Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients

This study was funded by Medical Research Scotland and Indica Labs, Inc., who also provided in-kind resource.Cellular subpopulations within the colorectal tumor microenvironment (TME) include CD3+ and CD8+ lymphocytes, CD68+ and CD163+ macrophages, and tumor buds (TBs), all of which have known prognostic significance in stage II colorectal cancer. However, the prognostic relevance of their spatial interactions remains unknown. Here, by applying automated image analysis and machine learning approaches, we evaluate the prognostic significance of these cellular subpopulations and their spatial interactions. Resultant data, from a training cohort retrospectively collated from Edinburgh, UK hospitals (n = 113), were used to create a combinatorial prognostic model, which identified a subpopulation of patients who exhibit 100% survival over a 5-year follow-up period. The combinatorial model integrated lymphocytic infiltration, the number of lymphocytes within 50-μm proximity to TBs, and the CD68+/CD163+ macrophage ratio. This finding was confirmed on an independent validation cohort, which included patients treated in Japan and Scotland (n = 117). This work shows that by analyzing multiple cellular subpopulations from the complex TME, it is possible to identify patients for whom surgical resection alone may be curative.Publisher PDFPeer reviewe

Multi-objective balancing of assembly lines by population heuristics

International audienceThis paper is concerned with the solution of the multi-objective single-model deterministic assembly line balancing problem (ALBP). Two bi-criteria objectives are considered: (1) minimizing the cycle time of the assembly line and the balance delay time of the workstations, and (2) minimizing the cycle time and the smoothness index of the workload of the line. A new population heuristic is proposed to solve the problem based on the general differential evolution (DE) method. The main characteristics of the proposed multi-objective DE (MODE) heuristic are: (a) it formulates the cost function of each individual ALB solution as a weighted-sum of multiple objectives functions with self-adapted weights. (b) It maintains a separate population with diverse Pareto-optimal solutions. (c) It injects the actual evolving population with some Pareto-optimal solutions. (d) It uses a new modified scheme for the creation of the mutant vectors. Moreover, special representation and encoding schemes are developed and discussed which adapt MODE on ALBPs. The efficiency of MODE is measured over known ALB benchmarks taken from the open literature and compared to that of two other previously proposed population heuristics, namely, a weighted-sum Pareto genetic algorithm (GA), and a Pareto-niched GA. The experimental comparisons showed a promising high quality performance for MODE approach

Scheduling with controllable processing times and compression costs using population-based heuristics

International audienceThis paper considers the single machine scheduling problem of jobs with controllable processing times and compression costs and the objective to minimize the total weighted job completion time plus the cost of compression. The problem is known to be intractable, and therefore it was decided to be tackled by population-based heuristics such as differential evolution (DE), particle swarm optimization (PSO), genetic algorithms (GAs), and evolution strategies (ES). Population-based heuristics have found wide application in most areas of production research including scheduling theory. It is therefore surprising that this problem has not yet received any attention from the corresponding heuristic algorithms community. This work aims at contributing to fill this gap. An appropriate problem representation scheme is developed together with a multi-objective procedure to quantify the trade-off between the total weighted job completion time and the cost of compression. The four heuristics are evaluated and compared over a large set of test instances ranging from 5 to 200 jobs. The experiments showed that a differential evolution algorithm is superior (with regard to the quality of the solutions obtained) and faster (with regard to the speed of convergence) to the other approaches