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Stellate ganglion blockade (SGB) can control ventricular arrhythmias (VAs), but outcomes are unclear. Percutaneous stellate ganglion (SG) recording and stimulation in humans has not been reported. The purpose of this study was to assess the outcomes of SGB and the feasibility of SG stimulation and recording in humans with VAs. Two patient cohorts were included—group 1: patients undergoing SGB for drug-refractory VAs. SGB was performed by injection of liposomal bupivacaine. Incidence of VAs at 24 and 72 hours and clinical outcomes were collected; group 2: patients undergoing SG stimulation and recording during VA ablation; a 2-F octapolar catheter was placed at the SG at the C7 level. Recording (30 kHz sampling, 0.5–2 kHz filter) and stimulation (up to 80 mA output, 50 Hz, 2 ms pulse width for 20–30 seconds) was performed. Group 1 included 25 patients [age 59.2 ± 12.8 years; 19 (76%) men] who underwent SGB for VAs. Nineteen patients (76.0%) were free of VA up to 72 hours postprocedure. However, 15 (60.0%) had VAs recurrence for a mean of 5.47 ± 4.52 days. Group 2 included 11 patients (mean age 63 ± 12.7 years; 82.7% men). SG stimulation caused consistent increases in systolic blood pressure. We recorded unequivocal signals with temporal association with arrhythmias in 4 of 11 patients. SGB provides short-term VA control, but has no benefit in the absence of definitive VA therapies. SG recording and stimulation is feasible and may have value to elicit VA and understand neural mechanisms of VA in the electrophysiology laboratory. [ABSTRACT FROM AUTHOR]

Background: Venous ethanol ablation (VEA) is effective for treatment of left ventricular (LV) summit (LVS) arrhythmias. The LVS venous anatomy is poorly understood and has inconsistent nomenclature.Objective: The purpose of this study was to delineate the LVS venous anatomy by selective venography and 3-dimensional (3D) mapping during VEA and by venous-phase coronary computed tomographic angiography (vCTA).Methods: We analyzed (1) LVS venograms and 3D maps of 53 patients undergoing VEA; and (2) 3D reconstructions of 52 vCTAs, tracing LVS veins.Results: Angiography identified the following LVS veins: (1) LV annular branch of the great cardiac vein (GCV) (19/53); (2) septal (rightward) branches of the anterior ventricular vein (AIV) (53/53); and (3) diagonal branches of the AIV (51/53). Collateral connections between LVS veins and outflow, conus, and retroaortic veins were common. VEA was delivered to target arrhythmias in 38 of 53 septal, 6 of 53 annular, and 2 of 53 diagonal veins. vCTA identified LVS veins (range 1-5) in a similar distribution. GCV-AIV transition could either form an angle close to the left main artery bifurcation (n = 16; 88° ± 13°) or cut diagonally (n = 36; 133°±12°) (P ≤.001). Twenty-one patients had LV annular vein. In 28 patients only septal LVS veins were visualized in vCTA, in 2 patients only diagonal veins and in 22 patients both septal and diagonal veins were seen. In 39 patients the LVS veins reached the outflow tracts and their vicinity.Conclusion: We provide a systematic atlas and nomenclature of LVS veins related to arrhythmogenic substrates. vCTA can be useful for noninvasive evaluation of LVS veins before ethanol ablation. [ABSTRACT FROM AUTHOR]

Technology-mediated learning (TML) has turned out to be essential in classroom in higher education. How to evaluate the effect of TML has become a critical issue in research and practice. Yet, there exists very little understanding of how to measure the effect of TML on learning process. The existing research results are difficult to be used in teaching design. This paper develops an instrument as a diagnostic tool to comprehensively evaluate the effect of TML on learning process. This instrument focuses on the relationship among IT, cognitive information processing, and instructional process. Based on cognitive information processing theory and Gagné’s instructional theory, definition and measurement of 8 constructs are identified and designed. In a sample of 181 undergraduates in nine majors, the study finds that this instrument has higher reliability, better convergent validity and discriminant validity. This instrument can help provide rich information about whether and how TML affects instructional events and learning activities effectively. At the same time, it can be a guiding tool for more effective applications of TML in instructional event design. [ABSTRACT FROM AUTHOR]

Agricultural water management is the core part of agricultural efficient water use in irrigation districts, which provides basic guarantee and decision-support for improving the efficiency of water production. This study was conducted in three typical irrigation districts in Huang-huai-hai river basin of north China. The evaluation index system of agricultural water management in irrigation districts was established which included technology index, engineering index, management index, environment index and economic index, classified 5 sorts of second-grade indices, 14 third-grade indices and 35 fourth-grade indices. The weight of each index was confirmed through improved AHP method. The grades of agricultural water management of Fenhe, Shijin and Renmin Shengliqu irrigation districts in Huang-huai-hai riverbasin were obtained using Grey Correlation method and Fuzzy Comprehensive Evaluation method. Results showed that the evaluation indices ranked according to the descending order of their weights as engineering index, 0.2147; management index, 0.2138; technology index, 0.2128; economics index, 0.1797; environment index, 0.1791 in the criterion level. The engineering and management index were the most two important factors which influenced the agricultural water management in irrigation districts. The grades of Fenhe, Shijin and Renmin Shengliqu irrigation districts were good, good and average, respectively. The study provides a theoretical basis in improving the level of agricultural water management and water use efficiency in irrigation districts of north China. [ABSTRACT FROM AUTHOR]

Abstract: Nutrient restriction (NR) prolongs longevity via enhanced mitochondrial function. We tested the hypothesis that NR enhances resistance to ischemia/reperfusion (IR) arrhythmias via preserved calcium (Ca) cycling and mitochondrial function. We examined the protective effects of NR on regional IR in cultured neonatal rat ventricular myocyte monolayers. Optical mapping of intracellular Ca and mitochondrial membrane potential Δψ m was performed using Rhod 2-AM and TMRE, respectively. Regional ischemia was mimicked by covering a portion of monolayer with a glass coverslip until loss of Ca propagation, and reperfusion was mimicked by removing the coverslip. NR was mimicked by culture in serum- and glucose-free medium for 24h. Relative to controls, NR monolayers: (1) sustained Ca oscillations during longer periods of ischemia (19.2±1.8min vs 10.4±1.4min, p <0.001); (2) had attenuated increases in Ca transient duration (CaD) and time decay constant (Tau) during ischemia; (3) had preserved conduction velocity (CV) during early reperfusion, leading to protection against reperfusion arrhythmias; (4) had minimal “rebound” decreased CaD and Tau during reperfusion; and (5) had no depolarization of Δψ m during IR. NR attenuates IR arrhythmias via (1) stable calcium cycling and (2) prevention of Δψ m depolarization during IR. Enhanced mitochondrial resistance to IR arrhythmias may play a role in NR-induced longevity prolongation. [Copyright &y& Elsevier]

A new kind of amphiphilic derivative of carboxymethylchitosan, a group of (2-hydroxyl-3-butoxyl)propylcarboxymethylchitosans (HBP-CMCHS), has been synthesized, and the surface and aggregate properties have been studied by means of surface tension, surface pressure and fluorescence measurements. HBP-CMCHS can adsorb on the surface to decrease the surface tension of the solution. The adsorption film was quite stable, which can make the relative compressed pressure increase dramatically with the decrease of the surface area. In solution, hydrophobic aggregations were identified by the decrease in the ratio of the fluorescence emission intensity of the first and third pyrene vibronic peaks (I1/I3). Results showed that the aggregation began to form at a concentration similar to that of the polymer transfer to the air–water interface. Aggregate formation of the polymers is a gradually compact process with hydrophobic associations. Increase of DS and addition of NaCl to the HBP-CMCHS solution can make the surface tension decrease, make the aggregation occur at lower concentration, and make the aggregation more hydrophobic. [Copyright &y& Elsevier]

Improving the spatial pattern of agricultural systems has become a promising approach for enhancing agricultural productivity and sustainability. However, previous studies have often focused on the influence of natural factors on crop distribution, ignoring factors such as human activities, socio-economic level and ecological environment. This study aims to investigate the influence of natural factors and social environmental drivers on the optimal pattern of multiple crops and evaluate the potential of optimal patterns to enhance agricultural productivity and sustainability. Here, we present a multi-criteria approach integrating natural and social environment system factors and set up three assessment scenarios: crop growth suitability (CGS), crop planting suitability (CPS), and crop planting competitiveness (CPC). Applying this approach to the Shiyang River basin in China as a case study, we assessed the suitability and competitiveness of single crops. To maximize agriculture competitiveness, we optimized the structure of the multi-crop system, and a method was proposed to identify highly suitable intercropping areas using crop competitiveness conflicts. Regional crop water consumption, water productivity, and economic benefits were calculated to analyze the potential for agricultural intensification under different optimization patterns. We found that the weights of four categories factors of location, socio-economic conditions, productivity, and environment protection accounted for 26.9%, 7.5%, 13.7%, and 4.5%, respectively, and the weight of social environmental influence indicators had accounted for about 43.5%, which cannot be ignored. The distribution area above moderate suitability (L2) in the CPS and CPC scenarios was about 7.92% - 30.03% and 6.14% - 26.4% higher than the CGS scenario, respectively. Social environmental factors are important to consider in assessing the suitability of crops. From the spatial structure, three optimization patterns all suggested increasing the planting proportion of wheat and potato in the future. The optimization patterns in CPC scenario could reduce total crop water demand by 91.86 to 175.77 million cubic meters compared with 2020 while showing great potential to improve crop water productivity and net output per cubic meter of water. Furthermore, we offered recommendations for the layouts of common intercropping systems in Northwest China based on the proposed method for identifying high suitability zones. This study emphasizes the importance of considering multiple environments to accurately assess crop suitability and achieve sustainable agricultural. The results could provide useful insights for managing and optimizing diverse planting systems, addressing growing concerns surrounding food and water security in resource-constrained regions. [Display omitted] • Human activities and social environment have not received sufficient attention for their impacts on agricultural patterns. • Proposed a novel approach to crop suitability assessment and structural optimization integrating natural and social systems. • Optimized patterns under the CPC scenario reduced water consumption and improved water productivity and net water benefits. • Findings emphasize the importance of considering human activities and social environmental drivers in agricultural planning. • Crop management based on dual environmental assessments provides a practical solution to promote sustainable agriculture. [ABSTRACT FROM AUTHOR]

• PET and ground-based ET 0 estimation are integrated to reflect the spatiotemporal variability and improve ET 0 accuracy. • A distributed interval nonlinear multiobjective programming (DINMP) model is proposed. • Uncertainties, conflicting objectives, spatial information, and marginal benefit are fully considered in DINMP model. • DINMP is applied to a real-world case and the results can contribute to the sustainable development of agriculture. Considering the uncertainties in agricultural system and spatiotemporal variability in evapotranspiration and precipitation, a distributed interval nonlinear multiobjective programming (DINMP) model was developed for optimal allocation of limited irrigation water resources in the middle reaches of Heihe River basin. The meteorological data from meteorological stations were used to estimate reference crop evapotranspiration (ET 0) through FAO56 Penman-Monteith (PM) equation, and then the remote sensing MOD16/PET data were fitted by linear regression model according to the FAO56 PM results. The 95% confidence interval was used to further improve the accuracy of the fitting results. Thus, satellite-based potential evapotranspiration (PET) and ground-based ET 0 estimation were integrated to not only reflect the spatial and temporal variability but also guarantee the accuracy of the ET 0. In the terms of precipitation, spatial interpolation was used to spatial information of precipitation. Based on these spatiotemporal data, a DINMP with three objectives, including maximizing economic benefits and water saving as well as minimizing water shortage of critical growth periods, was formulated, and further solved by fuzzy coordination method. The optimal allocation scheme improves the irrigation water productivity by [0.50, 0.66] kg/m3, and decreases net irrigation water allocation by [0.33, 1.01] ×108 m3. These results show that DINMP can not only consider the uncertainties and multiple objectives in agricultural water management, but also improve the spatial resolution of optimal water allocation strategies. The framework of this study can provide a reference for agricultural water managers in similar areas to obtain more reasonable water allocation schemes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classical optimal planting pattern (COPP) can improve regional water productivity. • The first assessment regarding the impact of COPP on food-energy-water-carbon nexus. • COPP can decrease carbon neutrality capacity by 13.09% in China's major arid area. • Need to redefine "optimum" planting pattern under the context of global warming. Global warming, water scarcity and limited land resources are the most challenging problems facing agriculture to ensure food security for the expected 9 billion people in 2050. To solve these problems, the classical optimal planting pattern, based on crop suitability evaluation method, is often adopted to reallocate water and land resources. However, whether or not the classical optimal planting pattern, which only considers environmental conditions in crop suitability evaluation, is beneficial to the regional carbon neutrality goal and saves water and energy resources has rarely been explored. Here, China's major arid food production area, the middle reaches of Heihe River Basin, is chosen as the demonstration to explore this issue. The classical optimal planting pattern obtained by crop suitability evaluation is compared with current planting in terms of planting distribution, carbon sequestration, energy consumption and water productivity from 2002 to 2016. Interestingly, the results indicate that optimal planting would reduce the regional net carbon sequestration capacity by up to 13.09% and increase regional carbon emissions by up to 22%, which is harmful to reach the commitment of carbon neutrality goal in China. Contrary, optimal crop planting pattern can increase regional water productivity by 1.74–32.59% and economic benefits by 1.52–30.55% while having little impact on energy consumption and water consumption. Considering the contradictions effects of classical optimal planting pattern on the food-energy-water-carbon nexus, we strongly recommend redefining the "optimal" in crop planting management by taking impacts on carbon into consideration to alleviate the crisis of global warming. [ABSTRACT FROM AUTHOR]

Abstract: Monitoring of spatial and temporal distribution of chlorophyll (Chl-a) concentrations in the aquatic milieu is always challenging and often interesting. However, the recent advancements in satellite digital data play a significant role in providing outstanding results for the marine environmental investigations. The present paper is aimed to review ‘remote sensing research in Chinese seas’ within the period of 24 years from 1978 to 2002. Owing to generalized distributional pattern, the Chl-a concentrations are recognized high towards northern Chinese seas than the southern. Moreover, the coastal waters, estuaries, and upwelling zones always exhibit relatively high Chl-a concentrations compared with offshore waters. On the basis of marine Chl-a estimates obtained from satellite and other field measured environmental parameters, we have further discussed on the applications of satellite remote sensing in the fields of harmful algal blooms (HABs), primary production and physical oceanographic currents of the regional seas. Concerned with studies of HABs, satellite remote sensing proved more advantageous than any other conventional methods for large-scale applications. Probably, it may be the only source of authentic information responsible for the evaluation of new research methodologies to detect HABs. At present, studies using remote sensing methods are mostly confined to observe algal bloom occurrences, hence, it is essential to coordinate the mechanism of marine ecological and oceanographic dynamic processes of HABs using satellite remote sensing data with in situ measurements of marine environmental parameters. The satellite remote sensing on marine environment and HABs is believed to have a great improvement with popular application of technology. [Copyright &y& Elsevier]

Spatial and temporal variability of soil properties, irrigation schedules, and fertilization management play an important role in crop growth and yield in agricultural systems. However, how to improve and accurately predict crop yield in different management zones (MZs) within a large-scale field are important but rarely discussed. In this study, coefficient of variation, linear correlations, partial least squares discriminant analysis and quantile regression were used to quantify relationships between crop and soil properties, and determine key explanatory factors of yield, and to identify suitable ways to improve crop yield among MZs. At the temporal scale, coefficients of variation of crop growth parameters (LAI and biomass), and correlations of crop growth parameters with soil water content and available nitrogen varied during crop growth stages in MZs across years. Crop–soil correlations varied with time and were most pronounced in heading and filling stages when LAI and biomass approached maximum values, respectively. At the spatial scale, 22 explanatory factors belonging to four categories: soil physical properties, initial soil properties, soil water and nitrogen dynamics, and topography were compared. Results showed that among the four categories, initial soil properties had the greatest influence on yield across the whole field, whereas soil water and nitrogen dynamics were the most influential for individual MZs. Key explanatory factors of yield varied across MZs in 2017 and 2018. Although key explanatory factors varied across years within a MZ, some remained unchanged, especially soil physical properties. Based on the quantile regression results, the same key explanatory factors might appear in MZs, but the reference ranges were different. For more sustainable and efficient agricultural production, distributed integrated management, including soil amelioration before sowing, and irrigation and N fertilization scheduling during the crop growth period should be implemented in MZs based on the spatial-temporal variability of soil properties. • Spatial variability of crop growth parameters varies during growth stages in management zones (MZs). • Correlations of crop growth parameters with soil and nitrogen dynamics vary in growth stages. • The relative contributions of explanatory factors in MZs are different from that across the whole field. • The combination of partial least squares discriminant analysis and quantile regression can determine the reference ranges of key explanatory factors in MZs. [ABSTRACT FROM AUTHOR]

Variability of soil properties within large-scale fields not only exists in the horizontal domain, but also in the vertical direction, causing spatial variability in yield. Three yield zones were delineated based on measured yield in 2017 and 2018 within a large field in northwest China. The Soil Water Heat Carbon Nitrogen Simulator (WHCNS) model was calibrated and used to simulate yield, nitrogen uptake (N u), water use efficiency (WUE), fertilizer N (nitrogen) use efficiency (FNUE), deep percolation (DP), nitrate leaching (NL) and residual nitrate (RN) at each sampling point in different yield zones. Based on the simulations, there were significant differences in N u , WUE, FNUE, DP, NL and RN in 0–100 cm and 100–160 cm soil layers among the three yield zones. DP, NL and RN in the layers were strongly determined by the interaction of zone and year (p < 0.05), thus yielding consistent patterns mainly determined by soil properties and meteorological factors. The modelled ranges of DP, NL, and RN (0–160 cm) were 25–119 mm, 15–94 kg ha−1, and 178–476 kg·ha−1 respectively, across the field. Soil texture in the maize main root zone (0–100 cm) has a great influence on yield and N u , and in the 100–160 cm layer upon DP and NL. RN was abundant after harvest and should be taken into account to determine the nitrogen fertilization demand for the following crop. The study showed that the process of delineating zones can be based on historical yield, making it feasibly easier than mapping soil properties. In view of the fact that there were large losses of water and nitrogen with uniform irrigation and fertilization management, the effects of vertically variable soil properties should be considered in future precision agriculture research, to achieve higher economic benefits and utilization efficiency. Unlabelled Image • Uniform management with excessive irrigation and fertilization caused a high risk of deep percolation and N leaching. • Deep percolation and N leaching were significantly different between yield zones within a field. • Residual N should be considered in fertilization management. • Precise irrigation and fertilization management could significantly reduce loss of water and nitrogen in northwest China. [ABSTRACT FROM AUTHOR]

When planning agricultural production, planting area and water allocation are two major subjects faced by decision makers. In this study, a framework integrated Nerlove supply response model (Nerlove model) and interval fuzzy credibility-constraint bi-level programming (IFCBP) model is developed for planning the agricultural production in arid and semi-arid regions. Through Nerlove model, the planning process of crop planting area was described as an economic problem for forecasting farmers' behavior rather than an optimization problem for allocating farmland resources, and the relationship between crop planting area and market price can be obtained and further provide credible future crop planting area information. The IFCBP model can not only deal with uncertainties presented as interval and fuzzy numbers but also examine the credibility of the constraints and handle tradeoffs between two-level decision makers. To solve the IFCBP model, a solution method based on the interval interactive algorithm and credibility-cut method is proposed. Then, to verify the validity of the developed framework and solving method for agricultural production planning, they were applied to a real-case in the middle reaches of the Heihe River basin, northwest China. The forecasting results obtained from Nerlove model have better performance in predicting the future planting area of corn and vegetable than wheat, indicating that wheat plays a more vulnerable role in the decision-making process of planting area owing to its higher substitutability. The results show that the proposed framework can tackle two-level decision makers' concerns under uncertainties featured as inexact and fuzzy numbers, which can help regional managers plan future resources effectively. Furthermore, a comparison was made between IFCBP and two corresponding single-level models in this study. The comparison indicates that the developed model provides an effective tradeoff between two decision makers from different decision-making levels in IFCBP. The developed framework provides managers an effective way to plan agricultural production in arid and semi-arid regions, and the developed model and related thinking may help solve similar problems. • An agricultural production planning approach based on interval fuzzy credibility-constrained bi-level programming (IFCBP) and Nerlove supply response theory. • Both objective and subject factors are considered through the entropy method and analytic hierarchy process method separately. • This approach is applied to a case study for planning crop planting area and irrigation-water allocation. • The results can support water managers formulate more efficient agricultural production planning strategy. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel regional grid-scale crop dynamic layout optimization model was constructed. • Multiyear crop layout plan was obtained by considering inter-annual impacts. • Spatial variance of parameters and neighbourhood effects of crop units were noted. • Overall government planning and farmers' crop rotation behaviour were regarded. • Stakeholders and their multi-objective planting preferences were balanced. Rapid population growth has led to an increasing demand for food, accompanied by an increase in the resources invested in producing food, and the extensive food production practices have placed a huge burden on the environment. There is an urgent desire of need to find a sustainable model of food production. Therefore, this study attempted to develop a dynamic regional grid-scale crop layout model (ABCAMODP-Crop) combining agent-based model, multi-objective programming, dynamic programming and cellular automata model to promote more sustainable food production from a supply-side perspective. The model can: (1) take into account the planting preferences and practical behaviours of government and farmer as stakeholders in crop layout; (2) further weigh the conflicts among the various objectives that stakeholders are concerned with from resource, environmental, nutritional and economic dimensions; (3) consider the impact of the previous year's crop layout on the next year's crop placement; and (4) obtain grid-scale accurate crop layout solutions by considering the spatial variability of optimization parameters and the neighborhood effects of crop layout cells. The model was applied to the Shiyang River Basin in China to verify its usefulness. Over the entire planning period, crop layout schemes optimized with the objective of integrating irrigation water productivity, environmental impact, nutrient availability and economic benefits have higher comprehensive benefits than the original scheme, with an increase in average irrigation water productivity per unit area of 14.55%, an increase in total yield of 14.45%, an increase in total carbohydrate availability of 7.39%, an increase in total calcium availability of 18.74%, an increase in total net carbon sequestration of 8.05%, an increase in total net benefit of 20.08%. The model developed allows for a holistic view of crop layout and guides decision-makers in a rational distribution of regional crops, thus promoting more sustainable food production. It can equally be extended to other regions as a generic modelling framework. [ABSTRACT FROM AUTHOR]

Rapidly growing population has led to increasing food demands and greater food system pressure. It is urged to find a way to improve the sustainability of food system. Therefore, this study attempted to develop a regional crop layout model (CAMOO-Crop) combining cellular automata model with multi-objective optimization model to improve food system sustainability. The model can (1) consider the carbon sequestration of farmland vegetation, carbon emissions from production inputs, and human demand for crop nutrient supply, (2) balance the trade-offs among multiple dimensions of resource, environment, economy and nutrition in the crop food system, (3) obtain grid-scale crop space layout schemes considering special distribution. The model was applied to the Shiyang River Basin in China to verify its effectiveness. After optimization, crop water productivity per unit area increased by 2.12%, carbon sequestration increased by 0.32%, carbon emission decreased by 1.87%, the net benefit increased by 2.66%, carbohydrate supply decreased by 0.19%, calcium supply increased by 5.74%. In addition, a cropland-scale food system sustainability evaluation index system was constructed, and the Food System Sustainability Index (FSSI) and Coupling Coordination Degree (CCD) were introduced to evaluate the sustainability of food systems under different crop layout scenarios. The results showed that compared with single-objective model scenario and the status quo, CAMOO-Crop model performed best, with FSSI from 0.70 to 0.91; CCD from 0.83 to 0.95. The developed model can promote sustainable development of the regional food system and guide decision-makers to rationally distribute regional crops. [Display omitted] • A novel crop layout framework was developed to improve food system sustainability. • A model for coupling cellular automata and multi-objective programming was provided. • Trade-offs between resource, environment, economy and nutrition were realized. • A farmland scale food system sustainability evaluation system was constructed. • Layout options were developed for decision-makers considering special distribution. [ABSTRACT FROM AUTHOR]

Ineffective control over industrialization and urbanization has exposed the shortcomings of land resource management. The overexploitation of limited high-quality land resources is exacerbating the global ecological and food crisis. There is an urgent need to change from "expansion" to "intensification" in land resource utilization. Taking the northwest oasis region of China as a case study, a spatio-temporal optimization model of land use and land cover (LULC) based on dynamic competitiveness evaluation was constructed, and four simulation scenarios, including natural growth without policy intervention (SN), economic priority (SE), food security (SF) and ecological protection (SP), were set up to evaluate and predict the contribution of land intensification to regional carbon neutrality. The results show that the total carbon emissions in the study region will rise sharply by 157.56% in 2030 compared with 2020 without policy intervention (SN). In contrast, all three land intensification scenarios have great emission reduction potential: total regional carbon emissions can be reduced by 21.15% (SE), 22.78% (SF), and 51.72% (SP), respectively, compared with SN. Notably, the implementation of the ecological protection policy (SP) in the region will result in an average carbon emission per unit of GDP of 0.57 t/104 yuan in 2030, which is 25.44% lower than in 2020, implying a downward trend in carbon emissions per GDP between 2020 and 2030, and SP scenario will see the region achieve carbon peaking before 2030. The results can provide novelty methods and policy opinions for optimal land management from the perspective of a low-carbon city. [ABSTRACT FROM AUTHOR]

It is urgent to determine the crop critical irrigation period and the priority of crop water irrigation in arid and semi-arid regions to cope with agricultural water shortage. In this study, a method combining Agricultural Production Systems Simulator (APSIM) crop model with grey relational analysis was proposed to determine the irrigation priority of wheat, maize, and sunflower in the irrigation water distribution to ensure food security, and applied to the Hetao Irrigation District (HID) in China. Crop irrigation priority was determined by crop yield, water supply demand ratio (SDR), and water productivity (WP). The maximum yield, maximum water productivity and minimum water stress (crop growth stage) were evaluated. The obtained results indicate that: (1) When the irrigation scenario was 100%, the critical irrigation period for wheat was the first 10 days after sowing and 40–61 days after sowing; The critical irrigation priority for maize was 20–30 days after sowing and 61–81 days after sowing; And the critical irrigation priority for sunflower was 30–61 days after sowing. (2) In early May, irrigation priority was wheat priority to maize, while maize was a priority to wheat in mid to late May. In June, sunflower priority to wheat and maize irrigation. From early to middle July, irrigation priority to maize, and from late July to late August, sunflower water irrigation priority to maize. The method of to identify irrigation priority and crop priority to provide irrigation decision for allocating agriculture water resources. [Display omitted] • A novel method based on crop model and grey relational analysis is proposed. • The best irrigation time after sowing wheat, maize, and sunflower is identified. • Priority for intercrop water irrigation at the same time is proposed. • The effects of water stress, water productivity, and yield is considered. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions from irrigation and nitrogen fertilizer applications in agriculture are attracting increasing attention, and the development of low-carbon agriculture has become an inevitable trend in the implementation of carbon neutrality. However, the optimization of water and nitrogen in crop planting still lacks precise management at the grid scale. From the perspective of agricultural carbon emission reduction, this study proposes a precise regional water and nitrogen management approach that can optimize irrigation and nitrogen fertilizer application in space and time. In this study, a coupled crop water-nitrogen production function model was constructed by analyzing the response relationship between nitrogen and water on crop yield. The verification results showed that the model had superior fitting accuracy, with R2 and NRMSE values of 0.88, 10.50%, 0.8 and 7.76% for maize and wheat, respectively. After combining the water-nitrogen coupling function with the cellular automaton model, a spatiotemporal optimization model was developed to realize grid-scale regional water and nitrogen management. The case study showed that the optimized irrigation and nitrogen scheme could save 2.73% of agricultural water, decrease nitrogen fertilizer use by 9.69%, and reduce carbon emissions by 4.39%. Correlation analysis of irrigation and fertilization can provide accurate guidance for regional irrigation and fertilization systems. Therefore, considering the contribution of agricultural "carbon reduction sources" to carbon neutrality, water saving, and nitrogen reduction should be considered in low-carbon agricultural development, which is conducive to the green and sustainable development of agriculture. [Display omitted] • Established the response relationship between water-nitrogen and yield. • A grid-scale water-nitrogen coupling optimization model is developed. • Realized the spatiotemporal optimization of crop water and nitrogen. • Maintaining crop yield while water-saving and nitrogen-reducing. • Provided irrigation and nitrogen fertilization solutions for low carbon emission. [ABSTRACT FROM AUTHOR]

The pressures of frequent extreme weather, water shortages and increasing food demand pose a continued challenge of maintaining the sustainable development of irrigated agricultural systems. Although rational arable land management is fundamental to alleviating these pressures, the relationship between cropping patterns and irrigation sustainability is understudied. Using the Hexi Corridor as an example, a maximum entropy machine learning model was used to determine the optimal cropping pattern based on crop suitability and to explore the impact mechanism of the optimal cropping pattern on the irrigation sustainability index (SI) from the perspective of reliability, resilience, and vulnerability. An optimal cropping pattern was conducive to sustainable irrigation and reduced irrigation water use by 21.03% from 1960 s to 2010 s with no continued agricultural expansion. Thus, the challenges of food security and sustainability for similar regions, and globally, can be met but will require major changes in cropping planning and management. [Display omitted] • Crop suitability is an important basis for determining optimal cropping patterns. • Irrigation sustainability is assessed under optimal cropping patterns. • Adjusting the cropping pattern is an effective way to alleviate water scarcity. • Sustainable irrigation development needs more attention. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that tropical cyclones can cause upwelling, decrease of sea surface temperature, increase of chlorophyll-a (Chl-a) concentration and enhancement of primary production. But little is known about the response of dissolved oxygen (DO) concentration to a typhoon in the open ocean. This paper investigates the impact of a typhoon on DO concentration and related ecological parameters using in situ and remote sensing data. The in situ data were collected 1week after the passage of the super-typhoon Nanmadol in the northern South China Sea in 2011. An increase in DO concentration, accompanied by a decrease in water temperature and an increase in salinity and Chl-a concentration, was measured at sampling stations close to the typhoon track. At these stations, maximum DO concentration was found at a depth of around 5m and maximum Chl-a concentration at depths between 50 and 75m. The layer of high DO concentration extends from the surface to a depth of 35m and the concentrations stay almost constant down to this depth. Due to the passage of the typhoon, also a large sea level anomaly (21.6cm) and a high value of Ekman pumping velocity (4.0×10−4 ms−1) are observed, indicating upwelling phenomenon. At the same time, also intrusion of Kuroshio waters in the form of a loop current into the South China Sea (SCS) was observed. We attribute the increase of DO concentration after the passage of the typhoon to three effects: (1) entrainment of oxygen from the air into the upper water layer and strong vertical mixing of the water body due to the typhoon winds, (2) upwelling of cold nutrient-rich water which stimulates photosynthesis of phytoplankton and thus the generation of oxygen, which also increases the DO concentration due to cold water since the solubility of oxygen increase with decreasing water temperature, and, possibly, (3) transport of DO enriched waters from the Western Pacific to the SCS via the intrusion of Kuroshio waters. [Copyright &y& Elsevier]

Abstract: Background: Prognosis of patients with gastric cancer (GC) is generally poor due to the lack of non-invasive tools for GC detection. The purpose of present study was to identify a serum microRNA (miRNA) expression profile that can serve as a novel diagnostic biomarker for GC detection and to assess its clinical applications in monitoring disease progression. Methods: Serum samples were taken from 164 GC patients and 127 age- and gender-matched tumour-free controls. An initial screening of miRNA expression by Solexa sequencing was performed using serum samples pooled from 20 patients and 20 controls, respectively. Differential expression was validated using hydrolysis probe-based stem-loop quantitative reverse transcription polymerase chain reaction (qRT-PCR) in individuals samples, the samples were arranged in two phases. Results: The Solexa sequencing results demonstrated that 19 serum miRNAs were markedly upregulated in the GC patients compared to the controls. The qRT-PCR analysis further identified a profile of five serum miRNAs (miR-1, miR-20a, miR-27a, miR-34 and miR-423-5p) as a biomarker for GC detection. The analysis results showed that the expression level of five serum miRNAs was correlated to tumour stage. The areas under the receiver operating characteristic (ROC) curve of this five-serum miRNA signature were 0.879 (95% confidence interval (CI) 0.822–0.936) and 0.831 (95% CI 0.767–0.898) for the two sets of serum samples, respectively, markedly higher than those of the biomarkers carcinoembryonic antigen (CEA) (0.503) and carbohydrate antigen 19-9 (CA19-9) (0.600). Conclusions: We identified five-miRNA signature for GC diagnosis by genome-wide serum miRNA expression profiling. Expression levels of this serum miRNA-based biomarker also indicate tumour progression stages. [Copyright &y& Elsevier]

The finite water resources are the main factors limiting agricultural sustainable development in arid and semiarid zones. In these water-scarce areas, optimizing limited water resources is an important way to reduce water consumption, increase crop yields and improve regional economic benefits. In this study, a CA-Jensen optimization model integrating cellular automata method and crop water production function into multi-objective dynamic optimization was developed and applied to the middle reaches of the Heihe River basin in the Northwest China to adjust the spatial distribution of cropland and the spatiotemporal allocation of crop water consumption. The net economic benefits of unit water, the crop water productivity and the value of the terrestrial ecosystem services represented the economic, social and ecological benefits in the objective function of the optimization model respectively. Adaptive cellular genetic algorithm was utilized to search and solve the optimization model. The obtained optimization results of the base year (2015) showed that the net benefits of unit water increased by 4.5% and the crop water productivity increased by 28.6%. In addition, for the target year (2030), although the reduction of agricultural water and cultivated land by 12.95% and 18.63% respectively led to a decrease of total economic benefits by 15.72%, the net benefits of unit water and social benefits got increased by 15.71% and 20.52%, respectively. Therefore, the optimization model can provide decision makers with guidance on the rational allocation of regional agricultural water resources in the future. • Established a spatio-temporal dynamic optimization model of crop water consumption. • Provided a method coupling cellular automata with Jensen model on grid-scale. • The comprehensive benefits of irrigation are improved under water limitation. • Helping decision-makers implement water-adapted sustainable agricultural policies. [ABSTRACT FROM AUTHOR]

Globally, agriculture is by far the largest water consuming sector and in areas where water is scarce, the spatial optimization of crop water consumption used to improve irrigation benefits becomes critical for regional water management. The spatial heterogeneity of environmental parameters brings great challenge to spatial optimization. Therefore, cellular automaton (CA), crop suitability (CS), spatial distributed crop water consumption model and optimization model were integrated and applied on the middle reaches of Heihe River basin, northwest of China. The cellular automata based Water Consumption Optimization (CA-WCSO) model is not only a spatial dynamic optimization model for crop water consumption, but also a decision support tool that reflects the interaction between water consumption at field level and management regulations at regional level. Six optimization paths: i) forward progressive (F P), ii) forward interlacing (F-IL), iii) forward interpolation (F-IP), iv) reverse progressive (R-P), v) reverse interlacing (R-IL) and vi) reverse interpolation (R-IP) of crop water consumption for the baseline year and the planning year were applied on the study site. Results for baseline year (2015) demonstrate that the six optimization paths can slightly reduce the water consumption (>1.4%) but significantly improve the irrigation benefits of the region by 20.56%. Using CA-WCSO model, decision makers can modify model's constraints and select appropriate optimization path to get the optimized crop planting patterns and make future regional water allocation plans. Unlabelled Image • Realize the dynamic optimization of spatial crop water consumption based on grids. • Suitability screening can ensure the effective use of local land-water resources. • The impact of environment on crop water consumption and growth is considered. • Six optimization paths are proposed in this optimization model. [ABSTRACT FROM AUTHOR]