Scanning Photo-Induced Impedance Microscopy - Resolution studies and polymer characterization

Scanning Photo-Induced Impedance Microscopy (SPIM) is an impedance imaging technique that is based on photocurrent measurements at field-effect structures. The material under investigation is deposited onto a semiconductor-insulator substrate. A thin metal film or an electrolyte solution with an immersed electrode serves as the gate contact. A modulated light beam focused into the space charge region of the semiconductor produces a photocurrent, which is directly related to the local impedance of the material. The absolute impedance of a polymer film can be measured by calibrating photocurrents using a known impedance in series with the sample. Depending on the wavelength of light used, charge carriers are not only generated in the focus but also throughout the bulk of the semiconductor. This can have adverse effects on the lateral resolution. Two-photon experiments were carried out to confine charge carrier generation to the spacecharge layer. The lateral resolution of SPIM is also limited by the lateral diffusion of charge carriers in the semiconductor. This problem can be solved by using thin silicon layers as semiconductor substrates. A resolution of better than 1 mu m was achieved using silicon on sapphire (SOS) substrates with a I l.Lm thick silicon layer

A high efficiency input/output coupler for small silicon photonic devices

Coupling light from an optical fibre to small optical waveguides is particularly problematic in semiconductors, since the refractive index of the silica fibre is very different from that of a semiconductor waveguide. There have been several published methods of achieving such coupling, but none are sufficiently efficient whilst being robust enough for commercial applications. In this paper experimental results of our approach called a Dual-Grating Assisted Directional Coupler, are presented. The principle of coupling by this novel method has been successfully demonstrated, and a coupling efficiency of 55% measured

third order bragg grating filters in small soi waveguides

Third order grating filters fabricated in small Silicon-on-Insulator rib waveguides are demonstrated. Variations in grating etch depth and duty cycle are considered, and a maximum experimental reflection of 42% is demonstrated for gratings of 1500 µm in length, with a grating period of approximately 689nm and an etch depth of 200nm. Agreement with modeling is shown to be good

Enumeration and Molecular Characterisation of Circulating Tumour Cells in Endometrial Cancer

BACKGROUND: This is a feasibility study to determine whether circulating tumour cells (CTCs) are detectable and suitable for molecular profiling in advanced endometrial cancer (aEC). METHOD: Between October 2012 and February 2014, 30 patients with aEC had baseline and up to 3 follow-up samples. CTCs and stathmin expression were evaluated using the CellSearch platform. Epithelial cell adhesion molecule (EpCAM) and stathmin immunohistochemistry were performed on FFPE tumour tissue. RESULTS: Eighteen from 30 (60%) patients had detectable CTCs during study [1 CTC (n = 7), 2 (n = 4), 3 (n = 1), 4 (n = 2), 7 (n = 1), 8 (n = 1), 22 (n = 1), 172 (n = 1) in 7.5 ml blood]. Ten from 18 patients had between 50 and 100% of detectable CTCs that were stathmin positive. More CTC-positive than CTC-negative patients had non-endometrioid versus endometrioid histology, tumour size ≥5 versus 0.05, 95% confidence interval 0.7-16.2]. Twenty-one tumour blocks were tested for EpCAM and stathmin immunohistochemistry (IHC). Stathmin tumour immunostaining scores (TIS) on IHC were higher in CTC-positive patients. CONCLUSION: CTC enumeration and molecular profiling with stathmin on the CellSearch platform is feasible in aEC. Stathmin TIS on IHC, a known prognostic marker in EC, was associated with CTC positivity

Evaluation of multi-layered graphene nano-platelet composite coatings for corrosion control part I - contact potentials and gas permeability

The electronic and diffusion-blocking properties of graphene nano-platelets (GNPs) are quantified with a view to understanding their action as (possible) additives to anti-corrosion coatings. Platelet size and thickness are determined by SEM and BET specific surface area measurements. A Scanning Kelvin probe is used to show that a contact potential of up to 1.4 V develops between GNP particles and various metal substrates: silver, copper, iron and zinc. A novel photochemical method is used to show that oxygen permeation rates through a PVB-GNP (polyvinylbutyral) composite coating decrease by over an order of magnitude as GNP volume fraction increases to 0.056

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma