MCM8-9 complex promotes resection of double-strand break ends by MRE11-RAD50-NBS1 complex.

MCM8-9 complex is required for homologous recombination (HR)-mediated repair of double-strand breaks (DSBs). Here we report that MCM8-9 is required for DNA resection by MRN (MRE11-RAD50-NBS1) at DSBs to generate ssDNA. MCM8-9 interacts with MRN and is required for the nuclease activity and stable association of MRN with DSBs. The ATPase motifs of MCM8-9 are required for recruitment of MRE11 to foci of DNA damage. Homozygous deletion of the MCM9 found in various cancers sensitizes a cancer cell line to interstrand-crosslinking (ICL) agents. A cancer-derived point mutation or an SNP on MCM8 associated with premature ovarian failure (POF) diminishes the functional activity of MCM8. Therefore, the MCM8-9 complex facilitates DNA resection by the MRN complex during HR repair, genetic or epigenetic inactivation of MCM8 or MCM9 are seen in human cancers, and genetic inactivation of MCM8 may be the basis of a POF syndrome

Recent development of inorganic nanoparticles for biomedical imaging

Inorganic nanoparticle-based biomedical imaging probes have been studied extensively as a potential alternative to conventional molecular imaging probes. Not only can they provide better imaging performance but they can also offer greater versatility of multimodal, stimuli-responsive, and targeted imaging. However, inorganic nanoparticle-based probes are still far from practical use in clinics due to safety concerns and less-optimized efficiency. In this context, it would be valuable to look over the underlying issues. This outlook highlights the recent advances in the development of inorganic nanoparticle-based probes for MRI, CT, and anti-Stokes shift-based optical imaging. Various issues and possibilities regarding the construction of imaging probes are discussed, and future research directions are suggested.

Prospects of deep learning for medical imaging

Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided

Classification of the glioma grading using radiomics analysis

Background Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading. Methods We considered 285 (high grade n = 210, low grade n = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort. Results Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts. Discussion Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas

Toroidal-Shaped Coils for a Wireless Power Transfer System for an Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) using communications, sensors, and navigation equipment will play a key role in future warfare. Currently, UAVs are monitored to prevent misfire and accidents, and the conventional method adopted uses wires for data transmission and power supply. The repeated connection and disconnection of cables increases maintenance time and harms the connector. For convenience and stability, a wireless power transfer system to power UAVs is needed. Unlike other wireless power transfer (WPT) applications, the size of the receiving coils must be small, so that the WPT systems can be embedded inside space-limited UAVs. The small size reduces the coupling coefficient and transfer efficiency between the transmitting and the receiving coils. In this study, we propose a toroidal-shaped coil for a WPT system for UAVs with high coupling coefficient with minimum space requirements. For validation, conventional coils and the proposed toroidal-shaped coil were used and their coupling coefficient and power transfer efficiency were compared using simulated and measured results. The simulated and measured results were strongly correlated, confirming that the proposed WPT system significantly improved efficiency with negligible change in the space requirement