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Water electrolysis has been considered as a green and sustainable technique for hydrogen production, while the sluggish kinetics of the oxygen evolution reaction (OER) and the insufficient active sites remain bottlenecks for practical performance. Herein, we present in situ electrochemical activation of nickel (Ni) alloys for water splitting assisted by the electro-oxidation of organic molecules. It has been discovered that in situ electrochemical activation results in an increased degree of metal oxidation, thereby inducing more high-valence active metal species (NiOOH). These species not only function as active sites for surface adsorption but also facilitate electron transfer. Subsequently, two representative organic molecules, methanol and ethanol, are introduced to further promote the water electrolysis. Through a combination of electrochemical testing and in situ Raman spectroscopy, it has been revealed that the activated Ni alloy demonstrates a stronger response to the adsorbed alcohol molecules and tends to form β-NiOOH at lower potentials compared to the pristine one, resulting in enhanced water splitting. The present work advances a simple activation method for transition metal-based electrocatalysts for highly efficient alcohol oxidation-assisted water electrolysis. [ABSTRACT FROM AUTHOR]

Background: Good wound healing is critical to infection prophylaxis and satisfactory rehabilitation in Total Knee Arthroplasty (TKA). Currently, two techniques, i.e., barbed continuous subcuticular suture without skin adhesive or combined use skin adhesive (n-butyl-2) are being used for superficial wound closure of TKA. While a new skin adhesive (2-octyl) with self-adhesive mesh has been employed as an alternative to conventional surgical skin closure in TKA, its superiority, especially in reducing wound complications and improving wound cosmetic outcomes has not been investigated. This study aimed to compare 2-octyl, n-butyl-2, and no skin adhesive in terms of safety and efficacy in TKA superficial wound closure. Methods: We conducted a multicenter, prospective, randomized controlled study in 105 patients undergoing primary TKA between May 2022 and October 2023. Each patient's knee was randomized to receive 2-octyl, n-butyl-2, or no skin adhesive skin closure with all using barbed continuous sutures in deep tissue. Wounds were followed 1, 3, 5 days, 2, 6 weeks, and 3 months after surgery. Wound discharge, complications, cosmetic outcomes, patient satisfaction, and wound-related costs were compared among these three methods. Results: Wound discharge was less in 2-octyl group and n-butyl-2 group than in non-adhesive group at 1 day, with the discharge only being less in 2-octyl group than in the non-adhesive group at day 3 and day 5 days (P < 0.05). There was no statistical difference in the incidence of other wound complications among the groups (P > 0.05). The 2-octyl group achieved better cosmetic effects than the other two groups in 6 weeks and 3 months (P < 0.05). Compared to the non-adhesive group, 2-octyl group scored higher in overall patient satisfaction score in 2 weeks and incurred lower costs (P < 0.05). Conclusions: Skin closure in TKA using 2-octyl adhesive material showed superiority when compared to no skin adhesive or n-butyl-2, in reducing wound discharge, improving the cosmetic outcomes, without increasing wound complications. In addition, the use of 2-octyl yielded better patient satisfaction and also was less costly compared to no skin adhesive. Our study exhibited that 2-octyl was a safe and effective wound closure technique for patients undergoing TKA. Trial registration: This study has been registered at Clinical Trials. Gov (No. ChiCTR210046442). [ABSTRACT FROM AUTHOR]

Biological membranes containing transmembrane channels play a crucial role in numerous cellular processes, and mimicking of cell membranes has garnered significant interest in various biomedical applications, particularly nanopore sequencing technology, where remarkable progress has been made with nanopore membranes. Considering the fragility of biomimetic membranes formed by artificial lipids and the limited mimicry of those formed by common block copolymers, this study developed a novel amphiphilic polymer by covalently linking hydrophilic heads of phospholipids to the ends of hydrophobic poly(dimethyl siloxane) (PDMS) chains. The absence of hydrophilic blocks allowed for good control over the polydispersity of this polymer within a narrow range. The high flexibility of PDMS chains, combined with relatively uniform molecular weights, would confer enhanced stability and robustness to polymeric membranes. Dynamic light scattering measurements and microdroplet formation tests demonstrated good amphipathic properties of these novel polymers when maintaining an appropriate hydrophilic–hydrophobic ratio. Moreover, the high similarity between the hydrophilic heads and natural phospholipids makes this polymer more compatible with biomolecules. A successful protein insertion experiment confirmed both the stability of this polymeric membrane and its compatibility with membrane proteins. As a result, this novel amphiphilic polymer exhibits great potential for biomembrane mimicking and paves a new path for material design in biomedical applications. [ABSTRACT FROM AUTHOR]

This manuscript reports on the interaction of a current-free monochromatic surface wave field with a wave-free uniform current field. The existing reasonable theories of wave–current interactions are primarily based on weak current assumptions and derived from linear theory, resulting in calculation bias in the analysis of nonlinear wave–current interactions. Moreover, experimental data on high-order wave–current interactions still need to be collected. Thus, steady-state solutions named the third-order wave–current theory based on the third-order wave dispersion relationship and the principle of wave–current energy conservation were derived. The wave–current interaction experiment was set up to cover 164 sets of experimental conditions, including 33 types of periodic waves from the second to the fifth order and six different current velocities. The effects of water depth, current velocity, wave period, and height on the wave height and wavelength in the wave–current interaction field were investigated. A comparison of the mean relative error (MRE) and the determination coefficient (R2) of the wavelength with the experimental data revealed that the third-order wave–current theory outperformed the traditional linear theory, with an optimal reduction of 75% and an enhancement of 25%, respectively. Additionally, the third-order wave–current theory reduces the MRE by 25%–40% in the wave height calculation, with R2 consistently outperforming the linear theory. The third-order wave–current theory can significantly improve the calculation accuracy of the theoretical method in solving nonlinear wave–current interactions. [ABSTRACT FROM AUTHOR]

The three-dimensional motion trajectories of bubbles in liquid metal have been extensively measured under a horizontal magnetic field (HMF) using ultrasonic phased array technology. This study focuses on the case of a streamwise magnetic field (SMF). Experimental results show that the anisotropy of bubble oscillation in HMFs is not observed in the SMF. Under a weak SMF, single bubbles exhibit zigzag oscillation motion whose axis is along the streamwise direction. The oscillation frequency and amplitude decrease as the SMF intensity increases. As the SMF becomes stronger, the bubble suddenly begins to exhibit low-frequency oblique oscillations, the main axis of which deviates from the streamwise direction. Further increases in the SMF intensity produce an oblique straight-line motion, with the oblique oscillations suppressed by the magnetic field. Under the SMF, the terminal velocity of the bubbles initially increases when the magnetic interaction parameter is N < 1 , with the attenuation of the oscillation frequency being the dominant cause. However, the magnitude of velocity growth under the SMF is smaller than that in the case of an HMF. When N > 1 , the bubble terminal velocity steadily decreases and exhibits a similar trend to that under the HMF, along with an increase in the drag coefficient. [ABSTRACT FROM AUTHOR]

Nanopore selective sequencing allows the targeted sequencing of DNA of interest using computational approaches rather than experimental methods such as targeted multiplex polymerase chain reaction or hybridization capture. Compared to sequence-alignment strategies, deep learning (DL) models for classifying target and nontarget DNA provide large speed advantages. However, the relatively low accuracy of these DL-based tools hinders their application in nanopore selective sequencing. Here, we present a DL-based tool named ReadCurrent for nanopore selective sequencing, which takes electric currents as inputs. ReadCurrent employs a modified very deep convolutional neural network (VDCNN) architecture, enabling significantly lower computational costs for training and quicker inference compared to conventional VDCNN. We evaluated the performance of ReadCurrent across 10 nanopore sequencing datasets spanning human, yeasts, bacteria, and viruses. We observed that ReadCurrent achieved a mean accuracy of 98.57% for classification, outperforming four other DL-based selective sequencing methods. In experimental validation that selectively sequenced microbial DNA from human DNA, ReadCurrent achieved an enrichment ratio of 2.85, which was higher than the 2.7 ratio achieved by MinKNOW using the sequence-alignment strategy. In summary, ReadCurrent can rapidly classify target and nontarget DNA with high accuracy, providing an alternative in the toolbox for nanopore selective sequencing. ReadCurrent is available at https://github.com/Ming-Ni-Group/ReadCurrent. [ABSTRACT FROM AUTHOR]

Land use and cover change (LUCC) significantly impacts global carbon cycles and land surface properties, accounting for 25% of the historical atmospheric CO2 increase. We explore a previously overlooked role of LUCC in driving the land carbon cycle by using a three-level meta-analysis and Land Use Harmonization data to drive an ecosystem model. Our findings reveal that a loss of 39.2% of soil organic carbon (SOC) change in China due to LUCC, mitigated by afforestation, doubles gross primary productivity at 0.02 Pg C yr−1, countering central China's urbanization decline. Indirect climate effects, especially soil bulk density, significantly impact SOC compared to direct climate effects. LUCC has significantly increased the Chinese terrestrial carbon sink, with net ecosystem productivity reaching 0.02 ± 0.12 Pg C yr−1. Our study underscores the importance of reforestation and afforestation in addressing climate change and enhancing carbon sinks in future carbon management. [ABSTRACT FROM AUTHOR]

Furfurylated wood exhibits excellent dimensional stability and corrosion resistance, making it a promising material for constructing buildings, but it is highly flammable. Herein, flame-retardant furfurylated poplar wood was produced via a two-step process utilizing boric acid (BA) and ammonium dihydrogen phosphate (ADP) as flame-retardant components, and biomass-derived furfuryl alcohol (FA) as a modifier. The acidity of BA and ADP allowed them to catalyze the polymerization of FA, which formed a cross-linked network that immobilized BA and ADP inside the wood. The addition of BA/ADP substantially delayed the time to ignition from 10 to 385 s and reduced the total heat release and total smoke release by 58.75% and 77.31%, respectively. Analysis of the pyrolysis process showed that the decomposition products of BA and ADP protected the underlying furfurylated wood and diluted combustible gases. This method significantly improved the fire retardancy and smokeless properties of furfurylated wood, providing promising prospects for its application as an engineering material. [ABSTRACT FROM AUTHOR]

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p <.001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p >.005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited. [ABSTRACT FROM AUTHOR]

The accurate prediction of blade loading is crucial to designing efficient turbomachinery, but traditional methods often neglect the impact of compressibility, leading to inaccuracies at high speeds. This study investigates the effect of compressibility on the blade loading parameters, particularly the Zweifel coefficient (Zw) and lift coefficient (CL), in turbine cascades. A novel intermediate method (IM), with averaged flow properties derived from both inlet and outlet conditions, is proposed to enhance the accuracy of Zw and CL calculations in compressible flow regimes. This method is based on the extended Kutta–Joukowski theorem for compressible flow and incorporates the Mach number directly into the modified definitions of Zw and CL. The analysis reveals that the averaged flow angle (αm), calculated by using a velocity-weighted approach, serves as a crucial parameter for blade similarity studies. The proposed correction method is applied and validated based on CFD simulations of the VKI-RG turbine cascade. The IM and modified definitions provide a robust framework for accurately predicting blade loading at high speeds, enabling improved design and analysis of turbomachinery. [ABSTRACT FROM AUTHOR]

We theoretically investigate the polarized terahertz (THz) generation in laser–gas interaction using circularly polarized (CP) two-color laser pulses with uncommon frequencies. We show that these laser pulses can be applied to generate polarized THz radiation, and the polarization of THz radiation, which ranges from linear to circular, can be controlled in a flexible manner by manipulating the laser parameters, such as the relative phase and phase angle. Furthermore, in contrast to traditional harmonic frequency combinations, the polarization states of the incommensurate lasers undergo complex evolution with the phase angle, resulting in a noteworthy influence on the THz yield. The obtained results could deepen our understanding of polarized THz radiation generation. [ABSTRACT FROM AUTHOR]

We study the magnetohydrodynamic flow around a circular cylinder confined in a rectangular duct. In this configuration, both the circular cylinder and the walls of the rectangular duct are electrically insulating, while the magnetic field aligns with the axial direction of the cylinder. The experimental measurements are performed by controlling two parameters Re and N (N is the ratio of electromagnetic forces to inertial forces) in the ranges of (180–722) and (0.8–264), respectively. Utilizing the electrical potential method, we employ both movable and wall probes to obtain the local flow velocities in the wake of the cylinder. By analyzing the space correlation of the signals obtained from the wall probes, a distinct transition in the flow behavior is observed, transitioning from a three-dimensional state to a quasi-two-dimensional (Q2D) state when the external magnetic field reaches a sufficient strength (N > 3). Additionally, the Q2D state allows for a further subdivision based on the scaling relationship R e / H a ∼ (0.41 – 0.42) in the stability map, thereby distinguishing between steady and unsteady flow states, which is consistent with findings from previous studies. [ABSTRACT FROM AUTHOR]

Background: Early-onset Alzheimer's disease (EOAD) exhibits a notable degree of heterogeneity as compared to late-onset Alzheimer's disease (LOAD). The proteins and pathways contributing to the pathophysiology of EOAD still need to be completed and elucidated. Objective: Using correlation network analysis and machine learning to analyze cerebrospinal fluid (CSF) proteomics data to identify potential biomarkers and pathways associated with EOAD. Methods: We employed mass spectrometry to conduct CSF proteomic analysis using the data-independent acquisition method in a Chinese cohort of 139 CSF samples, including 40 individuals with normal cognition (CN), 61 patients with EOAD, and 38 patients with LOAD. Correlation network analysis of differentially expressed proteins was performed to identify EOAD-associated pathways. Machine learning assisted in identifying crucial proteins differentiating EOAD. We validated the results in an Western cohort and examined the proteins expression by enzyme-linked immunosorbent assay (ELISA) in additional 9 EOAD, 9 LOAD, and 9 CN samples from our cohort. Results: We quantified 2,168 CSF proteins. Following adjustment for age and sex, EOAD exhibited a significantly greater number of differentially expressed proteins than LOAD compared to CN. Additionally, our data indicates that EOAD may exhibit more pronounced synaptic dysfunction than LOAD. Three potential biomarkers for EOAD were identified: SH3BGRL3, LRP8, and LY6 H, of which SH3BGRL3 also accurately classified EOAD in the Western cohort. LY6 H reduction was confirmed via ELISA, which was consistent with our proteomic results Conclusions: This study provides a comprehensive profile of the CSF proteome in EOAD and identifies three potential EOAD biomarker proteins. [ABSTRACT FROM AUTHOR]

Motivation Due to the varying delivery methods of mRNA vaccines, codon optimization plays a critical role in vaccine design to improve the stability and expression of proteins in specific tissues. Considering the many-to-one relationship between synonymous codons and amino acids, the number of mRNA sequences encoding the same amino acid sequence could be enormous. Finding stable and highly expressed mRNA sequences from the vast sequence space using in silico methods can generally be viewed as a path-search problem or a machine translation problem. However, current deep learning-based methods inspired by machine translation may have some limitations, such as recurrent neural networks, which have a weak ability to capture the long-term dependencies of codon preferences. Results We develop a BERT-based architecture that uses the cross-attention mechanism for codon optimization. In CodonBERT, the codon sequence is randomly masked with each codon serving as a key and a value. In the meantime, the amino acid sequence is used as the query. CodonBERT was trained on high-expression transcripts from Human Protein Atlas mixed with different proportions of high codon adaptation index codon sequences. The result showed that CodonBERT can effectively capture the long-term dependencies between codons and amino acids, suggesting that it can be used as a customized training framework for specific optimization targets. Availability and implementation CodonBERT is freely available on https://github.com/FPPGroup/CodonBERT. [ABSTRACT FROM AUTHOR]

Multi-fidelity simulations incorporate computational fluid dynamics (CFD) models into a thermodynamic model, enabling the simulation of the overall performance of an entire gas turbine with high-fidelity components. Traditional iterative coupled methods rely on characteristic maps, while fully coupled methods directly incorporate high-fidelity simulations. However, fully coupled methods face challenges in simulating rotating components, including weak convergence and complex implementation. To address these challenges, a fully coupled method with logarithmic transformations was developed to directly integrate high-fidelity CFD models of multiple rotating components. The developed fully coupled method was then applied to evaluate the overall performance of a KJ66 micro gas turbine across various off-design simulations. The developed fully coupled method was also compared with the traditional iterative coupled method. Furthermore, experimental data from ground tests were conducted to verify its effectiveness. The convergence history indicated that the proposed fully coupled method exhibited stable convergence, even under far-off-design simulations. The experimental verification demonstrated that the multi-fidelity simulation with the fully coupled method achieved high accuracy in off-design conditions. Further analysis revealed inherent differences in the coupling methods of CFD models between the developed fully coupled and traditional iterative coupled methods. These inherent differences provide valuable insights for reducing errors between the component-level model and CFD models in different coupling methods. The developed fully coupled method, introducing logarithmic transformations, offers more realistic support for the detailed and optimal design of high-fidelity rotating components within the overall performance platform of gas turbines. [ABSTRACT FROM AUTHOR]

Β-site amyloid precursor protein (APP) cleaving enzyme (BACE1) is a crucial protease in the production of amyloid-β (Aβ) in Alzheimer's disease (AD) patients. However, the side effects observed in clinical trials of BACE1 inhibitors, including reduction in brain volume and cognitive worsening, suggest that the exact role of BACE1 in AD pathology is not fully understood. To further investigate this, we examined cerebrospinal fluid (CSF) levels of BACE1 and its cleaved product sAPPβ that reflects BACE1 activity in the China Aging and Neurodegenerative Disorder Initiative cohort. We found significant correlations between CSF BACE1 or sAPPβ levels and CSF Aβ40, Aβ42, and Aβ42/Aβ40 ratio, but not with amyloid deposition detected by 18F-Florbetapir PET. Additionally, CSF BACE1 and sAPPβ levels were positively associated with cortical thickness in multiple brain regions, and higher levels of sAPPβ were linked to increased cortical glucose metabolism in frontal and supramarginal areas. Interestingly, individuals with higher baseline levels of CSF BACE1 exhibited slower rates of brain volume reduction and cognitive worsening over time. This suggests that increased levels and activity of BACE1 may not be the determining factor for amyloid deposition, but instead, may be associated with increased neuronal activity and potentially providing protection against neurodegeneration in AD. [ABSTRACT FROM AUTHOR]

Hydrogen has the potential to serve as a new energy resource, reducing greenhouse gas emissions that contribute to climate change. Natural hydrogenases exhibit impressive catalytic abilities for hydrogen production, but they often lack oxygen tolerance. Oxygen-tolerant hydrogenases can work under oxygen by reacting with oxygen to form inactive states, which can be reactivated to catalytic states by oxygen atom removal. Herein, we synthesized three NiFeSe complexes: (NiSe(CH3)FeCp, NiSe(CH3)FeCp* and NiSe(PhNMe2)FeCp) with features of active sites of [NiFeSe]-H2ases, which are the oxygen-tolerant hydrogenases, and we investigated the influence of electronic and steric factors on the oxygen reaction of these "biomimetic" complexes. In our research, we found that they react with oxygen, forming 1-oxygen species, which is related to the O2-damaged [NiFeSe] active site. Through a comparative analysis of oxygen reactions, we have discovered that electronic factors and steric hindrance on Se play a significant role in determining the oxygen reactivity of NiFe complexes related to hydrogenases' active sites. [ABSTRACT FROM AUTHOR]

Objective: To establish an automated, multitask, MRI-based deep learning system for the detailed evaluation of supraspinatus tendon (SST) injuries. Methods: According to arthroscopy findings, 3087 patients were divided into normal, degenerative, and tear groups (groups 0–2). Group 2 was further divided into bursal-side, articular-side, intratendinous, and full-thickness tear groups (groups 2.1–2.4), and external validation was performed with 573 patients. Visual geometry group network 16 (VGG16) was used for preliminary image screening. Then, the rotator cuff multitask learning (RC-MTL) model performed multitask classification (classifiers 1–4). A multistage decision model produced the final output. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis and calculation of related parameters. McNemar's test was used to compare the differences in the diagnostic effects between radiologists and the model. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 indicated statistical significance. Results: In the in-group dataset, the area under the ROC curve (AUC) of VGG16 was 0.92, and the average AUCs of RC-MTL classifiers 1–4 were 0.99, 0.98, 0.97, and 0.97, respectively. The average AUC of the automated multitask deep learning system for groups 0–2.4 was 0.98 and 0.97 in the in-group and out-group datasets, respectively. The ICCs of the radiologists were 0.97–0.99. The automated multitask deep learning system outperformed the radiologists in classifying groups 0–2.4 in both the in-group and out-group datasets (p < 0.001). Conclusion: The MRI-based automated multitask deep learning system performed well in diagnosing SST injuries and is comparable to experienced radiologists. Clinical relevance statement: Our study established an automated multitask deep learning system to evaluate supraspinatus tendon (SST) injuries and further determine the location of SST tears. The model can potentially improve radiologists' diagnostic efficiency, reduce diagnostic variability, and accurately assess SST injuries. Key Points: • A detailed classification of supraspinatus tendon tears can help clinical decision-making. • Deep learning enables the detailed classification of supraspinatus tendon injuries. • The proposed automated multitask deep learning system is comparable to radiologists. [ABSTRACT FROM AUTHOR]

Projections of spatial biodiversity dynamics under climate warming are often based on models including only climate variables, and when non-climatic factors (e.g. soil) are included, data are often at much coarser spatial resolutions than those experienced by plants. Field studies along elevation gradients permit the gathering of detailed soil data, while still covering a wide climatic gradient. Here, an intensive field survey of four spring forest herbs along an elevation gradient showed that soil properties had substantial impacts on the occurrence/abundance of all species, and that soil effects were more pronounced at higher elevations. For Trillium erectum and Claytonia caroliniana, very infrequent occurrences at high elevation were strongly associated with rare microsites with high pH or nutrients. In a seven-year transplant experiment with T. erectum, we found that individuals grew to much smaller sizes at high than low elevation, suggesting that environmental factors rather than dispersal limitation constrain the species' upper range limit, despite substantial warming in recent decades. Our study demonstrates that soil factors interact strongly with climate to determine plant range limits along climatic gradients. Unsuitable soils for plants at high elevations or latitudes may represent an important constraint on future plant migration and biodiversity change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'. [ABSTRACT FROM AUTHOR]

Cyber–physical distribution systems with renewable energy should have high resilience under extreme conditions. However, in a distribution system with deep cyber–physical coupling, the propagation of information risk impairs the resilience of the system. This paper proposes a strategy to improve the resilience of cyber–physical distribution systems that considers cross‐space propagation of information risk. First, the mechanism for interaction between the cyber space and physical space is analyzed, and an information risk cross‐space propagation model is proposed. Second, the risk status of key device nodes is quantified according to the risk propagation model, and an optimization strategy for information risk control is obtained, whose objective function is the minimum risk of the coupling network. Third, a fault restoration strategy that considers cyber space risk is proposed. Finally, the paper reports experiments using the modified IEEE 33‐node and 123‐node distribution networks to simulate the proposed strategy and verify its effectiveness. [ABSTRACT FROM AUTHOR]

Objective: To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. Methods: Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. Results: The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81–0.94) and accuracy (0.83–0.98) were observed across all diagnoses. Conclusion: ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. Clinical relevance statement: 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. Key Points: • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process. [ABSTRACT FROM AUTHOR]

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO. [ABSTRACT FROM AUTHOR]

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Objective: Osteonecrosis of the femoral head (ONFH) is a severe disease that primarily affects the middle-aged population, imposing a significant economic and social burden. Recent research has linked the progression of non-traumatic osteonecrosis of the femoral head (NONFH) to the composition of the gut microbiota. Steroids and alcohol are considered major contributing factors. However, the relationship between NONFH caused by two etiologies and the microbiota remains unclear. In this study, we examined the gut microbiota and fecal metabolic phenotypes of two groups of patients, and analyzed potential differences in the pathogenic mechanisms from both the microbial and metabolic perspectives. Methods: Utilizing fecal samples from 68 NONFH patients (32 steroid-induced, 36 alcohol-induced), high-throughput 16 S rDNA sequencing and liquid chromatography with tandem mass spectrometry (LC-MS/MS) metabolomics analyses were conducted. Univariate and multivariate analyses were applied to the omics data, employing linear discriminant analysis effect size to identify potential biomarkers. Additionally, functional annotation of differential metabolites and associated pathways was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Subsequently, Spearman correlation analysis was employed to assess the potential correlations between differential gut microbiota and metabolites. Results: High-throughput 16 S rDNA sequencing revealed significant gut microbial differences. At the genus level, the alcohol group had higher Lactobacillus and Roseburia, while the steroid group had more Megasphaera and Akkermansia. LC-MS/MS metabolomic analysis indicates significant differences in fecal metabolites between steroid- and alcohol-induced ONFH patients. Alcohol-induced ONFH (AONFH) showed elevated levels of L-Lysine and Oxoglutaric acid, while steroid-induced ONFH(SONFH) had increased Gluconic acid and Phosphoric acid. KEGG annotation revealed 10 pathways with metabolite differences between AONFH and SONFH patients. Correlation analysis revealed the association between differential gut flora and differential metabolites. Conclusions: Our results suggest that hormones and alcohol can induce changes in the gut microbiota, leading to alterations in fecal metabolites. These changes, driven by different pathways, contribute to the progression of the disease. The study opens new research directions for understanding the pathogenic mechanisms of hormone- or alcohol-induced NONFH, suggesting that differentiated preventive and therapeutic approaches may be needed for NONFH caused by different triggers. [ABSTRACT FROM AUTHOR]

Objective To evaluate the causal relationship between tea drinking and cholelithiasis by two-way Mendelian randomization. Methods The instrumental variables for tea drinking were determined from 447 485 participants in the UK Biobank genome-wide association study (GWAS). A total of 32 single nucleotide polymorphisms (SNPs) associated with tea drinking were used for two-sample Mendelian randomized analysis to evaluate the causal relationship between tea drinking and cholelithiasis. Cholelithiasis data were obtained from GWAS data of 35 712 cholelithiasis patients and 273 442 controls publicly available from the Finnegan Alliance. In this study, inverse variance weighted (IVW) was used as the main method to estimate causality, and sensitivity analysis was carried out to ensure the robustness of the results. Finally, reverse Mendelian randomization analysis was used to verify whether there was a reverse correlation. Results There was a causal re-lationship between tea drinking and cholelithiasis. In the IVW method, each additional standard deviation (2.85 cups/day) of tea consumption reduced the risk of cholelithiasis by 26.1% (OR =0.739, 95% CI: 0.536-0.990). Similar results were obtained in weighted mode (OR=0.652, 95% CI: 0.458-0.928), but there was no evidence to prove reverse correlation (IVW: P > 0.05). Conclusion The genetic evidence provided by two-way Mendelian randomized analysis shows that the increase of tea drinking reduces the risk of cholelithiasis, but there is no evidence of reverse association. [ABSTRACT FROM AUTHOR]

Our previous studies have discovered that long non-coding RNA (lncRNA) MALAT1 and its target microRNA-125b-5p (miR-125b-5p) are implicated in neurological diseases via regulating neuroinflammation and neuronal injury. This study aimed to further explore the relationship between lncRNA MALAT1 and miR-125b-5p, as well as their effect on microglial activation, neuroinflammation, and neural apoptosis in spinal cord injury (SCI). Primary microglia from Sprague Dawley rats were stimulated with lipopolysaccharide (LPS). Then, microglia were transfected with lncRNA MALAT1 overexpression or knock-down adenovirus-associated virus with or without miR-125b-5p mimic. The culture medium of microglia was incubated with primary neurons. SCI rats were established for in vivo validation. LncRNA MALAT1 expression was reduced by LPS treatment in a dose-dependent manner. LncRNA MALAT1 overexpression suppressed the microglial M1 polarization (decreased iNOS but increased ARG1), neuroinflammation (declined PTGS2, TNF-α, IL-1β, and IL-6), and microglia-induced neural apoptosis (lower TUNEL positive cells and C-caspase3 but higher BCL2) under LPS treatment; its knock-down displayed the opposite trend. Moreover, lncRNA MALAT1 directly bound to and negatively regulated miR-125b-5p. MiR-125b-5p mimic promoted microglial M1 polarization, neuroinflammation, and microglia-induced neural apoptosis following LPS treatment; also, it could attenuate the effect of lncRNA MALAT1. Further in vivo study displayed that lncRNA MALAT1 overexpression elevated the Basso-Beattie-Bresnahan motor function score and improved neural injury. Also, in vivo validation indicated a similar effect of lncRNA MALAT1 on microglial polarization and neuroinflammation as in vitro. LncRNA MALAT1 improves SCI recovery via miR-125b-5p mediated microglial M1 polarization, neuroinflammation, and neural apoptosis. [ABSTRACT FROM AUTHOR]

Electrocatalytic hydrogen production technology is essentially vital for future green and sustainable energy revolution while its large‐scale industrial application is still unsatisfactory due to the low efficiency of electrocatalysts and kinetically sluggish oxygen evolution reaction (OER). Developing novel electrocatalysts and coupling them with upgrading organic molecules are considered effective solutions. Herein, it reports a high‐entropy composite electrocatalyst consisting of FeCoNiAlMo alloy and carbon nanotube (CNT) for anodic benzyl alcohol (BA) electrooxidation coupled with cathodic hydrogen production. Because of the lower charge transfer resistance and faster reaction kinetics, the quinary FeCoNiAlMo/CNT is highly active to OER, remarkably outperforming the ternary FeCoNi/CNT counterpart. Then, the FeCoNiAlMo/CNT composite electrocatalyst is further utilized for BA oxidation reaction‐assisted hydrogen evolution. Combining the experimental and calculation results, the different activities when tested in BA‐containing or BA‐free electrolytes can be attributed to the different metal active sites with specific surface adsorption effects to water and BA molecules, respectively. This work develops a novel electrocatalyst for high‐efficient alcohol oxidation‐assisted electrolysis. [ABSTRACT FROM AUTHOR]

Buoyancy-assisted upward MHD flows and dynamical behaviors of flow channel insert (FCI) in the dual-coolant lead-lithium (DCLL) blanket are studied numerically. Based on our internally developed and validated solver, the dynamical behaviors of magneto-thermo-fluid-structure coupled multiphysical field are investigated. A large amplitude, low frequency, and quasiperiodic unsteady reverse flow at high Re (31000), high G r (3.5 × 1 0 11 ), and moderate magnetic field (0.7~1.7T) is found in the DCLL blanket. This intricate phenomenon has been discovered for the first time, representing a combination of separate experimental results from Melnikov et al. (2016) and Khanal and Lei (2012). In our study of this large amplitude, low frequency, and quasiperiodic unsteady reverse flows, the importance of the cold helium gas to the instability of fluid flow in the bulk region and the thermal conductivity of the FCI to convection structure and instability are first found and recognized. Additionally, we take into account the effects of the temperature field and flow field on structural deformation and mechanical behavior of FCI, and we have discovered several intriguing phenomena, such as (1) the stability of fluid flow in the bulk region depends on the strength of the heat source, the magnitude of the magnetic field, and thermal conductivity of FCI; (2) the instability and periodicity of the fluid flow are primarily related to the unsteady reverse flow, which rises up and falls down periodically in the bulk region; (3) the physical mechanism of unsteady flow influenced by reverse flow, pressure drop, and Lorentz force has been concluded. It has been discovered that the breakdown of a reverse flow vortex causes a rapid reduction in pressure drop. (4) To avoid this phenomenon in engineering, a phase map of unsteady and steady flows in the DCLL blanket has been created. (5) The quasiperiodic characteristics of solid (flow channel insert) affected by flow are found and analyzed. [ABSTRACT FROM AUTHOR]

The genus Rosa is globally popular with well-established applications since it has a high edible and medicinal value. However, relatively limited research has been conducted on the composition and quality of wild Rosa fruits. The present study aimed to compare the properties and chemical components of five wild edible Rosa fruits, Rosa roxburghii, Rosa sterilis, Rosa laevigata, Rosa davurica, and Rosa sericea. The UPLC-ESI-MS/MS approach identified the key metabolites among the five Rosa fruits as flavonoids, phenolic acids, and organic acids. The main differential metabolites among the five fruits are flavonoids (22.29–45.13%), phenolic acids (17–22.27%), and terpenoids (7.7–24%), respectively. In total, 125 compounds served as potential markers for the five Rosa species. Differential metabolic pathways of five Rosa fruits were analyzed using the KEGG approach. Rosa laevigata fruits showed the highest total polysaccharide (TPS) content of 64.48 g/100 g. All the five Rosa extracts effectively decreased the levels of malondialdehyde while increasing the activities of superoxide dismutase and glutathione peroxidase in the H2O2-induced HaCaT cell model, demonstrating high potential for antioxidant development. Our findings suggest that the five studied Rosa fruits exhibit biological activity and edible value worth further exploration. [ABSTRACT FROM AUTHOR]

Aim: As the direct oral anticoagulant most recently approved in China, data pertaining to clinical edoxaban use are still scarce. This study investigated the prevalence of and contemporary trends in edoxaban prescription among Chinese patients as well as factors associated with its inappropriate use in a multicentre registry of patients treated in real‐world clinical practice. Methods: This real‐world, prospective, multicentre and non‐interventional study included 1005 inpatients treated with edoxaban. According to National Medical Products Administration and European Heart Rhythm Association guidelines, edoxaban therapy was determined to be appropriate or inappropriate in each case. Results: The median patient age was 70.0 years (interquartile range 61.0‐78.0 years) and 46.3% were women. Overall, 456 (45.4%) patients received inappropriate edoxaban therapy, and common issues included an inappropriately low dosage (183, 18.2%) or wrong drug selection (109, 10.8%), high dosage (73, 7.3%), unreasonable off‐label use (49, 4.9%), contraindicated medication combinations (27, 2.7%) and incorrect administration timing (16, 1.6%). Several factors, such as age ≥75 years (odds ratio [OR] = 1.921, 95% confidence interval [CI] 1.355‐2.723, P < 0.001), weight >60 kg (OR = 2.657, 95%CI 1.970‐3.583, P < 0.001), severe renal insufficiency (OR = 1.988, 95% CI 1.043‐3.790, P = 0.037), current anaemia (OR = 1.556, 95% CI 1.151‐2.102, P = 0.004) and history of bleeding (OR = 2.931, 95% CI 1.605‐5.351, P < 0.001) were associated with an increased risk of inappropriate edoxaban therapy, whereas factors associated with cardiovascular specialties, such as admission to a cardiovascular department (OR = 0.637, 95% CI 0.464‐0.873, P = 0.005), dronedarone use (OR = 0.065, 95% CI 0.026‐0.165, P < 0.001) and amiodarone use (OR = 0.365, 95% CI 0.209‐0.637, P < 0.001) decreased this risk. Conclusion: In this real‐world study, 45.4% of patients received an inappropriate treatment with edoxaban. Multiple clinical characteristics can help identify patients who should receive edoxaban. Further development and implantation of educational activities and management strategies are needed to ensure the correct use of edoxaban. [ABSTRACT FROM AUTHOR]

The work aims to study the convection and diffusion of metallic fluid and the tritium in a cavity under the external magnetic field. The solver based on the finite volume method and the consistent and conservative scheme is developed to solve the Navier–Stokes equation considering the Lorentz force, concentration, and thermal buoyancy. The coupling effects of the magnetic field, the internal volumetric heat source, and the concentration difference between the left and right walls of the cavity are investigated. It is found that both the rotation direction and strength of the main circulation flow are controlled by the concentration buoyancy and the thermal buoyancy only regulates the global flow in the cavity. A larger concentration difference or a stronger internal heat source can lead to unstable flow. However, the stronger magnetic field suppresses the main circulation flow and small secondary vortices. The power law scaling of the Sherwood number vs the ratio of the Rayleigh number of the concentration to the Hartman number based on the force balance agrees with the numerical simulation. Four types of flow modes (large-amplitude low-frequency, large-amplitude high-frequency, small-amplitude low-frequency, and stable modes) are observed under the coupled multi-physics fields of the magnetic field, concentration difference, and heat source. The correlation function describing the influence of magnetic field and concentration strength on mass transfer is concluded. [ABSTRACT FROM AUTHOR]

Anthracyclines remain the cornerstone of numerous chemotherapeutic protocols, with beneficial effects against haematological malignancies and solid tumours. Unfortunately, the clinical usefulness of anthracyclines is compromised by the development of cardiotoxic side effects, leading to dose limitations or treatment discontinuation. There is no absolute linear correlation between the incidence of cardiotoxicity and the threshold dose, suggesting that genetic factors may modify the association between anthracyclines and cardiotoxicity risk. And the majority of single nucleotide polymorphisms (SNPs) associated with anthracycline pharmacogenomics were identified in the ATP‐binding cassette (ABC) and solute carrier (SLC) transporters, generating increasing interest in the pharmacogenetic implications of their genetic variations for anthracycline‐induced cardiotoxicity (AIC). This review focuses on the influence of SLC and ABC polymorphisms on AIC and highlights the prospects and clinical significance of pharmacogenetics for individualised preventive approaches. [ABSTRACT FROM AUTHOR]

Background: Diffusion magnetic resonsance imaging (dMRI) can potentially predict the postoperative outcome of cervical spondylotic myelopathy (CSM). Purpose: To explore preoperative dMRI parameters to predict the postoperative outcome of CSM through multifactor correlation analysis. Study Type: Prospective. Population: Post‐surgery CSM patients; 102 total, 73 male (52.42 ± 10.60 years old) and 29 female (52.0 ± 11.45 years old). Field Strength/Sequence: 3.0 T/Turbo spin echo T1/T2‐weighted, T2*‐weighted multiecho gradient echo and dMRI. Assessment: Spinal cord function was evaluated using modified Japanese Orthopedic Association (mJOA) scoring at different time points: preoperative and 3, 6, and 12 months postoperative. Single‐factor correlation and t test analyses were conducted based on fractional anisotropy (FA), mean diffusivity, intracellular volume fraction, isotropic volume fraction, orientation division index, increased signal intensity, compression ratio, age, sex, symptom duration and operation method, and multicollinearity was calculated. The linear quantile mixed model (LQMM) and the linear mixed‐effects regression model (LMER) were used for multifactor correlation analysis using the combinations of the above variables. Statistical Tests: Distance correlation, Pearson's correlation, multiscale graph correlation and t tests were used for the single‐factor correlation analyses. The variance inflation factor (VIF) was used to calculate multicollinearity. LQMM and LMER were used for multifactor correlation analyses. P < 0.05 was considered statistically significant. Results: The single‐factor correlation between all variables and the postoperative mJOA score was weak (all r < 0.3). The linear relationship was stronger than the nonlinear relationship, and there was no significant multicollinearity (VIF = 1.10–1.94). FA values in the LQMM and LMER models had a significant positive correlation with the mJOA score (r = 5.27–6.04), which was stronger than the other variables. Data Conclusion: The FA value based on dMRI significantly positively correlated with CSM patient postoperative outcomes, helping to predict the surgical outcome and formulate a treatment plan before surgery. Evidence Level: 1 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Background: Early cervical spondylotic myelopathy (CSM) is challenging to diagnose and easily missed. Diffusion MRI (dMRI) has the potential to identify early CSM. Methods: Using diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI), a 1:1 matched case-control study was conducted to evaluate the potential of dMRI in identifying early CSM and assessing uncompressed segments of CSM patients. CSM patients and volunteers were matched by age and spinal location. The differences in dMRI parameters between groups were assessed by the paired t-test, the multicollinearity of the dMRI parameters was evaluated by the variance inflation factor (VIF), and the value of dMRI parameters in distinguishing controls from CSM patients was determined by logistic regression. The univariate t-test was used to analyse differences between CSM patients and volunteers in adjacent uncompressed areas. Results: In total, 56 CSM patients and 56 control volunteers were included. Paired t-tests revealed significant differences in nine dMRI parameters between groups. Multicollinearity calculated through VIF and combined with logistic regression showed that the orientation division index (ODI) was significantly positively correlated (r = 2.12, p = 0.035), and the anisotropic water fraction (AWF) was significantly negatively correlated (r = −0.98, p = 0.015). The fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (ISOVF), ODI, and AWF were significantly different in the upper and lower uncompressed areas at all ages. Conclusion: dMRI can noninvasively identify early CSM patients and potentially identify the extent of CSM lesions involving the cervical spinal cord. Critical relevance statement: Diffusion MRI (dMRI) can identify early cervical spondylotic myelopathy (CSM) and has the potential to help determine the extent of CSM involvement. The application of dMRI can help screen for early CSM and develop clinical surgical and rehabilitation treatment plans. Key points: • Diffusion MRI can differentiate between normal and early-stage cervical spondylotic myelopathy patients. • Diffusion MRI has the ability to identify the extent of spinal cord involvement in cervical spondylotic myelopathy. • Diffusion MRI enables the early screening of cervical spondylotic myelopathy and helps guide clinical treatment. [ABSTRACT FROM AUTHOR]

Purpose: MR arthrography (MRA) is the most accurate method for preoperatively diagnosing superior labrum anterior–posterior (SLAP) lesions, but diagnostic results can vary considerably due to factors such as experience. In this study, deep learning was used to facilitate the preliminary identification of SLAP lesions and compared with radiologists of different seniority. Methods: MRA data from 636 patients were retrospectively collected, and all patients were classified as having/not having SLAP lesions according to shoulder arthroscopy. The SLAP-Net model was built and tested on 514 patients (dataset 1) and independently tested on data from two other MRI devices (122 patients, dataset 2). Manual diagnosis was performed by three radiologists with different seniority levels and compared with SLAP-Net outputs. Model performance was evaluated by the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), etc. McNemar's test was used to compare performance among models and between radiologists' models. The intraclass correlation coefficient (ICC) was used to assess the radiologists' reliability. p < 0.05 was considered statistically significant. Results: SLAP-Net had AUC = 0.98 and accuracy = 0.96 for classification in dataset 1 and AUC = 0.92 and accuracy = 0.85 in dataset 2. In dataset 1, SLAP-Net had diagnostic performance similar to that of senior radiologists (p = 0.055) but higher than that of early- and mid-career radiologists (p = 0.025 and 0.011). In dataset 2, SLAP-Net had similar diagnostic performance to radiologists of all three seniority levels (p = 0.468, 0.289, and 0.495, respectively). Conclusions: Deep learning can be used to identify SLAP lesions upon initial MR arthrography examination. SLAP-Net performs comparably to senior radiologists. [ABSTRACT FROM AUTHOR]

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The flow of liquid metal over a backward-facing step (BFS) exhibits unique flow characteristics due to the influence of strong magnetic field. In this study, direct numerical simulation of the BFS flow under a strong magnetic field is conducted based on a quasi-two-dimensional model (with a large interaction number, N ≫ 1 , and a large Hartmann number, H a ≫ 1). The Reynolds number (Re), Hartmann number (Ha), and expansion ratio (ER) are investigated within the ranges of [ 100 − 80 000 ] , [ 100 − 40 000 ] , and [ 1.67 − 5 ] , respectively. Three typical flow regimes are defined based on the evolution of the free shear layer vortices and the separation state of the boundary layer. Furthermore, a comprehensive flow regime map is presented for different ER, revealing a positive correlation between the critical Reynolds number (R e c 1 ) and Ha at the onset of instability. Specifically, R e c 1 is proportional to H a 0.5 , H a 0.54 , and H a 0.56 for ER = 5, 2.5, and 1.67, respectively. Moreover, the maximum relative thickness of the free shear layer at H a c 1 appears in the range of approximately 0.7 – 0.78 L r for ER = 5, while for ER = 2.5, it appears in the range of approximately 0.80 – 0.85 L r , indicating that the instability position of the free shear layer occurs earlier for large ER. Our numerical investigation also demonstrates that an increase in the transverse magnetic field compresses the free shear layer and delays the process of vortex pairing, thereby suppressing the oscillatory behavior of the shear layer. [ABSTRACT FROM AUTHOR]

Distributed generation and demand-side management are expected to play a more prominent role in future power systems. However, the increased number of generations and load demands pose new challenges to optimal energy management in a microgrid. In this paper, an economic dispatch model for microgrids considering Traditional Generators (TGs), energy storage units, wind turbines (WTs), and flexible loads is established. To tackle the Economic Dispatch Problem (EDP) over directed communication networks, a fully distributed algorithm developed by leveraging a two-step state information exchange mechanism is proposed. In addition, by employing a fixed stepsize, the proposed algorithm demonstrates rapid convergence. Furthermore, our algorithm is well-suited for nonquadratic convex cost functions. Subsequently, we extend our algorithm to address imperfect communication scenarios. Even in the presence of arbitrarily large yet bounded time delays, our algorithm exhibits robustness. Finally, several numerical examples are given to verify the correctness and effectiveness of the developed results. [ABSTRACT FROM AUTHOR]

Purposes: To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT). Materials and methods: Two VB-Net-based DL models for cervical vertebra segmentation and key-point detection were developed. Four-points and line-fitting methods were used to calculate the sagittal Cobb angle automatically. The average value of the sagittal Cobb angle was manually measured by two doctors as the reference standard. The percentage of correct key points (PCK), matched samples t test, intraclass correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), and Bland‒Altman plots were used to evaluate the performance of the DL model and the robustness and generalization of the model on the external test set. Results: A total of 991 patients were included in the internal data set, and 112 patients were included in the external data set. The PCK of the DL model ranged from 78 to 100% in the test set. The four-points method, line-fitting method, and reference standard measured sagittal Cobb angles were − 1.10 ± 18.29°, 0.30 ± 13.36°, and 0.50 ± 12.83° in the internal test set and 4.55 ± 20.01°, 3.66 ± 18.55°, and 1.83 ± 12.02° in the external test set, respectively. The sagittal Cobb angle calculated by the four-points method and the line-fitting method maintained high consistency with the reference standard (internal test set: ICC = 0.75 and 0.97; r = 0.64 and 0.94; MAE = 5.42° and 3.23°, respectively; external test set: ICC = 0.74 and 0.80, r = 0.66 and 0.974, MAE = 5.25° and 4.68°, respectively). Conclusions: The DL model can accurately measure the sagittal Cobb angle of the cervical spine on CT. The line-fitting method shows a higher consistency with the doctors and a minor average absolute error. [ABSTRACT FROM AUTHOR]

Pulmonary polymorphic carcinoma (PPC) is a rare and poorly differentiated form of non‐small cell lung cancer (NSCLC), accounting for just approximately 0.1% to 0.4% of all NSCLC cases. Historically, the conventional treatments for PPC have been linked to a grim prognosis. However, with the advent of immune checkpoint inhibitors (ICIs), including PD‐1 inhibitors, for the management of NSCLC, our center has witnessed encouraging outcomes in two PPC patients who underwent PD‐1 inhibitor therapy. The first patient was a 70‐year‐old male who initially came to our attention after the discovery of a lung mass during a routine physical examination. A lung biopsy confirmed the diagnosis of PPC, and further complications included brain metastasis. Surgical intervention was conducted for the brain metastases, while PD‐1 inhibitor therapy was employed for the lung tumors. The second patient was a 60‐year‐old male who was admitted with a history of persistent coughing and hemoptysis, which led to the diagnosis of a left lung tumor. Subsequent postoperative pathology revealed pulmonary adenocarcinoma coexisting with PPC. However, 2 months later, distant metastases were detected during a follow‐up examination. The patient encountered difficulty in tolerating the adverse effects of chemotherapy, prompting the initiation of PD‐1 inhibitor treatment. Notably, both patients underwent one cycle of PD‐1 inhibitor therapy without encountering significant adverse reactions, and their responses proved to be promising during re‐examinations. These findings suggest that surgery combined with immunotherapy PD‐1 inhibitor therapy may represent an effective approach for the treatment of PPC. [ABSTRACT FROM AUTHOR]

The investigation of magnetohydrodynamic (MHD) flow has been carried out through a two-dimensional approximation known as the PSM model within the FlowCube platform (Pothérat and Klein, 2014). The platform itself is a cubic vessel featuring alternating positive and negative electrodes that are uniformly located on its bottom. The flow is driven by the Lorentz force, which is induced by injected currents and imposed magnetic fields. The energy spectrum of two-dimensional flow agrees well with our three-dimensional one, confirming the two-dimensionality of MHD turbulence under strong magnetic fields. For quasi-two-dimensional (Q2D) flow in FlowCube, the energy spectrum displays ∼ k−3 and ∼ k2 slopes, corresponding to direct enstrophy cascade from forcing scale li to small scale and statistical equilibrium state of large scale (> li), where li is electrodes space in FlowCube. Moreover, a comprehensive study has been conducted on the transition from a laminar state to a turbulent state, revealing various flow states, including periodic, quasi-periodic, and chaotic states. Furthermore, under the same driving force, the periodic network of alternating vortices, which are typical structures in the FlowCube configuration, display different flow states. This observation may correspond to a large-scale intermittency in MHD flows due to the complex interaction between the driving force and dissipation. [ABSTRACT FROM AUTHOR]

Background: Diagnosing anterior talofibular ligament (ATFL) injuries differs among radiologists. Further assessment of ATFL tears is valuable for clinical decision‐making. Purpose: To establish a deep learning method for classifying ATFL injuries based on magnetic resonance imaging (MRI). Study Type: Retrospective. Population: One thousand seventy‐three patients from a single center with ankle MRI within 1 month of reference standard arthroscopy (in‐group dataset), were divided into training, validation, and test sets in a ratio of 8:1:1. Additionally, 167 patients from another center were used as an independent out‐group dataset. Field Strength/Sequence: Fat‐saturation proton density‐weighted fast spin–echo sequence at 1.5/3.0 T. Assessment: Patients were divided into normal, strain and degeneration, partial tear and complete tear groups (groups 0–3). The complete tear group was divided into five sub‐groups by location and the potential avulsion fracture (groups 3.1–3.5). All images were input into AlexNet, VGG11, Small‐Sample‐Attention Net (SSA‐Net), and SSA‐Net + Weight Loss for classification. The results were compared with four radiologists with 5–30 years of experience. Statistical Tests: Model performance was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and so on. McNemar's test was used to compare performance among the different models, and between the radiologists and models. The intraclass correlation coefficient (ICC) was used to assess the reliability of the radiologists. P < 0.05 was considered statistically significant. Results: The average AUC of AlexNet, VGG11, SAA‐Net, and SSA‐Net + Weight Loss was 0.95, 0.99, 0.99, 0.99 in groups 0–3 and 0.96, 0.99, 0.99, 0.99 in groups 3.1–3.5. The effect of SSA‐Net + Weight Loss was similar to SSA‐Net but better than AlexNet and VGG11. In the out‐group test set, the AUC of SSA‐Net + Weight Loss ranged from 0.89 to 0.99. The ICC of radiologists was 0.97–1.00. The effect of SSA‐Net + Weight Loss was better than each radiologist in the in‐group and out‐group test sets. Data Conclusion: Deep learning has potential to be used for classifying ATFL injuries. SSA‐Net + Weight Loss has a better diagnostic effect than radiologists with different experience levels. Level of Evidence: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

With the increasing deployment of advanced sensing and measurement devices, the modern distribution system is evolved into a cyber-physical power distribution system (CPPDS). Due to the extensive application of information and communication technology, CPPDS is prevalently exposed to a wide range of cybersecurity threats. In this paper, a novel security-oriented cyber-physical risk assessment method for CPPDS is proposed. Based on the information model composed of logical nodes, an attack graph of privilege promotion by exploiting the cyber vulnerabilities is constructed. The physical consequence caused by cyberattack is analyzed in detail. By using the Markov decision process (MDP) theory, the cyber-physical risk index (CPRI) is calculated. Furthermore, considering the allocation of the finite defense resource, the modified MDP approach with an attack–defense game is presented. The effectiveness of the proposed method is demonstrated with case studies on a four-feeder IEEE-RTBS test system. [ABSTRACT FROM AUTHOR]

Premise: The ever‐increasing temperatures of the Anthropocene may facilitate plant invasions. To date, studies of temperature effects on alien plants have mainly focused on aboveground plant traits but ignored belowground traits, which may confound predictions of plant invasion risks. Methods: The temperature effects on the root growth dynamics of two alien shrubs, invasive Mimosa sepiaria and naturalized Corchorus capsulari, were studied using a 3D, transparent growth system under five temperature treatments (day/night: 18°C/13°C to 34°C/29°C) that cover the present and future warming temperature scenarios in China. We measured root depth and width growth in response to temperature treatments over 84 days. We also investigated intra‐ and interspecific competition of paired plants of the two species grown together at the five temperatures. Results: Shoot growth of M. sepiaria and C. capsularis was optimal at the mid‐range temperature. Root growth, however, was faster at the highest temperature (34°C/29°C) for M. sepiaria, but decreased for C. capsularis as temperatures increased. Root depth growth was more sensitive than root width for both species during neighbor competition. Compared to C. capsularis, M. sepiaria had relatively greater advantage during intra‐ and interspecific competition with increasing temperature, possibly because of its better root growth at high temperatures. Conclusions: These results suggest that temperature increases can improve the performance of some alien plants by facilitating width and depth growth of their roots. This enhancement requires serious attention when managing and predicting invasion risk. [ABSTRACT FROM AUTHOR]

The six species of lungfish possess both lungs and gills and are the closest extant relatives of tetrapods. Here, we report a single-cell transcriptome atlas of the West African lungfish (Protopterus annectens). This species manifests the most extreme form of terrestrialization, a life history strategy to survive dry periods that can last for years, characterized by dormancy and reversible adaptive changes of the gills and lungs. Our atlas highlights the cell type diversity of the West African lungfish, including gene expression consistent with phenotype changes of terrestrialization. Comparison with terrestrial tetrapods and ray-finned fishes reveals broad homology between the swim bladder and lung cell types as well as shared and idiosyncratic changes of the external gills of the West African lungfish and the internal gills of Atlantic salmon. The single-cell atlas presented here provides a valuable resource for further exploration of the respiratory system evolution in vertebrates and the diversity of lungfish terrestrialization. The water-to-land transition of vertebrates is a landmark event in evolutionary history. This work utilizes single-cell RNA sequencing to explore the adaptations in African lungfish lungs and gills to terrestrialization. [ABSTRACT FROM AUTHOR]