Cancer cells overexpress IFsub.1, the endogenous protein that inhibits the hydrolytic activity of ATP synthase when mitochondrial membrane potential (Δμsub.H sup.+) falls, as in ischemia. Other roles ...have been ascribed to IFsub.1, but the associated molecular mechanisms are still under debate. We investigated the ability of IFsub.1 to promote survival and proliferation in osteosarcoma and colon carcinoma cells exposed to conditions mimicking ischemia and reperfusion, as occurs in vivo, particularly in solid tumors. IFsub.1-silenced and parental cells were exposed to the FCCP uncoupler to collapse Δμsub.H sup.+ and the bioenergetics of cell models were validated. All the uncoupled cells preserved mitochondrial mass, but the implemented mechanisms differed in IFsub.1-expressing and IFsub.1-silenced cells. Indeed, the membrane potential collapse and the energy charge preservation allowed an increase in both mitophagy and mitochondrial biogenesis in IFsub.1-expressing cells only. Interestingly, the presence of IFsub.1 also conferred a proliferative advantage to cells highly dependent on oxidative phosphorylation when the uncoupler was washed out, mimicking cell re-oxygenation. Overall, our results indicate that IFsub.1, by allowing energy preservation and promoting mitochondrial renewal, can favor proliferation of anoxic cells and tumor growth. Therefore, hindering the action of IFsub.1 may be promising for the therapy of tumors that rely on oxidative phosphorylation for energy production.
In recent years, with the increasing demand for high-quality Dendrobii caulis decoction piece, the identification of D. caulis decoction piece species has become an urgent issue. However, the current ...methods are primarily designed for professional quality control and supervision. Therefore, ordinary consumers should not rely on these methods to assess the quality of products when making purchases. This research proposes a deep learning network called improved YOLOv5 for detecting different types of D. caulis decoction piece from images. In the main architecture of improved YOLOv5, we have designed the C2S module to replace the C3 module in YOLOv5, thereby enhancing the network’s feature extraction capability for dense and small targets. Additionally, we have introduced the Reparameterized Generalized Feature Pyramid Network (RepGFPN) module and Optimal Transport Assignment (OTA) operator to more effectively integrate the high-dimensional and low-dimensional features of the network. Furthermore, a new large-scale dataset of Dendrobium images has been established. Compared to other models with similar computational complexity, improved YOLOv5 achieves the highest detection accuracy, with an average mAP@.05 of 96.5%. It is computationally equivalent to YOLOv5 but surpasses YOLOv5 by 2 percentage points in terms of accuracy.
Evaluating surface frequency components in the fabrication process is critical for controlling the machined surface quality. The presence of anisotropic ripples on diamond-turned surfaces makes this ...challenging. A multiscale frequency evaluation method, referred to as Surface Intrinsic Mode Decomposition (SIMD), is proposed for evaluating on-machine surface measurement (OMSM) data. It decomposes continuous surface probing profiles, incorporating both temporal and spatial frequency information. In comparison to the conventional power spectral density (PSD) analysis method, the approach enriches frequency details over a wider range, which contributes to a more comprehensive understanding of surface quality and helps to identify mid-spatial frequency (MSF) errors.
Background and Aim: Issues in patient positioning during chest X-ray (CXR) acquisition impair diagnostic quality and potentially increase radiation dose. Automated quality assessment was proposed to ...address this. Our objective is to determine thresholds on some quality control metrics following international guidelines, that represent expert knowledge and can be applied in a comprehensible and explainable AI approach for such an automatic quality assessment. Materials and Methods: An AI-method estimating collimation distance to the ribcage, balancing between both clavicle heads, and number of ribs above the diaphragm as metrics for collimation, rotation, and inhalation quality was applied on 64,315 posteroanterior CXR images from a public dataset (ChestX-ray8). From this set 920 CXR images were sampled and manually annotated to gain additional trusted reference metrics. Seven readers from different institutions then classified the acquisition quality of these images independently into okay, inadequate, or unacceptable following the criteria of international guidelines. Optimal thresholds on the metrics were determined to reproduce these classes using the metrics only. Results: A fair to moderate agreement between the experts was found. When disregarding all inadequate rates a classification on the metrics was able to separate okay rated cases from unacceptable cases for collimation (AUC > 0.97), rotation (AUC = 0.93) and inhalation (AUC = 0.97). Conclusion: Suitable thresholds were determined to reproduce expert opinions in the assessment of the most important quality criteria in CXR acquisition. These thresholds were finally applied on the AI-method's estimates to automatically classify image acquisition quality comprehensibly and according to the guidelines. KEYWORDS chest radiography, image quality, patient positioning, explainable artificial intelligence, quality management
A one-of-a-kind presentation of the major achievements in statistical profile monitoring methods Statistical profile monitoring is an area of statistical quality control that is growing in ...significance for researchers and practitioners, specifically because of its range of applicability across various service and manufacturing settings. Comprised of contributions from renowned academicians and practitioners in the field, Statistical Analysis of Profile Monitoring presents the latest state-of-the-art research on the use of control charts to monitor process and product quality profiles. The book presents comprehensive coverage of profile monitoring definitions, techniques, models, and application examples, particularly in various areas of engineering and statistics. The book begins with an introduction to the concept of profile monitoring and its applications in practice. Subsequent chapters explore the fundamental concepts, methods, and issues related to statistical profile monitoring, with topics of coverage including: Simple and multiple linear profiles Binary response profiles Parametric and nonparametric nonlinear profiles Multivariate linear profiles monitoring Statistical process control for geometric specifications Correlation and autocorrelation in profiles Nonparametric profile monitoring Throughout the book, more than two dozen real-world case studies highlight the discussed topics along with innovative examples and applications of profile monitoring. Statistical Analysis of Profile Monitoring is an excellent book for courses on statistical quality control at the graduate level. It also serves as a valuable reference for quality engineers, researchers and anyone who works in monitoring and improving statistical processes.
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