers, and deep deletion was the dominant mutation type. NR4A1, PSMC1, and EGR1 were selected as MIDN-interacted proteins, and these four molecules were co-expressed in pancreatic cancer, liver cancer, urothelial cancer, melanoma, and breast cancer. MIDN expression was significantly correlated with the infiltration of CD8+ T cell, CD4+ T cell, B cell, macrophage, neutrophil, and DC both in prostate adenocarcinoma and liver hepatocellular carcinoma. The MIDN level was correlated with several immune checkpoint genes, such as VEGFA, and RNA modification genes such as YTHDF1, YTHDF2, YTHDF3, and YTHDC1 in cancers. Furthermore, in breast cancer cells, the downregulation of MIDN suppressed the colony formation abilities and lessened cell-cycle-associated and stemness-associated genes; in gastric cancer, the knockdown of MIDN diminished the mRNA levels of Nanog and LDHA. Strikingly, silence of MIDN upregulated FTO protein expression in both breast and gastric cancer cells. Conclusions: Our findings demonstrate the expression, prognostic value, mutation status, interacting proteins, methylation status, and correlations with the tumor immune microenvironment of MIDN. MIDN will be developed as a potential therapeutic target and a prognosis biomarker. Full article

Influenza A viruses (IAVs) are highly contagious pathogens that cause zoonotic disease with limited availability of antiviral therapies, presenting ongoing challenges to both public health and the livestock industry. Unveiling host proteins that are crucial to the IAV life cycle can help clarify mechanisms of viral replication and identify potential targets for developing alternative host-directed therapies. Using a four-dimensional (4D), label-free methodology coupled with bioinformatics analysis, we analyzed the expression patterns of cellular proteins that changed following H9N2 virus infection. Compared to the control group, the H9N2 infected group displayed 732 differentially expressed proteins (DEPs), with 298 proteins showing upregulation and 434 proteins showing downregulation. Gene Ontology (GO) functional analysis showed that DEPs were catalog in 11 biological processes, three cellular components, and eight molecular functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that DEPs were involved in processes including cytokine signaling pathways induced by virus infection and protein digestion and absorption. Proteins including TP53, DDX58, and STAT3 were among the top hub proteins in the protein–protein interaction (PPI) analysis, suggesting that these signaling cascades could be essential for the propagation of IAVs. Furthermore, the host protein SNAPIN was chosen to ascertain the accuracy of expression changes identified through a proteomic analysis. The results indicated that SNAPIN was downregulated following infection with IAVs both in vitro and in vivo, which is consistent with the proteomics results, suggesting that SNAPIN may serve as a key regulatory factor in the viral life cycle of IAVs. Our research delineates an extensive interaction map of IAV infection within the A549 cells, facilitating the discovery of pivotal proteins that contribute to the virus’s propagation, potentially offering target candidates to screen for antiviral therapeutics. Full article

Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. Two anthropogenic geomorphic feature extraction problems are explored: the extraction of agricultural terraces and the mapping of surface coal mine reclamation-related valley fill faces. Light detection and ranging (LiDAR)-derived DTMs were used to generate LSPs. We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. Transfer learning between the different geomorphic datasets offered minimal benefits. We suggest that pre-trained models developed using large, image-based datasets may be of value for anthropogenic geomorphic feature extraction from LSPs even given the data and task disparities. More specifically, ImageNet-based parameters should be considered as an initialization state for the encoder component of semantic segmentation architectures applied to anthropogenic geomorphic feature extraction even when using non-RGB image-based predictor variables, such as LSPs. The value of transfer learning between the different geomorphic mapping tasks may have been limited due to smaller sample sizes, which highlights the need for continued research in using unsupervised and semi-supervised learning methods, especially given the large volume of digital terrain data available, despite the lack of associated labels. Full article

The removal of back armor marks the first stage in the comprehensive processing of river crabs. However, the current low level of mechanization undermines the effectiveness of this process. By integrating robotic systems with image recognition technology, the efficient removal of dorsal armor from river crabs is anticipated. This approach relies on the rapid identification and precise positioning of the processing location at the crab’s tail, both of which are essential for optimal results. In this paper, we propose a lightweight deep learning model called YOLOv7-SPSD for detecting river crab tails. The goal is to accurately determine the processing location for the robotic removal of river crab back armor. We start by constructing a crab tail dataset and completing the data labeling process. To enhance the lightweight nature of the YOLOv7-tiny model, we incorporate the Slimneck module, PConv, and the SimAM attention mechanism. These additions help achieve an initial reduction in model size while preserving detection accuracy. Furthermore, we optimize the model by removing redundant parameters using the DepGraph pruning algorithm, which facilitates its application on edge devices. Experimental results show that the lightweight YOLOv7-SPSD model achieves a mean Average Precision (mAP) of 99.6% at a threshold of 0.5, an F1-score of 99.6%, and processes frames at a rate of 7.1 frames per second (FPS) on a CPU. Compared to YOLOv7-tiny, the improved model increases FPS by 2.7, reduces GFLOPS by 74.6%, decreases the number of parameters by 71.6%, and lowers its size by 8.1 MB. This study enhances the deployment of models in river crab processing equipment and introduces innovative concepts and methodologies for advancing intelligent river crab deep processing technology. Full article

Protein arginine methyltransferase 5 (PRMT5) is a critical oncogenic factor in various cancers, and its inhibition has shown promise in suppressing tumor growth. However, the role of PRMT5 in squamous cell carcinoma (SCC) remains largely unexplored. In this study, we analyzed SCC patient data from The Cancer Genome Atlas (TCGA) and the Cancer Dependency Map (DepMap) to investigate the relationship between PRMT5 and SCC proliferation. We employed competition-based cell proliferation assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays, flow cytometry, and in vivo mouse modeling to examine the regulatory roles of PRMT5 and its binding partner WDR77 (WD repeat domain 77). We identified downstream targets, including the p63 isoform ΔNp63α and the cyclin-dependent kinase inhibitor p21, through single-cell RNA-seq, RT-qPCR, and Western blot analyses. Our findings demonstrate that upregulation of PRMT5 and WDR77 correlates with the poor survival of head and neck squamous cell carcinoma (HNSCC) patients. PRMT5/WDR77 regulates the HNSCC-specific transcriptome and facilitates SCC proliferation by promoting cell cycle progression. The PRMT5 and WDR77 stabilize the ΔNp63α Protein, which in turn, inhibits p21. Moreover, depletion of PRMT5 and WDR77 repress SCC in vivo. This study reveals for the first time that PRMT5 and WDR77 synergize to promote SCC proliferation via the ΔNp63α-p21 axis, highlighting a novel therapeutic target for SCC. Full article

Background: Renal cell carcinoma (RCC) is a highly aggressive malignancy accounting for the majority of kidney cancers. Despite recent advancements in therapeutic options, the prognosis for advanced-stage RCC remains poor. Niemann–Pick C1-Like 1 (NPC1L1) plays a crucial role in cholesterol absorption and has been implicated in cancer progression across various cancers. However, its expression patterns and prognostic significance in RCC remain unclear. Methods: In this study, NPC1L1 expression in normal and RCC tissues, including subtypes, was compared using TCGA, GEPIA2, and The Human Protein Atlas. Clinical correlations were assessed, and the impact of NPC1L1 on overall survival (OS) and progression-free survival (PFS) was evaluated. Gene effect scores were analyzed using the DepMap tool to determine the involvement of NPC1L1 in RCC progression. Results: NPC1L1 expression was significantly lower in RCC tissues compared to normal tissues, particularly in the clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) subtypes, but increased in advanced tumor stages. Higher NPC1L1 expression was associated with worse OS and PFS in RCC patients. Multivariable Cox regression confirmed NPC1L1 as an independent prognostic marker. Additionally, gene effect scores showed that NPC1L1 is essential for the survival of specific RCC cell lines. Conclusions: This study determines NPC1L1 as an independent prognostic indicator in RCC, with higher expression associated with poor survival outcomes. These findings suggest that NPC1L1 could serve as a valuable marker for identifying high-risk RCC patients. Further research is required to investigate the molecular mechanisms underlying the role of NPC1L1 in RCC progression. Full article

Cholangiocarcinoma (CCA), or bile duct cancer, is the second most common liver malignancy, with an increasing incidence in Western countries. The lack of effective treatments associated with the absence of early symptoms highlights the need to search for new therapeutic targets for CCA. Sulfatides (STs), a type of sulfoglycosphingolipids, have been found in the biliary tract, with increased levels in CCA and other types of cancer. STs are involved in protein trafficking and cell adhesion as part of the lipid rafts of the plasma membrane. We aimed to study the role of STs in CCA by the genetic targeting of GAL3ST1, an enzyme involved in ST synthesis. We used the CRISPR-Cas9 system to generate GAL3ST1-deficient TFK1 cells. GAL3ST1 KO cells showed lower proliferation and clonogenic activity and reduced glycolytic activity compared to TFK1 cells. Polarized TFK1 GAL3ST1 KO cells displayed increased transepithelial resistance and reduced permeability compared to TFK1 wt cells. The loss of GAL3ST1 showed a negative effect on growth in 30 out of 34 biliary tract cancer cell lines from the DepMap database. GAL3ST1 deficiency partially restored epithelial identity and barrier function and reduced proliferative activity in CCA cells. Sulfatide synthesis may provide a novel therapeutic target for CCA. Full article