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"Michael Rice"
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A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
This paper presents a history of the development of channel codes in deep-space telemetry and aeronautical mobile telemetry. The history emphasizes “firsts” and other remarkable achievements. Because coding was used first in deep-space telemetry, the history begins with the codes used for Mariner and Pioneer. History continues with the international standard for concatenated coding developed for the Voyager program and the remarkable role channel coding played in rescuing the nearly-doomed Galileo mission. The history culminates with the adoption of turbo codes and LDPC codes and the programs that relied on them. The history of coding in aeronautical mobile telemetry is characterized by a number of “near misses” as channel codes were explored, sometimes tested, and rarely adopted. Aeronautical mobile telemetry is characterized by bandwidth constraints that make use of low-rate codes and their accompanying bandwidth expansion, an unattractive option. The emergence of a family of high-rate LDPC codes coupled with a bandwidth-efficient modulation has nudged the aeronautical mobile telemetry community to adopt the codes in their standards.
Journal Article
Entertaining at home : inspirations from celebrated hosts
\"Entertaining at Home presents gatherings in the homes of leading tastemakers from the worlds of interior design, architecture, culinary arts, and society--including Lynn Wyatt, Suzanne Kasler, Julia Reed, Kimberly Schlegel Whitman, Carla McDonald, and Danielle Rollins, among others--who show readers how best to entertain with flair and finesse. Leading party aficionados offer their approaches to arranging flowers, setting the table, selecting menus, stocking the pantry, compiling killer playlists, and purchasing the perfect hostess gift. Included are a variety of easy-to-master delectable recipes, such as bacon-wrapped pretzels, seafood chowder, spicy gazpacho, and refreshing sangrias. From a summer social in New Orleans, a mother's day brunch and a lively luncheon in Texas to a lavish winter smorgasbord holiday dinner in Washington, D.C., the hosts offer a myriad of inspirational ideas\"-- Adapted from book jacket.
Book
Extensive evaluation of ATAC-seq protocols for native or formaldehyde-fixed nuclei
Background
The “Assay for Transposase Accessible Chromatin sequencing” (ATAC-seq) is an efficient and easy to implement protocol to measure chromatin accessibility that has been widely used in multiple applications studying gene regulation. While several modifications or variants of the protocol have been published since it was first described, there has not yet been an extensive evaluation of the effects of specific protocol choices head-to-head in a consistent experimental setting. In this study, we tested multiple protocol options for major ATAC-seq components (including three reaction buffers, two reaction temperatures, two enzyme sources, and the use of either native or fixed nuclei) in a well-characterized cell line. With all possible combinations of components, we created 24 experimental conditions with four replicates for each (a total of 96 samples). In addition, we tested the 12 native conditions in a primary sample type (mouse lung tissue) with two different input amounts. Through these extensive comparisons, we were able to observe the effect of different ATAC-seq conditions on data quality and to examine the utility and potential redundancy of various quality metrics.
Results
In general, native samples yielded more peaks (particularly at loci not overlapping transcription start sites) than fixed samples, and the temperature at which the enzymatic reaction was carried out had a major impact on data quality metrics for both fixed and native nuclei. However, the effect of various conditions tested was not always consistent between the native and fixed samples. For example, the Nextera and Omni buffers were largely interchangeable across all other conditions, while the THS buffer resulted in markedly different profiles in native samples. In-house and commercial enzymes performed similarly.
Conclusions
We found that the relationship between commonly used measures of library quality differed across temperature and fixation, and so evaluating multiple metrics in assessing the quality of a sample is recommended. Notably, we also found that these choices can bias the functional class of elements profiled and so we recommend evaluating several formulations in any new experiments. Finally, we hope the ATAC-seq workflow formulated in this study on crosslinked samples will help to profile archival clinical specimens.
Journal Article
γδ T cell receptor recognition of CD1d in a lipid-independent manner
The monomorphic antigen-presenting molecule CD1d presents lipid antigens to both αβ and γδ T cells. While type I natural killer T cells (NKT) display exquisite specificity for CD1d presenting α-galactosylceramide (α-GalCer), the extent of lipid specificity exhibited by CD1d-restricted γδ T cells remains unclear. Here, we demonstrate that human γδ T cell receptors (TCRs) can recognise CD1d in either a lipid-dependent or lipid-independent manner with weak to moderate affinity. Using small-angle X-Ray scattering, we find that γδ TCR-CD1d binding modality is conserved across distinct CD1d-restricted TCRs. In functional assays, CD1d γδ TCR affinity was a poor predictor of γδ T cell line activation. Moreover, CD1d presenting endogenous lipids was sufficient to stimulate T cell activation and induce γδ TCR-CD3 clustering and phosphorylation in a dose-dependent manner. Elongation of the γδ TCR-CD3 complex by the inclusion of the Cγ2 and Cγ3 -encoded constant domains perturbed cellular activation whilst maintaining the ability to form functional γδ TCR clusters. The crystal structure of a Vδ1 γδ + TCR-CD1d complex showed that the γδ TCR sat atop of the CD1d antigen-binding cleft but made no contacts with the presented lipid antigen. These findings provide a molecular basis for lipid-independent CD1d recognition by γδ TCRs.
Journal Article
Recognition of the antigen-presenting molecule MR1 by a Vδ3⁺ γδ T cell receptor
Unlike conventional αβ T cells, γδ T cells typically recognize non-peptide ligands independently of major histocompatibility complex (MHC) restriction. Accordingly, the γδ T cell receptor (TCR) can potentially recognize a wide array of ligands; however, few ligands have been described to date. While there is a growing appreciation of the molecular bases underpinning variable (V)δ1⁺ and Vδ2⁺ γδ TCR-mediated ligand recognition, the mode of Vδ3⁺ TCR ligand engagement is unknown. MHC class I–related protein, MR1, presents vitamin B metabolites to αβ T cells known as mucosal-associated invariant T cells, diverse MR1-restricted T cells, and a subset of human γδ T cells. Here, we identify Vδ1/2⁻ γδ T cells in the blood and duodenal biopsy specimens of children that showed metabolite-independent binding of MR1 tetramers. Characterization of one Vδ3Vγ8 TCR clone showed MR1 reactivity was independent of the presented antigen. Determination of two Vδ3Vγ8 TCR-MR1-antigen complex structures revealed a recognition mechanism by the Vδ3 TCR chain that mediated specific contacts to the side of the MR1 antigen-binding groove, representing a previously uncharacterized MR1 docking topology. The binding of the Vδ3⁺ TCR to MR1 did not involve contacts with the presented antigen, providing a basis for understanding its inherent MR1 autoreactivity. We provide molecular insight into antigen-independent recognition of MR1 by a Vδ3⁺ γδ TCR that strengthens an emerging paradigm of antibody-like ligand engagement by γδ TCRs.
Journal Article
Performance of a Deep Learning Model vs Human Reviewers in Grading Endoscopic Disease Severity of Patients With Ulcerative Colitis
Assessing endoscopic disease severity in ulcerative colitis (UC) is a key element in determining therapeutic response, but its use in clinical practice is limited by the requirement for experienced human reviewers.
To determine whether deep learning models can grade the endoscopic severity of UC as well as experienced human reviewers.
In this diagnostic study, retrospective grading of endoscopic images using the 4-level Mayo subscore was performed by 2 independent reviewers with score discrepancies adjudicated by a third reviewer. Using 16 514 images from 3082 patients with UC who underwent colonoscopy at a single tertiary care referral center in the United States between January 1, 2007, and December 31, 2017, a 159-layer convolutional neural network (CNN) was constructed as a deep learning model to train and categorize images into 2 clinically relevant groups: remission (Mayo subscore 0 or 1) and moderate to severe disease (Mayo subscore, 2 or 3). Ninety percent of the cohort was used to build the model and 10% was used to test it; the process was repeated 10 times. A set of 30 full-motion colonoscopy videos, unseen by the model, was then used for external validation to mimic real-world application.
Model performance was assessed using area under the receiver operating curve (AUROC), sensitivity and specificity, positive predictive value (PPV), and negative predictive value (NPV). Kappa statistics (κ) were used to measure agreement of the CNN relative to adjudicated human reference cores.
The authors included 16 514 images from 3082 unique patients (median [IQR] age, 41.3 [26.1-61.8] years, 1678 [54.4%] female), with 3980 images (24.1%) classified as moderate-to-severe disease by the adjudicated reference score. The CNN was excellent for distinguishing endoscopic remission from moderate-to-severe disease with an AUROC of 0.966 (95% CI, 0.967-0.972); a PPV of 0.87 (95% CI, 0.85-0.88) with a sensitivity of 83.0% (95% CI, 80.8%-85.4%) and specificty of 96.0% (95% CI, 95.1%-97.1%); and NPV of 0.94 (95% CI, 0.93-0.95). Weighted κ agreement between the CNN and the adjudicated reference score was also good for identifying exact Mayo subscores (κ = 0.84; 95% CI, 0.83-0.86) and was similar to the agreement between experienced reviewers (κ = 0.86; 95% CI, 0.85-0.87). Applying the CNN to entire colonoscopy videos had similar accuracy for identifying moderate to severe disease (AUROC, 0.97; 95% CI, 0.963-0.969).
This study found that deep learning model performance was similar to experienced human reviewers in grading endoscopic severity of UC. Given its scalability, this approach could improve the use of colonoscopy for UC in both research and routine practice.
Journal Article
A class of γδ T cell receptors recognize the underside of the antigen-presenting molecule MR1
T cell receptors (TCRs) recognize antigens presented by major histocompatibility complex (MHC) and MHC class I–like molecules. We describe a diverse population of human γδ T cells isolated from peripheral blood and tissues that exhibit autoreactivity to the monomorphic MHC-related protein 1 (MR1). The crystal structure of a γδTCR–MR1–antigen complex starkly contrasts with all other TCR–MHC and TCR–MHC-I-like complex structures. Namely, the γδTCR binds underneath the MR1 antigen-binding cleft, where contacts are dominated by the MR1 α3 domain. A similar pattern of reactivity was observed for diverse MR1-restricted γδTCRs from multiple individuals. Accordingly, we simultaneously report MR1 as a ligand for human γδ T cells and redefine the parameters for TCR recognition.
Journal Article