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Additional file 2: Table S1. Primers used for RT-PCR.

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Additional file 1: Supplementary Fig. 1 Principal component analysis of the internal quality parameters of self-rooted and grafted vines. Supplementary Fig. 2 Soil volumetric moisture content of well-watered and drought stress in self-rooted and grafted vines.

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Additional file 1: Figure S1. Radiomics feature selection using minimum redundancy and maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO). Figure S2. The contribution of each feature to the radiomic signature was shown by histogram. Figure S3. Performance of rad-score in discriminating between osteoporosis and osteopenia. Figure S4. Performance of rad-score in discriminating between normal and osteopenia, abnormal and osteoporosis. Table S1. Pyradiomics plug-in unit extraction parameters (v2.2). Table S2. Characteristics of Patients.

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3D facial landmark, which offers more expressive and occlusive information than its 2D counterpart, receives more and more attention from researchers in recent years. The top performing algorithms for 3D facial landmark detection are mainly divided into two categories: the two-step approach and the volume representation method. However, the former lacks the relation of depth with plat and the latter leads to large computation. In this paper, we propose the Combined Heatmap and Coordinate Regression (CHCR), which is an end-to-end method for 3D facial landmark detection from a single 2D image. To achieve that, we innovatively present the combined heatmap of three channels, and each channel of heatmap records the likelihoods of landmarks location of any two different axes. Such representation maintains the relation of various view while decreases either the channels for encoding or dimension for decoding. Then we retrieve the 3D coordinate vectors from corresponding combined heatmap by coordinate regression. Hence, an encoder-decoder network with a simple CNN attached to an hourglass module is designed to cope with the whole process. Experiments show our model is extremely light-weighted and runs faster than any other methods while on high performance of accuracy.

In recent years, geoparks (a geoheritage effort to protect geological features) have been vigorously promoted in China and abroad. The evaluation of a geopark is important for geopark development. However, current evaluation methods focus on geoheritage and their values, and often ignore residents’ situations, even though community participation is essential to the sustainable development of geoparks. In this paper, the Mt. Huaying Grand Canyon Geological Park was selected as a study area to evaluate resident’s perceptions of the park. Perception impact factors were selected from the perspective of the park residents. Quantitative evaluation models, based on data from questionnaires, were created using the hierarchy process model and expert evaluation method. The results showed that the following factors influenced residents’ perceptions, listed from the most significant to the least: residents’ understanding of geoheritage, participation level in commercial activities, participation level in planning decisions, satisfaction level regarding benefit distribution and level of participation willingness. It was found that residents’ level of understanding of geoheritage and their participation level in planning decisions were the main reasons for poorer perceptions of geoheritage. The keys to improving residents’ perceptions of geoheritage are to change the management system, implement people-centered policies and to establish a government-led management mode that encourages community participation and involves private business contracts.