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Accurate inference of the relationship between non-coding RNAs (ncRNAs) and drug resistance is essential for understanding the complicated mechanisms of drug actions and clinical treatment. Traditional biological experiments are time-consuming, laborious, and minor in scale. Although several databases provide relevant resources, computational method for predicting this type of association has not yet been developed. In this paper, we leverage the verified association data of ncRNA and drug resistance to construct a bipartite graph and then develop a linear residual graph convolution approach for predicting associations between non-coding RNA and drug resistance (LRGCPND) without introducing or defining additional data. LRGCPND first aggregates the potential features of neighboring nodes per graph convolutional layer. Next, we transform the information between layers through a linear function. Eventually, LRGCPND unites the embedding representations of each layer to complete the prediction. Results of comparison experiments demonstrate that LRGCPND has more reliable performance than seven other state-of-the-art approaches with an average AUC value of 0.8987. Case studies illustrate that LRGCPND is an effective tool for inferring the associations between ncRNA and drug resistance.

In this study, we analyzed the spatiotemporal variation of cold surges in Inner Mongolia between 1960 and 2012 and their possible driving factors using daily minimum temperature data from 121 meteorological stations in Inner Mongolia and the surrounding areas. These data were analyzed utilizing a piecewise regression model, a Sen+Mann-Kendall model, and a correlation analysis. Results demonstrated that (1) the frequency of single-station cold surges decreased in Inner Mongolia during the study period, with a linear tendency of-0.5 times/10a (-2.4 to 1.2 times/10a). Prior to 1991, a significant decreasing trend of-1.1 times/10a (-3.3 to 2.5 times/10a) was detected, while an increasing trend of 0.45 times/10a (-4.4 to 4.2 times/10a) was found after 1991. On a seasonal scale, the trend in spring cold surges was consistent with annual values, and the most obvious change in cold surges occurred during spring. Monthly cold surge frequency displayed a bimodal structure, and November witnessed the highest incidence of cold surge. (2) Spatially, the high incidence of cold surge is mainly observed in the northern and central parts of Inner Mongolia, with a higher occurrence observed in the northern than in the central part. Inter-decadal characteristic also revealed that high frequency and low frequency regions presented decreasing and increasing trends, respectively, between 1960 and 1990. High frequency regions expanded after the 1990s, and regions exhibiting high cold surge frequency were mainly distributed in Tulihe, Xiao'ergou, and Xi Ujimqin Banner. (3) On an annual scale, the cold surge was dominated by AO, NAO, CA, APVII, and CQ. However, seasonal differences in the driving forces of cold surges were detected. Winter cold surges were significantly correlated with AO, NAO, SHI, CA, TPI, APVII, CW, and IZ, indicating they were caused by multiple factors. Autumn cold surges were mainly affected by CA and IM, while spring cold surges were significantly correlated with CA and APVII.

Abstract:Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assisted estimators in various overall accuracies of classification and sampling fractions. In this study, the confusion matrix calibration direct estimator, confusion matrix calibration inverse estimator, ratio estimator, and simple regression estimator were implemented to infer the areas of several land cover classes using simple random sampling without replacement in two experiments: a case study using simulation data based on RapidEye images and that using actual RapidEye and Huan Jing (HJ)-1A images. In addition, the simple estimator using a simple random sample without replacement was regarded as a basic estimator. The comparison results suggested that the confusion matrix calibration estimators, ratio estimator, and simple regression estimator could provide more accurate and stable estimates than the simple random sampling estimator. In addition, high-quality classification data played a positive role in the estimation, and the confusion matrix inverse estimators were more sensitive to the classification accuracy. In the simulated experiment, the average deviation of a confusion matrix calibration inverse estimator decreased by about 0.195 with the increasing overall accuracy of classification; otherwise, the variation of the other three model-assisted estimators was 0.102. Moreover, the simple regression estimator was slightly superior to the confusion matrix calibration

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