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Thymic squamous cell carcinoma (TSCC) is a rare cancer that is typically characterized by low rates of brain metastases. However, when it does occur, brain metastases of TSCC can have significant clinical implications due to its rarity and lack of established management guidelines. In this literature review and case report, we explore the clinical presentation, management, and outcomes of patients with brain metastases of TSCC.Our literature review found that brain metastases of TSCC are relatively rare, with an estimated incidence of 5-10 % among patients with advanced-stage disease. Management of brain metastases of TSCC often involves a combination of surgical resection, radiation therapy, and chemotherapy. However, due to the rarity of this condition, there is currently limited evidence to guide optimal treatment strategies.We also present a case report of a patient with TSCC who was presented with a brain metastasis. This case highlights the importance of early diagnosis and prompt management of brain metastasis of TSCC to improve patient outcomes. Our review and case report underscore the need for further research to improve our understanding of the clinical characteristics, optimal management, and prognosis of patients with brain metastasis of TSCC.

Abstract Despite their importance, wetland ecosystems protected by the Ramsar Convention are under pressure from climate change and human activities. These drivers are altering water availability in these wetlands, changing water levels or surface water extent, in some cases, beyond historical variability. Attribution of the effects of human and climate activities is usually focused on changes within the wetlands or their upstream surface and groundwater inputs. However, the reliance of wetland water availability on upwind atmospheric moisture supply is less understood. Here, we assess the vulnerability of 40 Ramsar wetlands to precipitation changes caused by land use and hydroclimatic change occurring in their upwind moisture‐supplying regions. We use moisture flows from a Lagrangian tracking model, atmospheric reanalysis data, and historical land use change (LUC) data to assess and quantify these changes. Our analyses show that historical LUC has decreased precipitation and terrestrial moisture recycling in most wetland hydrological basins, decreasing surface water availability (precipitation minus evaporation). The most substantial effects on wetland water availability occurred in the tropic subtropical regions of Central Europe and Asia. Overall, we found wetlands in Central Asia and South America to be the most vulnerable by a combination of LUC‐driven effects on runoff, high terrestrial precipitation recycling, and recent decreases in surface water availability. This study stresses the need to incorporate upwind effects of land use changes in the restoration, management, and conservation of the world's wetlands.

Deep learning and remote sensing techniques have significantly advanced water surface monitoring; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a deep learning model inspired by knowledge distillation (a.k.a. teacher–student model) to generate labeled data automatically and eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images. To train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DeepAqua represents a significant advancement in computer vision techniques for water detection by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 3%, Intersection Over Union by 11%, and F1-score by 6%. This approach offers a practical solution for monitoring wetland water extent changes without the need of ground truth data, making it highly adaptable and scalable for wetland monitoring.

This article focuses on the condition of digital literatures as a medium for literary experimentation and its relationship with other artistic languages. The digital literary work of art is a space for interaction between different languag-es, where each medium manages to contribute to the con-struction of the overall meaning of the work. The reader is also a fundamental agent in the aesthetic experience of digital literature since he oversees the activation and gives meaning to the text through the offered languages. For this, the used theoretical framework goes from philos-ophy to literary theory to establish a corpus that allows a dialogue between the disciplines. In this way, it is possible to understand that the digital literary work of art expands the limits of the definition of literature, building bridges between artistic disciplines and dialoguing in a confluence of media. This article reflects the current concerns about new media and their impact on artistic disciplines.

Abstract The Magdalena River in Colombia is one of the world's largest (discharge = 7100 m3 s−1) tropical rivers, hosting > 170 aquatic vertebrate species. However, concise synthesis of the current ecological and environmental status is lacking. By documenting the anthropogenic stressors impacting the river on time scales ranging from centuries to decades, we found that the river system is subject to the compounding impacts of climate change, river impoundment, invasive alien species (IAS), catchment deforestation, and water pollution. We show that the Magdalena is a woefully understudied ecosystem relative to its critical importance to Colombia's economy, culture, and biodiversity compared with other similarly sized tropical rivers. We emphasize the need for research on (1) IAS population and ecological dynamics, (2) river damming and its links with IAS and climate change, and (3) land‐use changes as well as identifying sources of water pollution and strategies for mitigation.

Introduction: No standardized measurements of normal renal size exist in the Mexican pediatric population. The aims of this study were to (1) determine renal volume by ultrasound (US) adjusted for body surface area to define normal renal size and (2) develop normality tables for body-surface-area renal volume (BSARV) for the population aged 0-18 years in the state of Aguascalientes, Mexico. Methods: A prospective, cross-sectional study was conducted in a healthy population aged 0 to 18 years in the state of Aguascalientes, Mexico. The assessment was performed with renal US in grayscale. Age, height, sex, weight, and body surface area were recorded. The ultrasonographic parameters in both kidneys were length, volume, relative volume, and BSARV. Mean, standard deviation and 1 to 95 percentiles were calculated. Results: We included 1076 participants, 519 women, and 557 men. All had morphologically normal kidneys. There was a direct relationship between kidney length and age and height; the higher the age and height, the greater the kidney length. Normal kidney size according to the BSARV was 41.4 mL/m2 to 70.6 mL/m2 (10th and 90th percentiles, respectively) for the right kidney and 45.2 mL/m2 to 73.8 mL/m2 (10th and 90th percentiles, respectively) for the left kidney in both sexes, regardless of age and height. We defined normality charts of BSARV of the right and left kidney (percentiles 1 to 99) for the population aged 0-18 years. Conclusion: BSARV was the most accurate ultrasonographic parameter for kidney size in the Mexican population aged 0 to 18 years in one region of the country. BSARV is a simple and practical method for determining kidney size to assess normal kidney growth and development.

Abstract The global impacts of El Niño on precipitation have been long‐recognized, but more understanding of the mechanisms behind this influence is needed. For instance, previous studies have largely overlooked the potential impacts of El Niño on terrestrial moisture recycling (TMR). We perform a 40‐year forward tracking simulation to derive a global climatology of recycled precipitation and use a composite analysis to investigate how El Niño affects TMR. We identify seven regions where the El Niño impact on TMR is most significant and find that, in these regions, changes in precipitation and TMR are directly related: they increase or decrease together. In addition, we find a marked latitudinal contrast between the Southern Hemisphere, where TMR increases during El Niño, and the Northern Hemisphere and the tropics, where it decreases. Our results indicate that the weakening and strengthening of TMR can be behind precipitation changes caused by El Niño globally.