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Filamentous fungi have a structure called mycelium which is the vegetative part of the organism that forms the body or colony, which can function as a support, reproduction and absorption structure of nutrients and is composed of a tangle of hyphae that can grow without stopping while the fungus finds food and favorable conditions to survive. Inspired by fungi, it is possible to directly equate the structure of the mycelium with that of a communication network, so hyphae can be compared to links, and the tips and derivations of hyphae with nodes of the network. In this context, the growth process of filamentous fungi to explore the environment in which they live can serve as a metaphor for routing algorithms that seek a path between a source and a destination node. Based on this idea, this paper investigates a functional routing algorithm (HyphaNet) for wireless communication networks. Analytical modeling and validation tests proved that HyphaNet converges to more advantageous routes while exploring the search space. Finally, it can deliver good performance on the metrics packet delivery rate, average end-to-end delay and overhead, when compared to other well-known protocols.

The article published in the Blood Cancer Journal discusses a rare case of severe hemolytic anemia in a female carrier of the PGK1Ser320Asn mutation. The patient's condition was complicated by essential thrombocythemia, revealing a unique mechanism by which clonal hematopoiesis transformed a recessive genetic disorder into a dominant condition. The study delves into the genetic, molecular, and clinical aspects of the case, shedding light on the complex interplay between genetic mutations, X-chromosome inactivation, and somatic oncogenic events in the manifestation of hematological disorders. The findings highlight the need for further research and potential targeted therapies for such complex genetic conditions. [Extracted from the article]

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.

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]

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.

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