[Clinical study on the treatment of premature ejaculation with Nailifu Spray].

OBJECTIVE: To observe the effect of Nailifu Spray on the treatment of premature ejaculation. METHODS: A total of 90 patients were included in this study from January 1, 2022 to January 1, 2023. Nailifu spray was used to spray the surface of penile skin once a day, 2 sprays per session for 4 weeks.And the patients' premature ejaculation diagnostic tool (PEDT) scores, intravaginal ejaculation latency time (IELT), and international index of erectile function-5 (IIEF-5) scores were collected before and after treatment, respectively. RESUTS: The median (P25,P75) PEDT scores was 16.0(15.0,18.0) scores before treatment and 10.0(10.0,10.0) scores after treatment. The median (P25,P75) of IELT was 20.0 (10.0,30.0) s before treatment and 240.0 (180.0,300.0) s after treatment. The median (P25,P75) of IIEF-5 scores was 21.0 (21.0,22.0) scores before treatment and 21.0 (21.0,21.0) scores after treatment. Compared with baseline levels, IELT was significantly longer and PEDT scores were significantly lower, with statistically significant differences. No significant changes in IIEF-5 scores were seen. CONCLUSION: Nailifu spray treatment of premature ejaculation is accurate and effective, worthy of clinical promotion.

Study on the performance of high-voltage deep ultraviolet light-emitting diodes.

This study fabricated high-voltage, low-current DUV-LEDs by connecting two devices. Due to better current spreading and the enhanced reflective mirror effect, high-voltage devices present a higher dynamic resistance, emission output power, wall-plug efficiency, external quantum efficiency, and view angle than single traditional devices. The study found that when the injection current was 320 mA, the maximum output power was exhibited at 47.1 mW in the HV sample. The maximum WPE and EQE of high-voltage DUV-LEDs were 2.46% and 5.48%, respectively. Noteworthily, the redshift wavelength shifted from 287.5 to 280.5 nm, less than the traditional device-from 278 to 282 nm. Further, due to the uniform emission patterns in high-voltage devices, the view angle presents 130 degrees at 100 mA input current. In this study, the high-voltage device showed more excellent properties than the traditional device. In particular, it presented a high potential application in high-voltage circuits, which can remove transformers to eliminate extra power consumption.

Hedgehog signaling contributes to basic fibroblast growth factor-regulated fibroblast migration.

Fibroblast migration is a central process in skin wound healing, which requires the coordination of several types of growth factors. bFGF, a well-known fibroblast growth factor (FGF), is able to accelerate fibroblast migration; however, the underlying mechanism of bFGF regulation fibroblast migration remains unclear. Through the RNA-seq analysis, we had identified that the hedgehog (Hh) canonical pathway genes including Smoothened (Smo) and Gli1, were regulated by bFGF. Further analysis revealed that activation of the Hh pathway via up-regulation of Smo promoted fibroblast migration, invasion, and skin wound healing, but which significantly reduced by GANT61, a selective antagonist of Gli1/Gli2. Western blot analyses and siRNA transfection assays demonstrated that Smo acted upstream of phosphoinositide 3-kinase (PI3K)-c-Jun N-terminal kinase (JNK)-ß-catenin to promote cell migration. Moreover, RNA-seq and qRT-PCR analyses revealed that Hh pathway genes including Smo and Gli1 were under control of ß-catenin, suggesting that ß-catenin turn feedback activates Hh signaling. Taken together, our analyses identified a new bFGF-regulating mechanism by which Hh signaling regulates human fibroblast migration, and the data presented here opens a new avenue for the wound healing therapy.

A patch-based tensor decomposition algorithm for M-FISH image classification.

Multiplex-fluorescence in situ hybridization (M-FISH) is a chromosome imaging technique which can be used to detect chromosomal abnormalities such as translocations, deletions, duplications, and inversions. Chromosome classification from M-FISH imaging data is a key step to implement the technique. In the classified M-FISH image, each pixel in a chromosome is labeled with a class index and drawn with a pseudo-color so that geneticists can easily conduct diagnosis, for example, identifying chromosomal translocations by examining color changes between chromosomes. However, the information of pixels in a neighborhood is often overlooked by existing approaches. In this work, we assume that the pixels in a patch belong to the same class and use the patch to represent the center pixel's class information, by which we can use the correlations of neighboring pixels and the structural information across different spectral channels for the classification. On the basis of assumption, we propose a patch-based classification algorithm by using higher order singular value decomposition (HOSVD). The developed method has been tested on a comprehensive M-FISH database that we established, demonstrating improved performance. When compared with other pixel-wise M-FISH image classifiers such as fuzzy c-means clustering (FCM), adaptive fuzzy c-means clustering (AFCM), improved adaptive fuzzy c-means clustering (IAFCM), and sparse representation classification (SparseRC) methods, the proposed method gave the highest correct classification ratio (CCR), which can translate into improved diagnosis of genetic diseases and cancers. © 2016 International Society for Advancement of Cytometry.

Single image super-resolution via an iterative reproducing kernel Hilbert space method.

Image super-resolution, a process to enhance image resolution, has important applications in satellite imaging, high definition television, medical imaging, etc. Many existing approaches use multiple low-resolution images to recover one high-resolution image. In this paper, we present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.

Region-based active contours with cosine fitting energy for image segmentation.

In this paper, by employing the cosine function to express the so-called data fitting term in traditional active contour models, we propose an active contour model with the global cosine fitting energy for segmenting synthetic and real-world images. After that, in order to segment the image with intensity inhomogeneity, we extend the proposed global model to the local cosine fitting energy. In addition, we introduce level set regularization terms into the proposed models to avoid the expensive computational cost which is usually caused by the reinitialization of the evolving level set function. Experimental results indicate that the proposed models are accurate and effective when applied to segment different types of images. Moreover, our models are more efficient and robust for segmenting the images with strong noise and clutter than the Chan-Vese model and the local binary fitting model.

Variant of the region-scalable fitting energy for image segmentation.

This paper presents a variant of the level set function based on region-scalable fitting (RSF) model for segmenting a given image into different parts. In consideration of the image local characteristics, the RSF model can efficiently and effectively segment images with intensity inhomogeneity. Instead of utilizing n level set functions to define up to 2n phases in the RSF model, our method presents a piecewise constant level set formulation for image segmentation and each phase is represented by a unique constant value. In addition, our model avoids different segmentation results caused by different initializations. The energy functional of our method is locally differentiable and convex because we do not use the nondifferentiable Heaviside and Delta functions. Comparative experiment results demonstrate that our method is much more computationally efficient. Moreover, our algorithm is robust against destructive noise.

[Immune function and catch-up growth of 6-month infants with intrauterine growth retardation].

OBJECTIVE: To explore the influence of "developmental programming" on immunity function and the correlation between immunologic changes and physical growth. METHODS: Sixty singleton pregnancies with intrauterine growth retardation (IUGR) and twenty normal pregnant women were enrolled in this study at their third trimester of pregnancy. Birth weight, birth length, mode of delivery, complication of newborn were measured and/or recorded at the moment of delivery. Physical development of the infants was measured every month up to six months old using weight and length as indicators. The deviation of physical growth was evaluated by Z score. Blood samples were taken from the infants at the sixth month. Lymphocyte subpopulations were anaLyzed using flow cytometry. Humoral immunity were measured using immunoturbidimetry. RESULTS: About 27.27% and 29.09% of IUGR infants were found to have small for gestational age (SGA) and neonatal complications. They had lower birth body masses and birth lengths than those of controls (P<0.05). After correcting for gestational age, the IUGR boys had lower body masses at birth and one months old, as well as lower lengths at 0-6 months compared with the controls. The IUGR girls had lower weights at 0, 1, 3, 4, and 6 months, as well as lower lengths at 0, 3, 4, 5, and 6 months. The IUGR infants without intrauterine-catch-up growth had lower proportion of B lymphocyte than those with intrauterine-catch-up growth (P<0.05). The IUGR infants without extrauterine-catch-up growth had higher numbers of B lymphocyte and lower IgG than those of normal controls (P<0.05). CONCLUSION: IUGR infants without intrauterine-catch-up and extrauterine-catch-up have impaired immunity function. The theory of "development program" needs proof of studies with a large sample size and long-term follow-up.

On the signless laplacian spectral radius of bicyclic graphs with perfect matchings.

The graph with the largest signless Laplacian spectral radius among all bicyclic graphs with perfect matchings is determined.

The smallest spectral radius of graphs with a given clique number.

The first four smallest values of the spectral radius among all connected graphs with maximum clique size ω ≥ 2 are obtained.

Some refinements and generalizations of I. Schur type inequalities.

Recently, extensive researches on estimating the value of e have been studied. In this paper, the structural characteristics of I. Schur type inequalities are exploited to generalize the corresponding inequalities by variable parameter techniques. Some novel upper and lower bounds for the I. Schur inequality have also been obtained and the upper bounds may be obtained with the help of Maple and automated proving package (Bottema). Numerical examples are employed to demonstrate the reliability of the approximation of these new upper and lower bounds, which improve some known results in the recent literature.

Tensor Robust Kernel PCA for Multidimensional Data.

Recently, the tensor nuclear norm (TNN)-based tensor robust principle component analysis (TRPCA) has achieved impressive performance in multidimensional data processing. The underlying assumption in TNN is the low-rankness of frontal slices of the tensor in the transformed domain (e.g., Fourier domain). However, the low-rankness assumption is usually violative for real-world multidimensional data (e.g., video and image) due to their intrinsically nonlinear structure. How to effectively and efficiently exploit the intrinsic structure of multidimensional data remains a challenge. In this article, we first suggest a kernelized TNN (KTNN) by leveraging the nonlinear kernel mapping in the transform domain, which faithfully captures the intrinsic structure (i.e., implicit low-rankness) of multidimensional data and is computed at a lower cost by introducing kernel trick. Armed with KTNN, we propose a tensor robust kernel PCA (TRKPCA) model for handling multidimensional data, which decomposes the observed tensor into an implicit low-rank component and a sparse component. To tackle the nonlinear and nonconvex model, we develop an efficient alternating direction method of multipliers (ADMM)-based algorithm. Extensive experiments on real-world applications collectively verify that TRKPCA achieves superiority over the state-of-the-art RPCA methods.

High-order total variation-based multiplicative noise removal with spatially adapted parameter selection.

Multiplicative noise is one common type of noise in imaging science. For coherent image-acquisition systems, such as synthetic aperture radar, the observed images are often contaminated by multiplicative noise. Total variation (TV) regularization has been widely researched for multiplicative noise removal in the literature due to its edge-preserving feature. However, the TV-based solutions sometimes have an undesirable staircase artifact. In this paper, we propose a model to take advantage of the good nature of the TV norm and high-order TV norm to balance the edge and smoothness region. Besides, we adopt a spatially regularization parameter updating scheme. Numerical results illustrate the efficiency of our method in terms of the signal-to-noise ratio and structure similarity index.

Signal restoration combining Tikhonov regularization and multilevel method with thresholding strategy.

Singular value decomposition (SVD)-based approaches, e.g., truncated SVD and Tikhonov regularization methods, are effective ways to solve problems of small or moderate size. However, SVD, in the sense of computation, is expensive when it is applied in large-sized cases. A multilevel method (MLM) combining SVD-based methods with the thresholding technique for signal restoration is proposed in this paper. Our MLM will transfer large-sized problems to small- or moderate-sized problems in order to make the SVD-based methods available. The linear systems on the coarsest level in the multilevel process will be solved by the Tikhonov regularization method. No presmoothers are implemented in the multilevel process to avoid damaging the parameter choice on the coarsest level. Furthermore, the soft-thresholding denoising technique is employed for the postsmoothers aiming to eliminate the leaving high-frequency information due to the lack of presmoothers. Finally, computational experiments show that our method outperforms other SVD-based methods in signal restoration ability at a shorter CPU-time consumption.

Incomplete augmented Lagrangian preconditioner for steady incompressible Navier-Stokes equations.

An incomplete augmented Lagrangian preconditioner, for the steady incompressible Navier-Stokes equations discretized by stable finite elements, is proposed. The eigenvalues of the preconditioned matrix are analyzed. Numerical experiments show that the incomplete augmented Lagrangian-based preconditioner proposed is very robust and performs quite well by the Picard linearization or the Newton linearization over a wide range of values of the viscosity on both uniform and stretched grids.

A Triple-Double Convolutional Neural Network for Panchromatic Sharpening.

Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.

LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening.

Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan. The tensor completion technique is commonly used for image recovery, but it cannot directly perform the pansharpening or, more generally, the super-resolution problem because of the formulation gap. Different from previous variational methods, we first formulate a pioneering image super-resolution (ISR) degradation model, which equivalently removes the downsampling operator and transforms the tensor completion framework. Under such a framework, the original pansharpening problem is realized by the LRTC-based technique with some deblurring regularizers. From the perspective of regularizer, we further explore a local-similarity-based dynamic detail mapping (DDM) term to more accurately capture the spatial content of the panchromatic image. Moreover, the low-tubal-rank property of multispectral images is investigated, and the low-tubal-rank prior is introduced for better completion and global characterization. To solve the proposed LRTCFPan model, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Comprehensive experiments at reduced-resolution (i.e., simulated) and full-resolution (i.e., real) data exhibit that the LRTCFPan method significantly outperforms other state-of-the-art pansharpening methods. The code is publicly available at: https://github.com/zhongchengwu/code_LRTCFPan.

[The research on multi-spectral fitting of LED spectrum tunable light source].

Using a number of different spectral LED cells, the spectra of several typical spectral light source were fitted successfully. The research focuses on how to find the needed spectral distribution and the least number of LEDs by using the target spectrum to go. Furthermore the result is used to judge and improve the obtained original fitting model. Simulate The target spectrum obtained by the actual measurement was simulated and preferable prospective result was obtained.

[Epidemiological investigation of birth information and physique status in 9 to 15-year-old children from Chengdu City of Sichuan Province].

OBJECTIVE: As the intrauterine environment can affect childhood growth and development, this study aims to understand the relationship between birth gestational age, birth weight and physique development in 9 to 15-year-old children by a cross sectional investigation in Chengdu City, Sichuan Province. METHODS: A total of 7194 9 to 15-year-old school children were classified according to birth gestational age and birth weight: small for gestational age (SGA), appropriate for gestational age (AGA) and large for gestational age (LGA). Their heights and weights were measured. Parents completed a questionnaire. RESULTS: The prevalence of SGA was 6.23% (448 cases), and 5.13% of children in the SGA group did not undergo "catch-up growth" (lower than -2 SD). The mean height in these children at various stages was significantly lower than in the AGA group (P<0.05). The prevalence of LGA was 18.06% (1299 cases). A total of 179 children (13.78%) were found to be overweight and 57 children (4.39%) were found to be obese in the LGA group. The mean weight in the LGA group at various stages was significantly higher than in the AGA group (P<0.05). CONCLUSIONS: Height and weight development in children born SGA and LGA are different from normal children. More attention should be given to aspects of height and weight development in these school children.

Multiscale Feature Tensor Train Rank Minimization for Multidimensional Image Recovery.

The general tensor-based methods can recover missing values of multidimensional images by exploiting the low-rankness on the pixel level. However, especially when considerable pixels of an image are missing, the low-rankness is not reliable on the pixel level, resulting in some details losing in their results, which hinders the performance of subsequent image applications (e.g., image recognition and segmentation). In this article, we suggest a novel multiscale feature (MSF) tensorization by exploiting the MSFs of multidimensional images, which not only helps to recover the missing values on a higher level, that is, the feature level but also benefits subsequent image applications. By exploiting the low-rankness of the resulting MSF tensor constructed by the new tensorization, we propose the convex and nonconvex MSF tensor train rank minimization (MSF-TT) to conjointly recover the MSF tensor and the corresponding original tensor in a unified framework. We develop the alternating directional method of multipliers (ADMMs) to solve the convex MSF-TT and the proximal alternating minimization (PAM) to solve the nonconvex MSF-TT. Moreover, we establish the theoretical guarantee of convergence for the PAM algorithm. Numerical examples of real-world multidimensional images show that the proposed MSF-TT outperforms other compared approaches in image recovery and the recovered MSF tensor can benefit the subsequent image recognition.