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Abstract Formate oxidase (FOx), which contains 8-formyl flavin adenine dinucleotide (FAD), exhibits a distinct advantage in utilizing ambient oxygen molecules for the oxidation of formic acid compared to other glucose-methanol-choline (GMC) oxidoreductase enzymes that contain only the standard FAD cofactor. The FOx-mediated conversion of FAD to 8-formyl FAD results in an approximate 10-fold increase in formate oxidase activity. However, the mechanistic details underlying the autocatalytic formation of 8-formyl FAD are still not well understood, which impedes further utilization of FOx. In this study, we employ molecular dynamics simulation, QM/MM umbrella sampling simulation, enzyme activity assay, site-directed mutagenesis, and spectroscopic analysis to elucidate the oxidation mechanism of FAD to 8-formyl FAD. Our results reveal that a catalytic water molecule, rather than any catalytic amino acids, serves as a general base to deprotonate the C8 methyl group on FAD, thus facilitating the formation of a quinone-methide tautomer intermediate. An oxygen molecule subsequently oxidizes this intermediate, resulting in a C8 methyl hydroperoxide anion that is protonated and dissociated to form OHC-RP and OH−. During the oxidation of FAD to 8-formyl FAD, the energy barrier for the rate-limiting step is calculated to be 22.8 kcal/mol, which corresponds to the required 14-hour transformation time observed experimentally. Further, the elucidated oxidation mechanism reveals that the autocatalytic formation of 8-formyl FAD depends on the proximal arginine and serine residues, R87 and S94, respectively. Enzymatic activity assay validates that the mutation of R87 to lysine reduces the k cat value to 75% of the wild-type, while the mutation to histidine results in a complete loss of activity. Similarly, the mutant S94I also leads to the deactivation of enzyme. This dependency arises because the nucleophilic OH− group and the quinone-methide tautomer intermediate are stabilized through the noncovalent interaction provided by R87 and S94. These findings not only explain the mechanistic details of each reaction step but also clarify the functional role of R87 and S94 during the oxidative maturation of 8-formyl FAD, thereby providing crucial theoretical support for the development of novel flavoenzymes with enhanced redox properties.

Abstract Background Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strategy to identify LVE in HF patients by means of pulse wave signals. Method We constructed two high-quality pulse wave datasets comprising a non-LVE group and an LVE group based on the 264 HF patients. Fourier series calculations were employed to determine if significant frequency differences existed between the two datasets, thereby ensuring their validity. Then, the ML-based identification was undertaken by means of classification and regression models: a weighted random forest model was employed for binary classification of the datasets, and a densely connected convolutional network was utilized to directly estimate the left ventricular diastolic diameter index (LVDdI) through regression. Finally, the accuracy of the two models was validated by comparing their results with clinical measurements, using accuracy and the area under the receiver operating characteristic curve (AUC-ROC) to assess their capability for identifying LVE patients. Results The classification model exhibited superior performance with an accuracy of 0.91 and an AUC-ROC of 0.93. The regression model achieved an accuracy of 0.88 and an AUC-ROC of 0.89, indicating that both models can quickly and accurately identify LVE in HF patients. Conclusion The proposed ML methods are verified to achieve effective classification and regression with good performance for identifying LVE in HF patients based on pulse wave signals. This study thus demonstrates the feasibility and potential of the ML-based strategy for clinical practice while offering an effective and robust tool for diagnosing and intervening ventricular remodeling.

Abstract Background Centromeres play a crucial and conserved role in cell division, although their composition and evolutionary history in green algae, the evolutionary ancestors of land plants, remains largely unknown. Results We constructed near telomere-to-telomere (T2T) assemblies for two Trebouxiophyceae species, Chlorella sorokiniana NS4-2 and Chlorella pyrenoidosa DBH, with chromosome numbers of 12 and 13, and genome sizes of 58.11 Mb and 53.41 Mb, respectively. We identified and validated their centromere sequences using CENH3 ChIP-seq and found that, similar to humans and higher plants, the centromeric CENH3 signals of green algae display a pattern of hypomethylation. Interestingly, the centromeres of both species largely comprised transposable elements, although they differed significantly in their composition. Species within the Chlorella genus display a more diverse centromere composition, with major constituents including members of the LTR/Copia, LINE/L1, and LINE/RTEX families. This is in contrast to green algae including Chlamydomonas reinhardtii, Coccomyxa subellipsoidea, and Chromochloris zofingiensis, in which centromere composition instead has a pronounced single-element composition. Moreover, we observed significant differences in the composition and structure of centromeres among chromosomes with strong collinearity within the Chlorella genus, suggesting that centromeric sequence evolves more rapidly than sequence in non-centromeric regions. Conclusions This study not only provides high-quality genome data for comparative genomics of green algae but gives insight into the composition and evolutionary history of centromeres in early plants, laying an important foundation for further research on their evolution.

Abstract Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart’s blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland–Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error

In order to provide theoretical basis for developing and utilizing new varieties of hot peppers, five new varieties of pod peppers (Capsicum annum var. Conoides) hybridized with different combinations by three lines cultivation were selected and numbered as Sample 1, 2, 3, 4 and 5, and their agronomic traits and quality characteristics with an emphasis on capsaicinoids and volatile components were evaluated. The results showed that the peppers had different agronomic traits with their ranges of fruit length, fruit mass, seed number ratio and pulp ratio being 6.7~9.3 cm, 2.40~6.26 g, 15.3~28.1 grain/g, and 71.10%~83.93%, respectively. All the samples possessed high nutritional values but their contents of major nutrients were not the same. Sample 1 contained the highest carbohydrates, crude fats and ash. Sample 2 had the highest water content. Sample 3 contained the highest vitamin C. Sample 5 had the highest protein content. All the samples possessed higher capsaicinoids content and pungency degree, of which Sample 5 had the highest content of capsaicinoids (1.128 g/kg), Scoville index (17399 SHU) and pungency degree (116) while Sample 2 showed the lowest content of capsaicinoids (0.464 g/kg), Scoville index (7157 SHU) and pungency degree (48). A total of 100 volatile components were detected in the five samples, among which olefins were the most abundant component, ranging from 39.20% to 61.73%. Olefins, esters and alcohols were the common aromatic substances of the samples, but their types and contents were different. Pearson correlation analysis displayed that the pungency degrees of the five samples were extremely significantly negatively correlated with their fruit mass and pulp raio (P

Introduction Transcranial magnetic stimulation (TMS) over the left dorsolateral prefrontal cortex (lDLPFC) has been widely used as a treatment for major depressive disorder (MDD) in the past two decades. Different methods for localising the lDLPFC target include the ‘5 cm’ method, the F3 method and the neuro-navigational method. However, whether TMS efficacies differ between the three targeting methods remains unclear. We present a protocol for a systematic review and network meta-analysis (NMA) to compare the efficacies of TMS treatments using these three targeting methods in MDD.Methods and analysis Relevant studies reported in English or Chinese and published up to May 2023 will be identified from searches of the following databases: PubMed, Cochrane Central Register of Controlled Trials, Embase, PsycINFO, China National Knowledge Infrastructure, Wan Fang Database, Chinese BioMedical Literature Database, and China Science and Technology Journal Database. We will include all randomised controlled trials assessing the efficacy of an active TMS treatment using any one of the three targeting methods compared with sham TMS treatment or comparing efficacies between active TMS treatments using different targeting methods. Interventions must include a minimum of 10 sessions of high-frequency TMS over the lDLPFC. The primary outcome is the reduction score of the 17-item Hamilton Depression Rating Scale, 24-item Hamilton Depression Rating Scale or Montgomery-Asberg Depression Rating Scale. The dropout rate is a secondary outcome representing the TMS treatment’s acceptability. Pairwise meta-analyses and a random-effects NMA will be conducted using Stata. We will use the surface under the cumulative ranking curve to rank the different targeting methods in terms of efficacy and acceptability.Ethics and dissemination This systematic review and NMA does not require ethics approval. The results will be submitted for publication in a peer-reviewed journal.PROSPERO registration number CRD42023410273.

The discovery of alternative medicines with less adverse effects is extremely urgent for rheumatoid arthritis (RA). Phillyrin (Phil), the predominant lignan glycoside of Forsythia suspensa, has been reported to exert several pharmacological effects, such as antivirus, anti-inflammation, anti-oxidation, anti-obesity, and antipyretic activity. However, the effect of Phil on RA remains unknown. In this study, We utilized both in vivo collagen-induced arthritis (CIA) rat models and in vitro TNFα-induced fibroblast-like synoviocytes (FLSs) to study the inhibitory effects of Phil on RA. The in vivo studies revealed that Phil treatment effectively ameliorated synovial inflammation and bone erosion in CIA rats. The in vitro studies demonstrated that Phil could significantly suppress the proliferation and migration of arthritic FLSs. In addition, treatment with Phil resulted in decreased mRNA expression of proinflammatory cytokines including TNFα, IL-1β, IL-6, IL-8 and MMP9. Molecular mechanistic investigations revealed that the suppressive effects of Phil were mediated by blockade of the MAPK (ERK, p38, and JNK) and NF-κB pathways. Taken together, our findings suggest that Phil has an anti-arthritic effect in CIA rats by inhibiting the pathogenic characteristic of arthritic FLSs throught suppression of NF-κB and MAPKs signaling pathways. These results demonstrate the potential of Phil as a novel therapeutic agent for the treatment of RA.

A practical stochastic location-inventory-delivery problem with multi-item joint replenishment is studied. Unlike the conventional location-inventory model with a continuous-review (r, Q) inventory policy, the periodic-review inventory policy is adopted with multi-item joint replenishment under stochastic demand, and the coordinated delivery cost is considered. The proposed model considers the integrated optimization of strategic, tactical, and operational decisions by simultaneously determining (a) the number and location of distribution centers (DCs) to be opened, (b) the assignment of retailers to DCs, (c) the frequency and cycle interval of replenishment and delivery, and (d) the safety stock level for each item. An intelligent algorithm based on particle swarm optimization (PSO) and adaptive differential evolution (ADE) is proposed to address this complex problem. Numerical experiments verified the effectiveness of the proposed two-stage PSO-ADE algorithm. A sensitivity analysis is presented to reveal interesting insights that can guide managers in making reasonable decisions.

In order to explore the effect of low temperature treatment on the quality of yellow tea, this study took fresh leaves of ‘Jinxuan’ tea variety with one bud and one leaves as raw materials, taking natural lying at room temperature (25 ℃) as control, set 3 low temperature (10, 5, 0 ℃) and 5 treatment durations (8, 16, 24, 32, 40 h). The appearance characteristics of fresh leaves were observed and the relative conductivity, fat-soluble pigment and volatile components were determined. The sensory quality of processed yellow tea was evaluated and the main quality components were analyzed. The results showed that compared with the control group, the low temperature treatment of fresh leaves significantly increased the relative electrical conductivity, effectively reduced the chlorophyll content, and the aroma characteristics of volatiles in fresh leaves were significantly changed. Compared with the control yellow tea, low temperature treatment could effectively improve the yellow tea yellowing degree; After being treated at 10, 5 and 0 ℃ for 8 h, the contents of water extract and soluble sugar of tea increased significantly, while the contents of free amino acids in 5 ℃ and 0 ℃ treatment groups increased significantly. At the same time, the proportion of volatile substances with flower and fruit flavor in dry tea aroma increased. In conclusion, low temperature treatment of fresh leaves is helpful to improve the quality of ‘Jinxuan’ yellow tea, improve its taste mellow and aroma fragrance, and the best treatment temperature is 5~10 ℃ and duration is 16~24 h.

In order to explore the effect of low temperature treatment on the quality of Jinxuan white tea in the early stage of withering, the fresh leaves of Jinxuan tea variety with one bud and two leaves were used as raw materials. In the early stage of withering, the smart refrigerator was used for low temperature and different length treatment. The white tea was made according to the indoor natural withering and drying process. The aroma and taste of white tea were analyzed by sensory evaluation, gas chromatography-mass spectrometry and electronic tongue. The results showed that the quality of white tea processed at low temperature (5 ℃) treatment in the early withering stage was significantly different from that of the control. Low temperature treatment improved the persistence of white tea aroma and the intensity of sweet aroma and flower aroma, and increased the types and contents of fragrant volatile substances. The white tea treated at 5 ℃ for 40 h increased 21 kinds of fragrant volatile substances compared with the control. The total amount of fragrant volatile substances of white tea treated at 5 ℃ for 30 h increased the most, about 5 times that of the control. At the same time, low temperature treatment could increase the alcohol thickness of white tea taste and reduce the bitter taste. The taste sweetness of white tea treated at 5 ℃ for 50 h was the highest, which increased by 7.28% compared with the control. Therefore, low temperature treatment in the early stage of fresh leaves withering was helpful to improve the quality of Jinxuan white tea. The best comprehensive quality was the white tea treated at 5 ℃ for 30 h.

Abstract Background Uterine adenomyosis is associated with chronic pelvic pain, abnormal uterine bleeding, and infertility. The pathogenesis of adenomyosis is still unclear. Circular RNAs (circRNAs) have been implicated in several benign diseases and malignant tumors. We aimed to explore the co-dysregulated circular RNA profile in the eutopic endometrium and endometrial–myometrial interface (EMI) of adenomyosis. Methods Total RNA was extracted from the eutopic endometrium and EMI of 5 patients with adenomyosis and 3 patients without adenomyosis. Next-generation sequencing was performed to identify the circRNA expression profile of the two tissue types. Bioinformatics analysis was performed to predict circRNA-binding miRNAs and miRNA-binding mRNAs and construct ceRNA networks, and functional enrichment analysis was performed to predict the biological functions of circRNAs. Results Among the adenomyosis patients, 760 circRNAs were significantly upregulated and 119 circRNAs were significantly downregulated in the EMI of adenomyosis, while 47 circRNAs were significantly upregulated and 17 circRNAs were significantly downregulated in the eutopic endometrium of adenomyosis. We identified hsa_circ_0002144 and hsa_circ_0005806 as co-upregulated and hsa_circ_0079536 and hsa_circ_0024766 as co-downregulated in the eutopic endometrium and EMI. Bioinformatics analysis was performed to construct a ceRNA network of codifferentially expressed circRNAs. The MAPK signaling pathway is the most important signaling pathway involved in the function of the ceRNA network. Conclusions Co-dysregulated circRNAs were present in the eutopic endometrium and EMI of adenomyosis. MiRNA binding sites were observed for all of these circRNAs and found to regulate gene expression. Co-dysregulated circRNAs may induce the eutopic endometrial invagination process through the MAPK signaling pathway and promote the progression of adenomyosis.

To better conserve the ecology of the wild Rhododendron mucronulatum range, we studied the rhizosphere microenvironment of R. mucronulatum in Beijing’s Yunmeng Mountain National Forest Park. R. mucronulatum rhizosphere soil physicochemical properties and enzyme activities changed significantly with temporal and elevational gradients. The correlations between soil water content (SWC), electrical conductivity (EC), organic matter content (OM), total nitrogen content (TN), catalase activity (CAT), sucrose-converting enzyme activity (INV), and urease activity (URE) were significant and positive in the flowering and deciduous periods. The alpha diversity of the rhizosphere bacterial community was significantly higher in the flowering period than in the deciduous period, and the effect of elevation was insignificant. The diversity of the R. mucronulatum rhizosphere bacterial community changed significantly with the change in the growing period. A network analysis of the correlations revealed stronger linkages between the rhizosphere bacterial communities in the deciduous period than in the flowering period. Rhizomicrobium was the dominant genus in both periods, but its relative abundance decreased in the deciduous period. Changes in the relative abundance of Rhizomicrobium may be the main factor influencing the changes in the R. mucronulatum rhizosphere bacterial community. Moreover, the R. mucronulatum rhizosphere bacterial community and soil characteristics were significantly correlated. Additionally, the influence of soil physicochemical properties on the rhizosphere bacterial community was larger than that of enzyme activity on the bacterial community. We mainly analyzed the change patterns in the rhizosphere soil properties and rhizosphere bacterial diversity of R. mucronulatum during temporal and spatial variation, laying the foundation for further understanding of the ecology of wild R. mucronulatum.

Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs.Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs.Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5–12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot).Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

BackgroundPatients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients.MethodsWe collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups.ResultsCompared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores.ConclusionThis study reveals that MCS patients’ frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

Organizations that compete for attention in the marketplace face a strategic decision: whether to target a specialized niche or diversify to reach a broader market. Previous research has extensively analyzed the specialization dilemma faced by for-profit firms. We extend the analysis to knowledge-sharing groups in the marketplace of ideas. Using data on over 1,500 technology groups collected from an online event-organizing platform over a fifteen-year period, we measure the effect of topical focus, rarity, novelty, and technical exclusivity on audience growth, retention, and sustained engagement. We find that knowledge-sharing groups benefit marginally by specializing in rare topics but not in new topics. The strongest predictor of growth and survival is whether the group is associated with technically sophisticated topics, regardless of the breadth of focus, even though technical topics are less widely accessible. We conclude that what matters is not how specialized the organization, but how the organization is specialized.

IntroductionThe endometrial microbiota plays an essential role in the health of the female reproductive system. However, the interactions between the microbes in the endometrium and their effects on adenomyosis remain obscure.Materials and methodsWe profile endometrial samples from 38 women with (n=21) or without (n=17) adenomyosis to characterize the composition of the microbial community and its potential function in adenomyosis using 5R 16S rRNA gene sequencing.ResultsThe microbiota profiles of patients with adenomyosis were different from the control group without adenomyosis. Furthermore, analysis identified Lactobacillus zeae, Burkholderia cepacia, Weissella confusa, Prevotella copri, and Citrobacter freundii as potential biomarkers for adenomyosis. In addition, Citrobacter freundii, Prevotella copri, and Burkholderia cepacia had the most significant diagnostic value for adenomyosis. PICRUSt results identified 30 differentially regulated pathways between the two groups of patients. In particular, we found that protein export, glycolysis/gluconeogenesis, alanine, aspartate, and glutamate metabolism were upregulated in adenomyosis. Our results clarify the relationship between the endometrial microbiota and adenomyosis.DiscussionThe endometrial microbiota of adenomyosis exhibits a unique structure and Citrobacter freundii, Prevotella copri, and Burkholderia cepacia were identified as potential pathogenic microorganisms associated with adenomyosis. Our findings suggest that changes in the endometrial microbiota of patients with adenomyosis are of potential value for determining the occurrence, progression, early of diagnosis, and treatment oadenomyosis.

Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error

As the world’s largest developing country, China has played an important role in the achievement of the global CO2 emissions mitigation goal. The monitoring and analysis of CO2 emissions in the Yangtze River Economic Belt (YREB) urban agglomerations is strategic to the carbon peak and carbon neutrality in China. In this paper, we revealed the spatial and temporal variations of CO2 emissions in Cheng-Yu urban agglomeration (CY-UA), Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and Yangtze River Delta urban agglomeration (YRD-UA) in YREB and investigated the carbon emission development stage of YREB urban agglomerations. Particularly, a carbon emission development stage framework that considered the relationship between economic growth and carbon emissions was built based on Environmental Kuznets Curves (EKCs). Meanwhile, multiscale geographically weighted regression (MGWR) was used to analyze the impact of different influencing factors, including population (POP), GDP per capita (GDPPC), the proportion of secondary industry (SI), carbon emission intensity (CI), and urbanization (UR), on the CO2 emissions of three urban agglomerations. The results illustrate the following: (1) The CO2 emissions of YREB urban agglomerations decreased, with YRD-UA having the highest CO2 emissions among the three urban agglomerations and contributing 41.87% of YREB CO2 emissions in 2017. (2) CY-UA, YRMR-UA, and YRD-UA reached the CO2 emissions peak in 2012, 2011, and 2020, respectively, all of which are at the low-carbon stage. (3) POP and GDPPC show the greatest impact on the CO2 emissions of the three YREB urban agglomerations.

BackgroundNeural reorganization occurs after a stroke, and dynamic functional network connectivity (dFNC) pattern is associated with cognition. We hypothesized that dFNC alterations resulted from neural reorganization in post-stroke cognitive impairment (PSCI) patients, and specific dFNC patterns characterized different pathological types of PSCI.MethodsResting-state fMRI data were collected from 16 PSCI patients with hemorrhagic stroke (hPSCI group), 21 PSCI patients with ischemic stroke (iPSCI group), and 21 healthy controls (HC). We performed the dFNC analysis for the dynamic connectivity states, together with their topological and temporal features.ResultsWe identified 10 resting-state networks (RSNs), and the dFNCs could be clustered into four reoccurring states (modular, regional, sparse, and strong). Compared with HC, the hPSCI and iPSCI patients showed lower standard deviation (SD) and coefficient of variation (CV) in the regional and modular states, respectively (p < 0.05). Reduced connectivities within the primary network (visual, auditory, and sensorimotor networks) and between the primary and high-order cognitive control domains were observed (p < 0.01).ConclusionThe transition trend to suboptimal states may play a compensatory role in patients with PSCI through redundancy networks. The reduced exploratory capacity (SD and CV) in different suboptimal states characterized cognitive impairment and pathological types of PSCI. The functional disconnection between the primary and high-order cognitive control network and the frontoparietal network centered (FPN-centered) incomplete compensation may be the pathological mechanism of PSCI. These results emphasize the flexibility of neural reorganization during self-repair.

In recent years, when planning and determining a travel destination, residents often make the best of Internet techniques to access extensive travel information. Search engines undeniably reveal visitors' real-time preferences when planning to visit a destination. More and more researchers have adopted tourism-related search engine data in the field of tourism prediction. However, few studies use search engine data to conduct cluster analysis to identify residents' choice toward a tourism destination. In the present study, 146 keywords related to “Beijing tourism” are obtained from Baidu index and principal component analysis (PCA) is applied to reduce the dimensionality of keywords obtained by Baidu index. Modified affinity propagation (MAP) clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing. The result shows that residents in Hebei province are most likely to travel to Beijing. The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means, linkage, and Affinity Propogation (AP) in terms of silhouette coefficient and Calinski–Harabaz index. We also distinguish the difference of residents’ choice to travel to Beijing during the peak tourist season and off-season. The residents of Tianjing are inclined to travel to Beijing during the peak tourist season. The residents of Guangdong, Hebei, Henan, Jiangsu, Liaoning, Shanghai, Shandong, and Zhejiang have high attention to travel to Beijing during both seasons.

Abstract The transient characteristics of short‐circuit current (SCC) in doubly‐fed induction generator (DFIG) are quite different from the widely researched conventional synchronous machines. However, the second harmonic characteristics of SCC that impact the reliability of the transformer protection have not been accurately analysed. This paper presents the generation mechanism of second harmonic SCC in DFIG by deducing the dynamic response of rotor‐side converter, with particular attention to the influences of the voltage drop, harmonic component of stator voltage, frequency coupling, and deviation of the phase‐locked loop. When grid fault occurs, the terminal voltage sag leads to fluctuations in the active power, then the response of converter to power fluctuation and the frequency coupling leads to the second harmonic current in the abc reference frame. And the second harmonic current injected into the grid leads to fluctuations in the voltage disturbance term and deviation of the phase‐locked loop, which, in turn, affects the second harmonic current. Based on the analysis, the mitigation measures for the second harmonic current are proposed. Simulations are implemented to verify the theoretical analysis.

BackgroundStroke is an important cause of cognitive impairment. Rich club organization, a highly interconnected network brain core region, is closely related to cognition. We hypothesized that the disturbance of rich club organization exists in patients with post-stroke cognitive impairment (PSCI).MethodsWe collected data on resting-state functional magnetic resonance imaging (rs-fMRI) with 21 healthy controls (HC), 16 hemorrhagic stroke (hPSCI), and 21 infarct stroke (iPSCI). 3D shape features and first-order statistics of stroke lesions were extracted using 3D slicer software. Additionally, we assessed cognitive function using the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE).ResultsNormalized rich club coefficients were higher in hPSCI and iPSCI than HC at low-degree k-levels (k = 1–8 in iPSCI, k = 2–8 in hPSCI). Feeder and local connections were significantly decreased in PSCI patients versus HC, mainly distributed in salience network (SN), default-mode network (DMN), cerebellum network (CN), and orbitofrontal cortex (ORB), especially involving the right and left caudate with changed nodal efficiency. The feeder and local connections of significantly between-group difference were positively related to MMSE and MoCA scores, primarily distributed in the sensorimotor network (SMN) and visual network (VN) in hPSCI, SN, and DMN in iPSCI. Additionally, decreased local connections and low-degree ϕnorm(k) were correlated to 3D shape features and first-order statistics of stroke lesions.ConclusionThis study reveals the disrupted low-degree level rich club organization and relatively preserved functional core network in PSCI patients. Decreased feeder and local connections in cognition-related networks (DMN, SN, CN, and ORB), particularly involving the caudate nucleus, may offer insight into pathological mechanism of PSCI patients. The shape and signal features of stroke lesions may provide an essential clue for the damage of functional connectivity and the whole brain networks.

This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition (VMD) is adopted to decompose the wind speed data into multiple subseries where each subseries contains unique local characteristics, and all the subseries are converted into two-dimensional samples. (b) A gated recurrent unit (GRU) is sequentially modeled based on the obtained samples and makes the predictions for future wind speed. (c) The grid search with rolling cross-validation (GSRCV) is designed to simultaneously optimize the key parameters of VMD and GRU. To evaluate the effectiveness of the proposed VMD-GRU-GSRCV model, comparative experiments based on hourly wind speed data collected from the National Renewable Energy Laboratory are implemented. Numerical results show that the root mean square error, mean absolute error, mean absolute percentage error, and symmetric mean absolute percentage error of this proposed model reach 0.2047, 0.1435, 3.77%, and 3.74%, respectively, which outperform the benchmark predictions using popular parameter optimization methods, data processing techniques, and hybrid neural network forecasting models.

Stroke causes alterations in local spontaneous neuronal activity and related networks functional connectivity. We hypothesized that these changes occur in patients with post-stroke cognitive impairment (PSCI). Fractional amplitude of low-frequency fluctuations (fALFF) was calculated in 36 patients with cognitive impairment, including 16 patients with hemorrhagic stroke (hPSCI group), 20 patients with ischemic stroke (iPSCI group). Twenty healthy volunteers closely matched to the patient groups with respect to age and gender were selected as the healthy control group (HC group). Regions with significant alteration were regarded as regions of interest (ROIs) using the one-way analysis of variance, and then the seed-based functional connectivity (FC) with other regions in the brain was analyzed. Pearson correlation analyses were performed to investigate the correlation between functional indexes and cognitive performance in patients with PSCI. Our results showed that fALFF values of bilateral posterior cingulate cortex (PCC)/precuneus and bilateral anterior cingulate cortex in the hPSCI group were lower than those in the HC group. Compared with the HC group, fALFF values were lower in the superior frontal gyrus and basal ganglia in the iPSCI group. Correlation analysis showed that the fALFF value of left PCC was positively correlated with MMSE scores and MoCA scores in hPSCI. Besides, the reduction of seed-based FC values was reported, especially in regions of the default-mode network (DMN) and the salience network (SN). Abnormalities of spontaneous brain activity and functional connectivity are observed in PSCI patients. The decreased fALFF and FC values in DMN of patients with hemorrhagic and SN of patients with ischemic stroke may be the pathological mechanism of cognitive impairment. Besides, we showed how to use fALFF values and functional connectivity maps to specify a target map on the cortical surface for repetitive transcranial magnetic stimulation (rTMS).

Research in psychology, pedagogy, and physiology has shown that learning a second language will be a great benefit to preschool children. Compared with adults, children have more advantages in learning, such as better pronunciation and intonation. However, because of children’s inattention, the effects of the second language education in many preschools are not ideal in China. To make children learn the second language effectively, it should be integrated into their daily lives and in line with their interests. In this paper, an educational robot system with object recognition technology is introduced, which aims to provide innovative second language learning services for preschool children in China. The proposed system combines object recognition and projection with English teaching and makes objects in daily life more interesting with projected animation to attract children’s attention. On the projection screen, children can interact with a robot by touch or movement and can easily trigger more interactive effects. To evaluate the effectiveness of the proposed system, we conducted an experiment, and the results showed that the system can improve the language learning efficiency for the Chinese preschool children by interacting with objects under proper guidance.

Objective: Peritonitis is a severe complication in peritoneal dialysis (PD). This study was performed to identify predictors and establish a risk score for treatment failure in peritonitis patients.Methods: A single-center, retrospective observational study was conducted. The basic demographic characteristics, clinical and laboratory data of all patients with peritonitis during the study period were documented and analyzed. Multivariate logistic regression was applied to examine independent predictors of treatment failure, and a risk prediction score was established.Results: Three hundred fourteen episodes experienced by 241 patients were included in the final analysis. Logistic regression analysis indicated that PD duration (OR 1.017; P 0.005), fibrinogen (OR 1.327; P 0.021), high-density lipoprotein (OR 0.443; P 0.032), fungal infection (OR 63.413; P < 0.001), intestinal obstruction (OR 5.186, P 0.007), and diabetes mellitus (OR 2.451; P 0.018), hemodialysis history (OR 2.804, P 0.006) were independent predictors of treatment failure. The risk prediction score system showed a good calibration (P > 0.05) and discrimination (AUROC 0.80, P < 0.001).Conclusions: Fibrinogen, PD duration, fungal infection, hemodialysis history, concurrent intestinal obstruction, or diabetes mellitus were independent risk factors for a poor peritonitis outcome, while the high-density lipoprotein was a protective factor. This novel risk prediction score system may be used to predict a high risk of treatment failure effectively.

Objective Adequate dialysis is of great importance for continuous ambulatory peritoneal dialysis (CAPD) patients. This study aimed to develop and validate an easily applicable quantitative dialysis adequacy risk scoring system in CAPD patients based on laboratory parameters from a single blood draw. Methods A total of 634 CAPD patients from four study centers were enrolled in this study (345 and 289 patients in development and validation groups, respectively). A risk score model for inadequate dialysis was developed based on multivariate regression analysis, which was validated by the area under the receiver operator curve and calibrated by a calibration curve. Results Seven independent predictors for inadequate dialysis were identified in the development group (male sex, hypoalbuminemia, anemia, being overweight, hyperuricemia, estimated glomerular filtration rate 800 μmol/L). A risk prediction score model was established and validated in the development and validation groups. Further analysis indicated that this model is suitable for CAPD patients with a wide range of clinical manifestations. Conclusion An easily applicable novel risk scoring system was established to detect inadequate dialysis in CAPD patients.

The abnormal diameter of the coronary artery is twice or more than the normal diameter, which is a coronary artery aneurysm (CAA). According to the clinical statistics, CAA shows high occurrence on right coronary artery (RCA). The most common cause of CAA in adults is atherosclerosis, which destroys the elastic fibers in the middle layer of the blood vessel. Under the intravascular pressure, the weak wall bulges outward and form CAA. This article aims to explain the hemodynamic mechanism of coronary artery aneurysm shows high occurrence on RCA. Occurrence of CAA was simulated by the volume growth of coronary artery. Firstly, a 0–3D multi-scale model of normal coronary artery was constructed to obtain the hemodynamic environments of coronary artery. Then, fluid-structure interaction of normal and atherosclerotic blood vessel was performed to obtain volume growth rate of the coronary artery. Atherosclerosis was simulated by modifying Young’s modulus in middle layer of the blood vessel. Finally, creep simulation was performed to compare the deformation of the blood vessels under the accumulation of time. Under normal condition, the volume growth rate of the RCA is 2.28 times and 1.55 times of the LAD and the LCX. After atherosclerosis, the volume growth rate of the RCA was 2.69 times and 2.12 times of the LAD and the LCX. And the volume growth rate of the RCA was 3.85 times and 3.45 times of the LAD and the LCX after further deepening of atherosclerosis. The expansion time above the average volume growth rate of the RCA, the LAD and the LCX respectively were 0.194, 0.168 and 0.179 s. The RCA is 2.06 times the original, the LAD and LCX are 1.53 times and 1.56 times after 10 years in creep simulation. It can be concluded that the RCA is more prone to aneurysms originated from the larger expansion of the RCA under normal physiological condition, and the larger expansion is magnified under atherosclerosis condition with destroyed vessel elasticity, and further magnified during the time accumulated viscoelastic creep to develop to aneurysm eventually.

To explore hemodynamic interaction between the human respiratory system (RS) and cardiovascular system (CVS), here we propose an integrated computational model to predict the CVS hemodynamics with consideration of the respiratory fluctuation (RF). A submodule of the intrathoracic pressure (ITP) adjustment is developed and incorporated in a 0-1D multiscale hemodynamic model of the CVS specified for infant, adolescent, and adult individuals. The model is verified to enable reasonable estimation of the blood pressure waveforms accounting for the RF-induced pressure fluctuations in comparison with clinical data. The results show that the negative ITP caused by respiration increases the blood flow rates in superior and inferior vena cavae; the deep breathing improves the venous return in adolescents but has less influence on infants. It is found that a marked reduction in ITP under pathological conditions can excessively increase the flow rates in cavae independent of the individual ages, which may cause the hemodynamic instability and hence increase the risk of heart failure. Our results indicate that the present 0-1D multiscale CVS model incorporated with the RF effect is capable of providing a useful and effective tool to explore the physiological and pathological mechanisms in association with cardiopulmonary interactions and their clinical applications.

This study investigates three processes of multidirectional forging (MDF), namely, closed MDF (CMDF), single-open MDF, and double-open MDF, by using a constitutive equation and a dynamic recrystallization model of hot deformation of the GH4169 superalloy. The microstructure evolution of the three processes is simulated and compared. Among the three processes, the double-open MDF obtains the highest recrystallization degree, followed by the CMDF and the single-open MDF under the same reduction. The recrystallization degree of CMDF reaches 99.5% at 1000 °C and 9 passes, and the average recrystallized grain size is small, which is approximately 8.1 μm. The double-open MDF can obtain a fine grain size of forgings at 9 passes and 1000 °C, and it is easy to obtain forgings with the single-open MDF with uniform performance. The temperature is 850 °C–1000 °C, the compression rate is 0.15–0.2, and the pass is 5–9, which are the suitable parameter selection ranges for the CMDF. The temperature is 950 °C–1000 °C, the compression rate is 0.1–0.2, and the pass is 7–9, which are the suitable parameter selection ranges for single-open MDF. The temperature is 850 °C–1000 °C, the compression rate is 0.1–0.2, and the pass is 6–9, which are the suitable parameter selection ranges for the double-open MDF.

Few studies have explored the role of Cenozoic tectonic evolution in shaping the patterns and processes of extant animal distributions in and around East Asia. In this study, we selected South Chinese brown frogs as a model to examine the phylogenetic and biogeographical consequences of Miocene tectonic events within South China and its margins. We used mitochondrial and nuclear molecular data to reconstruct phylogenetic interrelationships among Chinese brown frogs using Bayesian and maximum likelihood analyses. The phylogeny results show that there are four main clades of Chinese brown frogs. Excepting the three commonly known Chinese brown frog species groups, R. maoershanensis forms an independent clade nearest to the R. japonica group. Phylogeny and P-distance analyses confirmed R. maoershanensis as a valid species. Among South Chinese brown frogs, there are four subclades associated with four geographical areas: (I) R. maoershanensis; (II) R. japonica; (III) R. chaochiaoensis; and (IV) other species of the R. longicrus species group. Divergence times, estimated using mitochondrial sequences, place the vicariance events among the four subclades in the middle to late Miocene epoch. Our results suggest that (1) South Chinese brown frogs originated due to a vicariance event separating them from the R. chensinensis species group at the time of the Geological movement (~18 million years ago, Ma) in southern Tibet and the Himalayan region; (2) the separation and speciation of R. maoershanensis from the R. japonica group occurred due to the dry climate at approximately 16 Ma; (3) South Chinese brown frogs migrated from South China to Japan at the time (~10.8 Ma) that the global sea-level fell and the East China Sea Shelf Basin was swamp facies, when a land gallery may have formed across the sea to connect the two areas; and (4) R. chaochiaoensis separated from other species of the R. longicrus species group during the uplift of the Tibetan Plateau at approximately 9.5 Ma.

The distribution area of moose in China has been shrinking back toward the north and northeast because of climate change and human disturbance, and the population number has been declining. Between 2011 and 2015, we studied moose at six sites in the northeast of China during the snowy seasons. We collected fecal samples and plant samples that were used to estimate population densities for moose, as well as their macro-nutrient selection. Out of a total of 257 fecal samples collected at six sites, we identified a total of 120 individual moose (57 females and 63 males). The population density (moose/km2 ± SE) was highest at Hanma with 0.305 ± 0.064 moose/km2 and lowest at Meitian with only 0.028 ± 0.013 moose/km2. Forage availability was different among sites, with the lowest availability at Mohe (58.17 number/20 m2) and highest was Zhanhe (250.44 number/20 m2). Moose at Zhanhe, Hanma, and Nanwenghe had a balanced diet with higher N:C (1:7), while at Meitian, Shuanghe and Mohe the N:C was 1:8. Our results indicate that the southern areas had low forage quality and quantity and this may be the reason for the distribution of the population of moose shrinking northward.

This paper presented a double-open multidirectional forging with relatively few deformation passes and a uniform deformation. The constitutive equation and dynamic recrystallization model of the GH4169 superalloy were identified based on a thermal compression test and imported into Deform simulation software. The microstructure evolution law of GH4169 superalloy undergoing double-open multidirectional forging was simulated. The evolution of the recrystallization volume fraction and recrystallized grain size of the GH4169 superalloy during double-open multidirectional forging was obtained. Both higher temperatures and more passes were found to produce more complete recrystallization and smaller recrystallization grain size. At the maximum temperature studied, 1000 °C, with nine passes, the recrystallization volume fraction exceeded 95%, and the recrystallized grain size reached 3−5.5 µm.