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Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. [https://www.selleckchem.com/products/pifithrin-u.html buy Pifithrin-μ] The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.We experimentally demonstrate the simultaneous enhancement of Raman and photoluminescence (PL) of core-shell hybrid nanoparticles consisting of Ag (core) and polydiacetylene (PDA, shell) through the assistance of localized surface plasmon (LSP) effect for the effective biosensor. Core-shell nanoparticles (NPs) are fabricated in deionized water through a sequential process of reprecipitation and self-assembly. The Raman signal of PDA on core-shell NPs is enhanced more than 100 times. Also, highly enhanced photoluminescence is observed on Ag/PDA hybrid NPs after coupling of the complementary t-DNA with p-DNA which are immobilized on PDA shell. This indicates that the core Ag affects the Raman and PL of PDA through the LSP resonance, which can be caused by the energy and/or charge transfer caused by the LSP coupling and the strong electromagnetic field near Ag NP surface. Only electrons present on the surface interact with the PDA shell, not involving the electrically neutral part of the electrons inside the Ag NP. Furthermore, this work shows that as prepared Ag/PDA NPs functionalized by probe DNA can sense the target DNA with an attomolar concentration (100 attomole).Worldwide, growth in the older population creates a pressing need to develop supportive environments that enhance quality of life as people age. Too often, built environments present barriers and challenges to older adults that compromise independent living and adversely affect health and life outcomes. Designing homes, buildings, and neighborhoods with older adults, through exercises in participatory or co-design, could help ensure that environments are better able to facilitate healthy aging. However, while it is potentially advantageous to involve this age group in environmental design decisions, doing so can be difficult. Analysis of and guidance on effective ways to involve older adults in these activities could make the challenge easier. With this aim in mind, this article provides critical perspectives on eight "less traditional" engagement techniques-walking interviews, photovoice, photo-elicitation, Talking Mats®, participatory mapping, drawing, model-making, and the "Design Fair". Insights into the strengths and limitations of these techniques, gained from observation of their use in participatory design activities, as well as feedback collected from older co-design participants, are presented. The article concludes by offering a number of practical recommendations for those interested in designing age-friendly homes and neighborhoods with older people.The ambiguity resolution (AR) and validation of the global navigation satellite system (GNSS) have been challenging tasks for some decades. Considering the reliability problem of extra-wide-lane (EWL) ambiguity in the triple-carrier ambiguity resolution (TCAR), a method for validating the reliability of the EWL ambiguity using a single epoch was proposed for the BeiDou Navigation Satellite System (BDS). For the initial EWL ambiguity, obtained using a rounding estimator with a geometry-free (GF) model, the double-difference ionospheric delay was first estimated to construct a relative positioning model with an initial fixed ambiguity. Second, based on the theory of gross error detection and the AR characteristics of EWL, the second-best ambiguity candidate was constructed. Finally, among the two sets of ambiguities, the one with the smaller posterior variance was taken as the reliable ambiguity. The study showed that, for a single epoch, when only one or two satellites had incorrect ambiguities, the AR success rate after ambiguity validation and correction could reach 100% for medium baselines. For long baselines, due to the increase of atmospheric error, the validation was affected to some extent. However, the AR success rates for two long baselines increased from 96.82% and 98.44% to 98.80% and 99.67%, respectively.Medulloblastoma, the most common pediatric malignant brain tumor, continues to have a high rate of morbidity and mortality in childhood. Recent advances in cancer genomics, single-cell sequencing, and sophisticated tumor models have revolutionized the characterization and stratification of medulloblastoma. In this review, we discuss heterogeneity associated with four major subgroups of medulloblastoma (WNT, SHH, Group 3, and Group 4) on the molecular and cellular levels, including histological features, genetic and epigenetic alterations, proteomic landscape, cell-of-origin, tumor microenvironment, and therapeutic approaches. The intratumoral molecular heterogeneity and intertumoral cellular diversity clearly underlie the divergent biology and clinical behavior of these lesions and highlight the future role of precision treatment in this devastating brain tumor in children.Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter.