Beyond Measurement: {E}xtracting Vegetation Height from High Resolution Imagery with Deep Learning

Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods, we designed Y-NET, a novel deep learning model to generate high resolution models of vegetation from highly recurrent multispectral aerial imagery and elevation data. Y-NET’s architecture uses convolutional layers to learn correlations between different input features and vegetation height, generating an accurate vegetation surface model (VSM) at 1×1 m resolution. We evaluated Y-NET on 235 km2 of the East San Francisco Bay Area and find that Y-NET achieves low error from LiDAR when tested on new locations. Y-NET also achieves an R2 of 0.83 and can effectively model complex vegetation through side-by-side visual comparisons. Furthermore, we show that Y-NET is able to identify instances of vegetation growth and mitigation by comparing aerial imagery and LiDAR collected at different times

Infrastructure Exposure, Extreme Weather Events & Climate Change - SF Bay - Napoli

Big Data analysis and computer modeling to compare the Mediterranean-type climate coastlands of San Francisco Bay, California (USA), and Naples Bay, southern Italy, prone to extreme weather events, sea storms and tsunami, climate change and sea level rise

Monte Carlo Simulation of a Knudsen Effusion Mass Spectrometer Sampling System

Knudsen flow is easily simulated with a Monte Carlo method. In this study we develop a Visual Basic for Excel (VBA) code to simulate the molecular beam from a vaporizing solid. The system at NASA Glenn uses the restricted collimation method of Chatillon and colleagues, which consists of two apertures between the effusion cell and the ionizer. The diameter of the first aperture is smaller than the diameter of the effusion cell orifice, so the ionizer effectively only sees inside the effusion cell. The code is able to calculate the transmission coefficient through the cell orifice, through the cell orifice and the first aperture, and through the cell orifice and first and second aperatures. Calculated transmission coefficients through the cell orifice are compared to tabulated values to validate the code. Then transmission coefficients are calculated through the cell orifice and both apertures to the ionizer. This allows the geometry (aperture spacing and diameters) of the sampling system the sampling system to be optimized. Calculated transmission factors are also compared to literature values calculated via an analytic method

Towards Visually Explaining Variational Autoencoders

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g. variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec-AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning improved latent space disentanglement, demonstrated on the Dsprites dataset

Measurements of the light-absorbing material inside cloud droplets and its effect on cloud albedo

Most of the measurements of light-absorbing aerosol particles made previously have been in non-cloudy air and therefore provide no insight into aerosol effects on cloud properties. Here, researchers describe an experiment designed to measure light absorption exclusively due to substances inside cloud droplets, compare the results to related light absorption measurements, and evaluate possible effects on the albedo of clouds. The results of this study validate those of Twomey and Cocks and show that the measured levels of light-absorbing material are negligible for the radiative properties of realistic clouds. For the measured clouds, which appear to have been moderately polluted, the amount of elemental carbon (EC) present was insufficient to affect albedo. Much higher contaminant levels or much larger droplets than those measured would be necessary to significantly alter the radiative properties. The effect of the concentrations of EC actually measured on the albedo of snow, however, would be much more pronounced since, in contrast to clouds, snowpacks are usually optically semi-infinite and have large particle sizes

Transcriptional repression by ApiAP2 factors is central to chronic toxoplasmosis

Tachyzoite to bradyzoite development in Toxoplasma is marked by major changes in gene expression resulting in a parasite that expresses a new repertoire of surface antigens hidden inside a modified parasitophorous vacuole called the tissue cyst. The factors that control this important life cycle transition are not well understood. Here we describe an important transcriptional repressor mechanism controlling bradyzoite differentiation that operates in the tachyzoite stage. The ApiAP2 factor, AP2IV-4, is a nuclear factor dynamically expressed in late S phase through mitosis/cytokinesis of the tachyzoite cell cycle. Remarkably, deletion of the AP2IV-4 locus resulted in the expression of a subset of bradyzoite-specific proteins in replicating tachyzoites that included tissue cyst wall components BPK1, MCP4, CST1 and the surface antigen SRS9. In the murine animal model, the mis-timing of bradyzoite antigens in tachyzoites lacking AP2IV-4 caused a potent inflammatory monocyte immune response that effectively eliminated this parasite and prevented tissue cyst formation in mouse brain tissue. Altogether, these results indicate that suppression of bradyzoite antigens by AP2IV-4 during acute infection is required for Toxoplasma to successfully establish a chronic infection in the immune-competent host

The transcriptome of Toxoplasma gondii

BACKGROUND: Toxoplasma gondii gives rise to toxoplasmosis, among the most prevalent parasitic diseases of animals and man. Transformation of the tachzyoite stage into the latent bradyzoite-cyst form underlies chronic disease and leads to a lifetime risk of recrudescence in individuals whose immune system becomes compromised. Given the importance of tissue cyst formation, there has been intensive focus on the development of methods to study bradyzoite differentiation, although the molecular basis for the developmental switch is still largely unknown. RESULTS: We have used serial analysis of gene expression (SAGE) to define the Toxoplasma gondii transcriptome of the intermediate-host life cycle that leads to the formation of the bradyzoite/tissue cyst. A broad view of gene expression is provided by >4-fold coverage from nine distinct libraries (~300,000 SAGE tags) representing key developmental transitions in primary parasite populations and in laboratory strains representing the three canonical genotypes. SAGE tags, and their corresponding mRNAs, were analyzed with respect to abundance, uniqueness, and antisense/sense polarity and chromosome distribution and developmental specificity. CONCLUSION: This study demonstrates that phenotypic transitions during parasite development were marked by unique stage-specific mRNAs that accounted for 18% of the total SAGE tags and varied from 1–5% of the tags in each developmental stage. We have also found that Toxoplasma mRNA pools have a unique parasite-specific composition with 1 in 5 transcripts encoding Apicomplexa-specific genes functioning in parasite invasion and transmission. Developmentally co-regulated genes were dispersed across all Toxoplasma chromosomes, as were tags representing each abundance class, and a variety of biochemical pathways indicating that trans-acting mechanisms likely control gene expression in this parasite. We observed distinct similarities in the specificity and expression levels of mRNAs in primary populations (Day-6 post-sporozoite infection) that occur prior to the onset of bradyzoite development that were uniquely shared with the virulent Type I-RH laboratory strain suggesting that development of RH may be arrested. By contrast, strains from Type II-Me49B7 and Type III-VEGmsj contain SAGE tags corresponding to bradyzoite genes, which suggests that priming of developmental expression likely plays a role in the greater capacity of these strains to complete bradyzoite development