Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines.

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis.

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics.

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result's metadata. An ontology for Evidence Graphs, EVI ( https://w3id.org/EVI ), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

Design and characterization of a low profile NaI(Tl) gamma camera for dedicated molecular breast tomosynthesis.

A new low profile gamma camera is being developed for use in a dual modality (x-ray transmission and gamma-ray emission) tomosynthesis system. Compared to the system's current gamma camera, the new camera has a larger field of view (~20×25 cm) to better match the system's x-ray detector (~23×29 cm), and is thinner (7.3 cm instead of 10.3 cm) permitting easier camera positioning near the top surface of the breast. It contains a pixelated NaI(Tl) array with a crystal pitch of 2.2 mm, which is optically coupled to a 4×5 array of Hamamatsu H8500C position sensitive photomultiplier tubes (PSPMTs). The manufacturer-provided connector board of each PSPMT was replaced with a custom designed board that a) reduces the 64 channel readout of the 8×8 electrode anode of the H8500C to 16 channels (8X and 8Y), b) performs gain non-uniformity correction, and c) reduces the height of the PSPMT-base assembly, 37.7 mm to 27.87 mm. The X and Y outputs of each module are connected in a lattice framework, and at two edges of this lattice, the X and Y outputs (32Y by 40X) are coupled to an amplifier/output board whose signals are fed via shielded ribbon cables to external ADCs. The camera uses parallel hole collimation. We describe the measured camera imaging performance, including intrinsic and extrinsic spatial resolution, detection sensitivity, uniformity of response, energy resolution for 140 keV gamma rays, and geometric linearity.