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Toughening the petroleum-based epoxy resin blends with bio-based modifiers without compromising their modulus, mechanical strength, and other properties is still a big challenge in view of the sustainability. In this study, a bio-based liquid crystal epoxy resin (THMT-EP) with an s-triazine ring structure was utilized to modify a petroleum-based bisphenol A epoxy resin (E51) with 4,4′-diaminodiphenylsulfone (DDS) as a curing agent, and the blended systems were evaluated for their thermal stability, mechanical properties, and flame retardancy. The results showed that the impact strength of the blended system initially increased and then decreased with the increase in THMT-EP content, and it reached the a maximum value of 26.5 kJ/m2 when the THMT-EP content was 5%, which was 31.2% higher than that of E51/DDS. Notably, the flexural strength, modulus, and glass transition temperature of the blended system were all simultaneously improved with the addition of THMT-EP. At the same time, the addition of THMT-EP enhanced the flame retardancy of the system by increasing the char yield at 700 °C and decreasing the peak heat release rate and total heat release rate. This work paves the way for a more sustainable improvement in the comprehensive performance of epoxy resin. [ABSTRACT FROM AUTHOR]

The aim of this study is to propose a new diagnostic model based on "segmentation + classification" to improve the routine screening of Thyroid nodule ultrasonography by utilizing the key domain knowledge of medical diagnostic tasks. A Multi-scale segmentation network based on a pyramidal pooling structure of multi-parallel void spaces is proposed. First, in the segmentation network, the exact information of the underlying feature space is obtained by an Attention Gate. Second, the inflated convolutional part of Atrous Spatial Pyramid Pooling (ASPP) is cascaded for multiple downsampling. Finally, a three-branch classification network combined with expert knowledge is designed, drawing on doctors' clinical diagnosis experience, to extract features from the original image of the nodule, the regional image of the nodule, and the edge image of the nodule, respectively, and to improve the classification accuracy of the model by utilizing the Coordinate attention (CA) mechanism and cross-level feature fusion. The Multi-scale segmentation network achieves 94.27%, 93.90% and 88.85% of mean precision (mPA), Dice value (Dice) and mean joint intersection (MIoU), respectively, and the accuracy, specificity and sensitivity of the classification network reaches 86.07%, 81.34% and 90.19%, respectively. Comparison tests show that this method outperforms the U-Net, AGU-Net and DeepLab V3+ classical models as well as the nnU-Net, Swin UNetr and MedFormer models that have emerged in recent years. This algorithm, as an auxiliary diagnostic tool, can help physicians more accurately assess the benign or malignant nature of Thyroid nodules. It can provide objective quantitative indicators, reduce the bias of subjective judgment, and improve the consistency and accuracy of diagnosis. Codes and models are available at https://github.com/enheliang/Thyroid-Segmentation-Network.git [ABSTRACT FROM AUTHOR]

Detecting low‐frequency DNA mutations hotspots cluster is critical for cancer diagnosis but remains challenging. Quantitative PCR (qPCR) is constrained by sensitivity, and allele‐specific PCR is restricted by throughput. Here we develop a long blocker displacement amplification (LBDA) coupled with qPCR for ultrasensitive and multiplexed variants detection. By designing long blocker oligos to perfectly match wildtype sequences while mispairing with mutants, long blockers enable 14–44 nt enrichment regions which is 2‐fold longer than normal BDA in the experiments. For wild template with a specific nucleotide, LBDA can detect different mutation types down to 0.5 % variant allele frequency (VAF) in one reaction, with median enrichment fold of 1,000 on 21 mutant DNA templates compared to the wild type. We applied LBDA‐qPCR to detect KRAS and NRAS mutation hotspots, utilizing a single plex assay capable of covering 81 mutations and tested in synthetic templates and colorectal cancer tissue samples. Moreover, the mutation types were verified through Sanger sequencing, demonstrating concordance with results obtained from next generation sequencing. Overall, LBDA‐qPCR provides a simple yet ultrasensitive approach for multiplexed detection of low VAF mutations hotspots, presenting a powerful tool for cancer diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

Detecting low‐frequency DNA mutations hotspots cluster is critical for cancer diagnosis but remains challenging. Quantitative PCR (qPCR) is constrained by sensitivity, and allele‐specific PCR is restricted by throughput. Here we develop a long blocker displacement amplification (LBDA) coupled with qPCR for ultrasensitive and multiplexed variants detection. By designing long blocker oligos to perfectly match wildtype sequences while mispairing with mutants, long blockers enable 14–44 nt enrichment regions which is 2‐fold longer than normal BDA in the experiments. For wild template with a specific nucleotide, LBDA can detect different mutation types down to 0.5 % variant allele frequency (VAF) in one reaction, with median enrichment fold of 1,000 on 21 mutant DNA templates compared to the wild type. We applied LBDA‐qPCR to detect KRAS and NRAS mutation hotspots, utilizing a single plex assay capable of covering 81 mutations and tested in synthetic templates and colorectal cancer tissue samples. Moreover, the mutation types were verified through Sanger sequencing, demonstrating concordance with results obtained from next generation sequencing. Overall, LBDA‐qPCR provides a simple yet ultrasensitive approach for multiplexed detection of low VAF mutations hotspots, presenting a powerful tool for cancer diagnosis and monitoring. [ABSTRACT FROM AUTHOR]

The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (KOA) stands as a degenerative ailment with a substantial and escalating prevalence. The practice of traditional Chinese non-pharmacological therapy has become a prevalent complementary and adjunctive approach. A mounting body of evidence suggests its efficacy in addressing KOA. Recent investigations have delved into its underlying mechanism, yielding some headway. Consequently, this comprehensive analysis seeks to encapsulate the clinical application and molecular mechanism of traditional Chinese non-pharmacological therapy in KOA treatment. The review reveals that various therapies, such as acupuncture, electroacupuncture, warm needle acupuncture, tuina, and acupotomy, primarily target localized knee components like cartilage, subchondral bone, and synovium. Moreover, their impact extends to the central nervous system and intestinal flora. More perfect experimental design and more comprehensive research remain a promising avenue in the future. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic cooperative learning control for high‐order output‐feedback systems under the output constraints. To avoid complex computation, we use a system transformation strategy in control design. Only one neural network (NN) is employed to approximate the unknown synthetic function for each agent. Subsequently, a NN‐based cooperative learning control mechanism is designed by introducing the barrier Lyapunov function (BLF). The proposed mechanism expands the NN approximation domain of the transformed systems, and the output of all subsystems remains constrained. Further, the NNs on identified uncertain system dynamics are used to construct the experience‐based controllers to carry out same control tasks. Finally, the theoretical authenticity is demonstrated by a numerical example. [ABSTRACT FROM AUTHOR]

The detection and identification of individual cattle plays an integral role in precision feeding and insurance claims, among others. Most current research is based on high-performance computing devices, which limits the application of deep learning techniques to mobile terminals. To this end, in this paper, we propose a channel-pruned YOLOv5 network-based method for cattle face detection on mobile terminals, referred to as NS-YOLO. First, the original model is sparsely trained and a sparse regularization penalty term is applied to the BN layers, then the corresponding mask values are labeled according to different weight thresholds, and the channels are pruned with global thresholds. Second, the detection precision is recovered by fine-tuning the model. Finally, the NCNN forward inference framework is used to quantize the model and an Android-based cattle face detection application is developed. Experimental results show that the model size, number of parameters and FLOPs are reduced by 86.10%, 88.19% and 63.25%, respectively, and the inference time is reduced by 35.53% compared to the original model, while mAP0.5 is reduced by only 1.6%. In particular, the 16-bit quantized model reduces the model size by 93.97% and the inference time by 34.97% compared to the original model on the mobile side. The results show that the proposed method can be easily deployed in resource-constrained mobile devices and has great advantages in practical engineering applications. [ABSTRACT FROM AUTHOR]

Tensorized type-2 single layer feedforward network extends the single layer feedforward network with tensorized type-2 fuzzy structure. In the tensorized type-2 single layer feedforward network, type-2 fuzzy sets are used to generate tensor with lower membership function, principal membership function and upper membership function. Other compositions of the single layer feedforward network, such as defuzzification results, weighted averaged results and different type reduction results could also be formed by the tensorized fuzzy construction method. In thus doing, the type-reduction or defuzzification approach is the unimportance procedure in the fuzzy network operation and construction. To deeply unveil the implicit information hidden in the type-2 fuzzy sets, cross-shaped convolution with irregular convolution kernel is used to form the tensor. The named irregular convolution kernel based tensorized type-2 single layer feedforward network adopts an iterative tensor equation solving algorithm with tensor inequality constraint (Huang and Ma in Linear Multilinear Algebra, 1–24, 2021, https://doi.org/10.1080/03081087.2021.1954140). Finally, the effectiveness of different convolution kernels for irregular convolution strategy based tensorized type-2 single layer feedforward network are tested. Comparisons are carried out on several benchmark datasets, and five different type-reduction methods for the irregular convolution strategy based tensorized type-2 single layer feedforward network are compared. Results show that the proposed learning method could be improved with this new information extraction strategy. [ABSTRACT FROM AUTHOR]

As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we propose a hairpin probe-mediated exponential amplification reaction (HEAR) strategy that combines DNA strand displacement with a "who triggers, who gets generated" mode, providing excellent single-base discrimination and a reduced background signal. The detection limit is 19 aM, which is reduced by 3 orders of magnitude compared to traditional exponential amplification approaches. This one-pot strategy also exhibits a wide dynamic range, high specificity and short detection time. It is expected to become a powerful tool for clinical diagnosis. [ABSTRACT FROM AUTHOR]

This study explored the role of P2X7 receptors in spinal cord astrocytes in the electroacupuncture-induced inhibition of visceral hypersensitivity (VH) in rats with irritable bowel syndrome (IBS). Visceral hypersensitivity of IBS was intracolonically induced by 2,4,6-trinitrobenzene sulfonic acid (TNBS). Visceromotor responses to colorectal distension (CRD-20,40,60,80 mmHg) and abdominal withdrawal reflex scoring (AWRs) were recorded after electroacupuncture at bilateral Zusanli (ST36) and Sanyinjiao (SP6) acupoints to evaluate the analgesic effect of electroacupuncture on visceral pain in rats with IBS. Fluorocitric acid (FCA), an astrocyte activity inhibitor, was injected intrathecally before electroacupuncture intervention and AWRs were recorded. Western blot and real-time qPCR were used to detect the expression of NMDA and P2X7 receptor to observe the regulation effect of electroacupuncture on NMDA receptor in the spinal cord of rats with visceral hypersensitivity. Intrathecal injection of P2X7 agonist or antagonist was administered before electroacupuncture treatment. To observe the effect of P2X7 receptor in spinal astrocytes on the inhibition of visceral hyperalgesia by electroacupuncture, the changes of AWR score, NMDA receptor in the spinal cord, and GFAP expression in astrocytes were detected. Inflammation of the colon had basically subsided at day 21 post-TNBS; persistent visceral hypersensitivity could be suppressed by electroacupuncture. This analgesic effect could be inhibited by FCA. The analgesic effect, downregulation of NMDA receptor NR1 subunit, and P2X7 protein of electroacupuncture were all reversed by FCA. P2X7 receptor antagonist A740003 can cooperate with EA to carry out analgesic effect in rats with visceral pain and downregulate the expression of NR1, NR2B, and GFAP in spinal dorsal horn. However, the P2X7 receptor agonist BzATP could partially reverse the analgesic effect of EA, inhibiting the downregulatory effect of EA on the expression of NR1, NR2B, and GFAP. These results indicate that EA may downregulate the expression of the NMDA receptor by inhibiting the P2X7 receptor in the spinal cord, thereby inhibiting spinal cord sensitization in IBS rats with visceral pain, in which astrocytes are an important medium. [ABSTRACT FROM AUTHOR]

With the Chinese data for solid rocket engines, traditional named entity recognition cannot be used to learn both character features and contextual sequence-related information from the input text, and there is a lack of research on the advantages of dual-channel networks. To address this problem, this paper proposes a BERT-based dual-channel named entity recognition model for solid rocket engines. This model uses a BERT pre-trained language model to encode individual characters, obtaining a vector representation corresponding to each character. The dual-channel network consists of a CNN and BiLSTM, using the convolutional layer for feature extraction and the BiLSTM layer to extract sequential and sequence-related information from the text. The experimental results showed that the model proposed in this paper achieved good results in the named entity recognition task using the solid rocket engine dataset. The accuracy, recall and F1-score were 85.40%, 87.70% and 86.53%, respectively, which were all higher than the results of the comparison models. [ABSTRACT FROM AUTHOR]

The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Objective: To explore whether acupuncture combined with moxibustion could inhibit epithelialmesenchymal transition in Crohn's disease by affecting the transforming growth factor β 1 (TGF- β 1)/Smad3/Snail pathway. Methods: Sixty-three patients with Crohn's disease were randomly divided into an observation group (31 cases) receiving moxibustion at 43 °C combined with acupuncture, and a control group (32 cases) receiving moxibustion at 37 °C combined with sham acupuncture using a random number table. Patients were treated for 12 weeks. Crohn's Disease Activity Index (CDAI) was used to evaluate disease activity. Hematoxylin-eosin staining and transmission electron microscopy were utilized to observe the morphological and ultrastructural changes. Immunohistochemistry was used to detect the expression of transforming growth factor β 1 (TGF-β 1), T β R1, T β R2, Smad3, Snail, E-cadherin and fibronectin in intestinal mucosal tissues. Results: The decrease of the CDAI score, morphological and ultrastructural changes were more significant in observation group. The expression levels of TGF- β 1, Tβ R2, Smad3, and Snail in the observation group were significantly lower than those before the treatment (P<0.05 or P<0.01). After treatment, the expression levels of TGF-β 1, TβR2, and Snail in the observation group were significantly lower than those in the control group (all P<0.05); compared with the control group, the expression of fibronectin in the observation group was significantly decreased, and the expression of E-cadherin was significantly increased (all P<0.05). Conclusions: Moxibustion at 43 °C combined with acupuncture may suppress TGF-β 1/Smad3/Snail pathway-mediated epithelial-mesenchymal transition of intestinal epithelial cells in Crohn's disease patients by inhibiting the expression levels of TGF-β 1, Tβ R2, Smad3, and Snail. (Registration No. ChiCTR-IIR-16007751) [ABSTRACT FROM AUTHOR]

This paper investigates the distributed cooperative learning (DCL) from adaptive neural network (NN) control for a group of strict-feedback systems, where the structure of all strict-feedback systems is identical. In order to achieve DCL easily, the system transformation method is employed for the strict-feedback systems. For an agent, only one radial basis function NN is used to approximate the lumped uncertainty in control design. Then the output tracking performance of all strict-feedback systems is guaranteed. What's more, we prove that weights of all NNs in a multi-agent system converge to a small neighborhood around their common optimal value if the topology of the multi-agent system is connected and undirected. Thus, the approximation domain of all NNs is enlarged. Further, the previous learned NNs are used to improve the control performance. Finally, we provide two examples to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

The underground mine environment is dangerous and harsh, tracking and detecting humans based on computer vision is of great significance for mine safety monitoring, which will also greatly facilitate identification of humans using the symmetrical image features of human organs. However, existing methods have difficulty solving the problems of accurate identification of humans and background, unstable human appearance characteristics, and humans occluded or lost. For these reasons, an improved aberrance repressed correlation filter (IARCF) tracker for human tracking in underground mines based on infrared videos is proposed. Firstly, the preprocess operations of edge sharpening, contrast adjustment, and denoising are used to enhance the image features of original videos. Secondly, the response map characteristics of peak shape and peak to side lobe ratio (PSLR) are analyzed to identify abnormal human locations in each frame, and the method of calculating the image similarity by generating virtual tracking boxes is used to accurately relocate the human. Finally, using the value of PSLR and the highest peak point of the response map, the appearance model is adaptively updated to further improve the robustness of the tracker. Experimental results show that the average precision and success rate of the IARCF tracker in the five underground scenarios reach 0.8985 and 0.7183, respectively, and the improvement of human tracking in difficult scenes is excellent. The IARCF tracker can effectively track underground human targets, especially occluded humans in complex scenes. [ABSTRACT FROM AUTHOR]

Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming. [ABSTRACT FROM AUTHOR]

Seven benzophenone compounds were synthesized in one or two steps, then their antitumor activity was evaluated. The total yields ranged from 9% to 44%. Compounds 3c–5c exhibited obvious antitumor activity. Among them, compounds 3c and 4c exhibited excellent and broad-spectrum antitumor activity. Compound 3c exhibited much stronger inhibitory activities against fourteen cancer cells than cisplatin. In particular, compound 3c exhibited stronger cytotoxicity against hepatocarcinoma SMMC-7721 cells than Taxol, with a half maximal inhibitory concentration (IC50) of approximately 0.111 μM. These results demonstrated that compounds 3c, 4c and 5c were very promising antitumor leads for further structural modification. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Acupuncture & Tuina Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sixteen substituted 1-hydroxy-3-methylxanthones were synthesized in one step. The yields ranged from 33 to 76%. Then, the antitumor, antioxidant, anti-tyrosinase, anti-pancreatic lipase, and antifungal activities of compounds 1–16 were evaluated. Compounds 10–12 and 14 inhibited tyrosinase and pancreatic lipase activity to a certain extent, respectively. Compound 16 exhibited obvious cytotoxicity against fifteen cancer cells, moderate antioxidant activity, and moderate inhibitory activity against Candida albicans. In particular, compound 16 exhibited strong inhibitory activity against A-549 and A549/Taxol cells. These results demonstrated that compounds 10–12, 14, and 16 are promising leads for further structural modification. [ABSTRACT FROM AUTHOR]

Combining symplectic algorithm, splitting technique and compact method, a compact splitting symplectic scheme is proposed to solve the fourth-order dispersive Schrödinger equation with cubic-quintic nonlinear term. The scheme has fourth-order accuracy in space and second-order accuracy in time. The discrete charge conservation law and stability of the scheme are analyzed. Numerical examples are given to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

Let {(ξni, ηni), 1 ≤ i ≤ n, n ≥ 1} be a triangular array of independent bivariate elliptical random vectors with the same distribution function as (S1,ρnS1+1−ρn2S2),ρn∈(0,1), where (S1, S2) is a bivariate spherical random vector. For the distribution function of radius S12+S22 belonging to the max-domain of attraction of the Weibull distribution, the limiting distribution of maximum of this triangular array is known as the convergence rate of ρn to 1 is given. In this paper, under the refinement of the rate of convergence of ρn to 1 and the second-order regular variation of the distributional tail of radius, precise second-order distributional expansions of the normalized maxima of bivariate elliptical triangular arrays are established. [ABSTRACT FROM AUTHOR]

This study aims to explore the efficacy of interferon-α (IFN-α) combined with either entecavir (ETV) or adefovir (ADV) therapy versus IFN-α mono-therapy for chronic hepatitis B (CHB) patients, and to identify the factors associated with treatment outcomes. Totally, 159 CHB patients receiving interferon-based treatment for 48 weeks were enrolled in this retrospective study, including IFN-α mono-therapy group (group A, n=44), IFN-α plus ADV group (group B, n=53) and IFN-α plus ETV group (group C, n=62). The primary measures of efficacy assessments were the changes in HBsAg. Cox regression analysis was used to identify the predictors of treatment outcomes. The predictive values of the factors were assessed by ROC analysis. For patients with baseline hepatitis B surface antigen (HBsAg) level <1000 IU/mL, the reductions in mean HBsAg levels at week 48 were greater in group C than that in group A ( P<0.05). Higher rate of HBeAg seroconversion was achieved in the combined therapy group than in IFN-α mono-therapy group at week 48 ( P<0.05). Two factors were independently associated with HBeAg seroconversion: baseline HBeAg level <2.215 log10 index/mL and ΔHBeAg (decline in HBeAg from baseline) >0.175 log10 at week 12. In conclusion, interferon-α plus ETV therapy can accelerate HBsAg decline as compared with interferon-α mono-therapy in CHB patients with lower baseline HBsAg levels, and the combination therapy was superior to IFN-α mono-therapy in increasing the rate of HBeAg seroconversion. Baseline HBeAg and ΔHBeAg at week 12 can independently predict HBeAg seroconversion in patients subject to interferon-based therapy for 48 weeks. [ABSTRACT FROM AUTHOR]

Genetic signaling and redox homeostasis are required for proper growth of blood vessels. Here, we report a novel function of peroxiredoxin1 (Prdx1) in vascular development in zebrafish. Knockdown of prdx1 impairs the growth of intersegmental vessel and caudal vein plexus (CVP), and reduces the expression of vascular markers, thus suggesting a role for prdx1 in vasculature and indicating that the antioxidant function of prdx1 is important. We found that H2O2-treated embryos also have CVP defects and observed synergistic effects when prdx1 knockdown was combined with H2O2 treatment. Moreover, N-acetyl-cysteine treatment rescues the vascular defects in prdx1 morphants. These results suggest that oxidative stress disturbs vascularization. Furthermore, we show that the regulation of prdx1 is mediated by Notch and BMP signals. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Acupuncture & Tuina Science is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The biologic studies of human neural crest stem cells (hNCSCs) are extremely challenging due to the limited source of hNCSCs as well as ethical and technical issues surrounding isolation of early human embryonic tissues. On the other hand, vast majority of studies on MycN have been conducted in human tumor cells, thus, the role of MycN in normal human neural crest development is completely unknown. In the present study, we determined the role of MycN in hNCSCs isolated from in vitro-differentiating human embryonic stem cells (hESCs). For the first time, we show that suppression of MycN in hNCSCs inhibits cell growth and cell cycle progression. Knockdown of MycN in hNCSCs increases the expression of Cdkn1a, Cdkn2a and Cdkn2b, which encodes the cyclin-dependent kinases p21CIP1, p16 INK4a and p15INK4b. In addition, MycN is involved in the regulation of human sympathetic neurogenesis, as knockdown of MycN enhances the expression of key transcription factors involved in sympathetic neuron differentiation, including Phox2a, Phox2b, Mash1, Hand2 and Gata3. We propose that unlimited source of hNCSCs provides an invaluable platform for the studies of human neural crest development and diseases. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint limit distributions of point processes of exceedances and partial sums of multivariate Gaussian sequences and show that the point processes and partial sums are asymptotically independent under some mild conditions. As a result, for a sequence of standardized stationary Gaussian vectors, we obtain that the point process of exceedances formed by the sequence (centered at the sample mean) converges in distribution to a Poisson process and it is asymptotically independent of the partial sums. The asymptotic joint limit distributions of order statistics and partial sums are also investigated under different conditions. [ABSTRACT FROM AUTHOR]

The effect of ammonium sulfamate (AS) content on the flame retardancy of polyamide 6 (PA6) was studied. It is found that the limiting oxygen index (LOI) of PA6 increases with the increase of AS content and the flame retardancy of PA6 is significantly improved. The morphology of the residues after combustion was examined by means of scanning electron microscopy (SEM). SEM results show that AS facilitates the formation of the intumescent char layer with honeycomb-like structure, which inhibits the transfer of heat and mass, and thus improves the flame retardancy of PA6. The thermal degradation of AS flame retarded PA6 was studied by thermogravimetric analysis (TGA). The Kissinger method was applied to estimate the activation energy ( Ea) of the degradation. The activation energy of the thermal degradation of PA6 decreases by adding AS, indicating that AS can promote the degradation of PA6. [ABSTRACT FROM AUTHOR]

ylonitrile/ N-[4-(aminosulfonyl)phenyl]acrylamide (AN/ASPAA) copolymers were synthesized and used as a host of lithium ion conducting electrolytes. The composition, molecular weight and molecular weight distribution of AN/ASPAA copolymers were determined, and the influence of copolymer composition on the glass temperature of AN/ASPAA copolymers and the ion conductivity of electrolytes were investigated. The molecular weights of AN/ASPAA copolymers were lower than those of AN and ASPAA homopolymers due to the cross-termination reaction. The glass temperatures of AN/ASPAA copolymers increased as the molar fraction of ASPAA units in copolymers increased. The lithium ion conductivities of the polymer electrolytes increased initially as the molar fraction of ASPAA units in copolymers increased, and a maximum conductivity was achieved when the molar fraction of ASPAA in the copolymer was 16.8%. [ABSTRACT FROM AUTHOR]