Evaluation of ozone deposition models over a subalpine forest in Niwot Ridge, Colorado

In this study, we evaluated three conceptually similar ozone gas deposition models. These dry deposition models are frequently used with chemical transport models for calculations over large spatial domains. However, large scale applications of surface-atmosphere exchange of reactive gases require modeling results as accurate as possible to avoid nonlinear accumulation of errors in the spatially representative results. In this paper, model evaluation and comparison against measured data over a coniferous forest at Niwot Ridge AmeriFlux site (Colorado, USA) is carried out. At this site, no previous model calibration took place for any of the models, therefore, we can test and compare their performances under similar conditions as they would perform in a spatial application. Our results show systematic model errors in all the three cases, model performance varies with time of the day, and the errors show a pronounced seasonal pattern as well. The introduction of soil moisture content stress in the model improved model performance regarding the magnitude of fluxes, but the correlation between measured and modeled ozone deposition values remains low. Our results suggest that ozone dry deposition model results should be interpreted carefully in large scale applications, where the accuracy can vary with land cover sometimes are biased

Spike-Timing Precision and Neuronal Synchrony Are Enhanced by an Interaction between Synaptic Inhibition and Membrane Oscillations in the Amygdala

The basolateral complex of the amygdala (BLA) is a critical component of the neural circuit regulating fear learning. During fear learning and recall, the amygdala and other brain regions, including the hippocampus and prefrontal cortex, exhibit phase-locked oscillations in the high delta/low theta frequency band (∼2–6 Hz) that have been shown to contribute to the learning process. Network oscillations are commonly generated by inhibitory synaptic input that coordinates action potentials in groups of neurons. In the rat BLA, principal neurons spontaneously receive synchronized, inhibitory input in the form of compound, rhythmic, inhibitory postsynaptic potentials (IPSPs), likely originating from burst-firing parvalbumin interneurons. Here we investigated the role of compound IPSPs in the rat and rhesus macaque BLA in regulating action potential synchrony and spike-timing precision. Furthermore, because principal neurons exhibit intrinsic oscillatory properties and resonance between 4 and 5 Hz, in the same frequency band observed during fear, we investigated whether compound IPSPs and intrinsic oscillations interact to promote rhythmic activity in the BLA at this frequency. Using whole-cell patch clamp in brain slices, we demonstrate that compound IPSPs, which occur spontaneously and are synchronized across principal neurons in both the rat and primate BLA, significantly improve spike-timing precision in BLA principal neurons for a window of ∼300 ms following each IPSP. We also show that compound IPSPs coordinate the firing of pairs of BLA principal neurons, and significantly improve spike synchrony for a window of ∼130 ms. Compound IPSPs enhance a 5 Hz calcium-dependent membrane potential oscillation (MPO) in these neurons, likely contributing to the improvement in spike-timing precision and synchronization of spiking. Activation of the cAMP-PKA signaling cascade enhanced the MPO, and inhibition of this cascade blocked the MPO. We discuss these results in the context of spike-timing dependent plasticity and modulation by neurotransmitters important for fear learning, such as dopamine

Evaluation of ozone deposition models over a subalpine forest in Niwot Ridge, Colorado

In this study, we evaluated three conceptually similar ozone gas deposition models. These dry deposition models are frequently used with chemical transport models for calculations over large spatial domains. However, large scale applications of surface-atmosphere exchange of reactive gases require modeling results as accurate as possible to avoid nonlinear accumulation of errors in the spatially representative results. In this paper, model evaluation and comparison against measured data over a coniferous forest at Niwot Ridge AmeriFlux site (Colorado, USA) is carried out. At this site, no previous model calibration took place for any of the models, therefore, we can test and compare their performances under similar conditions as they would perform in a spatial application. Our results show systematic model errors in all the three cases, model performance varies with time of the day, and the errors show a pronounced seasonal pattern as well. The introduction of soil moisture content stress in the model improved model performance regarding the magnitude of fluxes, but the correlation between measured and modeled ozone deposition values remains low. Our results suggest that ozone dry deposition model results should be interpreted carefully in large scale applications, where the accuracy can vary with land cover sometimes are biased

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars