Highlights d Affinity-tagging protocol enables proteomic profiling of individual HLA-II alleles d Even in ''hot'' tumors, professional APCs-not cancer cellsdrive HLA-II expression d Cellular localization influences which phagocytosed cancer proteins get presented d Machine-learning models for binding and processing improve HLA-II prediction

This paper proposes a simple adaptive sensing and group testing algorithm for sparse signal recovery. The algorithm, termed Compressive Adaptive Sense and Search (CASS), is shown to be near-optimal in that it succeeds at the lowest possible signal-to-noise-ratio (SNR) levels, improving on previous work in adaptive compressed sensing [2]-[5]. Like traditional compressed sensing based on random non-adaptive design matrices, the CASS algorithm requires only k log n measurements to recover a ksparse signal of dimension n. However, CASS succeeds at SNR levels that are a factor log n less than required by standard compressed sensing. From the point of view of constructing and implementing the sensing operation as well as computing the reconstruction, the proposed algorithm is substantially less computationally intensive than standard compressed sensing. CASS is also demonstrated to perform considerably better in practice through simulation. To the best of our knowledge, this is the first demonstration of an adaptive compressed sensing algorithm with near-optimal theoretical guarantees and excellent practical performance. This paper also shows that methods like compressed sensing, group testing, and pooling have an advantage beyond simply reducing the number of measurements or testsadaptive versions of such methods can also improve detection and estimation performance when compared to non-adaptive direct (uncompressed) sensing.

Background: The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2. Methods: We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8 + T cell responses. Results: We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8 + T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity.

Abstract-This paper studies the problem of high-dimensional multiple testing and sparse recovery from the perspective of sequential analysis. In this setting, the probability of error is a function of the dimension of the problem. A simple sequential testing procedure for this problem is proposed. We derive necessary conditions for reliable recovery in the non-sequential setting and contrast them with sufficient conditions for reliable recovery using the proposed sequential testing procedure. Applications of the main results to several commonly encountered models show that sequential testing can be exponentially more sensitive to the difference between the null and alternative distributions (in terms of the dependence on dimension), implying that subtle cases can be much more reliably determined using sequential methods.

The temperature of the low-density intergalactic medium (IGM) at high redshift is sensitive to the timing and nature of hydrogen and HeII reionization, and can be measured from Lyman-alpha (Ly-α) forest absorption spectra. Since the memory of intergalactic gas to heating during reionization gradually fades, measurements as close as possible to reionization are desirable. In addition, measuring the IGM temperature at sufficiently high redshifts should help to isolate the effects of hydrogen reionization since HeII reionization starts later, at lower redshift. Motivated by this, we model the IGM temperature at z 5 using semi-numeric models of patchy reionization. We construct mock Ly-α forest spectra from these models and consider their observable implications. We find that the small-scale structure in the Ly-α forest is sensitive to the temperature of the IGM even at redshifts where the average absorption in the forest is as high as 90%. We forecast the accuracy at which the z 5 IGM temperature can be measured using existing samples of high resolution quasar spectra, and find that interesting constraints are possible. For example, an early reionization model in which reionization ends at z ∼ 10 should be distinguishable -at high statistical significance -from a lower redshift model where reionization completes at z ∼ 6. We discuss improvements to our modeling that may be required to robustly interpret future measurements. Subject headings: cosmology: theory -intergalactic medium -large scale structure of universe