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Glycomimetics, which are synthetic molecules designed to mimic the structures and functions of natural carbohydrates, have been developed to overcome the limitations associated with natural carbohydrates. The fluorination of carbohydrates has emerged as a promising solution to dramatically enhance the metabolic stability, bioavailability, and protein-binding affinity of natural carbohydrates. In this review, the fluorination methods used to prepare the fluorinated carbohydrates, the effects of fluorination on the physical, chemical, and biological characteristics of natural sugars, and the biological activities of fluorinated sugars are presented.

PurposeGlobal climate change scenarios forecast the increasing frequency of extreme precipitation events in the Qinghai-Tibet Plateau (QTP), events that likely will result in increased annual rainfall. The effects of varying rainfall frequency on terrestrial ecosystems, especially in QTP regions impacted by global warming, have become a major research topic. However, the responses of soil labile organic carbon fractions to different rainfall frequencies in the QTP remain unknown.MethodsIn this study, we set five rainfall frequencies in the wet meadows of the QTP (control plots, CK; once a week, DF1; once every 2 weeks, DF2; once every 3 weeks, DF3; and once every 4 weeks, DF4). Rainfall frequency treatments received monthly ambient rainfall plus 25 mm of additional rainfall for each irrigation event, and the number of irrigation events was varied. The soil organic carbon fractions (soil organic carbon, SOC; microbial biomass carbon, MBC; and dissolved organic carbon, DOC) in the 0–10, 10–20, and 20–40 cm soil layers were determined.ResultsWe identified significant relationships of soil carbon fractions. With increased rainfall frequency, SOC content increased significantly and DOC content decreased significantly (P < 0.05). The MBC contents of DF3 were 25.1% (0–10 cm) and 32.14% (10–20 cm) higher than that of CK. During the plant growth season, soil carbon components had different patterns over time. The maximum SOC content was recorded in August, and the maximum soil DOC content was recorded in June or July. The minimum content of MBC was observed in July.ConclusionOur results show that the low frequency of extreme rainfall events increased microbial activity and promoted the decomposition of SOC, which was not conducive to the accumulation of soil carbon.

Ship detection in multispectral remote-sensing images is critical in marine surveillance applications. The previously proposed ship-detection methods for multispectral satellite imagery usually work well under ideal conditions. When meeting complex environments such as shadows, mists, or clouds, they fail to detect ships. To solve this problem, we propose a novel spectral-reflectance-based ship-detection method. Research has shown that different materials have unique reflectance curves in the same spectral wavelength range. Based on this observation, we present a new feature using the reflectance gradient across multispectral bands. Moreover, we propose a neural network called lightweight fusion networks (LFNet). This network combines the aforementioned reflectance and the color information of multispectral images to jointly verify the regions with ships. The method utilizes a coarse-to-fine detection framework because of the large-sense-sparse-targets situation in remote-sensing images. In the coarse stage, the proposed reflectance feature vector is used to input the classifier to rule out the regions without ships. In fine detection, the LFNet is used to verify true ships. Compared with some traditional methods that merely depend on appearance features in images, the proposed method takes advantage of employing the reflectance variance in objects between each band as additional information. Extensive experiments have been conducted on multispectral images from four satellites under different weather and environmental conditions to demonstrate the effectiveness and efficiency of the proposed method. The results show that our method can still achieve good performance even under harsh weather conditions.

Due to the complexity and untraceability of the grouting process and the underpinning of the slurry diffusion law, the current study on the grouting properties of alluvial filler soil lags behind the engineering application. Therefore, grouting model tests, including a laboratory soil test and a dynamic penetration test, are developed in this study to investigate the diffusion law of slurry and strength characteristics in alluvial filler soil. Through the excavation of the grouting model, the diffusion pattern of the grouting slurry can be observed precisely. Then an approach proposed in this study for estimating the shear strength growth of the grouting soil is verified by the grouting model tests. In addition, to assess the grouting volume, an analytical model considering the shrinkage coefficient of the slurry is developed. The good agreement between the test data and analytical results shows that the proposed method can effectively estimate the increase in shear strength and grouting amount. The excavation results show that the slurry is generally first filled and fractured along the interface between rock and soil and mainly fractured horizontally, with widths between 0.3~6.0 cm. The curves for the diffusion radius versus the distance from the grouting hole show a wavelike relationship in all directions (i.e., horizontal, up, and down).

Background Global climate change significantly affects photosynthesis in spring wheat. However, the successive dynamic effects of multiple environmental interactions on photosynthesis in spring wheat have been inadequately investigated. This study conducted pot control experiments to determine photosynthesis characteristics, namely light and CO 2 response curves, in spring wheat under progressive drought stress. Results Progressive drought stress caused all parameters of the light response curve to decrease logistically and all parameters of the CO 2 response curve to change exponentially. There were noticeable thresholds for these parameter changes. The ability of spring wheat to utilize light was weakened by progressive drought stress. Under all drought levels, the reduction in photosynthetic capacity was greater under strong light than under weak light. The effects on CO 2 utilization and the corresponding photosynthetic capacity depended on the drought level and CO 2 concentration. The optimal light intensity (I opt ) for spring wheat showed a logistic decreasing trend under progressive drought stress. Unexpectedly, the optimal atmospheric CO 2 concentration (CO 2opt ) remained at 800 µmol·mol − 1 under drought stress, which was less severe than extreme drought. Conclusions Our results showed that progressive drought stress, combined with different environmental factors, had distinct impacts on the photosynthetic efficiency and carbon assimilation capacity of spring wheat, providing a basis for rational carbon and water resource utilization in spring wheat under climate change.

This study aims to accurately characterize the nonlinear stress–strain evolution of rocks under uniaxial compression considering crack propagation. First, the rock meso-structure was generalized into intact rock unit cells, crack propagation damage unit cells, and pore unit cells according to phenomenological theory. A mesoscopic rock stress model considering crack propagation was established based on the static equilibrium relationship of the unit cells, and the effective stress of the crack propagation damage unit cells was solved based on fracture mechanics. Then, the geometric damage theory and conservation-of-energy principle were introduced to construct the damage evolution equation for rock crack propagation. On this basis, the effective stress of the damage unit cells and the crack propagation damage equation were incorporated into the rock meso-structure static equilibrium equation, and the effect of nonlinear deformation in the soft rock compaction stage was considered to establish a rock damage constitutive model based on mesoscopic crack propagation evolution. Finally, methods for determining model parameters were proposed, and the effects of the model parameters on rock stress–strain curves were explored. The results showed that the theoretical model calculations agreed well with the experimental results, thus verifying the rationality of the damage constitutive model and the clear physical meaning of the model parameters.

Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To overcome this drawback, a few methods try to select some key frames and then perform attacks based on them. Unfortunately, their selection strategy is independent of the attacking step, therefore the resulting performance is limited. Instead, we argue the frame selection phase is closely relevant with the attacking phase. The key frames should be adjusted according to the attacking results. For that, we formulate the black-box video attacks into a Reinforcement Learning (RL) framework. Specifically, the environment in RL is set as the recognition model, and the agent in RL plays the role of frame selecting. By continuously querying the recognition models and receiving the attacking feedback, the agent gradually adjusts its frame selection strategy and adversarial perturbations become smaller and smaller. We conduct a series of experiments with two mainstream video recognition models: C3D and LRCN on the public UCF-101 and HMDB-51 datasets. The results demonstrate that the proposed method can significantly reduce the adversarial perturbations with efficient query times.

In nature, organic molecules with great structural diversity and complexity are synthesized by utilizing a relatively small number of starting materials. A synthetic strategy adopted by nature is pathway branching, in which a common biosynthetic intermediate is transformed into different end products. A natural product can also be synthesized by the fusion of two or more precursors generated from separate metabolic pathways. This review article summarizes several representative branching and converging pathways in fungal natural product biosynthesis to illuminate how fungi are capable of synthesizing a diverse array of natural products.

Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is necessary to evaluate their robustness against adversarial examples in the real world. However, current few physical infrared attacks are complicated to implement in practical application because of their complex transformation from the digital world to physical world. To address this issue, in this paper, we propose a physically feasible infrared attack method called “infrared adversarial patches”. Considering the imaging mechanism of infrared cameras by capturing objects’ thermal radiation, infrared adversarial patches conduct attacks by attaching a patch of thermal insulation materials on the target object to manipulate its thermal distribution. To enhance adversarial attacks, we present a novel aggregation regularization to guide the simultaneous learning for the patch’s shape and location on the target object. Thus, a simple gradient-based optimization can be adapted to solve for them. We verify infrared adversarial patches in different object detection tasks with various object detectors. Experimental results show that our method achieves more than 90% Attack Success Rate (ASR) versus the pedestrian detector and vehicle detector in the physical environment, where the objects are captured in different angles, distances, postures, and scenes. More importantly, infrared adversarial patch is easy to implement, and it only needs 0.5 h to be manufactured in the physical world, which verifies its effectiveness and efficiency. Another advantage of our infrared adversarial patches is the ability to extend to attack the visible object detector in the physical world. As a consequence, we can simultaneously perform the infrared and visible physical attacks by a unified adversarial patch, which shows the good generalization.

Data-driven geotechnics is an emerging research field that contributes to the digitalization of geotechnical engineering. Among the numerous applications of digital techniques in geotechnical engineering, interpreting and simulating stratigraphic conditions with quantified uncertainty is an essential task and an open question in geotechnical practice. However, developing an uncertainty-aware integration of subjective engineering judgments (i.e., geological knowledge) and sparse objective site exploration results (i.e., borehole observations) is challenging. This investigation develops an effective three-dimensional stochastic geological modeling framework based on Markov random field (MRF) theory and Bayesian machine learning to characterize stratigraphic uncertainty. The proposed model considers both stratigraphic uncertainty (inherent) and model uncertainty (imperfect knowledge). A stratigraphic modeling example was studied to demonstrate the effectiveness of the proposed approach. We envision that this approach could be further generalized to industrial practices to improve risk control in geotechnical engineering.