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In the quest to streamline Korean text and speech annotation, this research introduces innovative automatic annotation methods that promise to revolutionize efficiency and technical prowess in constructing Korean annotation datasets. By leveraging the sophisticated Seq2Seq architecture with BERT and bidirectional GRU models, we significantly enhance the model’s ability to grasp contextual nuances, ensuring precise text annotations. The speech annotation frontier benefits from a novel amalgamation of the Hidden Markov Model’s forced alignment and semi-supervised learning, perfected with Seneff auditory features for meticulous phonological consonant boundary detection. Empirical validation across diverse datasets showcases our methodology’s superiority, achieving a remarkable 96.01% accuracy in text annotation and setting a new benchmark for phonological boundary detection at a 14.5ms minimum distance threshold. Our approach outperforms traditional algorithms, marking a pivotal step forward in Korean automatic annotation.

In recent years, the increase in the number of vehicles on the road has impacted the service life of bridges, and thus, their performance. In China, bridge inspections must be performed and documented at least once per year using labor-intensive and subjective approaches. In this study, a computationally efficient damage identification methodology for bridges was developed based on a deep convolutional generative adversarial network (DCGAN). To predict the damage to bridges, the vibration data of the structural damage are extracted from sensor measurements on the bridges and applied to the DCGAN model. The proposed damage identification method was validated on a simple supported beam bridge and a continuous girder bridge. The effects of sample size and noise on the identification accuracy were also evaluated. The performance of the DCGAN in the damage identification process is robust and reliable. This method for structural health monitoring offers a more objective evaluation of the overall performance and condition of a bridge, thus enabling bridge owners to efficiently allocate finite maintenance resources. [ABSTRACT FROM AUTHOR]

Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory estimation, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot be effectively achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range Light Detection and Ranging (LiDAR) data, which enables the measurement of individual tree height in forest plots with high precision. Besides, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and Synthetic Aperture Radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining basal area of individual tree within plots using UAV LiDAR data, this study calculated weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at large spatial scale, we also developed a machine learning-based mixed-effects model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 m and 13.3 m with standard deviations of 2.9 m and 3.3 m, respectively. The accuracy of mapped products was validated utilizing LiDAR and field measurement data. The correlation coefficient (푟) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while RMSE ranged from 2.6 to 4.1 m and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

With the rise of artificial magnetism and metamaterials, toroidal resonance has gained much attention for its special properties. In this paper, we propose a novel hybrid graphene-metal metamolecule consisting of a square bracket-like resonator and two asymmetric U-shaped resonators. By applying various Fermi energies to graphene, the amplitude of electromagnetically induced transparency (EIT) can be efficiently manipulated, and the maximum amplitude modulation depth can attain 81% in the microwave region. Numerical simulations and theoretical analysis demonstrate that the dynamic manipulation is mainly induced by the active tuning toroidal resonance through the recombination effect of the conductive graphene. Also, the maximum group delay of 85 ps can be attained and controlled with the increasing Fermi energy. The proposed hybrid graphene-metal metamolecule and dynamically manipulating mode presents a novel modulating strategy of EIT-like analog based on the toroidal response, which has great application for the design of efficient tunable resonators, filters, and sensors. [ABSTRACT FROM AUTHOR]

A high-resolution, spatially explicit forest age map is essential for quantifying forest carbon stocks and carbon sequestration potential. Prior attempts to estimate forest age on a national scale in China have been limited by sparse resolution and incomplete coverage of forest ecosystems, attributed to complex species composition, extensive forest areas, insufficient field measurements, and inadequate methods. To address these challenges, we developed a framework that combines machine learning algorithms (MLAs) and remote sensing time series analysis for estimating the age of China's forests. Initially, we identify and develop the optimal MLAs for forest age estimation across various vegetation divisions based on forest height, climate, terrain, soil, and forest-age field measurements, utilizing these MLAs to ascertain forest age information. Subsequently, we apply the LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Ultimately, the forest age data derived from LandTrendr are integrated with the result of MLAs to produce the 2020 forest age map of China. Validation against independent field plots yielded an R2 ranging from 0.51 to 0.63. On a national scale, the average forest age is 56.1 years (standard deviation of 32.7 years). The Qinghai–Tibet Plateau alpine vegetation zone possesses the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30 m-resolution forest age map offers crucial insights for comprehensively understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. The map is available at 10.5281/zenodo.8354262 (Cheng et al., 2023a). [ABSTRACT FROM AUTHOR]

Summary: Aiming to the problems of high circulating current and low efficiency caused by the traditional strategy of dual active bridge (DAB) converter under light load, in this paper, simple graphical optimization method (GSO) is proposed to improve the efficiency of dual active bridge converter. Through the contour plot of transmission power and current stress, optimal efficiency point can be obtained by tangent of contour map under condition of constant power, and heavily calculation or complex iterations of existing conventional control strategies are avoided. Moreover, analysis of proposed graphical current stress optimization is addressed, and controller design of proposed method is given and described. Efficiency comparison between traditional modulation strategy and GSO is given to show the advantage of proposed mothed. An experimental prototype is built to verify the analysis. [ABSTRACT FROM AUTHOR]

Dynamic-vibration-based structural damage identification (SDI) represents the main target for structural health monitoring (SHM). It is significant to consider the unavoidable uncertainties arising from both the structure and measuring noise. On the other hand, nonuniform measurement conditions often appear in actual SHM applications, which consist of two parts, i.e., spatial nonuniform characteristics for noises are induced by various intensities of input noise in every single sampling channel and multisensor stays in a damaged state. This paper proposes a new method for the SDI considering uncertainties in nonuniform measurement conditions integrating convolutional neural network (CNN). Herein, the great ability of feature extraction from the measurement associated with the convolutional network is used to handle the input data, and the mapping connection between the selected features and damage states is established. Time histories of structural responses, such as acceleration, are applied for damage identification. The application and accuracy of the CNN, which is trained with input uncertain parameters contaminated by stochastic noises, are verified by the finite element numerical and experimental results. Both uncertain parameters and measurement conditions are considered in the verification. The responses obtained from the numerical and experimental approach show that the proposed neural network model can identify the structural damage with high accuracy. The great robustness of the proposed method is examined by studying the influence of uncertainties, even considering the nonuniform measurement condition. [ABSTRACT FROM AUTHOR]

In recent years, the water–sand composition of the Yangtze River channel has changed due to the influence of human factors, especially the construction of water reservoirs such as the Three Gorges Project. Changing water–sand conditions have a long-term impact on the shaping of the river channel morphology in the middle and lower reaches of the Yangtze River, and the erosion retreat of local river sections has caused great harm to embankment projects. This paper focuses on the river evolution mechanism of the river channel from Chenglingji to Datong in the middle and lower reaches of the Yangtze River over the past 31 years. Landsat remote sensing images from 1989–2019 were used to extract and interpret water bodies, river shorelines, and central bars in the study area using the Modified Normalized Difference Water Index (MNDWI) combined with visual interpretation. We used near analysis to study the morphological evolution characteristics of the river, the channel, and selected typical river reaches for comparative analysis. We found out that the overall change in river morphology between 1989 and 2019 was small in the horizontal direction, but the local area changed significantly. Considerable scouring occurred in the vertical direction. Combining hydrological and meteorological data, we investigated the effects of the Three Gorges Dam, instream sand mining, boundary conditions, vegetation cover on both sides of the riverbanks, and aspects of storm flooding in the watershed on the evolution of the river. The study indicated that the geological conditions on both sides of the river, the implementation of the bank protection project, and the improvement of vegetation cover on both sides of the river have made the riverbanks more resistant to scouring. However, heavy rainfall floods, the operation of the Three Gorges Reservoir, and sand mining activities in the river channel make the river channel more susceptible to scouring. Based on the calculation of the slope change rate of the accumulated volume, it was found that the runoff is mainly influenced by precipitations, while the sand transport is mainly affected by human activities. This study shows that natural and anthropogenic activities affect the equilibrium state of the river's water and sediment to varying degree. [ABSTRACT FROM AUTHOR]

In order to address the issue of keeping the converter operating at a high efficiency level under different conditions, this paper suggests an adaptive step frequency adjustment online optimization search algorithm, which achieves the goal of fast adjustment and real‐time response to parameter changes. The converter is quickly adjusted to operate at a high efficiency by sampling the input and output current values and modulating the switching frequency utilizing the algorithm. Additionally, the algorithm shifts between two working models to finish the search for the best switching frequency while the converter's characteristics do not change. Moreover, it accomplishes the objective of instantly resynchronizing the algorithm after a change in the converter's parameters and starting a search for the best switching frequency in the new converter state. For various input voltages and output loads, the operating principle, optimal frequency, and efficiency are given. In addition, the principle analysis when the converter parameters are changed is provided, and the choice of two algorithm models is compared. Finally, a prototype buck converter with adjustable input voltage and load is constructed to test the algorithm's efficiency. [ABSTRACT FROM AUTHOR]

We present the design of a new type of irregular metamaterial structure that can achieve ultra-wideband absorption. The structure is created using 3D-printing to create a shell and contains multiple layers of water. The structure can achieve absorption levels greater than 0.9 in the 6.8–21.0 GHz range, with a relative bandwidth of 101.93%. The absorber also works in a wide range of incidence angles with different modes and is polarization insensitive. Measurement results obtained from a microwave experiment coincide well with the simulation results. The proposed metamaterial could be broadly applied in various civilian and military products in the future. [ABSTRACT FROM AUTHOR]

In this paper, a low-loss toroidal dipole metamaterial composed of four metal split ring resonators is proposed and verified at microwave range. Dual-band Fano resonances could be excited by normal incident electromagnetic waves at 6 GHz and 7.23 GHz. Analysis of the current distribution at the resonance frequency and the scattered power of multipoles shows that both Fano resonances derive from the predominant novel toroidal dipole. The simulation results exhibit that the sensitivity to refractive index of the analyte is 1.56 GHz/RIU and 1.8 GHz/RIU. Meanwhile, the group delay at two Fano peaks can reach to 11.38 ns and 12.85 ns, which means the presented toroidal metamaterial has significant slow light effects. The proposed dual-band toroidal dipole metamaterial may offer a new path for designing ultra-sensitive sensors, filters, modulators, slow light devices, and so on. [ABSTRACT FROM AUTHOR]

Dual-Fano resonances have been proposed with all-dielectric toroidal metamaterials in the microwave frequency. The presented structure consists of high permittivity rectangular bars of equal spacing. Two transparency peaks derived from the strong toroidal dipole with two modes can reach 0.92 and 0.9 at 10.51 GHz and 16.27 GHz, respectively. The excitation of these resonances could be explained by near-field distribution and multipoles scattered power. The trend of experimental results is virtually uniform with that of theoretical analysis and simulated curves. Such a compact all-dielectric structure may have important implications in many fields, such as ultra-sensitive sensors, stealth transmission, and so on. [ABSTRACT FROM AUTHOR]

A double E-shaped toroidal dipole metasurface is designed with the high Q-factor Fano and classical electromagnetically induced transparency (EIT) phenomena in the microwave frequency range. With the introduction of an asymmetric structure, the sharp Fano resonance can be excited and acquired a quite high Q-factor of 134 at a lower frequency of 4.58 GHz. It can be numerically and experimentally demonstrated that the singularity Fano response of designed construction is caused by the intensive toroidal dipole. In addition, due to destructive interference between the intensive toroidal dipole and electric dipole, the transmission peak of EIT can reach 0.95 with a Q-factor of 50 at 10.18 GHz. By calculating and comparing the radiated power of multipoles, the enhanced toroidal dipole response can be further verified. The designed planar toroidal dipole metamaterial with simple construction may have many possible applications in toroidal moment generators, sensing, and slow-light devices. [ABSTRACT FROM AUTHOR]

Background: Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved. Methods: Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism. Results: This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients. Conclusions: In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma. [ABSTRACT FROM AUTHOR]

The dual-band analog electromagnetically induced transparency (EIT) with intriguing toroidal has been presented at microwave frequency. The proposed construction is composed of the split resonant rings with different sizes in two layers to induce dual-toroidal resonance. By optimizing the structure, two transparency peaks derived from strengthened toroidal could attain to 0.97 at 5.36 GHz and 0.94 at 10.23 GHz, which could be certified by numerical simulation, near-field distribution and scattered power of far-field. The experiment and simulation are almost consistent, and the dual-toroidal analog-EIT could have potential applications in many fields, such as ultrasensitive sensing and secure data communication. [ABSTRACT FROM AUTHOR]

The stochastic pseudo excitation method (SPEM), which is based on the principle of pseudo excitation method (PEM), is introduced to represent the randomness of dynamic input in which the amplitude of excitation is adopted as a random variable. Based on the mathematic definition of power spectral density, a physical interpolation of the SPEM is discussed. Even if one random variable is involved in calculation, the effects of the uncertainties are required to be investigated. The SPEM offers a simple but quite effective way to solve the dynamic reliability problem. Through integrating the new algorithm into first-order reliability method (FORM), the dynamic reliability of uncertain structure subjected to random excitation is studied. A linear oscillator with three types of white noise is adopted to verify the SPEM for dynamic reliability of linear random vibration analysis. Also, the accuracy and efficiency of SPEM to handle the multi-degree-of-freedom structure is investigated in this paper. [ABSTRACT FROM AUTHOR]

Although there are some studies on the risk management of logistics service supply chain (LSSC), we still know little about the effects of risk evolution and interactional mechanism of subjects in different urgent scenarios of logistics service supply chain from a micro perspective. Based on the structures of the LSSC, this study thus analyzed the risk evolution and the main actors' reactions in the LSSC, including categorizing the urgent scenarios, analyzing their impacts on the diffusion intensity of pricing, profit and demand, and verifying the models' validity with the numerical analysis. The results indicate divergent impacts of the different urgent scenarios on the logistics service supply chain: the price fluctuation can cause similar profit declines of the supplier and integrator, the reduced capacity can cause the supply chain's price increase by 50%, and the demand risks can lead to similar changes of the upstream orders, prices and profits. The findings may provide clues for firms to develop effective countermeasures against the risks. [ABSTRACT FROM AUTHOR]

Based on the theory of pseudo excitation method, a so-called stochastic pseudo excitation method is proposed to acquire the sampling results of stochastic dynamic response of uncertain bridge structure directly, which cannot be achieved by the classical pseudo excitation method. In order to reduce the computational cost of the dynamic finite element analysis of the samples to gain the probability density function of structural responses, the response surface method was adopted to handle the sampling results. The stochastic pseudo excitation method combing response surface method is verified by comparing with Monte Carlo method in good agreement. As an example, the stochastic response of a super-high slender bridge tower subjected to the Davenport fluctuating wind excitations is evaluated taking into account the uncertainties of material properties and wind speed. Numerical results show that the effect of uncertainties of structure or excitations plays a constant role in the stochastic response at the different levels of external excitations. [ABSTRACT FROM AUTHOR]

An efficient algorithm is proposed for the stochastic responses of vehicle–bridge systems. The pseudo-excitation method is combined with the self-adaptive Gauss integration to handle the stochastic responses of vehicle–bridge systems, for which the statistic characteristics are calculated. The applicability and accuracy of the proposed hybrid numerical method are demonstrated by comparing the results with those from Monte Carlo simulation. Besides, nondimensional time is introduced to evaluate the stochastic responses of vehicles. [ABSTRACT FROM AUTHOR]

Highlight • We proposed tunable and polarization sensitive electromagnetically induced transparency metamaterial. • When incident angles vary from 0° to 60°, the transmission spectra has two transmission windows in TE mode. • In TM mode, the transmission window has broadened in the transmission spectra. • On the other hand, the proposed metamaterial has slow light effect and the largest value of group index is 30 in the transparency peak. • According to the property of the designed metamaterial, we believe it suggests the future possibility for a new type of applications in sensing and filtering device. Abstract We demonstrate a classical analogue of electromagnetically induced transparency (EIT) metamaterial that consisting of anomalous split resonance ring and cross-shaped wire with simulations and experiments. The transmission spectrum is tunable with the incident angles varies from 0° to 60° in different modes. Under normal incident wave, the metamaterial leads to polarization insensitive EIT-like effect. The distributions of surface current at peaks and dips of transmission have been analyzed. Furthermore, the group index presents maximum at the transparency peak, which verifies the slow light effect. The proposed metamaterial could provide an avenue for various applications, such as refractive index sensor and microwave switch. [ABSTRACT FROM AUTHOR]

A synthetic process was exploited to fabricate patchy CuO evenly planted on cubic NaTaO 3 for photocatalytically reducing CO 2 in isopropanol. The nano patches of CuO with about 15 nm in size were uniformly distributed on the surface of NaTaO 3 via a phase-transfer protocol and solvothermal synthesis. The crystal phase, morphology, composition, optical absorption and charge separation of as-prepared CuO-NaTaO 3 were characterized by XRD, SEM, TEM, EDX, XPS, UV–Vis and PL. The results of photocatalytic reduction of CO 2 confirmed that the CuO patched NaTaO 3 possessed better ability to separate charge carriers and selectively reduce CO 2 to methanol than CuO directly loaded NaTaO 3 using the traditional liquid phase reduction procedure after comparing the methanol yields. Furthermore, 5 wt% CuO patched NaTaO 3 led to the highest methanol yield of 1302.22 μmol g −1 h −1 . A redox mechanism was proposed and illustrated in a schematic diagram. [ABSTRACT FROM AUTHOR]

A compact planar construction composed of asymmetric split ring resonators was designed with a low-loss, high Q-factor resonance transparency at microwave frequency. The singularity property of the proposed metamaterial owing to the enhanced toroidal dipole T is demonstrated via numerical and experimental methods. The transmission peak can reach up to 0.91 and the loss is perfectly repressed, which can be testified by radiated power, H-field distributions, and the imaginary parts of effective permittivity and permeability. The designed planar metamaterial may have numerous potential applications at microwave, terahertz, and optical frequency, e.g., for ultrasensitive sensing, slow-light devices, lasing spacers, even invisible information transfer. [ABSTRACT FROM AUTHOR]

Different shape of bismuth sulfide (Bi2S3), including nanoparticles, and urchin-like, microspheres hierarchical nanostructures, have been successfully fabricated using a facile and template-free solvothermal method. Their crystal and porous structures, morphologies, as well as the optical absorption were characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-vis diffuse reflection spectroscopy (DRS) and nitrogen sorption. The electron microscopy observations showed that both the sulfur sources and solvents greatly affected the morphologies of the as-prepared Bi2S3. Compared with Bi2S3 nanoparticles, the hierarchical architectures exhibited higher activity for photocatalytic reduction of CO2 to methyl formate (MF) in methanol, and Bi2S3 microspheres showed the highest activity. This was attributed to their special hierarchical structure, good permeability and high light-harvesting capacity. [ABSTRACT FROM AUTHOR]

The reliability of vehicle–bridge dynamic interaction is evaluated. A time domain transfer matrix method for dynamic analysis is proposed. Using the idea of the Newmark-β method, the partial differential equation of beam vibration is transformed into a differential equation. Then, this differential equation is solved by the transfer matrix method. By using D’Alembert’s principle, the dynamic equations of vehicle–bridge interaction can be formulated. An iteration scheme is adopted to deal with the coupled terms in dynamic equations of vehicle–bridge system. Through integrating the first-order reliability method and transfer matrix method, the dynamic reliability of the vehicle–bridge system is studied. Through two numerical examples, the effects of system parameters on dynamic reliability of the vehicle–bridge system are investigated. [ABSTRACT FROM AUTHOR]

• Dopamine receptor agonists were intrastriatally injected into hemiparkinsonism and control rats. • SKF38393 and quinpirole produce a dose-related enhancement in locomotor activity. • SKF38393 and quinpirole induce more ameliorated movement in hemiparkinsonism rats than in control rats. • Quinpirole produced more ameliorated locomotor activity than SKF38393 in hemiparkinsonism rats. Dopamine (DA) in the striatum is essential to influence motor behavior and may lead to movement impairment in Parkinson's disease (PD). The present study examined the different functions of the DA D1 receptor (D1R) and DA D2 receptor (D2R) by intrastriatal injection of the D1R agonist SKF38393 and the D2R agonist quinpirole in 6-hydroxydopamine (6-OHDA)-lesioned and control rats. All rats separately underwent dose-response behavior testing for SKF38393 (0, 0.5, 1.0, and 1.5 μg/site) or quinpirole (0, 1.0, 2.0, and 3.0 μg/site) to determine the effects of the optimal modulating threshold dose. Two behavior assessment indices, the time of latency to fall and the number of steps on a rotating treadmill, were used as reliable readouts of motor stimulation variables for quantifying the motor effects of the drugs. The findings indicate that at threshold doses, SKF38393 (1.0 μg/site) and quinpirole (1.0 μg/site) produce a dose-dependent increase in locomotor activity compared to vehicle injection. The ameliorated behavioral responses to either SKF38393 or quinpirole in lesioned rats were greater than those in unlesioned control rats. Moreover, the dose-dependent increase in locomotor capacity for quinpirole was greater than that for SKF38393 in lesioned rats. These results can clarify several key issues related to DA receptors directly and may provide a basis for exploring the potential of future selective dopamine therapies for PD in humans. [ABSTRACT FROM AUTHOR]

Abstract Recent studies on the impact of Parkinson's disease (PD) on the thalamostriatal pathway have mainly focused on the structural and functional changes in the thalamus projection to the striatum. Alterations in the electrophysiological activity of the thalamostriatal circuit in PD have not been intensively studied. To further investigate this circuit, parafascicular nucleus (PF) single-unit spikes and dorsal striatum local field potential (LFP) activities were simultaneously recorded in control and 6-hydroxydopamine (6-OHDA)-lesioned rats during inattentive rest or treadmill walking states. We classified the PF neurons into two predominant subtypes (PF I and PF II). During rest state, after dopamine loss, increased PF I spike and striatal LFP coherence was observed in the beta-frequency (12–35 Hz), with changed PF I neuronal firing pattern and unchanged firing rates of the two neuron subtypes. However, in a treadmill walking state, PF II neurons displayed markedly increased coherence to striatal beta oscillations in the dopamine-depleted rats, as well as an altered PF II neuronal firing pattern and significantly decreased firing rates of the two neuron subtypes. The results indicate that in PD animals, state transition from rest to moving, such as treadmill walking, is associated with different PF neuron types and increased spike-LFP synchronization, which may provide new paradigms for understanding and treating PD. Highlights • PF neurons were classified into two predominant subtypes and dopamine loss altered PF neural firing activities. • The alterations of PF neural firing in PD are state-dependent. • Synchronization between single PF neurons with striatum β-frequency oscillations increased after dopamine loss. • The changes of synchronization are associated with different PF neuron subtypes in different states. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that gut microbiota (GM) and gut microbiota-derived metabolites are associated with gestational diabetes mellitus (GDM). However, the causal associations need to be treated with caution due to confounding factors and reverse causation., Methods: This study obtained genetic variants from genome-wide association study including GM ( N = 18,340), GM-derived metabolites ( N = 7,824), and GDM (5,687 cases and 117,89 controls). To examine the causal association, several methods were utilized, including inverse variance weighted, maximum likelihood, weighted median, MR-Egger, and MR.RAPS. Additionally, reverse Mendelian Randomization (MR) analysis and multivariable MR were conducted to confirm the causal direction and account for potential confounders, respectively. Furthermore, sensitivity analyses were performed to identify any potential heterogeneity and horizontal pleiotropy., Results: Greater abundance of Collinsella was detected to increase the risk of GDM. Our study also found suggestive associations among Coprobacter , Olsenella , Lachnoclostridium , Prevotella9 , Ruminococcus2 , Oscillibacte , and Methanobrevibacter with GDM. Besides, eight GM-derived metabolites were found to be causally associated with GDM. For the phenylalanine metabolism pathway, phenylacetic acid was found to be related to the risk of GDM., Conclusions: The study first used the MR approach to explore the causal associations among GM, GM-derived metabolites, and GDM. Our findings may contribute to the prevention and treatment strategies for GDM by targeting GM and metabolites, and offer novel insights into the underlying mechanism of the disease.

Background: Patients with neurogenic bladder (NGB) are at an increased risk of developing chronic kidney disease (CKD). However, data related to the real performance of the serum creatinine (Cr)-based estimated glomerular filtration rate (eGFR) equation in patients with NGB are limited. This study is to evaluate the performance of new Cr-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race and the GFR estimation equation for Chinese CKD patients for the estimation of GFR in Chinese patients with NGB., Materials and Methods: GFR was determined simultaneously by three methods: a) GFR measured by renal dynamic imaging with 99m Tc-DTPA (G-GFR), which was used as the reference GFR; b) GFR estimated by the new Cr-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race (EPI-GFR); and c) GFR estimated by the equation for Chinese CKD patients (C-GFR). Pearson correlation and linear regression were used to compare eGFR and G-GFR. Differences, absolute differences, precision, and accuracy were compared to identify which equation showed better performance in evaluating GFR in patients with NGB., Results: A total of 171 patients with NGB, including 121 men and 50 women from 20 provinces, 4 autonomous regions, and 3 municipalities in China, were enrolled in the final analysis, and the average age was 31.3 ± 11.9 years. Both C-GFR and EPI-GFR were moderately correlated with G-GFR and overestimated G-GFR. The difference between EPI-GFR and G-GFR was similar to that between C-GFR and G-GFR (median of 9.97 vs. 9.95 mL/min/1.73m 2 for difference, Wilcoxon signed ranks test, Z = -1.704, p = 0.088), but the absolute difference between EPI-GFR and G-GFR was significantly lower than that between C-GFR and G-GFR (median of 22.3 vs. 25.1 mL/min/1.73m 2 for absolute difference, Wilcoxon signed ranks test, Z = -4.806, p < 0.001). Both EPI-GFR and C-GFR displayed similar results of 15, 30, and 50% accuracies (χ 2 -test, p > 0.05), and there were no significant differences between EPI-GFR and C-GFR in misclassification percentages at different G-GFR levels (χ 2 -test, p > 0.05)., Conclusion: Our study indicated that for patients with NGB in China, Cr-based eGFR equations, which include the new CKD-EPI equation without race and the Chinese GFR estimation equation, showed suboptimal performance, and limited their application in GFR estimation. Further studies are needed to investigate whether incorporating additional biomarkers, such as cystatin C, could improve their performance of GFR estimating equations in patients with NGB.