The impact of interactive text communication on neonatal mortality in Kenya: a randomized controlled trial
Related Articles
Severity of neonatal influenza infection is driven by type I interferon and oxidative stress
Neonates exhibit increased susceptibility to respiratory viral infections, attributed to inflammation at the developing pulmonary air-blood interface. IFN I are antiviral cytokines critical to control viral replication, but also promote inflammation. Previously, we established a neonatal murine influenza virus (IV) model, which demonstrates increased mortality. Here, we sought to determine the role of IFN I in this increased mortality. We found that three-day-old IFNAR-deficient mice are highly protected from IV-induced mortality. In addition, exposure to IFNβ 24 h post IV infection accelerated death in WT neonatal animals but did not impact adult mortality. In contrast, IFN IIIs are protective to neonatal mice. IFNβ induced an oxidative stress imbalance specifically in primary neonatal IV-infected pulmonary type II epithelial cells (TIIEC), not in adult TIIECs. Moreover, neonates did not have an infection-induced increase in antioxidants, including a key antioxidant, superoxide dismutase 3, as compared to adults. Importantly, antioxidant treatment rescued IV-infected neonatal mice, but had no impact on adult morbidity. We propose that IFN I exacerbate an oxidative stress imbalance in the neonate because of IFN I-induced pulmonary TIIEC ROS production coupled with developmentally regulated, defective antioxidant production in response to IV infection. This age-specific imbalance contributes to mortality after respiratory infections in this vulnerable population.
Segment Anything for Microscopy
Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models.
Human-structure and human-structure-human interaction in electro-quasistatic regime
Augmented living equipped with electronic devices requires widespread connectivity and a low-loss communication medium for humans to interact with ambient technologies. However, traditional radiative radio frequency-based communications require wireless pairing to ensure specificity during information exchange, and with their broadcasting nature, these incur energy absorption from the surroundings. Recent advancements in electroquasistatic body-coupled communication have shown great promise by utilizing conductive objects like the human body as a communication medium. Here we propose a fundamental set of modalities of non-radiative interaction by guiding electroquasistatic signals through conductive structures between humans and surrounding electronic devices. Our approach offers pairing-free communication specificity and lower path loss during touch. Here, we propose two modalities: Human-Structure Interaction and Human-Structure Human Interaction with wearable devices. We validate our theoretical understanding with numerical electromagnetic simulations and experiments to show the feasibility of the proposed approach. A demonstration of the real-time transfer of an audio signal employing an human body communications-based Human-Structure Interaction link is presented to highlight the practical impact of this work. The proposed techniques can potentially influence Human-Machine Interaction research, including the development of assistive technology for augmented living and personalized healthcare.
Error-driven upregulation of memory representations
Learning an association does not always succeed on the first attempt. Previous studies associated increased error signals in posterior medial frontal cortex with improved memory formation. However, the neurophysiological mechanisms that facilitate post-error learning remain poorly understood. To address this gap, participants performed a feedback-based association learning task and a 1-back localizer task. Increased hemodynamic responses in posterior medial frontal cortex were found for internal and external origins of memory error evidence, and during post-error encoding success as quantified by subsequent recall of face-associated memories. A localizer-based machine learning model displayed a network of cognitive control regions, including posterior medial frontal and dorsolateral prefrontal cortices, whose activity was related to face-processing evidence in the fusiform face area. Representation strength was higher during failed recall and increased during encoding when subsequent recall succeeded. These data enhance our understanding of the neurophysiological mechanisms of adaptive learning by linking the need for learning with increased processing of the relevant stimulus category.
Dopamine in the tail of the striatum facilitates avoidance in threat–reward conflicts
Responding appropriately to potential threats before they materialize is critical to avoiding disastrous outcomes. Here we examine how threat-coping behavior is regulated by the tail of the striatum (TS) and its dopamine input. Mice were presented with a potential threat (a moving object) while pursuing rewards. Initially, the mice failed to obtain rewards but gradually improved in later trials. We found that dopamine in TS promoted avoidance of the threat, even at the expense of reward acquisition. Furthermore, the activity of dopamine D1 receptor-expressing neurons promoted threat avoidance and prediction. In contrast, D2 neurons suppressed threat avoidance and facilitated overcoming the potential threat. Dopamine axon activation in TS not only potentiated the responses of dopamine D1 receptor-expressing neurons to novel sensory stimuli but also boosted them acutely. These results demonstrate that an opponent interaction of D1 and D2 neurons in the TS, modulated by dopamine, dynamically regulates avoidance and overcoming potential threats.
Responses