Breast cancer (BC) and Thyroid cancer (TC) are prevalent malignancies in women that share epidemiological and molecular features. Emerging evidence indicates that non-coding RNAs are key regulators of cancer associated gene expression. Long non-coding RNA (lncRNA) drive tumor progression by acting as competing endogenous RNAs (ceRNAs), sponging microRNA (miRNA) to deregulate oncogenic messenger RNA (mRNA). The influence of functional genetic polymorphisms of lncRNAs on their expression, as well as the expression of their ceRNA components RNA in a direct comparative context of BC and TC, remains unexplored. 60 breast cancer and 60 thyroid cancer tissue samples, alongside matched adjacent healthy controls from a Pakistani female patient, were used. Genotyping of lncRNA H19 SNPs (rs3741219 and rs2839698) was performed using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) followed by quantitative real-time PCR (qRT-PCR) to assess the expression of lncRNA H19, miR-152, and DNMT1. Expression and genotype associations, association with clinical parameters, and diagnostic and prognostic utility of the studied RNA were statistically evaluated. Genotyping revealed that rs3741219 showed significant tumor–control differences in breast cancer (p < 0.05). Expression analysis revealed upregulation of lncRNA H19 and DNMT1, and downregulation of miR-152, in tumor samples compared with adjacent healthy controls in both cancers. In genotype–expression analysis, rs3741219 influenced lncRNA H19 expression in both cancer types. Receiver Operating Characteristic (ROC) analysis confirmed the strong diagnostic potential of H19 and DNMT1 (AUC 0.98-1.00). Correlation and regression analyses validated the proposed ceRNA interactions and their significant association with advanced cancer stage. A high-risk score from the H19/miR-152/DNMT1 axis was prognostic only in thyroid cancer (HR = 2.97).
Nature-based Solutions (NbS) for municipal wastewater treatment have emerged as sustainable alternatives to increase global access to sanitation. This study has analyzed the global distribution and key design characteristics of NbS over the past 30 years, focusing on full-scale municipal wastewater treatment systems. This systematic review has identified 393 NbS from 249 publications from 57 countries. Seven NbS types are presented as different designs of constructed wetlands. The analysis reported the advantages and limitations, the main design parameters, the types of substrates and macrophytes used, and the removal efficiency of the main physical–chemical and microbiological parameters for each NbS. The most common NbS identified were Horizontal subsurface Flow constructed wetlands (HF-CW), while Aerated constructed wetlands (Aerated-CW) were the least common. The Aerated-CW and Vertical subsurface Flow constructed wetlands (VF-CW) presented the highest hydraulic loading rates (210 and 120 mm d⁻¹, respectively) and the lowest specific surface area requirements (0.95 and 0.50 m2 PE-1, respectively). The research highlighted several types of substrates (n = 31) and a high number of macrophyte species (n=204) used in NbS worldwide. The most common macrophyte genera used in NbS were Phragmites, followed by Typha, Canna, Salix, Cyperus, and Scirpus. Gravel, sand, soil, and stone are the most common substrates identified. The Aerated-CW showed the highest average efficiencies for organic matter (BOD5: 97.3 ± 1.37 %; COD: 77.26 ± 31.69 %) and nutrients (TN: 74.00 ± 22.64 %, NH4+-N: 90.90 ± 15.56 %, and TP: 90.3 ± 9.33 %). This research can contribute to the increasing knowledge of NbS, offering practical perspectives for local policy-makers and designers, and promoting their large-scale implementation.
This study aimed to make the microdata from the National Survey of School Health (PeNSE 2024) available while preserving the participating student’s identity. To this end, the risk assessment was conducted considering a specific scenario where some key variables were selected. The treatments applied to risk control were Global Recoding (students age) and Local Suppression of two key variables. The results demonstrate that the treatments were effective in limiting the identity disclosure risk, considering the established scenario.
Let G be a simple graph, A(G) its adjacency matrix, and D(G) its diagonal degree matrix. In 2022, () defined the family of matrices L_α as the convex linear combination: L_α(G) = αD(G) + (α- 1)A(G), where α∈ [0,1]. The study of the spectrum of this family of matrices may provide a unified framework for analyzing the spectra of the adjacency, degree, and Laplacian matrices (D(G) - A(G)). In this work, we investigate the spectrum of L_α under graph operations and within specific families of graphs.
Este estudo avaliou o desempenho da arquitetura U-Net na identificação de favelas em ortoimagens de alta resolução, utilizando máscaras manuais como referência. Foram comparados modelos treinados com e sem data augmentation. O modelo com data augmentation apresentou melhor desempenho em IoU, F1-Score e Precisão, enquanto o modelo sem augmentation obteve maior Revocação, evidenciando o trade-off entre sensibilidade e controle de falsos positivos. Apesar das dificuldades em áreas pequenas e de baixo contraste visual, os resultados confirmam o potencial da U-Net para o mapeamento de assentamentos precários.
