Assessing remote sensing application on rangeland insurance in Canadian prairies

Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour I’Observation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program

Can Transfer Payment Reduce the Inequality of Compulsory Education in Poor Areas? An Empirical Study Based on the Data from 18 Counties in 6 Provinces in China

Transfer payment is of great significance for poverty alleviation and balanced regional development. Based on the first-hand survey data from 18 key counties of 6 provinces in China’s national poverty alleviation and development program, this paper uses propensity score matching to calculate the effects of transfer payment funds on the results of compulsory education in impoverished areas and uses Shapley value decomposition to decompose correlated factors. It finds that when the characteristics of students, families and schools controlled, transfer payment funds significantly lower student academic achievements in some subjects and aggravate the inequality of educational results, which may be the result of the reduction of local education funds caused by the “crowding out effect” of transfer payment. Suggestions are made in this paper to standardize the utilization of transfer payment funds, establish a linkage mechanism between the educational results of poor students and transfer payment funds, implement the assisting plan for teachers and students in poor areas, strengthen the “pertinent support for intelligence development”, and unite multiple agencies to increase input in education, with the purpose of reducing the inequality of compulsory education in poor areas

Epigenetic features are significantly associated with alternative splicing

<p>Abstract</p> <p>Background</p> <p>While alternative splicing (AS) contributes greatly to protein diversities, the relationship between various types of AS and epigenetic factors remains largely unknown.</p> <p>Results</p> <p>In this study, we discover that a number of epigenetic features, including DNA methylation, nucleosome occupancy, specific histone modifications and protein features, are strongly associated with AS. To further enhance our understanding of the association between these features and AS, we cluster our investigated features based on their association patterns with each AS type into four groups, with H3K36me3, EGR1, GABP, SRF, SIN3A and RNA Pol II grouped together and showing strongest association with AS. In addition, we find that the AS types can be classified into two general classes, namely the exon skipping related process (ESRP), and the alternative splice site selection process (ASSP), based on their association levels with the epigenetic features.</p> <p>Conclusion</p> <p>Our analysis thus suggests that epigenetic features are likely to play important roles in regulating AS.</p

Estimating Sparse Precision Matrix: Optimal Rates of Convergence and Adaptive Estimation

Precision matrix is of significant importance in a wide range of applications in multivariate analysis. This paper considers adaptive minimax estimation of sparse precision matrices in the high dimensional setting. Optimal rates of convergence are established for a range of matrix norm losses. A fully data driven estimator based on adaptive constrained ℓ1 minimization is proposed and its rate of convergence is obtained over a collection of parameter spaces. The estimator, called ACLIME, is easy to implement and performs well numerically. A major step in establishing the minimax rate of convergence is the derivation of a rate-sharp lower bound. A “two-directional” lower bound technique is applied to obtain the minimax lower bound. The upper and lower bounds together yield the optimal rates of convergence for sparse precision matrix estimation and show that the ACLIME estimator is adaptively minimax rate optimal for a collection of parameter spaces and a range of matrix norm losses simultaneously