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A comprehensive understanding of wind turbine wake characteristics is vital, particularly in the context of expanding large offshore wind farms. Existing wake measurement techniques provide only spatially sparse wake measurement data, limiting their utility in precise wind turbine design and control. This paper introduces a data-driven approach that combines proper orthogonal decomposition (POD) with machine learning (ML) techniques, designing a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. This framework establishes a nonlinear mapping between sensor measurements and low-dimensional POD coefficients. Two distinct sensor placements, wall-mounted and wake-mounted, are investigated for real measurement scenarios. The results highlight the effectiveness of the proposed wake flow estimation method in reconstructing a complete flow field from exceptionally sparse sensor data, with both wall-mounted and wake-mounted strategies, exhibiting promising results with maximum relative errors of 6.37% and 4.51%, respectively. From the reliability assessments considering various configurations of POD modes and sensor numbers, the ROM-WFE framework demonstrates its capability to estimate wake flow effectively, offering a cost-effective tool for practical applications. Furthermore, the framework maintains accuracy even with high-noise and low-frequency data, demonstrating robustness and generalization. This method significantly contributes to wind turbine wake prediction controller design, promising accurate and robust wake flow field estimation, potentially revolutionizing active wake control and enhancing wind farm operational efficiency. • Combining POD and ML techniques to establish a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. • Exploring the effectiveness of ROM-WFE in wall- and wake-mounted sensor placement strategies. • Utilizing fewer than three sensors enhances practicality for real-world applications and improves wind farm efficiency. • Demonstrating resilience and adaptability, maintaining accuracy even with high-noise, low-frequency data. [ABSTRACT FROM AUTHOR]

Hydrofoils play a crucial role in enhancing the efficiency of fluid machinery designed for ocean environments, reducing lift-induced drag and contributing to improved overall performance. To optimize hydrofoil design, a profound comprehension of the complex fluid flows around the hydrofoil structure is essential. In fluid mechanics, a precise and continuous representation of flow dynamics is essential for analysis and control purposes. Thus, obtaining a complete flow field, either through computational fluid dynamics (CFD) or experimental testing is of great significance. Nevertheless, the rigorous requirements of flow field tests render it impractical to directly measure complete flows around the airfoil using current instrumentation, especially for those with complex physical geometries. To tackle this issue, a novel deep learning framework is proposed to reconstruct the complete flow field by leveraging incomplete complementary flow data. As the representative benchmark problems, the flows around the Clark-Y hydrofoil at Re = 7 × 105 and the experimental NACA0012 2D hydrofoil at Re = 1800 have been investigated under different missing-flow scenarios of varying proportions, locations and orientations. Results demonstrate a remarkable agreement between the reconstructed flow field and the ground truth data, indicating the excellent performance of the proposed deep-learning model for missing flow reconstruction. A sensitivity analysis assesses the impact of the snapshot number and the latent space, revealing the method's robustness in selecting these parameters and simplifying its implementation in practical applications. This deep learning method offers the advantage of being implemented using paired incomplete flow fields, without the need for pre-known ground truth results as labels, holding the potential for addressing more complex full-field reconstruction problems in the future. [Display omitted] • Develop novel MS-AE framework for missing flow field reconstruction, surpassing fused POD and AE methods. • Conduct sensitivity analysis of AE-SE method to assess crucial parameters, ensuring reliability. • Apply unsupervised machine learning and multi-scale approach for innovative fluid mechanics reconstruction. [ABSTRACT FROM AUTHOR]

• We discussed on calculation approaches of human transformity. • We treated the transformity of teachers as a variable in the equation. • Results for human transformity needed comparisons and discussions. Human transformities are important for emergy accounting of human labor contributions in ecological, economic, and social systems. Lupinacci and Bonilla (2018) explored two different approaches to calculating teacher transformities in an English Language school in Minas Gerais, Brazil. This letter is not to criticize their approach and result, but instead to suggest one minor but potentially valuable amendment to their method. The innovation is to treat the transformity of the teacher as a variable on the input side of the equation. The new computed transformity of 1.21 E9 sej/J was 3.5 times larger than the transformity calculated by Lupinacci and Bonilla (2018). This E9 result is in line with other research that applied a multi-year cyclic calculation. The algebraic solution is one that could be applied more generally to other similar transformity equations. [ABSTRACT FROM AUTHOR]

Abstract: Sustainability evaluation of mariculture is needed for policy decision-making in its development. Zhao et al. (2013) developed the method of emergy ecological footprint to evaluate an offshore small fish farm in the East China Sea. After re-examination of their method, data analysis process and results, three questions are presented (1) what is the nature of this method? (2) How much impact do the recognition and classification of input flows on the results? (3) Do their evaluated results confirm to the real situation of this farm? Further discussions are also made on several main studies about the emergy ecological footprint method. Our results imply: (1) there is a need to improve this method; (2) the input flows should be carefully processed due to their great impacts on the results; (3) the essential data and comparative analysis are needed to support this evaluation. [Copyright &y& Elsevier]

• Inter-calibration between DMSP-OLS and NPP-VIIRS were conducted at the pixel scale. • CO 2 emissions in both lit area and unlit area were estimated. • Tradeoff between vegetation CO 2 sequestration and fossil CO 2 emission was evaluated. • Policy implications for CO 2 emissions reduction were suggested. Carbon neutrality has attracted tremendous attention. Cities contribute the most to CO 2 emissions. However, the contribution of vegetation to fossil-fuel-related CO 2 emissions in urban agglomeration is unclear. Clarifying the trade-off role of vegetation can disaggregate carbon reduction targets down to sub-units to adapt to and even mitigate global warming. In this study, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), one of the world's largest metropolitan areas, was studied using our proposed inter-calibration method. The results showed that the inter-calibration method is satisfactory and that the EANTLI model effectively decreases the blooming and saturation effects of nighttime light. In addition, fossil-fuel-related CO 2 emissions increased significantly (P < 0.0001) in the GBA during 2000–2018, while the variation in CO 2 sequestrations was far lower than that in the increase in emissions. CO 2 sequestrations by vegetation fully offset fossil-fuel-related CO 2 emissions in 2000, while the status reversed after 2001. Our findings illustrate the role of vegetation carbon sequestration in offsetting fossil-fuel-related CO 2 emissions and emphasize the importance of a CO 2 budget. Additionally, the one city, one policy strategy is a good choice for further adapting to and mitigating global warming. [ABSTRACT FROM AUTHOR]

A precise and cost-effective prediction tool for fluid-structure interaction (FSI) analysis is crucial for optimizing the structural design of tidal turbine blades. However, the high computational costs associated with fluid dynamic analysis pose a significant challenge, as the current lack of efficient FSI prediction methods hinders the advancement of cutting-edge tidal turbine designs. To address this issue, this paper proposes a novel consolidated framework that integrates deep learning convolutional neural networks (CNN) with blade element momentum (BEM) theory and finite element method (FEM) to perform deformation analysis of turbine blade structures. The proposed CNN-BEM-FEM integrated framework efficiently identifies the geometric features and predicts the hydrodynamic parameters of turbine blades and thus, achieving accurate assessments of the structural behavior of tidal turbines. The study applies two-step verification procedures to validate the prediction accuracy of the CNN-BEM-FEM framework and the result demonstrates excellent agreement with experimental tests for hydrodynamic performance and blade deformation. When compared with the static one-way FSI calculated by Ansys Workbench software, the computational efficiency of CNN-BEM-FEM framework increases by more than 18 times, with discrepancies in blade deformation and equivalent stress calculations generally less than 5 %. By applying the proposed method to predict the FSI performance of tidal turbine blades with various shear web structures, the practical applicability for composite turbine blade design is successfully demonstrated. The results underscore the potential of the CNN-BEM-FEM framework as an efficient and accurate prediction tool for optimizing the structural design of tidal turbine blades. • A high-efficient framework that combines CNN, BEM and FEM for tidal turbine rotor structural analysis is proposed. • Convolutional neural network (CNN) is used for identifying hydrodynamic performance of turbine blade section. • Blade element momentum (BEM) is for turbine prediction, while finite element method (FEM) is for structural analysis. • It shows great agreement with experiments, ensuring precise prediction of turbine blade deformations and hydrodynamics. • It outperforms benchmark software by more than 18 times, demonstrating exceptional computational efficiency. [ABSTRACT FROM AUTHOR]

• The genotype of rs2278294 in inosine monophosphate dehydrogenase-1 (IMPDH1) gene distributed differently between relapse and relapse free groups of neuromyelitis optica spectrum disorder (NMOSD) patients treated with mycophenolate mofetil (MMF). • Pharmacogenomics play a important role in the NMOSD patients therapy response. Different genotype indicated different results. • This article provides a new direction of drug efficacy research in NMOSD patients. Inosine monophosphate dehydrogenase-1 is the target of mycophenolate mofetil. This research investigated the association between the gene polymorphism of inosine monophosphate dehydrogenase-1 and effectiveness of mycophenolate mofetil therapy in neuromyelitis optica spectrum disorder patients. Fifty-nine neuromyelitis optica spectrum disorder patients accepted Mycophenolate Mofetil therapy for 1 year at least were divided into two groups: relapsing (n=21) and non-relapsing (n=38). Four single-nucleotide polymorphisms (SNPs: rs2228075, rs2278294, rs2288550, and rs3793165) in the inosine monophosphate dehydrogenase-1 gene were detected. Then we analyzed the allelic frequencies and the genotypes of SNPs in two groups. The allelic frequency of rs2278294 distributed differently between the relapse and non-relapsing patients (P=0.03), while no significant difference found in rs2228075, rs2288550 and rs3793165 between two groups. The genotypes C/C, C/T and T/T of rs2278294 (P = 0.031) also distributed differently between the two groups. Logistic regression analysis (adjusted by optic neuritis) showed that compared to the wild genotype C/C, C/T genotype had a 9-fold protection against relapse (OR=0.111 (0.022-0.548)), and T/T genotype had a 6.7-fold protection against relapse (OR=0.149 (0.026-0.854)). Our study provides preliminary evidence that the genotype of rs2278294 is associated with the response of neuromyelitis optica spectrum disorder patients to mycophenolate mofetil therapy. And compared to wild allelic C, the mutation to T tended to respond better to MMF. [ABSTRACT FROM AUTHOR]

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control. • Dynamic spatiotemporal wake reconstruction of a utility-scale yawed wind turbine is carried out in this paper. • A novel approach combining physics-informed neural network with sparse LiDAR measurement is proposed. • The neural network maps spatiotemporal coordinates [ t , x , y ] to flow physical quantities [ u , v , p ] integrated with NS equations. • Various cases with different LiDAR measurement parameters are investigated for sensitivity and robustness analysis. • The new approach shows great effectiveness in reconstructing dynamic wake behavior of trajectory and deflection changes. [ABSTRACT FROM AUTHOR]

Complete and clear global wind turbine wake data is very important for the study of wind turbine wake characteristics in increasingly large offshore wind farms. Existing wake measurement techniques can only obtain local high-resolution (HR) wake flow field, or sacrifice accuracy to obtain larger measurement area, which is insufficient for accurate modeling of wake effect. To overcome this challenge, this paper proposes a novel super-resolution (SR) reconstruction approach that can reconstruct the global HR wake flow field from low-resolution (LR) wake flow field measurement data effectively. The proposed approach utilizes a deep learning framework called down-sampled skip-connection and multi-scale network. The performance of the SR approach is evaluated by enhancing the resolution of the wake flow field at different scale factors, and its potential application is demonstrated by assessing the prediction accuracy of three typical wake models. The results indicate that the resolution of the global wind turbine wake can be improved by 16 times using the SR model, and the reconstructed global SR wake flow fields are consistent with the ground truth in terms of both the spatial distribution and the temporal variation. By comparing the prediction results of three different wake models with the LR or SR wake data, it is shown that the SR flow reconstruction method can be applied to more accurately evaluate the wake model prediction performance, which has the potential to improve wake models. Overall, this study presents an innovative solution to the problem of incomplete and inaccurate wake flow measurement in the wind energy industry, which could reduce the workload of experimental measurements and the cost burden of accurate measuring equipment for engineering applications. • A novel deep learning method is proposed to facilitate high resolution wake flow measurement of offshore wind turbine. • The deep learning framework adopts down-sampled skip-connection and multi-scale network. • Super-resolution flow field is achieved by reconstructing global high-resolution wake from local low-resolution wake. • Resolution of reconstructed wake flow attains 16 times improvement with great accuracy in spatiotemporal distribution. • Super-resolution reconstruction framework has been successfully applied for better assessing the prediction accuracy of traditional wake models. [ABSTRACT FROM AUTHOR]

A tidal turbine can benefit from exquisitely designed morphing blades with a flexible trailing edge by mitigating up to 90% of the load fluctuation in harsh ocean environments, which reduces the overall cost of tidal energy. However, existing fluid–structure interaction (FSI) methods of resolving flow-induced deformation of the blades is computationally expensive, which poses an important challenge to effective morphing blade design. This paper presents a novel static FSI tool based on deep learning to cost-efficiently analyze the fluid–structure coupling of a hydrofoil. Specifically, adopting a convolutional neural network (CNN) to predict the fluid force and finite element method (FEM) to solve the solid structure response, a new CNN-FEM framework with an iterative scheme for solving the FSI problem is developed to achieve equilibrium between the fluid and structural forces. The new framework is used to predict the elastic deformation of the flexible blade section of the hydrofoil to demonstrate its effectiveness in the FSI evaluation. Comparison of the results to those produced by commercially developed software (i.e., Ansys Workbench) shows that this method yields extremely close prediction results of average equivalent stress and an accuracy of more than 92%. Moreover, it is 100 times more computationally efficient than the commercial Ansys Workbench software, requiring less than 3s for one-way FSI calculation. Taking advantage of this cost-effectiveness, the CNN-FEM can be used to achieve the accurate prediction of the deformation characteristics of the flexible hydrofoil under various flow scenarios that lay a foundation for advanced morphing blade design in the future. [ABSTRACT FROM AUTHOR]

Abstract Sustainability evaluation of land resources could help policy decision-making in land use planning and management in a region. Zhang et al. (2018) presented a study on sustainability of land resources in Dengkou County based on emergy analysis. We commented on their major shortcomings including limitations of the energy system diagram, lacks of the emergy accounting of water resources, soil losses, machinery, environmental pollution and control, and the results of dynamic simulation. It is concluded that the emergy analysis method could serve land sustainability analysis, but it is of vital importance to create normative system diagrams and select proper emergy accounting items and dynamic simulation methods for ensuring the validity of evaluation results. Highlights • We commented on limitations of a land resources emergy analysis by Zhang et al. (2018). • Energy Systems Language should be normatively used. • Selection of emergy accounting items is of vital importance. • Land use change and emergy analysis need proper dynamic simulation methods. [ABSTRACT FROM AUTHOR]

A bibliometric approach, along with Citespace software, was used to quantitatively and visually evaluate global scientific research on emergy from 1996 to 2014. 637 publications – in accordance with the search criteria from the Science Citation Index Expanded (SCI-Expanded) and Social Science Citation Index (SSCI) of the Web of Science database – were statistically analyzed. The assessments on document type and language, publication year, authorship, subject categories and journals, countries/territories and institutions, most-frequently cited publications and author keywords were conducted with respect to seven categories. The amount of emergy publications per year has sharply increased in recent years. The most productive author was S. Ulgiati with 50 articles, who was also one of the most frequently cited publication authors. China produced 35.95% of all pertinent publications followed by the USA with 25.59% and Italy with 21.66%. Ecological Modeling, Ecological Engineering and Ecological Indicators were the three most common journals in this field. By synthetically analyzing the keywords, the dominant hot spots of emergy research could be concluded as “energy”, “sustainability”, “transformity”, and “indicators”. [ABSTRACT FROM AUTHOR]

The existing decentralized wind farm control incorporating the wind turbine wake interaction has almost all concentrated on the condition of fixed wind speed/direction without considering the variability of wind in nature. Meanwhile, most of the current wake models cannot meet the requirement for wind turbine control optimization research with the high demand of wake calculation speed and accuracy. In this paper, a novel decentralized control strategy, named the wind-interval-based (WIB) control which adopts the uniform operation among the same interval of variable wind speeds/directions, is proposed for the control optimization study of in-line two scaling wind turbines for demonstration. To prove the effectiveness of the new control mechanism, the ideal instantaneous control is introduced for the comparative study. At the same time, a wake model incorporating the two directly controllable parameters (i.e., the tip speed ratio λ and pitch angle γ) is established based on artificial neural network (ANN). The comparative results show that the total power output applying the instantaneous control and the new WIB control mechanisms are increased by 0.1%–4.1% and 0.45%–3.9% depending on the tested wind scenarios, respectively. By comparing the optimized power output with the two controls, it is found that the error between them is generally lower than 3%, while the proposed WIB control reduces the operational difficulty to a large extent facilitating its application to the real wind turbine operation in reality. In summary, this paper shows that the new proposed WIB control not only reduces the difficulties of the wind turbine control mechanism, but maintains the control effectiveness by achieving a comparable total power output with respect to the traditional instantaneous control, which is of great significance to the wind farm developer. [ABSTRACT FROM AUTHOR]

Horizontal axis tidal turbines (HATT) conversion of ocean tidal waves into electricity represents a promising source of clean and sustainable energy. However, the widespread adoption of these turbines has been hindered by persistent challenges, primarily stemming from the high costs associated with their construction and maintenance; and efficient conversion of tidal energy. Addressing these challenges is paramount for propelling tidal turbine technology and ensuring its economic viability. This study focuses on the efficient conversion of tidal energy into electricity by optimization of composite material turbine blades which is a complex problem that spans multiple physical domains, including hydrodynamics (the study of water flow) and structural mechanics (the study of material behavior under loads). To tackle these multifaceted challenges, we introduce an innovative DLFSI (Deep learning fluid-structure interaction) model which represents a groundbreaking approach to predict and optimize the hydrodynamic and structural performance of tidal turbine blades. DLFSI leverages the power of convolutional neural networks (CNN) to recognize intricate geometric features of turbine blades rapidly and accurately. By seamlessly integrating the blade element momentum (BEM) theory and finite element method (FEM), the DLFSI model facilitates comprehensive predictions of how composite blades will perform in real-world conditions. With this approach, we have achieved substantial improvements in critical performance metrics such as the power coefficient (a measure of energy conversion efficiency) and the maximum equivalent stress (a key indicator of structural integrity). The innovative DLFSI model presented in this study holds the potential for practical application within the realm of tidal turbine design and is poised to catalyze the sustainable progression of renewable energy technologies. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper proposes a novel cost-effective framework that combines deep learning convolutional neural network (CNN) and blade element momentum (BEM) models for optimizing the performance of three-dimensional (3D) horizontal axis tidal turbines (HATTs). The framework employs signed distance function (SDF) to reconstruct the three-dimensional blade geometry, utilizes CNN to identify the hydrodynamic performance of each blade section, and ultimately predicts the performance of HATT using BEM. On top of the new CNN-BEM model, the rotor blade geometrical optimization by multi-objective non-dominated sorting genetic algorithm (NSGA-II) is carried out to obtain a better trade-off solution with the maximal power coefficient of turbine and minimal hydrodynamic load exerted on the blades. The results show that the CNN-BEM model has good agreement with experimental data and reduces prediction time by 46.7% compared to the conventional Xfoil-BEM model, while reducing general optimization time by 20.1%. The new model's cost-efficiency allows for a better trade-off solution with reduced hydrodynamic load while maintaining the power coefficient. Thus, the proposed model has the capability to deliver both accurate and fast prediction and optimization of HATT performance, making it a valuable tool for guiding the design of tidal turbines. • A cost-effective framework coupling convolutional neural network (CNN) and blade element momentum (BEM). • The new coupled CNN-BEM model is applied for tidal turbine blade optimization. • Trade-off with maximal power coefficient and minimal hydrodynamic load is achieved by multi-objective optimization. • The new model reduces prediction time by 46.7% and general optimization time by 20.1%. • The new model's cost-efficiency allows for better trade-off solution than conventional Xfoil-BEM model. [ABSTRACT FROM AUTHOR]

• An assessment of conservation priorities for KBAs in highly urbanized regions was conducted. • Ecosystem conditions remain stable in most of KBAs over the past two decades. • Ecosystem conditions showed spatially significant negative correlations with anthropogenic threats. • Shenzhen Wutongshan is the highest priority KBA that needs to strengthen ecosystem conservation. The rapid and intense development in highly urbanized regions presents huge challenges to the conservation of biodiversity and ecosystem services. Progress on incorporating ecosystem conditions and anthropogenic threats into conservation assessments for key biodiversity areas (KBAs) in highly urbanized regions is still insufficient. To address this, this paper conducted an assessment of conservation priorities for key biodiversity areas in highly urbanized regions by integrating anthropogenic threats with ecosystem conditions. Taking 8 KBAs in the Guangdong - Hong Kong - Macao Greater Bay Area of China (GBA) as an example, this study found that the ecosystem condition of most of KBAs remains stable from 2000 to 2020. We found that the ecosystem conditions in most KBAs have not been negatively affected by anthropogenic threats. It is worth noting that Shenzhen Wutongshan (SZW) is the only KBA that faces a significant increase in anthropogenic threats with a significant decline in ecosystem condition during the past two decades. Overall, SZW is the highest priority KBA that needs to reduce the negative effects of anthropogenic threats and strengthen ecosystem conservation in the future. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network (CNN) is a commonly used deep learning algorithm due to its excellent capability in identification of structural features and parameter predictions in many domains. In addition, it has incomparable advantages of high analysis efficiency and generalization performance. However, it has been questioned in the research community on whether CNN method can be applied to effectively predict hydrofoil performance for hydraulic machinery design. To this end, this paper demonstrates a novel optimization platform using CNN for hydrofoil performance prediction, which can effectively and accurately obtain the optimized hydrofoils results in aid of the structural design of tidal turbine. The prediction model uses signed distance function (SDF) to graphically represent the shape of the hydrofoil which is subsequently imported into CNN as the network input. Three different hydrofoil performance properties including the lift coefficient, drag coefficient and pressure coefficient of surface are used as output to train the neural network. In order to guarantee the accuracy of the forecasting model, Computational Fluid Dynamics (CFD) method characterized by high precision is applied to generate the dataset for neural network training. The results show that it can accurately predict the hydrodynamic parameters at a lower angle of attack with extremely short period of time. On top of the established hydrofoil performance prediction model, the Pareto curve of the optimized hydrofoils is obtained and applied to the design of 3D horizontal axis tidal turbine (HATT) blades. It proves that the optimization platform is effective and versatile in a manner that achieves both accurate and rapid prediction/optimization of the hydrofoil, which greatly facilitates to apply it for the tidal turbine rotor design. • A novel platform for hydrofoil shape optimization based on CNN and CFD is established. • Through CNN, properties of hydrofoil including lift, drag, and pressure coefficients are accurately predicted. • By incorporating NSGA-Ⅱ, Pareto front of optimized hydrofoil shapes from UIUC database is achieved. • Optimized HATT yields the maximum 45% power coefficient and a wider high-efficiency operational range. • Best hydrofoil selection for HATT rotor design is concluded. [ABSTRACT FROM AUTHOR]

The gut microbial ecosystem, which is a collection of the host-microbiota interactions and the inter-species interplay among bacteria-dominated microbiota, has become a research hotspot due to its contribution to host health in recent years. Lactobacillus , which has worldwide usage in fermented dairy products, has aroused increasing attention and becomes one of the commonly used probiotics given its promising applications in intestinal health and disease, though it occupies a relatively small proportion of the intestinal microbiota. In the review, we first update the current understanding of determinants of Lactobacillus abundance in the intestinal tract. We then review evidence from animal models to human trials that provided insights into Lactobacillus 's applications in common intestinal disorders including the Helicobacter pylori infection, colorectal cancer, diarrhea, inflammatory bowel disease, and irritable bowel syndrome. Mechanisms underlying the probiotic role of Lactobacillus are finally discussed in five aspects: microbial interactions, the improvement of intestinal barrier function, the immunoregulation, the anticancer activity, and the metabolic regulation. This review aims to yield a profound understanding of how Lactobacillus will contribute to disease prevention and individualized therapies in future clinical practice, and to inspire novel microbial strategies utilizing both probiotics and their products in the fields of biology and medicine. [ABSTRACT FROM AUTHOR]

Amphibian populations are under threat worldwide, one such threat is frogs getting trapped in concrete-lined irrigation channels. Earlier studies have found that concrete ditches are acting as barriers to animal movement, fragmenting habitat, and trapping amphibians. China has over 3,000,000 kms of irrigation channels and they are a major threat to frog populations. In this study, we assessed the capacity of a common Chinese frog, Rana nigromaculata , to escape from a typical concrete-lined irrigation channel via 'frog-ways' cut into the walls of the channel. The twelve different designs varied gradients, substrates and widths of the frog-ways. The results showed that these simple and inexpensive retrofits of irrigation channels are very effective at allowing the great majority of frogs to escape. Male frogs were more likely to be trapped in concrete ditches, probably due to their smaller body size. The best design was N11 (55 degree slopes, a crushed stone surface, and 100 cm width) that can help the frogs to successfully escape in just 1 min. Taking water conveyance efficiency of irrigation water delivery systems into consideration, we recommend a longitudinal design (the frog-way is parallel to the water-flow direction) of simple concrete with slopes less than 55° and crushed stone. The findings can serve as a reference for technicians involved in future ecological engineering designs of irrigation channels throughout the world. • A simple and cost-effective frog-way design for irrigation channels • Physical characteristics of frogs and differences in behavioral ability • Capacity of a common Chinese frog to escape from a typical irrigation channel • Effects of different designs with gradients, substrates and widths [ABSTRACT FROM AUTHOR]

Diagram of the synthesis of MoSe 2 @ICG-PDA-HA. (B) Schematic illustration of the targeted photothermal/photodynamic synergistic therapy in tumor. • MoSe 2 @ICG-PDA-HA enables one-step simultaneous photothermal/photodynamic therapy. • Nanosystem can improve the optical stability of the loaded ICG. • Controlled release can be achieved via PDA. • Targeting tumors by HA reduces toxic side effects on normal tissues. The photothermal/photodynamic synergistic therapy is a promising tumor treatment, but developing nanosystems that achieve synchronous photothermal/photodynamic functions is still quite challenging. Here, we use a simple method to synthesize molybdenum selenide nanoparticles (MoSe 2 NPs) with a photothermal effect as a carrier, and load a photosensitizer ICG to form a nanosystem (MoSe 2 @ICG-PDA-HA)with dual photothermal/photodynamic functions under near-infrared irradiation. In addition, the surface modification of the nanosystem with acid-responsive release polydopamine (PDA) and tumor-targeted hyaluronic acid (HA) enhanced the stability of the photosensitizer ICG and the accumulation of ICG at tumor sites. The multicellular sphere assay simulated solid tumors and demonstrated that MoSe 2 @ICG-PDA-HA could significantly inhibit the 4T1 cell growth. The anti-tumor experiments in tumor-bearing mice showed that MoSe 2 @ICG-PDA-HA not only significantly inhibited the growth of 4T1 subcutaneous tumors, but also inhibited their metastasis. This study presented a nanosystem that could improve the photostability of optical materials and enhance the photothermal/photodynamic synergy effect, providing a new idea for finding a way to effectively treat breast cancer. [ABSTRACT FROM AUTHOR]

• Quantifying water footprint for per unit paddy rice product (WFP) for water management. • The path analysis method for the factors analysis. • Management strategies based on the WFP. Water footprint (WF) can help understanding of how to grow more food with less water. The aim of this paper is to distinguish the factors influencing the paddy rice water footprint, by using path analysis of the WF, its composition and distribution over 30 provinces, municipalities and autonomous regions (hereafter, provinces) in China during 1996–2015. The results show that the annual national WF was approximately 190.74 G m³, including 55.41 % green, 22.65 % blue and 21.94 % grey water respectively. WF basically remained stable over time. WF of the northeast increased from 16.70 G m³ to 39.19 G m³ during the observed period. WF for per unit paddy rice product (WFP) was 995.5 m³/t in the latest 20 years. Provinces with high WFP and blue water proportion were located in western China and the north China plain; all the low WFP values and blue water proportions were found south of the Yangtze River. The average temperature (ATE), irrigation water utilization coefficient (IWC) and fertilizer rate per sown area (FER) were the parameters closely and positively to WFP, both temporally and spatially. [ABSTRACT FROM AUTHOR]