Tracking air-sea exchange and upper-ocean variability in the Indonesian-Australian basin during the onset of the 2018/19 Australian summer monsoon

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(8), (2020): E1397-E1412, https://doi.org/10.1175/BAMS-D-19-0278.1.Sea surface temperatures (SSTs) north of Australia in the Indonesian–Australian Basin are significantly influenced by Madden–Julian oscillation (MJO), an eastward-moving atmospheric disturbance that traverses the globe in the tropics. The region also has large-amplitude diurnal SST variations, which may influence the air–sea heat and moisture fluxes, that provide feedback to the MJO evolution. During the 2018/19 austral summer, a field campaign aiming to better understand the influences of air–sea coupling on the MJO was conducted north of Australia in the Indonesian–Australian Basin. Surface meteorology from buoy observations and upper-ocean data from autonomous fast-profiling float observations were collected. Two MJO convective phases propagated eastward across the region in mid-December 2018 and late January 2019 and the second MJO was in conjunction with a tropical cyclone development. Observations showed that SST in the region was rather sensitive to the MJO forcing. Air–sea heat fluxes warmed the SST throughout the 2018/19 austral summer, punctuated by the MJO activities, with a 2°–3°C drop in SST during the two MJO events. Substantial diurnal SST variations during the suppressed phases of the MJOs were observed, and the near-surface thermal stratifications provided positive feedback for the peak diurnal SST amplitude, which may be a mechanism to influence the MJO evolution. Compared to traditionally vessel-based observation programs, we have relied on fast-profiling floats as the main vehicle in measuring the upper-ocean variability from diurnal to the MJO time scales, which may pave the way for using cost-effective technology in similar process studies.MF, SW, and JH are supported by the Centre for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales, and University of Tasmania. Y. Duan is supported by National Natural Science Foundation of China (41706032) and Basic Scientific Fund for National Public Research Institutes of China (2019Q03)

Wide-field-of-view near-eye display with dual-channel waveguide

We propose a wide-field-of-view near-eye display featuring a dual-channel waveguide with cholesteric liquid crystal gratings. Our dual-channel waveguide is capable of splitting the field of view through the orthogonal polarization division multiplexing. To explain its mechanism, a diagram of k-domain, which factors into both the waveguide size and the number of pupils, is depicted. Our results demonstrate that the diagonal field of view reaches up to 80°, eye relief is 10 mm, exit pupil is 4 × 3 mm2, and uniformity is 79%

A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images

Background: Postoperative complications following total hip arthroplasty (THA) often require revision surgery. X-rays are usually used to detect such complications, but manually identifying the location of the problem and making an accurate assessment can be subjective and time-consuming. Therefore, in this study, we propose a multi-branch network to automatically detect postoperative complications on X-ray images.Methods: We developed a multi-branch network using ResNet as the backbone and two additional branches with a global feature stream and a channel feature stream for extracting features of interest. Additionally, inspired by our domain knowledge, we designed a multi-coefficient class-specific residual attention block to learn the correlations between different complications to improve the performance of the system.Results: Our proposed method achieved state-of-the-art (SOTA) performance in detecting multiple complications, with mean average precision (mAP) and F1 scores of 0.346 and 0.429, respectively. The network also showed excellent performance at identifying aseptic loosening, with recall and precision rates of 0.929 and 0.897, respectively. Ablation experiments were conducted on detecting multiple complications and single complications, as well as internal and external datasets, demonstrating the effectiveness of our proposed modules.Conclusion: Our deep learning method provides an accurate end-to-end solution for detecting postoperative complications following THA

Functional Identification of Neuroprotective Molecules

The central nervous system has the capacity to activate profound neuroprotection following sub-lethal stress in a process termed preconditioning. To gain insight into this potent survival response we developed a functional cloning strategy that identified 31 putative neuroprotective genes of which 28 were confirmed to provide protection against oxygen-glucose deprivation (OGD) or excitotoxic exposure to N-methyl-D-aspartate (NMDA) in primary rat cortical neurons. These results reveal that the brain possesses a wide and diverse repertoire of neuroprotective genes. Further characterization of these and other protective signals could provide new treatment opportunities for neurological injury from ischemia or neurodegenerative disease

System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach

Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene

Multi-Point RCNN for Predicting Deformation in Deep Excavation Pit Surrounding Soil Mass

Accurate prediction and forecasting of soil mass deformation in deep excavation pits are pivotal for risk monitoring and safety assessment. Nonetheless, the complex underlying dynamics inherent in field sensing measurements pose challenges to the forecasting endeavor. In light of these challenges, the present study leverages recent strides in deep learning and introduces a spatiotemporal learning framework tailored to forecast soil mass deformation marked by resilient temporal interconnections and spatial associations. This study focuses on developing a Multi-Point Recurrent Convolutional Neural Network (RCNN) model for predicting sensor-based temporal patterns. This model integrates data feature fusion to extract spatiotemporal latent features from the dataset, thereby constructing a surrogate model for forecasting soil mass deformation. The proposed methodology is deployed to forecast strain responses in a deep excavation pit using a dataset spanning over five months. A comparative analysis is conducted, contrasting the performance of the proposed approach with that of a conventional temporal-only network. The analysis reveals that the prediction errors generated by the Multi-Point RCNN are predominantly concentrated within the range of 10% for all sensors, with a high-confidence interval (CI) of 96%, compared to the RCNN model (82%) and the LSTM model (79%). The compelling outcomes underscore the efficacy of the Multi-Point RCNN approach as a promising, dependable, and computationally efficient method for accurately predicting soil mass deformation in deep excavation pits, grounded in data-driven principles

Local and Global Geometric Structure Preserving and Application to Hyperspectral Image Classification

Locality Preserving Projection (LPP) has shown great efficiency in feature extraction. LPP captures the locality by the K-nearest neighborhoods. However, recent progress has demonstrated the importance of global geometric structure in discriminant analysis. Thus, both the locality and global geometric structure are critical for dimension reduction. In this paper, a novel linear supervised dimensionality reduction algorithm, called Locality and Global Geometric Structure Preserving (LGGSP) projection, is proposed for dimension reduction. LGGSP encodes not only the local structure information into the optimal objective functions, but also the global structure information. To be specific, two adjacent matrices, that is, similarity matrix and variance matrix, are constructed to detect the local intrinsic structure. Besides, a margin matrix is defined to capture the global structure of different classes. Finally, the three matrices are integrated into the framework of graph embedding for optimal solution. The proposed scheme is illustrated using both simulated data points and the well-known Indian Pines hyperspectral data set, and the experimental results are promising