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Abstract Native mucus is heterogeneous, displays high inter-individual variation and is prone to changes during harvesting and storage. To overcome the lack of reproducibility and availability of native mucus, commercially available purified mucins, porcine gastric mucin (PGM) and mucin from bovine submaxillary gland (BSM), have been widely used. However, the question is to which extent the choice of mucin matters in studies of their interaction with polymers as their composition, structure and hence physicochemical properties differ. Accordingly, the interactions between PGM or BSM with two widely used polymers in drug delivery, polyethylene oxide and chitosan, was studied with orthogonal methods: turbidity, dynamic light scattering, and quartz crystal microbalance with dissipation monitoring. Polymer binding and adsorption to the two commercially available and purified mucins, PGM and BSM, is different depending on the mucin type. PEO, known to interact weakly with mucin, only displayed limited interaction with both mucins as confirmed by all employed methods. In contrast, chitosan was able to bind to both PGM and BSM. Interestingly, the results suggest that chitosan interacts with BSM to a greater extent than with PGM indicating that the choice of mucin, PGM or BSM, can affect the outcome of studies of mucin interactions with polymers.

IntroductionTo investigate the protective effects of anisodamine (654-1/654-2) against acute kidney injury (AKI) in LPS-induced septic shock rats and explore its molecular mechanisms.Methods56 rats were randomly divided into 8 groups: control, LPS, LPS + 654-1, and LPS + 654-2 (1.25, 2.5 and 5 mg/kg). The model was evaluated by monitoring MAP, HR, and plasma LD levels. ELISA and biochemical assay kits were used to measure the levels of inflammatory cytokines (IL-1β, IL-6, and TNF-α) and kidney injury markers (BUN and CRE). Additionally, RNA-seq and bioinformatic analysis were performed to explore the mechanism of action of 654-1/654-2, and verification was conducted by western blotting and RT-PCR.Results654-1/654-2 significantly restored the levels of MAP, HR, and plasma LD in septic shock rats. Furthermore, 654-1/654-2 (5 mg/kg) effectively ameliorated LPS-induced kidney structural damage and exhibited a dose-dependent reduction in levels of inflammatory cytokines and kidney injury markers. In addition, RNA-seq, WB, and RT-PCR analyses revealed that 654-1/654-2 exerted its effects by inhibiting the expressions of the NF-κB and MAPK pathways and activating the Pi3K/Akt/Bcl-2 signaling pathway, thereby mitigating AKI.DiscussionThis study suggested that 654-1/654-2 could alleviate AKI in septic shock rats by improving inflammation invasion and cell apoptosis. Notably, 654-1/654-2 collectively suppressed inflammation response through the p38/JNK/AP-1/NF-κB pathway. Additionally, 654-1 promotes survival signaling via the Pi3K/Akt/Bcl-2 pathway, whereas 654-2 reduces apoptosis through the P53/Bax pathway. These findings provided a theoretical basis for the clinical application of 654-1/654-2 in treating organ damage caused by septic shock.

AbstractPurpose This study aimed to improve the accuracy of brain-computer interface (BCI) systems based on motor imagery (MI) and mental arithmetic (MA) by utilizing functional near-infrared spectroscopy (fNIRS) and an improved dilation CapsuleNet (ID-CapsuleNet) model.Methods The study focused on the characteristics of fNIRS and employed large-kernel dilation convolution to extract hemodynamic features from fNIRS data. Inspired by CapsuleNet’s success in image classification, an ID-CapsuleNet model was designed, combining large-kernel dilation convolution and CapsuleNet. Four publicly available datasets (A, B, C, and D) were utilized for evaluating the proposed model. Datasets A and B were MA type, while datasets C and D were MI type. Ablation experiments were conducted to assess the usefulness of large-kernel convolution, dynamic routing, and dilation convolution.Results The average accuracies for each dataset were 95.01%, 76.88%, 74.03%, and 80.29% respectively. Cross-subject average accuracies were 88.72%, 75.80%, 75.78%, and 80.34%. Ablation experiments confirmed the importance of large-kernel convolution, dynamic routing, and dilation convolution in the ID-CapsuleNet model.Conclusion The developed ID-CapsuleNet model demonstrated promising potential for enhancing the performance of BCI systems based on MI and MA. The findings contribute to the advancement of BCI technology, offering improved assistive tools for disabled individuals.

Summary: Previous research indicates that individuals with Internet gaming disorder (IGD) show impaired inhibitory control and abnormal EEG/ERP patterns. However, it is unclear how those individuals with excessive Internet game use (EUG) but without addiction differ. This study examined inhibitory control, resting EEG, and ERP in EUG gamers compared to non-gamers. Fifteen participants in each group underwent 4-min eyes-closed EEG recordings and a color-word Stroop task. Results showed no significant differences in reaction time, accuracy, or P3 amplitude between EUG gamers and non-gamers. However, EUG gamers exhibited shortened P3 latency, which may suggest enhanced inhibitory control. Additionally, EUG gamers showed reduced theta and alpha band power during the resting state compared to non-gamers. These findings suggest that excessive gaming without addiction may enhance inhibitory control and influence brain activity differently from IGD.

Abstract The vaginal microbiome plays an essential role in the reproductive health of human females. As infertility increases worldwide, understanding the roles that the vaginal microbiome may have in infertility and in vitro fertilization (IVF) treatment outcomes is critical. To determine the vaginal microbiome composition of 1411 individuals (1255 undergoing embryo transplantation) and their associations with reproductive outcomes, clinical and biochemical features are measured, and vaginal samples are 16S rRNA sequenced. Our results suggest that both too high and too low abundance of Lactobacillus is not beneficial for pregnancy; a moderate abundance is more beneficial. A moderate abundance of Lactobacillus crispatus and Lactobacillus iners (~80%) (with a pregnancy rate of I‐B: 54.35% and III‐B: 57.73%) is found beneficial for pregnancy outcomes compared with a higher abundance (>90%) of Lactobacillus (I‐A: 44.81% and III‐A: 51.06%, respectively). The community state type (CST) IV‐B (contains a high to moderate relative abundance of Gardnerella vaginalis) shows a similar pregnant ratio (48.09%) with I‐A and III‐A, and the pregnant women in this CST have a higher abundance of Lactobacillus species. Metagenome analysis of 71 samples shows that nonpregnant women are detected with more antibiotic‐resistance genes, and Proteobacteria and Firmicutes are the main hosts. The inherent differences within and between women in different infertility groups suggest that vaginal microbes might be used to detect infertility and potentially improve IVF outcomes.

The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS) unified framework to summarize the correlation analysis (CA)-based SSVEP spatial filtering methods from a machine learning perspective. Within this framework, the commonalities and differences between various spatial filtering methods appear apparent, the interpretation of computational factors becomes intuitive, and spatial filters can be determined by solving a generalized optimization problem with non-linear and regularization items. Moreover, the proposed LS framework provides the foundation of utilizing the knowledge behind these spatial filtering methods in further classification/regression model designs. Through a comparative analysis of existing representative spatial filtering methods, recommendations are made for the superior and robust design strategies. These recommended strategies are further integrated to fill the research gaps and demonstrate the ability of the proposed LS framework to promote algorithmic improvements, resulting in five new spatial filtering methods. This study could offer significant insights in understanding the relationships between various design strategies in the spatial filtering methods from the machine learning perspective, and would also contribute to the development of the SSVEP recognition methods with high performance.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain–computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG $\boldsymbol{\mu }$ and $\boldsymbol{\beta }$ rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

We propose a novel reception scheme for twin single-sideband (twin-SSB) signals using just a single photodetector (PD), significantly reducing the system complexity and cost. To detect a twin-SSB with power-unbalanced quadrature-phase shift keying (QPSK) sidebands upon detection via a single PD at the receiver side, two QPSKs carried in two sidebands are coherently superposed and detected in a 16-ary quadrature amplitude modulation (16-QAM) format. This technique notably diminishes the linearity and effective number of bits required for the transmitter components in high-speed optical transmission systems. Moreover, a hierarchical blind-phase search (HBPS) algorithm is proposed to compensate for the imperfect phase rotation of QPSK signals during transmission. To demonstrate the effectiveness of our proposed method, we successfully conducted simulations of 112 Gb/s 16-QAM signal transmission over a 10 km standard single-mode fiber (SSMF), achieving bit error ratios (BERs) of 7.84×10−4, well below the 7% hard-decision forward error correction (HD-FEC) threshold of 3.8×10−3. In addition, the synthetic transmission scheme proposed in this paper is compared with the traditional 16-QAM signal transmission scheme, and the results show that the proposed scheme does not introduce a performance cost with the same received optical power (ROP) and transmission distance.

The lower valence compensation of YMn1-xCuxO3 (x = 0.00, 0.05, and 0.10) is prepared by the solid-state reaction, and the effects of divalent cation Cu-doping on the construction and magnetic and dielectric attributes of multiferroic YMnO3 are systemically researched. Powder X-ray diffraction shows YMn1-xCuxO3 has a single-phase hexagonal construction with a P63cm space group as the parent YMnO3, and lattice parameters decrease systematically as Cu concentration increases. Using the scanning electric microscope, structure morphologies analysis shows that the mean grain size varies between 1.90 and 2.20 μm as Cu content increases. YMn1-xCuxO3 magnetization increases as Cu doping concentration increases, and the antiferromagnetic transition temperature declines from 71 K for x = 0.00 to 58 K for x = 0.10. The valence distributions of Mn ions conduce to the modified magnetic attributes. Due to Cu substitution, the dielectric loss and dielectric constant decline as frequency increases from 400 to 700 K, showing representative relaxation behaviors. Indeed, that is a thermally activated process. In addition, the peak of the dielectric loss complies with the Arrhenius law. The relaxation correlates to the dipole effect regarding carrier hopping between Mn3+ and Mn4+, and also correlates to oxygen vacancies generated by Mn2+.

Abstract Glioblastoma (GBM) is a lethal cancer characterized by hypervascularity and necrosis associated with hypoxia. Here, it is found that hypoxia preferentially induces the actin‐binding protein, Transgelin (TAGLN), in GBM stem cells (GSCs). Mechanistically, TAGLN regulates HIF1α transcription and stabilizes HDAC2 to deacetylate p53 and maintain GSC self‐renewal. To translate these findings into preclinical therapeutic paradigm, it is found that sodium valproate (VPA) is a specific inhibitor of TAGLN/HDAC2 function, with augmented efficacy when combined with natural borneol (NB) in vivo. Thus, TAGLN promotes cancer stem cell survival in hypoxia and informs a novel therapeutic paradigm.

Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p

Abstract Signal transducer and activator of transcription 4 (STAT4) is a critical transcription factor for T helper cell differentiation and tumor cells. Although its prognostic role and gene function have been reported in several carcinomas, the role of STAT4 in vitro and in vivo in breast cancer remains poorly understood. The effect of STAT4 in immunotherapy is also unclear. Therefore, we integrated bulk transcriptomics, experiments, and single‐cell transcriptomics to systematically analyze its function in prognosis and signaling pathway. Several clinical breast cancer cohorts confirmed STAT4 as a T‐cell relevant prognostic biomarker. Overexpressed STAT4 increased programmed cell death ligand 1 (PD‐L1) and major histocompatibility complex class II levels in breast cancer cells. In molecular mechanism, transcriptional synergy between STAT4 and STAT3 transactivated interleukin (IL)‐12R and involved a positive feedback loop: STAT4/IL‐12R/JAK2–STAT3–STAT4, which contributed to the upregulation of PD‐L1 expression. The above signaling axis was defined as the STAT4‐related pathway and its score was used to predict T‐cell expansion and anti‐PD1 treatment response. These findings highlight a novel molecular mechanism indirectly regulating PD‐L1 through the STAT4‐related pathway: IL‐12R/JAK2–STAT3–STAT4/PD‐L1, and it has potential application in predicting anti‐PD‐1 immunotherapy response, which may pave the way for stratified immunotherapy in breast cancer.

Strong-field quantum electrodynamics (SF-QED) plays a crucial role in ultraintense laser–matter interactions and demands sophisticated techniques to understand the related physics with new degrees of freedom, including spin angular momentum. To investigate the impact of SF-QED processes, we have introduced spin/polarization-resolved nonlinear Compton scattering, nonlinear Breit–Wheeler, and vacuum birefringence processes into our particle-in-cell (PIC) code. In this article, we provide details of the implementation of these SF-QED modules and share known results that demonstrate exact agreement with existing single-particle codes. By coupling normal PIC simulations with spin/polarization-resolved SF-QED processes, we create a new theoretical platform to study strong-field physics in currently running or planned petawatt or multi-petawatt laser facilities.

Background: Acute ischemic stroke (AIS) is a leading cause of death and disability worldwide. This study aimed to evaluate the efficacy and safety of anisodine hydrobromide (Ani) injection in the treatment of AIS.Methods: Randomized controlled trials (RCTs) based on Ani injection for the treatment of AIS were retrieved from both Chinese and English databases. The retrieval period was from the databases’ inception to May 2023. The Cochrane Collaboration Risk of Bias Tool was used to assess the methodological quality. The outcome indicators were analyzed using RevMan 5.3 software.Results: We included the findings of 11 RCTs encompassing 1,337 patients with AIS. Our meta-analysis revealed that Ani injection supplementation significantly reduced the National Institutes of Health Stroke Scale [MD = −1.53, 95%CI = (−1.94, −1.12), p < 0.00001], modified Rankin Scale [MD = −0.89, 95%CI = (−0.97, −0.81), p < 0.00001], and the relative time to peak [SMD = −0.81, 95%CI = (−1.08, −0.55), p < 0.00001] significantly. Additionally, Ani injection significantly increased the Barthel Index [MD = 10.65, 95%CI = (4.30, 17.00), p = 0.001], relative cerebral blood volume [SMD = 0.28, 95%CI = (0.02, 0.53), p = 0.03], and clinical efficacy [RR = 1.2, 95%CI = (1.08, 1.34), p = 0.001]. No statistically significant difference in the rate of adverse events was observed between the Ani injection supplemental group and the control group.Conclusion: Based on currently published evidence, Ani injection was found to be effective and safe in improving AIS outcome. Nevertheless, limitations of the included RCTs still exist, and thus, more multi-center, large-sample, high-quality RCTs are required to further verify the efficacy and safety of Ani injection in patients with AIS.Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427591], identifier [PROSPERO 2023 CRD42023427591].

Background Pathological cardiac hypertrophy is a major cause of heart failure morbidity. The complex mechanism of intermolecular interactions underlying the pathogenesis of cardiac hypertrophy has led to a lack of development and application of therapeutic methods. Methods and Results Our study provides the first evidence that TRAF4, a member of the tumor necrosis factor receptor–associated factor (TRAF) family, acts as a promoter of cardiac hypertrophy. Here, Western blotting assays demonstrated that TRAF4 is upregulated in cardiac hypertrophy. Additionally, TRAF4 deletion inhibits the development of cardiac hypertrophy in a mouse model after transverse aortic constriction surgery, whereas its overexpression promotes phenylephrine stimulation‐induced cardiomyocyte hypertrophy in primary neonatal rat cardiomyocytes. Mechanistically, RNA‐seq analysis revealed that TRAF4 promoted the activation of the protein kinase B pathway during cardiac hypertrophy. Moreover, we found that inhibition of protein kinase B phosphorylation rescued the aggravated cardiomyocyte hypertrophic phenotypes caused by TRAF4 overexpression in phenylephrine‐treated neonatal rat cardiomyocytes, suggesting that TRAF4 may regulate cardiac hypertrophy in a protein kinase B–dependent manner. Conclusions Our results revealed the regulatory function of TRAF4 in cardiac hypertrophy, which may provide new insights into developing therapeutic and preventive targets for this disease.

Objective: To comparatively investigate the ameliorative effect of Phellinus igniarius (P. igniarius) on renal aging in a rat model of focal and segmental glomerulosclerosis (FSGS). Methods: The FSGS model was established in rats by uninephrectomy combined with tail vein injection of doxorubicin. The FSGS rats were randomly divided into the model group, the P. igniarius decoction group, the P. igniarius polysaccharides group, and the P. igniarius polyphenols group. Molecular indicators of cell senescence, renal function indexes, and podocyte injury markers were tested after ten weeks of intragastric administration. Besides, the pathological renal lesions and the ultrastructural changes were observed. Results: FSGS developed in the model group within ten weeks and showed segmental glomerular scarring and renal aging. Following the 10-week intervention, 24 h proteinuria, serum creatinine, blood urea nitrogen, P16INK4α, thrombospondin-1, and transforming growth factor-β1 were decreased in each treatment group, whereas albumin, erythropoietin, nephrin, and podocin were increased; the pathological renal injury was alleviated, and the number of senescent cells was reduced, especially in rats treated with P. igniarius decoction. Conclusions: P. igniarius ameliorates renal aging and renal injury in the FSGS rat model. Compared with the effective constituents (polysaccharides and polyphenols), P. igniarius decoction has a better curative effect, which is expected to provide a new therapeutic idea for FSGS.

Objective: Recently, artificial neural networks (ANNs) have been proven effective and promising for the steady-state visual evoked potential (SSVEP) target recognition. Nevertheless, they usually have lots of trainable parameters and thus require a significant amount of calibration data, which becomes a major obstacle due to the costly EEG collection procedures. This paper aims to design a compact network that can avoid the over-fitting of the ANNs in the individual SSVEP recognition. Method: This study integrates the prior knowledge of SSVEP recognition tasks into the attention neural network design. First, benefiting from the high model interpretability of the attention mechanism, the attention layer is applied to convert the operations in conventional spatial filtering algorithms to the ANN structure, which reduces network connections between layers. Then, the SSVEP signal models and the common weights shared across stimuli are adopted to design constraints, which further condenses the trainable parameters. Results: A simulation study on two widely-used datasets demonstrates the proposed compact ANN structure with proposed constraints effectively eliminates redundant parameters. Compared to existing prominent deep neural network (DNN)-based and correlation analysis (CA)-based recognition algorithms, the proposed method reduces the trainable parameters by more than ${90}\%$ and ${80}\%$ respectively, and boosts the individual recognition performance by at least ${57}\%$ and ${7}\%$ respectively. Conclusion: Incorporating the prior knowledge of task into the ANN can make it more effective and efficient. The proposed ANN has a compact structure with less trainable parameters and thus requires less calibration with the prominent individual SSVEP recognition performance.

Objective: Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance. Approach: To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases. Main results: We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III. Significance: The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.

A brain-computer interface (BCI), which provides an advanced direct human-machine interaction, has gained substantial research interest in the last decade for its great potential in various applications including rehabilitation and communication. Among them, the P300-based BCI speller is a typical application that is capable of identifying the expected stimulated characters. However, the applicability of the P300 speller is hampered for the low recognition rate partially attributed to the complex spatio-temporal characteristics of the EEG signals. Here, we developed a deep-learning analysis framework named ST-CapsNet to overcome the challenges regarding better P300 detection using a capsule network with both spatial and temporal attention modules. Specifically, we first employed spatial and temporal attention modules to obtain refined EEG signals by capturing event-related information. Then the obtained signals were fed into the capsule network for discriminative feature extraction and P300 det- ection. In order to quantitatively assess the performance of the proposed ST-CapsNet, two publicly-available datasets (i.e., Dataset IIb of BCI Competition 2003 and Dataset II of BCI Competition III) were applied. A new metric of averaged symbols under repetitions (ASUR) was adopted to evaluate the cumulative effect of symbol recognition under different repetitions. In comparison with several widely-used methods (i.e., LDA, ERP-CapsNet, CNN, MCNN, SWFP, and MsCNN-TL-ESVM), the proposed ST-CapsNet framework significantly outperformed the state-of-the-art methods in terms of ASUR. More interestingly, the absolute values of the spatial filters learned by ST-CapsNet are higher in the parietal lobe and occipital region, which is consistent with the generation mechanism of P300.

Abstract Biallelic variants in Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) are the main pathogenic factor of congenital absence of the vas deferens (CAVD), including congenital bilateral absence of the vas deferens (CBAVD) and congenital unilateral absence of the vas deferens (CUAVD). However, there are few reports about the correlation between CFTR variant and outcomes of assisted reproductive technology (ART) in CAVD patients of China. In this study, 104 patients with CAVD were recruited in Central China, and provided gene detection by the whole-exome sequencing, among them 69% (72/104) carried at least one variant in CFTR and one carried adhesion G protein-coupled receptor G2 (ADGRG2) variant. A total of 81 CAVD patients were treated with ART, of which 21 and 60 carried none or at least one variant in CFTR, respectively. The fertilization rate, cleavage rate, effective embryo rate, implantation rate, clinical pregnancy rate and live birth rate per fresh embryo transfer were compared between patients with and without CFTR variants. It was found that the ART outcomes had no significant difference whether the patients carried the CFTR variant or not. In addition, all of the offspring were healthy after follow-up. In conclusion, rare CFTR variants may play a major role in patients with CAVD in Central China, which were greatly different from other descent. There was no significant difference in ART outcomes in CAVD patients with or without CFTR variants. The limitations of this study were that there was no statistical analysis of the sperm quality through TESA and conclusions were relatively limited due to the small sample size of the study.

Abstract Background Shone’s syndrome is a rare complex congenital anomaly. The classical definition consists of four anomalies: supravalvular mitral membrane, parachute mitral valve (PMV), subaortic stenosis, and coarctation of the aorta (CoA). Few studies have been reported on Shone’s syndrome in adults, particularly the primary surgical correction of the anomalies. Case presentation A 22-year-old female patient presented with chest distress and tachypnea. Echocardiography and CT revealed supravalvular mitral membrane, PMV, Bicuspid aortic valve stenosis, CoA and patent ductus arteriosus. She underwent primary definitive surgical correction successfully and was discharged from hospital with symptoms free. Conclusions Our case report highlights the importance of echocardiographic evaluation in the diagnosis of Shone’s syndrome. The surgical strategy should be tailored according to both the patient’s profile and the surgeon’s personal surgical experience. Extra-anatomical bypass procedure is an appropriate technique for adult patients with long-segment coarctation and concomitant cardiac lesions. The outcomes of the case study indicate that the primary definitive surgery is encouraging.

Abstract Implementation of quantum gates are important for quantum computations in physical system made of polar molecules. We investigate the feasibility of implementing gates based on pendular states of the molecular system by two different quantum optical control methods. Firstly, the Multi-Target optimal control theory and the Multi-Constraint optimal control theory are described for optimizing control fields and accomplish the optimization of quantum gates. Numerical results show that the controlled NOT gate (CNOT) can be realized under the control of above methods with high fidelities (0.975 and 0.999) respectively. In addition, in order to examine the dependence of the fidelity on energy difference in the same molecular system, the SWAP gate in the molecular system is also optimized with high fidelity (0.999) by the Multi-Constraint optimal control theory with the zero-area and constant-fluence constraints.

Abstract Background Skeletal muscle‐secreted myokines widely participate in lipids metabolism through autocrine, paracrine and endocrine actions. The myokines represented by FGF21 and Irisin can promote the browning of adipocytes and serve as promising targets for treating obesity. Although recombinant myokines replacement therapy and AAV (adeno‐associated virus)‐based myokines overexpression have shown a definite effect in ameliorating obesity, novel myokine activation strategies with higher efficacy and safety are still in pressing need. This study aimed to evaluate the therapeutic potential of a novel CRISPR‐based myokines activation strategy in obesity treatments. Methods In this study, we used lentivirus and a single AAV vector containing dCas9‐VP64 with a single‐guide RNA to selectively activate Fgf21 and Fndc5 expression in skeletal muscles both in vitro and in vivo. The activation efficacy of the CRISPRa system was determined by qRT‐PCR, Western blotting and ELISA. The treatment effect of CRISPR‐based myokines activation was tested in 3T3‐L1‐derived adipocytes and diet‐induced obese (DIO) mice (male C57BL/6 mice, induced at 6‐week‐old for 10 weeks). Results The virus upregulates myokines expression in both mRNA and protein levels of muscle cells in vitro and in vivo. Myokines secreted by muscle cells promoted browning of 3T3‐L1‐derived adipocytes. In vivo activation of myokines by AAVs can reduce body weight and fat mass, increase the adipocytes browning and improve glucose tolerance and insulin sensitivity in DIO mice. Conclusions Our study provides a novel CRISPR‐based myokines activation strategy that can ameliorate obesity by promoting adipocytes browning.

Abstract Purpose This study aimed to analyze the distribution of IgG subclass in diabetic nephropathy (DN) and its association with clinicopathological features. Methods This is a single-center retrospective study enrolling 108 patients with biopsy-proven DN. Immunofluorescence and immunohistochemistry staining were applied, and clinicopathological features and renal outcomes were compared between patients with different patterns or categories of IgG subclass deposition. Results Both IgG and its subclasses colocalized with collagen IV α5 on glomerular basement membrane (GBM) and some of tubular basement membrane (TBM). IgG1 and the Mixed type were two predominant types of deposition, no matter on GBM or TBM, and IgG1 showed a much higher deposition rate on GBM than that on TBM (P = 0.004). IgG subclass deposit on multi-location was more associated with a shorter duration of nephropathy and severer tubular interstitial injury (P

Generation of arbitrarily spin-polarized electron and positron beams has been investigated in the single-shot interaction of high-energy polarized γ-photons with an ultraintense asymmetric laser pulse via nonlinear Breit-Wheeler pair production. We develop a fully spin-resolved semi-classical Monte Carlo method to describe the pair creation and polarization. In the considered general setup, there are two sources of the polarization of created pairs: the spin angular momentum transfer from the polarized parent γ-photons, as well as the asymmetry and polarization of the driving laser field. This allows to develop a highly sensitive tool to control the polarization of created electrons and positrons. Thus, dense GeV lepton beams with average polarization degree up to about 80%, adjustable continuously between the transverse and longitudinal components, can be obtained by our all-optical method with currently achievable laser facilities, which could find an application as injectors of the polarized e+e− collider to search for new physics beyond the Standard Model.

Abstract The recovery of low-quality waste heat is a major problem in energy utilization. In order to solve this problem and improve energy utilization, the research group designed a low-quality waste heat power generation device with Roots power machine as the core. However, the device has poor ability to adjust the rotation speed and it is difficult to generate electricity stably. The fundamental reason is that the system has many variables and strong coupling. According to the actual working conditions, the power of the device is 10 kW, and the fluctuation range should be within ± 7%. On the one hand, it can be improved by hardware, on the other hand, the design of software is also very critical. At present, through the investigation of domestic and foreign researches on the control system, it is found that the stability of the system is gradually improved, but the problem of strong coupling between variables has not been effectively solved. Therefore, the research group modeled the variables in the system and obtained a coupled model. Based on the couple model, the research group introduced nonlinear multi-model adaptive closed-loop decoupling control and designed a control system. The simulation results show that the maximum overshoot of the control system is 3.9%, the adjustment time is also reduced, and it is stable in low quality waste heat recovery device. Experimental results show that under the control of the system, the rotational speed of roots motor can keep stable, the maximum deviation is not more than 21.4 r/min, and the fluctuation range is within ± 7%, which meets the requirements of the index. This has laid the foundation for the follow-up research of grid-connected power generation.

Abstract Aim The aim of this study was to investigate the renoprotective effects of exosomes derived from rat bone marrow mesenchymal stem cells (rBMSCs) in a rat model of 5/6 nephrectomy (Nx)-induced chronic kidney disease (CKD). Methods A rat model of 5/6 Nx-induced CKD was established using conventional method. rBMSC-derived exosomes were isolated using ultracentrifugation and characterized. The exosomes were injected into 5/6 Nx rats through the caudal vein. After 12 weeks, 24 h proteinuria, serum creatinine (SCr), and blood urea nitrogen (BUN) levels were evaluated, and renal pathology was analyzed by H&E and Masson staining, and transmission electron microscopy. The expression of klotho was analyzed and the activity of the klotho promoter was evaluated using a luciferase reporter assay. Results The isolated exosomes showed typical morphological features. Exosomes transplantation reduced 24 h urinary protein excretion, and SCr and BUN levels in 5/6 Nx-induced CKD rats. Furthermore, renal pathology was improved in the exosome-treated 5/6 Nx rats. Mechanistically, the exosomes significantly upregulated the activity of klotho promoter and its expression. Conclusions Transplantation of rBMSC-derived exosomes may protect against kidney injury, probably by regulating klotho activity and expression. Our results provide a theoretical basis for the application of rBMSC-derived exosomes in CKD therapy.

The rapid development of ultrafast ultraintense laser technology continues to create opportunities for studying strong-field physics under extreme conditions. However, accurate determination of the spatial and temporal characteristics of a laser pulse is still a great challenge, especially when laser powers higher than hundreds of terawatts are involved. In this paper, by utilizing the radiative spin-flip effect, we find that the spin depolarization of an electron beam can be employed to diagnose characteristics of ultrafast ultraintense lasers with peak intensities around 1020–1022 W/cm2. With three shots, our machine-learning-assisted model can predict, simultaneously, the pulse duration, peak intensity, and focal radius of a focused Gaussian ultrafast ultraintense laser (in principle, the profile can be arbitrary) with relative errors of 0.1%–10%. The underlying physics and an alternative diagnosis method (without the assistance of machine learning) are revealed by the asymptotic approximation of the final spin degree of polarization. Our proposed scheme exhibits robustness and detection accuracy with respect to fluctuations in the electron beam parameters. Accurate measurements of ultrafast ultraintense laser parameters will lead to much higher precision in, for example, laser nuclear physics investigations and laboratory astrophysics studies. Robust machine learning techniques may also find applications in more general strong-field physics scenarios.

Anisodamine is an anticholinergic drug extracted and isolated from the Anisodus tanguticus (Maxim.) Pascher of the Solanaceae family which is also a muscarinic receptor antagonist. Owing to the lack of natural sources of anisodamine, synthetic products are now used. Using ornithine and arginine as precursor compounds, putrescine is catalyzed by different enzymes and then undergoes a series of reactions to produce anisodamine. It has been used clinically to protect cardiac function and treat septic shock, acute pancreatitis, calculous renal colic, bronchial asthma, blood circulation disturbances, jaundice, analgesia, vertigo, acute poisoning, and other conditions.This review describes the relevant pharmacokinetic parameters. Anisodamine is poorly absorbed in the gastrointestinal tract, and it is not as effective as intravenous administration. For clinical medication, intravenous infusion should be used rather than rapid intravenous injection. With the advancement of research in recent years, the application scope of anisodamine has expanded, with significant developments and application values surging.This review systematically describes the sources, pharmacokinetics, pharmacological effects and clinical application of anisodamine, in order to provide a basis for clinical use.

Triptolide, a controversial natural compound due to its significant pharmacological activities and multiorgan toxicity, has gained much attention since it was isolated from the traditional Chinese herb Tripterygium wilfordii Hook F. However, in addition to its severe toxicity, triptolide also presents powerful therapeutic potency in the same organs, such as the liver, kidney, and heart, which corresponds to the Chinese medicine theory of You Gu Wu Yun (anti-fire with fire) and deeply interested us. To determine the possible mechanisms involved in the dual role of triptolide, we reviewed related articles about the application of triptolide in both physiological and pathological conditions. Inflammation and oxidative stress are the two main ways triptolide exerts different roles, and the cross-talk between NF-κB and Nrf2 may be one of the mechanisms responsible for the dual role of triptolide and may represent the scientific connotation of You Gu Wu Yun. For the first time, we present a review of the dual role of triptolide in the same organ and propose the possible scientific connotation of the Chinese medicine theory of You Gu Wu Yun, hoping to promote the safe and efficient use of triptolide and other controversial medicines.

A novel scheme has been proposed to enhance neutron yields, in which a multi-channel target consisting of a row of parallel micro-wires and a plane substrate is irradiated by a relativistic femtosecond laser. Two-dimensional particle-in-cell simulations show that the multi-channel target can significantly enhance the neutron yield, which is about 4 orders of magnitude greater than the plane target. Different from the case of nanowire target, we find that when the laser penetrates into the channel, the excited transverse sheath electric field can effectively accelerate the D+ ions in the transverse direction. When these energetic D+ ions move towards the nearby wire, they will collide with the bulk D+ ions to trigger D-D fusion reaction and produce neutrons, which is much more effective than the plane target case. Due to the unique trajectory of the incident D+ ions, the angular distribution of the produced neutrons is modulated from isotropic to two peaks around ±90°. Meanwhile, this enhancement and modulation is further verified in a wide range of target parameters.

BackgroundSingle recurrence in the sub-frontal region after cerebellar medulloblastoma (MB) resection is rare and the underlying molecular characteristics have not been specifically addressed.MethodsWe summarized two such cases in our center. All five samples were molecularly profiled for their genome and transcriptome signatures.ResultsThe recurrent tumors displayed genomic and transcriptomic divergence. Pathway analysis of recurrent tumors showed functional convergence in metabolism, cancer, neuroactive ligand–receptor interaction, and PI3K-AKT signaling pathways. Notably, the sub-frontal recurrent tumors had a much higher proportion (50–86%) of acquired driver mutations than that reported in other recurrent locations. The acquired putative driver genes in the sub-frontal recurrent tumors functionally enriched for chromatin remodeler-associated genes, such as KDM6B, SPEN, CHD4, and CHD7. Furthermore, the germline mutations of our cases showed a significant functional convergence in focal adhesion, cell adhesion molecules, and ECM–receptor interaction. Evolutionary analysis showed that the recurrence could be derived from a single primary tumor lineage or had an intermediate phylogenetic similarity to the matched primary one.ConclusionRare single sub-frontal recurrent MBs presented specific mutation signatures that might be related to the under-dose radiation. Particular attention should be paid to optimally covering the sub-frontal cribriform plate during postoperative radiotherapy targeting.

Pyrochlore has been extensively studied as one of the most ideal candidates for nuclear wastes immobilization, due to its excellent geological stability, radiation tolerance, low chemical leaching rate, and high thermal stability. However, the artificial pyrochlore has a relatively high selectivity for curing nuclides, which limits the types and quantity of its target species. To tackle the challenge, a variety of simulated nuclides(Eu, Sm and Nd) were successfully incorporated into the pyrochlore lattice by solid-state reactions. The experimental results show that the multi-pyrochlore exhibits a homogeneous structure with single phase. Leaching tests indicate that the multi-pyrochlore owns excellent leaching resistance and chemical stability, which enables multi-pyrochlore a promising immobilization material for high-level waste.

The dynamic balance of intestinal flora is one of the important signs of maintaining human health, and the development of many human diseases have been confirmed to be closely related to intestinal flora disorders. Environment is a crucial factor affecting intestinal flora, which can be further divided into conventional environment and extreme environment, however there are few studies on the interrelationship between the extreme environment and the intestinal flora, which is of great significance for the special crowds who work under special environment. Therefore, this article focuses on extreme environment, exploring the changes of human intestinal flora in the plateau, aerospace, navigation and other environments, as well as the regulation of intestinal flora through dietary intervention, in order to provide references for adapting to extreme environments and improving the health of special populations.

Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, etc., in EEG signals would lead to a positive or a negative masking effect on deep learning-based sleep staging systems was investigated in this paper. We systematically analyzed several traditional pre-processing methods involving fast Independent Component Analysis (FastICA), Information Maximization (Infomax), and Second-order Blind Source Separation (SOBI). On top of these methods, a SOBI-WT method based on the joint use of the SOBI and Wavelet Transform (WT) is proposed. It offered an effective solution for suppressing artifact components while retaining residual informative data. To provide a comprehensive comparative analysis, these pre-processing methods were applied to eliminate the intra-artifacts and the processed signals were fed to two ready-to-use deep learning models, namely two-step hierarchical neural network (THNN) and SimpleSleepNet for automatic sleep staging. The evaluation was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDF Expanded, and a clinical dataset that was collected in Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed SOBI-WT method increased the accuracy from 79.0% to 81.3% on MASS, 83.3% to 85.7% on Sleep-EDF Expanded, and 75.5% to 77.1% on HSFU compared with the raw EEG signal, respectively. Experimental results demonstrate that the intra-artifacts bring out a masking negative impact on the deep learning-based sleep staging systems and the proposed SOBI-WT method has the best performance in diminishing this negative impact compared with other artifact elimination methods.

The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms requires a large amount of labelled data. Data labelling is often done with a lot of human efforts, which is very time-consuming. In this study, we propose a hybrid system integrating an unsupervised learning (UL) module and a supervised learning (SL) module, where the UL module can significantly reduce the workload of data labelling. For preliminary seizure screening, UL synthesizes amplitude-integrated EEG (aEEG) extraction, isolation forest-based anomaly detection, adaptive segmentation, and silhouette coefficient-based anomaly detection evaluation. The UL module serves to quickly locate the determinate subjects (seizure segments and seizure-free segments) and the indeterminate subjects (potential seizure candidates). Afterwards, more robust seizure detection for the indeterminate subjects is performed by the SL using an EasyEnsemble algorithm. EasyEnsemble, as a class-imbalance learning method, can potentially decrease the generalization error of the seizure-free segments. The proposed method can significantly reduce the workload of data labelling while guaranteeing satisfactory performance. The proposed seizure detection system is evaluated using the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG dataset, and it achieves a mean accuracy of 92.62%, a mean sensitivity of 95.55%, and a mean specificity of 92.57%. To the best of our knowledge, this is the first epilepsy seizure detection study employing the integration of both the UL and the SL modules, achieving a competitive performance superior or similar to that of the state-of-the-art methods.

Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05 ± 8.03, p = 0.001), FMA shoulder/elbow (6.2 ± 5.94, p = 0.0004) and FMA wrist/hand (4.3 ± 2.83, p = 0.007), and WMFT (5.1 ± 5.53, p = 0.037). The improvement of FMA was significantly correlated with BCI accuracy (r = 0.714, p = 0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.

At the site of continuous rolling of lithium battery electrode, most of its correction units use traditional PID control algorithms, coupled with the complex structure of the rolling equipment and the large number of control system actuators, result in the deviation of the pole piece position being controlled within 10mm only. In order to avoid the undesirable industrial problems caused by untimely correction and insufficient control accuracy, this paper proposes a control strategy for lithium battery roll press deflection device with the introduction of GA(Genetic Algorithm) optimized integral separation PID. For the problems of poor self-adaptive capability of deflection control and interference with equipment and environment during the rolling process. Analyze the causes of runout of the pole piece and the structure of the deflection correction system, establish a mathematical model and an integral separation fuzzy PID controller to realize the deflection control. Optimize the key parameters of the controller using GA to improve the control accuracy and performance of the deflection control. Simulations and experiments show that the deviation control strategy proposed in this paper can reduce the position deviation of the polar strip to within 4mm, which can effectively improve the deviation correction accuracy and the anti-interference capability of equipment operation, and also provide a good solution for the deviation control of other large and complex mechanical equipment.

Background: Coronavirus disease 2019 (COVID-19) pandemic continues to escalate intensively worldwide. Massive studies on general populations with SARS-CoV-2 infection have revealed that pre-existing comorbidities were a major risk factor for the poor prognosis of COVID-19. Notably, 49–75% of COVID-19 patients had no comorbidities, but this cohort would also progress to severe COVID-19 or even death. However, risk factors contributing to disease progression and death in patients without chronic comorbidities are largely unknown; thus, specific clinical interventions for those patients are challenging. Methods: A multicenter, retrospective study based on 4806 COVID-19 patients without chronic comorbidities was performed to identify potential risk factors contributing to COVID-19 progression and death using LASSO and a stepwise logistic regression model. Results: Among 4806 patients without pre-existing comorbidities, the proportions with severe progression and mortality were 34.29% and 2.10%, respectively. The median age was 47.00 years [interquartile range, 36.00–56.00], and 2162 (44.99%) were men. Among 51 clinical parameters on admission, age ≥ 47, oxygen saturation < 95%, increased lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen levels, dyspnea, increased blood glucose and prothrombin time levels were associated with COVID-19 mortality in the entire cohort. Of the 3647 patients diagnosed with non-severe COVID-19 on admission, 489(13.41%) progressed to severe disease. The risk factors associated with COVID-19 progression from non-severe to severe illness were increased procalcitonin levels, SpO2 < 95%, age ≥ 47, increased LDH, activated partial thromboplastin time levels, decreased high-density lipoprotein cholesterol levels, dyspnea and increased D-dimer levels. Conclusions: COVID-19 patients without pre-existing chronic comorbidities have specific traits and disease patterns. COVID-19 accompanied by severe bacterial infections, as indicated by increased procalcitonin levels, was highly associated with disease progression from non-severe to severe. Aging, impaired respiratory function, coagulation dysfunction, tissue injury, and lipid metabolism dysregulation were also associated with disease progression. Once factors for multi-organ damage were elevated and glucose increased at admission, these findings indicated a higher risk for mortality. This study provides information that helps to predict COVID-19 prognosis specifically in patients without chronic comorbidities.

Due to the wide variety of production orders and complex processes in the lithium battery cell rolling shop, it is easy to cause conflict deadlock problems when multiple devices are operating at the same time, thus blocking the tasks and affecting productivity. In this paper, we build a task scheduling framework for the rolling shop by combining the production process of lithium battery cells, and establish a production scheduling model for the rolling shop by considering the scheduling rules of order splitting and order prioritization. An improved genetic algorithm is proposed to solve the model with the completion time and delay time as the optimization objectives. First, heuristic rules are combined with GLR initialization methods to generate initial populations to improve population quality. Secondly, a simulated annealing algorithm with an elite retention strategy is introduced to screen the new population, and a neighborhood exchange strategy based on order priority ranking is proposed to overcome the problem that the iterative process is prone to illegal solutions. Finally, the encoding method and mutation repair mechanism are improved, and adaptive crossover and mutation operations are used to improve the search capability of the algorithm. The experimental results show that the improved genetic algorithm is feasible and effective in solving the optimization problem of rolling operation shop scheduling, and the algorithm has certain application value in improving the efficiency of smart shop operation.

Tension control is very important for the intelligent control system of rapier looms because stable tension guarantees a tight fabric structure, moderate elasticity and good forming. To solve the problems of low-tension measurement accuracy of the existing rapier loom, the complex structure of the tension control strategy and algorithm, and the high research and development cost, this paper proposes new research from the perspective of high-precision and nonlinear processing of tension detection signals. The median filtering and limiting filtering algorithms are integrated to solve the uncertainty problem caused by the disturbance and fluctuation of the tension signal, and an excellent sample dataset is obtained. The attenuation factor and the number of learning times are introduced to design and adjust the learning rate of the back propagation neural network algorithm. In addition, the overfitting problem of the backpropagation neural network model in the current research process is solved. The experimental simulation results show that the tension detection fluctuation range of this method is 0.8 kg, and the tension detection error is within 0.1%. On the basis of the existing tension control algorithm, the tension detection accuracy is improved, which presents a new research perspective for the wide application of high-precision tension control strategies in rapier looms. It is of great importance to improve the production efficiency and fabric quality of rapier looms.

Traffic prediction aims to predict the future traffic state by mining features from history traffic information, and it is a crucial component for the intelligent transportation system. However, most existing traffic prediction methods focus on road segment prediction while ignore the fine-grainedlane-level traffic prediction. From observations, we found that different lanes on the same road segment have similar but not identical patterns of variation. Lane-level traffic prediction can provide more accurate prediction results for humans or autonomous driving systems to make appropriate and efficient decisions. In traffic prediction, the mining of spatial features is an important step and graph-based methods are effective methods. While most existing graph-based methods construct a static adjacent matrix, these methods are difficult to respond to spatio-temporal changes in time. In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. The data-driven adjacent matrix consists of the fundamental distance-based adjacent matrix and the dynamic lane correlation matrix. The temporal features are extracted with the gated recurrent unit (GRU). Then, we adaptively fuse spatial and temporal features with the gating mechanism to get the final spatio-temporal features for lane-level traffic prediction. Extensive experiments on a real-world dataset validate the effectiveness of our model.

A mid-story-isolated structure is developed from a base-isolated structure. Mid-story-isolated structures located in sloping ground have become a research hotspot in recent years. It is important to consider the soil–structure interaction (SSI) effects and multi-dimensional earthquakes on these structures. This paper established a model of the mid-story-isolated structure considering SSI in sloping ground. An elastic–plastic time history analysis was carried out under the one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) earthquakes. Under 3D earthquakes, the traditional 2D isolated bearing has limited damping capacity. Therefore, two kinds of 3D isolated bearings were designed. Results show that the seismic response of the mid-story-isolated structure considering SSI in sloping ground can be amplified compared with that of the mid-story-isolated structure without considering SSI. The seismic response of the structure under 3D earthquakes is more significant than that under 2D earthquakes and 1D earthquakes. For the two kinds of 3D isolated bearings, the minimum reduction rate of tensile and compressive stress is about 46% compared with that of the traditional 2D isolated bearings. When the 3D isolated bearings are used, the stress of the soil foundation decreases, which is more conducive to the stability of the soil foundation.

A scheme, applying a linearly polarized laser irradiating on a T-type target which contains a longitudinal target followed by a transverse target, is proposed to collimate the terahertz radiation. The results show that the interaction between the laser and the longitudinal target can effectively reduce the electron transverse divergence while increasing the electron maximum cut-off energy. In this way, the terahertz radiation can be well collimated while enhancing its intensity. By using two-dimensional particle-in-cell simulations, we show that the pointing angle is about −13.6° and 17.5° when the length and thickness of the longitudianl target are 90.0 μm and 4.0 μm, respectively. In addition, the impact of the laser and target parameters on the collimation of terahertz radiation are also investigated separately. Such a scheme may pave a new way for enhancing and collimating the terahertz radiation via laser-solid interactions.

Stem cell-based therapy has been used to treat ischaemic heart diseases for two decades. However, optimal cell types and transplantation methods remain unclear. This study evaluated the therapeutic effects of human umbilical cord mesenchymal stem cell (hUCMSC) sheet on myocardial infarction (MI). Methods: hUCMSCs expressing luciferase were generated by lentiviral transduction for in vivo bio-luminescent imaging tracking of cells. We applied a temperature-responsive cell culture surface-based method to form the hUCMSC sheet. Cell retention was evaluated using an in vivo bio-luminescent imaging tracking system. Unbiased transcriptional profiling of infarcted hearts and further immunohistochemical assessment of monocyte and macrophage subtypes were used to determine the mechanisms underlying the therapeutic effects of the hUCMSC sheet. Echocardiography and pathological analyses of heart sections were performed to evaluate cardiac function, angiogenesis and left ventricular remodelling. Results: When transplanted to the infarcted mouse hearts, hUCMSC sheet significantly improved the retention and survival compared with cell suspension. At the early stage of MI, hUCMSC sheet modulated inflammation by decreasing Mcp1-positive monocytes and CD68-positive macrophages and increasing Cx3cr1-positive non-classical macrophages, preserving the cardiomyocytes from acute injury. Moreover, the extracellular matrix produced by hUCMSC sheet then served as bioactive scaffold for the host cells to graft and generate new epicardial tissue, providing mechanical support and routes for revascularsation. These effects of hUCMSC sheet treatment significantly improved the cardiac function at days 7 and 28 post-MI. Conclusions: hUCMSC sheet formation dramatically improved the biological functions of hUCMSCs, mitigating adverse post-MI remodelling by modulating the inflammatory response and providing bioactive scaffold upon transplantation into the heart. Translational perspective: Due to its excellent availability as well as superior local cellular retention and survival, allogenic transplantation of hUCMSC sheets can more effectively acquire the biological functions of hUCMSCs, such as modulating inflammation and enhancing angiogenesis. Moreover, the hUCMSC sheet method allows the transfer of an intact extracellular matrix without introducing exogenous or synthetic biomaterial, further improving its clinical applicability.

Human alpha oscillation (7–13 Hz) has been extensively studied over the years for its connection with cognition. The individual alpha frequency (IAF), defined as the frequency that provides the highest power in the alpha band, shows a positive correlation with cognitive processes. The modulation of alpha activities has been accomplished through various approaches aimed at improving cognitive performance. However, very few studies focused on the direct modulation of IAF by shifting the peak frequency, and the understanding of IAF modulation remains highly limited. In this study, IAFs of healthy young adults were up-regulated through short-term neurofeedback training using haptic feedback. The results suggest that IAFs have good trainability and are up-regulated, also that IAFs are correlated with the enhanced cognitive performance in mental rotation and n-back tests compared to sham-neurofeedback control. This study demonstrates the feasibility of self-regulating IAF for cognition enhancement and provides potential therapeutic benefits for cognitive-impaired patients.

Abstract Background This study aimed to analyze the clinicopathological characteristics of patients with paraproteinemia and renal damage. Methods Ninety-six patients from 2014 to 2018 with paraproteinemia and renal damage were enrolled and the clinical data, renal pathology, treatment and prognosis data were collected. Results A total of 96 patients (54 male and 42 female), accounting for 2.7% of all renal biopsies, were enrolled in this study. Among them, 42 were monoclonal gammopathy of renal significance (MGRS), 21 were renal monotypic immunoglobulin alone (renal monoIg), and 19 were monoclonal gammopathy of undetermined significance (MGUS). Individuals with multiple myeloma (MM) accounted for the fewest number of patients (n = 14). In the MGRS group, the main diseases were amyloidosis (n = 25) and cryoglobulinemic glomerulonephritis (n = 7), while in the MM group, the main diseases were cast nephropathy (n = 9) and light chain deposit disease (n = 3). In the MGUS group, it was mainly IgA nephropathy (IgAN, n = 10) and idiopathic membranous nephropathy (n = 5); while in the renal monoIg group, most of the cases were IgAN (n = 19). Chemotherapy was mainly administered to patients in the MM group, while immunosuppression therapy was mostly administered to patients in the renal monoIg group. Most patients with renal monoIg exhibited a major response, followed by the patients with MGUS and MGRS, while most of the patients with MM had a partial response but none had a major response. Approximately more than half (57.1%) of the patients with MM progressed to end-stage renal disease (ESRD), followed by MGRS (33.3%); however, the mortality rate was low in both the MGRS and MM groups. The survival analysis reviewed that serum creatinine, hemoglobin levels, and the serum κ/λ ratio were independent risk factors for ESRD in patients with MGRS. Conclusions The clinicopathological changes in patients with MGRS were between those in patients with MM and MGUS. The treatment for MGRS and MM was more intensive, and the overall mortality rate was low. Both MGUS and renal monoIg alone exhibited slighter clinicopathological features than MGRS and MM, and the treatment was focused mostly on primary renal diseases.

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.