Novel mutations in ATP7B in Chinese patients with Wilson's disease and identification of kidney disorder of thinning of the glomerular basement membrane

IntroductionWilson's disease is an autosomal recessive disorder caused by ATP7B pathogenic mutations. The hallmark of this disorder mainly consists of liver involvement, neurologic dysfunction and psychiatric features. In addition, the kidneys can also be affected by excessive copper deposition.MethodsA total of 34 patients clinically diagnosed with WD were recruited. They underwent ATP7B gene sequencing and clinical data of symptoms, examination, and treatment were collected. Moreover, renal pathology information was also investigated.ResultsWe identified 25 potentially pathogenic ATP7B variants (16 missense, 5 frameshift, 3 splicing variants and 1 large deletion mutation) in these 34 WD patients, 5 of which were novel. In our cases, the most frequent variant was c.2333G>T (R778L, 39.06%, exon 8), followed by c.2621C>T (A874V, 10.94%, exon 11) and c.3316G>A (V1106I, 7.81%, exon 11). Furthermore, we described the thinning of the glomerular basement membrane as a rare pathologically damaging feature of Wilson's disease for the first time. Additionally, two patients who received liver transplant were observed with good prognosis in present study.DiscussionOur work expanded the spectrum of ATP7B variants and presented rare renal pathological feature in WD patients, which may facilitate the development of early diagnosis, counseling, treatment regimens of WD

Fabrication of Silica/PMMA Composite Based Superhydrophobic Coating by Drop Casting Method

The dirt particles are detached and carried away by freely rolling water drops from superhydrophobic surfaces performing self-cleaning ability. Hence, the self-cleaning superhydrophobic surfaces are gaining huge attention of industries due to their useful day-to-day applications. Herein, we synthesized the hydrophobic silica nanoparticles by sol-gel processing of methyltrimethoxysilane (MTMS). The nanocomposite solution consisting suspension of silica nanoparticles in poly(methylmethacrylate) (PMMA) was applied on glass substrate by simple drop casting method. The microscale roughness of the coating facilitated air trapping in the rough protrusions resulting water contact angle higher than 168°. The self-cleaning ability and mechanical durability of the superhydrophobic coating were also evaluated

Characterize Artistic Style based on the Entropy Rate of the Imaginary Stroke Sequences

Recent progress in neuroaesthetics showed that when people appreciate a piece of visual artworks, they can also reconstruct the imaginary stroke sequences from static brushstrokes. This subjective feature is consistent with the general art practice of copying works of art, that is, imitators can interpret the original creative process of practitioners and physically copy their creative tracks. However, this feature has not been measured to characterize artistic styles. This paper attempts to design an experiment to measure this subjective characteristics. 34 pieces of calligraphy and painting works in different styles were chosen as the materials, and 130 testers were invited to draw their imaginary stroke sequences on the chosen pieces. The obtained traces is reinterpreted as AOI(Area of Interests) sequence and modeled as a Markov chain, and its entropy rate with standard error is calculated to measure the randomness. Result show that the measurement can differentiate artistic styles

Characterize Artistic Style based on the Entropy Rate of the Imaginary Stroke Sequences

Recent progress in neuroaesthetics showed that when people appreciate a piece of visual artworks, they can also reconstruct the imaginary stroke sequences from static brushstrokes. This subjective feature is consistent with the general art practice of copying works of art, that is, imitators can interpret the original creative process of practitioners and physically copy their creative tracks. However, this feature has not been measured to characterize artistic styles. This paper attempts to design an experiment to measure this subjective characteristics. 34 pieces of calligraphy and painting works in different styles were chosen as the materials, and 130 testers were invited to draw their imaginary stroke sequences on the chosen pieces. The obtained traces is reinterpreted as AOI(Area of Interests) sequence and modeled as a Markov chain, and its entropy rate with standard error is calculated to measure the randomness. Result show that the measurement can differentiate artistic styles

Land Use/Cover Dynamics in Response to Changes in Environmental and Socio-Political Forces in the Upper Reaches of Yangtze River, China

Land use/cover change (LUCC), which results from the complex interaction of social, ecological and geophysical processes, is a major issue and the main cause of global environmental change. This study analyzed the land use/cover dynamics and their environmental and socio-political forces in the upper reaches of Yangtze River from 1980 to 2000 by using remote sensing, climatic and socio-economic data from both research institutes and government departments. The results indicated that there had been significant land use/cover changes between 1980 and 2000 in the study area, which were characterized by a severe replacement of cropland and woodland with grassland and built-up land. The transition matrices highlight the dominant dynamic events and the internal conversions between land use/cover types during the study period and reveal two distinct transition phases. Land use/cover changes in the upper reaches of Yangtze River during 1980 to 2000, while restricted by environmental attributes, were strongly driven by socio-political factors. However, excessively pursuing higher land use benefits likely results in serious environmental degradation. This study suggests that the restructuring of land use should be based on land suitability and sustainable protection of fragile environment in the upper reaches of Yangtze River. A thorough comprehension of historical changes will enhance our capability to predict future land use change and contribute to effective management strategies and policies for the rational land use

Investigation on nursing service satisfaction of the elderly living in nursing home and influencing factors--Taking Zhengzhou City as an example

Objective: Investigate the status quo of the elderly living in the nursing home and their satisfaction and needs of nursing services, and analyze the service needs of the elderly living in the nursing home of Zhengzhou City. Methods: Randomly select 312 elderly people from 84 nursing homes for questionnaire survey and in-depth interview. Results: More than 60% of the respondents show their satisfaction on the current life in the nursing home. Sex, occupation, physical condition, dietary level, accommodation level, service level and health care conditions of the nursing home have a significant impact on the satisfaction of the elderly. Conclusion: With the growth of the aging of population, improving the level of service for the elderly and improve the service facilities is an essential way to improve the satisfaction and urgent needs of the elderly

Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model

Study on Ultra-short-time Power Forecast of Photovoltaic System based on Ground-based Cloud Image Recognition and Key Impact Factors

In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants

Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine

Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms