Partial asynchrony of coniferous forest carbon sources and sinks at the intra-annual time scale.

As major terrestrial carbon sinks, forests play an important role in mitigating climate change. The relationship between the seasonal uptake of carbon and its allocation to woody biomass remains poorly understood, leaving a significant gap in our capacity to predict carbon sequestration by forests. Here, we compare the intra-annual dynamics of carbon fluxes and wood formation across the Northern hemisphere, from carbon assimilation and the formation of non-structural carbon compounds to their incorporation in woody tissues. We show temporally coupled seasonal peaks of carbon assimilation (GPP) and wood cell differentiation, while the two processes are substantially decoupled during off-peak periods. Peaks of cambial activity occur substantially earlier compared to GPP, suggesting the buffer role of non-structural carbohydrates between the processes of carbon assimilation and allocation to wood. Our findings suggest that high-resolution seasonal data of ecosystem carbon fluxes, wood formation and the associated physiological processes may reduce uncertainties in carbon source-sink relationships at different spatial scales, from stand to ecosystem levels

Clinical Imaging Characteristics and Pathological Features of Sarcomatoid Hepatocellular Carcinoma

Objective: To explore the imaging manifestations and clinical-pathological features of sarcomatoid hepatocellular carcinoma (SHC). Methods: The clinical data, CT/MRI findings and pathological features of 7 patients with SHC were retrospectively analyzed and the consistency was evaluated. Results: SHC showed unclear boundary and low density by plain CT images, with the size ranging from 4.2 cm×4.7 cm–14.0 cm×11.0 cm. By enhanced CT images, SHC showed ring or early enhancement with large areas of necrosis. SHC showed heterogeneous signal intensity on T1WI and T2WI with visible cystic degeneration and necrotic area while DWI displayed obvious diffusion restricted changes. On enhanced MRI, the tumor showed progressive enhancement of peripheral and solid components with delayed enhanced pseudo-capsule. 6 cases had different degree of necrosis in the center of lesion, and lymph node metastasis in the livers hilar was found in 5 patients. Pathological features polygonal and fusiform tumor cells with extensive necrosis and hemorrhage were found on HE staining. Vimentin and CK were positive by immunohistochemical staining. Conclusions: The imaging findings of SHC were consistent with the pathological features, which was helpful to deep understanding of specific imaging manifeatations, and the diagnosis and differential diagnosis of liver tumors

Construction of Artificial Forest Point Clouds by Laser SLAM Technology and Estimation of Carbon Storage

In order to reduce the impact of global warming, forestry carbon sink trading is an effective approach to achieving carbon neutrality, while carbon storage estimation plays an important role as the basis of the whole carbon sink trading. Therefore, an accurate estimation of carbon storage is conducive to the sustainable development of carbon sink trading. In this paper, we use laser SLAM technology to model an artificial forest in three dimensions, extract the tree parameters by the point cloud processing software, and calculate the carbon storage according to the allometric growth equation of the tree species. The experimental results show that the loop path is the best among the three-path planning of ZEB-HORIZON scanner data acquisition. For large-scale plantations, the fusion data acquisition of linear and loop paths by Livox Mid-40 and ZEB-HORIZON LIDAR can be adopted with a highly precise and a complete 3D point cloud obtained. The Lidar360 software is used for single wood segmentation and parameter extraction, and the manual measurement is taken as the quasi-true value. After the measurement accuracy analysis, the carbon storage estimation is met. Using the volume source biomass method in the sample plot inventory method, the carbon storages of camphor and cypress in the experimental area were estimated through the allometric growth equation of camphor and cypress and the international conversion rate

Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

In this study, hybrid state-action-reward-state-action (SARSAλ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess

Regulation of Iron Homeostasis and Related Diseases

The liver is the organ for iron storage and regulation; it senses circulating iron concentrations in the body through the BMP-SMAD pathway and regulates the iron intake from food and erythrocyte recovery into the bloodstream by secreting hepcidin. Under iron deficiency, hypoxia, and hemorrhage, the liver reduces the expression of hepcidin to ensure the erythropoiesis but increases the excretion of hepcidin during infection and inflammation to reduce the usage of iron by pathogens. Excessive iron causes system iron overload; it accumulates in never system and damages neurocyte leading to neurodegenerative diseases such as Parkinson’s syndrome. When some gene mutations affect the perception of iron and iron regulation ability in the liver, then they decrease the expression of hepcidin, causing hereditary diseases such as hereditary hemochromatosis. This review summarizes the source and utilization of iron in the body, the liver regulates systemic iron homeostasis by sensing the circulating iron concentration, and the expression of hepcidin regulated by various signaling pathways, thereby understanding the pathogenesis of iron-related diseases

Construction of Artificial Forest Point Clouds by Laser SLAM Technology and Estimation of Carbon Storage

In order to reduce the impact of global warming, forestry carbon sink trading is an effective approach to achieving carbon neutrality, while carbon storage estimation plays an important role as the basis of the whole carbon sink trading. Therefore, an accurate estimation of carbon storage is conducive to the sustainable development of carbon sink trading. In this paper, we use laser SLAM technology to model an artificial forest in three dimensions, extract the tree parameters by the point cloud processing software, and calculate the carbon storage according to the allometric growth equation of the tree species. The experimental results show that the loop path is the best among the three-path planning of ZEB-HORIZON scanner data acquisition. For large-scale plantations, the fusion data acquisition of linear and loop paths by Livox Mid-40 and ZEB-HORIZON LIDAR can be adopted with a highly precise and a complete 3D point cloud obtained. The Lidar360 software is used for single wood segmentation and parameter extraction, and the manual measurement is taken as the quasi-true value. After the measurement accuracy analysis, the carbon storage estimation is met. Using the volume source biomass method in the sample plot inventory method, the carbon storages of camphor and cypress in the experimental area were estimated through the allometric growth equation of camphor and cypress and the international conversion rate

FnnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm

The learning speed of online sequential extreme learning machine (OS-ELM) algorithms is much higher than that of convolutional neural networks (CNNs) or recurrent neural network (RNNs) on regression and simple classification datasets. However, the general feature extraction of OS-ELM makes it difficult to conveniently and effectively perform classification on some large and complex datasets, e.g., CIFAR. In this paper, we propose a flexible OS-ELM-mixed neural network, termed as fnnmOS-ELM. In this mixed structure, the OS-ELM can replace a part of fully connected layers in CNNs or RNNs. Our framework not only exploits the strong feature representation of CNNs or RNNs, but also performs at a fast speed in terms of classification. Additionally, it avoids the problem of long training time and large parameter size of CNNs or RNNs to some extent. Further, we propose a method for optimizing network performance by splicing OS-ELM after CNN or RNN structures. Iris, IMDb, CIFAR-10, and CIFAR-100 datasets are employed to verify the performance of the fnnmOS-ELM. The relationship between hyper-parameters and the performance of the fnnmOS-ELM is explored, which sheds light on the optimization of network performance. Finally, the experimental results demonstrate that the fnnmOS-ELM has a stronger feature representation and higher classification performance than contemporary methods

Effect of aerobic exercise on GRP78 and ATF6 expressions in mice with non-alcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is a prevalent medical condition with an ever-growing trend. Although multiple intracellular mechanisms are involved, endoplasmic reticulum (ER) stress has been demonstrated to play a significant role in the genesis and progression. Most of the research supports the advantages of exercise for NAFLD. However, little is known about the molecular mechanism(s) that underpin the effectiveness of exercise training in NAFLD. This study aimed to identify how aerobic exercise affected hepatic ER stress in a mouse NAFLD model. In this study, the mice were fed either a standard diet (SD) or a high-fat diet (HFD) for 17 weeks. HFD mice were trained on a treadmill during the last eight weeks. All animals were tested for serum levels of biochemical assays, protein expression, and gene expression. The hematoxylin and eosin, Oil red O, and immunohistochemistry staining were also performed. The results indicated that a high-fat diet generated NAFLD, with serum lipid disruption and hepatic function impairment, and increased GRP78 and ATF6 expressions. However, aerobic training reversed the majority of these alterations. It is concluded that NAFLD appears to be associated with hepatic ER stress response, and aerobic exercise mitigates NAFLD via lowering ER stress proteins GRP78 and ATF6

Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use

MicroRNA-29c-3p in dual-labeled exosome is a potential diagnostic marker of subjective cognitive decline

Objective: The present study aimed to determine whether peripheral blood neural cell adhesion molecule (NCAM)/amphiphysin 1 dual-labeled exosomal proteins and microRNAs (miRs) might serve as a marker for the early diagnosis of Alzheimer's disease (AD). Methods: This observational, retrospective, multicenter study used a two-stage design conducted in Beijing and Shanghai, China. The subjects included 76 patients with subjective cognitive decline (SCD), 80 with amnestic mild cognitive impairment (aMCI), 76 with dementia of Alzheimer's type (AD), 40 with vascular dementia (VaD), and 40 controls in the discovery stage. These results were confirmed in the verification stage. The levels of Aβ42, Aβ42/40, T-Tau, P-T181-tau, neurofilament light chain (NfL), and miR-29c-3p in peripheral blood amphiphysin 1 single-labeled and NCAM/amphiphysin 1 dual-labeled exosomes were captured and detected by immunoassay. Results: In the discovery stage, the levels of Aβ42 and miR-29c-3p in peripheral blood NCAM/amphiphysin 1 dual-labeled exosome of the SCD group were significantly higher than those in control and VaD groups (all P < 0.05). The verification stage further confirmed the results of the discovery stage. Plasma NCAM/amphiphysin 1 dual-labeled exosomal miR-29c-3p showed a good diagnostic performance. The NCAM/amphiphysin 1 dual-labeled exosomal miR-29c-3p had the highest AUC for diagnosis of SCD. The levels of Aβ42, Aβ42/40, Tau, P-T181-tau, and miR-29c-3p in peripheral blood exosomes were correlated to those in CSF (all P < 0.05). The combination of exosomal biomarkers had slightly higher diagnostic efficiency than the individual biomarkers and that the exosomal biomarkers had the same diagnostic power as the CSF biomarkers. Conclusion: The plasma NCAM/amphiphysin 1 dual-labeled exosomal miR-29c-3p had potential advantages in the diagnosis of SCD