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Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

This study aims to find out the possible optimal therapy and assess the prognosis properly for patient with spontaneous rupture of hepatocellular carcinoma (HCC).Propensity score matching (PSM) analysis was used to study the data from 325 patients with ruptured HCC (RHCC) and 2,291 patients with non-RHCC.The incidence and hospital mortality of RHCC were 5.1% and 0.8% respectively, with a median overall survival (OS) time of 17 months. There was no difference between ruptured and non-RHCC patients undergoing conservation treatment in terms of OS. Trans-arterial embolization (TAE) was carried out in 69 (21.2%) cases with RHCC, with a median OS of 7 months, which was no difference from that of non-RHCC (pre- and post-PSM). One hundred and sixty-nine (52.0%) RHCC cases underwent one-stage hepatectomy, with a median OS and disease-free survival (DFS) of 30 and 6 months respectively, which were shorter than that of non-RHCC (post-PSM). TAE plus two-stage hepatectomy was performed in 30 RHCC cases, with a median OS and DFS of 28 and 10 months respectively; these outcomes were better than that from RHCC patients undergoing TAE alone or one-stage hepatectomy (post-PSM), which were no difference from that of non-RHCC patients undergoing hepatectomy. The risk of death for RHCC patient undergoing one-stage hepatectomy is 1.545 times higher than that of one undergoing TAE + two-stage hepatectomy.TAE plus two-stage hepatectomy might be the optimal treatment for RHCC patient. Under the premise of the same pathological properties, there is no difference in prognosis between ruptured and non-RHCC patients if the therapy is appropriate.

Introduction Legume crops are an important source of protein and oil for human health and in fixing atmospheric N2 for soil enrichment. With an objective to accelerate much-needed genetic analyses and breeding applications, draft genome assemblies were generated in several legume crops; many of them are not high quality because they are mainly based on short reads. However, the superior quality of genome assembly is crucial for a detailed understanding of genomic architecture, genome evolution, and crop improvement. Objectives Present study was undertaken with an objective of developing improved chromosome-length genome assemblies in six different legumes followed by their systematic investigation to unravel different aspects of genome organization and legume evolution. Methods We employed in situ Hi-C data to improve the existing draft genomes and performed different evolutionary and comparative analyses using improved genome assemblies. Results We have developed chromosome-length genome assemblies in chickpea, pigeonpea, soybean, subterranean clover, and two wild progenitor species of cultivated groundnut (A. duranensis and A. ipaensis). A comprehensive comparative analysis of these genome assemblies offered improved insights into various evolutionary events that shaped the present-day legume species. We highlighted the expansion of gene families contributing to unique traits such as nodulation in legumes, gravitropism in groundnut, and oil biosynthesis in oilseed legume crops such as groundnut and soybean. As examples, we have demonstrated the utility of improved genome assemblies for enhancing the resolution of “QTL-hotspot” identification for drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea through genome-wide association study. Genomic resources developed in this study are publicly available through an online repository, ‘Legumepedia’. Conclusion This study reports chromosome-length genome assemblies of six legume species and demonstrates the utility of these assemblies in crop improvement. The genomic resources developed here will have significant role in accelerating genetic improvement applications of legume crops.

The problem of finite-time prescribed performance control (PPC) for spacecraft attitude maneuvering is researched. To the best of the authors' knowledge, limited results have been reported. How to achieve the prescribed performance attitude tracking within a preset time interval is still an open problem, especially in the presence of inertia perturbations, external disturbances, actuator saturations, and faults. On account of this, a finite-time performance function is first constructed as the predefined boundary of tracking errors. Second, Chebyshev neural network is utilized to approximate the lumped uncertainties. Then, the Nussbaum gain technique compensating for actuator saturations and faults is incorporated into the backstepping design to develop a new fault-tolerant attitude controller. Both transient and steady-state performances (e.g., the maximum overshoot, steady-state error, and settling time) are guaranteed. Compared with the common PPC or finite-time control achievements on spacecraft attitude tracking, the setting time herein can be set in advance without relying on initial states. Finally, experiments are performed to verify the effectiveness of the solution and comparisons with related works are displayed. National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61773108]; Natural Sciences and Engineering Research Council of CanadaNatural Sciences and Engineering Research Council of Canada (NSERC)CGIAR This work was supported in part by the National Natural Science Foundation of China under Grant 61773108, and in part by the Natural Sciences and Engineering Research Council of Canada.

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods. The datasets, codes, and trained models are publicly available at https://github.com/JunMa11/AbdomenCT-1K., 20 pages

High-Altitude Long-Endurance (HALE) solar-powered Unmanned Aircraft Vehicles (UAVs) can utilize solar energy as power source and maintain extremely long cruise endurance, which has attracted extensive attentions from researchers. Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day. In this work, a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning (RL) framework is introduced. According to flight and environment information, a neural network controller outputs commands of thrust, attack angle, and bank angle to realize an autonomous flight based on energy maximization. The validity of the proposed method was evaluated in a 5-km radius area in simulation, and results have shown that after one day-night cycle, the battery energy of the RL-controller was improved by 31% and 17% compared with those of a steady-state strategy with a constant speed and a constant altitude and a kind of state-machine strategy, respectively. In addition, results of an uninterrupted flight test have shown that the endurance of the RL controller was longer than those of the control cases.

Microplastic pollution is a growing global concern and a serious threat to aquatic life. Microplastics (MPs) have been found in the natural environment in the last few years. However, levels of microplastic pollution in aquaculture areas of Hainan Island have been less studied than in wild environments. Xincun bay (XC) and Gangmen bay (GM) are lagoons that are rich in fishery resources. Here, we investigated microplastic pollution in 8 surface seawater samples, 17 species of fish, and mussel samples from XC bay and GM bay, Hainan, China. The MPs were isolated from surface seawater, organisms and identified by microscopic, Raman spectroscopic and micro-Fourier Transform Infrared spectrometer methods. The mean abundance of MPs in seawater samples was 523 items/m3 and that of fish was 7.1 items/individual and the percentages of microplastics detected in the gastrointestinal tracts (GITs) and gill were 99% and 1%, respectively. MPs were also observed in the soft tissues of mussel samples with a mean abundance of 0.36 ± 0.81 items/individual. Most MPs were identified as polystyrene and their sizes ranged between 0.5 and 5 mm in both seawater and organism samples. Our results indicated that the uptake of microplastics by organisms was closely related to microplastic pollution in the aquaculture environment. Foam pollution is common in organisms and surface seawater in the aquaculture area of our study region, which provides a way for human exposure.

Abstract: Estrogen receptors (ERs) are steroid hormone receptors, which belong to a large nuclear receptor family. Endocrine diseases correlate strongly with dysregulated ER signaling. Traditional therapies continue to rely on small molecule inhibitors, including aromatase inhibitors (AIs) and selective estrogen receptor modulators (SERMs), all of which permit acquired resistance to endocrine therapy. Proteolytic targeting chimeras (PROTACs) offer unprecedented potential for solving acquired endocrine resistance. ARV-471, an ER-targeting PROTAC developed by Arvinas, was approved by the US FDA to entered clinical trials in 2019 to treat patients suffering from locally advanced or metastatic ER-positive/HER2-negative breast cancer, and has since been approved by the US FDA. In this review, we will focus on progress in developing ER-targeting PROTACs from publications and patents aimed at the treatment of endocrine diseases.

The multi-hop protocols are proved effective in the railway disaster wireless monitoring system. However, farther transmission distance with the larger data will decline the valid lifetime and reliability of the system. Most existing studies focused primarily on the communication protocols optimization, and some works tried to utilize the limited computation ability at the network-level or node-level, which are insufficient for the stiff disaster information monitoring demands. This paper presents an adaptive hybrid computation and communication strategy to fully taking advantage of the sensor processing ability, and improve the energy efficiency at the link-level. Furthermore, an adaptive optimization model is designed to meet the different monitoring demands of the system, and the valid lifetime is improved accordingly. Numerical examples with various operational scenarios are developed to demonstrate the superiority and practicality of the proposed protocol in the lifetime improvement, energy consumption minimization and equalization compared with other outstanding protocols.

Background To investigate antibiotic resistance of pathogens responsible for neonatal invasive bacterial infections (IBIs) in China. Methods Cross-sectional study of neonates with IBI evaluated in nine hospitals in China (January 2012–August 2019). Antibiotic resistance patterns of pathogens responsible for neonatal IBIs were analyzed. Results Of 3770 full-term neonates who were subjected to lumbar puncture and a blood culture, IBIs were diagnosed in 460 neonates (12.2%). Escherichia coli and Group B Streptococcus (GBS) were the leading pathogens, followed by Enterococcus spp, and Staphylococcus aureus. E. coli expressed high resistance to ampicillin (72.0%) and third-generation cephalosporins (cefotaxime, 34.8%; ceftriaxone, 38.1%). The prevalence of extended spectrum beta-lactamase (ESBL)-producing E. coli was 34.1%. The proportions of E. spp resistant to penicillin and ampicillin were 60% and 54.1%. All S. aureus showed resistance to ampicillin and penicillin. The resistance rate of S. aureus to methicillin was 50%. Although all GBS were susceptible to penicillin and ampicillin, the proportions of GBS resistant to erythromycin and clindamycin were 75.9% and 77.3%. Antibiotic susceptibility appeared to improve in 2019. Susceptibility of E. coli to ampicillin, cefotaxime, and ceftriaxone improved to 42.9%, 76.9%, and 71.4% in 2019, compared with 12.5%, 37.5%, and 50% in 2012. The prevalence of ESBL-producing E. coli declined to 20% in 2019, lower than 100% in 2012. Susceptibility of GBS to erythromycin and clindamycin improved from 0% in 2012 to 28.6% and 25% in 2019. Conclusions The prevalence of antibiotic resistance is high in neonates in China, although there is a favorable declining trend in recent years.

Background: Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces. Methods: In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality. Results: The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively. Conclusions: We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions.

Objective: To verify the association between CD44 and CD133 expression levels and the prognosis of patients with lower grade gliomas (LGGs) and constructing radiomic models to predict those two genes’ expression levels before surgery. Materials & methods: Genomic data of patients with LGG and the corresponding T2-weighted fluid-attenuated inversion recovery images were downloaded from The Cancer Genome Atlas and The Cancer Imaging Archive, which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. Results & conclusion: CD44 and CD133 expression levels in LGG can significantly affect the prognosis of patients with LGG. Based on the T2-weighted fluid-attenuated inversion recovery images, the radiomic features can effectively predict the expression levels of CD44 and CD133 before surgery.

Nowadays, energy costs are the most important factor in cloud computing. Therefore, the implementation of energy-aware task scheduling methods is of utmost importance. A task scheduling framework considering deadlines, data locality and resource utilization is proposed to save on energy costs in heterogeneous clusters. The framework consists of task list construction, task scheduling and slot list updating. In terms of deadline constraints, number of job slots allocated and possible processing times of jobs, a new job sequence is proposed to construct an reasonable task list. Tasks are scheduled to promising slots from their rack-local servers, cluster-local servers and remote servers in the produced task scheduling, which greatly improves data locality. After the assignment among tasks and slots, an update of available slots in clusters is proposed not only to find available slots but also to improve server resource utilization using fuzzy logic with the available number of slots according to current CPU, memory and bandwidth utilization. Experimental results show that the proposed heuristic results in lower energy consumption than the adapted existing algorithms with a variable total number of slots.

Electricity prices differ during different time periods and change from place to place. Cloud workflow applications often require geo-distributed data which is transmitted among heterogeneous servers in intra- and inter- data centers. Such varying electricity prices and data transmission time bring great challenges when optimizing the energy cost for scheduling tasks in workflow applications to heterogeneous servers in cloud data centers. In this paper, we minimize the total electricity cost in a deadline constrained energy-aware workflow scheduling problem with data being geographically distributed across data centers. A scheduling framework is proposed. Strategies are developed to sequence workflow applications, divide deadlines and sort tasks. An adaptive local search method is presented to improve solutions during the search process which dynamically balances intensification using neighborhood structures of increasing size. Components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. Experimental results demonstrate the effectiveness of the proposal for the considered problem.

Human visual system has a limitation of sensitivity in detecting small distortion in an image/video and the minimum perceptual threshold is so called Just Noticeable Difference (JND). JND modelling is challenging since it highly depends on visual contents and perceptual factors are not fully understood. In this paper, we propose deep learning based JND and perceptual quality prediction models, which are able to predict the Satisfied User Ratio (SUR) and Video Wise JND (VWJND) of compressed videos with different resolutions and coding parameters. Firstly, the SUR prediction is modeled as a regression problem that fits deep learning tools. Then, Video Wise Spatial SUR method (VW-SSUR) is proposed to predict the SUR value for compressed video, which mainly considers the spatial distortion. Thirdly, we further propose Video Wise Spatial-Temporal SUR (VW-STSUR) method to improve the SUR prediction accuracy by considering the spatial and temporal information. Two fusion schemes that fuse the spatial and temporal information in quality score level and in feature level, respectively, are investigated. Finally, key factors including key frame and patch selections, cross resolution prediction and complexity are analyzed. Experimental results demonstrate the proposed VW-SSUR method outperforms in both SUR and VWJND prediction as compared with the state-of-the-art schemes. Moreover, the proposed VW-STSUR further improves the accuracy as compared with the VW-SSUR and the conventional JND models, where the mean SUR prediction error is 0.049, and mean VWJND prediction error is 1.69 in quantization parameter and 0.84 dB in peak signal-to-noise ratio.

Feature selection is important in both machine learning and pattern recognition. Successfully selecting informative features can significantly increase learning accuracy and improve result comprehensibility. Various methods have been proposed to identify informative features from high-dimensional data by removing redundant and irrelevant features to improve classification accuracy. In this article, we systematically survey existing sparse learning models for feature selection from the perspectives of individual sparse feature selection and group sparse feature selection, and analyze the differences and connections among various sparse learning models. Promising research directions and topics on sparse learning models are analyzed.

Tumor-induced osteomalacia TIO is a rare paraneoplastic syndrome caused by excessive production of fibroblast growth factor 23 FGF23 by a tumor. Previous studies have revealed generalized mineralization defects and low areal bone mineral density aBMD in TIO. However, data on the bone microarchitecture in TIO are limited. In this study, we evaluated the microarchitecture in the peripheral distal radius and tibia and axial lumbar spine skeleton using high-resolution peripheral quantitative computed tomography HR-pQCT and trabecular bone score TBS and investigated related factors in a large cohort of Chinese patients with TIO. A total of 186 patients with TIO who had undergone dual-energy X-ray absorptiometry DXA or HR-pQCT scans were enrolled. Compared with age-, sex- and BMI-matched healthy controls, TIO patients n=113 had lower vBMD, damaged microstructure and reduced bone strength in the peripheral skeleton, especially at the tibia. The average TBS obtained from 173 patients was 1.15 ± 0.16. The proportion of patients with abnormal TBS

A simple approach is proposed to design a new type endfire antenna, which simultaneously possesses bidirectional and unidirectional radiation pattern in wideband. By the dispersion engineering, different frequency bands spoof SPP modes are excited at the two ports of a proposed splitter structure, and the two ports of the splitter structure are directly connected with two radiator structures, which are utilized to decouple the different frequency bands spoof SPP to free space wave by dispersion mismatching, achieving bidirectional and unidirectional radiation pattern in different frequency bands. From 8.4 GHz to 21.6 GHz, this designed spoof SPP antenna possesses bidirectional enfire radiation pattern, and from 21.6 GHz to 45.0 GHz, this designed spoof SPP antenna possesses unidirectional enfire radiation pattern. This designed spoof SPP antenna has an average gain over 10.6 dB in the whole working bandwidth, and the relative bandwidth of this antenna is 137%. Both the simulation and measurement results verify that the stable bidirectional and unidirectional radiation pattern of this proposed endfire spoof SPP antenna in wide bandwidth are achieved.

Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor's learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20,000 aerial image pairs of size 256 x 256. Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset.

For cumulative change detection using time-series polarimetric synthetic aperture radar (SAR) (PolSAR) data, pairwise comparisons using bi-temporal data change detection are often performed over each successive pair of images in the entire time series. In this way, due to the impact of the quality of the difference image (DI) of bitemporal data and the classification accuracy of the DI into the change and nonchange classes, there is a great risk of many false alarms or omissions caused by gradually occurred changes. Therefore, this letter proposes a symmetric similarity matrix based on a likelihood ratio test (LRT), which groups the similarity between any two-time point data in time series. Then, Shannon entropy is applied to the similarity matrix to calculate the DI of the entire time-series data. Finally, an image segmentation method based on Markov random field (MRF) is used to classify the calculated DI into the change and nonchange classes. The experiments using the multitemporal fully polarimetric Radarsat-2 data acquired at three different time points prove the effectiveness of the proposed method compared with some existing methods.

Sub-pixel mapping (SPM) has been widely adopted to alleviate the mixed pixel problem in hyperspectral image, as an extension of spectral unmixing (SU), providing a way to observe the spatial location of the endmember within mixed pixel. However, most of the SPM methods are unmixing-then-mapping (UTM), i.e., SPM process relies on the abundance images generated from SU, in which process uncertainty inherently exists and would be propagated to SPM. Furthermore, the prior knowledge toward the sub-pixel scale distribution is mainly model-driven/handcrafted, which has limitation for geographical-realistic distribution representation. In this letter, we proposed spectral-spatial fusion SPM based on deep neural network (SSNET), to realize the integrative modeling of SU and SPM problem in a unified network fashion to avoid uncertainty accumulation in UTM process, and it can simultaneously generate SU result and SPM result. Besides, SSNET provides a supervised manner to learn prior knowledge with external exemplar pairs of low- and high-resolution images for a geographical-realistic distribution representation. The experiment with two hyperspectral images validated the superiority of the proposed SSNET.

Oxytocin (OXT) is a nine amino acid neuropeptide hormone that has become one of the most intensively studied molecules in the past few decades. The vast majority of OXT is synthesized in the periventricular nucleus and supraoptic nucleus of the hypothalamus, and a few are synthesized in some peripheral organs (such as the uterus, ovaries, adrenal glands, thymus, pancreas, etc.) OXT modulates a series of physiological processes, including lactation, parturition, as well as some social behaviors. In addition, more and more attention has recently been focused on the analgesic effects of oxytocin. It has been reported that OXT can relieve tension and pain without other adverse effects. However, the critical role and detailed mechanism of OXT in analgesia remain unclear. Here, this review aims to summarize the mechanism of OXT in analgesia and some ideas about the mechanism.

Today, remote sensing (RS) data are already regarded as ``big data.'' Developments in computer science have made it possible to explore the potential treasure within remote sensing big data, but only limited remote sensing research has made use of big data technology due to gaps in techniques between big data and remote sensing. In this research, we analyzed the full processing flow of remote sensing big data from the perspective of both computer science and remote sensing science and proposed a modular framework. Computation ready data (CRD), a dynamic data type for computation based on analysis ready data (ARD), is proposed to connect the two main modules of the framework, the data module and computation module. Compared with existing research, the proposed framework classifies and abstracts the key technical and research points of the processing of remote sensing big data as replaceable modules and bridges them through an open organization. Subsequently, we built a prototype platform with open-source technologies and carried out three experiments to validate the feasibility and advantages of the framework, namely normalized difference vegetation index (NDVI) production, water body change detection, and land use classification. Results indicate that this framework can greatly reduce experimental costs for remote sensing researchers. While the proposed framework has proven flexible and practical, further research is needed for the technical implementation of certain modules to achieve the original intention of the framework.

Although the cartoon industry has developed rapidly in recent years, few studies pay special attention to cartoon image quality assessment (IQA). Unfortunately, applying blind natural IQA algorithms directly to cartoons often leads to inconsistent results with subjective visual perception. Hence, this brief proposes a blind cartoon IQA method based on convolutional neural networks (CNNs). Note that training a robust CNN depends on manually labeled training sets. However, for a large number of cartoon images, it is very time-consuming and costly to manually generate enough mean opinion scores (MOSs). Therefore, this brief first proposes a full reference (FR) cartoon IQA metric based on cartoon-texture decomposition and then uses the estimated FR index to guide the no-reference IQA network. Moreover, in order to improve the robustness of the proposed network, a large-scale dataset is established in the training stage, and a stochastic degradation strategy is presented, which randomly implements different degradations with random parameters. Experimental results on both synthetic and real-world cartoon image datasets demonstrate the effectiveness and robustness of the proposed method.

BACKGROUNDSince December 2019, an outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, and is now becoming a global threat. We aimed to delineate and compare the immunological features of severe and moderate COVID-19.METHODSIn this retrospective study, the clinical and immunological characteristics of 21 patients (17 male and 4 female) with COVID-19 were analyzed. These patients were classified as severe (11 cases) and moderate (10 cases) according to the guidelines released by the National Health Commission of China.RESULTSThe median age of severe and moderate cases was 61.0 and 52.0 years, respectively. Common clinical manifestations included fever, cough, and fatigue. Compared with moderate cases, severe cases more frequently had dyspnea, lymphopenia, and hypoalbuminemia, with higher levels of alanine aminotransferase, lactate dehydrogenase, C-reactive protein, ferritin, and D-dimer as well as markedly higher levels of IL-2R, IL-6, IL-10, and TNF-α. Absolute numbers of T lymphocytes, CD4+ T cells, and CD8+ T cells decreased in nearly all the patients, and were markedly lower in severe cases (294.0, 177.5, and 89.0 × 106/L, respectively) than moderate cases (640.5, 381.5, and 254.0 × 106/L, respectively). The expression of IFN-γ by CD4+ T cells tended to be lower in severe cases (14.1%) than in moderate cases (22.8%).CONCLUSIONThe SARS-CoV-2 infection may affect primarily T lymphocytes, particularly CD4+ and CD8+ T cells, resulting in a decrease in numbers as well as IFN-γ production by CD4+ T cells. These potential immunological markers may be of importance because of their correlation with disease severity in COVID-19.TRIAL REGISTRATIONThis is a retrospective observational study without a trial registration number.FUNDINGThis work is funded by grants from Tongji Hospital for the Pilot Scheme Project, and partly supported by the Chinese National Thirteenth Five Years Project in Science and Technology for Infectious Disease (2017ZX10202201).

Dexmedetomidine (DEX) has been reported to attenuate cecal ligation perforation (CLP)-stimulated acute lung injury (ALI) by downregulating HMGB1 and RAGE. This study aimed to further investigate the specific mechanisms of RAGE and its potential-related mechanisms of DEX on ALI models in vitro and in vivo. The in vitro and in vivo ALI models were established by lipopolysaccharide treatment in MLE-12 cells and CLP in mice, respectively. The effect of DEX on pathological alteration was investigated by HE staining. Thereafter, the myeloperoxidase (MPO) activity and inflammatory cytokine levels were respectively detected to assess the lung injury of mice using commercial kits. The expression levels of HMGB1, RAGE, NF-κB, and pyroptosis-related molecules were detected by RT-qPCR and Western blot. HE staining showed that lung injury, increased inflammatory cell infiltration, and lung permeability was found in the ALI mice, and DEX treatment significantly attenuated lung tissue damage induced by CLP. The MPO activity and inflammatory cytokines (TNF-α, IL-1β, and NLRP3) levels were also significantly reduced after DEX treatment compared with those in the ALI mice. Moreover, DEX activated the HMGB1/RAGE/NF-κB pathway and upregulated the pyroptosis-related proteins. However, the protective DEX effect was impaired by RAGE overexpression in ALI mice and MLE-12 cells. Additionally, DEX treatment significantly suppressed HMGB1 translocation from the nucleus region to the cytoplasm, and this effect was reversed by RAGE overexpression. These findings suggested that DEX may be a useful ALI treatment, and the protective effects on ALI mice may be through the inhibition of HMGB1/RAGE/NF-κB pathway and cell pyroptosis., Graphical abstractDexmedetomidine (DEX) has protective effects on acute lung injury (ALI) in vitro and in vivo. The possible mechanisms may be closely associated with inflammatory response, caspase-1-mediated cell pyroptosis, and high-mobility group protein 1 (HMGB1)/receptor for advanced glycation end products (RAGE)/nuclear factor-κB pathway. Moreover, DEX could promote the HMGB1 translocation from the cytoplasm to the nucleus in lipopolysaccharide-activated MLE-12 cells, whereas the action of RAGE overexpression was opposite.

PURPOSE Growing evidence proved the efficacy of multi-parametric MRI (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT)-guided targeted biopsy (TB) in prostate cancer (PCa) diagnosis, but there is no direct comparison between mpMRI-TB and PSMA PET/CT-TB. Gastrin-releasing peptide receptor (GRPR) is highly expressed in PCa, which can compensate for the unstable expression of PSMA in PCa. Therefore, we designed a study to compare the efficiency of mpMRI-TB, dual-tracer (GRPR and PSMA) PET/CT-TB, systematic biopsy, and combined biopsy for the diagnosis of prostate cancer. METHODS One hundred twelve suspicious PCa patients were enrolled from September 2020 to June 2021. Patients with anyone of positive dual-tracer PET/CT or mpMRI underwent TB, and all enrolled patients underwent systematic biopsy (SB) after TB. The primary outcome was the detection rates of PCa in different biopsy strategies. Secondary outcomes were the performance of three imaging methods, omission diagnostic rates, and upgrading and downgrading of biopsy samples relative to those of prostatectomy specimens in different biopsy strategies. McNemar's tests and Bonferroni correction in multiple comparisons were used to compare the primary and secondary outcomes. RESULTS In 112 men, clinically significant PCa (grade group[GG] ≥ 2) accounted for 34.82% (39/112), and nonclinically significant PCa (GG = 1) accounted for 4.46% (5/112). 68 Ga-PSMA PET/CT-TB achieved higher PCa detection rate (69.77%) and positive ratio of biopsy cores (0.44) compared with SB (39.29% and 0.12) and mpMRI-TB (36.14% and 0.23), respectively (P

Background Bacterial infection is one of the most frequent complications in acute-on-chronic liver failure (ACLF), which leads to high mortality. However, a specific prognostic model for ACLF patients with bacterial infection has not been well established. Aim To establish and validate a nomogram for predicting 30-day mortality of hepatitis B virus-related ACLF (HBV-ACLF) patients with bacterial infection. Methods A total of 513 ACLF patients for HBV reactivation were enrolled in the prospective cohort, and 224 patients with bacterial infection were for derivation. Independent predictors were identified using multivariate logistic model and then assembled into a nomogram to predict 30-day mortality. The performance of the nomogram was assessed based on its calibration, discrimination and clinical utility in a retrospective cohort of 192 HBV-ACLF patients with bacterial infection. Results Age, total bilirubin, lactate dehydrogenase, international normalized ratio and soluble interleukin-2 receptor were shown to be independent risk factors for 30-day mortality of HBV-ACLF patients with bacterial infection and the nomogram was constructed. The nomogram showed a good calibration and discrimination in the derivation cohort, with an area under the receiver operating characteristic curve (AUC) of 0.883. Application of the nomogram in the validation cohort also showed a good calibration and discrimination, with the AUC of 0.852. Decision curve analysis confirmed the clinical utility of the nomogram. Conclusion The nomogram was established and validated for predicting 30-day mortality of HBV-ACLF patients with bacterial infection, which may facilitate optimal therapeutic strategies to improve the prognosis of these patients.

Objective Alström syndrome is an autosomal recessive genetic disease caused by a mutation in the ALMS1 gene. Alström syndrome is clinically characterized by multisystem involvement, including sensorineural deafness, cone-rod dystrophy, nystagmus, obesity, insulin resistance, type 2 diabetes and hypogonadism. The diagnosis is thus challenging for patients without this characteristic set of clinical symptoms. We explored the effectiveness of whole-exome sequencing in the diagnosis of Alström syndrome. Methods A girl with symptoms of Alström syndrome was tested and diagnosed with the disease by whole-exome sequencing. Results Whole-exome sequencing revealed two novel variants, c.6160_6161insAT: p.Lys2054Asnfs*21 (exon 8) and c.10823_10824 delAG:p.Glu 3608Alafs*9 (exon16) in the ALMS1 gene, leading to premature termination codons and the domain of ALMS1 protein. Blood sample testing of her asymptomatic parents revealed them to be heterozygous carriers of the same mutations. Assembly showed that the mutations on both alleles were located in conserved sequences. A review of the ALMS1 gene nonsense mutation status was performed. Conclusion We herein report two novel variants of the ALMS1 gene discovered in a Chinese Alström syndrome patient that expand the mutational spectrum of ALMS1 and provided new insight into the molecular mechanism underlying Alström syndrome. Our findings add to the current knowledge concerning the diagnosis and treatment of Alström syndrome.

Because of the extraordinary properties including high atomic numbers and large μτ products, metal halide perovskites have been widely employed and used for radiation detecting. Cs4PbI6 material has a high X-ray attenuation coefficient and excellent electrical properties that have a good potential in X-ray detection applications. Here, we have designed a flexible polymer-encapsulated Au/Cs4PbI6/Au X-ray detector with outstanding sensitivity of 256.20 μC Gy-1 cm-2 irradiated by 30 keV X-ray at 10 V bias, long-time stability, and durable flexibility without obvious degradation after bending for 600 cycles. These features demonstrate that this polymer-encapsulated durable flexible and sensitive X-ray detector could open a new possibility for next-generation radiation applications in dosimeter, imaging technologies.

Dynamic network analysis is a promising research area with a wide range of applications. Criminal network investigation is one of them. People may be interested in using dynamic network analysis to detect criminal communities in a dynamic social network, track the evolution of those communities, identify critical criminal members, or predict links between criminal members and others. One difficulty in applying dynamic network analysis to real-world data is that real-world dynamic networks may vary sparse, which can cause overfitting problem and compromise the performance of the proposed model. Another problem is that each node in a complex real-world network may have multiple features, making it complicated to compute the distance between nodes. We propose a higher-order multiple-feature-based community evolution model (HFCE) to address those two issues. The model uses a higher-order representation of the neighbouring information among nodes to alleviate the sparsity problem. It also introduces first-order similarity regularization to clarify the distance between nodes with multiple features. Experiment results show that the HFCE model outperforms five other popular dynamic network models (ESPRA, AFECT, GenLouvain, ECD and DYNMOGA) in terms of community on the real-world sparse dataset detection and link prediction precision. The HFCE model can also effectively track the evolution of the communities and identify the important nodes in the network over time, which makes it a desirable model in criminal network investigation.

Vasorin (VASN) is an important transmembrane protein associated with development and disease. However, it is not clear whether the death of mice with VASN deficiency (VASN −/−) is related to cardiac dysfunction. The aim of this research was to ascertain whether VASN induces pathological cardiac hypertrophy by targeting myosin light chain 7 (MYL7). VASN −/− mice were produced by CRISPR/Cas9 technology and inbreeding. PCR amplification, electrophoresis, real‐time PCR and Western blotting were used to confirm VASN deficiency. Cardiac hypertrophy was examined by blood tests, histological analysis and real‐time PCR, and key downstream factors were identified by RNA sequencing and real‐time PCR. Western blotting, immunohistochemistry and electron microscopy analysis were used to confirm the downregulation of MYL7 production and cardiac structural changes. Our results showed that sudden death of VASN −/− mice occurred 21–28 days after birth. The obvious increases in cardiovascular risk, heart weight and myocardial volume and the upregulation of hypertrophy marker gene expression indicated that cardiac hypertrophy may be the cause of death in young VASN −/− mice. Transcriptome analysis revealed that VASN deficiency led to MYL7 downregulation, which induced myocardial structure abnormalities and disorders. Our results revealed a pathological phenomenon in which VASN deficiency may lead to cardiac hypertrophy by downregulating MYL7 production. However, more research is necessary to elucidate the underlying mechanism.

Background As one of the most common body malignant cancers, skin cancers contain a group of highly heterogeneous tumors with different malignant potential, prognosis and treatment methods. Despite the progress in the treatment of skin cancers worldwide, the overall prognosis is still poor. Recent studies indicated single nucleotide polymorphisms (SNPs) of interleukin-6 (IL-6), including 174G/C and 597G/A, might be associated with susceptibility to skin cancer. This meta-analysis aims to clarify the relationship between IL-6 gene polymorphisms and skin cancers. Methods Eligible studies were identified from searching PubMed, Embase, Web of Science and Cochrane. Pooled odds ratio (OR) and corresponding 95% confidence interval (CI) were obtained for the relationships between IL-6 174G/C and 597G/A polymorphisms and skin cancer using random-effects models. For the included studies, the Newcastle-Ottawa scale (NOS) score was calculated to assess study quality. Heterogeneity tests, sensitivity analysis, and publication bias assessments were also performed. Trim-and-fill method was used when publication bias existed aiming to adjusting OR. All data were analyzed in R (version 4.0.2). Results This meta-analysis included 1,705 cases and 1,987 controls for 174G/C polymorphism (10 publications), and 968 cases and 998 controls for 597G/A polymorphism (3 publications). No elevated risk of skin cancer was found in all comparisons for 174G/C polymorphism: CC vs. GC + GG, OR =1.03 (95% CI: 0.81–1.31); GC + CC vs. GG, OR =1.16 (95% CI: 0.96–1.39); CC vs. GG, OR =1.14 (95% CI: 0.86–1.53); GC vs. GG, OR =1.16 (95% CI: 0.99–1.37); C vs. G, OR =1.07 (95% CI: 0.92–1.24). Then we performed subgroup analysis based on publication year, the cancer type, sample size, NOS score. Significant differences were observed in the subgroup of publication year before 2010 (GC + CC vs. GG, OR =1.255, P=0.012; GC vs. GG, OR =1.277, P=0.01), while there is no statistical significance in the subgroup of publication year after 2010 (P>0.05 for all comparisons). After publication bias adjustment, the results further suggested that 174G/C polymorphism is not associated with the risk of skin cancer. No elevated risk of skin cancer was found in the comparisons for 597G/A polymorphism. Discussion Current evidence showed that IL-6 gene polymorphisms might not be associated with the susceptibility to skin cancer.

As one of the main pollution sources in China, haze can blur the railway monitoring and threaten railway safety. In this paper, we propose an end-to-end multi-scale residual network (MSRN) which can achieve remarkable dehazing effect on railway monitoring images. The method optimizes the image dehazing algorithm in three aspects: network structure, loss function and hazy dataset. Firstly, inspired by the residual network, the paper presents a method of fusing multi-scale feature information based on the residual network, which can extract more effective information at different scales. Secondly, a combined loss function is designed to achieve better convergent results by balancing training time, training calculations, and precision. Thirdly, the paper synthesizes an outdoor dataset specifically for railway scenarios, which relies on real depth maps and various outdoor images. Extensive experimental results on both full reference image quality assessment and no reference image quality assessment of image restoration demonstrate that the proposed algorithm shows higher performance than the state-of-the-art algorithms. Moreover, the haze of railway monitoring images is removed under hazy weather, and the detection algorithm achieved higher detection accuracy on the images after dehazing. The proposed network structure, loss function, and hazy dataset are discussed and analyzed in detail to verify the effectiveness of the proposed method.

Mixed pixel problem is omnipresent in remote sensing images for urban land use interpretation due to the hardware limitations. Subpixel mapping (SPM) is a usual way to solve this problem by improving the observation scale and realizing a finer spatial resolution land cover mapping. Recently, deep learning-based subpixel mapping network (DLSMNet) was proposed, benefited from its strong representation and learning ability, to restore a visually pleasing finer mapping. However, the spatial context features of artifacts are usually aggregated and progressively lost during the forward pass of the network without sufficient representation, which make it difficult to be learned and restored. In this article, a semantic information modulated (SIM) deep subpixel mapping network (SIMNet) is proposed, which uses low-resolution semantic images as prior, to reinforce the representation of spatial context features. In SIMNet, SIM module is proposed to parametrically incorporate the semantic prior into the state-of-the-art (SOTA) feed forward network architecture in an end-to-end training fashion. Furthermore, stacked SIM module with residual blocks (SIM_ResBlock) is adopted to pass the representation of spatial context feature to the deep layers, to get it fully learned during backpropagation. Experiments have been implemented on three public urban scenario data sets, and the SIMNet generates a clearer outline of artificial facilities with sufficient spatial context, and is distinctive for even individual building, which is challenging for other SOTA DLSMNet. The results demonstrate that the proposed SIMNet is a promising way for high-resolution urban land use mapping from easily available lower resolution remote sensing images.Mixed pixel problem is omnipresent in remote sensing images for urban land use interpretation due to the hardware limitations. Subpixel mapping (SPM) is a usual way to solve this problem by improving the observation scale and realizing a finer spatial resolution land cover mapping. Recently, deep learning-based subpixel mapping network (DLSMNet) was proposed, benefited from its strong representation and learning ability, to restore a visually pleasing finer mapping. However, the spatial context features of artifacts are usually aggregated and progressively lost during the forward pass of the network without sufficient representation, which make it difficult to be learned and restored. In this article, a semantic information modulated (SIM) deep subpixel mapping network (SIMNet) is proposed, which uses low-resolution semantic images as prior, to reinforce the representation of spatial context features. In SIMNet, SIM module is proposed to parametrically incorporate the semantic prior into the state-of-the-art (SOTA) feed forward network architecture in an end-to-end training fashion. Furthermore, stacked SIM module with residual blocks (SIM_ResBlock) is adopted to pass the representation of spatial context feature to the deep layers, to get it fully learned during backpropagation. Experiments have been implemented on three public urban scenario data sets, and the SIMNet generates a clearer outline of artificial facilities with sufficient spatial context, and is distinctive for even individual building, which is challenging for other SOTA DLSMNet. The results demonstrate that the proposed SIMNet is a promising way for high-resolution urban land use mapping from easily available lower resolution remote sensing images.

Low doses of nitrite, close to physiological levels, increase blood flow in normal and ischemic tissues through a nitric oxide (NO) dependent mechanism. Given that nitrite therapy and dietary supplementation with vegetables high in nitrate (e.g. beets) are gaining popularity we decided to determine if low doses of nitrite impact the development of choroidal neovascularization (CNV), a key feature of wet age related macular degeneration (AMD). Sodium nitrite (at 50 mg/L, 150 mg/L, and 300 mg/L), nitrate (1 g/L) or water alone were provided in the drinking water of C57BL/6J mice aged 2 or 12 months. Mice were allowed to drink ad libitum for 1 week at which time laser-induced choroidal neovascularization (L-CNV) was induced. The mice continued to drink the supplemented water ad libitum for a further 14 days at which point optical coherence tomography (OCT) was performed to determine the volume of the CNV lesion. Blood was drawn to determine nitrite and nitrate levels and eyes taken for histology. CNV volume was 2.86 × 10(7) μm(3) (±0.4×10(7)) in young mice on water alone but CNV volume more than doubled to >6.9 × 10(7) μm(3) (±0.8×10(7)) in mice receiving 300 mg/L nitrite or 7.34 × 10(7) μm(3) (±1.4×10(7)) in 1 g/L nitrate (p

Background Although several systematic reviews and meta-analyses have been conducted to assess the relationship between fruit intake and health outcomes, the results of the relationship are inconclusive. Scope and approach We conducted an umbrella evaluation of existing research on the relationship between fruit intake and various health outcomes in humans. We used A Measurement Tool to Assess Systematic Reviews 2 and Grade of Recommendations, Assessment, Development and Evaluation to comprehensively evaluate the methodological quality and evidence quality of the research. We also performed a subgroup analysis based on the study design. Key findings and conclusions The umbrella review covered 59 qualified studies, which included 77 meta-analyses and 66 health outcomes. No adverse relationships between fruit intake and health outcomes were found. Moderate-quality evidence suggested that one serving increment of fruit per day could decrease the risks of cardiovascular disease (RR: 0.97, 95% CI: 0.95 to 0.99), stroke (RR: 0.92, 95% CI: 0.88 to 0.96), coronary heart disease (RR: 0.96, 95% CI: 0.93 to 0.99), and oral cancer (OR: 0.51, 95% CI: 0.40 to 0.65). And moderate-quality evidence presented that 200 g increment of fruit intake per day was associated with lower risk of breast cancer (RR: 0.94, 95% CI: 0.89 to 0.99), and the risk of type 2 diabetes mellitus could be reduced by 8%-12% when the fruit intake increasing to 100-500 g/d. For the remaining studies, the quality of evidence was either low or very low. We also found that the study design could influence the significance of the association between fruit intake and health outcomes. Large-sample, multi-center, and multi-country randomized controlled trials or prospective studies are required to verify our results.

Purpose What remains uncertain with corporate universities is the contribution they provide to their organizations, particularly when considered from the perspective of managers. Managers are important stakeholders, as they may participate in carrying out the mission and policies that govern the corporate university and participate in the programs offered. Organizations would benefit from knowing more about the perceptions of managers. The purpose of this paper is to study managers’ perceptions of the accountability of two corporate university programs in China and its relationship with the managers’ overall involvement in corporate university programs and their commitment to the organization. Design/methodology/approach The study was conducted in two state-owned organizations in China, a country in which many organizations have adopted corporate universities. A mixed-method approach was used to carry out the study. Findings The results showed that there was a moderate relationship between the managers’ perceptions of the accountability of corporate university programs, based on two management development programs and managers’ involvement in corporate university programs and their commitment to the organization. The results also showed a low relationship between managers’ involvement in their corporate university programs and commitment to the organization. Originality/value The findings provide implications for managing corporate universities in China and enhancing the accountability of these initiatives. The study serves as a basis for future studies involving corporate universities and on the accountability of and involvement in training.

Paget's disease of bone (PDB) is a rare metabolic bone disorder, which is extremely rare in Asian population. This study aimed to investigate the phenotypes and the pathogenic mutations of woman with early-onset PDB. The clinical features, bone mineral density, x-ray, radionuclide bone scan, and serum levels of alkaline phosphatase (ALP), procollagen type 1 N-terminal propeptide (P1NP), and β-carboxy-terminal cross-linked telopeptide of type 1 collagen (β-CTX) were measured in detail. The pathogenic mutations were identified by whole-exon sequencing and confirmed by Sanger sequencing. We also evaluated the effects of intravenous infusion of zoledronic acid on the bones of the patient and summarized the phenotypic characteristics of reported patients with mutation at position 155 of the valosin-containing protein (VCP). The patient only exhibited bone pain as the initial manifestation with vertebral compression fracture and extremely elevated ALP, P1NP, and β-CTX levels; she had no inclusion body myopathy and frontotemporal dementia. The missense mutation in exon 5 of the VCP gene (p.Arg155His) was identified by whole-exome sequencing and further confirmed by Sanger sequencing. No mutation in candidate genes of PDB, such as SQSTM1, CSF1, TM7SF4, OPTN, PFN1, and TNFRSF11A, were identified in the patient by Sanger sequencing. Rapid relief of bone pain and a marked decline in ALP, P1NP, and β-CTX levels were observed after zoledronic acid treatment. Previously reported patients with VCP missense mutation at position 155 (R155H) always had myopathy, frontotemporal dementia, and PDB, but the patient in this study exhibited only PDB. This was the first report of R155H mutation-induced early-onset in the VCP gene in Asian population. PDB was the only manifestation having a favorable response to zoledronic acid treatment. We broadened the genetic and clinical phenotype spectra of the VCP mutation.

Whether the definition of hypertension according to 2017 AHA/ACC guidelines and blood pressure (BP) changes was related to the increased risk of chronic kidney disease (CKD) remained debated. This prospective cohort study aimed to investigate the association of BP and long‐term BP change with CKD risk with different glucose metabolism according to the new hypertension guidelines. This study examined 12 951 participants and 11 183 participants derived from the older people cohort study, respectively. Participants were divided into three groups based on blood glucose and the risks were assessmented by the logistic regression model. During a 10 years of follow‐up period, 2727 individuals developed CKD (21.1%). Compared with those with BP

Site planning not only involves the arrangement of a large number of elements but also considers the complexity of urban systems; hence, the design process involves large workloads and is time consuming. Retrieving similar existing cases helps architects optimize or accelerate the design process. This paper proposes a computational approach that provides valuable references by retrieving similar cases. Three types of attributes are extracted to represent a given parcel: land-use attributes, geometric attributes (area, orientation, etc.), and environmental attributes (greening rate, surrounding facilities, etc.). The complete hierarchical retrieval process is divided into three phases. The first phase selects cases whose land-use attributes are consistent with the target parcel. Then, the similarity distances between the given target parcel and the selected cases are calculated using geometric attributes. The eXtreme Gradient Boosting (XGBoost) classifier is adopted to determine which case is similar to the target parcel. Finally, similarity scores of the retrieved cases are calculated based on the environmental attributes to provide more options during the actual design. In total, 1189 cases with different land-use types in Beijing were collected for the case base. The comparative experimental results confirmed that the proposed ensemble learning-based hierarchical retrieval of similar cases approach improves the accuracy of retrieval results. Furthermore, we use a real-world target parcel to demonstrate the superiority and flexibility of the retrieval process.

In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algorithm to obtain the segmented MRI images of brain tissue, and the validity was verified. Then, 70 children with cerebral palsy were rolled into the observation group (n = 35), which received MRI for diagnosis after segmentation of brain tissue, and control group (n = 35), which were diagnosed by computed tomography (CT). The diagnosis results of the two groups were compared. The validity experiment verified that the image segmentation method based on CNN algorithm can obtain effective style graphics. In clinical trials, the diagnostic accuracy of 88.6% in the observation group was evidently superior to that of 80% in the control group ( P < 0.05 ). In the observation group, one patient was diagnosed as normal, four patients had white matter lesions, 17 patients had corpus callosum lesions, and five patients had basal ganglia softening foci. In the control group, two patients were diagnosed as normal, two patients had white matter lesions, 19 patients had corpus callosum lesions, and four patients had basal ganglia softening foci. No notable difference was found between the two groups ( P > 0.05 ). According to the research results, in the diagnosis of cerebral palsy in children, the image segmentation of brain 3D model based on CNN to obtain the MRI image of segmented brain tissue can effectively improve the detection accuracy. Moreover, the specific symptoms can be diagnosed clearly. It can provide the corresponding diagnostic basis for clinical diagnosis and treatment and was worthy of clinical promotion.

Objectives Australian guidelines for dementia cover 109 recommendations for dementia care. Knowing which recommendations to implement poses a challenge for general practitioners (GPs). This study aimed to gather general practice perspectives of priority recommendations for GPs in their practice. Methods To explore which recommendations are most important and those requiring the greatest support in GP implementation, a Delphi study was conducted. Thirty-six GPs, 4 practice nurses and 1 medical services director completed two rounds of email questionnaires. Results Recommendations requiring support for GP implementation relate to early assessment of behavioural and psychological symptoms; mental health interventions tailored to the person's preferences and abilities; language and cultural barriers in access to information and services; and cognitive and learning needs in treatment delivery. Conclusion It is critical to understand where support may be needed for GPs to successfully implement recommendations to improve care provided to people with dementia and their carers.

BACKGROUND Leucocytes for individuals during pregnancy may form into different trajectory patterns. Since no studies have been conducted, we aim to examine the associations between leucocyte trajectory across pregnancy and offspring's birth outcomes and growth during the first 2 years. METHODS We conducted a retrospective study enrolled 1070 singleton pregnancies aged 21-46 years old between 2014 and 2018 in Huazhong University of Science and Technology Union Shenzhen Hospital, China. Leucocyte trajectories were modelled using growth mixture modelling and four trajectories were identified: moderate-increasing (n = 41), low-stable (n = 828), high-decreasing (n = 145) and low-increasing (n = 56). RESULTS Relative to the low-stable group, logistic regression analysis after adjusting for covariates indicated that the odds ratios of preterm were 3.06 (95% confidence interval (CI): 1.43-6.23) for moderate-increasing, 0.78 (95% CI: 0.38-1.47) for high-decreasing and 0.68 (95% CI: 0.23-1.61) for the low-increasing group, respectively. By using generalized estimating equation analysis, we observed that infants in the moderate-increasing and low-increasing group had -0.35 and -0.21 (P

A surface modified-CsPbBr3/polybutylmethacrylate (PBMA) nanocomposite is reported to be a scintillator that enables us to provide a high contrast X-ray image using a common charge-coupled device (CCD) camera. Bis(2-(methacryloyloxy)ethyl) phosphate (BMEP) was employed to alter the ratio of the original ligands on the CsPbBr3 nanocrystal (NC) surface for optimizing the optical performance of the CsPbBr3/PBMA nanocomposites. The nanocomposites with a concentration of 0.02 wt % NCs exhibit more than 70% transmittance in the visible region and show a green emission at 515 nm, the fast decay time is 13 ns, while the photoluminescence quantum yield value is 99.2%. Under X-ray excitation, the emission peak wavelength is centered at 524 nm and shows a narrow full width at half-maximum of 26.6 nm; the result nicely matches with the peak quantum efficiency of most commercial CCD/complementary metal oxide semiconductor cameras. The high contrast X-ray image is recorded at a low dose rate of 4.6 μGyair/s, which enables read out with software. Our results demonstrate that these CsPbBr3/PBMA nanocomposites have promising application prospects for ionizing radiation detection, especially for X-ray imaging.

PurposeTo confirm the exact break-point of a novel long-range deletion discovered in one female parathyroid carcinoma (PC) patient who has a strong family history suggesting familial hyperparathyroidism, and to investigate the expression of parafibromin in the patient’s affected lesion.MethodsClinical information of one female patient as well as five of her relatives were collected. Their genomic DNA extracted from peripheral blood went through Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA). After completing whole genome sequencing (WGS), clone sequencing was also performed, whose result was aligned with standard human genome database after Sanger sequencing.ResultsThe medical history of recurrent hypercalcemia after parathyroidectomy and histopathological investigation confirmed that the female patient with the diagnosis of PC. WGS displayed a novel 130kb long-range deletion spanning UCHL5 to CDC73 which was later confirmed by clone sequencing. MLPA showed similar results in four of her five relatives, suggesting these people to be carriers of the same long-range deletion, and three among them had a history of primary hyperparathyroidism (PHPT) ahead of the proband’s first visit.Conclusion We discovered a novel 130kb long-range deletion spanning CDC73 in a family of five-person, and the existence of the deletion was related to PHPT and PC. Our discovery validated the role of CDC73 mutation in the occurrence of PHPT and PC, which provided new information to the genetic studies of PC.

About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.