ct2vl: A Robust Public Resource for Converting SARS-CoV-2 Ct Values to Viral Loads.

The amount of SARS-CoV-2 in a sample is often measured using Ct values. However, the same Ct value may correspond to different viral loads on different platforms and assays, making them difficult to compare from study to study. To address this problem, we developed ct2vl, a Python package that converts Ct values to viral loads for any RT-qPCR assay/platform. The method is novel in that it is based on determining the maximum PCR replication efficiency, as opposed to fitting a sigmoid (S-shaped) curve relating signal to cycle number. We calibrated ct2vl on two FDA-approved platforms and validated its performance using reference-standard material, including sensitivity analysis. We found that ct2vl-predicted viral loads were highly accurate across five orders of magnitude, with 1.6-fold median error (for comparison, viral loads in clinical samples vary over 10 orders of magnitude). The package has 100% test coverage. We describe installation and usage both from the Unix command-line and from interactive Python environments. ct2vl is freely available via the Python Package Index (PyPI). It facilitates conversion of Ct values to viral loads for clinical investigators, basic researchers, and test developers for any RT-qPCR platform. It thus facilitates comparison among the many quantitative studies of SARS-CoV-2 by helping render observations in a natural, universal unit of measure.

Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires.

Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function (Z), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately 1010 sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating Z this way for only a few models: it then uses these expensive estimates to estimate Z more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally.

$\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets.

Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed $\textit{greylock}$, a Python package that calculates diversity measures and is tailored to large datasets. $\textit{greylock}$ can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). $\textit{greylock}$ also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe $\textit{greylock}$'s key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating $\textit{greylock}$'s applicability across a range of dataset types and fields.

Shape retaining injectable hydrogels for minimally invasive bulking.

PURPOSE: Particle migration, poor shape definition and/or rapid resorption limit the success of current urethral bulking agents. We propose that shape defining porous scaffolds that allow cell infiltration and anchoring, and may be delivered in a minimally invasive manner may provide many advantageous features. MATERIALS AND METHODS: Alginate hydrogels were prepared with varying degrees of covalent cross-linking and different pore characteristics. Dehydrated scaffolds were compressed into smaller, temporary forms, introduced into the dorsal subcutaneous space of CD-1 mice by minimally invasive delivery through a 10 gauge angiocatheter and rehydrated in situ with a saline solution delivered through the same catheter. Ionically cross-linked calcium alginate gel served as a control. Specimens were harvested at 2, 6, 12 and 24 weeks to evaluate implant shape retention and volume, cell infiltration and calcification, and the presence of an inflammatory response. RESULTS: A total of 90 scaffolds were implanted and 95% were recovered at the site of injection. All of these scaffolds successfully rehydrated and 80% recovered and maintained their original 3-dimensional shape for 6 months. Scaffold volume and tissue infiltration varied depending on the degree of alginate cross-linking. Highly cross-linked materials (20% and 35%) demonstrated the best volume maintenance with the latter facilitating the most tissue infiltration. The inflammatory response was minimal except with the 80% cross-linked material. Calcification was not observed in covalently cross-linked scaffolds. In contrast, 98% of calcium alginate implants were calcified. CONCLUSIONS: Shape retaining porous hydrogels meet many of the requirements necessary for a successful injectable bulking agent and offer advantages over currently used agents.