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Abstract DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

Abstract Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.

The identification, evaluation, and spatial distribution of “Production–Living–Ecological” space (PLEs) have been widely studied, but there is still little attention paid to whether their distribution characteristics will vary with scale changes. This article takes the organic whole of the PLEs composed of production space, living space, and ecological space on Hainan Island as the research object. Starting from the perspective of spatial heterogeneity, it quantitatively evaluates its spatial functions and explores the regularities of its aggregation and coordination characteristics with changes in scale, revealing the evolution of its distribution pattern with changes in scale. The results show that: (1) The distribution of PLEs in Hainan Island has obvious heterogeneity. The functional values of production and living space are distributed in a pattern of high in the south and north, low in the middle; The ecological space shows a high distribution pattern in the middle and low around it. (2) The PLEs in Hainan Island is significantly affected by scale effects. The degree of aggregation decreases as the scale increases, with the ecological space showing the most obvious downward trend, the living space showing a weaker downward trend and the production space being moderate. (3) The overall level of coupling-coordination of PLEs in Hainan Island is low, which decreases as the scale increases, with 500 m × 500 m being the peak value. The research results of this article indicate that there are scale effects in the functional distribution characteristics of PLEs, which can provide decision support for the national spatial planning at different scales.

Few studies have attempted to investigate the impact of airports on the ecological environment. This paper compares the effects of airports in China and Germany on the surrounding ecological environment and discusses the underlying institutional mechanisms that lead to these impacts. We used remote-sensing ecological indicators to assess ecological environment quality. A buffer analysis was used to determine the influence scopes of airports on the ecological environment. The institutional analysis development framework was adopted to investigate the functions of different institutions on the influence scopes and intensities. The results showed that the Chinese airport had obvious negative effects and that its impact scope was wide, while the impact intensity of the German airport was weaker. These significant differences stem from the distinct institutional systems that structure the two airports. Our findings ultimately provide insight into how to improve the relationship between infrastructure construction and environmental protection in China.