The gold standard of histopathology for the diagnosis of Barrett’s esophagus (BE) is hindered by inter-observer variability among gastrointestinal pathologists. Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). We performed a comparative study to elucidate the characteristics and behaviors of different deep learning-based feature representation approaches for the WSI-based diagnosis of diseased esophageal architectures, namely, dysplastic and non-dysplastic BE. The results showed that if appropriate settings are chosen, the unsupervised feature representation approach is capable of extracting more relevant image features from WSIs to classify and locate the precursors of esophageal cancer compared to weakly supervised and fully supervised approaches.

Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem.Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm.Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm.Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.

The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods for MTSC tasks. The success of these approaches relies on access to the massive amount of labeled data (i.e., annotating or assigning tags to each sample that shows its corresponding category). However, obtaining a massive amount of labeled data is usually very time-consuming and expensive in many real-world applications such as medicine, because it requires domain experts’ knowledge to annotate data. Insufficient labeled data prevents these models from learning discriminative features, resulting in poor margins that reduce generalization performance. To address this challenge, we propose a novel approach: supervised contrastive learning for time series classification (SupCon-TSC). This approach improves the classification performance by learning the discriminative low-dimensional representations of multivariate time series, and its end-to-end structure allows for interpretable outcomes. It is based on supervised contrastive (SupCon) loss to learn the inherent structure of multivariate time series. First, two separate augmentation families, including strong and weak augmentation methods, are utilized to generate augmented data for the source and target networks, respectively. Second, we propose the instance-level, and cluster-level SupCon learning approaches to capture contextual information to learn the discriminative and universal representation for multivariate time series datasets. In the instance-level SupCon learning approach, for each given anchor instance that comes from the source network, the low-variance output encodings from the target network are sampled as positive and negative instances based on their labels. However, the cluster-level approach is performed between each instance and cluster centers among batches, as opposed to the instance-level approach. The cluster-level SupCon loss attempts to maximize the similarities between each instance and cluster centers among batches. We tested this novel approach on two small cardiopulmonary exercise testing (CPET) datasets and the real-world UEA Multivariate time series archive. The results of the SupCon-TSC model on CPET datasets indicate its capability to learn more discriminative features than existing approaches in situations where the size of the dataset is small. Moreover, the results on the UEA archive show that training a classifier on top of the universal representation features learned by our proposed method outperforms the state-of-the-art approaches.

BACKGROUND: Phenotypic plasticity of smooth muscle cells (SMCs) and endothelial cells (ECs) contributes to atherosclerotic plaque composition and stability, yet how shifts in one population influence the contribution and function of the other under conditions of vascular stress, such as irradiation, is poorly understood. A major limitation has been the inability to simultaneously fate-map both cell types within the same lesion, with most studies mapping one lineage while inferring the other using unreliable dynamically changing marker genes, risking false-positive and false-negative assignment. METHODS: We generated dual lineage tracing Apoe -deficient mice, enabling simultaneous fate mapping of SMCs and ECs. This model was used to extend prior findings from single lineage tracing models demonstrating irradiation-induced loss of SMC lesion investment and expansion of EC-derived cells. Dual lineage tracing mice were subjected to irradiation followed by Western diet feeding to induce atherosclerosis. Lineage tracing, immunostaining, and single-cell RNA sequencing analysis were used to define coordinated SMC and EC responses and identify changes relevant to plaque instability. RESULTS: Dual lineage tracing simultaneously labeled SMC- and EC-derived cells in healthy and atherosclerotic vessels. Irradiation induced divergent responses: SMC-derived cells failed to invest in lesions and upregulated stress-activated inflammatory genes, whereas EC-derived cells expanded and upregulated SMC-associated genes. However, EC-derived cells within lesions failed to induce extracellular matrix genes, and lesions from irradiated mice exhibited reduced collagen content and fewer ACTA2 (smooth muscle actin alpha 2) ⁺ cells within the fibrous cap, consistent with reduced plaque stability. CONCLUSIONS: Dual lineage tracing of SMCs and ECs demonstrated that irradiation-induced loss of lesional SMC and expansion of EC-derived ACTA2 ⁺ cells are not artifacts of false lineage assignment. By resolving SMC and EC fate within the same lesion, we identify irradiation-induced cell dynamics including inflammatory reprogramming of SMCs, EC phenotypic modulation, impaired extracellular matrix organization, and reduced ACTA2 ⁺ fibrous cap cellularity that may contribute to radiotherapy-associated increased atherosclerotic cardiovascular disease risk.