Search Results (175)

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder that predominantly affects synovial joints, leading to inflammation, pain, and progressive joint damage. Despite therapeutic advancements, the molecular basis of established RA remains poorly defined. Methods: In this study, we conducted an untargeted plasma proteomic analysis using two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in samples from RA patients and healthy controls in the discovery phase. Results: Significantly (ANOVA, p ≤ 0.05, fold change > 1.5) differentially abundant proteins (DAPs) were identified. Notably, upregulated proteins included mitochondrial dicarboxylate carrier, hemopexin, and 28S ribosomal protein S18c, while CCDC124, osteocalcin, apolipoproteins A-I and A-IV, and haptoglobin were downregulated. Receiver operating characteristic (ROC) analysis identified CCDC124, osteocalcin, and metallothionein-2 with high diagnostic potential (AUC = 0.98). Proteins with the highest selected frequency were quantitatively verified by multiple reaction monitoring (MRM) analysis in the validation cohort. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) revealed the underlying molecular pathways and key interaction networks involved STAT1, TNF, and CD40. These central nodes were associated with immune regulation, cell-to-cell signaling, and hematological system development. Conclusions: Our combined proteomic and bioinformatic approaches underscore the involvement of dysregulated immune pathways in RA pathogenesis and highlight potential diagnostic biomarkers. The utility of these markers needs to be evaluated in further studies and in a larger cohort of patients. Full article

Background: Obesity significantly impacts disease burden, with waist-to-hip ratio (WHR) as a key obesity indicator, but the genetic and biological pathways underlying WHR, particularly its sex-specific differences, remain poorly understood. Methods: This study explored WHR’s sexual dimorphism and its links to complex traits using cross-sectional surveys and genetic data from Giant and UK Biobank (UKB). We analyzed WHR heritability, performed tissue-specific transcriptome-wide association studies (TWAS) using FUSION, and conducted genetic correlation analyses with linkage disequilibrium score regression (LDSC) and Local Analysis of [co]Variant Association (LAVA). Polygenic scores (PGS) for WHR were constructed using the clumping and thresholding method (CT), and associations with complex traits were assessed via logistic or linear models. Results: The genetic analysis showed sex-specific heritability for WHR, with TWAS identifying female-specific (e.g., CCDC92) and male-specific (e.g., UQCC1) genes. Global genetic correlation analysis revealed sex-specific associations between WHR and 23 traits, while local analysis identified eight sex-specific loci across five diseases. Regression analysis highlighted sex-specific associations for 70 traits with WHR and 45 traits with WHR PGS, with stronger effects in females. Predictive models also performed better in females. Conclusions: This study underscores WHR’s sexual dimorphism and its distinct associations with complex traits, offering insights into sex-specific biological differences, health management, and clinical advancements. Full article

Background: Comprehensive molecular profiling is essential for precision oncology in non-small cell lung cancer (NSCLC). However, genomic data from Eastern European populations, including Romania, remain limited. Methods: We analyzed 398 consecutive NSCLC cases tested at the PATHOS Molecular Pathology Laboratory (Cluj-Napoca, Romania) between April 2024 and February 2025 using the Ion Torrent™ Genexus™ System and the Oncomine™ Dx Target Test, which evaluates SNVs/indels in 46 genes, fusions in 23 genes, and CNVs in 19 genes from FFPE samples. Results: The cohort was predominantly male (66%) with a median age of 67 years. Adenocarcinoma represented 70% of cases with known histology. Genomic profiling revealed a high frequency of actionable alterations. KRAS mutations were the most common (29.1%), with p.G12C detected in 10.3% of all the cases. EGFR mutations were present in 14.3% of patients, mostly exon 19 deletions and L858R substitutions. BRAF alterations (5.3%) included both V600E and non-V600E variants. RET alterations were detected as eight missense mutations, two canonical fusions (KIF5B–RET, CCDC6–RET), one amplification, and three transcript imbalances. EML4-ALK fusions (1.77%), ERBB2 mutations/amplifications (3.0%), and FGFR1/FGFR3 amplifications were also observed. Conclusions: This study provides the first large-scale molecular snapshot of NSCLC in Romania. While the overall genomic profiles align with Western populations, the higher frequency of KRAS p.G12C and FGFR amplifications highlights the value of region-specific data to support targeted therapies in Eastern Europe. Full article

Crohn’s disease (CD) is a chronic inflammatory disorder with potential progression to stricturing (B2) or penetrating (B3) phenotypes, leading to significant complications. Early identification of patients at risk for these complications is critical for personalized management. This study aimed to develop a predictive model using clinical data and a Korean-specific transcriptome-wide association study (TWAS) to forecast early progression in CD patients. A retrospective analysis of 430 Korean CD patients from 15 hospitals was conducted. Genotyping was performed using the Korea Biobank Array, and gene expression predictions were derived from a TWAS model based on terminal ileum data. Logistic regression models incorporating clinical and gene expression data predicted progression to B2 or B3 within 24 months of diagnosis. Among the cohort, 13.9% (60 patients) progressed to B2 and 16.9% (73 patients) to B3. The combined model achieved mean area under the curve (AUC) values of 0.788 for B2 and 0.785 for B3 progression. Key predictive genes for B2 included CCDC154, FAM189A2, and TAS2R19, while PUS7, CCDC146, and MLXIP were linked to B3 progression. This integrative model provides a robust approach for identifying high-risk CD patients, potentially enabling early, targeted interventions to reduce disease progression and associated complications. Full article

Accurate long-term and high-resolution forest disturbance monitoring are pivotal for forest carbon modeling and forest management. Many algorithms have been developed for this purpose based on the Landsat time series, but their nationwide performance across different regions and disturbance types remains unexplored. Here, we conducted a comprehensive comparison and validation of six widely used forest disturbance- monitoring algorithms using 12,328 reference samples in China. The algorithms included three annual-scale (VCT, LandTrendr, mLandTrendr) and three daily-scale (BFAST, CCDC, COLD) algorithms. Results indicated that COLD achieved the highest accuracy, with F1 and F2 scores of 81.81% and 81.25%, respectively. Among annual-scale algorithms, mLandTrendr exhibited the best performance, with F1 and F2 scores of 73.04% and 72.71%, and even outperformed the daily-scale BFAST algorithm. Across China’s six regions, COLD consistently achieved the highest F1 and F2 scores, showcasing its robustness and adaptability. However, regional variations in accuracy were observed, with the northern region exhibiting the highest accuracy and the southwestern region the lowest. When considering different forest disturbance types, COLD achieved the highest accuracies for Fire, Harvest, and Other disturbances, while CCDC was most accurate for Forestation. These findings highlight the necessity of region-specific calibration and parameter optimization tailored to specific disturbance types to improve forest disturbance monitoring accuracy, and also provide a solid foundation for future studies on algorithm modifications and ensembles. Full article

This review provides an analysis of non-perovskite hybrid halides of coinage metals templated by metal–organic cations (CCDC November 2023). These materials display remarkable structural diversity, from zero-dimensional molecular complexes to intricate three-dimensional frameworks, allowing fine-tuning of their properties. A total of 208 crystal structures, comprising haloargentates, mixed-metal haloargentates, and halocuprates, are categorized and examined. Their potential in photocatalysis is discussed. Special attention is given to the structural adaptability of these materials for the generation of functional interfaces. This review highlights key compounds and aims to inspire further research into optimizing hybrid halides for advanced technological applications. Full article

Glycosylation plays a critical role in various biological processes, yet identifying specific glycosyltransferase substrates remains a challenge due to the complexity of glycosylation. Here, we employ proximity labeling with biotin ligases BASU and TurboID to map the proximitome of MGAT3, a glycosyltransferase responsible for the biosynthesis of the bisecting GlcNAc structure, in HEK293T cells. This approach enriched 116 and 189 proteins, respectively, identifying 17 common substrates shared with bisecting GlcNAc-bearing proteome obtained via intact glycopeptide enrichment methods. Gene ontology analysis revealed that the enriched proteins were predominantly localized in the exosome, endoplasmic reticulum, and Golgi apparatus, consistent with subcellular localization of MGAT3 substrates. Notably, four novel substrates, GOLM2, CCDC134, ASPH, and ERO1A, were confirmed to bear bisecting GlcNAc modification, validating the utility of the proximity labeling method. Furthermore, we observed that bisecting GlcNAc modification inhibits breast cancer progression by promoting the degradation of α-galactosidase A (GLA). These findings demonstrate the efficacy of proximity labeling in identifying glycosyltransferase substrates and provide insights into the functional impact of bisecting GlcNAc modification. Full article

Semen quality and persistence are critical for evaluating the usability of individual boars in AI, a standard practice in pig breeding. We conducted GWASs on various semen traits of Duroc boars, including MOT, DEN, ABN, MMP, AIR, and ROS levels. These traits were assessed using FCM and CASA. A total of 1183 Duroc boars were genotyped using the GeneSeek GGP Porcine 50 K SNP BeadChip. The GWAS was performed using three different models: GLM, MLM, and FarmCPU. Additionally, trait heritability was estimated using single- and multiple-trait PBLUP models, yielding 0.19, 0.29, 0.13, 0.18, 0.11, and 0.14 heritability for MOT, DEN, ABN, MMP, AIR, and ROS, respectively. All semen traits exhibited low heritability except ABN, which demonstrated medium heritability. Nine candidate genes (GPX5, AWN, PSP-II, CCDC62, TMEM65, SLC8B1, TRPV4, UBE3B, and SIRT5) were potentially associated with semen traits. These genes are associated with antioxidant and mitochondrial functions in porcine sperm. Our findings provide insight into the genetic architecture of semen traits in Duroc boars, and the identified SNPs and candidate genes may enhance economic outcomes in the pig breeding industry while improving sperm quality through targeted breeding strategies. Full article

Although osteoclasts play crucial roles in the skeletal system, the mechanisms that underlie oxidative stress during osteoclastogenesis remain unclear. The transcription factor Nrf2 and its suppressor, Keap1, function as central mediators of oxidative stress. To further elucidate the function of Nrf2/Keap1-mediated oxidative stress regulation in osteoclastogenesis, DNA microarray analysis was conducted in this study using wild-type (WT), Keap1 knockout (Keap1 KO), and Nrf2 knockout (Nrf2 KO) osteoclasts. Principal component analysis showed that 403 genes, including Nqo1, Il1f9, and Mmp12, were upregulated in Keap1 KO compared with WT osteoclasts, whereas 24 genes, including Snhg6, Ccdc109b, and Wfdc17, were upregulated in Nrf2 KO compared with WT osteoclasts. Moreover, 683 genes, including Car2, Calcr, and Pate4, were upregulated in Nrf2 KO cells compared to Keap1 KO cells. Functional analysis by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed upregulated genes in Nrf2 KO osteoclasts were mostly enriched in oxidative phosphorylation. Furthermore, GeneMANIA predicted the protein–protein interaction network of novel molecules such as Rufy4 from genes upregulated in Nrf2 KO osteoclasts. Understanding the complex interactions between these molecules may pave the way for developing promising therapeutic strategies against bone metabolic diseases caused by increased osteoclast differentiation under oxidative stress. Full article

A study of the solid-state intermolecular interactions of twenty-nine benzopyrans substituted with polyhaloalkyl groups was carried out by quantum chemical calculations using the Mercury and WinGX computer programs. Molecular structures were obtained from crystallographic information files (CIF) of the CCDC database. C-H—O, C-H—X, C-X—O and C-X—X type contacts, characterized as unconventional hydrogen bonds, were identified and calculated. The criteria used for distances and angles were d(D—A) < R(D) + R(A) + 0.50 and d(H—A) < R(H) + R(A)—0.12°, where D-H—A > 100.0°. D is the donor atom, A is the acceptor atom, R is the Van der Waals radius and d is the interatomic distance. In addition, Etter’s notation was used to describe sets of hydrogen bonds in organic crystals, detailing the intermolecular contacts and periodic arrangements of the crystal packing. It was corroborated that certain positions of halogen atoms and their interactions play an important role in stabilizing the crystal lattice. Full article

The aim of this study was to identify genetic variants and pathways associated with the total number of piglets born and to investigate the potential negative consequences of the intensive selection for reproductive traits, particularly the formation of bumps on the legs of pigs. We used genome-wide association analysis and methods for identifying selection signatures. As a result, 47 SNPs were identified, localized in genes that play a significant role during sow pregnancy. These genes are involved in follicle growth and development (SGC), early embryonic development (CCDC3, LRRC8C, LRFN3, TNFRSF19), endometrial receptivity and implantation (NEBL), placentation, and embryonic development (ESRRG, GHRHR, TUSC3, NBAS). Several genes are associated with disorders of the nervous system and brain development (BCL11B, CDNF, ULK4, CC2D2A, KCNK2). Additionally, six SNPs are associated with the formation of bumps on the legs of pigs. These variants include intronic variants in the CCDC3, ULK4, and MINDY4 genes, as well as intergenic variants, regulatory region variants, and variants in the exons of non-coding transcripts. The results suggest important biological pathways and genetic variants associated with sow fertility and highlight the potential negative impacts on the health and physical condition of pigs. Full article

Due to the significant threat to forest health posed by beetle infestations on pine trees, timely and accurate predictions are crucial for effective forest management. This study developed a pine tree stress probability prediction workflow based on monthly cloud-free Sentinel-2 composite images to address this challenge. First, representative pine tree stress samples were selected by combining long-term forest disturbance data using the Continuous Change Detection and Classification (CCDC) algorithm with high-resolution remote sensing imagery. Monthly cloud-free Sentinel-2 images were then composited using the Multifactor Weighting (MFW) method. Finally, a Random Forest (RF) algorithm was employed to build the pine tree stress probability model and analyze the importance of spectral, topographic, and meteorological features. The model achieved prediction precisions of 0.876, 0.900, and 0.883, and overall accuracies of 89.5%, 91.6%, and 90.2% for January, February, and March 2023, respectively. The results indicate that spectral features, such as band reflectance and vegetation indices, ranked among the top five in importance (i.e., SWIR2, SWIR1, Red band, NDVI, and NBR). They more effectively reflected changes in canopy pigments and leaf moisture content under stress compared with topographic and meteorological features. Additionally, combining long-term stress disturbance data with high-resolution imagery to select training samples improved their spatial and temporal representativeness, enhancing the model’s predictive capability. This approach provides valuable insights for improving forest health monitoring and uncovers opportunities to predict future beetle outbreaks and take preventive measures. Full article

Despite the existence of effective therapy options for patients with localized colorectal cancer, advanced-stage patients have limited therapies. Genomic profiling is a promising tool for guiding treatment selection as well as patient monitoring. Here, we describe a novel gene rearrangement (CCDC6::CASP7) detected in a patient with advanced colorectal cancer that could be a therapeutic target. The patient underwent surgical resection but died after the operation from fecal peritonitis. To our knowledge, this is the first report in which the CCDC6::CASP7 gene rearrangement has been described in an advanced colorectal adenocarcinoma patient. Full article

Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies. Full article