Hybrid Metaheuristic Technique Based Tabu Search and Simulated Annealing

This paper presents hybrid technique using two metahueristic methods; which are simulated annealing (SA) and tabu search (TS). The aim is to exhibit the facility of adaptive memory in tabu search method to resolve the long computation times of simulated annealing metaheuristic method. This can be done by keeping the best path which is found in each iteration. As a result, the proposed hybrid technique gives the optimum solution by finding the shortest path with minimum cost when applied on travelling salesman problem (TSP) since it reduces the time complexity by finding the optimum path with a few numbers of iterations when compared with SA and TS

Room temperature thermally evaporated thin Au film on Si suitable for application of thiol self-assembled monolayers in MEMS/NEMS sensors

Gold is a standard surface for attachment of thiol-based self-assembled monolayers (SAMs). To achieve uniform defect free SAM coatings, which are essential for bio/chemical sensing applications, the gold surface must have low roughness, and be highly orientated. These requirements are normally achieved by either heating during Au deposition or post deposition Au surface annealing. This paper shows that room temperature deposited gold, can afford equivalent gold surfaces, if the gold deposition parameters are carefully controlled. This observation is an important result as heating (or annealing) of the deposited gold can have a detrimental effect on the mechanical properties of the silicon on which the gold is deposited used in microsensors. The paper presents the investigation of the morphology and crystalline structure of Au film prepared by thermal evaporation at room temperature on silicon. The effect of gold deposition rate is studied, and it is shown that by increasing the deposition rate from 0.02 nm s-1 to 0.14 nm s-1 the gold surface RMS roughness decreases, whereas the grain size of the deposited gold is seen to follow a step function decreasing suddenly between 0.06 and 0.10 nm s-1. The XRD intensity of the preferentially [111] orientated gold crystallites is also seen to increase as the deposition rate increases up to a deposition rate of 0.14 nm s-1. Formation and characterization of 1-dodecanethiol on these Au coated samples is also studied using contact angle. It is shown that by increasing the Au deposition rate the contact angle hysteresis (CAH) decreases until it plateaus, for a deposition rate greater than 0.14 nm s-1, where the CAH is smaller than 9 degrees which is an indication of homogeneous SAM formation, on a smooth surface

The T cell differentiation landscape is shaped by tumour mutations in lung cancer

Tumour mutational burden (TMB) predicts immunotherapy outcome in non-small cell lung cancer (NSCLC), consistent with immune recognition of tumour neoantigens. However, persistent antigen exposure is detrimental for T cell function. How TMB affects CD4 and CD8 T cell differentiation in untreated tumours and whether this affects patient outcomes is unknown. Here, we paired high-dimensional flow cytometry, exome, single-cell and bulk RNA sequencing from patients with resected, untreated NSCLC to examine these relationships. TMB was associated with compartment-wide T cell differentiation skewing, characterized by loss of TCF7-expressing progenitor-like CD4 T cells, and an increased abundance of dysfunctional CD8 and CD4 T cell subsets with strong phenotypic and transcriptional similarity to neoantigen-reactive CD8 T cells. A gene signature of redistribution from progenitor-like to dysfunctional states was associated with poor survival in lung and other cancer cohorts. Single-cell characterization of these populations informs potential strategies for therapeutic manipulation in NSCLC

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

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

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC