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Synopsis Predicting performance responses of insects to climate change is crucial for biodiversity conservation and pest management. While most projections on insects’ performance under climate change have used macro-scale weather station data, few incorporated the microclimates within vegetation that insects inhabit and their feeding behaviors (e.g., leaf-nesting: building leaf nests or feeding inside). Here, taking advantage of relatively homogenous vegetation structures in agricultural fields, we built microclimate models to examine fine-scale air temperatures within two important crop systems (maize and rice) and compared microclimate air temperatures to temperatures from weather stations. We deployed physical models of caterpillars and quantified effects of leaf-nesting behavior on operative temperatures of two Lepidoptera pests: Ostrinia furnacalis (Pyralidae) and Cnaphalocrocis medinalis (Crambidae). We built temperature-growth rate curves and predicted the growth rate of caterpillars with and without leaf-nesting behavior based on downscaled microclimate changes under different climate change scenarios. We identified widespread differences between microclimates in our crop systems and air temperatures reported by local weather stations. Leaf-nesting individuals in general had much lower body temperatures compared to non-leaf-nesting individuals. When considering microclimates, we predicted leaf-nesting individuals grow slower compared to non-leaf-nesting individuals with rising temperature. Our findings highlight the importance of considering microclimate and habitat-modifying behavior in predicting performance responses to climate change. Understanding the thermal biology of pests and other insects would allow us to make more accurate projections on crop yields and biodiversity responses to environmental changes. 

Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors. 

Abstract Thrombosis is a major cause of mortality in patients with myeloproliferative neoplasms (MPNs), though there is currently little to offer patients with MPN beyond aspirin and cytoreductive therapies such as hydroxyurea for primary prevention. Thrombogenesis in MPN involves multiple cellular mechanisms, including platelet activation and neutrophil-extracellular trap formation; therefore, an antithrombotic agent that targets one or more of these processes would be of therapeutic benefit in MPN. Here, we treated the JAK2V617F knockin mouse model of polycythemia vera with N-acetylcysteine (NAC), a sulfhydryl-containing compound with broad effects on glutathione replenishment, free radical scavenging, and reducing disulfide bonds, to investigate its antithrombotic effects in the context of MPN. Strikingly, NAC treatment extended the lifespan of JAK2V617F mice without impacting blood counts or splenomegaly. Using an acute pulmonary thrombosis model in vivo, we found that NAC reduced thrombus formation to a similar extent as the irreversible platelet inhibitor aspirin. In vitro analysis of platelet activation revealed that NAC reduced thrombin-induced platelet-leukocyte aggregate formation in JAK2V617F mice. Furthermore, NAC reduced neutrophil extracellular trap formation in primary human neutrophils from patients with MPN as well as healthy controls. These results provide evidence that N-acetylcysteine inhibits thrombosis in JAK2V617F mice and provide a pre-clinical rationale for investigating NAC as a therapeutic to reduce thrombotic risk in MPN.