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Immunotherapy is a powerful technique where immune cells are modified to improve cytotoxicity against cancerous cells to treat cancers that do not respond to surgery, chemotherapy, or radiotherapy. Expressing chimeric antigen receptor (CAR) in immune cells, typically T lymphocytes, is a practical modification that drives an immune response against cancerous tissue. CAR-T efficacy is suboptimal in solid tumors due to the tumor microenvironment (TME) that limits T lymphocyte cytotoxicity. In this study, we demonstrate that neutrophils differentiated from human pluripotent stem cells modified with AAVS1-inserted CAR constructs showed a robust cytotoxic effect against prostate-specific membrane antigen (PSMA) expressing LNCaP cells as a model for prostate cancer in vitro. Our results suggest that engineered CAR can significantly enhance the neutrophil anti-tumor effect, providing a new avenue in treating prostate cancers. 

Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. Both ligand and target molecules are represented as graphs with node and edge features encoding information about atomic elements and bonds respectively. Although existing deep learning models perform remarkably well at predicting physicochemical properties and binding affinities, the generation of new molecules with optimized properties remains challenging. Inherently, most GNNs perform poorly in whole-graph representation due to the limitations of the message-passing paradigm. Furthermore, step-by-step graph generation frameworks that use reinforcement learning or other sequential processing can be slow and result in a high proportion of invalid molecules with substantial post-processing needed in order to satisfy the principles of stoichiometry. To address these issues, we propose a representation-first approach to molecular graph generation. We guide the latent representation of an autoencoder by capturing graph structure information with the geometric scattering transform and apply penalties that structure the representation also by molecular properties. We show that this highly structured latent space can be directly used for molecular graph generation by the use of a GAN. We demonstrate that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis. 

Websites are malleable: users can run code in the browser to customize them. However, this malleability is typically only accessible to programmers with knowledge of HTML and Javascript. Previously, we developed a tool called Wildcard which empowers end-users to customize websites through a spreadsheet-like table interface without doing traditional programming. However, there is a limit to end-user agency with Wildcard, because programmers need to first create site-specific adapters mapping website data to the table interface. This means that end-users can only customize a website if a programmer has written an adapter for it, and cannot extend or repair existing adapters. In this paper, we extend Wildcard with a new system for enduser web scraping for customization. It enables end-users to create, extend and repair adapters, by performing concrete demonstrations of how the website user interface maps to a data table. We describe three design principles that guided our system’s development and are applicable to other end-user web scraping and customization systems: (a) users should be able to scrape data and use it in a single, unified environment, (b) users should be able to extend and repair the programs that scrape data via demonstration and (c) users should receive live feedback during their demonstrations. We have successfully used our system to create, extend and repair adapters by demonstration on a variety of websites and we provide example usage scenarios that showcase each of our design principles. Our ultimate goal is to empower end-users to customize websites in the course of their daily use in an intuitive and flexible way, and thus making the web more malleable for all of its users. 

Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management. 

We present a method for establishing confidence in the decisions of an autonomous car which accounts for errors not only in control but also in perception. The key idea is that the controller generates a certificate, which is a kind its proposed action is safe. of proof that its interpretation of the scene is accurate and its proposed action is safe. Checking the certificate is faster and simpler than generating it, which allows for a monitor that comprises a much smaller trusted base than the system as a whole. Simulation experiments suggest that the approach is practical.