Alex's Lemonade Stand Foundation

Voltage-hold (V-hold) protocols have shown promise toward calendar lifetime analysis of cells with graphite (Gr) and silicon (Si) anodes. In this work, repeat V-holds are performed on Gr and Si cells paired with lithium iron phosphate cathodes to delineate their beneficial role in formation and conditioning. We find that V-hold at the top of charge supplements constant current cycling in conditioning the cell to higher capacities for both Gr and Si cells after the first V-hold. A reduced order model provides the irreversible capacity proportions of each V-hold. With each repeat V-hold, parasitic loss of lithium to the solid electrolyte interphase (SEI) decreases on both Gr and Si cells. Gr cells show the square-root-of-time capacity loss behavior within 200 h of V-hold, indicative of its fast relaxation and low impact of reference performance test cycles on the SEI growth. Lifetime estimates from repeat V-holds on Gr can reach years. Si exhibits longer transition times from kinetic to diffusion-limited SEI growth, evidenced by the 400 h and 200 h holds showing square-root-of-time and linear behavior, respectively. Lifetime predictions from repeat V-holds on Si only reach 1–2 months, highlighting its limitations. Recommended duration of V-holds for Si cells should be ≥400 h.

Abstract With the rise of single-cell technologies, it has become increasingly apparent that pediatric tumors exhibit substantial transcriptomic heterogeneity. Many studies have even shown that specific tumor cell subpopulations and recurrent gene expression programs are associated with clinical outcome and may serve as therapeutic targets, revealing the importance of properly identifying these programs. Typical workflows for identifying recurrent gene expression programs, also referred to as metaprograms, use non-negative matrix factorization (NMF) on each individual sample and then combine these individual NMF programs into metaprograms using hierarchical clustering. However, this approach relies on both selecting the number of factors for NMF and choosing the appropriate number of clusters for hierarchical clustering. Because each dataset has its own unique characteristics, it is difficult to pre-determine the appropriate number of clusters or metaprograms. To address this problem, we curated a set of metrics to evaluate what constitutes a robust metaprogram: 1) non-redundancy, 2) sample diversity, and 3) biological interpretability. Here we present metafactory, a Nextflow workflow to identify metaprograms using a data-driven approach to select the optimal number of metaprograms (k) for each sample group. First, cNMF is run on each individual sample across a range of ranks. Sample-specific NMF programs are removed, and all remaining NMF programs across all samples are then clustered into metaprograms based on their Pearson correlation coefficient. The process of generating metaprograms is repeated across a wide range of k values, and a set of data-driven metrics are calculated for each value of k to assess the quality of resulting metaprograms. Each metric is used to rank the values of k, and the average rank is used to determine the optimal value of k and identify the final set of metaprograms for each sample group. This workflow can be applied to any set of single-cell RNA-sequencing datasets to identify recurrent gene expression programs. We applied metafactory to the Single-cell Pediatric Cancer Atlas (https://scpca.alexslemonade.org/), a data resource for uniformly processed single-cell and single-nuclei RNA sequencing data that contains data from over 700 samples across more than 50 cancer types. The use of metafactory provided a data-driven approach to identify recurrent gene expression programs across multiple disease types represented in the ScPCA Portal, accelerating the discovery of key programs that shape disease biology and may carry clinical significance. Citation Format: Allegra G. Hawkins, Joshua A. Shapiro, Stephanie J. Spielman, Jaclyn N. Taroni. Robust identification of recurrent gene expression programs across samples from the Single-cell Pediatric Cancer Atlas Portal [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Bridging Discovery and Clinical Impact in Pediatric Cancer; 2026 Sep 22-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_1):Abstract nr PR019.

High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.

Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a method for interpreting new transcriptomic datasets through instant comparison to public datasets without high-performance computing requirements. We apply Principal Component Analysis on 536 studies comprising 44,890 human RNA sequencing profiles and aggregate sufficiently similar loading vectors to form Replicable Axes of Variation (RAV). RAVs are annotated with metadata of originating studies and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrate the efficient and coherent database search, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature aids in analyzing new gene expression data in the context of existing databases using minimal computing resources.

Beamlines capable of merging beams with different energies are critical to many applications related to advanced accelerator concepts and energy-recovery linacs (ERLs). In an ERL, a low-energy “fresh” bright bunch is generally injected into a superconducting linac for acceleration using the fields established by a decelerated “spent” beam traveling on the same axis. A straight-merger system composed of a selecting cavity with a superimposed dipole magnet was proposed and recently tested at AWA. This paper reports on the experimental results obtained so far along with detailed beam dynamics investigations of the merger concept and its ability to conserve the beam brightness associated with the fresh bunch.