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Obtaining reliable flow data is essential for the fluid mechanics analysis and control, and various measurement techniques have been proposed to achieve this goal. However, imperfect data can occur in experimental scenarios, particularly in the particle image velocimetry technique, resulting in insufficient flow data for accurate analysis. To address this issue, a novel machine learning-based multi-scale autoencoder (MS-AE) framework is proposed to reconstruct missing flow fields from imperfect turbulent flows. The framework includes two missing flow reconstruction strategies: complementary flow reconstruction and non-complementary flow reconstruction. The former requires two independent measurements of complementary paired flow fields, posing challenges for real-world implementation, whereas the latter requires only a single measurement, offering greater flexibility. A benchmark case study of channel flow with ordinary missing configuration is used to assess the performance of the MS-AE framework. The results demonstrate that the MS-AE framework outperforms the traditional fused proper orthogonal decomposition method in reconstructing missing turbulent flow, irrespective of the availability of complementary paired faulty flow fields. Furthermore, the robustness of the proposed MS-AE approach is assessed by exploring its sensitivity to various factors, such as latent size, overlap proportion, reconstruction efficiency, and suitability for multiscale turbulent flow structures. The new method has the potential to contribute to more effective flow control in the future, thanks to its characteristic that eliminates the requirement for complementary flow fields. [ABSTRACT FROM AUTHOR]

The reconstruction of accurate and robust unsteady flow fields from sparse and noisy data in real-life engineering tasks is challenging, particularly when sensors are randomly placed. To address this challenge, a novel Autoencoder State Estimation (AE-SE) framework is introduced in this paper. The framework integrates sensor measurements into a machine learning-based reduced-order model (ROM) by leveraging the low-dimensional representation of flow fields. The proposed approach is tested on two direct numerical simulation benchmark examples, namely, circular and square cylinders and wake flow fields at Re = 100. The results demonstrate satisfactory performance in terms of accuracy and reconstruction efficiency. It achieves the same accuracy as traditional methods while improving reconstruction efficiency by 70%. Moreover, it preserves essential physical properties and flow characteristics even in the noisy data, indicating its practical applicability and robustness. Experimental data validation confirms a relative error below 5% even at a noise level of 12%. The flexibility of the model is further evaluated by testing it with a trained ROM under varying Reynolds numbers and benchmark cases, demonstrating its ability to accurately estimate and recognize previously unseen flow fields with appropriate training datasets. Overall, the proposed AE-SE flow reconstruction method efficiently and flexibly leverages ROM for the low-dimensional representation of complex flow fields from sparse measurements. This approach contributes significantly to the development of downstream applications such as design optimization and optimal control. [ABSTRACT FROM AUTHOR]

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]

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPPp), actual NPP (NPPa), and human-induced NPP (NPPh), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPPa variability were quantitatively analyzed and identified. The results showed that the NPPp, NPPa, and NPPh had heterogeneous spatial distributions, and the average NPPp and NPPa values over the whole study area increased at rates of 3.63 and 6.94 gC·m−2·yr−1 from 2000 to 2020, respectively, while the NPPh decreased at a rate of −4.43 gC·m−2·yr−1. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPPa increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPPa values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPPa over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA. [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]

The article reports that Foreign aid of China follows the concepts of building a community with a shared future for mankind and serving the head-of-state diplomacy, and focuses on international anti-pandemic cooperation and small yet smart assistance programs. It mentions that the aid promotes the pandemic response and socioeconomic recovery of countries, but also strongly serves China in building a new development paradigm and safeguarding its sovereignty, security and development interests.

Fluid data are of great significance for analyzing the fluid structure and understanding the law of fluid movement. Apart from the experimental test, the computational fluid dynamics (CFD) method has been widely applied in the field of fluid dynamics over the past few decades. However, due to the high computational costs of CFD method and the limitation of computational resources, it is still challenging to accurately calculate and obtain the high-resolution (HR) flow fields. To this end, a novel framework based on the super-resolution (SR) algorithm, namely, new enhanced down-sampled skip-connection and multi-scale (E-DSC/MS), is reported to achieve the HR global flow reconstruction from low-resolution data. Through the new SR flow reconstruction method, the HR flow fields of two benchmark 2D cases (i.e., cylinder and hydrofoil) are precisely and efficiently predicted using a universal SR model. The effectiveness of the new E-DSC/MS algorithm is tested by comparing it with the traditional super-resolution convolution neural network and U-net in terms of the velocity field prediction of the self-region (training region) and other-region (untrained region). The result shows that the universal SR flow reconstruction framework is able to increase the spatial resolution of velocity field by 16 times, and flow fields reconstructed by E-DSC/MS are in good agreement with the ground-truth data. In addition, the E-DSC/MS model could reconstruct the global flow field with a correlation coefficient of more than 99% regardless of the selection of the arbitrary region/window for SR training. The present method overcomes the limitation of the existing techniques in efficiently reconstructing HR flow field, which helps to reduce the requirement for expensive experimental equipment and to accelerate the CFD simulation process. [ABSTRACT FROM AUTHOR]

Antibody-secreting cells (ASCs) play a fundamental role in humoral immunity. The aberrant function of ASCs is related to a number of disease states, including autoimmune diseases and cancer. Recent insights into activated B cell subsets, including naïve B cell to ASC stages and their resultant cellular disturbances, suggest that aberrant ASC differentiation occurs during autoimmune diseases and is closely related to disease severity. However, the mechanisms underlying highly active ASC differentiation and the B cell subsets in autoimmune patients remain undefined. Here, we first review the processes of ASC generation. From the perspective of novel therapeutic target discovery, prediction of disease progression, and current clinical challenges, we further summarize the aberrant activity of B cell subsets including specialized memory CD11chiT-bet+ B cells that participate in the maintenance of autoreactive ASC populations. An improved understanding of subgroups may also enhance the knowledge of antigen-specific B cell differentiation. We further discuss the influence of current B cell therapies on B cell subsets, specifically focusing on systemic lupus erythematosus, rheumatoid arthritis, and myasthenia gravis. [ABSTRACT FROM AUTHOR]

Agrilus mali Matsumara (Coleoptera: Buprestidae) is a wood‐boring beetle distributed to eastern China that occasionally injures apple species. However, this wood‐boring beetle is new to the wild apple forests (Malus sieversii) of the Tianshan Mountains (western China) and has caused extensive tree mortality. The development of a biological control program for these wild apple forests is a high priority that requires exploration of the life cycle, DNA barcoding and taxonomic status of A. mali. In this study, to determine the diversity of invasive beetles, a fragment of the mitochondrial cytochrome oxidase gene was analyzed. Based on the results, beetles from Gongliu and Xinyuan counties of Xinjiang were identical but differed from those in the apple nursery of Gongliu by a single‐nucleotide substitution. We summarize the taxonomic status, relationships, and genetic distances of A. mali among other Agrilus species using the Tajima‐Nei model in maximum likelihood phylogeny. Analysis revealed that A. mali was closely related to Agrilus mendax and both belong to the Sinuatiagrulus subgenus. The life cycle of A. mali was investigated based on a monthly regular inspection in the wild apple forests of Tianshan. Similar to congeneric species, hosts are injured by larvae of A. mali feeding on phloem tissue, resulting in serpentine galleries constructed between bark and xylem that prevent nutrient transport and leading to tree mortality. Future studies will focus on plant physiological responses to the invasive beetles and include surveys of natural enemies for a potential classical biological control program. This study summarizes the taxonomic status and relationship of Agrilus mali among other Agrilus species using DNA barcodes. Insect and life history were described. [ABSTRACT FROM AUTHOR]

Abstract Karst water is an important resource for drinking water supply. To determine the impacts of urbanization on karst water quality, we performed a case study in the rapidly urbanized Guiyang-Anshun region, Guizhou province, southwestern China. We interpret data from regional reconnaissance and long-term monitoring related to major ion chemistry, stable isotopes (Sr, C, S (for SO 4 2−), and N and O (for NO 3 −)), remote sensing, and socio-economic development. We identify groundwater SO 4 2− and NO 3 − sources by combined use of δ34S and Ca2+/Na+ molar ratio and δ15N and NO 3 −/Na+ ratio, respectively. We find that carbonate, sulfide, silicate, and gypsum weathering, anthropogenic inputs, and hydrodynamic conditions account for karst water composition and its seasonal variations. Atmospheric N and S deposition, nitrification of soil N, and sulfide oxidation control the background levels of groundwater NO 3 − and SO 4 2−. The elevated concentrations of NO 3 − and SO 4 2− at residential sites in rural and urban areas mainly arise from domestic sewage. Nitrification and fertilizer application are major reasons for the high levels of NO 3 − in regional groundwater systems. Vegetated/forested land area decreases as constructed land area increases, which results in declining biogenic CO 2 production and inputs into the aquifer. Although the local government has attempted to control SO 2 emissions, substantial increases in fossil fuel utilization and fertilizer consumption as well as population growth may have increased atmospheric HNO 3 deposition and induced increasingly severe contamination of groundwater with NO 3 − and SO 4 2−. Our results improve the understanding of urbanization impacts on water quality and are important for water resource management in karst regions. Graphical abstract Unlabelled Image Highlights • Combined use of δ34S-Ca2+/Na+, δ15N-NO 3 −/Na+ may identify water SO 4 2−, NO 3 − sources. • Domestic sewage mainly accounts for high levels of NO 3 −, SO 4 2− in residential sites. • Elevated NO 3 − loads in regional groundwater arise from nitrification and fertilizers. • Urbanization may have induced groundwater NO 3 − and SO 4 2− contamination. • Rise in constructed land area decreases biogenic CO 2 inputs into the groundwater. [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]

Accurate quantification of the contributions of climatic and anthropogenic factors to the variation in NPP is critical for elucidating the relevant driving mechanisms. In this study, the spatiotemporal variation in net primary productivity (NPP) in China during 2000–2020, the interactive effects of climatic and anthropogenic factors on NPP and the optimal characteristics of driving forces were explored. Our results indicate that NPP had obvious spatial differentiation, an overall increasing trend was identified and this trend will continue in the future for more than half of the pixels. Land use and Land cover and precipitation were the main factors regulating NPP variation at both the national scale and the sub-region scale, except in southwest China, which was dominated by altitude and temperature. Moreover, an interactive effect between each pair of factors was observed and the effect of any pair of driving factors was greater than that of any single factor, manifested as either bivariate enhancement or nonlinear enhancement. Furthermore, the responses and optimal characteristics of NPP concerning driving forces were diverse. The findings provide a critical understanding of the impacts of driving forces on NPP and could help to create optimal conditions for vegetation growth to mitigate and adapt to climate changes. [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]

Dysregulation of microRNA (miRNA) biogenesis is implicated in cancer development and progression. Dicer and Drosha are established regulators of miRNA biogenesis. In this study, we used a miRNA array to evaluate the miRNA expression profiles in nasopharyngeal carcinoma (NPC) samples. The significance analysis of microarrays showed a global downregulation of miRNA expression in NPC samples compared with normal nasopharyngeal epithelial tissues. Notably, miR-18a, a member of the oncogenic miR-17–92 cluster, was upregulated in the NPC samples and ell lines. Clinical parameter studies showed that higher levels of miR-18a correlated with NPC advanced stage, lymph node metastasis, Epstein-Barr virus infection and a higher death rate from NPC, indicating oncogenic roles in NPC development. The expression levels of miR-18a and Dicer1 were inversely related in NPC tissues. Further studies demonstrated that miR-18a negatively regulated Dicer1 by binding to the 3′ untranslated regions of Dicer1. In vitro and in vivo biological function assays showed that miR-18a promoted the growth, migration and invasion of NPC cells by regulating Dicer1 expression, which caused the global downregulation of miRNA expression levels including miR-200 family and miR-143. Furthermore, we found that the epithelial mesenchymal transition marker E-cadherin and the oncogene K-Ras were aberrantly expressed after miR-18a transduction, and these alterations were directly induced by downregulation of the miR-200 family and miR-143. Collectively, our findings indicate that miR-18a plays an oncogenic role in the development of NPC by widespread downregulation of the miRNome and could be a potential therapeutic target for NPC. [ABSTRACT FROM PUBLISHER]

Abstract: A logic-enriched type theory (LTT) is a type theory extended with a primitive mechanism for forming and proving propositions. We construct two LTTs, named and , which we claim correspond closely to the classical predicative systems of second order arithmetic and . We justify this claim by translating each second order system into the corresponding LTT, and proving that these translations are conservative. This is part of an ongoing research project to investigate how LTTs may be used to formalise different approaches to the foundations of mathematics. The two LTTs we construct are subsystems of the logic-enriched type theory , which is intended to formalise the classical predicative foundation presented by Herman Weyl in his monograph Das Kontinuum. The system has also been claimed to correspond to Weyl’s foundation. By casting and as LTTs, we are able to compare them with . It is a consequence of the work in this paper that is strictly stronger than . The conservativity proof makes use of a novel technique for proving one LTT conservative over another, involving defining an interpretation of the stronger system out of the expressions of the weaker. This technique should be applicable in a wide variety of different cases outside the present work. [Copyright &y& Elsevier]

Pyrene is a high molecular weight polycyclic aromatic hydrocarbon (PAH) that is found in water systems worldwide. It is harmful to living organisms, even when taken in very small amounts. The photocatalytic degradation of pyrene in porous Pt/TiO2–SiO2 photocatalyst (PPtPC) suspension under UV irradiation was investigated in this study. PPtPC was prepared by a simple heat treatment of the compacted powder mixtures of anatase TiO2 and amorphous SiO2 with camphor as a pore directing template, followed by coating platinum by the dip-coating method. X-ray diffraction (XRD), scanning electron microscopy (SEM) with an integrated energy-dispersive analysis of the X-ray (EDX) system, and Brunauer–Emmett–Teller (BET) were used to characterize PPtPC. The degradation kinetics of pyrene in different experimental conditions, such as initial concentration of pyrene, oxygen concentrations, pH, and temperature, were investigated. The durability of PPtPC was also tested. The results indicate that the structure of TiO2 in PPtPC is anatase. The aggregated size of PPtPC is in the range of 10–100 μm, the mean pore diameter is 3 nm, and the BET surface area is 109 m2 g-1. The photocatalytic degradation process of pyrene follows pseudo-first-order kinetics. The rate constants increase as the initial concentration of pyrene and pH decrease. Higher temperature slightly enhances the rate constant. The dissolved oxygen in the photocatalytic degradation process is not as important as in the photolysis process. The recovered PPtPC still shows high photoactivity. This work suggests that PPtPC offers a promising method for high molecular weight PAH removal. [ABSTRACT FROM AUTHOR]

Abstract: Details on using analyte gas-phase basicity (GB) or proton affinity (PA) as an additional analysis dimension in GC/FT-ICR MS are reported. Ion–molecule reactions of chemical ionization (CI) reagent ions with the GC separated C1, C2, and C3 aromatic neutral (viz., toluene, m-xylene, and mesitylene) and ketone (acetone, 3-pentanone, and 4-heptanone) molecules were monitored. A suite of CI reagent ions was generated by self-chemical ionization (SCI) of ethanol in an ICR cell. Multiplexed reactant ion monitoring (MRIM) of the CI reagent ions (R i H+) was used for GB bracketing of the neutral analytes eluting from the GC column. The PAs of the conjugate bases (R i ) of the reagent/reactant ions spanned a wide range (∼170 to ∼200kcalmol−1). A kinetic model was constructed to relate the degree of reactant ion depletion to analyte concentration and proton transfer reaction efficiency. Our experimental GB estimations, based on the thermokinetic data, were in broad agreement with the GB values reported in the NIST database and primary sources (experimental and ab initio). Experimentally measured MRIM chromatogram profiles and kinetic analysis were used to estimate analyte gas-phase pressures in the ICR cell and losses to the surfaces in the ICR vacuum chamber. The MRIM approach permits GB determinations of multiple analytes in a single GC run and provides an additional dimension to improve analytical resolution in GC/FT-ICR MS. [Copyright &y& Elsevier]

Abstract: Coercive subtyping is a general approach to abbreviation and subtyping in dependent type theories with inductive types. Coherence and admissibility of transitivity are important both for understanding of the framework and for its correct implementation. In this paper, we study the issue of transitivity in the context of subtyping for parameterised inductive types. In particular, we propose and study the notion of weak transitivity and show that, for a large class of parameterised inductive types, the natural subtyping rules are coherent and weak transitivity is admissible in an intensional type theory. A possible extension of type theory with certain extensional computation rules is also discussed for achieving admissibility of transitivity in general. [Copyright &y& Elsevier]

A thermal resistor-capacitor (RG) model is introduced for the power insulated gate bipolar transistor (IGBT) modules used in a three-phase inverter. The parameters of the model are extracted from the experimental data for the transient thermal impedance from-junction-to-case Zje and case-to-ambient Zca. The accuracy of the RC model is verified by comparing its predictions with those resulting from the three-dimensional finite element method simulation. The parameter extraction algorithm is easy to adapt to other types of power modules in an industrial application environment. [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 article discusses a paper which aims to develop an effective methodology for assessing the sustainability of metro systems by using emergy analysis. Information on a proposed composite index called the emergy cost-benefit ratio (EmCBR) for understanding the feasibility of metro systems better is provided. Using the project life cycle to evaluate the total costs or benefits is suggested.

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]

Tidal turbines play a critical role in converting the kinetic energy of water into electricity, contributing significantly to energy conversion. However, the current optimization design of these turbines involves computationally intensive simulations, leading to higher design costs. Additionally, traditional parameterized modeling methods, constrained by predefined design parameters, limit the exploration of innovative designs. In response, this study introduces an innovative data-driven "generative–predictive" design approach comprising a generative model and a predictive model. The generative model autonomously learns feature representations from existing turbines and leverages this knowledge to generate a novel set of turbines with superior hydrodynamic performance. Subsequently, an efficient performance evaluation is conducted using a predictive model for the generated turbines. Compared to the current parameterized modeling approaches, the proposed approach is combined with multi-objective optimization algorithm to optimize the tidal turbine hydrodynamic performance. Research results demonstrate that the generative model, trained on gradients, can generate highly complex turbines with minimal latent vectors. Through transfer learning, the predictive model exhibits robustness and accuracy, effectively guiding the design process. In the final optimization comparison, the proposed generative–predictive design approach requires only 4% of the optimization time while achieving results similar to or surpassing traditional design approaches. This approach proves to be a powerful tool for guiding the efficient and optimized design of turbines. [ABSTRACT FROM AUTHOR]

Purpose: To decipher the discrepancies between muscle-specific kinase antibody-positive myasthenia gravis (MuSK-MG) and double-seropositive myasthenia gravis (DSP-MG), and to determine prognostic factors for minimal manifestation status (MMS) achievement in MG patients with MuSK autoantibodies (MuSK-Ab). Patients and Methods: A total of 34 MG patients seropositive for MuSK-Ab were enrolled in this study. The demographic and clinical features were compared between MuSK-MG (n = 28) and DSP-MG (n = 6) patients, and factors affecting MMS induction in all patients with MuSK-Ab were identified using Cox regression analysis. Results: Compared to MuSK-MG patients, those with DSP-MG had similar clinical characteristics, except that they had a lower frequency of bulbar muscle involvement at nadir (50% vs 92.9%; P =  0.029) and higher proportions of comorbidities with diabetes mellitus (33.3% vs 0%; P = 0.027) and thymic abnormalities (33.3% vs 0%; P = 0.027). Higher MG Activities of Daily Living (MG-ADL) scores (HR = 0.16, 95% CI: 0.037– 0.7, P = 0.015) and axial muscle involvement at nadir (HR = 0.39, 95% CI: 0.16– 0.94, P = 0.035) were negative prognostic factors for MMS achievement in patients with MuSK-Ab regardless of acetylcholine receptor antibody (AChR-Ab) positivity. Multivariable Cox regression analysis further established higher MG-ADL scores at the nadir (HR = 0.19, 95% CI: 0.04– 0.94; P = 0.042) as an independent risk factor for MMS achievement. Conclusion: DSP-MG was comparable to MuSK-MG and could be considered a single entity in our cohort. In all MG patients with MuSK-Ab, a higher MG-ADL score at nadir may herald a lower chance of MMS achievement, with no observed potential effect of AChR-Ab presence. [ABSTRACT FROM AUTHOR]

Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m−2∙a−1 and 0.16 kg∙C∙m−2∙a−1 for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m−2∙a−1 and 0.10 kg∙C∙m−2∙a−1, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10−2 kg∙C∙m−2∙a−1 due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m−2 a−1)/°C and −0.254 (g∙C∙m−2∙a−1)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m−2·a−1)/(MJ m−2). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau. [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]

Amid surging population and industrial growth, there is a need to assess the vulnerability of groundwater to pollution. In this study, the case of Xingshan County in South China where karst groundwater is a major water source is presented. Field survey revealed hazardous landuse practices in highly karstified areas. To delineate areas of high vulnerability, an intrinsic vulnerability map (IVM) was created based on the EPIK (Epikarst, Protective cover, Infiltration condition, Karst network) method. Results show that 10.1, 25.8, 58.7 and 5.3% of the area are of low, medium, high and very high vulnerability. Although IVMs are powerful tools since they highlight areas of high "intrinsic" susceptibility to pollution, they do not answer the important question, "what sources are at immediate risk." With this limitation, tracer technique was coupled with IVM to establish a link to resources/sources. Rhodamine and uranine were injected into two sinkholes in high vulnerability zones near hazardous activities. Both were detected 57.3 and 5.65 h after injection at a major spring (Bailongquan). This established link validates the IVM and highlights specific sources at risk. This study demonstrates the need to couple IVMs with tracer test in order to prioritize areas to probe in order to determine the extent of their impact by establishing active karstic groundwater flow paths. The case here is an example of source/resource vulnerability assessment, hazard/risk assessment and validation of an IVM. Our findings present a cost-effective approach to vulnerability assessment by emphasizing the reliability of EPIK method as it requires less data. [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]

Myasthenia gravis (MG) is a neurological autoimmune disease characterized by fluctuating weakness of certain voluntary muscles. Current treatments for MG are largely directed at suppressing the whole immune system by using immunosuppressants or glucocorticoids and often cause several side effects. The ideal therapeutic methods for MG should suppress aberrant immunoactivation specifically, while retaining normal function of the immune system. In this study, we first produced exosomes from microRNA-146a overexpressing dendritic cells (DCs). Then, we observed suppressive effects of those exosomes in experimental autoimmune myasthenia gravis (EAMG) mice. Results showed that exosomes from microRNA-146a overexpressing DCs expressed decreased levels of CD80 and CD86. In experimental autoimmune MG, exosomes from microRNA-146a overexpressing DCs suppressed ongoing clinical MG in mice and altered T helper cell profiles from Th1/Th17 to Th2/Treg both in serum and spleen, and the therapeutic effects of those exosomes were antigen-specific and partly dose dependent. All the findings provide experimental basis for antigen-specific therapy of MG. [ABSTRACT FROM AUTHOR]

Energy-restricted diet is a specific dietary regimen, including the continuous energy-restricted diet and the intermittent energy-restricted diet. It has been proven effective not only to reduce weight and extend the lifespan in animal models, but also to regulate the development and progression of various neurological diseases such as epilepsy, cerebrovascular diseases (stroke), neurodegenerative disorders (Alzheimer's disease and Parkinson's disease) and autoimmune diseases (multiple sclerosis). However, the mechanism in this field is still not clear and a systematic neurological summary is still missing. In this review, we first give a brief summary of the definition and mainstream strategies of energy restrictions. We then review evidence about the effects of energy-restricted diet from both animal models and human trials, and update the current understanding of mechanisms underlying the biological role of energy-restricted diet in the fight against neurological diseases. Our review thus contributes to the modification of dietary regimen and the search for special diet mimics. [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]

Simple Summary: Glioma is a life-threatening malignancy where conventional therapies are ineffective, and microorganisms are a promising weapon that can be used in cancer treatment. In order to promote the use of microbial therapies in glioma, this article summarizes the microorganisms that have been used for glioma treatment in recent years and their mechanisms and how the gut flora affect glioma progression, suggesting the current limitations of this approach and possible future directions for its development. This paper may bring new inspiration to those who investigate glioma, promote the progress of glioma therapy research, and bring new promise to glioma patients. Glioma is the most common primary malignancy of the central nervous system (CNS), and 50% of patients present with glioblastoma (GBM), which is the most aggressive type. Currently, the most popular therapies are progressive chemotherapy and treatment with temozolomide (TMZ), but the median survival of glioma patients is still low as a result of the emergence of drug resistance, so we urgently need to find new therapies. A growing number of studies have shown that the diversity, bioactivity, and manipulability of microorganisms make microbial therapy a promising approach for cancer treatment. However, the many studies on the research progress of microorganisms and their derivatives in the development and treatment of glioma are scattered, and nobody has yet provided a comprehensive summary of them. Therefore, in this paper, we review the research progress of microorganisms and their derivatives in the development and treatment of glioma and conclude that it is possible to treat glioma by exogenous microbial therapies and targeting the gut–brain axis. In this article, we discuss the prospects and pressing issues relating to these therapies with the aim of providing new ideas for the treatment of glioma. [ABSTRACT FROM AUTHOR]

Aims: Gut microbiota and metabolites have a profound impact on the maintenance of body health. In this study, we assessed the association between gut microbiota and serum metabolite changes in myositis using 16S rRNA gene sequencing and metabolomics to provide new ideas for screening and treating myositis. Methods and results: Blood and faecal samples were collected from 20 myositis patients and 20 healthy control subjects. Then, 16S rRNA gene sequencing, enzyme‐linked immunosorbent assays and untargeted metabolomics study were performed to evaluate the relationship between gut microbiota and serum metabolites in patients with myositis. Compared to healthy control subjects, the blood samples from the patients with myositis had elevated levels of interleukin‐4 (IL‐4), tumour necrosis factor‐α (TNF‐α), and malondialdehyde (MDA) and decreased superoxide dismutase (SOD) levels. The increase in Bacteroidota (including Bacteroides and Parabacteroides, but not Prevotella) and the decrease in Firmicutes in the patients were accompanied by functional changes in amino acid and lipid metabolism. The gut microbiota (Bacteroides and Parabacteroides) were negatively correlated with the differential serum metabolites (glutamate and taurine). The differential serum metabolites (glutamate, pyrrolidonecarboxylic acid, and taurine) were also correlated with inflammatory factors (IL‐4 and TNF‐α) and oxidative stress indexes (MDA and SOD). Conclusion: Dysbiosis of gut microbiota in patients with myositis was accompanied by changes in inflammatory factors, oxidative stress indexes, and small molecule metabolites in serum. Significance and impact of study: Blood and faecal biomarkers could be used for screening myositis. [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]

Objective. To find a preoperative computed tomography-based method to predict the incidence of sacral screw loosening and assist surgical planning. Methods. Surgically treated patients for degenerative lumbosacral disorders with rigid pedicle screw fixation of patients with L5-S1 vertebra in our center from January 2016 to January 2021 were retrospectively included in the current study. CT scan attenuation of the horizontal plane of the sacrum was measured with Hounsfield units (HU). Postoperative X-ray tests were used to diagnose screw loosening. The data was analyzed by independent sample t -tests, X 2 analysis, Pearson correlation analysis, and ROC curve analysis. Results. A total of 162 (114 male, 48 female, average age 63.7 ± 7.3 years) patients were included in the final analysis. Significant differences were found between the screw loosening group and nonloosening group concerning the HU value of the sacrum at the horizontal plane (P < 0.01). In ROC curve analysis, AUC was 0.674 (95% CI: 0.592-0.756). A cutoff of 200 HU provided 64.8% sensitivity and 62.4% specificity, and a cutoff of 150 HU provided 90.2% sensitivity. Conclusions. Analyzing 162 patients with at least 12 months of follow-up, we propose cutoff CT attenuation values of 200 HU and 150 HU to take moderate and radical measures of screw augmentation to prevent screw loosening in the sacral bone. [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]

Background: The incidence of myasthenia gravis (MG) is increasing, and its characteristics in elderly patients are believed to differ from those in younger patients. However, only a few studies have focused on elderly patients with MG. Objective: To review the characteristics of MG in elderly patients and evaluate whether older age is an independent factor associated with achieving minimal manifestation status (MMS). Methods: This retrospective cohort study included 367 patients (319 non-elderly and 48 elderly patients) with MG enrolled at Xiangya Hospital from September 1, 2016, to December 31, 2018. We collected demographic data and information regarding comorbidities, antibody status, Myasthenia Gravis Foundation of America classification, affected muscle groups, thymoma, and treatment. MMS was defined as the primary outcome. Results: Comorbidities were more common in elderly than in younger patients with MG. Anti-acetylcholine receptor antibody was the dominant subtype, whereas anti-muscle-specific tyrosine kinase antibody was rare and detected only in non-elderly patients. Elderly patients were more likely than younger patients to have generalized MG, but the frequency of thymoma was lower (28.5% vs. 10.4%, p = 0.0078). MMS or better was achieved in 154 (48.3%) and 13 (27.1%) non-elderly and elderly patients, respectively. Older age did not appear to be an independent factor associated with MMS (hazard ratio = 0.625; 95% confidence interval, 0.345–1.131). Conclusions: Older age was not an independent factor for a worse prognosis in patients with MG. The treatment of elderly patients with MG should be individually tailored. [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]

[Display omitted] • Hydrochemical characteristics of the initial geothermal fluid were reconstructed. • Inverse models were used to identify the initial geothermal fluid formation. • Initial geothermal fluids originate from hydrothermal sulfide oxidation. • Ductile deformation and metamorphic domes provided energy and channel. Analyzing the genesis and resource potential of orogenic geothermal systems poses significant challenges due to their complex geological backgrounds and evolutionary histories. This paper presents a novel synthesis of hydrogeochemical and isotope geochemical data, coupled with geological and geophysical results, to comprehensively understand the origins of deep, high-temperature orogenic geothermal systems. This understanding is achieved through the utilization of advanced geochemical modeling techniques to analyze the geochemical attributes of initial geothermal fluids. The hydrochemical and H/O/S isotopic composition of a total of 67 geothermal and non-geothermal water samples from the Dabie orogenic belt (DOB) were analyzed. Integrated multicomponent geothermometry (IMG) was employed to reconstruct the characteristics of the initial geothermal fluids, and PHREEQC inverse simulations creatively determined the most likely pathways of their genesis and evolution. The findings indicate that all geothermal waters from the three major geothermal fields of Luotian, Yingshan, and Yuexi are alkali-sulfate type waters. The initial geothermal fluids are acidic sulfate type with reservoir temperatures of 156–222 °C, 115–179 °C, and 100–189 °C, respectively. The primary processes responsible for the formation of these initial geothermal fluids are the dissolution of anhydrite from sulfide oxidation and, to a lesser extent, direct sulfide oxidation. Generally, the formation of geothermal systems in the investigated geothermal fields is associated with the extensional collapse and lithospheric thinning of the Dabie orogen since the Early Cretaceous Period. The spatial distribution of the three geothermal fields in this study corresponds to the tectonic-magmatic framework of magmatic intrusion-induced domes (Luotian and Yuexi) in the DOB. The reactivation of the Tan-Lu Fault during the Cenozoic Era suggests the presence of a deep heat source comprising magmatic and metamorphic components. This heat source, combined with the infiltration of paleo-atmospheric and modern atmospheric precipitation water along regional fault systems, results in the formation and gradual dilution of initial geothermal fluids. Neutralization occurs through interactions with calcite and fine-grained sodium aluminosilicates, ultimately leading to the formation of the collected geothermal water. The conceptual model established in this study elucidates the distinctive genesis and formation mechanism of the orogenic geothermal system exemplified by the DOB. [ABSTRACT FROM AUTHOR]

Background: Leucine rich repeat containing 4 (LRRC4), also known as netrin-G ligand-2 (NGL-2), belongs to the superfamily of LRR proteins and serves as a receptor for netrin-G2. LRRC4 regulates the formation of excitatory synapses and promotes axon differentiation. Mutations in LRRC4 occur in Autism Spectrum Disorder (ASD) and intellectual disability. Multiple sclerosis (MS) is a chronic neuroinflammatory disease with spinal cords demyelination and neurodegeneration. Here, we sought to investigate whether LRRC4 is involved in spinal cords neuron-associated diseases. Methods: LRRC4 was detected in the CNS of experimental autoimmune encephalomyelitis (EAE) mice by the use of real-time PCR and western blotting. LRRC4−/− mice were created and immunized with myelin oligodendrocyte glycoprotein peptide (MOG)35–55. Pathological changes in spinal cords of LRRC4−/− and WT mice 15 days after immunization were examined by using hematoxylin and eosin (H&E), Luxol Fast Blue (LFB) staining and immunohistochemistry. The number of Th1/Th2/Th17/Treg cells in spleens and blood were measured with flow cytometry. Differential gene expression in the spinal cords from WT and LRRC4−/− mice was analyzed by using RNA sequencing (RNA-seq). Adeno-associated virus (AAV) vectors were used to overexpress LRRC4 (AAV-LRRC4) and were injected into EAE mice to assess the therapeutic effect of AAV-LRRC4 ectopic expression on EAE. Results: We report that LRRC4 is mainly expressed in neuron of spinal cords, and is decreased in the spinal cords of the EAE mice. Knockout of LRRC4 have a disease progression quickened and exacerbated with more severe myelin degeneration and infiltration of leukocytes into the spinal cords. We also first found that Rab7b is high expressed in EAE mice, and the deficiency of LRRC4 induces the elevated NF-κB p65 by up-regulating Rab7b, and up-regulation of IL-6, IFN-γ and down-regulation of TNF-α, results in more severe Th1 immune response in LRRC4−/− mice. Ectopic expression of LRRC4 alleviates the clinical symptoms of EAE mice and protects the neurons from immune damages. Conclusions: We identified a neuroprotective role of LRRC4 in the progression of EAE, which may be used as a potential target for auxiliary support therapeutic treatment of MS. [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]

Representative pathogenic bacteria such as Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) are widespread in nature and pose a threat to human health. To control the propagation of these pathogens from the source, the key is to design broad-spectrum antibacterial materials to reduce the serious damage of pathogenic bacteria. At present, more and more nanoparticles are widely researched and applied due to their multi-pathway antibacterial properties, such as regulating physiology, biochemistry and physical chemistry. In this work, we synthesized a uniformly dispersed and stable spherical nanoparticle (TiO2@V2O5) synthesized by self-assembly of tianium dioxide and vanadium pentoxide. Based on its excellent photosensitive properties, TiO2@V2O5 nanoparticles have showed excellent antibacterial properties under the light irradiation due to the production of hydroxyl radicals in antibacterial and mechanism tests. In addtion, related cell and plant experiments have showed that TiO2@V2O5 nanoparticles are excellent biocompatible materials, it could be widely used in environmental pollution control, limiting the serious damage caused by pathogens. [ABSTRACT FROM AUTHOR]

Myasthenia gravis (MG) is an autoimmune disease caused by antibodies directed against different components of the neuromuscular junction (NMJ), and pathogenic mechanism associated thymic pathologies. Microbiota invading thymus is hypothesized to trigger human autoimmune response by providing antigens to produce NMJ antibodies and clinical manifestations of myasthenia gravis. There are many reports of microbial nucleic acid isolation and protein expression and even a few studies of viable microbes isolated from the human thymus. However, high-resolution investigation of microbial infectious agents from the human thymus that may contribute to myasthenia gravis is very limited. To investigate potential microbial infection within human thymus tissue samples, we performed quantitative real-time PCR of the bacterial 16S ribosomal RNA (rRNA) and fungal 18S rRNA gene analysis on (1) a total of 23 abnormal thymus including 13 thymomas and 4 thymus hyperplasias obtained from MG patients and 6 thymoma-nonMG patients who underwent thymectomy. (2) Another 14 normal thymus were selected as control from patients who underwent congenital heart disease surgery. Results show that a wide range of bacteria agents are exhibited in the thymus, rather than fungus. And an intriguing new observation that shows the microbiota displayed differences among MG patients, thymoma-nonMG patients, and normal thymus as judged from the taxonomic profiles in these three groups. Compared with the normal thymus group, the Klebsiella and Escherichia coli show higher abundance in MG patient group. The thymus harbors bacteria agents and Klebsiella and Escherichia coli may be potential pathogens associated with MG. Further investigations are needed to elucidate the possible associations of Klebsiella and E. coli and MG. [ABSTRACT FROM AUTHOR]

Aims: Temporal lobe epilepsy (TLE) is the most common focal epilepsy syndrome in adults and frequently develops drug resistance. Studies have investigated the value of peripheral DNA methylation signature as molecular biomarker for diagnosis or prognosis. We aimed to explore methylation biomarkers for TLE diagnosis and pharmacoresistance prediction. Methods: We initially conducted genome‐wide DNA methylation profiling in TLE patients, and then selected candidate CpGs in training cohort and validated in another independent cohort by employing machine learning algorithms. Furthermore, nomogram comprising DNA methylation and clinicopathological data was generated to predict the drug response in the entire patient cohort. Lastly, bioinformatics analysis for CpG‐associated genes was performed using Ingenuity Pathway Analysis. Results: After screening and validation, eight CpGs were identified for diagnostic biomarker with an area under the curve (AUC) of 0.81 and six CpGs for drug‐resistant prediction biomarker with an AUC of 0.79. The nomogram for drug‐resistant prediction comprised methylation risk score, disease course, seizure frequency, and hippocampal sclerosis, with AUC as high as 0.96. Bioinformatics analysis indicated drug response–related CpGs corresponding genes closely related to DNA methylation. Conclusions: This study demonstrates the ability to use peripheral DNA methylation signature as molecular biomarker for epilepsy diagnosis and drug‐resistant prediction. [ABSTRACT FROM AUTHOR]

This study is aimed at exploring the expression pattern and methylation level of G0S2 in the peripheral blood mononuclear cells (PBMCs) of myasthenia gravis (MG) patients with positive acetylcholine receptor (AChR) autoantibodies and revealing the relationship between the G0S2 methylation pattern and MG. The relationship between the NFAT family members and G0S2 was explored to reveal the regulatory mechanism of G0S2 in the pathogenesis and treatment of AChR MG. Moreover, we attempted to demonstrate the potential therapeutic mechanism of tacrolimus in AChR MG. The relative G0S2 expression level in the PBMCs of healthy people was compared with that in the PBMCs of AChR MG patients with quantitative real-time PCR (qRT-PCR). The methylation frequency of the G0S2 promoter was detected by bisulfite sequencing PCR (BSP) and pyrosequencing. A dual-luciferase reporter system was used to reveal the relationship between the G0S2 promoter and nuclear factor of activated T cells 5 (NFAT5). The qRT-PCR results showed that G0S2 expression was significantly upregulated in the B cells and CD8+ T cells of AChR MG patients but not in the CD4+ T cells, and these expression differences were significantly associated with a decrease in G0S2 methylation. NFAT5, which was speculated to bind to island 1 (p1) in the G0S2 promoter, may regulate the lymphocyte balance by regulating G0S2 gene expression but failed to affect the methylation of the G0S2 promoter. Tacrolimus therapy-induced methylation and overexpression of NFAT5 could significantly reduce the expression of G0S2 in AChR MG patients. The G0S2 gene was remarkably upregulated in the PBMCs of MG patients. NFAT5 may affect transcription initiation and downregulate G0S2 expression through p1 in the promoter, thus controlling G0S2 gene expression and regulating the lymphocyte balance. Therefore, G0S2 could be an immune regulatory factor in both AChR MG occurrence and treatment with tacrolimus. [ABSTRACT FROM AUTHOR]

Background: The Epstein-Barr virus (EBV) is closely associated with several types of malignancies. EBV is normally present in the latent state in the peripheral blood B cell compartment. The EBV latent-to-lytic switch is required for virus spread and virus-induced carinogenesis. Immunosuppression or DNA damage can induce the reactivation of EBV replication. EBV alone is rarely sufficient to cause cancer. In this study, we investigated the roles of host microRNAs and environmental factors, such as DNA-damage agents, in EBV reactivation and its association with lymphomagenesis.Methods: We first analyzed the publicly available microRNA array data containing 45 diffuse large B-cell lymphoma patients and 10 control lymph nodes or B cells with or without EBV infection. In situ hybridization for miR-18a and immunohistochemitry were performed to evaluate the correlation between the expression of miR-18a and nuclear EBV protein EBNA1 in lymphoid neoplasm. The proliferative effects of miR-18a were investigated in EBV-positive or -negative lymphoid neoplasm cell lines. EBV viral load was measured by a quantitative real-time EBV PCR and FISH assay. The genomic instability was evaluated by CGH-array.Results: In this study, we analyzed the publicly available microRNA array data and observed that the expression of the miR-17-92 cluster was associated with EBV status. In situ hybridization for miR-18a, which is a member of the miR-17-92 cluster, showed a significant upregulation in lymphoma samples. miR-18a, which shares the homolog sequence with EBV-encoded BART-5, promoted the proliferation of lymphoma cells in an EBV status-dependent manner. The DNA-damaging agent UV or hypoxia stress induced EBV activation, and miR-18a contributed to DNA damaging-induced EBV reactivation. In contrast to the promoting effect of ATM on the lytic EBV reactivation in normoxia, ATM inhibited lytic EBV gene expression and decreased the EBV viral load in the prescence of hypoxia-induced DNA damage. miR-18a reactivated EBV through inhibiting the ATM-mediated DNA damage response (DDR) and caused genomic instability.Conclusions: Taken together, these results indicate that DNA-damaging agents and host microRNAs play roles in EBV reactivation. Our study supported the interplay between host cell DDR, environmental genotoxic stress and EBV. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disease characterized by progressive cognitive decline. It can be divided into familial AD (FAD) and sporadic AD (SAD) based on the family history. Recently dysregulation of cholesterol homeostasis has been implicated in the development of late-onset AD. ATP-binding cassette transporter A7 ( ABCA7 ) gene, regulating the transport of cholesterol, has been recently identified as a susceptible gene of AD by several large genome-wide association studies. To test the genetic effect of ABCA7 rs3764650 on blood lipid levels in Southern Chinese Han population and investigate the risk factors of SAD, a total of 118 SAD patients and 120 healthy matched controls were recruited and the genotyping in ABCA7 rs3764650 was conducted on the Sequenom MassARRAY iPLEX platform. Meanwhile, the levels of fasting lipid profile and mini-mental state examination (MMSE) scores were tested. There was significant difference in genotype distribution between SAD patients and controls ( p = 0.001). While the difference of ABCA7 rs3764650 allele distribution between SAD patients and controls was only significant in APOEε4 -noncarriers ( p = 0.039). The association between blood lipid levels and ABCA7 rs3764650 genotypes was influenced by APOEε4 status. In APOEε4 -noncarriers of SAD, the total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C) levels in GG genotype group were significantly lower than those in GT and TT genotype groups (all p < 0.05). Whereas no significant difference of blood lipid levels was found among three genotypes in APOEε4 -carriers of SAD and controls. Additionally, logistic regression analysis showed that lower high-density lipoprotein cholesterol (HDL-C) levels ( p = 0.015, OR = 5.669) and GG genotype ( p = 0.013, OR = 8.318) were positively associated with SAD. Our results suggest that GG genotype of ABCA7 rs3764650 was a risk factor of SAD in Southern Chinese Han population as well as lipid homeostasis. [ABSTRACT FROM AUTHOR]

Abnormal CD4 + T cell activation is known to play roles in the pathogenesis of myasthenia gravis (MG). However, little is known about the mechanisms underlying the roles of lncRNAs in regulating CD4 + T cell. In this study, we discovered that the lncRNA IFNG-AS1 is abnormally expressed in MG patients associated with quantitative myasthenia gravis (QMG) and the positive anti-AchR Ab levels patients. IFNG-AS1 influenced Th1/Treg cell proliferation and regulated the expression levels of their transcription factors in an experimental autoimmune myasthenia gravis (EAMG)model. IFNG-AS1 could reduce the expression of HLA-DRB and HLA-DOB and they had a negative correlation in MG. Furthermore IFNG-AS1 influenced the expression levels of CD40L and CD4 + T cells activation in MG patient partly depend on effecting the HLA-DRB1 expression. It suggests that IFNG-AS1 may be involved in CD4 + T cell-mediated immune responses in MG. [ABSTRACT FROM AUTHOR]