Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?

Accurately monitoring global vegetation dynamics with modern remote sensing is critical for understanding the functions and processes of the biosphere and its interactions with the planetary climate. The MODerate resolution Imaging Spectroradiometer (MODIS) vegetation index (VI) product has been a primary data source for this purpose. To date, the MODIS team had released several versions of VI products that have widely used in global change studies and practical applications. In this study, we re-examined the global vegetation activity by comparing the recent MODIS Collection 6 (C6) VIs with Collection 5 (C5) VIs including Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Terra (2001â2015) and Aqua Satellites (2003â2015). We found substantial differences in global vegetation trends betweenTerra-C5 and -C6 VIs, especially EVI. From 2001 to 2015, global vegetation showed a remarkable greening trend in annual EVI from the Terra-C6 (0.28% yearâ1; P<0.001), in contrast to the decreasing EVI trend from the Terra-C5 (â0.14% yearâ1, P<0.01). Spatially, large portions of the browning areas in tropical regions identified by Terra-C5 VIs were not evident in Terra-C6 VIs. In contrast, the widespread greening areas in Terra-C6 VIs were consistent with Aqua-C6 VIs and GIMMS3g NDVI. Our finding of a greening Earth supports the recent studies suggesting an enhanced land carbon sink. Our study suggests that most of the vegeta

Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN

Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited embedded memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by separating training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantages: First, the memory demand for training on high-resolution images is amortized among subvolumes. Furthermore, anchoring the high-resolution subvolumes to a single low-resolution image ensures anatomical consistency between subvolumes. During inference, our model can directly generate full high-resolution images. We also incorporate an encoder with a similar hierarchical structure into the model to extract features from the images. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation and clinical-relevant feature extraction.Comment: 12 pages, 9 figures. Under revie

Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability.Comment: 18 pages, 12 figures, 8 table

Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum

An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum

Load shifting of a supplier-based demand response of multi-class subscribers in smart grid

We propose a demand response (DR) solution approach for a real-time pricing model with multi-class users to determine the electricity supply mix. The model aims to address the problem of power consumption overloading in peak hours using the real-time information obtained from the interaction between suppliers and users in a smart grid. The proposed DR algorithm allocates the overloaded demand assigned to a supplier to other electricity suppliers in order to satisfy all users’ demand while the supplier ensures to maximize the utility or reserved demand of users. Furthermore, a priority approach based on different user groups is developed for allocating the extra demand to other suppliers. Numerical experiments have been conducted to analyze the performance of the algorithm and compare the real-time electricity price with the fixed price

Arsenic and Cadmium Accumulation in Soil as Affected by Continuous Organic Fertilizer Application: Implications for Clean Production

As and Cd in soil can be assimilated and accumulated by vegetables and can be subsequently ingested by humans. Contradictory effects of organic fertilizer application on As and Cd accumulation in soil have been reported in previous studies. An eight-year greenhouse study was conducted on a sandy loam soil in Beijing, China to investigate the effects of organic fertilizer application rate on soil properties, and As and Cd accumulation in soil. The contamination risk of pak choi grown after eight years’ application of organic fertilizer was also evaluated. Soil organic carbon increased 3.0–3.8 times with low, medium and high rates of fertilizer application in 2018 compared to the initial soil. Organic fertilizer application significantly increased soil nutrients and microbial biomass while it mildly affected soil pH. The bioavailability of As/Cd has decreased after eight years’ application of organic fertilizer. Pak choi crop harvested from all three treatments in 2018 did not pose a threat to human health, even for life-time consumption. Soil total As content significantly decreased with organic fertilizer application, mainly due to the lower As content in the applied fertilizer than that in soil. Continuous application of clean organic fertilizer can be adopted to reduce the contamination risk of highly contaminated soil in the soil–plant system

The role of Akkermansia muciniphila in inflammatory bowel disease: Current knowledge and perspectives

Inflammatory bowel diseases, including Crohn’s disease and ulcerative colitis, is a chronic relapsing gastrointestinal inflammatory disease mediated by dysregulated immune responses to resident intestinal microbiota. Current conventional approaches including aminosalicylates, corticosteroids, immunosuppressive agents, and biological therapies are focused on reducing intestinal inflammation besides inducing and maintaining disease remission, and managing complications. However, these therapies are not curative and are associated with various limitations, such as drug resistance, low responsiveness and adverse events. Recent accumulated evidence has revealed the involvement of mucin-degrading bacterium Akkermansia muciniphila (A. muciniphila) in the regulation of host barrier function and immune response, and how reduced intestinal colonisation of probiotic A. muciniphila can contribute to the process and development of inflammatory bowel diseases, suggesting that it may be a potential target and promising strategy for the therapy of inflammatory bowel disease. In this review, we summarise the current knowledge of the role of A. muciniphila in IBD, especially focusing on the related mechanisms, as well as the strategies based on supplementation with A. muciniphila, probiotics and prebiotics, natural diets, drugs, and herbs to promote its colonisation in the gut, and holds promise for A. muciniphila-targeted and -based therapies in the treatment of inflammatory bowel disease

Generation of a Urine-Derived Ips Cell Line from a Patient with a Ventricular Septal Defect and Heart Failure and the Robust Differentiation of These Cells to Cardiomyocytes via Small Molecules

Background/Aims: Ventricular septal defects (VSDs) are one of the most common types of congenital heart malformations. Volume overload resulting from large VSDs can lead to heart failure (HF) and constitutes a major cause of pediatric HF with a series of often-fatal consequences. The etiology of VSD with HF is complex, and increasing evidence points toward a genetic basis. Indeed, we identified an L2483R mutation in the ryanodine receptor type 2 (RyR2) in a 2-month-old male patient with VSD with HF. Methods: We generated integration-free induced pluripotent stem cells from urine samples (UiPSCs) of this patient using Sendai virus containing the Yamanaka factors and characterized these cells based on alkaline phosphatase activity, pluripotency marker expression, and teratoma formation. Then, we induced the derived UiPSCs to rapidly and efficiently differentiate into functional cardiomyocytes through temporal modulation of canonical Wnt signaling with small molecules. Real-time PCR and immunofluorescence were used to verify the expression of myocardium-specific markers in the differentiated cardiomyocytes. The ultrastructure of the derived myocardial cells was further analyzed by using transmission electron microscopy. Results: The established UiPSC lines were positive for alkaline phosphatase activity, retained the RyR2 mutation, expressed pluripotency markers, and displayed differentiation potential to three germ layers in vivo. The UiPSC-derived cells showed hallmarks of cardiomyocytes, including spontaneous contraction and strong expression of cardiac-specific proteins and genes. However, compared with cardiomyocytes derived from H9 cells, they had a higher level of autophagy, implying that autophagy may play an important role in the development of VSD with HF. Conclusion: The protocol described here yields abundant myocardial cells and provides a solid platform for further investigation of the pathogenesis, pharmacotherapy, and gene therapy of VSD with HF

Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum

An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum