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Oceanic water quality monitoring is essential for environmental protection, resource management, and ecosystem vitality. Optical remote sensing from space plays a pivotal role in global surveillance of oceanic water quality. However, the spatial resolution of current ocean color data products falls short of scrutinizing intricate small-scale marine features. This study introduces a hybrid model that fuses MODIS (Moderate Resolution lmaging Spectroradiometer) ocean color products with Sentinel-2 ‘s remote sensing reflectance data to generate high-resolution ocean color imagery, specifically investigating the diffuse attenuation coefficient at a wavelength of 490 nm (Kd490). To address the intricacies of coastal environments, we propose two complementary strategies to improve the accuracy of inversion. The first strategy leverages MODIS ocean color products alongside a geographic segmentation model to perform distinct inversions for separate marine zones, enhancing spatial resolution and specificity in coastal regions. The second strategy bolsters model interpretability during training by integrating predictions from conventional physical models into a Random Forest-based Regression Ensemble (RFRE) model. This study focuses on the coastal regions surrounding the Beibu Gulf, near Hainan Island in China. Our findings exhibit a strong concordance with MODIS products, achieving a monthly average coefficient of determination (R²) of 0.90, peaking at 0.97, and sustaining a monthly average root-mean-square error (RMSE) of less than 0.02. These results substantiate the model’s efficacy. Moreover, the annual trend analysis and localized assessment of the reconstructed Kd490 offer nuanced insights that surpass MODIS data, establishing a robust foundation for high-resolution water quality monitoring in coastal zones.

Abstract Background Currently, creating more effector T cells and augmenting their functions is a focal point in pancreatic ductal adenocarcinoma (PDAC) treatment research. T cell immunoglobulin domain and mucin domain molecule 4 (TIM‐4), known for promoting cancer progression in various malignancies, is implicated in the suppressive immune microenvironment of tumors. Analyzing of the role of TIM‐4 in the immune regulation of PDAC can offer novel insights for immune therapy. Methods We analyzed the TIM‐4 expression in tumor specimens from PDAC patients. Meanwhile, multiple fluorescent immunohistochemical staining was used to study the distribution characteristics of TIM‐4, and through tissue microarrays, we explored its correlation with patient prognosis. The influence of TIM‐4 overexpression on cell function was analyzed using RNA‐seq. Flow cytometry and ELISA were used for verification. Finally, the relationship between TIM‐4 and T lymphocytes was analyzed by tissue microarray, and the impacts of TIM‐4 on T cell subsets were observed by cell coculture technology and a mouse pancreatic cancer in situ model. Results In PDAC, TIM‐4 is mainly expressed in tumor cells and negatively correlated with patient prognosis. TIM‐4 influences the differentiation of Treg by inhibiting IL‐6 secretion in pancreatic cancer cells and facilitates the proliferation of pancreatic cancer in mice. Additionally, the mechanism may be through the CD8+ effector T cells (CD8+Tc). Conclusion TIM‐4 has the potential to be an immunotherapeutic target or to improve the efficacy of chemotherapy for PDAC.

Elevated homocysteine (Hcy) levels, referred to hyperhomocysteinemia, are associated with an increased risk of several neurological disorders. Ferroptosis and inflammation play a vital role in Hcy-induced neuronal dysfunction. Amentoflavone (AMF), an active natural biflavone compound, exhibits antioxidative, anti-inflammatory, and neuroprotective activities. This study aimed to explore the potential effects of AMF on Hcy-induced neuronal injury, with a particular focus on the underlying mechanisms involving ferroptosis and inflammation. We assessed neuronal damage in HT22 cells by measuring cell viability, lactate dehydrogenase (LDH) release, and proliferation rates. Additionally, we evaluated oxidative stress markers including the levels of reactive oxygen species (ROS), MitoSOX, mitochondrial membrane potential (MMP), malondialdehyde (MDA), and glutathione (GSH). Iron metabolism and ferroptosis-related gene expressions (Ptgs2, Tfr1, and Fth1) were quantified. TheSLC7A11/GPX4 axis was also detected. Our results showed that AMF treatment dramatically mitigated Hcy-induced neuronal injury by increasing cell viability, decreasing LDH release, and promoting cell proliferation. AMF treatment also reduced Hcy-induced oxidative stress and lipid peroxidation, as evidenced by reduced ROS, MitoSOX, MMP, and MDA levels, along with an increased GSH content in HT22 cells. In addition, AMF treatment reduced iron content and ferroptosis-related gene mRNA levels. However, Erastin, a ferroptosis inducer, blocked these neuroprotective effects of AMF. Ferroptosis inhibitor Ferrostatin-1 also attenuated Hcy-induced ferroptosis. Moreover, both AMF and Ferrostatin-1 effectively mitigated Hcy-induced inflammation, which was again antagonized by Erastin. Mechanistically, AMF treatment enhanced SLC7A11/GPX4 axis in Hcy-treated HT22 cells. In conclusion, these findings suggest that AMF possesses neuroprotection against Hcy-induced injury primarily by inhibiting ferroptosis-mediated inflammation, partly through the activation of SLC7A11/GPX4 axis.

Methane, the second most prevalent greenhouse gas globally, is characterized by its high global warming potential and short atmospheric lifetime. It is primarily emitted from sectors such as coal mining, oil and gas production, agriculture, livestock rearing, and waste management. Active, prudent, and orderly control of methane emissions offers a multifaceted benefit: it mitigates global warming, enhances the economic value through the resource utilization of energy, provides environmental advantages by control of pollutants, and improves safety by reducing production accidents. Methane is also the principal greenhouse gas emitted in coal mine operations. Currently, monitoring and researching the distribution of methane emissions and their concentration within the three-dimensional space of coal mines have become a key focus in China’s methane emission control efforts, which is of significant importance for coal mining enterprises to align with national strategies. A study conducted on a high-gas mine in Shanxi and a low-gas mine in Shaanxi for methane emission monitoring and accounting utilized fixed monitoring, manual monitoring, drone monitoring, and post-mining activity monitoring to investigate the distribution of methane in high and low gas mines in China and to analyze their methane emissions. The results indicate that: ① The methane emission volumes for a certain high-gas mine in Shanxi and a low-gas mine in Shaanxi published by the U.S.-based Climate TRACE website for the year 2022 show a significant discrepancy from the results calculated using the ground measurement method, with the published annual methane emission volume for a certain low-gas mine being 10.92 times that of the ground measurement calculation. The top-down monitoring and accounting methods have been found to be insufficiently accurate and unable to provide reliable data support for carbon trading. ② In the monitoring and accounting of post-mining activities, the use of default values recommended by the IPCC results in methane emissions that are 3 to 5 times higher than those calculated using the ground measurement method, and there is a significant variation in the emissions calculated based on coal samples of different sizes and exposure durations. There is an urgent need for a unified and precise testing standard to ensure fair and accurate data for future carbon market transactions.

Abstract Background Metabolic dysfunction-associated fatty liver disease (MAFLD) has been proposed as a new term for diagnosing fatty liver disease, which is considered to be a multi-systemic disease with multiple extrahepatic manifestations, including sarcopenia. The link between sarcopenia and MAFLD remains uncertain, especially among young and middle-aged adults. Thus, we examined the relationship between MAFLD and sarcopenia in young and middle-aged individuals in this study. Methods A total of 2214 individuals with laboratory tests, dual-energy X-ray absorptiometry and ultrasound transient elastography from NHANES 2017–2018 were selected for this study. MAFLD was diagnosed as fatty liver disease with any one of the situations: overweight/obesity, diabetes mellitus, presence of metabolic dysregulation. Sarcopenia was defined by appendicular lean mass adjusted for body mass index (BMI). Multivariable logistic regression and restricted cubic spline (RCS) model were applied to explore the relationship between MAFLD and sarcopenia, and the mediation analyses were also conducted. Moreover, subgroup analyses stratified by BMI and lifestyles were done. Results The prevalence of MAFLD was 47.85%, and nearly 8.05% of participants had sarcopenia. The prevalence of sarcopenia was higher in participants with MAFLD (12.75%; 95% CI 10.18–15.31%) than in the non-MAFLD (3.73%; 95% CI 2.16–5.31%). MAFLD was significantly positively associated with sarcopenia after adjustments [OR = 2.87 (95% CI: 1.62–5.09)]. Moreover, significant positive associations were observed between liver fibrosis and sarcopenia prevalence in MAFLD patients (OR = 2.16; 95% CI 1.13–4.15). The RCS curve revealed that MAFLD was linearly associated with sarcopenia. The relationship between the MAFLD and sarcopenia were mediated by C-reactive protein (mediation proportion: 15.9%) and high-density lipoprotein cholesterol (mediation proportion: 18.9%). Subgroup analyses confirmed the association between MAFLD and sarcopenia differed in different lifestyle groups. Conclusions Both MAFLD prevalence and severity was significantly associated with sarcopenia. Thus, clinicians should advise comorbidity screening and lifestyle changes to young and middle-aged patients.

As a type of novel material, open-cell copper (Cu) foam has been widely applied in electrode production of batteries, due to its functional and structural properties. The use of ultrasonic welding technique to join Cu foam and aluminium (Al) plate has been proven to be feasible, but quality evaluation of the joints remains a blank to be filled in. In this study, orthogonal test design is used to investigate the effects of welding pressure, welding amplitude, welding energy and the interaction among the three parameters on the shear-tensile strength of ultrasonically welded joints of Cu foam/Al plate, and it is influenced by the three parameters in the following order: welding pressure > welding energy > welding amplitude. Based on the results of the orthogonal test, a Cu foam/Al plate ultrasonic welding joint evaluation system is established, based on which, qualitative and quantitative analysis on the quality of the joints classified the joints into three grades. The optimal parameter combination of the welding joint of Cu foam/Al plate is obtained as follows: 0.5 MPa, 65 %, 130 J, under which the porous structure of the joint is complete while its tensile strength is equal to or even better than the base material.

Abstract The primary methods of assessing the reliability of distribution networks comprise analytic and simulation methods. However, both approaches require the identification and computation of network topology, which precludes their expression in explicit, continuous functions, consequently impeding the incorporation of reliability constraints into planning and operational optimization models. To tackle this restriction, the present work puts forth a novel linear‐programming‐based reliability assessment method that is mathematically formulated, considering distribution automation (DA) and distributed generations (DGs), consisting of both conventional and renewable energy sources. In this paper, the clustering method and the scenario‐based method are used to model DGs. Next, a mixed integer linear programming (MILP) model, considering the DA and DGs with the System Average Interruption Duration Index (SAIDI) as the optimization objective, is proposed. Finally, the feasibility and effectiveness of the proposed method are verified in a 37‐node distribution network system.

Background Focused ultrasound ablation surgery (FUAS) has been widely employed to treat patients with uterine fibroid (UF). This study aimed to estimate myometrial stiffness changes in patients who received FUAS for UFs or myomectomy (ME) and compare the recovery of surrounding myometrium between FUAS and ME groups. Our results may provide more evidence for guiding the proper conception timing in patients with UF.Methods This study enrolled 173 patients from May 2022 to August 2023. Shear wave elastography (SWE) was used to dynamically monitor myometrial elasticity changes in patients before and after surgery. Moreover, our study monitored and analyzed the stiffness changes in the targeted fibroid after FUAS, as well as in the myometrium around after FUAS or ME.Results The stiffness of the myometrium around the resected fibroid was significantly higher than at the preoperative level until 6 months. Conversely, the stiffness of the surrounding myometrium was only temporarily increased 1 day after FUAS. The comparison between FUAS and ME groups regarding the stiffness of the surrounding myometrium showed that nonsignificant differences were detected between the two groups before the treatment. The stiffness of the surrounding myometrium in the ME group was statistically significantly higher than that of the FUAS group 1 day as well as 1, 3, and 6 months after the treatment, respectively.Conclusion The FUAS had less impact on the surrounding myometrium than the ME, which may be more conducive to the recovery of myometrial elasticity in patients with UF.

The modified coconut shell biochars (MCSBCs) were fabricated and their adsorptions for Pb(II) were evaluated, in which waste coconut shell was used as the raw material, both ZnCl2 and KMnO4 were applied as the inorganic modifiers. FT-IR spectra, TGA, SEM and BET techniques were utilized to characterize their properties. It was spotted that the thermal stability of UCSBC could arrive at 500 °C. The BET specific surface areas of both Zn- and Mn-modified MCSBCs (485.137, 476.734 m2/g) were highly decreased as compared with that of UCSBC (3528.78 m2/g). In contrast, the average pore diameters of both Zn- and Mn-modified MCSBCs (3.295, 3.803 nm) were smaller than that of UCSBC (3.814 nm). These findings reveal that the modification of CSBC didn't change its pore size. Their adsorptions for Pb(II) were performed and some controlling factors involving pH, contact time, starting concentration and temperature were explored. Moreover, the experiment data were fitted via linear and non-linear techniques. It was found that the Langmuir maximal adsorption amounts of un-modified coconut shell biochar (UCSBC), Zn-modified and Mn-modified MCSBCs for Pb(II) could reach 31.653, 86.547 and 93.666 mg/g, respectively. Two-parameter kinetic models exposed that Pb(II) adsorption on UCSBC, Zn-modified and Mn-modified MCSBCs obeyed both the Lagergren first-order (non-linear R2 = 0.990, 0.954, 0.953, respectively) and Avrami fractional-order (non-linear R2 = 0.989, 0.946, 0.945, respectively) kinetic models. Two-parameter and three-parameter isotherm models verified that Pb(II) adsorption on UCSBC, Zn-modified and Mn-modified MCSBCs followed the Langmuir (non-linear R2 = 0.992, 0.997, 0.993, respectively) as well as Sips (non-linear R2 = 0.992, 0.997, 0.992, respectively) isotherm models. The computation of thermodynamic parameters evidenced that the modification of UCSBC via KMnO4 and ZnCl2 can effectively rise its adsorption for Pb(II), exhibiting promising applications in the handling of metal-bearing water.

Abstract Sainfoin (Onobrychis viciifolia), which belongs to subfamily Papilionoideae of Leguminosae, is a vital perennial forage known as “holy hay” due to its high contents of crude proteins and proanthocyanidins (PAs, also called condensed tannins) that have various pharmacological properties in animal feed, such as alleviating rumen tympanic disease in ruminants. In this study, we select an autotetraploid common sainfoin (2n = 4x = 28) and report its high-quality chromosome-level genome assembly with 28 pseudochromosomes and four haplotypes (~1950.14 Mb, contig N50 = 10.91 Mb). The copy numbers of genes involved in PA biosynthesis in sainfoin are significantly greater than those in four selected Fabales species, namely, autotetraploid Medicago sativa and three other diploid species, Lotus japonicus, Medicago truncatula, and Glycine max. Furthermore, gene expansion is confirmed to be the key contributor to the increased expression of these genes and subsequent PA enhancement in sainfoin. Transcriptomic analyses reveal that the expression of genes involved in the PA biosynthesis pathway is significantly increased in the lines with high PA content compared to the lines with medium and low PA content. The sainfoin genome assembly will improve our understanding of leguminous genome evolution and biosynthesis of secondary metabolites in sainfoin.

Cross-view geo-localization via scene matching is crucial in unmanned aerial vehicle (UAV) systems in global navigation satellite system denial environment. However, images in the same scene may undergo geometric distortion and occlusion due to differences in capture viewpoint, time, and platform. The existing methods mainly extract consistent features between images by CNNs, while ignoring the semantic distribution and structural information of the objects. Aiming at addressing this issue, we introduce a semantic-aware graph convolutional network (SeGCN). To improve consistent representation of object features from different viewpoints, potential semantic features are inferred via cross-attention of image context. Then, for exploring the structural information of objects, SeGCN performs graph convolution on graph structures constructed from the same semantic features. Finally, the composite features generated by SeGCN and backbone are utilized for scene matching. Comprehensive experiments conducted on the University-1652 and SUES-200 benchmarks establish that the proposed approach attains the highest levels of accuracy in both localization and navigation tasks. Furthermore, we conducted localization simulation experiments on our real UAV datasets, confirming the effectiveness of SeGCN in real world application scenarios.

Abstract Objectives Dental caries is one of the most prevalent oral diseases and causes of tooth loss. Cross-sectional studies observed epidemiological associations between dental caries and brain degeneration disorders, while it is unknown whether dental caries causally affect the cerebral structures. This study tested whether genetically proxied DMFS (the sum of Decayed, Missing, and Filled tooth Surfaces) causally impacts the brain cortical structure using Mendelian randomization (MR). Methods The summary-level GWAS meta-analysis data from the GLIDE consortium were used for DMFS, including 26,792 participants. ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) consortium GWAS summary data of 51,665 patients were used for brain structure. This study estimated the causal effects of DMFS on the surface area (SA) and thickness (TH) of the global cortex and functional cortical regions accessed by magnetic resonance imaging (MRI). Inverse-variance weighted (IVW) was used as the primary estimate, the MR pleiotropy residual sum and outlier (MR-PRESSO), the MR-Egger intercept test, and leave-one-out analyses were used to examine the potential horizontal pleiotropy. Results Genetically proxied DMFS decreases the TH of the banks of the superior temporal sulcus (BANSSTS) with or without global weighted (weighted, β = − 0.0277 mm, 95% CI: − 0.0470 mm to − 0.0085 mm, P = 0.0047; unweighted, β = − 0.0311 mm, 95% CI: − 0.0609 mm to − 0.0012 mm, P = 0.0412). The causal associations were robust in various sensitivity analyses. Conclusions Dental caries causally decrease the cerebral cortical thickness of the BANKSSTS, a cerebral cortical region crucial for language-related functions, and is the most affected brain region in Alzheimer’s disease. This investigation provides the first evidence that dental caries causally affects brain structure, proving the existence of teeth-brain axes. This study also suggested that clinicians should highlight the causal effects of dental caries on brain disorders during the diagnosis and treatments, the cortical thickness of BANKSSTS is a promising diagnostic measurement for dental caries-related brain degeneration.

Abstract Background Migraine is a common disabling neurological disorder with severe physical and psychological damage, but there is a lack of convenient and effective non-invasive early prediction methods. This study aimed to develop a new series of non-invasive prediction models for migraine with external validation. Methods A total of 188 and 94 subjects were included in the training and validation sets, respectively. A standardized professional questionnaire was used to collect the subjects' 9-item traditional Chinese medicine constitution (TCMC) scores, Pittsburgh Sleep Quality Index (PSQI) score, Zung's Self-rating Anxiety Scale and Self-rating Depression Scale scores. Logistic regression was used to analyze the risk predictors of migraine, and a series of prediction models for migraine were developed. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the discrimination and calibration of the models. The predictive performance of the models were further validated using external datasets and subgroup analyses were conducted. Results PSQI score and Qi-depression score were significantly and positively associated with the risk of migraine, with the area of the ROC curves (AUCs) predicting migraine of 0.83 (95% CI:0.77–0.89) and 0.76 (95% CI:0.68–0.84), respectively. Eight non-invasive predictive models for migraine containing one to eight variables were developed using logistic regression, with AUCs ranging from 0.83 (95% CI: 0.77–0.89) to 0.92 (95% CI: 0.89–0.96) for the training set and from 0.76 (95% CI: 0.66–0.85) to 0.83 (95% CI: 0.75–0.91) for the validation set. Subgroup analyses showed that the AUCs of the eight prediction models for predicting migraine in the training and validation sets of different gender and age subgroups ranged from 0.80 (95% CI: 0.63–0.97) to 0.95 (95% CI: 0.91–1.00) and 0.73 (95% CI: 0.64–0.84) to 0.93 (95% CI: 0.82–1.00), respectively. Conclusions This study developed and validated a series of convenient and novel non-invasive prediction models for migraine, which have good predictive ability for migraine in Chinese adults of different genders and ages. It is of great significance for the early prevention, screening, and diagnosis of migraine.

In the context of energy shortages and the development of new energy sources, tidal current energy has emerged as a promising alternative. It is typically harnessed by deploying arrays of multiple water turbines offshore. Vertical axis water turbines (VAWTs), as key units in these arrays, have wake effects that influence array spacing and energy efficiency. However, existing studies on wake velocity distribution models for VAWTs are limited in number, accuracy, and consideration of influencing factors. A precise theoretical model (Lam’s formula) for wake lateral velocity can better predict wake decay, aiding in the optimization of tidal current energy array designs. Turbulence in the ocean, serving as a medium for energy exchange between high-energy and low-energy water flows, significantly impacts the wake recovery of water turbines. To simplify the problem, this study uses software ANSYS Fluent 2020 R2 for two-dimensional simulations of VAWT wake decay under different turbulence intensities, confirming the critical role of turbulence intensity in wake velocity decay. Based on the obtained data, a new mathematical approach was employed to incorporate turbulence intensity into Lam’s wake formula for VAWTs, improving its predictive accuracy with a minimum error of 1%, and refining some parameter calculations. The results show that this model effectively reflects the impact of turbulence on VAWT wake recovery and can be used to predict wake decay under various turbulence conditions, providing a theoretical basis for VAWT design, optimization, and array layout.

Purpose: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. Methods: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody. Linear regression was used to predict the peak of knee medial force with the peaks of the surrogate parameters and walking speed. Results: There was no significant difference in walking speed among these three tasks. The peak knee adduction moment (pKAM) was a significant predictor of the peak knee medial force (pKMF) for SW, ST, and CT (p < 0.001), while the peak knee flexion moment (pKFM) was only a significant predictor of the pKMF for SW (p = 0.034). The statistical analysis showed that the pKMF increased, while the pKFM and the peak knee adduction angle (pKAA) decreased significantly during CT compared to those of SW and ST (p < 0.001). The correlation analysis indicated that the knee parameters during SW and ST were quite similar. Conclusions: This study investigated the relationship between knee medial force and some surrogate parameters during walking and turning. KAM was still the best surrogate parameter for SW, ST, and CT. It is necessary to consider the type of movement when comparing the surrogate predictors of knee medial force, as the prediction equations differ significantly among movement types.

Abstract Background Intranasal agents may be ideal for the treatment of migraine patients. Many new acute intranasal-specific therapies have been developed, but few of them have been directly compared. The aim of this network meta-analysis (NMA) was to compare the efficacy and safety of various intranasal agents for the treatment of acute migraine in adult patients. Methods The Cochrane Register of Controlled Trials, Embase, and PubMed were searched from inception to 15 August 2023. Randomized controlled trials (RCTs) using intranasal agents (no restrictions on dose, formulation, dosing regimen or timing of the first dose) to treat adult patients with acute migraine were included. The primary efficacy endpoint was pain freedom at 2 h, and the primary safety endpoint was adverse events (AEs). The analysis process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results Nineteen studies (21 RCTs, 9738 participants) were included. Compared to the placebo, 5 mg of zolmitriptan using a conventional liquid nasal spray device was the most effective for pain freedom at 2 h [odds ratio (OR): 4.67, 95% confidence interval (CI): 3.43 to 6.43] and 24 h (OR: 5.49, 95% CI: 3.58 to 8.42) among all the interventions. Butorphanol nasal spray 1 mg was the most effective (OR: 8.62, 95% CI: 1.11 to 66.92) for pain freedom at 1 h, but with low-quality evidence. DFN-02 presented the highest freedom from nausea (OR: 4.95, 95% CI: 1.29 to 19.01) and phonophobia (OR: 5.36, 95% CI: 1.67 to 17.22) at 2 h, albeit with lower odds of achieving complete pain freedom. ROX-828 showed the highest improvement in freedom from photophobia at 2 h (OR: 4.03, 95% CI: 1.66 to 9.81). Dihydroergotamine nasal spray was significantly associated with the highest risk of AEs (OR: 9.65, 95% CI: 4.39 to 21.22) and was not recommended for routine use. Zavegepant nasal spray demonstrated the lowest risk of AEs (OR: 2.04, 95% CI: 1.37 to 3.03). The results of sensitivity analyses for the primary endpoints (pain freedom at 2 h and AEs) were generally consistent with those of the base case model. Conclusions Compared with other new intranasal-specific therapies in treating migraine attacks, zolmitriptan nasal spray 5 mg was the most effective agent for pain freedom at 2 h. Zavegepant nasal spray 10 mg had the fewest adverse side effects.

The incidence of knee injuries during firefighter training is high, but there is a lack of research on predictive factors and risk assessment for such injuries. Biomechanical assessments can provide a better understanding of how the body’s load changes during exercise, which may alter the risk of injury. Ten firefighters were recruited for our study. Each participant completed the FMS test and the single- and double-leg jump tasks. Motion information was collected and musculoskeletal models of the participants were constructed using OpenSim 4.4 to obtain the joint angle and joint moment. The peak GRF and CoM-CoP angle were also calculated. The findings showed a significantly larger PKFM (p = 0.0195), VAFM (p = 0.0039), and peak AP GRF (p = 0.0039) during the single-leg jump. The opposite performance was observed for KFA (p = 0.0098) and MPA (p = 0.0273). A stepwise multiple linear regression analysis was used to explore the relationship between these parameters and both the FMS score and the times of injuries. The risk of injury is higher in the single-leg jump compared to the double-leg jump. The biomechanical parameters of these two jumps can be used to assess sports injuries and to provide methods and references for injury risk monitoring during firefighter occupational training.

Background and purpose: Anatomical labeling of the cerebral vasculature is a crucial topic in determining the morphological nature and characterizing the vital variations of vessels, yet precise labeling of the intracranial arteries is time-consuming and challenging, given anatomical structural variability and surging imaging data. We present a U-Net-based deep learning (DL) model to automatically label detailed anatomical segments in computed tomography angiography (CTA) for the first time. The trained DL algorithm was further tested on a clinically relevant set for the localization of intracranial aneurysms (IAs).Methods: 457 examinations with varying degrees of arterial stenosis were used to train, validate, and test the model, aiming to automatically label 42 segments of the intracranial arteries [e.g., 7 segments of the internal carotid artery (ICA)]. Evaluation metrics included Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD). Additionally, 96 examinations containing at least one IA were enrolled to assess the model’s potential in enhancing clinicians’ precision in IA localization. A total of 5 clinicians with different experience levels participated as readers in the clinical experiment and identified the precise location of IA without and with algorithm assistance, where there was a washout period of 14 days between two interpretations. The diagnostic accuracy, time, and mean interrater agreement (Fleiss’ Kappa) were calculated to assess the differences in clinical performance of clinicians.Results: The proposed model exhibited notable labeling performance on 42 segments that included 7 anatomical segments of ICA, with the mean DSC of 0.88, MSD of 0.82 mm and HD of 6.59 mm. Furthermore, the model demonstrated superior labeling performance in healthy subjects compared to patients with stenosis (DSC: 0.91 vs. 0.89, p < 0.05; HD: 4.75 vs. 6.19, p < 0.05). Concurrently, clinicians with model predictions achieved significant improvements when interpreting the precise location of IA. The clinicians’ mean accuracy increased by 0.04 (p = 0.003), mean time to diagnosis reduced by 9.76 s (p < 0.001), and mean interrater agreement (Fleiss’ Kappa) increased by 0.07 (p = 0.029).Conclusion: Our model stands proficient for labeling intracranial arteries using the largest CTA dataset. Crucially, it demonstrates clinical utility, helping prioritize the patients with high risks and ease clinical workload.

Here the authors report the first experimental observation of topological antichiral surface states by constructing a three-dimensional modified Haldane model in a magnetic Weyl photonic crystal.

Current CNN-based methods for infrared and visible image fusion are limited by the low discrimination of extracted structural features, the adoption of uniform loss functions, and the lack of inter-modal feature interaction, which make it difficult to obtain optimal fusion results. To alleviate the above problems, a framework for multimodal feature learning fusion using a cross-attention Transformer is proposed. To extract rich structural features at different scales, residual U-Nets with mixed receptive fields are adopted to capture salient object information at various granularities. Then, a hybrid attention fusion strategy is employed to integrate the complementing information from the input images. Finally, adaptive loss functions are designed to achieve optimal fusion results for different modal features. The fusion framework proposed in this study is thoroughly evaluated using the TNO, FLIR, and LLVIP datasets, encompassing diverse scenes and varying illumination conditions. In the comparative experiments, HATF achieved competitive results on three datasets, with EN, SD, MI, and SSIM metrics reaching the best performance on the TNO dataset, surpassing the second-best method by 2.3%, 18.8%, 4.2%, and 2.2%, respectively. These results validate the effectiveness of the proposed method in terms of both robustness and image fusion quality compared to several popular methods.

Calcium (Ca2+) is a versatile intracellular second messenger that regulates several signaling pathways involved in growth, development, stress tolerance, and immune response in plants. Autoinhibited Ca2+-ATPases (ACAs) play an important role in the regulation of cellular Ca2+ homeostasis. Here, we systematically analyzed the putative OsACA family members in rice, and according to the phylogenetic tree of OsACAs, OsACA9 was clustered into a separated branch in which its homologous gene in Arabidopsis thaliana was reported to be involved in defense response. When the OsACA9 gene was knocked out by CRISPR/Cas9, significant accumulation of reactive oxygen species (ROS) was detected in the mutant lines. Meanwhile, the OsACA9 knock out lines showed enhanced disease resistance to both rice bacterial blight (BB) and bacterial leaf streak (BLS). In addition, compared to the wild-type (WT), the mutant lines displayed an early leaf senescence phenotype, and the agronomy traits of their plant height, panicle length, and grain yield were significantly decreased. Transcriptome analysis by RNA-Seq showed that the differentially expressed genes (DEGs) between WT and the Osaca9 mutant were mainly enriched in basal immune pathways and antibacterial metabolite synthesis pathways. Among them, multiple genes related to rice disease resistance, receptor-like cytoplasmic kinases (RLCKs) and cell wall-associated kinases (WAKs) genes were upregulated. Our results suggest that the Ca2+-ATPase OsACA9 may trigger oxidative burst in response to various pathogens and synergically regulate disease resistance and leaf senescence in rice.

A microgrid is a promising small-scale power generation and distribution system. The selling prices of wind turbine equipment (WT), photovoltaic generation equipment (PV), and battery energy storage equipment (BES) have a significant impact on microgrid profits, which, in turn, affects the planning capacity of renewable energy. However, existing research has not yet conducted in-depth modeling and analysis for different kinds of energy generation electricity prices. This paper proposes an optimal capacity planning method for wind-photovoltaic-storage equipment, considering different energy selling incomes in microgrids. Stochastic characteristics of renewable energy (WT and PV), selling prices of different types of energy, and timing coupling characteristic are considered in the proposed model. In addition, the configuration capacities of WT, PV, and BES are modeled as discrete decision variables, according to the type of specific equipment. The comprehensive life cycle cost (LCC) is considered an objective function. It can be found that the proposed collaborative capacity planning model is a mathematical programming problem with complex nonlinear constraints and integer variables. To solve this problem, a cultural gray wolf optimization algorithm (CGWO) is applied in this paper. The proposed method’s efficiency, convergence, superiority, and effectiveness are verified through a case study. Moreover, the impact of different new energy sales prices on capacity planning results is also revealed in the article.

Abstract Background Other iatrogenic immunodeficiency-associated (OIIA) T- and natural killer (NK)-cell lymphoproliferative disorders (TNK-LPDs) are rare in patients with rheumatoid arthritis (RA). Methods We investigated the clinicopathological characteristics, Epstein–Barr virus (EBV) infection, genetic findings, therapeutic response, and prognostic factors in 21 RA patients with OIIA TNK-LPDs and compared these with those of 39 with OIIA B-cell LPDs (B-LPDs) and 22 with non-OIIA B-LPDs. Results Immunohistologically, 11 patients (52%) showed CD4+ T-LPDs, and 7 had a T follicular helper (TFH) phenotype. The other nine patients (43%) showed CD8+ T-LPDs, and the remaining one (5%) had features of CD3+ CD4− CD8− nasal type TNK-cell lymphoma. CD30+, p53+, and CMYC+ atypical lymphocytes were identified in seven (33%), eight (38%), and five (24%) patients, respectively. In situ hybridisation detected EBV-encoded RNA (EBER) + large atypical lymphocytes in five patients (24%). Nine of 17 patients (53%) showed clonal peaks of TCRγ by polymerase chain reaction. Withdrawal of MTX and biologic drugs was effective in 12 patients (57%), and 8 (38%) received chemotherapies. Two patients with TFH+ or EBV+ CD4+ CD30+ large cell peripheral T-cell lymphoma, one with CD8+ systemic anaplastic large cell lymphoma, and two with systemic EBV+ CD8+ T-cell lymphoma of childhood showed a lethal progressive clinical course within 13 months. Moreover, > 500 U/L LDH, large atypical lymphocytes, expression of CD30, p53, and CMYC, and EBER+ atypical lymphocytes were significantly poor prognostic factors for overall survival (p

Abstract FAT atypical cadherin 1 (FAT1) is one of the most mutagenic genes in tumors, and several critical studies have revealed its role in tumors, although no pan-cancer studies are currently available. Therefore, we explored the potential oncogenic role of FAT1 in 33 tumors based on The Cancer Genome Atlas and Gene Expression Omibus datasets. We found that FAT1 was strongly expressed in most tumors and significantly correlated with their prognosis. Additionally, we analyzed the association of FAT1 with tumors from multiple perspectives, including single-cell sequencing, mutations, high tumor mutational burden, microsatellite instability, immune cell infiltration, and immune microenvironment. Our first pan-cancer study provided a relatively comprehensive understanding of the oncogenic role of FAT1 in tumors.

Within this work we demonstrate the highly efficient nonlinear spectral broadening and subsequent temporal compression of 1.49 mJ pulses at 101 kHz repetition rate from an ultrafast thulium-doped fiber laser system employing a gas-filled multi-pass cell (MPC). To achieve spectral broadening, we use a krypton and helium-filled Herriott-type MPC with highly reflective broadband dielectric mirrors. The spectrally broadened pulses are subsequently compressed using fused-silica plates, resulting in a pulse duration of 20 fs and an overall excellent transmission of 96%. Furthermore, the beam quality is preserved up to the maximum output power of 144 W. It provides, to the best of our knowledge, the highest average power with few-cycle pulses at 2 µ m wavelength with almost 10 times more pulse energy and 3 times more average power than previous 2 µ m MPCs, enabling future secondary source experiments.

BackgroundAnxiety and depression are the most common psychiatric comorbidities in migraine, but their impact on the risk of developing migraine and their gender and age differences are unclear, and research on their associations with migraine-related burdens are limited.ObjectiveTo systematically explore the association between anxiety and depression with migraine and migraine-related burdens, including the risk of developing migraine, as well as migraine frequency, severity, disability, headache impact, quality of life and sleep quality.MethodsA total of 170 migraineurs and 85 sex-and age-matched healthy control subjects were recruited consecutively for this study. Anxiety and depression were assessed using Zung’s Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS), respectively. Logistic regression and linear regression analyses were used to explore the associations between anxiety and depression with migraine and its burdens. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of SAS score and SDS score on migraine and its severe burdens.ResultsAfter adjusting for confounders, anxiety and depression remained significantly associated with an increased risk of developing migraine, with odds ratios of 5.186 (95% CI:1.755–15.322) and 3.147 (95% CI:1.387–7.141), respectively. Meanwhile, there were significant additive interactions between the association of anxiety and depression with the risk of developing migraine in gender and age (P for interaction

The paradoxical effects of cobalt in biological processes have caused controversy regarding the application of cobalt-based biomaterials. Cobalt has recently been shown to be a trace element that promotes bone growth. Qingzhuan Dark Tea polysaccharides (TPS) has been shown to be a biomaterial with antioxidant and immunomodulatory effects. In order to develop a novel immunomodulatory biomaterial, we synthesized polysaccharide cobalt complex (TPS-Co) to prevent the paradoxical effects of cobalt while maintaining its beneficial effects, and evaluated its morphology, structure, and antioxidant activity. Fourier-transform infrared spectroscopy, X-ray diffraction and X-ray photoelectron spectroscopy demonstrated that cobalt complexed successfully with TPS. Scanning electron microscopy and atomic mechanical microscopy demonstrated that TPS-Co has a more homogeneous and concentrated morphological distribution compared to TPS. Thermal performance analysis demonstrated that TPS-Co has higher thermal stability. Atomic absorption spectroscopy showed a cobalt content of 3.8%. Ultraviolet spectroscopy indicated that TPS-Co does not contain nucleic acids and proteins. Antioxidant activity assays showed that TPS-Co has better antioxidant activity than TPS in the concentration range of 0.4–2 mg/mL. Proliferation assay of MC3T3-E1 cells demonstrated that TPS-Co has the best cell proliferation effect at a cobalt concentration of 2 ppm. Therefore, TPS-Co may have potential applications in bone regeneration.

Abstract In recent years, ultrasound, as an external stimuli that can activate different types of naonocatalysts for therapy, has attracted extensive attention. One characteristic that makes ultrasound a particularly attractive trigger stimulus for nanomedicine is that it can be applied to the deep regions of the body noninvasively in a focused way. Different biological effects can be achieved by integrating ultrasound with nanocatalysts, and nanodroplets. Gas therapy, as a green antitumor treatment, has attracted substantial attention. The development of nanotechnology and nanomedicine has made gas therapy more precious by controlled release under internal, and outside factors and targeted delivery. In this article, an overview of ultrasound‐based gas therapy on antitumor therapy has been provided. First, we explored the mechanism of ultrasound‐triggered gas release. Second, we list the common gas release pathways and their mechanism in response to ultrasound activity. Third, exemplary instances of gas‐generating facilities under ultrasound controllable are explored, with an emphasis on their originality and guiding principles. The impact of the gas‐generating platform as a tumor therapy has also been considered. Finally, the difficulties and future prospects for this effective therapeutic approach are examined.

We explore the dynamics and determinants of volatility spillover between financial technology (FinTech) and the traditional financial industry (TFI). The total, directional, and net spillover indices are measured, and we further decompose volatility spillovers in a time–frequency framework. Economic fundamentals, risk contagion, and market attention are examined as potential determinants of volatility spillovers. We reveal that the total spillover index is time-varying and exhibits an inverse U-shape between 2017 and 2021. FinTech is a net volatility receiver in most cases, and exported volatility to the TFI during the COVID-19 pandemic. The long-term components are the main driving force of volatility spillovers based on the frequency domain decomposition. Meanwhile, the economic fundamental determinants are the main contributing factors of volatility spillovers, explaining over 60% of total spillover between FinTech and the TFI. The findings remain robust under different settings of forecasting horizons, lag lengths, and proxy of the risk contagion determinant.

A novel entry guidance command generation (EGCG) method for hypersonic glide vehicles (HGVs) is proposed in this paper. Apart from the conventional path constraints, terminal constraints, and multiple stationary geographic constraints, the method takes into account several threats that HGVs must avoid during the entry process. The threats are classified into covert threats and dynamic threats. The information of covert threats needs to be detected during the entry process, and the positions of dynamic threats are even unfixed. A piecewise analytical polynomial height-velocity profile is used to derive an analytical magnitude expression for bank angle commands. The profile is capable of taking full advantage of the width of the entry corridor and satisfying large range requirements in flight missions. An improved artificial potential field (IAPF) is introduced to formulate the lateral guidance law, which allows the HGV to pass all the waypoints, circumvent no-fly zones, and maneuver to avoid threats. Finally, several simulations are conducted to demonstrate the effectiveness of the designed method. The proposed EGCG method exhibits a superior ability to satisfy multiple constraints and avoid threats, accuracy to target point arrival, and strong robustness against uncertainties and deviations.

Abstract Background Biological mutagens (such as transposon) with sequences inserted, play a crucial role to link observed phenotype and genotype in reverse genetic studies. For this reason, accurate and efficient software tools for identifying insertion sites based on the analysis of sequencing reads are desired. Results We developed a bioinformatics tool, a Finder, to identify genome-wide Insertions in Mutagenesis (named as “InMut-Finder”), based on target sequences and flanking sequences from long reads, such as Oxford Nanopore Sequencing. InMut-Finder succeeded in identify > 100 insertion sites in Medicago truncatula and soybean mutants based on sequencing reads of whole-genome DNA or enriched insertion-site DNA fragments. Insertion sites discovered by InMut-Finder were validated by PCR experiments. Conclusion InMut-Finder is a comprehensive and powerful tool for automated insertion detection from Nanopore long reads. The simplicity, efficiency, and flexibility of InMut-Finder make it a valuable tool for functional genomics and forward and reverse genetics. InMut-Finder was implemented with Perl, R, and Shell scripts, which are independent of the OS. The source code and instructions can be accessed at https://github.com/jsg200830/InMut-Finder .

Abstract Titanium has been widely used in prosthetic valves, but they are associated with serious defects in titanium‐based prosthetic valves, such as thrombosis, calcification, and decay. Therefore, it is very important to biofunctionalize titanium‐based valves to reduce inflammation and accelerate endothelialization of stents and antithrombosis. The titanium dioxide nanotubes were prepared from pure titanium (Ti) by anodic oxidation method in this study. The effects of titanium dioxide nanotubes on the metabolism of macrophages and the inflammatory reaction as implants were studied in vitro. The polarization state of macrophages and the ability to accelerate endothelialization were analyzed. The results demonstrated that titanium nanotubes promote M2 polarization of macrophages by inhibiting glycolysis and activating the Adenosine monophosphate‐activated protein kinase (AMPK) signaling pathway. In general, biofunctionalization titanium with nanotube could inhibit macrophage glycolysis, reduce inflammatory factor release and promote M2 polarization by activating the AMPK signaling pathway. And endothelialization was accelerated in vitro. Our result demonstrated that titanium nanotube could act as a potential approach to biofunctionlize titanium‐based prosthetic valves for endothelialization.

Load behaviors significantly impact the planning, dispatching, and operation of the modern power systems. Load classification has been proved as one of the most effective ways of analyzing the load behaviors. However, due to the issues of data collection, transmission, and storage in current power systems, data missing problems frequently occur, which prevents the load classification tasks from precisely identifying the load classes. Simultaneously, because of the diversities of the load categories, different loads contribute various amounts of data, which causes the class imbalance issue. The traditional load data classification algorithms lack the ability to solve the aforementioned issues, which may deteriorate the load classification accuracy. Therefore, this study proposed an improved deep learning algorithm based on the load classification approach in terms of raising the classification performances with solving the data missing and class imbalance issues. First, the LATC (low-rank autoregressive tensor completion) algorithm is used to solve the data missing issue to improve the quality of the training dataset. A Borderline-SMOTE algorithm is further adopted to improve the class distribution in the training dataset to improve the training performances of biGRU (bidirectional gated recurrent unit). Afterward, to improve the classification accuracy in the classification task, the biGRU algorithm, combined with the attention mechanism, is used as the underlying infrastructure. The experimental results show the effectiveness of the proposed approach.

BackgroundIn-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.Materials and methodsA retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.ResultsA total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.ConclusionThe prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.

BackgroundThe relationship between sleep and migraine is well known to be bidirectional. However, few studies have systematically assessed the association between sleep quality and the risk of developing migraine, and its gender and age differences are unclear. And there is currently limited evidence on the associations between sleep quality and migraine-related burdens.ObjectiveThe objectives of this study were to: (1) explore the association between sleep quality and the risk of developing migraine, and its gender and age differences; (2) investigate the associations between sleep quality and the total pain burden, severity, disability, headache impact, quality of life, anxiety, and depression of migraine patients.MethodsThis study consecutively enrolled 134 migraine patients and 70 sex- and age-matched healthy control subjects. Sleep quality was assessed through the Pittsburgh Sleep Quality Index (PSQI). Logistic regression and linear regression analyses were used to explore the associations between sleep quality with the risk of developing migraine and the migraine-related burdens.ResultsThe prevalence of poor sleep quality in migraine patients was significantly higher than that in subjects without migraine (P < 0.001). After adjusting for various confounding factors, the risk of migraine with poor sleep quality remained 3.981 times that of those with good sleep quality. The subgroup analysis showed that there were significant additive interactions between poor sleep quality and the risk of migraine in gender, age, and education level (P for interaction < 0.05), and the stronger correlations were found in females, populations with ages more than 35 years old, and with lower education levels. In addition, multivariate linear regression analysis showed that poor sleep quality was significantly and independently associated with the total pain burden, severity, headache impact, quality of life, anxiety, and depression in migraine patients (P trend < 0.05).ConclusionPoor sleep quality was significantly independently associated with an increased risk of developing migraine and the migraine-related burdens. Strengthening PSQI assessment is valuable for the early prevention and treatment of migraine patients.

Abstract Background Systemic Epstein-Barr virus+ T-cell lymphoma (sEBV+ TCL) occurs in childhood and young adults, and is exceptionally rare in older adults. Methods We investigated clinicopathological features in 16 patients of various ages with systemic EBV+ CD8+ T-lymphoproliferative diseases. Results Eight younger patients and four of eight older adults had sEBV+ CD8+ TCL, with invasion by medium-sized to/or large atypical lymphocytes primarily in bone marrow and lymph nodes, hemophagocytic lymphohistiocytosis (HLH), and progressive clinicopathological course. A further two patients demonstrated EBV+ node-based CD8+ large TCL without HLH, while the remaining two had the systemic form of chronic active EBV infection (sCAEBV) with CD8+ small lymphocytes. Past history of sCAEBV-like lesions was observed in one sEBV+ TCL patient (8.3%). Immunohistologically, in 12 sEBV+ TCL patients, atypical lymphocytes were positive for phosphate signal transducer and activator of transcription 3 (66.7%), CMYC (83.3%), and p53 (75%). Strong reactions of programmed cell death-ligand (PD-L)1+ tumor or non-neoplastic cells were detected in nine sEBV+ TCL patients (75%). Clonal peaks of the T-cell receptor (TCR) γ gene were detected in eight sEBV+ TCL patients by polymerase chain reaction. Four younger patients in sEBV+ TCL (33.3%) are in remission with chemotherapies including etoposide, and three of the four underwent allogeneic stem cell transplantation (SCT). Conclusion sEBV+ CD8+ TCL was observed in younger and older adults with less history of sCAEBV. HLH, tumor cell atypia, immunohistological findings, and progressive clinical course were characteristic of sEBV+ CD8+ TCL. Prompt chemotherapy and SCT induced tumor regression in sEBV+ CD8+ TCL patients.

Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.

This study was performed to investigate the effects of selenium yeast (Se-yeast) on the growth, intestinal health, immune status and body composition of juvenile Apostichopus japonicus before and after aestivation. Five experimental diets were formulated with increasing addition of Se-yeast (0, 0.5, 1.0, 1.5 and 2.0 mg/kg), with the diet without Se-yeast as the control. Each diet was randomly assigned to three tanks of juvenile A. japonicus (initial body weight: 2.96 ± 0.04 g). The whole experiment lasted for 135 days, which included a 45-day feeding experiment before aestivation, a 60-day aestivation phage and a 30-day feeding experiment after aestivation. The results showed that weight gain rate (WGR) was significantly increased by the increasing addition of Se-yeast before aestivation. After aestivation, WGR was markedly elevated by 1.0 mg/kg Se-yeast but was inhibited by 1.5–2.0 mg/kg Se-yeast. The evisceration rate (ER) of A. japonicus was obviously inhibited by the relatively higher addition level of Se-yeast (1.5–2.0 mg/kg) before aestivation. After aestivation, the ER was significantly inhibited by Se-yeast at an addition of 1.5 mg/kg. Before aestivation, the highest activities of all digestive enzymes were obtained by Se-yeast addition at a level equal to or above 1.0 mg/kg Se-yeast. After aestivation, the activities of most digestive enzymes were maximized by Se-yeast at the addition level of no more than 1.0 mg/kg. Before aestivation, the activities of nitric oxide synthase and alkaline phosphatase significantly increased by the increasing addition of Se-yeast. After aestivation, immune related parameters exhibited the highest or comparable values when the addition level of Se-yeast was equal to or less than 1.0 mg/kg. Notably, the selenium content in the body wall of A. japonicus increased significantly as Se-yeast addition increased in the diets. These results showed that Se-yeast at the addition level of 1.5–2.0 mg/kg before aestivation and 1.0–1.5 mg/kg after aestivation was beneficial for the promotion of growth, intestinal health and immune status of juvenile A. japonicus.