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Pack-ice seals are key indicator species in the Southern Ocean. Their large size (2–4 m) and continent-wide distribution make them ideal candidates for monitoring programs via very-high-resolution satellite imagery. The sheer volume of imagery required, however, hampers our ability to rely on manual annotation alone. Here, we present SealNet 2.0, a fully automated approach to seal detection that couples a sea ice segmentation model to find potential seal habitats with an ensemble of semantic segmentation convolutional neural network models for seal detection. Our best ensemble attains 0.806 precision and 0.640 recall on an out-of-sample test dataset, surpassing two trained human observers. Built upon the original SealNet, it outperforms its predecessor by using annotation datasets focused on sea ice only, a comprehensive hyperparameter study leveraging substantial high-performance computing resources, and post-processing through regression head outputs and segmentation head logits at predicted seal locations. Even with a simplified version of our ensemble model, using AI predictions as a guide dramatically boosted the precision and recall of two human experts, showing potential as a training device for novice seal annotators. Like human observers, the performance of our automated approach deteriorates with terrain ruggedness, highlighting the need for statistical treatment to draw global population estimates from AI output.

Fine-scale sea ice conditions are key to our efforts to understand and model climate change. We propose the first deep learning pipeline to extract fine-scale sea ice layers from high-resolution satellite imagery (Worldview-3). Extracting sea ice from imagery is often challenging due to the potentially complex texture from older ice floes (i.e., floating chunks of sea ice) and surrounding slush ice, making ice floes less distinctive from the surrounding water. We propose a pipeline using a U-Net variant with a Resnet encoder to retrieve ice floe pixel masks from very-high-resolution multispectral satellite imagery. Even with a modest-sized hand-labeled training set and the most basic hyperparameter choices, our CNN-based approach attains an out-of-sample F1 score of 0.698–a nearly 60% improvement when compared to a watershed segmentation baseline. We then supplement our training set with a much larger sample of images weak-labeled by a watershed segmentation algorithm. To ensure watershed derived pack-ice masks were a good representation of the underlying images, we created a synthetic version for each weak-labeled image, where areas outside the mask are replaced by open water scenery. Adding our synthetic image dataset, obtained at minimal effort when compared with hand-labeling, further improves the out-of-sample F1 score to 0.734. Finally, we use an ensemble of four test metrics and evaluated after mosaicing outputs for entire scenes to mimic production setting during model selection, reaching an out-of-sample F1 score of 0.753. Our fully-automated pipeline is capable of detecting, monitoring, and segmenting ice floes at a very fine level of detail, and provides a roadmap for other use-cases where partial results can be obtained with threshold-based methods but a context-robust segmentation pipeline is desired.

We introduce a semiautomated machine learning method that employs high‐resolution imagery for the species‐level classification of Antarctic pack‐ice seals. By incorporating the spatial distribution of hauled‐out seals on ice into our analytical framework, we significantly enhance the accuracy of species identification. Employing a Random Forest model, we achieved 97.4% accuracy for crabeater seals and 98.0% for Weddell seals. To further refine our classification, we included three linearity measures: mean distance to a group's regression line, straightness index, and sinuosity index. Additional variables, such as the number of neighboring seals within a 250 m radius and distance of individual seals to the sea ice edge, also contributed to improved accuracy. Our study marks a significant advancement in the development of a cost‐effective, unified Antarctic seal monitoring system, enhancing our understanding of seal spatial behavior and enabling more effective population tracking amid environmental changes. [ABSTRACT FROM AUTHOR]

Abstract Background Evolution is a unifying theme in biology and its understanding is essential to address sustainability problems. However, many people across the world do not understand evolution. Textbooks are among the most widely used educational resources and the way they depict evolution may greatly impact students’ scientific literacy in evolution. In this paper we investigate which evolution concepts are addressed in European science textbooks, from the 1st to the 9th grade. A content analysis using the ‘Framework for the Assessment of school Curricula on the presence of Evolutionary concepts”, was performed on textbooks from eight European countries: Belgium, Cyprus, Czech Republic, Greece, Italy, Netherlands, Portugal and Slovenia. At least two experienced coders per country independently analysed the texts, images and activities, discussed the results and reached a final consensus. Results Our results show that textbooks lack or rarely address concepts considered important for evolution understanding, notably the processes driving evolution and their daily life implications, limiting opportunities for education for sustainability. Conclusions We recommend that science textbooks emphasize evolution and its processes since the first school years. This recommendation is particularly relevant for the analysed textbooks from Cyprus and Belgium, that cover less than 45% of the concepts important to evolution literacy.

Renal medullary cells are exposed to elevated and variable osmolarities and low oxygen tension. Despite the harsh environment, these cells are resistant to the effects of many harmful events. To test the hypothesis that this resistance is a consequence of these cells developing a stress tolerance phenotype to survive in this milieu, we created osmotically tolerant cells [hypertonic (HT) cells] by gradually adapting murine inner medullary collecting duct 3 cells to hyperosmotic medium containing NaC1 and urea. HT cells have a reduced DNA synthesis rate, with the majority of cells arrested in the [G.sub.0]/[G.sub.1] phase of the cell cycle, and show constitutive expression of heat shock protein 70 that is proportional to the degree of hyperosmolarity. Unlike acute hyperosmolarity, chronic hyperosmolarity failed to activate MAPKs. Moreover, HT cells acquired protein translational tolerance to further stress treatment, suggesting that HT ceils have an osmotolerant phenotype that is analogous to thermotolerance but is a permanent condition. In addition to osmotic shock, HT cells were more resistant to heat, [H.sub.2][O.sub.2], cyclosporin, and apoptotic inducers, compared with isotonic murine inner medullary duct 3 cells, but less resistant to amphotericin B and cadmium. HT cells demonstrate that in renal medullary cells, hyperosmotic stress activates biological processes that confer cross-tolerance to other stressful conditions. heat shock protein 70; nephrotoxins; thermotolerance; cell cycle; inner medullary collecting duct 3

An experiment was conducted to determine the physiologic effects of hyperosmolar sodium chloride (NaCl) and urea, individually and in combination, on the resistance and response of murine inner medullary collecting duct (mIMCD3) cells. Hyperosmolar NaCl is believed to an activator of mitogen-induced protein kinases while urea stimulates the production of protein kinase C. Results showed that the combination of NaCl and urea was linked with improved resistance and survival of mIMCD3 cells.

A study was undertaken to examine the effects of physiological stresses such as hyperosmolality and heat stress on the mRNA expression of the Hsp110/SSE subfamily of heat and osmotic stress proteins in the murine renal medulla. Hsp/110 stress proteins were also associated with the Hsp70/BiP gene superfamily. Results revealed that Osp94, Hsp70 and Hsp110 were stimulated by hypertonic sodium chloride and heat shock. These stress proteins were discovered in all tissues used in the study. Hypertonicity, rather than hyperosmolality, increased expression of the stress proteins.

O artigo descreve as conquistas realizadas na logística do varejo farmacêutico nacional nos últimos anos. As farmácias organizaram-se em redes; melhoraram seu aspecto visual; muitas operam 24 horas; usam fórmulas sofisticadas, teoricamente corretas, na gestão de estoques; dispõem de softwares avançados; operam depósitos racionalizados, por vezes automatizados; as entregas às lojas são diárias; o código de barras facilita o controle dos produtos. Subsistem problemas que impedem a obtenção de autêntico just in time e de falta-zero: dificuldade de prever a demanda; limitação da capacidade dos bancos de dados para registrar dados relativos a 5.000 apresentações em 100 lojas; entrosamento precário entre os setores de vendas e compras na empresa; freqüência e rapidez ainda insuficientes de reposição dos produtos no depósito e nas lojas; existência de numerosos medicamentos de baixo giro. Recomendações são apresentadas para a remoção desses entraves e o advento do sonhado Supply Chain Management.