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Abstract The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

Abstract To rescue ischemic myocardium from progressing to myocardial infarction, timely identification of the infarct size and reperfusion is crucial. However, fast and accurate identification, as well as the targeted protection of injured cardiomyocytes following ischemia/reperfusion (I/R) injury, remain significantly challenging. Here, a near infrared heptamethine dye IR‐780 is shown that has the potential to quickly monitor the area at risk following I/R injury by selectively entering the cardiomyocytes of the at‐risk heart tissues. Preconditioning with IR‐780 or timely IR‐780 administration before reperfusion significantly protects the heart from ischemia and oxidative stress‐induced cell death, myocardial remodeling, and heart failure in both rat and pig models. Furthermore, IR‐780 can directly bind to F0F1‐ATP synthase of cardiomyocytes, rapidly decrease the mitochondrial membrane potential, and subsequently slow down the mitochondrial energy metabolism, which induces the mitochondria into a “quiescent state” and results in mitochondrial permeability transition pore inhibition by preventing mitochondrial calcium overload. Collectively, the findings show the feasibility of IR‐780‐based imaging and protection strategy for I/R injury in a preclinical context and indicate that moderate mitochondrial function depression is a mode of action that can be targeted in the development of cardioprotective reagents.

IntroductionSugarcane/soybean intercropping with reduced nitrogen (N) addition has improved soil fertility and sustainable agricultural development in China. However, the effects of intercropping pattern and N fertilizer addition on the allocation of photosynthesized carbon (C) in plant-soil system were far less understood.MethodsIn this study, we performed an 13CO2 pulse labeling experiment to trace C footprints in plant-soil system under different cropping patterns [sugarcane monoculture (MS), sugarcane/soybean intercropping (SB)] and N addition levels [reduced N addition (N1) and conventional N addition (N2)].Results and discussionOur results showed that compared to sugarcane monoculture, sugarcane/soybean intercropping with N reduced addition increased sugarcane biomass and root/shoot ratio, which in turn led to 23.48% increase in total root biomass. The higher root biomass facilitated the flow of shoot fixed 13C to the soil in the form of rhizodeposits. More than 40% of the retained 13C in the soil was incorporated into the labile C pool [microbial biomass C (MBC) and dissolved organic C (DOC)] on day 1 after labeling. On day 27 after labeling, sugarcane/soybean intercropping with N reduced addition showed the highest 13C content in the MBC as well as in the soil, 1.89 and 1.14 times higher than the sugarcane monoculture, respectively. Moreover, intercropping pattern increased the content of labile C and labile N (alkaline N, ammonium N and nitrate N) in the soil. The structural equation model indicated that the cropping pattern regulated 13C sequestration in the soil mainly by driving changes in labile C, labile N content and root biomass in the soil. Our findings demonstrate that sugarcane/soybean intercropping with reduced N addition increases photosynthesized C sequestration in the soil, enhances the C sink capacity of agroecosystems.

Human–wildlife conflicts are becoming increasingly common worldwide and are a challenge to biodiversity management. Compared with compensatory management, which often focuses on solving emergency conflicts, mitigation management allows decision-makers to better understand where the damage is distributed, how the species are distributed and when the species conduct their activity. Here, we integrated data collected from 90 districts/counties’ damage surveys and 1271 camera traps to understand the damage status, abundance, density and activity rhythms of wild boar (Sus scrofa) in Zhejiang, Eastern China, from January 2019 to August 2023. We found that (1) wild boar–human conflicts were mainly distributed in the northwest and southwest mountainous regions of Zhejiang Province; (2) the total abundance of wild boar was 115,156 ± 24,072 individuals, indicating a growing trend over the past decade and a higher density in the western and southern regions; (3) wild boar exhibited different activity patterns across different damage regions, and the periods around 7:00, 11:00 and 16:00 represented activity peaks for wild boar in seriously damaged regions. The damage distribution, density, distribution and activity rhythms provide specific priority regions and activity intensity peaks for conflict mitigation. We believe that these findings based on the damage, distribution and activity could provide a scientific basis for mitigation management at the county level and enrich the framework of human–wildlife conflict mitigation.

Green spaces have a positive impact on the mood of urban residents. However, previous studies have focused primarily on parks or residential areas, neglecting the influence of green spaces in different socioeconomic locations on public sentiment. This oversight fails to acknowledge that most young individuals are exposed to places beyond their homes and parks throughout the day. Using web crawlers, we collected 105,214 Sina Weibo posts from 14,651 geographical check-in points in Hangzhou, Zhejiang Province. We developed a mixed ordered logistic regression model to quantify the relationship between public sentiment (negative/neutral/positive) and the surrounding green space. The findings are as follows: (1) the correlation between GVI and public sentiment is stronger than that between public sentiment and NDVI; (2) among different socioeconomic regions, residential areas are associated with lower levels of public sentiment, while parks are associated with higher levels; and (3) at a scale of 1000 m, an increase of 1% in GVI significantly improves public sentiment regarding transportation hubs, with a regression coefficient of 0.0333. The relationship between green space and public sentiment is intricate and nuanced, and it is influenced by both public activities and spatiotemporal contexts. Urban green space planners should consider additional factors to enhance the effectiveness of green space in improving public sentiment.

Firmiana hainanensis Kosterm. is a commercially valuable endemic tree species in China and has long been considered a globally vulnerable species. We assembled and characterized the complete chloroplast genome of this species by using Illumina pair-end sequencing data. The total chloroplast genome size was 161,559 bp, including two inverted repeats (IRs) of 25,612 bp, separated by a large single copy (LSC) and a small single copy (SSC) regions of 90,057 and 20,277 bp, respectively. A total of 130 genes were identified, including 85 protein-coding genes, 37 tRNA, and eight rRNA genes. Phylogenetic analysis showed that F. hainanensis was the most basal species in the genus Firmiana. The chloroplast genome of this species will provide a theoretical basis to understand the taxa’s evolution further and is expected to contribute to its conservation efforts.

Trailliaedoxa gracilis (Rubiaceae) is a Chinese endemic monotypic genus distributed in southwestern China. This study reported the complete chloroplast genome of T. gracilis assembled from Illumina sequencing reads. The chloroplast genome size is 152,407 bp, containing a single large copy (LSC) region of 82,957 bp, a short single copy (SSC) region of 17,936 bp, and a pair of inverted repeats (IRs) of 25,757 bp. A total of 127 genes were found, including 82 protein-coding genes, 37 tRNA genes, and eight rRNA genes. A phylogenetic analysis using the maximum likelihood algorithm revealed that T. gracilis belonged to the subfamily Ixoroideae and had the closest relationship with Scyphiphora hydrophyllacea.

The return of crop residues and application of chemical nitrogen (N) can influence the soil organic carbon (SOC) turnover. However, the changes in the response of the priming effect (PE) to N management in real farming systems are not fully understood. In this research, we launched a 270-day in situ experiment in three N management plots (N0, no N; N1, 300 kg hm−2; and N2, 360 kg hm−2) on a long-term maize farm in order to examine the microbial mechanisms that trigger the PE in the presence of 13C-labeled maize residues. We found that N1 decreased SOC mineralization and the positive PE, but increased the residual C mineralization and microbial C use efficiency in comparison with N0 and N2, respectively. The positive PE can be explained by the microbial nutrient mining theory for N0 and by the microbial stoichiometry decomposition theory for N1 and N2, as reflected by the increased abundance of oligotrophic phyla in N0 and the increased abundance of copiotrophic phyla in N1 and N2. The microbial biomass C (MBC), residue-derived MBC, and the communities’ complexity were decreased in N2 due to the acidification of the soil environment, but N1 enhanced the MBC, residue-derived MBC, and bacterial communities’ complexity. The keystone bacterial taxa of Vicinamibacteraceae and Gemmatimonas preferred the recalcitrant C of SOC in N0 and N2, respectively. However, Acidibacter favored the labile residual C in N1. The keystone fungal taxa of Penicillium, Sarocladium, and Cladophialophora exhibited wide substrate-use abilities in N0, N1, and N2, respectively. Our research depicts the mechanisms of how microbial communities’ structures are reshaped through N management and emphasizes the functions of the keystone microbial taxa in C turnover and the PE in farming systems.

The clock synchronization is considered as a key technology in the time-sensitive networking (TSN) of 5G fronthaul. This paper proposes a clock synchronization enhancement method to optimize the link delays, in order to improve synchronization accuracy. First, all the synchronization dates are filtered twice to get the good calculation results in the processor, and then FPGA adjust the timer on the slave side to complete clock synchronization. This method is implemented by Xilinx Zynq UltraScale+ MPSoC (multiprocessor system-on-chip), using FPGA+ARM software and hardware co-design platform. The master and slave output Pulse Per-Second signals (PPS). The synchronization accuracy was evaluated by measuring the time offset between PPS signals. Contraposing the TSN, this paper compares the performance of the proposed scheme with some previous methods to show the efficacy of the proposed work. The results show that the slave clock of proposed method is synchronized with the master clock, leading to better robustness and significant improvement in accuracy, with time offset within the range of 40 nanoseconds. This method can be applied to the time synchronization of the 5G open fronthaul network and meets some special service needs in 5G communication.

Concurrent exposure to cadmium (Cd) and lead (Pb) is prevalent in the environment, but information on the long-term impacts of complex Cd–Pb exposure on herbivorous insects, especially at low doses, is scant. We studied the effects of complex Cd–Pb exposure (4.06 mg/kg Cd and 12.5 mg/kg Pb) on the growth and food utilization of the herbivorous insect Spodoptera litura for 10 continuous generations. Cd or Pb ingestion, excretion and accumulation by insect at the different developmental stages was determined for ten generations. The weighted scores calculated by the analytic hierarchy process (AHP) on the basis of the parameters of survival, growth and food utilization indicated that complex Cd–Pb exposure had positive impacts on the insects, regardless of generations. Compared with Cd (4.06 mg/kg Cd) and Pb (12.5 mg/kg Pb) alone, complex Cd–Pb exposure showed antagonistic interactions. After exposure to complex Cd–Pb, although larvae significantly increased heavy metal uptake along with the elevated ECD and ECI, insects enhanced heavy metal excretion via the feces and puparium; consequently, heavy metal accumulation in the insect body significantly decreased. With increasing generations, the positive impacts of complex Cd–Pb stress on insects became increasingly obvious, and the uptake of Cd or Pb decreased while the excretion of Cd or Pb increased, which demonstrated that insect tolerance to Cd–Pb exposure at low concentrations increased over generations.

Constructed wetlands (CWs) have been proven as a reliable alternative to traditional wastewater treatment technologies. Microorganisms in CWs, as an important component, play a key role in processes such as pollutant degradation and nutrient transformation. Therefore, an in-depth analysis of the community structure and diversity of microorganisms, especially for functional microorganisms, in CWs is important to understand its performance patterns and explore optimized strategies. With advances in molecular biotechnology, it is now possible to analyze and study microbial communities and species composition in complex environments. This review performed bibliometric analysis of microbial studies in CWs to evaluate research trends and identify the most studied pollutants. On this basis, the main functional microorganisms of CWs involved in the removal of these pollutants are summarized, and the effects of these pollutants on microbial diversity are investigated. The result showed that the main phylum involved in functional microorganisms in CWs include Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes. These functional microorganisms can remove pollutants from CWs by catalyzing chemical reactions, biodegradation, biosorption, and supporting plant growth, etc. Regarding microbial alpha diversity, heavy metals and high concentrations of nitrogen and phosphorus significantly reduce microbial richness and diversity, whereas antibiotics can cause large fluctuations in alpha diversity. Overall, this review can provide new ideas and directions for the research of microorganisms in CWs.

There is currently no effective antiviral therapy or immune-based treatment for coronavirus disease (COVID-19). The urgent challenge is to prevent the transition of COVID-19 from mild to severe infection. This paper discussed nocturnal oxygen therapy as a new option for people with COVID-19 under home quarantine. It suggested that nocturnal oxygen therapy in the early stages may be helpful in preventing disease progression by inhibiting the rapid replication of the virus and improving the body's antiviral ability.

Designing efficient systems capable emulating the muscular excitation-contraction signatures, remains a challenge. Here, the authors propose cytoadhesion-inspired hybrids as locally-coupled electromechanical interfaces capable retrieving the myoelectric and mechanical signals.

Objective To develop a method for the determination of 23 pesticides in vegetables by atmospheric pressure gas chromatography-tandem mass spectrometry (APGC-MS/MS). Methods Acetone was used to extract target compounds in vegetable samples, while multi-walled carbon nanotubes was used as purifying material. The extracts were separated by DB-5MS column (30 m×0.25 mm, 0.25 μm). APGC-MS/MS was operated in positive ion multiple reaction monitoring mode (MRM) for pesticides detection. External standard calibration method was used for quantification. Results The 23 pesticides showed good linearity in the range of 0.01-20 μg/L, with correlation coefficient (R2) above 0.99. The detection limits and quantification limits of 23 pesticide residues in vegetable substrates were 0.02-0.40 and 0.05-1.25 μg/kg, respectively. The recoveries were between 66.7%-110.7% with relative standard deviation (RSD) ≤14.8%. Conclusion The method had high sensitivity, reproductivity and specificity and was suitable for rapid detection of multiple pesticide residues in vegetables.

The early detection of Diabetic Retinopathy (DR) is critical for diabetics to lower the blindness risks. Many studies represent that Deep Convolutional Neural Network (CNN) based approaches are effective to enable automatic DR detection through classifying retinal images of patients. Such approaches usually depend on a very large dataset composed of retinal images with predefined classification labels to support their CNN training. However, in some occasions, it is not so easy to get enough well-labelled images to act as model training samples. At the same time, when a CNN becomes deeper, its training will not only take much longer time, but also be more likely to lead to overfitting, especially on a large training dataset. Therefore, it is meaningful to explore a simpler CNN based approach that is still effective on small datasets to classify retinal images. In this paper, an approach to retinal image classification is proposed based on the integration of multi-scale shallow CNNs. Experiments on public datasets show that, on small datasets, the proposed approach can improve the classification accuracy by 3% compared with current representative integrated CNN learning approaches. On the bigger dataset, the proposed approach can improve the classification accuracy by 3% to 9% compared with other representative approaches such as traditional CNN, LCNN and VGG16noFC. The evaluation also represents that, though the classification accuracy of the proposed approach declines by 6% on the smallest dataset containing only 10% samples of the original dataset, its time cost declines to about 30% of that on the original dataset.

To solve the complex background problems (e.g. Noise interference, object overlap, and different illumination) that affect the classification performance on plant leaf images, this paper proposes an instance segmentation and classification method for plant leaf images based on IFPN SNMS CFFI-Mask R-CNN (ISC-MRCNN) and ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN). To obtain the foreground of plant leaf images, the lateral connection structure of the feature map pyramid in ISC-MRCNN fuses the feature maps of different depths, so that the network learns more detailed features. Then, the Soft Non-Maximum Suppression Algorithm is employed to improve the detection performance of overlapping objects. Next, the pooling method of integrating the continuous function can reduce the precision loss during the alignment of the mapping between the feature map and the original image. Finally, by constructing a mask filter layer, complex backgrounds are masked. To distinguish the similarity between plant leaf images, APS-DCCNN is used to classify the foreground images. In this process, the Support Vector Machine is used to replace softmax and then an Adaptive Chaotic Particle Swarm Algorithm is employed to optimize it. The experimental results show that compared with Mask R-CNN, the average precision of ISC-MRCNN has increased by 1.89% under different thresholds. The proposed method is suitable for the object detection and instance segmentation problems with complex background. Besides, compared with traditional CNN, the average precision of the classification results obtained by APS-DCCNN has improved by 1.59%. This has shown that the proposed method is suitable for the classification of plant leaves.

Salicylic acid (SA) plays a critical role in allergic reactions of plants to pathogens and acquired systemic resistance. Thus far, although some research has been conducted on the direct effects of different concentrations of SA on the chemical defense response of treated plant parts (leaves) after at multiple post-treatments times, few research has reported on the systematic effects of non-treated parts (roots). Therefore, we examined direct and systemic effects of SA concentration and time following foliar application on chemical defense responses in maize variety 5422 with two fully expanded leaves. In the experiments, maize leaves were treated with different SA concentrations of 0.1, 0.5, 1.0, 2.5, 5.0 mM, and then, the presence of defense chemicals and enzymes in treated leaves and non-treated roots was measured at different time points of 3, 12, 24, 48, 72 h following SA foliar application. The results showed that direct and systemic effects of SA treatment to the leaf on chemical defense responses were related to SA concentration and time of measurement after spraying SA. In treated leaves, total phenolics content increased directly by 28.65% at the time point of 12 h following foliar application of 0.5 mM SA. DIMBOA (2,4-dihydroxy-7-methoxy-2H, 1, 4-benzoxazin-3 (4H)-one) content was directly enhanced by 80.56~551.05% after 3~72 h following 0.5~5.0 mM SA treatments. Polyphenol oxidase and superoxide dismutase activities were directly enhanced after 12~72 h following 0.5~5.0 mM SA treatments, whereas peroxidase and catalase activities were increased after 3~24 h following application of 1.0~5.0 mM SA. In non-treated roots, DIMBOA content and polyphenol oxidase activity were enhanced systematically after 3~48 h following 1.0~5.0 mM SA foliar treatments. Superoxide dismutase activities were enhanced after 3~24 h following 0.5~2.5 mM SA applications, but total phenolics content, peroxidase and catalase activity decreased in some particular concentrations or at the different times of measurement in the SA treatment. It can be concluded that SA foliar application at 1.0 and 2.5 mM produces strong chemical defense responses in maize, with the optimal induction time being 24 h following the foliar application.

Maximizing the carbon sequestration of forested land is important for achieving carbon neutrality. Although some studies have discussed forest carbon sequestration efficiency (FCSE) from the perspective of total factor production, it is being increasingly recognized that forestland use regulates sequestration and emissions. When viewing forestland use as input and carbon emissions as output, there is a lack of empirical evidence on FCSE and its influencing factors. Here, a superefficiency slacks-based measurement model was applied to estimate FCSE for 66 counties in Zhejiang Province, China. The influencing factors and spatial spillover effects of FCSE were also analyzed using a spatial autocorrelation model. The findings showed that over the sample observation period, county FCSE ranged from 0.199 to 1.258, with considerable gaps. The global Moran’s I index showed that county-level FCSE was markedly spatially autocorrelated. Spatially, forestland use, cutting, pests, and diseases had negative spatial spillover effects on FCSE, whereas average annual temperature and precipitation displayed positive spillover effects. These findings suggest that the overall coordination of forest resource supervision and management among counties should be strengthened. The implementation of forestry management models aimed at consolidating or increasing forest carbon sequestration should be emphasized to improve forest quality, thereby promoting FCSE enhancement.

How nitrogen (N) supply affects the induced defense of plants remains poorly understood. Here, we investigated the impacts of N supply on the defense induced in maize (Zea mays) against the fall armyworm (Spodoptera frugiperda). In the absence of herbivore attack or exogenous jasmonic acid (JA) application, N supply increased plant biomass and enhanced maize nutrient (soluble sugar and amino acid) contents and leaf area fed by S. frugiperda (the feeding leaf area of S. frugiperda larvae in maize supplemented with 52.2 and 156.6 mg/kg of N was 4.08 and 3.83 times that of the control, respectively). When coupled with herbivore attack or JA application, maize supplemented with 52.2 mg/kg of N showed an increased susceptibility to pests, while the maize supplemented with 156.6 mg/kg of N showed an improved defense against pests. The changes in the levels of nutrients, and the emissions of volatile organic compounds (VOCs) caused by N supply could explain the above opposite induced defense in maize. Compared with herbivore attack treatment, JA application enhanced the insect resistance in maize supplemented with 156.6 mg/kg of N more intensely, mainly reflecting a smaller feeding leaf area, which was due to indole emission and two upregulated defensive genes, MPI (maize proteinase inhibitor) and PAL (phenylalanine ammonia-lyase). Hence, the optimal N level and appropriate JA application can enhance plant-induced defense against pests.

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.

In order to explore the influence of external environmental factors, the initial width of cracks and the placement of glass fiber tubes on the repairing effect of self-repairing concrete, glass fiber tubes are built in self-repairing concrete specimens, Self-repair test using repair adhesive. The repair rate α is characterized by the ratio of the initial crack width l to the time t used for repair completion, and the effects of the above three factors on the repair rate were analyzed. The results show that at –15~30 °C, the repair rate increases with the increase of temperature; at 0~30 °C, the growth rate of repair rate is obviously less than the growth rate of –15~0 °C. When the temperature is below 0 °C, the temperature plays a leading role in the improvement of the repair rate; The repair rate increases first and then decreases with the increase of the initial crack width. When the crack width is from 0.4to 0.6mm, the repair rate increases significantly faster than the crack width from 0.6to 1.0mm. The rate of repair of the crack width from 1.0 to 1.5 mm is significantly higher than that of the crack width from 1.5 mm to 2.0 mm. And when the initial crack width is about 1.0 mm, the repair rate reaches the highest level. When the initial crack width and the repair temperature are between –15 and 30 °C, the repair rate of the glass fiber tube is slightly better than that of the diamond when the inverted trapezoid is placed. The initial width of the crack has the greatest influence on the repair rate. Followed by temperature conditions and placement of fiberglass tubes. The corresponding factor levels at the maximum repair rate are 1.0mm, 30 °C and inverted trapezoids.

Abstract Background HMA4 transporters are involved in the transport and binding of divalent heavy metals (Cd, Zn, Pb [lead] and Co [cobalt]). In general, as efflux pumps, HMA4 transporters can increase the heavy metal tolerance of yeast and Escherichia coli. Additional research has shown that the C-terminus of HMA4 contains a heavy metal-binding domain and that heterologous expression of a portion of peptides from this C-terminal domain in yeast provides a high level of Cd tolerance and Cd hyperaccumulation. Results We cloned BjHMA4 from Brassica juncea, and quantitative real-time PCR analysis revealed that BjHMA4 was upregulated by Zn and Cd in the roots, stems and leaves. Overexpression of BjHMA4 dramatically affects Zn/Cd distribution in rice and wheat seedlings. Interestingly, BjHMA4 contains a repeat region named BjHMA4R within the C-terminal region; this repeat region is not far from the last transmembrane domain. We further characterized the detailed function of BjHMA4R via yeast and E. coli experiments. Notably, BjHMA4R greatly and specifically improved Cd tolerance, and BjHMA4R transformants both grew on solid media that contained 500 μM CdCl2 and presented improved Cd accumulation (approximately twice that of wild-type [WT] strains). Additionally, visualization via fluorescence microscopy indicated that BjHMA4R clearly localizes in the cytosol of yeast. Overall, these findings suggest that BjHMA4R specifically improves Cd tolerance and Cd accumulation in yeast by specifically binding Cd2+ in the cytosol under low heavy metal concentrations. Moreover, similar results in E. coli experiments corroborate this postulation. Conclusion BjHMA4R can specifically bind Cd2+ in the cytosol, thereby substantially and specifically improving Cd tolerance and accumulation under low heavy metal concentrations.

Owing to its scalability and high fault-tolerance even on a distributed environment built up with personal computers, MapReduce has been introduced to parallelise the training of Back Propagation Neural Networks (BPNNs) on high-dimensional big datasets. Based on the evolution of local BPNNs produced by distributed Map tasks with different data splits, the paper proposes a novel approach to the distributed data-parallel training of BPNNs in MapReduce. The approach provides a reasonable measure to get global convergent BPNN candidates from local BPNNs only convergent on the specific data splits. Further, it not only can reduce the iterations to get the global convergent BPNN, but also shows great advantages in avoiding the training to get trapped into a local optimum on high-dimensional big datasets. To improve the training efficiency further, local BPNNs from the same computing node are merged based on the average of their weight matrices before they act as individuals of the population for the global evolution. Our approach also leverages Random Project based sampling techniques to evaluate the fitness of each individual in order to lower the computation cost in the evolution stage. Experiments show that our proposed approach improves the training efficiency highly compared to the stand-alone or traditional MapReduce BPNN training, and improves model accuracy for larger datasets. The comparison with 23 other popular classification approaches also shows that our proposed approach has big advantages in accuracy.

Bt (Bacillus thuringiensis) corn is one of the top three large-scale commercialized anti-insect transgenic crops around the world. In the present study, we tested the Bt protein content, defense chemicals contents, and defense enzyme activities in both the leaves and roots of Bt corn varieties 5422Bt1 and 5422CBCL, as well as their conventional corn 5422 seedlings, with two fully expanded leaves which had been treated with 2.5 mM exogenous salicylic acid (SA) to the aboveground part for 24 h. The result showed that the SA treatment to the aboveground part could significantly increase the polyphenol oxidase activity of conventional corn 5422, the Bt protein content, and peroxidase activities of Bt corn 5422Bt1, as well as the polyphenol oxidase and peroxidase activity of Bt corn 5422CBCL in the leaves. In the roots, the polyphenol oxidase and peroxidase activity of conventional corn 5422, the polyphenol oxidase and superoxide dismutase activities of Bt corn 5422Bt1, the DIMBOA (2,4-dihydroxy-7-methoxy-2H, 1, 4-benzoxazin-3 (4H)-one) content, and four defense enzymes activities of Bt corn 5422CBCL were systematically increased. These findings suggest that the direct effect of SA application to aboveground part on the leaf defense responses in Bt corn 5422CBCL is stronger than that in non-Bt corn. Meanwhile, the systemic effect of SA on the root defense responses in Bt corn 5422CBCL is stronger than that in conventional corn 5422 and Bt corn 5422Bt1. It can be concluded that the Bt gene introduction and endogenous chemical defense responses of corns act synergistically during the SA-induced defense processes to the aboveground part. Different transformation events affected the root defense response when the SA treatment was applied to the aboveground part.

Vehicle loads and environmental differences are the key technical factors in the model construction of concrete pavement damage prediction. According to the data of the 168-month actual number of actions of different vehicle axle types, average temperature, average wind speed, rainfall, snowfall and days below 0 ℃ collected from the Mudanjiang-provincial section of the He-da highway in China, the broken slab ratio of cement concrete (DBL) was calculated. Cracking rate(CRK) and environmental factor(SF) were introduced into the model. This paper uses SPSS analysis method to carry out correlation analysis and partial correlation analysis by introducing SF to the model of DBL and CRK, so that the concrete pavement damage prediction model in seasonally frozen regions can be constructed and tested. Results show that CRK and SF both have positive linear relationship with DBL; Concrete pavement damage in seasonally frozen regions can be predicted by analyzing parameters like actual number of actions of different vehicle axle types, road service time and freezing index, etc. No multiple collinearity exists in the parameters of the model and the construction of model for concrete pavement damage prediction in seasonally frozen regions is of great theoretical significance for timely and effective pavement maintenance. The model has achieved good results in damage prediction accuracy and efficiency.

Diabetic bladder dysfunction (DBD) afflicts nearly half of diabetic patients, but effective treatment is lacking. In this study, IR-61, a novel heptamethine cyanine dye with potential antioxidant effects, was investigated to determine whether it can alleviate DBD. Rats were intraperitoneally injected with IR-61 or vehicle after diabetes was induced with streptozotocin. Before evaluating the effects of IR-61 in improving DBD by filling cystometry, we detected its distribution in tissues and subcellular organelles by confocal fluorescence imaging. Near infrared (NIR) imaging showed that IR-61 could accumulate at high levels in the bladders of diabetic rats, and confocal images demonstrated that it was mainly taken up by bladder smooth muscle cells (BSMCs) and localized in mitochondria. Then, filling cystometry illustrated that IR-61 significantly improved the bladder function of diabetic rats. The histomorphometry results showed that IR-61 effectively mitigated the pathological changes in bladder smooth muscle (BSM) in diabetic rats. Furthermore, IR-61 remarkably reduced the number of apoptotic BSMCs and the unfavorable expression of proteins related to the mitochondrial apoptotic pathway (Bcl-2, BAX, Cytochrome C, and cleaved Caspase-9) in diabetic rats. Moreover, the frozen section staining and transmission electron microscopy results proved that IR-61 significantly reduced the reactive oxygen species (ROS) levels and prevented the mitochondrial mass and morphology damage in the BSM of diabetic rats. In addition, IR-61 upregulated the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and its associated antioxidant proteins in the BSM of diabetic rats. Together, these results indicate that IR-61 can improve the voiding function of rats with DBD by protecting the mitochondria of BSMCs from oxidative stress, which is possibly mediated through the activation of the Nrf2 pathway.

We executed a pot experiment to examine the differences of absorption, chemical forms, subcellular distribution, and toxicity of Cd between two cultivars of Chinese flowering cabbage Brassica campestris [Lvbao701 (low-Cd cultivar) and Chicaixin No.4 (high-Cd cultivar)]. Compared to Chicaixin No.4, the presence of Lvbao701 enhanced the proportion of insoluble Cd forms in soil, Lvbao701 roots and leaves had higher proportion of Cd converted into insoluble phosphate precipitates and pectate-or protein-bound forms and lower proportion of inorganic Cd, which result in low accumulation and toxicity of Cd to Lvbao701 and cutworm Spodoptera litura fed on Lvbao701 leaves. Instead of total Cd, Cd transfer and toxicity in B. campestris-S. litura system depend on chemical Cd forms in soil and cabbages and subcellular Cd distributions in cabbages and insects, and the proportions of them were not the highest among all chemical forms and subcellular distributions of Cd. Although exchangeable Cd was major Cd chemical form in cabbage planted soil, Cd bound to iron and manganese oxides and to organic matter were significantly correlated with growth indices and photosynthesis parameters of cabbages. Despite major part of Cd was precipitated in cell wall of roots, Cd in organelle fraction was closely associated with the fitness of cabbages. Metal-rich granules, not cytosolic fraction (the major subcellular Cd distribution), affected the food utilization of S. litura. Therefore, cabbage cultivars significantly affected Cd transfer and toxicity in B. campestris-S. litura system, and the use of Lvbao701 in Cd polluted soil could reduce potential risks for Cd entering food chains.

Objective To investigate the effects of 1,25(OH) 2 D 3 on renal fibrosis associated with the AMP-activated protein kinase (AMPK)α/mechanistic target of rapamycin (mTOR) signalling pathway in a rat model of unilateral ureteral obstruction (UUO). Methods A total of 54 male Sprague Dawley rats were randomly divided into three groups: sham-operation group, UUO group, and UUO plus calcitriol (3 ng/100 g) group. Renal tissue was excised for histological examination by immunohistochemistry and Western blot, and for gene expression analysis using real-time polymerase chain reaction. Results 1,25(OH) 2 D 3 enhanced AMPKα levels, inhibited mTOR levels and slowed the development of interstitial fibrosis in kidney tissue. Compared with the UUO plus calcitriol group, UUO rats demonstrated more severe renal damage characterized by marked tubular atrophy, interstitial fibrosis and significant induction of fibrogenic transforming growth factor-β1 and increased extra-cellular matrix proteins (α-smooth muscle actin and collagen type III), and decreased E-cadherin. Conclusion Treatment with 1,25(OH) 2 D 3 altered the AMPKα/mTOR signalling pathway to suppress excessive fibroblast activation observed in UUO rats. This may serve as a novel mechanism to ameliorate renal dysfunction and fibrotic lesions.

The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R2, MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

This study was to identify the effects of prebiotics supplemented in infant formula on enzyme activity and phosphate uptake in the small intestine of Sprague Dawley (SD) rats. Forty-eight healthy SD rats at 15 days old (a week before weaning) with similar weight were randomly divided into 3 groups: A (control group), B, C, with 16 rats per group. Rats in groups A, B, C were fed a standard infant formula, the standard infant formula supplemented with oligosaccharides, and the standard infant formula supplemented with polysaccharides, respectively. The feeding test was conducted for 28 d. Compared with group A, the results showed the following: 1) the activities of sucrose and lactase in the small intestine were significantly increased in SD rats of group C (P < 0.05); 2) the relative expressions of lactase gene in the anterior and posterior segments of the small intestine were significantly increased by 1.68 and 2.26 in SD rats of group C (P < 0.05), and the relative expression of Mgam gene in the posterior segment of the small intestine was significantly increased by 0.99 in SD rats of group C (P < 0.05); 3) the relative expressions of Na/Pi-IIb gene in the anterior and posterior segments of the small intestine were significantly increased by 1.85 and 2.28 in SD rats of group C (P < 0.05). These results indicate that the supplementation of infant formula with prebiotics can promote enzyme activity in the small intestine by increasing the relative expression of enzyme gene or by decreasing intestinal injury, and can increase the relative expression of Na/Pi-IIb gene. The effect of polysaccharides is better than that of oligosaccharides. Keywords: Infant formula, Prebiotics, Ussing chamber, Intestinal enzyme activities, Phosphate absorption

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument onboard NASA’s Terra (launched in 1999) and Aqua (launched in 2002) satellite missions as part of the more extensive Earth Observation System (EOS). By measuring the reflection and emission by the Earth-Atmosphere system in 36 spectral bands from the visible to thermal infrared with near-daily global coverage and high-spatial-resolution (250 m ~ 1 km at nadir), MODIS is playing a vital role in developing validated, global, interactive Earth system models. MODIS products are processed into three levels, i.e., Level-1 (L1), Level-2 (L2) and Level-3 (L3). To shift the current static and “one-size-fits-all” data provision method of MODIS products, in this paper, we propose a service-oriented flexible and efficient MODIS aggregation framework. Using this framework, users only need to get aggregated MODIS L3 data based on their unique requirements and the aggregation can run in parallel to achieve a speedup. The experiments show that our aggregation results are almost identical to the current MODIS L3 products and our parallel execution with 8 computing nodes can work 88.63 times faster than a serial code execution on a single node.

Numerous studies have indicated that El Niño and the southern oscillation (ENSO) could have determinant impacts on remote weather and climate using the conventional correlation-based methods, which however, cannot identify the cause-and-effect of such linkage, and ultimately determine a direction of causality. This study employs the vector auto-regressive (VAR) model estimation method with the long-term observational sea surface temperature (SST) data and the NCEP/NCAR reanalysis data to demonstrate the Granger causality between ENSO and other climate attributes. Results showed that ENSO as the modulating factor can result in abnormal surface temperature, pressure, precipitation and wind circulation remotely, not vice versa. We also carry out the global climate model sensitivity simulations using the parallel computing techniques to double confirm the causality relations between ENSO and abnormal events in remote regions. Our statistical and climate model-based analyses may enrich our current understanding on the occurrences of extreme events worldwide caused by different ENSO strengths through teleconnections.

In order to control lepidopteran and coleopteran insects, the genes expressing Bacillus thuringiensis (Bt) insecticidal proteins have been transferred into crops. Ecological risk assessments of the transgenic plants have included impacts on non-target entomophagous insects, such as parasitoid wasps. Herbivore-induced plant volatiles are considered to be important defensive traits of plants because these compounds play as an important role in recruitment of natural enemies. Here, we evaluated induced volatile emissions of maize seedlings of two Bt cultivars (5422Bt1, event Bt11 and 5422CBCL, event Mon810), and their nearly isogenic non-Bt line 5422. We damaged plants mechanically and then applied with the regurgitant of Spodoptera litura (F.) caterpillars (Lepidoptera: Noctuidae), or treated the plants with the plant hormone jasmonic acid (JA), to trigger similar defensive responses of plants. Compared to the non-Bt isoline 5422 and the Bt maize 5422CBCL, the other Bt maize 5422Bt1 released more (3E)-4,8-dimethyl-1,3,7-nonatriene (DMNT) when they were all treated by artificial wounds and caterpillar regurgitant; and released more linalool, DMNT and (E)-β-farnesene when applied with JA solution. As a result, the total volatile emission of the 5422Bt1 was highest. However, the difference in volatile emission did not affect the attractiveness of the Bt maize plants to the egg parasitoid Trichogramma ostriniae Pang et Chen (Hymenoptera: Trichogrammatidae) compared to the nearly isogenic non-Bt plants. The variability of induced volatiles of maize cultivars derived from conventional breeding programs and transgenic methods are discussed.

The cultivation of transgenic Bacillus thuringiensis (Bt) has received worldwide attention since Bt crops were first released. Its ecological risks on arbuscular mycorrhizal fungi (AMF) have been widely studied. In this study, after cultivation for five seasons, the AMF diversity and community composition of two Bt maize varieties, 5422Bt1 (event Bt11) and 5422CBCL (event MO10), which both express Cry1Ab protein, and their isoline non-Bt maize 5422, as well as Bt straw after cultivation had been returned to subsequent conventional maize variety, were analyzed using Illumina MiSeq sequencing. A total of 263 OTUs (operational taxonomic units) from 511,847 sequenced affiliated with the AMF which belonged to Mucoromycota phylum Glomeromycotina subphylum were obtained. No significant difference was detected in the AMF diversity and richness (Shannon, Simpson, ACE, and Chao 1 indices) and community composition in rhizosphere soils and roots between Bt and non-Bt treatment revealed by NMDS (non-metric multidimensional scaling) and NPMANOVA (non-parametric multivariate analysis). Moreover, Glomus was the most dominant genus in all samples. Although there was no significant difference in the AMF community in roots and rhizosphere soils between the Bt and non-Bt maize treatments, total phosphorus (TP), total nitrogen (TN), organic carbon (OC), and pH were driving factors affecting the AMF community, and their composition varied between rhizosphere soils and roots during the maturity period of the fifth season. Compared to our previous study, the results were identical. In conclusion, no significant difference was observed between the Bt and non-Bt treatments, and the Illumina MiSeq method had higher throughput and higher quality read cover, which gave us comprehensive insight into AMF communities in agro-ecosystems.

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional Neural Network (CNN), to identify dust aerosols in the daytime satellite images from the Visible Infrared Imaging Radiometer Suite (VIIRS) under cloud-free conditions on a global scale. In order to train the ML algorithms, we collocated the state-of-the-art dust detection product from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) with the VIIRS observations along the CALIOP track. The 16 VIIRS M-band observations with the center wavelength ranging from deep blue to thermal infrared, together with solar-viewing geometries and pixel time and locations, are used as the predictor variables. Four different sets of training input data are constructed based on different combinations of VIIRS pixel and predictor variables. The validation and comparison results based on the collocated CALIOP data indicate that the FFNN method based on all available predictor variables is the best performing one among all methods. It has an averaged dust detection accuracy of about 81%, 89%, and 85% over land, ocean and whole globe, respectively, compared with collocated CALIOP. When applied to off-track VIIRS pixels, the FFNN method retrieves geographical distributions of dust that are in good agreement with on-track results as well as CALIOP statistics. For further evaluation, we compared our results based on the ML algorithms to NOAA’s Aerosol Detection Product (ADP), which is a product that classifies dust, smoke, and ash using physical-based methods. The comparison reveals both similarity and differences. Overall, this study demonstrates the great potential of ML methods for dust detection and proves that these methods can be trained on the CALIOP track and then applied to the whole granule of VIIRS granule.

The antiskid performance of the cement concrete pavement in the seasonal frost regions is an important factor determining the safety of road use. However, due to the low efficiency and high cost of on-site detection, it is very important to reasonably predict it. Five key factors such as ice film thickness, tire pressure, tire load, driving speed, and structural depth were determined. The response surface test was performed to determine the corresponding range of the five factors when the model was optimally predicted. A prediction model of antiskid performance for cement concrete pavement in the seasonal frost regions was created, the goodness of model fitting and the normal distribution of the model were tested, and the applicability of the model was verified. The results show that when the thickness of the ice film is 1.5 mm ～ 3 mm, the tire pressure is 180 kPa ～ 240 kPa, the driving speed is 40 km/h ～ 80 km/h, the structural depth is 0.24 mm ～ 0.62 mm and the tire load is 3250 N ～ 4000 N , the prediction level of the model is the best; SRI can be interpreted by the model accounting for 98.5 %, and the regression model has a high degree of fit, which meets the assumption of normal distribution; the model's SRI predicted value fits the field measured SRI value to 0.995, and the degree of fit is high. The prediction model of antiskid performance is of great significance to prolong the service life of cement concrete pavement in seasonal frost regions.

The wide-use airport cement concrete direct-thickening double-deck pavement slabs (ACCDDPS) were selected as the research object to study their mechanical properties. The airport runway simulation test station (ARSTS) was used to conduct indoor tests to demonstrate the distribution of tension stress at the bottom of slabs and slabs deflection. Furthermore, ANSYS software was applied to establish finite element model (FEM) of ACCDDPS and analyze the mechanical laws under different loads. The indoor tests results are in good agreement with the ANSYS simulation results, and some consistent conclusions can be obtained that the maximum tension stress increases with wheel load, and the slab middle of the longitudinal edge is a critical position. In addition, we studied the influence of covered layer thickness, elastic modulus, and slab size on pavement slab mechanical properties by ANSYS, and we concluded that although the structural parameters are different, the critical position of ACCDDPS is still in the middle of the longitudinal edge. However, for the covered layer and the original surface layer, the law that the tension stress values vary with the structural parameters is different, but the maximum deflection value is about 0.1.

Helianthemum songaricum is an ecologically important endangered relic plant species growing in the arid and semi-arid regions of northwestern China. It is also a valuable germplasm resource. Consequently, we herein report the complete H. songaricum chloroplast genome, which comprises 152,042 bp, including a large single copy (LSC) region (84,389 bp) and a small single copy (SSC) region (18,926 bp) that are separated by a pair of 24,298-bp inverted repeats (IRs). The LSC, SSC, and IR regions account for 55.50%, 12.45%, and 31.96% of the chloroplast genome, respectively. Moreover, the GC content of this genome is 36.6%. Furthermore, the 110 annotated genes consist of 75 protein-coding genes, 4 rRNAs, and 31 tRNAs. A phylogenetic tree based on 14 chloroplast genomes revealed that H. songaricum is closely related to Clematoclethra scandens and Actinidia arguta.

In recent years, with the further adoption of the Internet of Things and sensor technology, all kinds of intelligent transportation system (ITS) applications based on a wide range of traffic sensor data have had rapid development. Traffic sensor data gathered by large amounts of sensors show some new features, such as massiveness, continuity, streaming, and spatio-temporality. ITS applications utilizing traffic sensor data can be divided into three main types: 1) offline processing of historical data; 2) online processing of streaming data; and 3) hybrid processing of both. Current research tends to solve these problems in separate solutions, such as stream computing and batch processing. In this paper, we propose a hybrid processing approach and present corresponding system implementation for both streaming and historical traffic sensor data, which combines spatio-temporal data partitioning, pipelined parallel processing, and stream computing techniques to support hybrid processing of traffic sensor data in real-time. Three types of real-world applications are explained in detail to show the usability and generality of our approach and system. Our experiments show that the system can achieve better performance than a popular open-source streaming system called Storm.

Bile salt-stimulated lipase (BSSL) is a lipolytic digestive enzyme with broad substrate specificity secreted from exocrine pancreas into the intestinal lumen in all species and from the lactating mammary gland into the milk of some species, notably humans but not cows. BSSL in breast milk facilitates digestion and absorption of milk fat and promotes growth of small for gestational age preterm infants. Thus, purified recombinant human BSSL (rhBSSL) can be used for treatment of patients with fat malabsorption and expressing rhBSSL in the milk of transgenic cloned cows would therefore be a mean to meet a medical need. In the present study, a vector pBAC-hLF-hBSSL was constructed, which efficiently expressed active rhBSSL in milk of transgenic cloned cows to a concentration of 9.8 mg/ml. The rhBSSL purified from cow milk had the same enzymatic activity, N-terminal amino acid sequence, amino acid composition and isoelectric point and similar physicochemical characteristics as human native BSSL. Our study supports the use of transgenic cattle for the cost-competitive, large-scale production of therapeutic rhBSSL.

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints. Second, it is quite difficult to select a learner from numerous BN structure learning algorithms to consistently achieve good learning accuracy. Lastly, there is a lack of an intelligent method that merges separately learned BN structures into a well structured BN network. To address these shortcomings, we introduce a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning). PEnBayes starts with an adaptive data preprocessing phase that calculates the Appropriate Learning Size and intelligently divides a big dataset for fast distributed local structure learning. Then, PEnBayes learns a collection of local BN Structures in parallel using a two-layered weighted adjacent matrix-based structure ensemble method. Lastly, PEnBayes merges the local BN Structures into a global network structure using the structure ensemble method at the global layer. For the experiment, we generate big data sets by simulating sensor data from patient monitoring, transportation, and disease diagnosis domains. The Experimental results show that PEnBayes achieves a significantly improved execution performance with more consistent and stable results compared with three baseline learning algorithms.

The cultivation of genetically modified plants (GMP) has raised concerns regarding the plants' ecological safety. A greenhouse experiment was conducted to assess the impact of five seasons of continuous Bt (Bacillus thuringiensis) maize cultivation on the colonisation and community structure of the non-target organisms arbuscular mycorrhizal fungi (AMF) in the maize roots, bulk soils and rhizospheric soils using the terminal restriction fragment length polymorphism (T-RFLP) analysis of the 28S ribosomal DNA and sequencing methods. AMF colonisation was significantly higher in the two Bt maize lines that express Cry1Ab, 5422Bt1 (event Bt11) and 5422CBCL (MON810) than in the non-Bt isoline 5422. No significant differences were observed in the diversity of the AMF community between the roots, bulk soils and rhizospheric soils of the Bt and non-Bt maize cultivars. The AMF genus Glomus was dominant in most of the samples, as detected by DNA sequencing. A clustering analysis based on the DNA sequence data suggested that the sample types (i.e., the samples from the roots, bulk soils or rhizospheric soils) might have greater influence on the AMF community phylotypes than the maize cultivars. This study indicated that the Cry1Ab protein has minor effects on the AMF communities after five seasons of continuous Bt maize cultivation.

The use of transgenic livestock is providing new methods for obtaining pharmaceutically useful proteins. However, the protein expression profiles of the transgenic animals, including expression of milk fat globule membrane (MFGM) proteins, have not been well characterized. In this study, we compared the MFGM protein expression profile of the colostrum and mature milk from three lines of transgenic cloned (TC) cattle, i.e., expressing recombinant human α-lactalbumin (TC-LA), lactoferrin (TC-LF) or lysozyme (TC-LZ) in the mammary gland, with those from cloned non-transgenic (C) and conventionally bred normal animals (N). We identified 1, 225 proteins in milk MFGM, 166 of which were specifically expressed only in the TC-LA group, 265 only in the TC-LF group, and 184 only in the TC-LZ group. There were 43 proteins expressed only in the transgenic cloned animals, but the concentrations of these proteins were below the detection limit of silver staining. Functional analysis also showed that the 43 proteins had no obvious influence on the bovine mammary gland. Quantitative comparison revealed that MFGM proteins were up- or down-regulated more than twofold in the TC and C groups compared to N group: 126 in colostrum and 77 in mature milk of the TC-LA group; 157 in colostrum and 222 in mature milk of the TC-LF group; 49 in colostrum and 98 in mature milk of the TC-LZ group; 98 in colostrum and 132 in mature milk in the C group. These up- and down-regulated proteins in the transgenic animals were not associated with a particular biological function or pathway, which appears that expression of certain exogenous proteins has no general deleterious effects on the cattle mammary gland.

In order to systematically explore and understand the structure–activity relationship (SAR) of a lesinurad-based hit (1c) derived from the replacement of the S atom in lesinurad with CH2, 18 compounds (1a–1r) were designed, synthesized and subjected to in vitro URAT1 inhibitory assay. The SAR exploration led to the discovery of a highly potent flexible URAT1 inhibitor, 1q, which was 31-fold more potent than parent lesinurad (IC50 = 0.23 μM against human URAT1 for 1q vs 7.18 μM for lesinurad). The present study discovered a flexible molecular scaffold, as represented by 1q, which might serve as a promising prototype scaffold for further development of potent URAT1 inhibitors, and also demonstrated that the S atom in lesinurad was not indispensable for its URAT1 inhibitory activity.

As the number of transgenic livestock increases, reliable detection and molecular characterization of transgene integration sites and copy number are crucial not only for interpreting the relationship between the integration site and the specific phenotype but also for commercial and economic demands. However, the ability of conventional PCR techniques to detect incomplete and multiple integration events is limited, making it technically challenging to characterize transgenes. Next-generation sequencing has enabled cost-effective, routine and widespread high-throughput genomic analysis. Here, we demonstrate the use of next-generation sequencing to extensively characterize cattle harboring a 150-kb human lactoferrin transgene that was initially analyzed by chromosome walking without success. Using this approach, the sites upstream and downstream of the target gene integration site in the host genome were identified at the single nucleotide level. The sequencing result was verified by event-specific PCR for the integration sites and FISH for the chromosomal location. Sequencing depth analysis revealed that multiple copies of the incomplete target gene and the vector backbone were present in the host genome. Upon integration, complex recombination was also observed between the target gene and the vector backbone. These findings indicate that next-generation sequencing is a reliable and accurate approach for the molecular characterization of the transgene sequence, integration sites and copy number in transgenic species.

The development of transgenic cloned animals offers new opportunities for agriculture, biomedicine and environmental science. Expressing recombinant proteins in dairy animals to alter their milk composition is considered beneficial for human health. However, relatively little is known about the expression profile of the proteins in milk derived from transgenic cloned animals. In this study, we compared the proteome and nutrient composition of the colostrum and mature milk from three lines of transgenic cloned (TC) cattle that specifically express human α-lactalbumin (TC-LA), lactoferrin (TC-LF) or lysozyme (TC-LZ) in the mammary gland with those from cloned non-transgenic (C) and conventionally bred normal animals (N). Protein expression profile identification was performed, 37 proteins were specifically expressed in the TC animals and 70 protein spots that were classified as 22 proteins with significantly altered expression levels in the TC and C groups compared to N group. Assessment of the relationship of the transgene effect and normal variability in the milk protein profiles in each group indicated that the variation in the endogenous protein profiles of the three TC groups was within the limit of natural variability. More than 50 parameters for the colostrum and mature milk were compared between each TC group and the N controls. The data revealed essentially similar profiles for all groups. This comprehensive study demonstrated that in TC cattle the mean values for the measured milk parameters were all within the normal range, suggesting that the expression of a transgene does not affect the composition of milk.