Analyzing spatial patterns and driving factors of cropland change in China's National Protected Areas for sustainable management.

Farming in protected areas frequently challenges ecological conservation goals while supporting local livelihoods. To balance protection and agriculture, a comprehensive understanding of cropland dynamics in protected areas is of paramount importance. However, studies addressing this trade-off are relatively scarce, especially considering explicit Chinese government regulations on population relocation and cropland retirement in National Protected Areas (NPAs). Our study examined the spatial and temporal pattern of cropland in NPAs and explored the covariance between cropland density and species richness. Concurrently, the driving factors of cropland development in NPAs were analyzed using Multiple Linear Regression. The results indicate that the cropland area in NPAs continued to expand, growing from 1.93 to 2.34 million hectares in 2000-2020, with a cropland density of approximately 0.4. Cropland expansion in the northern NPAs, particularly in the resource-rich Northeast (28.12 %) and the Northwest with high marginal agricultural returns (38.26 %), have encroached upon species habitats and aggravated biodiversity loss. Moreover, cities with higher cropland densities in NPAs are usually located at borders, possibly due to decentralized management. The Multiple Linear Regression results show that high cropland density is usually associated with a high population density (ß = 0.156) and lower levels of rural education (ß = -0.101) and income (ß = -0.122). To mitigate the issue of cropland development in NPAs, it is crucial to avoid one-size-fits-all management strategies, strengthen regional legal supervision, adjust fiscal incentives, and promote eco-friendly agriculture. In the north regions, the expansion of cropland in NPAs should be strictly controlled. For the southwest, the positive role of preserving cropland in NPAs for alleviating human-nature conflict and maintaining social stability should be emphasized. This study provides research support for China's exploration of geographically suitable strategies for controlling cropland in NPAs. Moreover, the findings could serve as a reference for the governance of NPAs in other countries.

Fulfilling global climate pledges can lead to major increase in forest land on Tibetan Plateau.

The UN Climate Change Conference in Glasgow spawned the enhancement and updating of many nations' climate pledges. Previous research has investigated the effects of these pledges on limiting planetary warming, but their spatially explicit effects on land use/cover are unknown. Here, we linked the Glasgow pledges and the spatially explicit responses of the Tibetan Plateau's land systems. We found that while fulfilling global climate pledges may not significantly affect the global shares of forestland, grassland/pasture, shrubland, and cropland, it needs a 9.4% increase in the forest area of the Tibetan Plateau. This need is an area 11.4 times the increase of the plateau's forest in the 2010s, or greater than the size of Belgium. The new forest comes mainly from the medium-density grassland in the Yangtze River basin, calling for more proactive environmental management for the headwaters area of this longest river in Asia.

CLUMondo-BNU for simulating land system changes based on many-to-many demand-supply relationships with adaptive conversion orders.

Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand-supply relationships. In this study, we first investigated the source code of CLUMondo, providing a complete, detailed mechanism of this model. We found that the featured function of CLUMondo-balancing demands and supplies in a many-to-many mode-relies on a parameter called conversion order. The setting of this parameter is a manual process and requires expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. Comparative experiments demonstrated the validity and effectiveness of the proposed automated method. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting in CLUMondo-BNU v1.0. This study facilitates the application of CLUMondo and helps to exploit its full potential.

Sensitive and selective detection of carbamazepine in serum samples by bionic double-antibody sandwich method based on cucurbit[7]uril and molecular imprinted polymers.

A novel bionic enzyme-linked immunosorbent assay (BELISA) based on double-antibody sandwich method is firstly designed for the detection of carbamazepine (CBZ) in human serum samples. In this BELISA system, cucurbit[7]uril (CB[7]) is employed as an artificial capture antibody (cAb), and molecularly imprinted polymers (MIPs) is used as an artificial detection antibody (dAb). Nanozymes (PdNPs) as signal generators are integrated with MIPs. This couple of bionic antibodies exhibits not only the excellent physical and chemical stability, but also the superior molecular recognition ability. Based on two bionic antibodies that can selectively recognize different sites of CBZ molecule, a new BELISA method has been constructed for the first time. The proposed BELISA method displays a good linear relationship ranging from 2 to 20 µg mL-1. The detection limit is 0.37 µg mL-1, which can well meet clinical testing demand. It provides a more stable and economical method for clinical therapeutic drug monitoring (TDM).

The spatiotemporal variation in heavy metals in China's farmland soil over the past 20 years: A meta-analysis.

Accurate information on farmland soil heavy metal elements is needed for pollution management and strategic decision making at the national level. In this paper, we review the Chinese literature on soil heavy metal elements (i.e., arsenic, cadmium, chromium, copper, lead, mercury, and zinc) over the past 20 years using meta-analysis. The overall pollution status, spatiotemporal distribution patterns and driving factors of heavy metals in China's farmland soil are explored by using the geoaccumulation index, the standard deviation ellipse method and the PCA/APCS model, respectively. The results show that most heavy metals in farmland soil from the study cases are similar to the world average. Seven types of elements have increased compared with background values. Cd and Hg have become the top polluting elements in China and industrial and agricultural activities are the main sources of current heavy metal element enrichment. Regional natural-social-economic differences have led to significant spatial heterogeneity of heavy metal pollution, showing an intensity pattern unfavourable to national food security. In the time period, the overall distribution range gradually increased with the accelerated growth of regional industrial output, and there was a tendency for the gravity centre of the pollution studies to migrate inland to the northwest and southwest. Regionally differentiated environmental regulation and pollution remediation measures should be developed for pollution prevention and control in the future.

Study on performance of mimic uricase and its application in enzyme-free analysis.

Nanozymes were the novel research field to replace natural enzymes because of stability and low cost. However, the research on nanozymes was mainly focused on peroxidase, and there was little research about nanozymes with oxidase-like activity, especially mimic oxidase of small molecules related to human physiology. High levels of uric acid (UA) in the body can cause hyperuricemia and gout. And natural uricase cured this disease because it could oxidize UA. The oxidase-like activity of mixed valence state metal organic frameworks with cerium (MVSM) had been studied, but MVSM was found to have uricase-like activity in this article. The catalytic process of UA with MVSM was studied by a variety of analytical methods, which was similar to the natural uricase except for further oxidation of H2O2. The catalytic activity constants of MVSM were acquired by the Michaelis-Menten equation. MVSM had a better ability to catalyze UA in in vivo and in vitro experiments. An enzyme-free analysis-based mimic uricase for UA was established. All the experimental results proved that MVSM had a good prospect to replace the natural uricase. A nanomaterial, mixed valence state Ce-MOF (MVSM), with uricase-like activity has been found in vivo and in vitro. This material has potential to be a fluorescent analysis for detecting uric acid without uricase.

Nanozyme based on graphene oxide modified with Fe3O4, CuO, and cucurbit[6]uril for colorimetric determination of homocysteine.

A nanozyme based on graphene oxide modified with Fe3O4 NPs, CuO NPs, and cucurbit[6]uril has been successfully fabricated by a simple sonochemical technique. By employing CB[6] as a specific binding pocket and Fe3O4@CuO-GO as a peroxidase mimic, this novel nanozyme (BN I) is equipped with molecular recognition ability and enhanced peroxidase-like activity. On the basis of the inhibition effect of homocysteine (Hcy) towards the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) catalyzed by BN I, a simple colorimetric method is established for the sensitive and selective determination of Hcy. This proposed method displays a good linear response in the range 5-200 µM with a detection limit of 1.8 µM. In the practical assay of human plasma samples, the relative standard deviations (RSD) are lower than 11% and the recoveries are between 98.0 and 104.9%. In the assay of human urine samples, the RSD are below 9.0% and the recoveries range from 94.0 to 103.5%. The colorimetric method presented offers a convenient and accurate way for the determination of biomarkers in point-of-care testing (POCT).

Cucurbiturils regulating Fe3O4-Au nanoparticles as a multi-functional platform for Cd2+ sensing and nitrocompound catalysis.

Herein, through the interfacial regulation of cucurbiturils (CBs) on Fe3O4-Au nanoparticles, a novel multifunctional platform is constructed for the sensitive detection of Cd2+ and the selective catalytic reduction (SCR) of nitrocompounds. The reported surface modification strategy provides an efficient approach to prepare a new platform for multiple purposes.

ResNet-Locust-BN Network-Based Automatic Identification of East Asian Migratory Locust Species and Instars from RGB Images.

Locusts are agricultural pests found in many parts of the world. Developing efficient and accurate locust information acquisition techniques helps in understanding the relation between locust distribution density and structural changes in locust communities. It also helps in understanding the hydrothermal and vegetation growth conditions that affect locusts in their habitats in various parts of the world as well as in providing rapid and accurate warnings on locust plague outbreak. This study is a preliminary attempt to explore whether the batch normalization-based convolutional neural network (CNN) model can be applied used to perform automatic classification of East Asian migratory locust (AM locust), Oxya chinensis (rice locusts), and cotton locusts. In this paper, we present a way of applying the CNN technique to identify species and instars of locusts using the proposed ResNet-Locust-BN model. This model is based on the ResNet architecture and involves introduction of a BatchNorm function before each convolution layer to improve the network's stability, convergence speed, and classification accuracy. Subsequently, locust image data collected in the field were used as input to train the model. By performing comparison experiments of the activation function, initial learning rate, and batch size, we selected ReLU as the preferred activation function. The initial learning rate and batch size were set to 0.1 and 32, respectively. Experiments performed to evaluate the accuracy of the proposed ResNet-Locust-BN model show that the model can effectively distinguish AM locust from rice locusts (93.60% accuracy) and cotton locusts (97.80% accuracy). The model also performed well in identifying the growth status information of AM locusts (third-instar (77.20% accuracy), fifth-instar (88.40% accuracy), and adult (93.80% accuracy)) with an overall accuracy of 90.16%. This is higher than the accuracy scores obtained by using other typical models: AlexNet (73.68%), GoogLeNet (69.12%), ResNet 18 (67.60%), ResNet 50 (80.84%), and VggNet (81.70%). Further, the model has good robustness and fast convergence rate.