Improved mechanical and electrical properties in electrospun polyimide/multiwalled carbon nanotubes nanofibrous composites

Highly aligned polyimide (PI) and PI/multi-walled carbon nanotubes (PI/MWCNTs) nanofibrous composites by incorporating poly(ethylene oxide) as the dispersing medium were fabricated using electrospinning technique. The morphology, mechanical, and electrical properties of the electrospun nanofibrous composites were investigated. Scanning electron microscope showed that the functionalized MWCNTs (f-MWCNTs) were well dispersed and oriented along the nanofiber axis. Analysis of electrical properties indicated a remarkable improvement on the alternating current conductivity by introduction of the aligned f-MWCNTs. Besides, with addition of 3 vol.% f-MWCNTs, the obvious enhancement of tensile modulus and strength was achieved. Thus, the electrospun PI/MWCNTs nanofibrous composites have great potential applications in multifunctional engineering materials

Combined Tracking Strategy Based on Unscented Kalman Filter for Global Positioning System L2C CM/CL Signal

In a global positioning system receiver, the tracking algorithm plays a dominant role since the code delay and Doppler frequency shift need to be accurately estimated as well as their variation over time need to be continuously updated. Combine unscented Kalman filter (UKF) with CM/CL signal to improve the signal tracking precision is proposed. It allow weighting assignment between CM code and CL code incoming signal, masked by a mass of noise, and to describe a UKF tracking loop aiming at decreasing numerical errors. UKF here involves state and measuring equations which calculate absolute offsets to adjust initial code and carrier phase then dramatically decrease the tracking error. In particular, the algorithm is implemented in both open space and jammed environment to highlight the advantages of tracking approach, by comparing single code and combined code, UKF and EKF tracking loop. It proves that signal tracking based on UKF, with low energy dissipation as well as high precision, is particularly appealing for a software receiver implementation

Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach for decoding the oculomotor decision than several classical and state-of-the-art single-view and multi-view learning approaches

A Comment on "A direct approach for determining the switch soints in the Karnik-Mendel algorithm"

This letter is a supplement to the previous paper “A Direct Approach for Determining the Switch Points in the Karnik-Mendel Algorithm”. In the previous paper, the enhanced iterative algorithm with stop condition (EIASC) was shown to be the most inefficient in R. Such outcome is apparently different from the results in another paper in which EIASC was illustrated to be the most efficient in Matlab. An investigation has been made into this apparent inconsistency and it can be confirmed that both the results in R and Matlab are valid for the EIASC algorithm. The main reason for such phenomenon is the efficiency difference of loop operations in R and Matlab. It should be noted that the efficiency of an algorithm is closely related to its implementation in practice. In this letter, we update the comparisons of the three algorithms in the previous paper based on optimised implementations under five programming languages (Matlab, R, Python, C and Java). From this, we conclude that results in one programming language cannot be simply extended to all languages

A Comprehensive Survey on Distributed Training of Graph Neural Networks

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.Comment: To Appear in Proceedings of the IEE

A Comprehensive Study of the Efficiency of Type-Reduction Algorithms

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karnik-Mendel (EKM) and the enhanced iterative algorithm with stopping condition (EIASC). In a previous paper, we found that the computational efficiency of an algorithm is closely related to the platform, and how it is implemented. In computer science, the dependence on languages is usually avoided by focusing on the complexity of algorithms (using big O notation). In this paper, the main contribution is the proposal of two novel type-reduction algorithms. Also, for the first time, a comprehensive study on both existing and new type-reduction approaches is made based on both algorithm complexity and practical computational time under a variety of programming languages. Based on the results, suggestions are given for the preferred algorithms in different scenarios depending on implementation platform and application context

Pharmacokinetic study of isoquercitrin in rat plasma after intravenous administration at three different doses

O objetivo deste estudo é desenvolver um método simples e específico de HPLC usando vitexina como padrão interno para investigar a farmacocinética do isoquercitrina (ISOQ) após três doses diferentes administradas por via intravenosa a ratos. Os parâmetros farmacocinéticos foram calculados pelas abordagens compartimental e não compartimental. Os resultados mostraram que ISOQ se encaixa no modelo de três compartimentos. Os valores de AUC aumentaram proporcionalmente na faixa de 5-10 mg·kg-1. Além disso, a meia-vida, b meia-vida, ªCL, MRT0-t and MRT0→∞ de ISOQ em ratos mostraram diferenças significativas entre 20 mg·kg-1 e outras doses, o que significa que ISOQ apresenta farmacocinética dose-dependente no intervalo de 5-10 mg·kg-1 e farmacocinética não linear em doses mais elevadas.The aim of this study is to develop a simple and specific HPLC method using vitexin as the internal standard to investigate the pharmacokinetics of isoquercitrin (ISOQ) after three different doses administrated intravenously to rats. The pharmacokinetic parameters were calculated by both compartmental and non-compartmental approaches. The results showed that ISOQ fitted a three-compartment open model. The values of AUC increased proportionally within the range of 5-10 mg·kg-1. Moreover, a half-life, b half-life, ªCL, MRT0-t and MRT0→∞ of ISOQ in rats showed significant differences between 20 mg·kg-1 and other doses, indicating that ISOQ presented dose-dependent pharmacokinetics in the range of 5-10 mg·kg-1 and non-linear pharmacokinetics at higher doses

Occupational exposure in swine farm defines human skin and nasal microbiota

Anthropogenic environments take an active part in shaping the human microbiome. Herein, we studied skin and nasal microbiota dynamics in response to the exposure in confined and controlled swine farms to decipher the impact of occupational exposure on microbiome formation. The microbiota of volunteers was longitudinally profiled in a 9-months survey, in which the volunteers underwent occupational exposure during 3-month internships in swine farms. By high-throughput sequencing, we showed that occupational exposure compositionally and functionally reshaped the volunteers’ skin and nasal microbiota. The exposure in farm A reduced the microbial diversity of skin and nasal microbiota, whereas the microbiota of skin and nose increased after exposure in farm B. The exposure in different farms resulted in compositionally different microbial patterns, as the abundance of Actinobacteria sharply increased at expense of Firmicutes after exposure in farm A, yet Proteobacteria became the most predominant in the volunteers in farm B. The remodeled microbiota composition due to exposure in farm A appeared to stall and persist, whereas the microbiota of volunteers in farm B showed better resilience to revert to the pre-exposure state within 9 months after the exposure. Several metabolic pathways, for example, the styrene, aminobenzoate, and N-glycan biosynthesis, were significantly altered through our PICRUSt analysis, and notably, the function of beta-lactam resistance was predicted to enrich after exposure in farm A yet decrease in farm B. We proposed that the differently modified microbiota patterns might be coordinated by microbial and non-microbial factors in different swine farms, which were always environment-specific. This study highlights the active role of occupational exposure in defining the skin and nasal microbiota and sheds light on the dynamics of microbial patterns in response to environmental conversion

Proteinuria, Estimated Glomerular Filtration Rate and Urinary Retinol-Binding Protein as Clinical Predictors of Long-Term Allograft Outcomes in Transplant Glomerulopathy

Background/Aims: We aimed to explore the associations between clinical parameters and long-term allograft outcomes in transplant glomerulopathy (TG) in a large retrospective cohort with long follow-up. Methods: Clinical and laboratory data at biopsy from 180 cases of TG with an estimated glomerular filtration rate (eGFR)> 15ml/min/1.73m2 from January 2004 to December 2016 at our center were retrospectively analyzed. The main outcome of this study was initiation of replacement therapy or an eGFR declined to < 15 ml/min/1.73m2. Results: During a median follow-up of 5 years (interquartile range 2.6-8.2 years), 117 cases (65.0%) achieved the combined event. Kaplan-Meier method yielded the 1-year and 5-year cumulative renal allograft survival rates after a histopathologic diagnosis of TG were 84% (95% confidence interval [CI] 81-87%) and 33% (95% CI 27–39%) respectively. In univariate analysis, allograft outcome differed significantly by eGFR, proteinuria, blood hemoglobin level, urinary retinol-binding protein (urRBP) and urinary N-acetyl-β-D-glucosaminidase (urNAG) level at the time of biopsy. Multivariate Cox analysis revealed that a higher level of eGFR was the most powerful predictor of allograft survival. Compared with those with eGFR≥60, the hazard ratio (HR) increased from 4.50 (95% CI: 1.03-19.71, p=0.0462) for patients with eGFR between 30 and 59 ml/min/1.73m2 to 9.14 (95% CI 1.97-42.45, P=0.0047) when eGFR decreased to 15 to 29 ml/min/1.73m2. Additionally, proteinuria and higher urRBP values (≥2.85mg/dl) were found to confer much worse survival rates for TG patients in multivariate Cox analysis. Male sex (HR 0.48, P=0.02) and HCV infection (HR 1.78, P=0.0499) were also found to be independent risk factors for worse allograft survival. Conclusion: Five clinical features—impaired renal function, higher proteinuria, higher urRBP level, male sex and HCV infection—are independent predictors of an unfavorable renal allograft outcome. urRBP is a simple and useful parameter that can add invaluable information for the clinical follow-up of patients with TG