Infection by hepatitis E virus (HEV) via the oral route causes acute hepatitis. Extra-hepatic manifestations of HEV infection may stem from various causes; however, its distribution in organs such as ...the liver, as well as the mechanisms underlying HEV-induced cell injury, remain unclear. The objective of this study was to determine the chronological distribution of HEV in various tissues of HEV-challenged miniature pigs and to investigate the mechanisms underlying HEV-induced cell death in the pancreas and liver. Virological and serological analyses were performed on blood and faecal samples. Histopathology of the liver and extra-hepatic tissues was analysed. Cell death pathways and immune cell characterisation in inflammatory lesions were analysed using immunohistochemistry. The liver and pancreas displayed inflammation and cellular injury, and a large amount of HEV was observed in the lesions. The liver was infiltrated by T and natural killer cells. HEV was identified in all organs except the heart, and was associated with immune cells. Although the liver and the pancreas strongly expressed TNF-α and TRAIL, TUNEL assay results were negative. RIP3 and pMLKL were expressed in the pancreas. RIP3, but not pMLKL, was expressed in the liver. Pancreatitis induced in HEV-infected miniature pigs is associated with necroptosis.
Recently, computer-aided diagnosis (CAD) systems powered by deep learning (DL) algorithms have shown excellent performance in the evaluation of digital mammography for breast cancer diagnosis. ...However, such systems typically require pixel-level annotations by expert radiologists which is prohibitively time-consuming and expensive. Medical institutes would wonder if a high-performance breast cancer CAD system can be trained by exploring their own huge amount of historical imaging data and corresponding diagnosis reports, without additional annotations workload of their radiologists. In this study, we show that a DL classification model trained on historical mammograms with only image-level pathology labels (which can be automatically extracted from medical reports) can achieve surprisingly good diagnostic performance on newly incoming exams compared with experienced radiologists. A DL model called DenseNet was trained and cross-validated with 5979 historical exams acquired before September 2017 with biopsy-verified pathology and tested with 1194 newly obtained cases after that. For both cross-validation and test sets, the ROCs generated by DL predictions were above the ROCs generated by ratings from radiologists. For the suspicious cases which radiologists suggest biopsy (BI-RADS category 4 and 5), the DL model can reject 60% of false biopsies on benign breasts while keeping 95% sensitivity. For the mammograms based on which radiologists were not able to make a diagnosis (BI-RADS 0), the DL model still achieved an AUC score of 79%. Moreover, the model is able to localize lesions on mammograms although such information was not provided in the training phase. Finally, the impact of input image resolution and different DL model architectures on the diagnostic accuracy were also presented and analyzed.
Emerging power sources and load demands penetrating the power system on demand side lead to diversified dynamic processes of distribution networks and thus create challenges for transient simulation ...of large‐scale active distribution networks. This paper proposes a network‐decomposition‐based multi‐rate parallel transient simulation technique for active distribution networks. Prominent advantages of the proposed method are accentuated by balancing computational complexities of CPU cores and devised core‐oriented adaptive step sizes in terms of the simulated elements with various dynamic characteristics. First, an optimised decomposition strategy of active distribution networks is proposed with the goals of balancing the computational burden of each core and minimising interactive data between cores. On that basis, coordinating models of multi‐port networks and equivalent models of interfaces are established. Then, a parallel simulation method of coordinated adaptive step sizes is proposed to accelerate the transient simulation. Case studies on eight feeders connecting to a common 10‐kV bus are carried out to verify the simulation speed and precision of the proposed parallel simulation method.
Predicting the trajectories of pedestrians is an important and difficult task for many applications, such as robot navigation and autonomous driving. Most of the existing methods believe that an ...accurate prediction of the pedestrian intention can improve the prediction quality. These works tend to predict a fixed destination coordinate as the agent intention and predict the future trajectory accordingly. However, in the process of moving, the intention of a pedestrian could be a definite location or a general direction and area, and may change dynamically with the changes of surrounding. Thus, regarding the agent intention as a fixed 2-d coordinate is insufficient to improve the future trajectory prediction. To address this problem, we propose Dynamic Target Driven Network for pedestrian trajectory prediction (DTDNet), which employs a multi-precision pedestrian intention analysis module to capture this dynamic. To ensure that this extracted feature contains comprehensive intention information, we design three sub-tasks: predicting coarse-precision endpoint coordinate, predicting fine-precision endpoint coordinate and scoring scene sub-regions. In addition, we propose a original multi-precision trajectory data extraction method to achieve multi-resolution representation of future intention and make it easier to extract local scene information. We compare our model with previous methods on two publicly available datasets (ETH-UCY and Stanford Drone Dataset). The experimental results show that our DTDNet achieves better trajectory prediction performance, and conducts better pedestrian intention feature representation.
Autophagy caused by ischemia/reperfusion (I/R) increases the extent of cardiomyocyte damage. Melatonin (Mel) diminishes cardiac injury through regulating autophagy and mitochondrial dynamics. ...However, illustrating the specific role of mitophagy in the cardioprotective effects of melatonin remains a challenge. The aim of our research was to investigate the impact and underlying mechanisms of melatonin in connection with mitophagy during anoxia/reoxygenation (A/R) injury in H9c2 cells.
H9c2 cells were pretreated with melatonin with or without the melatonin membrane receptor 2 (MT2) antagonist 4-P-PDOT, the MT2 agonist IIK7 and the sirtuin 3 (SIRT3) inhibitor 3-TYP for 4 hours and then subjected to A/R injury. Cell viability, cellular apoptosis, necrosis levels and oxidative markers were assessed. The expression of SIRT3 and forkhead box O3a (FoxO3a), mitochondrial function and the levels of mitophagy-related proteins were also evaluated.
A/R injury provoked enhanced mitophagy in H9c2 myocytes. In addition, increased mitophagy was correlated with decreased cellular viability, increased oxidative stress and mitochondrial dysfunction in H9c2 cells. However, melatonin pretreatment notably increased cell survival and decreased cell apoptosis and oxidative response after A/R injury, accompanied by restored mitochondrial function. The inhibition of excessive mitophagy is involved in the cardioprotective effects of melatonin, as shown by the decreased expression of the mitophagy-related molecules Parkin, Beclin1, and BCL2-interacting protein 3-like (BNIP3L, best known as NIX) and decreased light chain 3 II/light chain 3 I (LC3 II/LC3 I) ratio and upregulation of p62 expression. Moreover, the decreased expression of SIRT3 and FoxO3a in A/R-injured H9c2 cells was abrogated by melatonin, but these beneficial effects were attenuated by the MT2 antagonist 4-P-PDOT or the SIRT3 inhibitor 3-TYP and enhanced by the MT2 agonist IIK7.
These results indicate that melatonin protects H9c2 cells during A/R injury through suppressing excessive mitophagy by activating the MT2/SIRT3/FoxO3a pathway. Melatonin may be a useful candidate for alleviating myocardial ischemia/reperfusion (MI/R) injury in the future, and the MT2 receptor might become a therapeutic target.
PURPOSE: To build and validate a radiomics-based nomogram for the prediction of pre-operation lymph node (LN) metastasis in esophageal cancer. PATIENTS AND METHODS: A total of 197 esophageal cancer ...patients were enrolled in this study, and their LN metastases have been pathologically confirmed. The data were collected from January 2016 to May 2016; patients in the first three months were set in the training cohort, and patients in April 2016 were set in the validation cohort. About 788 radiomics features were extracted from computed tomography (CT) images of the patients. The elastic-net approach was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to build the radiomics signature and another predictive nomogram model. The predictive nomogram model was composed of three factors with the radiomics signature, where CT reported the LN number and position risk level. The performance and usefulness of the built model were assessed by the calibration and decision curve analysis. RESULTS: Thirteen radiomics features were selected to build the radiomics signature. The radiomics signature was significantly associated with the LN metastasis (P<0.001). The area under the curve (AUC) of the radiomics signature performance in the training cohort was 0.806 (95% CI: 0.732-0.881), and in the validation cohort it was 0.771 (95% CI: 0.632-0.910). The model showed good discrimination, with a Harrell’s Concordance Index of 0.768 (0.672 to 0.864, 95% CI) in the training cohort and 0.754 (0.603 to 0.895, 95% CI) in the validation cohort. Decision curve analysis showed our model will receive benefit when the threshold probability was larger than 0.15. CONCLUSION: The present study proposed a radiomics-based nomogram involving the radiomics signature, so the CT reported the status of the suspected LN and the dummy variable of the tumor position. It can be potentially applied in the individual preoperative prediction of the LN metastasis status in esophageal cancer patients.
Continuous sign language recognition (CSLR) is an essential task for communication between hearing-impaired and people without limitations, which aims at aligning low-density video sequences with ...high-density text sequences. The current methods for CSLR were mainly based on convolutional neural networks. However, these methods perform poorly in balancing spatial and temporal features during visual feature extraction, making them difficult to improve the accuracy of recognition. To address this issue, we designed an end-to-end CSLR network: Spatial–Temporal Transformer Network (STTN). The model encodes and decodes the sign language video as a predicted sequence that is aligned with a given text sequence. First, since the image sequences are too long for the model to handle directly, we chunk the sign language video frames, i.e., ”image to patch”, which reduces the computational complexity. Second, global features of the sign language video are modeled at the beginning of the model, and the spatial action features of the current video frame and the semantic features of consecutive frames in the temporal dimension are extracted separately, giving rise to fully extracting visual features. Finally, the model uses a simple cross-entropy loss to align video and text. We extensively evaluated the proposed network on two publicly available datasets, CSL and RWTH-PHOENIX-Weather multi-signer 2014 (PHOENIX-2014), which demonstrated the superior performance of our work in CSLR task compared to the state-of-the-art methods.
•I proposed a detachable carbon cycle model which contains 14 carbon pools to simulate carbon cycle and driven carbon pools to equilibrium state, each carbon pool or carbon flow process can detach ...from the main model as independent component for study.•Each carbon pool size and the internal carbon exchange was highly assessed by using detachable carbon cycle model, slow pool released the most CO2 among all carbon pools.•The carbon influx-efflux (IE) function of each carbon pool can serve as a logistic function.
Terrestrial carbon storage plays a crucial role in determining the global carbon cycle and regulating global climate. However, the carbon storage, as simulated by terrestrial biosphere models (TBMs), exhibits considerable inconsistency at the regional or global scale because the model structure, input dataset, and key parameter are quite different in TBMs. In this study, I proposed a detachable carbon cycle (DCC) model to simulate terrestrial carbon storage, internal carbon exchange, and the influx–efflux (IE) function of each carbon pool. The model was established based on a pool–and–flux scheme and contained 14 carbon pools, or carbon flow processes. Each process can be detached from the primary model and evaluated as an independent component. The average net primary productivity (NPP) from 1982 to 2008 was used as the influx carbon to drive the DCC model. The magnitude of the internal carbon exchanges of the DCC model was explicitly represented, and the carbon IE function of each carbon pool was fitted based on the characteristics of carbon flux. Results indicated that the terrestrial carbon storage was 2766.25 Pg, and carbon stored in vegetation and soil was 705.85 and 2022.00 Pg, respectively. Carbon stored in slow and passive pools accounted for 70.42% of the terrestrial carbon storage, and the slow pool contributes the highest amount of released CO2 among of all carbon pools during carbon decomposition. The IE functions exhibited a nonlinear curve feature and satisfactory adjust-R2. This study aimed to contribute to our understanding of the carbon cycle from non-equilibrium state to equilibrium state and can serve as a reference and framework for global carbon storage simulation research.
During China's first gas hydrate drilling expedition, a gas hydrate-bearing layer (GHBL) of ≈25 m thickness was identified above a bottom-simulating reflection (BSR) at site SH2 in the Shenhu area, ...South China Sea. This study considers the GHBL and the underlying free gas-bearing layer (FGBL) as a whole and evaluates them using quantitative seismic characterization. The reprocessed seismic data, correlated to well log data, reveal the BSR to be accompanied by newly-identified reflections of positive polarity from the top of the GHBL and the base of the FGBL. Of note are phase reversals along individual reflections that cross the top of the GHBL and the base of the FGBL. Phase reversals at the top of the GHBL are a previously little known seismic feature, and seismic forward modeling shows them to provide a direct indicator for the presence of gas hydrate. Amplitude versus offset (AVO) analysis indicates that the two new reflections have characteristics distinct from those of the BSR. Gas hydrate and free gas saturations derived by AVO analysis and seismic impedance inversion, respectively, are consistent with values estimated from sonic velocities, chloride anomalies and pressure cores. The saturations calculated from acoustic impedance inversion show that both gas hydrate and free gas of high saturation are discontinuous, and that free gas has a clear stratified distribution. The distribution of gas hydrate is generally consistent with that of free gas below the BSR, indicating that gas hydrate near site SH2 is mainly controlled by free gas in the underlying FGBL. The quantitative interpretation presented in this study provides a new and reliable method for the characterization of gas hydrate-bearing sediments in areas with no wells, and for the pre-drilling assessment of free gas hazard.
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•Two normal polarity reflections associated with phase reversals in cross-cutting reflections are identified at the top of a gas hydrate-bearing layer (GHBL) and the base of a free gas-bearing layer (FGBL).•AVO characteristics and polarities of reflections from the top of the GHBL and the base of the FGBL are shown to be distinct from those of the BSR.•AVO analysis is extended to derive gas hydrate saturation at the top of the GHBL and free gas saturation at the base of the FGBL.•Gas hydrate and free gas are uniformly distributed at pore scale but areas of high saturation are discontinuous at seismic scale.
Background To evaluate the postoperative morbidity and mortality of patients undergoing cardiovascular surgery during the 2022 nationwide Omicron variant infection wave in China. Methods This ...retrospective cohort study included 403 patients who underwent cardiovascular surgery for the first time during the 2022 wave of the pandemic within 1 month. Among them, 328 patients were preoperatively diagnosed with COVID-19 Omicron variant infection during the pandemic, and 75 patients were negative. The association between Omicron variant exposure and postoperative prognosis was explored by comparing patients with and without COVID-19 exposure. The primary outcome was in-hospital death after cardiovascular surgery. The secondary outcomes were major postoperative morbidity, including myocardial infarction (MI), acute kidney injury (AKI), postoperative mechanical ventilation hours, ICU stay hours, and postoperative length of stay. The data were analyzed using inverse probability of treatment weighting (IPTW) to minimize bias. Results We identified 403 patients who underwent cardiovascular surgery, 328 (81.39%) had Omicron variant infections. In total, 10 patients died in the hospital. Omicron variant infection was associated with a much greater risk of death during cardiovascular surgery after adjustment for IPTW (2.8% vs. 1.3%, adjusted OR 2.185, 95%CI = 1.193 to 10.251, P = 0.041). For major postoperative morbidity, there were no significant differences in terms of myocardial infarction between the two groups (adjusted OR = 0.861, 95%CI = 0.444 to 1.657, P = 0.653), acute kidney injury (adjusted OR = 1.157, 95%CI = 0.287 to 5.155, P = 0.820), postoperative mechanical ventilation hours (B -0.375, 95%CI=-8.438 to 7.808, P = 0.939), ICU stay hours (B 2.452, 95%CI=-13.269 to 8.419, P = 0.660) or postoperative stay (B -1.118, 95%CI=-2.237 to 1.154, P = 0.259) between the two groups. Conclusion Perioperative COVID-19 infection was associated with an increased risk of in-hospital death among patients who underwent cardiovascular surgery during the Omicron variant wave of the pandemic. Keywords: COVID-19, Cardiovascular surgery, Morbidity, Mortality
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