Ionic and electronic properties of the topological insulator Bi2_2Te2_2Se investigated using β\beta-detected nuclear magnetic relaxation and resonance of 8^8Li

We report measurements on the high temperature ionic and low temperature electronic properties of the 3D topological insulator Bi$_2$Te$_2$Se using ion-implanted $^8$Li $\beta$-detected nuclear magnetic relaxation and resonance. With implantation energies in the range 5-28 keV, the probes penetrate beyond the expected range of the topological surface state, but are still within 250 nm of the surface. At temperatures above ~150 K, spin-lattice relaxation measurements reveal isolated $^8$Li$^{+}$ diffusion with an activation energy $E_{A} = 0.185(8)$ eV and attempt frequency $\tau_{0}^{-1} = 8(3) \times 10^{11}$ s$^{-1}$ for atomic site-to-site hopping. At lower temperature, we find a linear Korringa-like relaxation mechanism with a field dependent slope and intercept, which is accompanied by an anomalous field dependence to the resonance shift. We suggest that these may be related to a strong contribution from orbital currents or the magnetic freezeout of charge carriers in this heavily compensated semiconductor, but that conventional theories are unable to account for the extent of the field dependence. Conventional NMR of the stable host nuclei may help elucidate their origin.Comment: 17 pages, 12 figures, submitted to Phys. Rev.

Nuclear magnetic resonance of ion implanted 8^8Li in ZnO

We report on the stability and magnetic state of ion implanted $^8$Li in single crystals of the semiconductor ZnO using $\beta$-detected nuclear magnetic resonance. At ultradilute concentrations, the spectra reveal distinct Li sites from 7.6 to 400 K. Ionized shallow donor interstitial Li is stable across the entire temperature range, confirming its ability to self-compensate the acceptor character of its (Zn) substitutional counterpart. Above 300 K, spin-lattice relaxation indicates the onset of correlated local motion of interacting defects, and the spectra show a site change transition from disordered configurations to substitutional. Like the interstitial, the substitutional shows no resolved hyperfine splitting, indicating it is also fully ionized above 210 K. The electric field gradient at the interstitial $^8$Li exhibits substantial temperature dependence with a power law typical of non-cubic metals.Comment: 15 pages and 11 figure

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

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

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: 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

Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images

Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral clustering is an image segmentation method making it possible to combine features and cues. This study presents a new spectral clustering method using unsupervised feature learning (UFL). In this method, the SAR image is primarily processed by the non‐negative matrix factorisation (NMF) algorithm and then non‐negative features containing spatial structure information are extracted. Afterwards, the extracted features are learned using a sparse coding algorithm to increase the discrimination power of the features. Sparse coding is an unsupervised learning algorithm which finds the patterns or high‐level semantics of the data. Ultimately, the SAR image segmentation operation is performed by applying spectral clustering on learned features. In this method, sparse coding learns features and simultaneously creates the similarity function required in spectral clustering through the production of sparse coefficients. Therefore this method avoids the Gaussian similarity function, which has a problem with scale parameter adjustment that is one of the drawbacks of spectral clustering methods. The results demonstrate that, compared with wavelet and GLCM features, NMF features manage to obtain more meaningful information and provide a better SAR image segmentation result. The results have also demonstrated that SAR image segmentation using learned features is significantly improved compared with segmentation by unlearned features. The experimental results indicate the effect of UFL on SAR image segmentation

Screening activated carbons produced from recycled petroleum coke for acid gas separation

Activated carbons derived from petroleum coke (petcoke) have the potential to (a) help reduce sulfur dioxide emissions through desulfurization, (b) help reduce carbon dioxide and (c) utilize a common waste product. Herein we present results for the selective adsorption of H2S and CO2 from a synthetic sour gas mixture using 7 activated carbons, four derived from petcoke and three obtained commercially. The petcoke activated with sodium hydroxide (P_Na) showed an H2S/CH4 selectivity up to SH₂S/CH₄ = 152 in temperature swing adsorption experiments. The H2S/CH4 selectivity was observed to be inversely proportional to the BET apparent surface area and directly proportional to the oxygen content of the activated carbons. H2S/CH4 and H2S/CO2 selectivity for P_Na was found to increase with increasing temperature. The P_Na activated carbon maintained a high H2S selectivity (SH₂S/CH₄ > 50 and SH₂S/CO₂ > 20) after regeneration at temperatures of T = 423 – 723 K. Pure component CH4, CO2, and H2S adsorption isotherms at T = 288.15 K, 298.15 K and 308.15 K were collected and used to estimate the multi-component adsorption. The results of these studies indicate that the petcoke activated carbons are viable materials for separating H2S and CO2 from sour natural gas streams or biogas

Sol–Gel-Derived 2D Nanostructures of Aluminum Hydroxide Acetate: Toward the Understanding of Nanostructure Formation

Two-dimensional (2D) metal oxide nanostructures have generated a great deal of attention in material science for their prospective wide-ranging applications; therefore, a scalable and economical method for producing these structures is an asset. In this research, a simple procedure for the preparation of 2D aluminum hydroxide acetate macromolecules ([Al­(OH)­(OAc)₂]_<i>m</i>) has been developed via a nonaqueous sol–gel route at a mild reaction temperature and ambient pressure. To gain a greater understanding of the mechanism for how the self-assembly of these 2D structures occurs, a combination of in situ Fourier transform infrared (FTIR) measurements and density functional theory (DFT) calculations were utilized. It was found that the bridging OH^–1 and coordination modes of the organic ligands guide the assembly of the planar nanostructures. The theoretical calculation results show that the structures of the [Al­(OH)­(OAc)₂]₈ oligomer can be either a linear or a planar structure, and the latter is more thermodynamically favorable than its linear counterpart. The simple synthesis method described herein could possibly open a new avenue for designing 2D nanostructures via ligand-directed anisotropic condensation reactions