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Objectives: In this study, we aimed to evaluate the non-inferiority of a quadrivalent influenza vaccine (QIV) developed by Sinovac Biotech Co., Ltd. (Sinovac, Beijing, China) by comparing its immunogenicity and safety with a comparator QIV (Vaxigrip Tetra®) in a population aged 3 years and older in Chile and the Philippines. Methods: A phase 3, non-inferiority, double-blind, randomized controlled, multicenter clinical trial was conducted in the southern hemisphere (SH) 2023 influenza season. Participants aged ≥ 3 years old with stable health were randomized 1:1 to receive either Sinovac QIV or comparator QIV. The co-primary outcomes were immunological non-inferiority for Sinovac QIV versus the comparator against each strain contained in the vaccines in terms of seroconversion rates (SCRs) and geometric mean titers (GMTs) of hemagglutination inhibition (HI) antibodies 28 days after final vaccination. Results: A total of 2039 participants were vaccinated (1019 Sinovac QIV; 1020 comparator QIV). Sinovac QIV induced non-inferior immune responses to all four strains as compared to comparator QIV, with slightly higher GMTs than those of comparator QIV: GMT ratios (lower limit 95% confidence interval (CI)) were 1.8 (1.6) for A(H1N1), 1.4 (1.3) for A (H3N2), 1.3 (1.1) for B Victoria and 1.2 (1.1) for B Yamagata; observed seroconversion rate differences (lower limit 95% CI) were 9.6% (6.7) for A(H1N1), 7.0% (3.5) for A(H3N2), 2.4% (−0.03) for B Victoria and 6.8% (3.0) for B Yamagata. Adverse reactions were similar across the two groups and no vaccine-related serious adverse events were reported. Conclusions: The immunogenicity of Sinovac QIV was non-inferior to that of the comparator QIV in these populations aged 3 years and older, and safety was comparable. [ABSTRACT FROM AUTHOR]

The efficacy of functional lipids with antioxidant properties in reducing cardiovascular risk has not been consistent. Randomized controlled trials (RCTs) reporting estimates for the effects of antioxidant functional lipid supplementations on cardiometabolic risk factors were searched up to 1 May 2024. Overall, antioxidant lipid supplementations, compared with placebo, had favorable effects on systolic blood pressure (lycopene: −1.95 [−3.54, −0.36] mmHg), low-density lipoprotein cholesterol (n6 fatty acid: −0.39 [−0.71, −0.06] mmol/L; astaxanthin: −0.11 [−0.21, −0.01] mmol/L), high-density lipoprotein cholesterol (n3 fatty acid: 0.20 [0.13, 0.27] mmol/L; n6 fatty acid: 0.08 [0.01, 0.14] mmol/L; astaxanthin: 0.13 [0.05, 0.21] mmol/L), total cholesterol (n6 fatty acid: −0.24 [−0.37, −0.11] mmol/L; astaxanthin: −0.22 [−0.32, −0.12] mmol/L; beta-carotene: −0.13 [−0.23, −0.04] mmol/L), triglyceride (n3 fatty acid: −0.37 [−0.47, −0.28] mmol/L; astaxanthin: −0.46 [−0.83, −0.10] mmol/L), and fasting blood insulin (astaxanthin: −2.66 [−3.98, −1.34] pmol/L). The benefits of antioxidant lipid supplementations appeared to be most evident in blood pressure and blood lipids in participants with different cardiometabolic health statuses. Notably, n9 fatty acid increased triglyceride and hemoglobin A1C in the total population, which increases CVD risk. Antioxidant lipid supplementations ameliorate cardiometabolic risk factors, while their effect may depend on type and cardiometabolic health status. Long-term RCTs are needed to corroborate risk–benefit ratios across different antioxidant functional lipid supplementation settings. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is an age-related condition characterized by progressive loss of muscle mass, strength, and function. The occurrence of sarcopenia has a huge impact on physical, psychological, and social health. Therefore, the prevention and treatment of sarcopenia is becoming an important public health issue. Method: 35 six-week-old male C57BL/6 mice were randomly divided into five groups, one of which served as a control group, while the rest of the groups were constructed as a model of sarcopenia by intraperitoneal injection of D-galactose. The intervention with lactoferrin, creatine, and their mixtures, respectively, was carried out through gavage for 8 weeks. Muscle function was assessed based on their endurance, hanging time, and grip strength. The muscle tissues were weighed to assess the changes in mass, and the muscle RNA was extracted for myogenic factor expression and transcriptome sequencing to speculate on the potential mechanism of action by GO and KEGG enrichment analysis. Result: The muscle mass (lean mass, GAS index), and muscle function (endurance, hanging time, and grip strength) decreased, and the size and structure of myofiber was smaller in the model group compared to the control group. The intervention with lactoferrin and creatine, either alone or combination, improved muscle mass and function, restored muscle tissue, and increased the expression of myogenic regulators. The combined group demonstrated the most significant improvement in these indexes. The RNA-seq results revealed enrichment in the longevity-regulated pathway, MAPK pathway, focal adhesion, and ECM–receptor interaction pathway in the intervention group. The intervention group may influence muscle function by affecting the proliferation, differentiation, senescence of skeletal muscle cell, and contraction of muscle fiber. The combined group also enriched the mTOR-S6K/4E-BPs signaling pathway, PI3K-Akt signaling pathway, and energy metabolism-related pathways, including Apelin signaling, insulin resistance pathway, and adipocytokine signaling pathway, which affect energy metabolism in muscle. Conclusions: Lactoferrin and creatine, either alone or in combination, were found to inhibit the progression of sarcopenia by influencing the number and cross-sectional area of muscle fibers and muscle protein synthesis. The combined intervention appears to exert a more significant effect on energy metabolism. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) transition metal dichalcogenides (TMDs) are garnering considerable scientific interest, prompting discussion regarding their prospective applications in the fields of nanoelectronics and spintronics while also fueling groundbreaking discoveries in phenomena such as the fractional quantum anomalous Hall effect (FQAHE) and exciton dynamics. The abundance of binary compound TMDs, such as MX2 (M = Mo, W; X = S, Se, Te), has unlocked myriad avenues of exploration. However, the exploration of ternary compound TMDs remains relatively limited, with notable examples being Ta2NiS5 and Ta2NiSe5. In this study, we report the synthesis of a new 2D ternary compound TMD materials, Ta3VSe8, employing the chemical vapor transport (CVT) method. The as-grown bulk crystal is shiny and can be easily exfoliated. The crystal quality and structure are verified by X-ray diffraction (XRD), while the surface morphology, stoichiometric ratio, and uniformity are determined by scanning electron microscopy (SEM). Although the phonon property is found stable at different temperatures, magneto-resistivity evolves. These findings provide a possible approach for the realization and exploration of ternary compound TMDs. [ABSTRACT FROM AUTHOR]

The increasing occurrence of severe weather phenomena presents substantial difficulties for electricity distribution systems. This study investigates the integration of hydrogen production plants (HPPs) into power distribution networks to bolster resilience against the increasing frequency of extreme weather events. It highlights the innovative use of hydrogen, generated from renewable sources, as an energy storage medium to ensure a stable power supply during disruptions. By employing stochastic optimization, the study aims to effectively manage hydrogen production and utilization, considering uncertainties in disaster scenarios and energy demands. It addresses critical research gaps, such as the lack of focus on pre-disaster preventive scheduling using hydrogen, the under-explored application of stochastic optimization in such contexts, and the predominance of real-time response strategies over pre-emptive measures. This approach significantly advances current understanding by proposing novel strategies that leverage hydrogen production and sophisticated optimization to enhance the resilience of power networks against extreme weather events. In some scenarios, using our method can reduce the cost of pre-disaster prevention by approximately 15.68% while ensuring that the disaster recovery effect remains unchanged. [ABSTRACT FROM AUTHOR]

When the underwater submersible encounters an internal solitary wave (ISW), its loadings and motions are significantly disturbed. To investigate the interaction mechanism between the suspended submersible and the ISW, a three-dimensional ISW–submersible-interaction numerical model was established, based on the computational fluid dynamics (CFD) method. The generation and propagation of the ISW was simulated in a two-layer fluid numerical wave tank, according to the eKdV theory. The standard operation equation of the submersible was introduced to simulate the six degree of freedom (6DoF) motions of the submersible combined with the overset dynamic mesh method. The motion simulation method was effectively validated by comparing it with published experimental results on the motion responses of a slender body under the ISW. Based on the constructed numerical model, the dynamic mechanisms between the suspended submersible and the ISW were studied, and the effects of the initial submerged depths and the ISW amplitudes on the dynamic responses of the submersible were revealed. According to the numerical results, the motions of the submersible have been significantly determined by its initial submerged depths. The submersible located above the ISW interface has a significant motion along the propagation direction of the ISW and its motion trajectory resembles a counterclockwise semi ellipse. The motion of the submersible located below the ISW interface follows the trace of the lower layer of fluid, which presents as an unclosed clockwise ellipse. The corresponding motions of the submersible would be increased with the increase in the ISW amplitudes. [ABSTRACT FROM AUTHOR]

Whether China can achieve the United Nations' Sustainable Development Goals (SDGs) largely depends on the ability of main food-producing areas to cope with multiple land use change challenges. Despite the fact that the Yangtze River basin is one of the key regions for China's food security, the spatiotemporal dynamics of cropland abandonment and recultivation remain largely unexplored in this region. The present study assesses the evolution of the agricultural system within the Yangtze River basin between 2000 and 2020 by mapping cropland abandonment and recultivation using MODIS time series and multiple land cover products. The results highlight a widespread cropland abandonment process (i.e., 10.5% of the total study area between 2000 and 2020), predominantly in Western Sichuan, Eastern Yunnan, and Central Jiangxi. Although 70% of abandoned cropland is situated in areas with slopes less than 5°, the highest rates of abandonment are in mountainous regions. However, by 2020, 74% of this abandoned cropland had been recultivated at least once, whereas half of the abandoned croplands got recultivated within three years of their initial abandonment. Hence, as this is one of the first studies that unravels the complex interaction between cropland abandonment and recultivation in a spatiotemporal explicit context, it offers (i) scientists a novel methodological framework to assess agricultural land use issues across large geographical entities, and (ii) policy-makers new insights to support the sustainable transition of the agricultural sector. [ABSTRACT FROM AUTHOR]

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system's operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model's scale. To ensure an efficient solution, a Benders' decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM's siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits. [ABSTRACT FROM AUTHOR]

Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n) . Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly connected important features by involving the known biological network when constructing the forest. The algorithm can identify effective features that form highly connected sub-graphs and achieve equivalent classification accuracy to RF. To evaluate the capability of our proposed method, we conducted simulation experiments and applied the method to two real datasets—non-small cell lung cancer RNA-seq data from The Cancer Genome Atlas, and human embryonic stem cell RNA-seq dataset (GSE93593). The resulting high classification accuracy, connectivity of selected sub-graphs, and interpretable feature selection results suggest the method is a helpful addition to graph-based classification models and feature selection procedures. [ABSTRACT FROM AUTHOR]

Photocatalytic CO2 reduction to valuable hydrocarbon solar fuel is of great significance but still challenging. Strong CO2 enrichment ability and easily adjustable structures make metal-organic frameworks (MOFs) potential photocatalysts for CO2 conversion. Even though pure MOFs have the potential for photoreduction of CO2, the efficiency is still quite low due to rapid photogenerated electron–hole recombination and other drawbacks. In this work, graphene quantum dots (GQDs) were in situ encapsulated into highly stable MOFs via a solvothermal method for this challenging task. The GQDs@PCN-222 with encapsulated GQDs showed similar Powder X-ray Diffraction (PXRD) patterns to PCN-222, indicating the retained structure. The porous structure was also retained with a Brunauer–Emmett–Teller (BET) surface area of 2066 m2/g. After incorporation of GQDs, the shape of GQDs@PCN-222 particles remained, as revealed by the scanning electron microscope (SEM). As most of the GQDs were covered by thick PCN-222, it was hard to observe those GQDs using a transmission electron microscope (TEM) and a high-resolution transmission electron microscope (HRTEM) directly, the treatment of digested GQDs@PCN-222 particles by immersion in a 1 mM aqueous KOH solution can make the incorporated GQDs visible in TEM and HRTEM. The linker, deep purple porphyrins, make MOFs a highly visible light harvester up to 800 nm. The introduction of GQDs inside PCN-222 can effectively promote the spatial separation of the photogenerated electron–hole pairs during the photocatalytic process, which was proved by the transient photocurrent plot and photoluminescence emission spectra. Compared with pure PCN-222, the obtained GQDs@PCN-222 displayed dramatically enhanced CO production derived from CO2 photoreduction with 147.8 μmol/g/h in a 10 h period under visible light irradiation with triethanolamine (TEOA) as a sacrificial agent. This study demonstrated that the combination of GQDs and high light absorption MOFs provides a new platform for photocatalytic CO2 reduction. [ABSTRACT FROM AUTHOR]

Cr(VI) is one of the most toxic heavy metals, posing multiple threats to humans and ecosystems. Photoreduction of toxic Cr(VI) to para-toxic Cr(III) is one of the most effective ways to remove heavy metal chromium but is still challenging. Herein, Ti3C2/UiO-66-NH2 composites with different ratio of Ti3C2 were synthesized via an in situ solvothermal process and used for the enhanced photocatalytic removal of Cr(VI) for the first time. The UiO-66-NH2 nanoparticles were dispersed on the surface and slits of accordion-like Ti3C2 homogeneously. A strong interfacial interaction between Ti3C2 and UiO-66-NH2 was formed, which was indicated by the XPS. The Fermi level of the MXene cocatalyst is lower than UiO-66-NH2; thus, Ti3C2 can serve as the electron sink and accumulate photogenerated electrons from UiO-66-NH2 on its surface. A depletion layer was also formed due to the different Fermi levels of UiO-66-NH2 and Ti3C2, which prevents electrons from flowing back to UiO-66-NH. The strong interfacial interaction and formed depletion layer are beneficial for the following charge transfer from UiO-66-NH2 to Ti3C2 after light irradiation and for suppressing the photogenerated charge recombination. With suitable band positions and enhanced charge separation ability, Ti3C2/UiO-66-NH2 composites exhibited better photoreduction efficiency of Cr2O72− than pure Ti3C2 and UiO-66-NH2, with optimized samples reaching 100% in 40 min. The photoreduction kinetics of Cr2O72− by 2-T/U was also studied, with a photoreduction rate of 0.0871 min−1, which is about 2.6 times higher than that of the pure UiO-66-NH. This research provides a new type of efficient and environmentally friendly photocatalysts for the photoreduction of Cr2O72−. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values. [ABSTRACT FROM AUTHOR]

OpenMOC-HEX, a neutron transport calculation code with hexagonal modular ray tracing, has the capability of domain decomposition parallelism based on an MPI parallel programming model. In this paper, the optimization of inter-node communication was studied. Starting from the specific geometric arrangement of hexagonal reactors and the communication features of the Method of Characteristics, the computation and communication of all the hexagonal assemblies are mapped to a graph structure. Then, the METIS library is used for graph partitioning to minimize the inter-node communication under the premise of load balance on each node. Numerical results of an example hexagonal core with 1968 energy groups and 1027 assemblies demonstrate that the communication time is reduced by about 90%, and the MPI parallel efficiency is increased from 82.0% to 91.5%. [ABSTRACT FROM AUTHOR]

A total of 39 novel cyclotryptamine alkaloid derivatives were prepared from 2-(1H-indol-3-yl) acetonitrile. The prepared compounds were evaluated against six plant pathogen fungi. Bioassay results revealed that most of the compounds displayed higher in vitro antifungal activities than the positive control. Notably, compound b2 displayed the broadest and most effective activity among the tested cyclotryptamine alkaloid derivatives and might be a novel potential leading compound for further development as an antifungal agent. [ABSTRACT FROM AUTHOR]

Tobacco smoke exposure is a major environmental risk factor that facilitates the development and progression of asthma. Our previous study showed that CpG oligodeoxynucleotide (CpG-ODN) inhibits thymic stromal lymphopoietin (TSLP)-dendritic cells (DCs) to reduce Th2/Th17-related inflammatory response in smoke-related asthma. However, the mechanism underlying CpG-ODN -downregulated TSLP remains unclear. A combined house dust mite (HDM)/cigarette smoke extract (CSE) model was used to assess the effects of CpG-ODN on airway inflammation, Th2/Th17 immune response, and amount of IL-33/ST2 and TSLP in mice with smoke-related asthma induced by adoptive transfer of bone-marrow-derived dendritic cells (BMDCs) and in the cultured human bronchial epithelium (HBE) cells administered anti-ST2, HDM, and/or CSE. In vivo, compared to the HDM alone model, the combined HDM/CSE model had aggravated inflammatory responses, while CpG-ODN attenuated airway inflammation, airway collagen deposition, and goblet cell hyperplasia and reduced the levels of IL-33/ST2, TSLP, and Th2/Th17-cytokines in the combined model. In vitro, IL-33/ST2 pathway activation promoted TSLP production in HBE cells, which could be inhibited by CpG-ODN. CpG-ODN administration alleviated Th2/Th17 inflammatory response, decreased the infiltration of inflammatory cells into the airway, and improved the remodeling of smoke-related asthma. The underlying mechanism may be that CpG-ODN inhibits the TSLP-DCs pathway by downregulating the IL-33/ST2 axis. [ABSTRACT FROM AUTHOR]

Over the last decades, growing crop production across China has had far-reaching consequences for both the environment and human welfare. One of the emerging questions is "how to meet the growing food demand in China?" In essence, the consensus is that the best way forward would be to increase crop yield rather than further extend the current cropland area. However, assessing progress in crop production is challenging as it is driven by multiple factors. To date, there are no studies to determine how multiple factors affect the crop production increase, considering both intensive farming (using yield and multiple cropping index) and large-scale farming (using mean parcel size and number of parcels). Using the Logarithmic-Mean-Divisia-Index (LMDI) decomposition method combined with statistical data and land cover data (GlobeLand30), we assess the contribution of intensive farming and large-scale farming changes to crop production dynamics at the national and county scale. Despite a negative contribution from MPS (mean parcel size, −25%), national crop production increased due to positive contributions from yield (+77%), MCI (multiple cropping index, +27%), as well as NP (number of parcels, +21%). This allowed China to meet the growing national crop demand. We further find that large differences across regions persist over time. For most counties, the increase in crop production is a consequence of improved yields. However, in the North China Plain, NP is another important factor leading to crop production improvement. On the other hand, regions witnessing a decrease in crop production (e.g., the southeast coastal area of China) were characterized by a remarkable decrease in yield and MCI. Our detailed analyses of crop production provide accurate estimates and therefore can guide policymakers in addressing food security issues. Specifically, besides stabilizing yield and maintaining the total NP, it would be advantageous for crop production to increase the mean parcel size and MCI through land consolidation and financial assistance for land transfer and advanced agricultural infrastructure. [ABSTRACT FROM AUTHOR]

Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree's width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection. [ABSTRACT FROM AUTHOR]

As the population increases and climate extremes become more frequent, the pressure on food supply increases. A better understanding of the influence of climate variations on crop yield in China would be of great benefit to global food security. In this study, gridded, daily meteorological data and county-level annual yield data were used to quantify the climate sensitivity of corn, rice, and spring wheat yields, and identify the spatiotemporal variation relationship between climate and yields from 1980 to 2014. The results showed that rice and corn were more sensitive to climate variations than spring wheat, both spatially and temporally. Photosynthetic active radiation (PAR) was found to be beneficial to rice in northeast China and the Yangtze River basin, as well as corn in the south and spring wheat in Xinjiang, but not to rice in the south of the Yangtze River and spring wheat in the southeast coast. The temperature centroid shift was the main driving factor causing the movement of the centroid of the three crops. For every 1 km shift of the temperature centroid, the corn and rice yield centroids moved 0.97 km and 0.34 km, respectively. These findings improve our understanding of the impacts of climate variations on agricultural yields in different regions of China. [ABSTRACT FROM AUTHOR]

The slow pace of discovery of bioactive natural products can be attributed to the difficulty in rapidly identifying them in complex mixtures such as plant extracts. To overcome these hurdles, we explored the utility of two machine learning techniques, i.e., Elastic Net and Random Forests, for identifying the individual anti-inflammatory principle(s) of an extract of the inflorescences of the hops (Humulus lupulus) containing hundreds of natural products. We fractionated a hop extract by column chromatography to obtain 40 impure fractions, determined their anti-inflammatory activity using a macrophage-based bioassay that measures inhibition of iNOS-mediated formation of nitric oxide, and characterized the chemical composition of the fractions by flow-injection HRAM mass spectrometry and LC-MS/MS. Among the top 10 predictors of bioactivity were prenylated flavonoids and humulones. The top Random Forests predictor of bioactivity, xanthohumol, was tested in pure form in the same bioassay to validate the predicted result (IC50 7 µM). Other predictors of bioactivity were identified by spectral similarity with known hop natural products using the Global Natural Products Social Networking (GNPS) algorithm. Our machine learning approach demonstrated that individual bioactive natural products can be identified without the need for extensive and repetitive bioassay-guided fractionation of a plant extract. [ABSTRACT FROM AUTHOR]

A common situation arising in flow shops is that the job processing order must be the same on each machine; this is referred to as a permutation flow shop scheduling problem (PFSSP). Although many algorithms have been designed to solve PFSSPs, machine availability is typically ignored. Healthy machine conditions are essential for the production process, which can ensure productivity and quality; thus, machine deteriorating effects and periodic preventive maintenance (PM) activities are considered in this paper. Moreover, distributed production networks, which can manufacture products quickly, are of increasing interest to factories. To this end, this paper investigates an integrated optimization of the distributed PFSSP with flexible PM. With the introduction of machine maintenance constraints in multi-factory production scheduling, the complexity and computation time of solving the problem increases substantially in large-scale arithmetic cases. In order to solve it, a deep Q network-based solution framework is designed with a diminishing greedy rate in this paper. The proposed solution framework is compared to the DQN with fixed greedy rate, in addition to two well-known metaheuristic algorithms, including the genetic algorithm and the iterated greedy algorithm. Numerical studies show that the application of the proposed approach in the studied production-maintenance joint scheduling problem exhibits strong solution performance and generalization abilities. Moreover, a suitable maintenance interval is also obtained, in addition to some managerial insights. [ABSTRACT FROM AUTHOR]

For hypersonic-vehicle fairing separation, the reliable separation of the fairing and rapid suppression of the separation disturbance are required in scenarios where there is high dynamic pressure and there are abrupt changes in the aerodynamic shape of the aircraft as well as overall parameters. A switching-based control method is proposed in this paper for the interference suppression of hypersonic-vehicle fairing separation, aiming to ensure the stability of the control system in cases of abrupt changes in the controlled object. First, an unsteady-flow calculation method is adopted to clarify the aerodynamic interference characteristics of the aircraft in fairing separation, and the aerodynamic calculation model of the hypersonic-vehicle fairing-separation process is established; then, two states of the aircraft, with and without fairing, are transformed into a subsystem with switching characteristics. The attitude-stability problem in the fairing-separation process is transformed into a problem of solving the arbitrary switching of the switched system by using the linear matrix inequality (LMI) approach. Meanwhile, the control system for interference suppression of the fairing separation is designed by considering the aerodynamic interaction of the fairing movement with the aircraft during the separation. The simulation results showed that the designed control system can realize the reliable separation of the fairing under high dynamic pressure and effectively suppress interference in the separation process. [ABSTRACT FROM AUTHOR]

Cropland fields are the basic spatial units for agricultural management, and information about their distribution is critical for analyzing agricultural investments and management. However, the extraction of cropland fields of smallholder farms is a challenging task because of their irregular shapes and diverse spectrum. In this paper, we proposed a new object-based Genetic Programming (GP) approach to extract cropland fields. The proposed approach used the multiresolution segmentation (MRS) method to acquire objects from a very high resolution (VHR) image, and extracted spectral, shape and texture features as inputs for GP. Then GP was used to automatically evolve the optimal classifier to extract cropland fields. The results show that the proposed approach has obtained high accuracy in two areas with different landscape complexities. Further analysis show that the GP approach significantly outperforms five commonly used classifiers, including K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). By using different numbers of training samples, GP can maintain high accuracy with any volume of samples compared to other classifiers. [ABSTRACT FROM AUTHOR]

Marrubium friwaldskyanum Boiss (Lamiaceae) is a Bulgarian endemic species. Overall, the essential oil (EO) composition of M. friwaldskyanum was different from that of the other Marrubium species reported in the literature. The main EO constituents of M. friwaldskyanum were (E)-caryophyllene, germacrene D, and caryophyllene oxide. The effect of the harvest stage was significant only on α-copaene, (E)-caryophyllene, caryophyllene oxide, and τ-muurolol. The concentration of α-copaene (1.26–1.83% range of the total oil), (E)-caryophyllene (31–41%), caryophyllene oxide (6.4–11.8%), and τ-muurolol (1.3–2.8%) were the highest at 2–3 pair of leaves or before flowering and lower at flowering. The harvest stage did not significantly affect the concentrations of the other six identified EO compounds β-bourbonene (1.1%), α-humulene (2.8%), germacrene D (23.3%), bicyclogermacrene (2.85%), δ-cadinene (1.1%), and spathulenol (2.8%). In a separate experiment, grinding of the biomass prior to EO extraction had a significant effect only on the concentrations of D-limonene (0.24–3.3%) and bicyclogermacrene (3.6–9.1%). Grinding in water or without water, maceration, and addition of Tween®20 had rather small effects on the EO profile. The identified EO constituents and their mean concentrations in this experiment were (E)-caryophyllene (25.4%), germacrene D (17.6%), caryophyllene oxide (9.1%), spathulenol (6.5%), τ-muurolol (5.0%), carvacrol (3.9%), α-copaene (2.5%), β-bourbonene (2.5%), δ-cadinene (2.4%), α-humulene (1.8%), and Z-β-farnesene (1.3%). Embryological studies observed anther and the development of the male gametophyte and ovule and development of the female gametophyte of M. friwaldskyanum. Furthermore, pollen and seed viability assays were conducted, and mass spectrometry-based metabolomics analysis of an extract from shoots revealed the presence of 45 natural products, identified as flavonoids, phenolic acids, and (tri)terpenoids. Overall, the phytochemistry and some of the microscopic analyses distinguished this endemic species from other species in Marrubium. [ABSTRACT FROM AUTHOR]

Lithium cobalt oxide (LCO) is the most widely used cathode materials in electronic devices due to the high working potential and dense tap density, but the performance is limited by the unstable interfaces at high potential. Herein, LiF thin film is sputtered on the surface of LCO electrodes for enhancing the electrochemical performance and reducing the voltage polarization. The polarization components are discussed and quantified by analyzing the relationship between electrochemical polarization and charger transfer resistance, as well as that between concentration polarization and Li-ion diffusion coefficients. In addition, the decreased charge transfer resistance, increased lithium-ion diffusion coefficients, and stabilized crystal structure of LiF-coated LCO are confirmed by various electrochemical tests and in-situ XRD experiments. Compared to that of pristine LCO, the capacity and cycling performance of LiF-coated LCO is improved, and the overpotential is reduced upon cycling. This work provides reference for quantifying the various polarization components, and the strategy of coating LiF film could be applied in developing other analogous cathode materials. [ABSTRACT FROM AUTHOR]

Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas. [ABSTRACT FROM AUTHOR]

Yellow rust is a worldwide disease that poses a serious threat to the safety of wheat production. Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images. [ABSTRACT FROM AUTHOR]

Rapeseed is an important oil product in China. China's current soybean trade issues with major soybean producing countries have caused a large decline in soybean import since 2017. This may bring the increasing needs of rapeseed import, which would have an impact on domestic production. However, our knowledge on the effects of international rapeseed trade on domestic production remains unknown. It is thus important to understand the pattern of rapeseed in China under this scenario, as it may provide necessary information for all relevant stakeholders. With this goal, this study aims to investigate the spatial and temporal patterns of winter rape in China's major winter rape production region, the Middle Reaches of the Yangtze River Valley (MYR), during 2003–2015 using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data. A decision tree according to the difference in enhanced vegetation index (EVI) profiles of land-cover types was built to extract winter rape. The results show that there is an essential decrease in both the number and density of winter rape patches under the opening global rapeseed market. There are significant hotspots of winter rape gain and loss, within which the loss dominated the trend. The significant cost advantage of rapeseed in the international market may largely reduce the domestic cultivation in China through telecoupling effects. Understanding the spatio-temporal dynamics of winter rape on the MYR has significant economic and policy implications and can provide great supports for the agricultural production, policy-making, and oil products trade in the international market. [ABSTRACT FROM AUTHOR]

Cropland maps at regional or global scales typically have large uncertainty and are also inconsistent with each other. The substantial uncertainty in these cropland maps limits their use in research and management efforts. Many synergy approaches have been developed to generate hybrid cropland maps with higher accuracy from existing cropland maps. However, few studies have compared the advantages, disadvantages, and regional suitability of these approaches. To close this knowledge gap, this study aims to compare two representative synergy methods of cropland mapping: Geographically weighted regression (GWR) and modified fuzzy agreement scoring (MFAS). We assessed how the sample size, quality of input satellite-based maps, and various landscapes influence the accuracy of the synergy maps based on these two methods. The GWR model is a regression analysis predominantly dependent on the cropland percentage of the training samples, while the MFAS method is largely influenced by the consistency of input datasets, and the training samples only play an auxiliary role. Therefore, the GWR method was relatively more sensitive to the number of training samples than the MFAS method. The quality of input maps had a significant impact on both methods, particularly on MFAS. In regions with heterogeneous landscapes and high elevations, the croplands are generally more fragmented, and the consistency of the input satellite-based maps was lower; the application of cropland percentage samples could compensate for the low dataset consistency. Therefore, GWR is more suitable for regions with heterogeneous landscapes, while MFAS is more appropriate for regions with homogeneous landscapes. The MFAS method uses cropland area from the agricultural statistics to calibrate the initial synergy maps, while the GWR model only considers the spatial distribution of cropland and does not make use of the distribution information of cropland area. The MFAS method showed a higher correlation with the statistical data, while GWR model exhibited a stronger relationship with cropland percentage. Our study reveals the advantages, disadvantages, and regional suitability of the two main types of synergy methods (regression analysis methods and data consistency scoring methods) and can inform future synergy cropland mapping efforts. [ABSTRACT FROM AUTHOR]

Global food demand will increase over the next few decades, and sustainable agricultural intensification on current cropland may be a preferred option to meet this demand. Mapping cropping intensity with remote sensing data is of great importance for agricultural production, food security, and agricultural sustainability in the context of global climate change. However, there are some challenges in large-scale cropping intensity mapping. First, existing indicators are too coarse, and fine indicators for measuring cropping intensity are lacking. Second, the regional, intra-class variations detected in time-series remote sensing data across vast areas represent environment-related clusters for each cropping intensity level. However, few existing studies have taken into account the intra-class variations caused by varied crop patterns, crop phenology, and geographical differentiation. In this research, we first presented a new definition, a normalized cropping intensity index (CII), to quantify cropping intensity precisely. We then proposed a Bayesian network model fusing prior knowledge (BNPK) to address the issue of intra-class variations when mapping CII over large areas. This method can fuse regional differentiation factors as prior knowledge into the model to reduce the uncertainty. Experiments on five sample areas covering the main grain-producing areas of mainland China proved the effectiveness of the model. Our research proposes the framework of obtain a CII map with both a finer spatial resolution and a fine temporal resolution at a national scale. [ABSTRACT FROM AUTHOR]

Soybean cultivation in China has significantly decreased due to the rising import of genetically modified soybeans from other countries. Understanding soybean’s extent and change information is of great value for national agricultural policy implications and global food security. Some previous studies have explored the quantitative relationships between crop area and spectral variables derived from remote sensing data. However, both those linear or non-linear relationships were expressed by global regression models, which ignored the spatial non-stationarity of crop spectral signature and may limit the prediction accuracy. This study presented a geographically weighted regression model (GWR) to estimate fractional soybean at 250 m spatial resolution in Heilongjiang Province, one of the most important food production regions in China, using time-series MODIS data and high-quality calibration information derived from Landsat data. A forward stepwise optimization strategy was embedded with the GWR model to select the optimal subset of independent variables for soybeans. Normalized Difference Vegetation Index (NDVI) of Julian day 233 to 257 when soybeans are filling seed was found to be the most important temporal period for sub-pixel soybean area estimation. Our MODIS-based soybean area compared well with Landsat-based results at pixel-level. Also, there was a good agreement between the MODIS-based result and census data at county level, with the coefficient of determination (R2) of 0.80 and the root mean square error (RMSE) was 340.21 km2. Additionally, F-test results showed GWR model had better model goodness-of-fit and higher prediction accuracy than the traditional ordinary least squares (OLS) model. These promising results suggest crop spectral variations both at temporal and spatial scales should be considered when exploring its relationship with pixel-level crop acreage. The optimized GWR model by combining an automated feature selection strategy has great potential for estimating sub-pixel crop area at regional scale based on remote sensing time-series data. [ABSTRACT FROM AUTHOR]

Wide-coverage spatial information on irrigated croplands is a vital foundation for food security and water resources studies at the regional level. Several global irrigated-cropland maps have been released to the public over the past decade due to the efforts of the remote sensing community. However, the consistency and discrepancy between these maps is largely unknown because of a lack of comparative studies, limiting their use and improvement. To close this knowledge gap, we compared the latest four irrigated-cropland datasets (GMIA, GRIPC, GlobCover, and GFSAD) in mainland China. First, the four maps were compared quantitatively and neutral regional- and provincial-level statistics of the relative proportions of irrigated land were obtained through regression analysis. Second, we compared the similarities and discrepancies of the datasets on spatial grids. Furthermore, the contributions of mosaic cropland pixels in GlobCover and GFSAD were also analyzed because of their extensive distribution and ambiguous content. Results showed that GMIA has the lowest dispersion and best statistical correlation followed by GRIPC, while the corresponding features of GlobCover and GFSAD are approximately equal. Spatial agreement of the four maps is higher in eastern than western China, and disagreement is contributed mostly by GlobCover and GFSAD. However, divergence exists in the ratios of the different agreement levels, as well as their sources, on a regional scale. Mosaic pixels provide more than half of the irrigated areas for GlobCover and GFSAD, and they include both correct and incorrect information. Our results indicate a need for a uniform quantitative classification system and for greater focus on heterogeneous regions. Furthermore, the results demonstrate the advantage of numerical restriction in the calculations. Therefore, special attention should be paid to integrating databases and to exploring remote sensing features and methods for spatial reconstruction and identification of untypical irrigation areas., Competing Interests: The authors declare no conflicts of interest.

Mapping crop patterns with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. In this paper, a hierarchical clustering method was proposed to map cropping frequency from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Indices (EVI) data and the spatial and temporal patterns of cropping frequency from 2001 to 2015 in Hubei Province of China were analyzed. The results are as follows: (1) The total double crop areas decreased slightly, while total single crop areas decreased significantly during 2001 and 2015; (2) The transfer between double crop and single crop was frequent in Hubei with about 11~15% croplands changed their cropping frequency every 5 years; (3) The crop system has obvious regional differentiation for their change trend at the county level., Competing Interests: The authors declare no conflict of interest.

Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ considerably from statistics. In this study, we propose a hierarchical optimization synergy approach (HOSA) to develop a hybrid cropland map of China, circa 2010, by fusing five existing cropland products, i.e., GlobeLand30, Climate Change Initiative Land Cover (CCI-LC), GlobCover 2009, MODIS Collection 5 (MODIS C5), and MODIS Cropland, and sub-national statistics of cropland area. HOSA simplifies the widely used method of score assignment into two steps, including determination of optimal agreement level and identification of the best product combination. The accuracy assessment indicates that the synergy map has higher accuracy of spatial locations and better consistency with statistics than the five existing datasets individually. This suggests that the synergy approach can improve the accuracy of cropland mapping and enhance consistency with statistics., Competing Interests: The authors declare no conflict of interest.

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human-land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.