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The Moon has a magmatic and thermal history that is distinct from that of the terrestrial planets 1 . Radioisotope dating of lunar samples suggests that most lunar basaltic magmatism ceased by around 2.9–2.8 billion years ago (Ga) 2 , 3 , although younger basalts between 3 Ga and 1 Ga have been suggested by crater-counting chronology, which has large uncertainties owing to the lack of returned samples for calibration 4 , 5 . Here we report a precise lead–lead age of 2,030 ± 4 million years ago for basalt clasts returned by the Chang’e-5 mission, and a 238 U/ 204 Pb ratio ( µ value) 6 of about 680 for a source that evolved through two stages of differentiation. This is the youngest crystallization age reported so far for lunar basalts by radiometric dating, extending the duration of lunar volcanism by approximately 800–900 million years. The µ value of the Chang’e-5 basalt mantle source is within the range of low-titanium and high-titanium basalts from Apollo sites ( µ value of about 300–1,000), but notably lower than those of potassium, rare-earth elements and phosphorus (KREEP) and high-aluminium basalts 7 ( µ value of about 2,600–3,700), indicating that the Chang’e-5 basalts were produced by melting of a KREEP-poor source. This age provides a pivotal calibration point for crater-counting chronology in the inner Solar System and provides insight on the volcanic and thermal history of the Moon. Basalt samples returned from the Moon by the Chang’e-5 mission are revealed to be two billion years old by radioisotopic dating, providing insight on the volcanic history of the Moon.

Due to frequent changes in the humid and hot environment, the residual soil with a particle-size distribution (PSD) from gravel to clay experiences multiple drying–wetting cycles. The pressure plate test and nuclear magnetic resonance (NMR) spectroscopy were used to investigate the influence of drying–wetting cycles on the soil–water characteristic curve (SWCC) and pore-size distribution (POSD) of undisturbed residual soil. The results showed that the water-holding capacity of the residual soil decreased as the number of drying–wetting cycles increased and gradually stablilized, and then the van Genuchten (VG) model was found to perform well on the SWCC during the drying–wetting processes. The NMR results indicated a double-pore structure, and the porosity of the residual soil as well as the internal water content increased smoothly with more drying–wetting cycles. The obtained POSD curve of soil implied that drying–wetting cycles had a more obvious effect on small pores and macro-pores than on micro-pores and meso-pores. Theoretical calculations evinced that the product of the matric suction and relaxation time should be constant at a constant temperature. However, the experimental results did not effectively reflect such a relation between the matric suction and relaxation time. A modified VG model based on the cumulative pore volume was utilized to describe the POSD under drying–wetting cycles. Subsequently, the proposed Rational2D surface equation was used to accurately reflect the internal relationship between the SWCC and POSD curve under different numbers of drying–wetting cycles. Moreover, the fractal model for the SWCC derived from the capillary theory confirmed that the matric suction had a strong linear relationship with the relative volumetric water content in the log-log scale. Also, the fractal dimension can be approximated as a constant, because its attenuation is small with more drying–wetting cycles.

Agricultural machinery input (AMI) plays a crucial role in promoting agricultural green development and advancing agricultural modernization. Based on panel data from 130 cities in the Yangtze River Economic Belt (YREB) from 2013 to 2022, we employ the spatial Durbin model for empirical analysis and draws the following. (1) The AMI in the YREB promotes local agricultural green production efficiency (AGPE) but inhibits that of neighboring areas. Moreover, with the increase in AMI, the impacts exhibit an “inverted U-shaped” and “U-shaped” trend, respectively. (2) The positive direct impact of AMI on AGPE is smaller than its negative spillover effect. (3) The expansion of agricultural operation scale (AOS) and the increase in grain yield per unit area (GYPUA) facilitate AMI in promoting local AGPE. Moreover, the expansion of AOS mitigates the negative impact of AMI on the AGPE of neighboring areas. (4) Notably, a threshold effect exists in the relationship between AMI and AGPE for both AOS and GYPUA. When AOS and GYPUA exceed certain threshold values, the promoting effect of AMI on AGPE is further strengthened. (5) Heterogeneity analysis shows that AMI in the upper, middle, and lower reaches of the YREB exhibits distinct patterns. Based on these findings, the following policy recommendations are proposed: establishing an agricultural machinery information service platform, strengthening vocational education and training for farmers, increasing support for energy-efficient and environmentally friendly agricultural machinery, and promoting land transfer.

Flavonoids and isoflavonoids are polyphenolic secondary metabolites usually produced by plants adapting to changing ecological environments over a long period of time. Therefore, their biosynthesis pathways are considered as the most distinctive natural product pathway in plants. Seemingly, the flavonoids and isoflavones from fungi and actinomycetes have been relatively overlooked. In this review, we summarized and classified the isoflavones and flavonoids derived from fungi and actinomycetes and described their biological activities. Increasing attention has been paid to bioactive substances derived from microorganism whole-cell biotransformation. Additionally, we described the utilization of isoflavones and flavonoids as substrates by fungi and actinomycetes for biotransformation through hydroxylation, methylation, halogenation, glycosylation, dehydrogenation, cyclisation, and hydrogenation reactions to obtain rare and highly active biofunctional derivatives. Overall, among all microorganisms, actinomycetes are the main producers of flavonoids. In our review, we also summarized the functional genes involved in flavonoid biosynthesis.

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.

The presence of cracks significantly influences the engineering properties of unsaturated residual soil, particularly under external air pressure fluctuation (i.e. Δua). A formula is developed that integrates existing crack depth theories with factors such as soil shear strengths, effective stress parameters, effective tensile strength reduction coefficients, and environmental changes like infiltration and evaporation. In addition, a further equation is deduced to determine the surface flow rate cracking value Q0, which can reflect the influence of seasonal climate on the matric suction of the surface soil. Our findings reveal that crack depth is quite reactive to short-term fluctuations in external air pressure, effective stress parameters and evaporation intensity, with remarkable increases in crack depth linked to these variations. The theoretical depth of soil crack increases by 0.12 ∼ 0.27m when the external air pressure fluctuation (i.e. Δua) is 3 kPa. Soils subjected to climatic conditions crack only when Q>Q0, and early cracking of soils under Δua>0 is conditioned by increasing infiltration and effective cohesion or reducing evaporation. The variation rule of the theoretical crack depth calculated in this research shows consistency with the previous equations, and the model predictions at Δua = 3kPa are proved to match well with the measured results of the practical engineering subjected to various climate environment factors.

Background Liver cancer is a common malignant tumor in China, with high mortality. Its occurrence and development were thoroughly studied by high-throughput expression microarray, which produced abundant data on gene expression, mRNA quantification and the clinical data of liver cancer. However, the hub genes, which can be served as biomarkers for diagnosis and treatment of early liver cancer, are not well screened. Results Here we present a new method for getting 6 key genes, aiming to diagnose and treat the early liver cancer. We firstly analyzed the different expression microarrays based on TCGA database, and a total of 1564 differentially expressed genes were obtained, of which 1400 were up-regulated and 164 were down-regulated. Furthermore, these differentially expressed genes were studied by using GO and KEGG enrichment analysis, a PPI network was constructed based on the STRING database, and 15 hub genes were obtained. Finally, 15 hub genes were verified by applying the survival analysis method on Oncomine database, and 6 key genes were ultimately identified, including PLK1, CDC20, CCNB2, BUB1, MAD2L1 and CCNA2. The robustness analysis of four independent data sets verifies the accuracy of the key gene’s classification of the data set. Conclusions Although there are complicated differences between cancer and normal cells in gene functions, cancer cells could be differentiated in case that a group of special genes expresses abnormally. Here we presented a new method to identify the 6 key genes for diagnosis and treatment of early liver cancer, and these key genes can help us understand the pathogenesis of liver cancer more deeply.

Image captioning generates a semantic description of an image. It deals with image understanding and text mining, which has made great progress in recent years. However, it is still a great challenge to bridge the “semantic gap” between low-level features and high-level semantics in remote sensing images, in spite of the improvement of image resolutions. In this paper, we present a new model with an attribute attention mechanism for the description generation of remote sensing images. Therefore, we have explored the impact of the attributes extracted from remote sensing images on the attention mechanism. The results of our experiments demonstrate the validity of our proposed model. The proposed method obtains six higher scores and one slightly lower, compared against several state of the art techniques, on the Sydney Dataset and Remote Sensing Image Caption Dataset (RSICD), and receives all seven higher scores on the UCM Dataset for remote sensing image captioning, indicating that the proposed framework achieves robust performance for semantic description in high-resolution remote sensing images.

Global Navigation Satellite Systems (GNSSs) can provide high-precision positioning services, which can be applied to fields including navigation and positioning, autonomous driving, unmanned aerial vehicles and so on. However, GNSS signals are easily disrupted in complex environments, which results in a positioning performance with a significantly inferior accuracy and lengthier convergence time, particularly for the single GNSS system. In this paper, multi-GNSS precise point positioning (PPP) with tightly integrating ultra-wide band (UWB) technology is presented to implement fast and precise navigation and positioning. The validity of the algorithm is evaluated by a set of GNSS and UWB data. The statistics indicate that multi-GNSS/UWB integration can significantly improve positioning performance in terms of the positioning accuracy and convergence time. The improvement of the positioning performance for the GNSS/UWB tightly coupled integration mainly concerns the north and east directions, and to a lesser extent, the vertical direction. Furthermore, the convergence performance of GNSS/UWB solution is analyzed by simulating GNSS signal interruption. The reliability and robustness of GNSS/UWB solution during GNSS signal interruption is verified. The results show that multi-GNSS/UWB solution can significantly improve the accuracy and convergence speed of PPP.

Background Targeting specificity has been a barrier to applying genome editing systems in functional genomics, precise medicine and plant breeding. In plants, only limited studies have used whole-genome sequencing (WGS) to test off-target effects of Cas9. The cause of numerous discovered mutations is still controversial. Furthermore, WGS-based off-target analysis of Cpf1 (Cas12a) has not been reported in any higher organism to date. Results We conduct a WGS analysis of 34 plants edited by Cas9 and 15 plants edited by Cpf1 in T0 and T1 generations along with 20 diverse control plants in rice. The sequencing depths range from 45× to 105× with read mapping rates above 96%. Our results clearly show that most mutations in edited plants are created by the tissue culture process, which causes approximately 102 to 148 single nucleotide variations (SNVs) and approximately 32 to 83 insertions/deletions (indels) per plant. Among 12 Cas9 single guide RNAs (sgRNAs) and three Cpf1 CRISPR RNAs (crRNAs) assessed by WGS, only one Cas9 sgRNA resulted in off-target mutations in T0 lines at sites predicted by computer programs. Moreover, we cannot find evidence for bona fide off-target mutations due to continued expression of Cas9 or Cpf1 with guide RNAs in T1 generation. Conclusions Our comprehensive and rigorous analysis of WGS data across multiple sample types suggests both Cas9 and Cpf1 nucleases are very specific in generating targeted DNA modifications and off-targeting can be avoided by designing guide RNAs with high specificity.