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Abstract Simultaneous multi-target detection and multi-site gene editing are two key factors restricting the development of disease diagnostic and treatment technologies. Despite numerous explorations on the source, classification, functional features, crystal structure, applications and engineering of CRISPR-Cas13a, all reports use the contiguous target RNA activation paradigm that only enables single-target detection in vitro and one-site gene editing in vivo. Here we propose a noncontiguous target RNA activation paradigm of Cas13a and establish a CRISPR-Cas13a Gemini System composed of two Cas13a:crRNA binary complexes, which can provide rapid, simultaneous, highly specific and sensitive detection of two RNAs in a single readout, as well as parallel dual transgene knockdown. CRISPR-Cas13a Gemini System are demonstrated in the detection of two miRNAs (miR-155 and miR-375) for breast cancer diagnosis and two small RNAs (EBER-1 and EBER-2) for Epstein-Barr virus diagnosis using multiple diagnostic platforms, including fluorescence and colorimetric-based lateral flow systems. We also show that CRISPR-Cas13a Gemini System can knockdown two foreign genes (EGFP and mCherry transcripts) in mammalian cells simultaneously. These findings suggest the potential of highly effective and simultaneous detection of multiple biomarkers and gene editing of multiple sites.

The environment of mountain ecosystems can change greatly in short distances as elevation increases. The effects of elevation change on the distribution and body size of carabid beetles were investigated at elevations of 750–2600 m in the Changbaishan Nature Reserve (Northeast China). The richness and abundance of carabid species decreased significantly as elevation increased. However, the change trends are different in forests and tundra. In the broad-leaved Korean pine forest and coniferous forest at low elevations, carabid beetle species have high richness and abundance. The community composition of carabid beetles was significantly different at different elevations and among different vegetation types. Some species only occurred at specific elevations. There were fewer indicator species in high-elevation areas, but Carabus macleayi Dejean, Nebria pektusanica Horratovich and Pterostichus jaechi Kirschenhofer were mainly found in high-elevation areas. The average body size of species in the carabid beetle community was negatively correlated with elevation. The sizes of the larger Carabus canaliculatus Adams and Carabus venustus Morawitz were negatively correlated with elevation. Their body sizes decreased obviously in the tundra at elevations above 2000 m. Changes in vegetation types at high elevations affect the distribution and body sizes of beetles along the elevation gradient. Some large carabid species may be smaller at high elevations where a unique insect fauna has developed. The body size and distribution range of the carabid may be the factors that affect body size reduction at high elevation. Although some high-elevation species also occur in low-elevation areas, the protection of species diversity in high-elevation areas should be emphasized in the context of global climate change. The results illustrate the mechanisms of carabid beetles’ response to elevation change and the need for carabid beetles’ diversity conservation under global climate change.

Geomatics is an interdisciplinary subject. Many disciplines have teaching demands in this field. A new course on “Geomatics Technology” has been suggested by the Weiyang College of Tsinghua University of China for the major of “Mathematical and Scientific Basic Science+Civil, Hydraulic and Marine Engineering”. This paper offers a data-led geomatics teaching mode, developing a customized teaching cloud platform, to explore the cross-integrated innovative teaching methods. Teachers and students can assign and submit assignments on this platform. The platform constitutes a data flow with the data download, data processing and result sharing. It encourages communication among students in various majors, grades and units using data as the medium, from data processing to application upstream and downstream. In the “Geomatics Technology” course, geospatial data has emerged as a vital element of the multidisciplinary approach. This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management. Furthermore, the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering, to offer a novel idea for the reform of courses linked to geospatial informatics.

Rapid urban expansion caused by vigorous urban population growth brought up various socioeconomic and eco-environmental problems, which have important ramifications for sustainable development across the world. Along with the accelerated urbanization process, accurate and realistic prediction of urban expansion is of great importance to optimize urban planning and urban development. This study proposed a new hybrid model, which combined the urban scaling law (USL) with the ANN-CA model to predict urban expansion. To employ urban scaling law in the model, we innovatively calculated the law exponent at a single-city scale. Based on USL, we estimated urban land demand in the future by panel data regression. Finally, we added the area constraint and ecological constraint into the ANN-CA model to simulate urban expansion spatially. This frame of urban expansion has been successfully applied in Shenzhen, of which the urban land area would increase from 816.45 km2 in 2020 to 842.48 km2 in 2025. By comparing this model with the traditional prediction method, we proved its effectiveness and accuracy. Besides, we found that the scaling exponent can reflect urbanization level and distinguish overconstructed cities.

Albeit hyperspectral image (HSI) classification methods based on deep learning have presented high accuracy in supervised classification, these traditional methods required quite a few labeled samples for parameter optimization. When processing HSIs, however, artificially labeled samples are always insufficient, and class imbalance in limited samples is inevitable. This study proposed a Transformer-based framework of spatial–spectral–associative contrastive learning classification methods to extract both spatial and spectral features of HSIs by the self-supervised method. Firstly, the label information required for contrastive learning is generated by a spatial–spectral augmentation transform and image entropy. Then, the spatial and spectral Transformer modules are used to learn the high-level semantic features of the spatial domain and the spectral domain, respectively, from which the cross-domain features are fused by associative optimization. Finally, we design a classifier based on the Transformer. The invariant features distinguished from spatial–spectral properties are used in the classification of satellite HSIs to further extract the discriminant features between different pixels, and the class intersection over union is imported into the loss function to avoid the classification collapse caused by class imbalance. Conducting experiments on two satellite HSI datasets, this study verified the classification performance of the model. The results showed that the self-supervised contrastive learning model can extract effective features for classification, and the classification generated from this model is more accurate compared with that of the supervised deep learning model, especially in the average accuracy of the various classifications.

Runoff complexity is an important indicator reflecting the sustainability of a watershed ecosystem. In order to explore the multiscale characteristics of runoff complexity and analyze its variation and influencing factors in the Yanhe watershed in China during the period 1991–2020, we established a new analysis method for watershed runoff complexity based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method for the decomposition of multiscale characteristics and the refined composite multiscale entropy (RCMSE) method for the quantification of the system complexity. The results show that runoff and its components all present multiscale complexity characteristics that are different from random signals, and the intermediate frequency modes contribute the most to runoff complexity. The runoff complexity of the Yanhe watershed has decreased gradually since 1991, and 2010 was a turning point of runoff complexity, when it changed from a decline to an increase, indicating that the ecological sustainability of this basin has improved since 2010, which was mainly related to the ecological restoration measures of the Grain for Green Project. This study expands the research perspective for analyzing the variation characteristics of runoff at the multiscale, and provides a reference for the study of watershed ecological sustainability and ecological management.

Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised learning-based algorithm, 3S-HSID, for denoising real satellite hyperspectral images without requiring external data support. The 3S-HSID framework can perform robust denoising of a single satellite hyperspectral image in all bands simultaneously. It first conducts a Bernoulli sampling of the input data, then uses the Bernoulli sampling results to construct the training pairs. Furthermore, the global spectral consistency and minimum local variance are used in the loss function to train the network. We use the training model to predict different Bernoulli sampling results, and the average of multiple predicted values is used as the denoising result. To prevent overfitting, we adopt a dropout strategy during training and testing. The results of denoising experiments on the simulated hyperspectral data show that the denoising performance of 3S-HSID is better than most state-of-the-art algorithms, especially in terms of maintaining the spectral characteristics of hyperspectral images. The denoising results for different types of real satellite hyperspectral data also demonstrate the reliability of the proposed method. The 3S-HSID framework provides a new technical means for real satellite hyperspectral image preprocessing.

Urban scaling laws describe powerful universalities of the scaling relationships between urban attributes and the city size across different countries and times. There are still challenges in precise statistical estimation of the scaling exponent; the properties of variance require further study. In this paper, a statistical regression method based on the maximum likelihood estimation considering the lower bound constraints and the heterogeneous variance of error structure, termed as CHVR, is proposed for urban scaling estimation. In the CHVR method, the heterogeneous properties of variance are explored and modeled in the form of a power-of-the-mean variance model. The maximum likelihood fitting method is supplemented to satisfy the lower bound constraints in empirical data. The CHVR method has been applied to estimating the scaling exponents of six urban attributes covering three scaling regimes in China and compared with two traditional methods. Method evaluations based on three different criteria validate that compared with both classical methods, the CHVR method is more effective and robust. Moreover, a statistical test and long-term variations of the parameter in the variance function demonstrate that the proposed heterogeneous variance function can not only describe the heterogeneity in empirical data adequately but also provide more meaningful urban information. Therefore, the CHVR method shows great potential to provide a valuable tool for effective urban scaling studies across the world and be applied to scaling law estimation in other complex systems in the future.

An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation.

Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established.

Distinguishing and characterizing different landscape patterns have long been the primary concerns of quantitative landscape ecology. Information theory and entropy-related metrics have provided the deepest insights in complex system analysis, and have high relevance in landscape ecology. However, ideal methods to compare different landscape patterns from an entropy view are still lacking. The overall aim of this research is to propose a new form of spatial entropy (Hs) in order to distinguish and characterize different landscape patterns. Hs is an entropy-related index based on information theory, and integrates proximity as a key spatial component into the measurement of spatial diversity. Proximity contains two aspects, i.e., total edge length and distance, and by including both aspects gives richer information about spatial pattern than metrics that only consider one aspect. Thus, Hs provides a novel way to study the spatial structures of landscape patterns where both the edge length and distance relationships are relevant. We compare the performances of Hs and other similar approaches through both simulated and real-life landscape patterns. Results show that Hs is more flexible and objective in distinguishing and characterizing different landscape patterns. We believe that this metric will facilitate the exploration of relationships between landscape patterns and ecological processes.

A significant difficulty in urban studies is obtaining urban areas. Nighttime light (NTL) data provide efficient approaches to map urban areas. Previous methods have utilized visual particularities of cities with ancillary data to obtain the optimal thresholds. How cities behave differently from rural areas should be considered. A Zipf’s law-based method is proposed to bootstrap the optimal threshold based on the statistical properties of a Zipf’s law model on continuous thresholds at the country scale. In our method, the Zipf’s law model is utilized to quantify fractal, self-organized, and agglomeration behaviors of cities. The three-phase cluster dynamics are discovered and the abrupt transition between Phase 1 and Phase 2 denotes the rural-urban demarcation point. The urban areas are derived by the proposed method from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data in 2013 in China. An accuracy assessment is conducted to compare it with the GlobeLand30-2010 data and the overall accuracy has directly confirmed the effectiveness of the method. The validation using point of interest (POI) data verifies that the urban areas show strong responses to social interactions and production with R2 values of 0.91 and 0.92, respectively, implying that the city areas extracted by our method can be a proxy for human activities. Comparisons with existing methods validate the effectiveness and high degree of automation of the proposed method in mapping urban areas at the country level. According to our analyses, the Zipf’s law-based method shows great potential to provide a universal criterion to map urban areas from the perspective of the behaviors of urban systems without ancillary data, and a valuable tool for spatial and temporal urban studies.