Tile2Vec: Unsupervised representation learning for spatially distributed data

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.Comment: 8 pages, 4 figures in main text; 9 pages, 11 figures in appendi

In Situ-Targeting of Dendritic Cells with Donor-Derived Apoptotic Cells Restrains Indirect Allorecognition and Ameliorates Allograft Vasculopathy

Chronic allograft vasculopathy (CAV) is an atheromatous-like lesion that affects vessels of transplanted organs. It is a component of chronic rejection that conventional immuno-suppression fails to prevent, and is a major cause of graft loss. Indirect allo-recognition through T cells and allo-Abs are critical during CAV pathogenesis. We tested whether the indirect allo-response and its impact on CAV is down-regulated by in situ-delivery of donor Ags to recipient's dendritic cells (DCs) in lymphoid organs in a pro-tolerogenic fashion, through administration of donor splenocytes undergoing early apoptosis. Following systemic injection, donor apoptotic cells were internalized by splenic CD11chi CD8α+ and CD8− DCs, but not by CD11cint plasmacytoid DCs. Those DCs that phagocytosed apoptotic cells in vivo remained quiescent, resisted ex vivo-maturation, and presented allo-Ag for up to 3 days. Administration of donor apoptotic splenocytes, unlike cells alive, (i) promoted deletion, FoxP3 expression and IL-10 secretion, and decreased IFN-γ-release in indirect pathway CD4 T cells; and (ii) reduced cross-priming of anti-donor CD8 T cells in vivo. Targeting recipient's DCs with donor apoptotic cells reduced significantly CAV in a fully-mismatched aortic allograft model. The effect was donor specific, dependent on the physical characteristics of the apoptotic cells, and was associated to down-regulation of the indirect type-1 T cell allo-response and secretion of allo-Abs, when compared to recipients treated with donor cells alive or necrotic. Down-regulation of indirect allo-recognition through in situ-delivery of donor-Ag to recipient's quiescent DCs constitutes a promising strategy to prevent/ameliorate indirect allorecognition and CAV

SJS/TEN 2019: From science to translation.

Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) are potentially life-threatening, immune-mediated adverse reactions characterized by widespread erythema, epidermal necrosis, and detachment of skin and mucosa. Efforts to grow and develop functional international collaborations and a multidisciplinary interactive network focusing on SJS/TEN as an uncommon but high burden disease will be necessary to improve efforts in prevention, early diagnosis and improved acute and long-term management. SJS/TEN 2019: From Science to Translation was a 1.5-day scientific program held April 26-27, 2019, in Vancouver, Canada. The meeting successfully engaged clinicians, researchers, and patients and conducted many productive discussions on research and patient care needs

Systemic HIV and SIV latency reversal via non-canonical NF-κB signalling in vivo

Long-lasting, latently infected resting CD4+ T cells are the greatest obstacle to obtaining a cure for HIV infection, as these cells can persist despite decades of treatment with antiretroviral therapy (ART). Estimates indicate that more than 70 years of continuous, fully suppressive ART are needed to eliminate the HIV reservoir1. Alternatively, induction of HIV from its latent state could accelerate the decrease in the reservoir, thus reducing the time to eradication. Previous attempts to reactivate latent HIV in preclinical animal models and in clinical trials have measured HIV induction in the peripheral blood with minimal focus on tissue reservoirs and have had limited effect2–9. Here we show that activation of the non-canonical NF-κB signalling pathway by AZD5582 results in the induction of HIV and SIV RNA expression in the blood and tissues of ART-suppressed bone-marrow–liver–thymus (BLT) humanized mice and rhesus macaques infected with HIV and SIV, respectively. Analysis of resting CD4+ T cells from tissues after AZD5582 treatment revealed increased SIV RNA expression in the lymph nodes of macaques and robust induction of HIV in almost all tissues analysed in humanized mice, including the lymph nodes, thymus, bone marrow, liver and lung. This promising approach to latency reversal—in combination with appropriate tools for systemic clearance of persistent HIV infection—greatly increases opportunities for HIV eradication

Unlocking Large-Scale Crop Field Delineation in Smallholder Farming Systems with Transfer Learning and Weak Supervision

Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learning to delineating field boundaries in industrial agricultural systems, but field boundary datasets remain missing in smallholder systems due to (1) small fields that require high resolution satellite imagery to delineate and (2) a lack of ground labels for model training and validation. In this work, we use newly-accessible high-resolution satellite imagery and combine transfer learning with weak supervision to address these challenges in India. Our best model uses 1.5 m resolution Airbus SPOT imagery as input, pre-trains a state-of-the-art neural network on France field boundaries, and fine-tunes on India labels to achieve a median Intersection over Union (mIoU) of 0.85 in India. When we decouple field delineation from cropland classification, a model trained in France and applied as-is to India Airbus SPOT imagery delineates fields with a mIoU of 0.74. If using 4.8 m resolution PlanetScope imagery instead, high average performance (mIoU > 0.8) is only achievable for fields larger than 1 hectare. Experiments also show that pre-training in France reduces the number of India field labels needed to achieve a given performance level by as much as 10× when datasets are small. These findings suggest our method is a scalable approach for delineating crop fields in regions of the world that currently lack field boundary datasets. We publicly release 10,000 Indian field boundary labels and our delineation model to facilitate the creation of field boundary maps and new methods by the community

Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained on optical satellite features often exhibit low performance when transferred across geographies. Here we explore the use of NASA’s global ecosystem dynamics investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three major cropped regions (in China, France, and the United States) we first demonstrate that GEDI energy profiles can reliably distinguish maize, a crop typically above 2 m in height, from crops like rice and soybean that are shorter. We further show that these GEDI profiles provide much more invariant features across geographies compared to spectral and phenological features detected by passive optical sensors. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84%, and able to transfer across regions with accuracies higher than 82%, compared to 64% for transfer of optical features. Finally, we show that GEDI profiles can be used to generate training labels for models based on optical imagery from Sentinel-2, thereby enabling the creation of 10 m wall-to-wall maps of tall versus short crops in label-scarce regions. As maize is the second most widely-grown crop in the world and often the only tall crop grown within a landscape, we conclude that GEDI offers great promise for improving global crop type maps