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Abstract Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease. 

In solid tumours, the abundance of macrophages is typically associated with a poor prognosis. However, macrophage clusters in tumour-cell nests have been associated with survival in some tumour types. Here, by using tumour organoids comprising macrophages and cancer cells opsonized via a monoclonal antibody, we show that highly ordered clusters of macrophages cooperatively phagocytose cancer cells to suppress tumour growth. In mice with poorly immunogenic tumours, the systemic delivery of macrophages with signal-regulatory protein alpha (SIRPα) genetically knocked out or else with blockade of the CD47–SIRPα macrophage checkpoint was combined with the monoclonal antibody and subsequently triggered the production of endogenous tumour-opsonizing immunoglobulin G, substantially increased the survival of the animals and helped confer durable protection from tumour re-challenge and metastasis. Maximizing phagocytic potency by increasing macrophage numbers, by tumour-cell opsonization and by disrupting the phagocytic checkpoint CD47– 

In the past decade, academia and industry have embraced machine learning (ML) for database management system (DBMS) automation. These efforts have focused on designing ML models that predict DBMS behavior to support picking actions (e.g., building indexes) that improve the system's performance. Recent developments in ML have created automated methods for finding good models. Such advances shift the bottleneck from DBMS model design to obtaining the training data necessary for building these models. But generating good training data is challenging and requires encoding subject matter expertise into DBMS instrumentation. Existing methods for training data collection are bespoke to individual DBMS components and do not account for (1) how workload trends affect the system and (2) the subtle interactions between internal system components. Consequently, the models created from this data do not support holistic tuning across subsystems and require frequent retraining to boost their accuracy. This paper presents the architecture of a database gym, an integrated environment that provides a unified API of pluggable components for obtaining high-quality training data. The goal of a database gym is to simplify ML model training and evaluation to accelerate autonomous DBMS research. But unlike gyms in other domains that rely on custom simulators, a database gym uses the DBMS itself to create simulation environments for ML training. Thus, we discuss and prescribe methods for overcoming challenges in DBMS simulation, which include demanding requirements for performance, simulation fidelity, and DBMS-generated hints for guiding training processes.