Isotopic and spin selectivity of H_2 adsorbed in bundles of carbon nanotubes

Due to its large surface area and strongly attractive potential, a bundle of carbon nanotubes is an ideal substrate material for gas storage. In addition, adsorption in nanotubes can be exploited in order to separate the components of a mixture. In this paper, we investigate the preferential adsorption of D_2 versus H_2(isotope selectivity) and of ortho versus para(spin selectivity) molecules confined in the one-dimensional grooves and interstitial channels of carbon nanotube bundles. We perform selectivity calculations in the low coverage regime, neglecting interactions between adsorbate molecules. We find substantial spin selectivity for a range of temperatures up to 100 K, and even greater isotope selectivity for an extended range of temperatures,up to 300 K. This isotope selectivity is consistent with recent experimental data, which exhibit a large difference between the isosteric heats of D_2 and H_2 adsorbed in these bundles.Comment: Paper submitted to Phys.Rev. B; 17 pages, 2 tables, 6 figure

Introduction: Shakespeare's public spheres

Habermas’ sense of a “cultural Public Sphere” is a notoriously complex term and, when applied to Early Modern cultures, needs careful definition. This essay both introduces the variety of methods by which we might approach playtexts with a view to their public – auditory – impact and contributes to a debate about an audience's understanding of Shakespeare's plays. By selecting two words and their spread of use in one play, Twelfth Night, we might appreciate the potential for meaningful ambiguity latent in how we hear the language of live performance. If we search for how certain terms (in this case, the cluster of semes derived from repetitions of “fancy” and “play”), we might find at times incompatible senses, yet we get near to appreciating the range of Early Modern dramatic language

Ethnoracial Disparities in SARS-CoV-2 Seroprevalence in a Large Cohort of Individuals in Central North Carolina from April to December 2020

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths around the world within the past 2 years. Transmission within the United States has been heterogeneously distributed by geography and social factors with little data from North Carolina. Here, we describe results from a weekly cross-sectional study of 12,471 unique hospital remnant samples from 19 April to 26 December 2020 collected by four clinical sites within the University of North Carolina Health system, with a majority of samples from urban, outpatient populations in central North Carolina. We employed a Bayesian inference model to calculate SARS-CoV-2 spike protein immunoglobulin prevalence estimates and conditional odds ratios for seropositivity. Furthermore, we analyzed a subset of these seropositive samples for neutralizing antibodies. We observed an increase in seroprevalence from 2.9 (95% confidence interval [CI], 1.8 to 4.5) to 12.8 (95% CI, 10.6 to 15.2) over the course of the study. Latinx individuals had the highest odds ratio of SARS-CoV-2 exposure at 6.56 (95% CI, 4.66 to 9.44). Our findings aid in quantifying the degree of asymmetric SARS-CoV-2 exposure by ethnoracial grouping. We also find that 49% of a subset of seropositive individuals had detectable neutralizing antibodies, which was skewed toward those with recent respiratory infection symptoms

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia

Comparison of some nonlinear smoothing methods. [Nuclear medicine images]

Due to the poor quality of many nuclear medicine images, computer-driven smoothing procedures are frequently employed to enhance the diagnostic utility of these images. While linear methods were first tried, it was discovered that nonlinear techniques produced superior smoothing with little detail suppression. We have compared four methods: Gaussian smoothing (linear), two-dimensional least-squares smoothing (linear), two-dimensional least-squares bounding (nonlinear), and two-dimensional median smoothing (nonlinear). The two dimensional least-squares procedures have yielded the most satisfactorily enhanced images, with the median smoothers providing quite good images, even in the presence of widely aberrant points

Non-linear image processing

Processing of nuclear medicine images is generally performed by essentially linear methods with the non-negativity condition being applied as the only non-linear process. The various methods used: matrix methods in signal space and Fourier or Hadamard transforms in frequency or sequency space are essentially equivalent. Further improvement in images can be obtained by the use of inherently non-linear methods. The recent development of an approximation to a least-difference method (as opposed to a least-square method) has led to an appreciation of the effects of data bounding and to the development of a more powerful process. Data bounding (modification of statistically improbable data values) is an inherently non-linear method with considerable promise. Strong bounding depending on two-dimensional least-squares fitting yields a reduction of mottling (buttermilk effect) not attainable with linear processes. A pre- bounding process removing very bad points is used to protect the strong bounding process from incorrectly modifying data points due to the weight of an extreme but yet unbounded point as the fitting area approaches it. (auth