Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos

Toll-Like Receptor 4 Reduces Oxidative Injury via Glutathione Activity in Sheep

Toll-like receptor 4 (TLR4) is an important sensor of Gram-negative bacteria and can trigger activation of the innate immune system. Increased activation of TLR4 can lead to the induction of oxidative stress. Herein, the pathway whereby TLR4 affects antioxidant activity was studied. In TLR4-overexpressing sheep, TLR4 expression was found to be related to the integration copy number when monocytes were challenged with lipopolysaccharide (LPS). Consequently, production of malondialdehyde (MDA) was increased, which could increase the activation of prooxidative stress enzymes. Meanwhile, activation of an antioxidative enzyme, glutathione peroxidase (GSH-Px), was increased. Real-time PCR showed that expression of activating protein-1 (AP-1) and the antioxidative-related genes was increased. By contrast, the expression levels of superoxide dismutase 1 (SOD1) and catalase (CAT) were reduced. In transgenic sheep, glutathione (GSH) levels were dramatically reduced. Furthermore, transgenic sheep were intradermally injected with LPS in each ear. The amounts of inflammatory infiltrates were correlated with the number of TLR4 copies that were integrated in the genome. Additionally, the translation of γ-glutamylcysteine synthetase (γ-GCS) was increased. Our findings indicated that overexpression of TLR4 in sheep could ameliorate oxidative injury through GSH secretion that was induced by LPS stimulation. Furthermore, TLR4 promoted γ-GCS translation through the AP-1 pathway, which was essential for GSH synthesis

Analysis of risk factors and short-term prognostic factors of arrhythmia in patients infected with mild/moderate SARS-CoV-2 Omicron variant

BackgroundComplications, including arrhythmia, following severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection continue to be of concern. Omicron is the mainstream SARS-CoV-2 mutant circulating in mainland China. At present, there are few epidemiological studies concerning the relationship between arrhythmia and Omicron variant infection in mainland China.ObjectivesTo investigate the risk factors of arrhythmia in patients infected with the SARS-CoV-2 Omicron variant and the factors influencing prognosis.MethodsData from 192 Omicron infected patients with symptoms of arrhythmia (AH group) and 100 Omicron infected patients without arrhythmia (Control group) were collected. Patients in the AH group were divided into the good and poor prognosis groups, according to the follow-up results 4–6 weeks after infection. The general and clinical data between the AH and Control groups, and between the good and poor prognosis groups were compared. The variables with differences between the groups were included in the multivariate logistic regression analysis, and the quantitative variables were analyzed by receiver operating characteristic curve to obtain their cut-off values.ResultsCompared with the control group, the body mass index (BMI), proportion of patients with a history of arrhythmia, proportion of antibiotics taken, heart rate, moderate disease severity, white blood cell (WBC) count, and the aspartate aminotransferase, creatine kinase (CK), CK isoenzyme (CK-MB), myoglobin (Mb), high-sensitive troponin I (hs-cTnI), lymphocyte ratio and high sensitivity C-reactive protein (hs-CRP) levels in the AH group were significantly higher (p < 0.05). In addition, obesity (BMI ≥24 kg/m2), fast heart rate (≥100 times/min), moderate disease severity, and WBC, CK-MB and hs-cTnI levels were independent risk factors of arrhythmia for patients with Omicron infection (p < 0.05), and hs-CRP was a protective factor (p < 0.05). Compared with the good prognosis group, the age, proportion of patients with a history of arrhythmia, heart rate, proportion of moderate disease severity, and hs-CRP, CK, Mb and hs-cTnI levels were significantly higher in the poor prognosis group, while the proportion of vaccination was lower in the poor prognosis group (p < 0.05). Advanced age (≥65 years old), proportion of history of arrhythmia, moderate disease severity, vaccination, and hs-CRP, Mb and cTnI levels were independent factors for poor prognosis of patients with arrhythmia (p < 0.05).ConclusionThe factors that affect arrhythmia and the prognosis of patients infected with Omicron include obesity, high heart rate, severity of the disease, age. history of arrhythmia, WBC, hs-CRP, and myocardial injury indexes, which could be used to evaluate and prevent arrhythmia complications in patients in the future

Multisite rainfall downscaling and disaggregation in a tropical urban area

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.Accepted versio

Study of climate change impact on flood frequencies : a combined weather generator and hydrological modeling approach

Climate change is expected to lead to more frequent and intensive flooding problems for watersheds in the south part of China. This study presented a coupled Long Ashton Research Station Weather Generator (LARS-WG) and Semidistributed Land Use–Based Runoff Processes (SLURP) approach for flood frequency analysis and applied it to the Heshui watershed, China. LARS-WG, as a weather generator, was used to offer 46 sets of climate data from seven general circulation models (GCMs) under various emission scenarios (i.e., A1B, B1, and A2) over near-term and future periods (i.e., T1, 2011–30; T2, 2046–65; and T3, 2080–99). SLURP is a continuous, spatially distributed hydrological model that uses parameters from physiographic data to simulate the hydrological cycle from precipitation to runoff. Flood frequency analysis based on Pearson type III distributions was followed to analyze statistics of annual peaks. The final results (from ensembles of multimodels and multiscenarios) indicated that the magnitudes of a 200-yr return flood for T1, T2, and T3 would increase by 5.23%, 4.08%, and 12.92%, respectively, in comparison to the baseline level; those under the most extreme condition (i.e., worst scenario) would be 25.18%, 31.00%, and 44.46%, respectively. Various GCMs and emission scenarios suggested different results. But the ECHAM5/Max Planck Institute Ocean Model was found to give a more worrying intensification of flood risks and the Commonwealth Scientific and Industrial Research Organisation Mark, version 3.0, and the Community Climate System Model, version 3, were relatively conservative. The study results were useful in helping gain insight into the flood risks and its uncertainty under future climate change conditions for the Heshui watershed, and the proposed methodology is also applicable to many other watersheds in Southeast Asia with similar climatic conditions.Published versio

on Oscillation Criteria for First Order Neutral Differential Equations with Positive and Negative Coefficients

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A Novel Routing Algorithm for Power Line Communication over a Low-voltage Distribution Network in a Smart Grid

A novel artificial cobweb routing algorithm (ACRA) for routing the tree-type physical topology of a low-voltage distribution network in a smart grid is proposed and analyzed in this paper. The establishment, maintenance and reconstruction of the route are presented. The artificial cobweb routing algorithm is shown to have broad general applicability for power line communication. To provide a theoretical foundation for further research, the communication delay of the network is calculated accurately. Simulation analysis of the communication delay and throughputs, which were based on Opnet14.5, demonstrate the accuracy of the theoretical calculation. For the performance evaluation of ACRA, a test-bed that includes PLC nodes with the ACRA is set up in a noisy environment. Experimental results show the feasibility of the ACRA algorithm. These indicate that ACRA is effective for guaranteeing Quality of Service (QoS) and reliability in power line communication

Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China

Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations