Fumarylacetoacetate Hydrolase Knock-out Rabbit Model for Hereditary Tyrosinemia Type 1.

Hereditary tyrosinemia type 1 (HT1) is a severe human autosomal recessive disorder caused by the deficiency of fumarylacetoacetate hydroxylase (FAH), an enzyme catalyzing the last step in the tyrosine degradation pathway. Lack of FAH causes accumulation of toxic metabolites (fumarylacetoacetate and succinylacetone) in blood and tissues, ultimately resulting in severe liver and kidney damage with onset that ranges from infancy to adolescence. This tissue damage is lethal but can be controlled by administration of 2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione (NTBC), which inhibits tyrosine catabolism upstream of the generation of fumarylacetoacetate and succinylacetone. Notably, in animals lacking FAH, transient withdrawal of NTBC can be used to induce liver damage and a concomitant regenerative response that stimulates the growth of healthy hepatocytes. Among other things, this model has raised tremendous interest for the in vivo expansion of human primary hepatocytes inside these animals and for exploring experimental gene therapy and cell-based therapies. Here, we report the generation of FAH knock-out rabbits via pronuclear stage embryo microinjection of transcription activator-like effector nucleases. FAH-/- rabbits exhibit phenotypic features of HT1 including liver and kidney abnormalities but additionally develop frequent ocular manifestations likely caused by local accumulation of tyrosine upon NTBC administration. We also show that allogeneic transplantation of wild-type rabbit primary hepatocytes into FAH-/- rabbits enables highly efficient liver repopulation and prevents liver insufficiency and death. Because of significant advantages over rodents and their ease of breeding, maintenance, and manipulation compared with larger animals including pigs, FAH-/- rabbits are an attractive alternative for modeling the consequences of HT1.Wellcome Trus

Water Ecosystem Service Quality Evaluation and Value Assessment of Taihu Lake in China

Taihu Lake is the third largest freshwater lake in China. Water ecosystems play an important role in the survival and development of human society. The evaluation of water ecosystem services is helpful to understand and grasp the changing rules of Taihu Lake’s ecosystem services value in recent years. First, we used the Water Environment Qualities Index (WQI) to evaluate the water ecological quality of Taihu Lake; second, on the basis of the survey data from 2010 to 2018, we combined economic and ecological methods to evaluate the water ecosystem of Taihu Lake. The evaluation system includes four major service functions, 11 second-class evaluation indicators and 19 index factor. Research indicates that, (1) in the past 8 years, the WQI of Taihu Lake increased year by year and Taihu Lake changed from moderate pollution to light pollution; (2) provisioning services are the main service of Taihu Lake’s water ecosystem and the order of various service values was provisioning service value > regulation service value > cultural service value > support service value, with water supply as the core function of provisioning services; and (3) the total values in 2010, 2014, and 2018 were 115.39 billion yuan, 113.31 billion yuan, and 119.96 billion yuan, respectively, showing a trend of first decreasing and then increasing. To a certain extent, the improvement in Taihu Lake’s water ecological quality has led to an increase in the value of regulation services

Water Ecosystem Service Quality Evaluation and Value Assessment of Taihu Lake in China

Taihu Lake is the third largest freshwater lake in China. Water ecosystems play an important role in the survival and development of human society. The evaluation of water ecosystem services is helpful to understand and grasp the changing rules of Taihu Lake’s ecosystem services value in recent years. First, we used the Water Environment Qualities Index (WQI) to evaluate the water ecological quality of Taihu Lake; second, on the basis of the survey data from 2010 to 2018, we combined economic and ecological methods to evaluate the water ecosystem of Taihu Lake. The evaluation system includes four major service functions, 11 second-class evaluation indicators and 19 index factor. Research indicates that, (1) in the past 8 years, the WQI of Taihu Lake increased year by year and Taihu Lake changed from moderate pollution to light pollution; (2) provisioning services are the main service of Taihu Lake’s water ecosystem and the order of various service values was provisioning service value > regulation service value > cultural service value > support service value, with water supply as the core function of provisioning services; and (3) the total values in 2010, 2014, and 2018 were 115.39 billion yuan, 113.31 billion yuan, and 119.96 billion yuan, respectively, showing a trend of first decreasing and then increasing. To a certain extent, the improvement in Taihu Lake’s water ecological quality has led to an increase in the value of regulation services

Dynamical Properties of a Herbivore-Plankton Impulsive Semidynamic System with Eating Behavior

In this paper, an impulsive semidynamic system of the relationship between plankton and herbivore is established, and the Poincaré map method is used to extend the new properties of the model. We define the Poincaré map of the impulsive point series in phase concentration and analyze the characteristics. A comprehensive and detailed analysis of the periodic solution is performed. In addition, the numerical simulations illustrate the correctness of our arguments. The results show that plankton and herbivore can survive stably under effective control

DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Superresolution

The application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult to deploy in real remote sensing tasks. In this article, we propose a compact and efficient distance attention residual network (DARN) to achieve a better compromise between model accuracy and complexity. The distance attention residual connection block (DARCB), the core component of the DARN, uses multistage feature aggregation to learn more accurate feature representations. The main branch of the DARCB adopts a shallow residual block (SRB) to flexibly learn residual information to ensure the robustness of the model. We also propose a distance attention block (DAB) as a bridge between the main branch and the branch of the DARCB; the DAB can effectively alleviate the loss of detail features in the deep CNN extraction process. Experimental results on two remote sensing and five super-resolution benchmark datasets demonstrate that the DARN achieves a better compromise than existing methods in terms of performance and model complexity. In addition, the DARN achieves the optimal solution compared with the state-of-the-art lightweight remote sensing SISR method in terms of parameter amount, computation amount, and inference speed. Our code will be available at https://github.com/candygogogogo/DARN

RAFFIA: Short-term Forest Fire Danger Rating Prediction via Multiclass Logistic Regression

Forest fire prevention is important because of human communities near forests or in the wildland-urban interfaces. Short-term forest fire danger rating prediction is an effective way to provide early guidance for forest fire managers. It can therefore effectively protect the forest resources and enhance the sustainability of the forest ecosystem. However, relevant existing forest fire danger rating prediction models operate well only when applied to distinct climates and fuel types separately. There are desires for an effective methodology, which can construct a specific short-term prediction model according to an evaluation of the data from that specific region. Moreover, a suitable method for prediction model construction needs to deal with some big data related computing challenges (i.e., data diversity coupled with complexity of solution space, and the requirement of real-time forest fire prevention application) when massively observed heterogeneous parameters are available for prediction (e.g., meteorology factor, the amount of litter in the area, soil moisture, etc.). To capture the influences of multiple prediction factors on the prediction results and effectively learn from fast cumulative historical big data, artificial intelligence methods are investigated in this paper, yielding a short-term Ratings of Forest Fire Danger Prediction via Multiclass Logistic Regression (or RAFFIA) model for forest fire danger rating online prediction. Experimental evaluations conducted on a sensor-based forest fire prevention experimental station show that RAFFIA (with 98.71% precision and 0.081 root mean square error) is more effective than the Least Square Fitting Regression (LSFR) and Random Forests (RF) prediction models

Passive model order reduction algorithm based on Chebyshev expansion of impulse response of interconnect networks

In this paper, weprovide a new passive model order reduction algorithm on interconnects, which is based on the Chebyshev expansion of their impulse response. The Chebyshev coefficient matrices of the impulse response of the reduced order model up to a given order remain the same as those of the original network, so that the time domain transient response of the reduced order model matches that of the original network well. Compared with the model order reduction algorithms based on the frequency domain response, it is more efficient in dealing with complicated transientwaveforms of interconnects where strong inductance effects are involved