It was found that failure was triggered by a critical material volume of around the half thickness, measured from the inner surface, for the both path-independent metrics analyzed.This work examines the differences between a human and a machine in object recognition tasks. The machine is useful as much as the output classification labels are correct and match the dataset-provided labels. However, very often a discrepancy occurs because the dataset label is different than the one expected by a human. To correct this, the concept of the target user population is introduced. The paper presents a complete methodology for either adapting the output of a pre-trained, state-of-the-art object classification algorithm to the target population or inferring a proper, user-friendly categorization from the target population. The process is called 'user population re-targeting'. The methodology includes a set of specially designed population tests, which provide crucial data about the categorization that the target population prefers. Ferroptosis inhibitor The transformation between the dataset-bound categorization and the new, population-specific categorization is called the 'Cognitive Relevance Transform'. The results of the experiments on the well-known datasets have shown that the target population preferred such a transformed categorization by a large margin, that the performance of human observers is probably better than previously thought, and that the outcome of re-targeting may be difficult to predict without actual tests on the target population.For precision cancer radiotherapy, high linear energy transfer (LET) particle irradiation offers a substantial advantage over photon-based irradiation. In contrast to the sparse deposition of low-density energy by χ- or γ-rays, particle irradiation causes focal DNA damage through high-density energy deposition along the particle tracks. This is characterized by the formation of multiple damage sites, comprising localized clustered patterns of DNA single- and double-strand breaks as well as base damage. These clustered DNA lesions are key determinants of the enhanced relative biological effectiveness (RBE) of energetic nuclei. However, the search for a fingerprint of particle exposure remains open, while the mechanisms underlying the induction of chromothripsis-like chromosomal rearrangements by high-LET radiation (resembling chromothripsis in tumors) await to be elucidated. In this work, we investigate the transformation of clustered DNA lesions into chromosome fragmentation, as indicated by the induction and to the new model we propose for the mechanistic origin of chromothripsis-like rearrangements.It is proposed that the bioactive lipid, arachidonic acid (AA, 204 n-6), can inactivate severe acute respiratory syndrome(SARS-CoV-2), facilitate M1 and M2 macrophage generation, suppress inflammation, prevent vascular endothelial cell damage, and regulate inflammation resolution processes based on the timely formation of prostaglandin E2 (PGE2) and lipoxin A4 (LXA4) based on the context. Thus, AA may be useful both to prevent and manage coronavrus disease-2019(COVID-19).For the past 100 years, oncologists have relentlessly pursued the destruction of tumor cells by surgical, chemotherapeutic or radiation oncological means. Consistent with this focus, treatment plans are typically based on key characteristics of the tumor itself such as disease site, histology and staging based on local, regional and systemic dissemination. Precision medicine is similarly built on the premise that detailed knowledge of molecular alterations of tumor cells themselves enables better and more effective tumor cell destruction. Recently, host factors within the tumor microenvironment including the vasculature and immune systems have been recognized as modifiers of disease progression and are being targeted for therapeutic gain. In this review, we argue that-to optimize the impact of old and new treatment options-we need to take account of an epidemic that occurs independently of-but has major impact on-the development and treatment of malignant diseases. This is the rapidly increasing number of patients with excess weight and its' attendant metabolic consequences, commonly described as metabolic syndrome. It is well established that patients with altered metabolism manifesting as obesity, metabolic syndrome and chronic inflammation have an increased incidence of cancer. Here, we focus on evidence that these patients also respond differently to cancer therapy including radiation and provide a perspective how exercise, diet or pharmacological agents may be harnessed to improve therapeutic responses in this patient population.As many, if not most, ligands at G protein-coupled receptor antagonists are inverse agonists, we systematically reviewed inverse agonism at the nine adrenoceptor subtypes. Except for β3-adrenoceptors, inverse agonism has been reported for each of the adrenoceptor subtypes, most often for β2-adrenoceptors, including endogenously expressed receptors in human tissues. As with other receptors, the detection and degree of inverse agonism depend on the cells and tissues under investigation, i.e., they are greatest when the model has a high intrinsic tone/constitutive activity for the response being studied. Accordingly, they may differ between parts of a tissue, for instance, atria vs. ventricles of the heart, and within a cell type, between cellular responses. The basal tone of endogenously expressed receptors is often low, leading to less consistent detection and a lesser extent of observed inverse agonism. Extent inverse agonism depends on specific molecular properties of a compound, but inverse agonism appears to be more common in certain chemical classes. While inverse agonism is a fascinating facet in attempts to mechanistically understand observed drug effects, we are skeptical whether an a priori definition of the extent of inverse agonism in the target product profile of a developmental candidate is a meaningful option in drug discovery and development.To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine learning techniques for big data analytics has become a possibility recently. Due to the high false classification rates of the current methods, our goal is to build a practical and accurate method for road safety predictions that automatically determine if the driving behaviour is safe on public transportation. In this paper, our main contributions include (1) a novel feature extraction method because of the lack of informative features in raw CAN bus data, (2) a novel boosting method for driving behaviour classification (safe or unsafe) to combine advantages of deep learning and shallow learning methods with much improved performance, and (3) an evaluation of our method using a real-world data to provide accurate labels from domain experts in the public transportation industry for the first time.