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Motivation Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge. Results We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 h. We also demonstrated that our pipeline could be applied to the vascular analysis. Availability and implementation The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.

Lake Ulansuhai, a typical shallow lake in an arid area that is economically and ecologically important along the Yellow River, is currently eutrophic. Long-term (2010–2020) data on chlorophyll-a, nutrient, and environmental factors were obtained from three Lake Ulansuhai monitoring stations. The temporal and spatial distribution characteristics of Chl-a were analyzed. Additionally, a hybrid evolutionary algorithm was established to simulate and predict Chl-a, and sensitivity analysis revealed the interaction between environmental factors and eutrophication. The results indicated that (1) the seasonal variation of eutrophication showed an obvious trend of spring > summer > autumn > winter, and the concentration of Chl-a in the inlet was significantly higher than that in the outlet; (2) The inlet, center, and outlet of Ulansuhai Lake are satisfactorily affected by HEA in the best suited method. The fitting coefficients (R2) of the optimal models were 0.58, 0.59, and 0.62 for the three monitoring stations, and the root mean square errors (RMSE) were 3.89, 3.21, and 3.56, respectively; (3) under certain range and threshold conditions, Chl-a increased with the increase of permanganate index, water temperature, dissolved oxygen concentration, and ammonia nitrogen concentration, but decreased with the increase of water depth, Secchi disk depth, pH, and fluoride concentration. The results indicate that the HEA can simulate and predict the dynamics of Chl-a, and identify and quantify the relationships between eutrophication and the threshold data. The research results provide theoretical basis and technical support for the prediction and have great significance for the improvement of water quality and environmental protection in arid and semi-arid inland lakes.

Wearable electronics have enriched daily lives by providing smart functions as well as monitoring body health conditions. However, the realization of wearable electronics with personal healthcare and thermal comfort management of the human body is still a great challenge. Furthermore, manufacturing such on-skin wearable electronics on traditional thin-film substrates results in limited gas permeability and inflammation. Herein, we proposed a personal healthcare and thermal management smart textile with a three-dimensional (3D) interconnected conductive network, formed by silver nanowires (AgNWs) bridging lamellar structured transition-metal carbide/carbonitride (MXene) nanosheets deposited on nonwoven fabrics. Benefiting from the interconnected conductive network synergistic effect of one-dimensional (1D) AgNWs bridging two-dimensional (2D) MXene, the strain sensor exhibits excellent durability (>1500 stretching cycles) and high sensitivity (gauge factor (GF) = 1085) with a wide strain range limit (∼100%), and the details of human body activities can be accurately recognized and monitored. Moreover, thanks to the excellent Joule heating and photothermal effect endowed by AgNWs and MXene, the multifunctional smart textile with direct temperature visualization and solar-powered temperature regulation functions was successfully developed, after further combination of thermochromic and phase-change functional layers, respectively. The smart textiles with a stretchable AgNW-MXene 3D conductive network hold great promise for next-generation personal healthcare and thermal management wearable systems.

In this study, the potential effects of N, N’-dodecanedioic bis(cyclopentanecarboxylic acid) dihydrazide (BCADD) as a new additive in poly(L-lactic acid) (PLLA) was estimated. The comparative study on the melt-crystallization showed that the BCADD as heterogeneous nuclei facilitated crystallization of PLLA in cooling, which indicated by the obvious crystallization exotherms and sharp melt-crystallization peak. Unfortunately, with increasing of BCADD from 0.5 wt% to 3 wt%, it is unexpected that the melt-crystallization peak of the BCADD-nucleated PLLA shifted toward the lower temperature and became flatter, evidencing the importance of BCADD loading for PLLA’s crystallization. Additionally, the cooling rate and the final melting temperature were also proved to be important influence factors during PLLA’s melt-crystallization process, but in contrast with the effect of the final melting temperature on the melt-crystallization, a higher cooling rate could more seriously weaken crystallization ability of the BCADD-nucleated PLLA. The chemical nucleation mechanism was proposed to explain the promoting effect of BCADD on the crystallization of PLLA via the analysis of frontier orbital energy. The melting behaviors after crystallization further confirmed the crystallization accelerating role of BCADD, and the melting behaviors were affected by the heating rate, crystallization temperature and BCADD loading. Although the onset thermal decomposition of the BCADD-nucleated PLLA occurred at lower temperature comparing with the pure PLLA, the intermolecular interaction of PLLA with BCADD attempted to prevent the decrease of thermal stability. Overall, the addition of BCADD resulted in the complicated effect on the tensile modulus and tensile strength of PLLA, but the elongation at break continuously decreased when increasing BCADD loading.

Monte Carlo computation has been widely applied in the field of dynamic systems. This paper focuses on the general framework in the implementation of sequential importance sampling by combining red...

Background:Maternal depression was an increasing risk of child neurodevelopment difficulties. However, maternal depression was always chronic and recurrent, and few studies have explored the association between the severity and duration of maternal depression and child development. We aimed to explore the relationship between trajectories of maternal depressive symptoms from pregnancy to the first six months postpartum and child development at eight months.Methods:Data was collected from 988 mother-child pairs who participated in Shenzhen Birth Cohort Study. Maternal depressive symptoms were evaluated by the Edinburgh Postnatal Depression Scale (EPDS) at four points: late pregnancy, 1 month, 3 months, and 6 months postpartum. Child emotional and behavioral developments were assessed by Ages and Stages Questionnaires: Social-Emotional (ASQ-SE) and Ages and Stages Questionnaires-Third Edition (ASQ-3) at 8 months postpartum. Latent profile class analysis (LPA) was used to identify the trajectories of maternal depressive symptoms. Univariate and multivariate linear regression were conducted to explore the association between the depressive symptoms trajectories and child development.Results:Four trajectories of maternal depressive symptoms were identified by LPA: low(n=605), subclinical(n=93), moderate-low and increasing(n=255) and persistent high (54). Multivariable regressions showed that children of mothers assigned to persistent high depressive symptoms were more likely to have low scores in ASQ-3 total, three ASQ-3 domains (fine, problem-solving, and personal-society motor) and ASQ-se. Children of mothers assigned to class2 were more likely to have lower scores in ASQ-3 total, two ASQ-3 domains (communication motor and gross motor) and ASQ-se, compared to children of mothers assigned to class1. Risk factors identified in the multi-model included low birth weight, sleep quality at late pregnancy, low education level, and breastfeeding at 3month.Conclusion:The impact of high levels of maternal depression symptoms on the increased risk of child developmental delay highlights the importance of early intervention and addressing maternal depression from pregnancy through early childhood. These findings draw attention to the need to move beyond a “depressed/not depressed” approach to screening for depression in the perinatal period.

Flexible transparent electrodes demand high transparency, low sheet resistance, as well as excellent mechanical flexibility simultaneously, however they still remain to be a great challenge due to"trade-off" effect. Herein, inspired by a hollow interconnected leaf vein, we developed robust transparent conductive mesh with biomimetic interwoven structure via hierarchically self-assembles silver nanowires interwoven metal carbide/nitride (MXene) sheets along directional microfibers. Strong interfacial interactions between plant fibers and conductive units facilitate hierarchically interwoven conductive mesh constructed orderly on flexible and lightweight veins while maintaining high transparency, effectively avoiding the trade-off effect between optoelectronic properties. The flexible transparent electrodes exhibit sheet resistance of 0.5 Ω sq

For the linear combinator of the slope vector in the multiple measurement error model, existing confidence interval is so seriously affected by Gleser–Hwang effect that it is subject to have poor e...

Since the emergence of the novel coronavirus in December 2019, it has rapidly swept across the globe, with a huge impact on daily life, public health and the economy around the world. There is an urgent necessary for a rapid and economical detection method for the Covid-19. In this study, we used the transformers-based deep learning method to analyze the chest X-rays of normal, Covid-19 and viral pneumonia patients. Covid-Vision-Transformers (CovidViT) is proposed to detect Covid-19 cases through X-ray images. CovidViT is based on transformers block with the self-attention mechanism. In order to demonstrate its superiority, this research is also compared with other popular deep learning models, and the experimental result shows CovidViT outperforms other deep learning models and achieves 98.0% accuracy on test set, which means that the proposed model is excellent in Covid-19 detection. Besides, an online system for quick Covid-19 diagnosis is built on http://yanghang.site/covid19.

Climate change is going to be one that presents major challenges facing pastoral system, especially in arid and semi-arid region. How pastoralists perceive and adapt has become increasingly important part in providing foundation for households and governments to develop adaptation policies. This article aims to investigate the pastoralists' perceptions on climatic change variability and impacts, in addition, to explore the pastoralists' adaptation strategies and determinants using a Multivariate Probit Model, as well as barriers and needs in the adaptive process. We collected questionnaires from the pastoral areas across four districts in Inner Mongolia. The findings revealed that pastoralists' perception of the inter-annual temperature variation is relatively coincide with actual meteorological data. This study found that 11 adaptation strategies have been commonly used by pastoralists, moreover, household production capital, risk-buffering capacity, and social network influenced the pastoralists' adaptations. However, barriers remain for pastoralists, such as inadequate capital and labor, lack of water, limited access to credit, technological knowledge, and timely weather information. Further, our findings indicated that pastoralists need hazard relief fund, improved pasture facilities, technical knowledge training, active weather-based information, integrated pest and disease management and off-farm employment opportunities. This combination of our findings potentially provides some support for developing appropriate and long-term specific policies to mitigate the adverse impacts of climate change.

This paper focuses on inference based on the confidence distributions of the nonparametric regression function and its derivatives, in which dependent inferences are combined by obtaining informati...

The rapid development of information technology has brought great changes to the field of education, and the information technology has also been upgrading in an unprecedented speed. As the ideological and political course plays the role of the main position and channel of national moral education, it is an urgent issue to be solved by the teachers of this course in colleges and universities that how to take advantage of the emerging information technology, improve the teaching methods of this course, increase the classroom interests and enhance the teaching effectiveness of it, and improve the teaching ability of the teachers of this course. By describing the positive role of the emerging information technology in the teaching of the ideological and political course, this paper expounds the importance of the emerging information technology in the teaching of the ideological and political course. After that, it analyzes the buckets effect phenomenon existing in the information-based teaching of the ideological and political course in colleges and universities, points out several effective ways to promote the deep combination of the information technology and the teaching of the ideological and political course, and provides beneficial references for the organic combination of the ideological and political course and the information technology in colleges and universities.

In this paper, we study the metric geometric mean introduced by Pusz and Woronowicz and the spectral geometric mean introduced by Fiedler and Pt\'ak, originally for positive definite matrices. The relation between $t$-metric geometric mean and $t$-spectral geometric mean is established via log majorization. The result is then extended in the context of symmetric space associated with a noncompact semisimple Lie group. For any Hermitian matrices $X$ and $Y$, So's matrix exponential formula asserts that there are unitary matrices $U$ and $V$ such that $$e^{X/2}e^Ye^{X/2} = e^{UXU^*+VYV^*}.$$ In other words, the Hermitian matrix $\log (e^{X/2}e^Ye^{X/2})$ lies in the sum of the unitary orbits of $X$ and $Y$. So's result is also extended to a formula for adjoint orbits associated with a noncompact semisimple Lie group., Comment: 14 pages, 2 figures, latest revision on Aug 28, 2021

Increasing evidence indicates that the Aquaporin1 (AQP1) aberrant expression may be related to a wide variety of human cancers, including breast cancer (BC). In the present study, we explore the effects and possible mechanism of miR-3194-3p on the biological behaviors of BC. At first, miR-3194-3p is found to modulate AQP1 expression targeting the 3′-UTR using miRNA target prediction algorithms. MiR-3194-3p expression is markedly downregulated, and AQP1 expression is upregulated in BC tissues compared with adjacent normal breast tissues. Moreover, the differential expression of miR-3194-3p and AQP1 are observed in four BC cells with different malignancy degree. Meanwhile, a significant negative correlation between AQP1 and miR-3194-3p expressions in tumor tissues from 30 BC patients is revealed. miR-3194-3p mimic remarkably inhibits cell proliferation, migration, and invasion as well as promotes apoptosis in MDA-MB-231 cells while miR-3194-3p inhibitors exert an opposite role in MCF-7 cells. Dual-luciferase reporter system demonstrates that AQP1 is a direct target gene of miR-3194-3p. Overexpression of AQP1 by pBABE-puro-AQP1 vector partially abrogates the effect of miR-3194-3p mimic in MDA-MB-231 cells. In short, our results suggest that miR-3194-3p suppresses BC cell proliferation, migration, and invasion by targeting AQP1, providing a novel insight into BC tumorigenesis and treatment.

In this paper we propose a Generalized Fiducial Inference inspired method for finding a robust consensus of several independently derived collection of confidence distributions (CDs) for a quantity of interest. The resulting fused CD is robust to the existence of potentially discrepant CDs in the collection. The method uses computationally efficient fiducial model averaging to obtain a robust consensus distribution without the need to eliminate discrepant CDs from the analysis. This work is motivated by a commonly occurring problem in inter-laboratory trials, where different national laboratories all measure the same unknown true value of a quantity and report their CDs. These CDs need to be fused to obtain a consensus CD for the quantity of interest. When some of the CDs appear to be discrepant, simply eliminating them from the analysis is often not an acceptable approach, particularly so in view of the fact that the true value being measured is not known and a discrepant result from a lab may be closer to the true value than the rest of the results. Additionally, eliminating one or more labs from the analysis can lead to political complications since all labs are regarded as equally competent. These considerations make the proposed method well suited for the task since no laboratory is explicitly eliminated from consideration. We report results of three simulation experiments showing that the proposed fiducial approach has better small sample properties than the currently used naive approaches. Finally, we apply the proposed method to obtain consensus CDs for gauge block calibration inter-laboratory trials and measurements of Newton’s constant of gravitation ( G ) by several laboratories.

This article mainly considers interval estimation of the scale and shape parameters of the generalized exponential (GE) distribution. We adopt the generalized fiducial method to construct a kind of new confidence intervals for the parameters of interest and compare them with the frequentist and Bayesian methods. In addition, we give the comparison of the point estimation based on the frequentist, generalized fiducial and Bayesian methods. Simulation results show that a new procedure based on generalized fiducial inference is more applicable than the non-fiducial methods for the point and interval estimation of the GE distribution. Finally, two lifetime data sets are used to illustrate the application of our new procedure.

Twin support vector machine (TSVM) has attracted significant attention in recent years, but it is suitable for solving the single-task learning (STL) problems. It trains each task independently and neglects the relationships among all tasks. Conversely, multi-task learning (MTL) explores the shared information between multiple correlated tasks, which obtains a better classifier than STL. Nevertheless, the existing multi-task twin support vector machines give the same penalties to the misclassified samples. In fact, the misclassified samples play a different role in generating separating hyperplane. Motivated by above studies, we put forward a rough margin-based multi-task ν -twin support vector machine (rough MT- ν -TSVM) in this paper. The proposed rough MT- ν -TSVM gives different penalties to the misclassified samples depending on their positions. It not only takes full advantage of rough ν -TSVM, but also discovers the commonality among tasks and maintains the individuality of each task. Therefore, compared with the state-of-the-art algorithms, our method yields better classification performance. In addition, we apply it to Chinese wine dataset to verify the effectiveness. Finally, the related extensions are further discussed, especially a fast SMO-type decomposition method (SDM) is introduced to handle relatively large-scale problems for acceleration. Comprehensive experiments are conducted on eleven benchmark datasets and an image dataset. The results demonstrate that our proposed algorithm can avoid over-fitting and achieve better classification accuracy, meanwhile it does not increase computational time compared with DMTSVM and MT- ν -TSVM.

Self-driven smart materials have a wide range of applications in smart textiles, artificial muscles, soft robots, human-machine interactions and so on. Flexible electrothermally driven smart materials are essentially required in self-driven smart devices due to their excellent flexibility and simple operation. However, there are still some issues should be solved such as the high driving voltage, how to achieve excellent driving performance at low voltage is highly desired. In this work, three kinds of flexible double-layer electrothermal actuators (ETAs) are made of “U”-shaped graphite paper and polyimide (PI) film. The thermal expansion coefficients of graphite and PI vary widely and graphite has excellent electrothermal performance, which enable flexible double-layer ETA has a good driving performance at low voltage in a short time. The bending angle of the actuator can reach 248.6° and the bending curvature can reach 1.23 cm −1 at 6 V such low driving voltage in ten seconds, meanwhile, exhibits excellent driving performance and high durability. Two ETAs were assembled into a flexible smart gripper and the gripper can achieve the desired driving performance while applying a low voltage. By combining the actuator with the robot design the smart robot that can execute commands for grasping objects smoothly while moving freely. It has great potential in various human-machine interactions fields and intelligent drive fields.

In this paper, we construct a new kind of confidence intervals for the among-group variance component through constructing confidence distribution in one-way random effects model with unequal error variances. At first, a combined asymptotic confidence distribution is constructed for the parameter of interest, then the confidence interval is derived naturally by the property of confidence distribution. Simulation results demonstrate that the new confidence intervals perform very well in terms of both empirical coverage probability and average interval length. Finally, a real data example is illustrated.

In this paper, a new kind of confidence intervals for the common mean of several inverse Gaussian populations are constructed based on a combined confidence distribution. The simulation results demonstrate that the new method is very satisfactory.

In this paper, we make use of least squares idea to construct new fiducial generalized pivotal quantities of variance components in two-component normal mixed linear model, then obtain generalized confidence intervals for two variance components and the ratio of the two variance components. The simulation results demonstrate that the new method performs very well in terms of both empirical coverage probability and average interval length. The newly proposed method also is illustrated by a real data example.

Based on the generalized inference idea, a new kind of generalized confidence intervals is derived for the among-group variance component in the heteroscedastic one-way random effects model. We construct structure equations of all variance components in the model based on their minimal sufficient statistics; meanwhile, the fiducial generalized pivotal quantity (FGPQ) can be obtained through solving an implicit equation of the parameter of interest. Then, the confidence interval is derived naturally from the FGPQ. Simulation results demonstrate that the new procedure performs very well in terms of both empirical coverage probability and average interval length.

In this paper, we construct a new kind of confidence intervals for the parameters of interest through constructing confidence distributions for them in one-way random effects model. At first, we use the method of Singh et al. (Ann Stat 33:159–183, 2005) to derive combined asymptotic confidence distribution, then obtain the confidence interval naturally by the property of confidence distribution. Simulation results demonstrate that the new confidence intervals perform very well in terms of empirical coverage probability and average interval length. Although we focus on confidence interval estimation, our method can also be used to carry out hypothesis tests about the parameters of interest.

SiZer (SIgnificant ZERo crossing of the derivatives) is a powerful scale-space visualization technique for exploratory data analysis. In this paper a new version of SiZer based on regression spline is proposed for comparing multiple regression curves. The new SiZer is constructed on the basis of p-values for testing the equality of multiple regression functions at different locations and scales. Fiducial inference and regression spline are applied to gain the p-values. In addition, multiple testing adjustments are carried out to control the row-wise false discovery rate and family-wise error rate of the proposed SiZer, respectively. The new SiZer is more powerful even in the case of small sample size case due to the good properties of p-value and FDR control. Simulation results show that the new SiZer performs well compared with the existing SiZers. Finally, a real data example is carried out to illustrate its usage in applications.

In this article, we introduce two goodness-of-fit tests for testing normality through the concept of the posterior predictive p-value. The discrepancy variables selected are the Kolmogorov-Smirnov (KS) and Berk-Jones (BJ) statistics and the prior chosen is Jeffreys’ prior. The constructed posterior predictive p-values are shown to be distributed independently of the unknown parameters under the null hypothesis, thus they can be taken as the test statistics. It emerges from the simulation that the new tests are more powerful than the corresponding classical tests against most of the alternatives concerned.

We present some new characterizations of unitary similarity of a square complex matrix to a symmetric matrix. Our approach uses the singular value decomposition. A result of Vermeer is extended in the context of an orthogonal symmetric Lie algebra of the compact type.

Let G be a connected semisimple Lie group and its Lie algebra. Let be the Cartan decomposition corresponding to a Cartan involution θ of . The Killing form B induces a positive definite symmetric bilinear form B θ on defined by B θ(X, Y) = − B(X, θY). Given , we consider the optimization problem where the norm ‖ · ‖ is induced by B θ and K is the connected subgroup of the G with the Lie algebra . We obtain the gradient flow of the corresponding smooth function on the manifold K × ··· × K. Our results give unified extensions of several results of Li, Poon and Schulte-Herbruggen [C.K. Li, Y.T. Poon, and T. Schulte-Herbruggen, Least-squares approximation by elements from matrix orbits achieved by gradient flows on compact Lie groups, Math. Comp. 80 (2011), 1601–1621]. They are also true for reductive Lie groups.

This article presents a new procedure for testing homogeneity of scale parameters from k independent inverse Gaussian populations. Based on the idea of generalized likelihood ratio method, a new generalized p-value is derived. Some simulation results are presented to compare the performance of the proposed method and existing methods. Numerical results show that the proposed test has good size and power performance.