Vale (Brazil)

Furipterus horrens es un murciélago insectívoro endémico de los trópicos del Nuevo Mundo. Se refugia principalmente en cuevas y, debido a su modulación de banda ancha y a sus llamados de ecolocalización de baja intensidad, F. horrens rara vez se detecta en el monitoreo acústico y, en general, los datos acústicos y la historia natural de esta especie son poco conocidos. Con base en datos recopilados de mayo de 2021 a diciembre de 2024, describimos aquí los pulsos de ecolocalización de individuos de F. horrens al emerger de sus refugios en cuevas ubicadas en el Bosque Nacional de Carajás, Amazonia oriental, Brasil. También describimos y analizamos por primera vez clics audibles emitidos por F. horrens dentro de estos refugios. Los pulsos presentaron una estructura de frecuencia modulada, escalonada y de banda ancha, e identificamos dos sonotipos de los pulsos de ecolocalización de la especie. Ambos sonotipos presentaron las frecuencias Fmax más altas reportadas para esta especie y para todas las demás especies de murciélagos neotropicales (> 200 kHz). La duración del pulso, su pendiente y su ciclo de trabajo se mantuvieron dentro de los intervalos esperados para la especie, pero el ancho de banda varió en ambos sonotipos, posiblemente relacionado con la baja intensidad de los cantos. Encontramos diferencias en cuatro parámetros evaluados entre animales dentro de las jaulas de vuelo e individuos en vuelo libre, lo que sugiere que el comportamiento acústico puede variar según el entorno y las circunstancias. Los clicks fueron cortos (5,5 ± 4,7 ms) y de banda ancha (35,3 kHz), con frecuencias que oscilaron entre 3,0 ± 1,2 kHz (Fmín) y 39,4 kHz (Fmáx), lo que cubre la mayor parte del espectro audible humano. Nuestra hipótesis es que los clicks podrían funcionar como sonidos de alarma producidos por las alas del murciélago y que esta acción podría ser desencadenada por amenazas, como depredadores, y tener un origen comunicativo en lugar de un pulso de ecolocalización para la navegación. El reconocimiento de estos clicks podría ayudar a detectar F. horrens dentro de sus abrigos, convirtiendo esto en una estrategia complementaria y eficaz a las redes de niebla y el monitoreo acústico pasivo en futuros monitoreos y estudios de impacto ambiental.

BACKGROUND: The understanding of ovarian cancer pathogenesis has been fundamentally revised. High-grade serous carcinoma (HGSC), the commonest lethal subtype, is now known to mostly originate from  serous tubal intraepithelial carcinoma (STIC). This finding came with significant implications for prevention, diagnosis, and clinical management of this condition. OBJECTIVES: This systematic review put together the current evidence on STIC, particularly focusing on its role in ovarian carcinogenesis, the consequent choice of opportunistic salpingectomy (OS) for prevention, and the persistent clinical/pathological challenges in its diagnosis. METHODS: A systematic search of PubMed/MEDLINE and Embase was conducted for studies published between 2016 and 2024. Search terms included "serous tubal intraepithelial carcinoma," "STIC," "ovarian cancer origin," "opportunistic salpingectomy," and "fallopian tube precursor." Studies were screened for relevance to the pathological criteria, clinical management, and preventive strategies related to STIC. RESULTS: The fallopian tube fimbria as the site of origin for most of HGSCs. OS is emerging as a effective  prevention strategy, with data showing a significant reduction in ovarian malignancy incidence. Pathologically, the diagnosis of STIC relies on a combination of severe morphological atypia and an aberrant p53 immunohistochemical pattern. The management of an incidental STIC, particularly in risk-reducing surgery, is controversial due to uncertainties about its metastatic potential. CONCLUSION: STIC discovery has significantly changed ovarian cancer prevention, making opportunistic salpingectomy a new standard of care. Diagnostic challenges and a lack of consensus on the clinical management of isolated STIC lesions reveal critical areas for future research.

Considerando a necessidade de compreender a influência de variáveis microestruturais nas propriedades mecânicas de materiais metálicos, este artigo apresenta uma análise estatística teórica baseada em dados da literatura científica. Objetiva-se investigar como parâmetros como tamanho de grão, fração de fase e composição química impactam a resistência e a ductilidade dos metais. Para tanto, procede-se a um levantamento bibliográfico abrangente aliado à aplicação de métodos estatísticos descritivos e inferenciais, permitindo correlacionar os dados obtidos em estudos anteriores. Desse modo, observa-se que variáveis microestruturais exercem influência significativa sobre o comportamento mecânico dos materiais, podendo servir como preditores na estimativa de propriedades como limite de escoamento, resistência à tração e dureza. O estudo permite concluir que a integração entre Ciência dos Materiais e Estatística é essencial para a modelagem preditiva e otimização das ligas metálicas, contribuindo para avanços na engenharia de materiais e no desenvolvimento de metais com desempenho controlado.

Abstract Iron oxides constitute an important class of materials, exhibiting a rich and intricate range of behaviors. Despite their significance, the structural and mechanical properties, particularly of Hematite ($\alpha$-Fe2O3), have been scarcely investigated in the literature. At the same time, recent developments in machine learning for interatomic potentials have revolutionized computational materials science by enabling highly accurate and efficient simulations of atomic interactions. Traditional methods, such as density functional theory (DFT) and classical force fields, often struggle with high computational costs or lack the flexibility to generalize across diverse chemical environments. ML-based approaches have emerged as powerful alternatives, learning complex potential energy surfaces from quantum-mechanical data. These models can achieve DFT accuracy at a fraction of the computational cost, facilitating large-scale molecular dynamics (MD) simulations. In this work, we present a graph neural network interatomic potential for hematite. The model was trained on datasets generated from DFT+U calculations to account for strong electronic correlations, using atomic configurations sampled across a wide range of temperatures and pressures. Our potential accurately reproduces fundamental material properties, including the elastic moduli, anisotropic elastic constants, vibrational frequencies, and surface energies. Furthermore, we demonstrate its transferability to other bulk iron oxides. This work enables large-scale molecular dynamics (MD) simulations of iron-based materials with ab initio accuracy at a computational cost comparable to that of classical potentials, opening new opportunities for investigating these complex systems.

The accurate assessment of undrained shear strength in natural soils is essential, as these materials often form the foundation of critical geotechnical structures such as embankments, slopes, and dams. Their behaviour is complex, influenced by stress history, depositional environment, and over-consolidation ratio (OCR). Conventional analysis methods typically assume homogeneity and uniform stress conditions, which do not represent the variability of natural soils, particularly those of pleistocene origin, often leading to conservative or unreliable stability estimates. Cone penetration tests with pore pressure measurement have improved interpretation by indirectly capturing stress history and stratigraphic variability; however, challenges remain in consistently defining the transition between drained and undrained behaviour and in relating undrained shear strength to effective stress parameters. To address these limitations, this study proposes a methodological framework introducing sectorisation of undrained shear strength ratios, considering both OCR and in situ stress state. The approach was applied to a sediment retention dam, demonstrating practical value in capturing subsurface heterogeneity and refining soil behaviour interpretation. Results indicate improved accuracy in defining shear strength parameters, thereby supporting safer and more reliable geotechnical design in complex geological settings.

The increasing adoption of Multiprocessor System-on-Chip (MPSoC) architectures in space applications demands highly reliable interconnection solutions resilient to radiation-induced faults. This paper evaluates the reliability of the eXtensible Interconnect Network Architecture (XINA), a fault-tolerant Network-on-Chip (NoC) featuring a robust, AXI-compatible Network Interface (NI), specifically designed to maintain reliable communication under harsh environmental conditions. XINA incorporates Triple Modular Redundancy (TMR) and Hamming Error-Correcting Codes (ECC) to enhance the robustness of both NoC routers and NI components, while remaining fully compatible with standard AMBA AXI IP cores. Using a simulation-based fault injection methodology applied to a large-scale 100-core MPSoC implementation, we quantify the fault tolerance provided by both the NoC and its NI, and analyze their combined impact on performance, area, and power consumption. Four design variants combining different protection levels were evaluated. Results demonstrate the system sustained a peak global throughput of 2.12 Gbps in the unprotected baseline configuration, while the fully protected configuration reduced error occurrences at the cost of increased resource utilization and reduced operating frequency.

The hydrodynamic classification of two minerals of very different density, silica and hematite, was investigated using a modified REFLUX™ Classifier known as the GradePro™. The primary objective was to achieve efficient classification with no by-pass, meaning no entrainment of relatively fine particles with those that report to the coarse underflow stream. By-pass is a common feature of cyclones in comminution circuits, responsible for overgrinding and hence lower mineral recovery, excessive energy consumption, poor mill utilisation, and increased tailings and water loss. A series of continuous steady state classification experiments was conducted covering a range of separation sizes. The partition curves were produced as a function of the particle size. Pycnometry was used to convert the data into a binary data set composed of the hematite and silica portions. The classification was found to be very sharp, exhibiting closure of the partition curves and no by-pass. The hematite separation size was typically half that of the silica, but calculations indicated the two minerals had the same slip velocity due to the differential levels of hindered settling, governed by the suspension density. This shift in separation size offers a range of benefits which are discussed.

Abstract Cultivating threatened plant species is vital for conservation, safeguarding against extinction by ensuring propagation and maintaining genetic diversity. Here, we conducted an integrative morphological, physiological, and quantitative proteomic analysis of Stephanopodium engleri plants grown under different substrates and nutritional sources. Stephanopodium engleri is a threatened tree endemic to the Iron Quadrangle, a Brazilian mining region. Different nutritional sources resulted in changes in the growth of S. engleri , with N, P, and micronutrients identified as the most limiting nutrients. The species exhibited maximum growth with half of the applied fertilization, suggesting a low nutritional requirement. The highest growth was related to adjustments in the photosynthetic apparatus and protein regulation, with no significant differences in morpho-anatomical traits. The most representative proteins displayed a diverse array of metabolic functions, particularly related to iron and zinc homeostasis, photosynthesis and energy pathways and reactive oxygen species signaling. Plants with lower carbon assimilation and growth exhibited down-regulation of these proteins. Our findings provide valuable insights into S . engleri physiology and adaptations, emphasizing the importance of minimum fertilization to produce high-quality seedlings. These results also support broader conservation efforts aimed at preventing the species from becoming extinct, including reinforcing natural populations, establishing new ones, and supporting the maintenance of germplasm banks and living collections.