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A novel and moderate synthesis of 2,5-disubstituted oxazoles and oxazolines involving ruthenium(II) porphyrin-copper chloride catalyzed cyclization was developed. These reactions using readily available benzene carboxylic acids and phenylethenes or phenylacetylenes are performed under mild conditions. The reactions proceed in series, giving rise to the formation of an intermolecular C-N bond and an intramolecular C-O bond, which yield oxazole or oxazoline derivatives simultaneously.

Explosive developments in modern society bring huge fire loads. Previous fire detections at early stages are basically enabled by recognizing abnormal high-temperatures, smoke particles, and flame light signals. However, the identification of these characteristic signals is generally accompanied by an open flame or smoke, which makes it difficult to prevent further serious damage. Herein, a latent-firedetecting strategy of trace ammonia (NH 3 ) analysis based on nanohybrid Ti 3 C 2 T x MXene/MoS 2 is proposed. Benefiting from nanoscale high-density Schottky heterojunctions between MoS 2 and Ti 3 C 2 T x MXene, ultrafast (3 s @100 ppm), sub-ppm (200 ppb minimum), and high-sensitivity (81.7% @100 ppm and 10.2% @200 ppb) detection of NH 3 are enabled. An assembled latent-fire-detecting olfactory system (LFOS) based on MXene/MoS 2 and interdigital electrodes can monitor trace NH 3 releases from different materials (wool, leather, foam, and nylon) during thermal decomposition at latent stages. Notably, the LFOS can detect fire threats at least 84 s earlier than commercialized smoke detectors, providing more fire dealing time and an escape period; this offers a promising latent-fire-warning approach for eliminating fire treats at an early stage.

Breast cancer (BC) is the most common female malignancy and the second leading cause of cancer-related death worldwide. In spite of significant advances in clinical management, the mortality of BC continues to increase due to the frequent occurrence of treatment resistance. Intensive studies have been conducted to elucidate the molecular mechanisms underlying BC therapeutic resistance, including increased drug efflux, altered drug targets, activated bypass signaling pathways, maintenance of cancer stemness, and deregulated immune response. Emerging evidence suggests that long noncoding RNAs (lncRNAs) are intimately involved in BC therapy resistance through multiple modes of action. Therefore, an in-depth understanding of the implication of lncRNAs in resistance to clinical therapies may improve the clinical outcome of BC patients. Here, we highlight the role and underlying mechanisms of lncRNAs in regulating BC treatment resistance with an emphasis on lncRNAs-mediated resistance in different clinical scenarios, and discuss the potential of lncRNAs as novel biomarkers or therapeutic targets to improve BC therapy response.

Survival analysis is a branch of statistics to analyze the time duration that is expected until some events of interest happen, like the death in the organisms of biology. Currently, survival analysis based on pathological images has turned out to be a truly energetic area in the research of healthcare for making primary decisions on therapy and improving patients' quality of treatment. In this regard, the interest to design convolutional neural networks for survival analysis with pathological images is increasing greatly at present. Furthermore, to consider the important spatial hierarchies between features and improve the robustness to affine transformation, capsule network (referred to as CapsNet) has been put forward in recent years. A novel capsule network named CapSurv is introduced in this paper, with a new loss function named survival loss to make survival analysis with whole slide pathological images. In addition, to train CapSurv preferably, semantic-level features extracted by VGG16, are used to distinguish discriminative patches from whole slide pathological images. Our method is applied to the predictions of the survival of glioblastoma and lung squamous cell carcinoma with a public cancer dataset. The results illustrate the proposed CapSurv model has the ability to improve the performance of the prediction by comparing with state-of-the-art survival models. INDEX TERMS Survival analysis, capsule networks, pathological images, deep learning.

Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision‐based surface defect inspection for steel products including hardware system, automated vision‐based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre‐processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

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Converting CS2 into high-value productions represents an emerging and effective strategy for disposing CS2, which can avoid generating various toxic pollutants caused by the traditional method of incinerating CS2. However,...