Preparation and Mechanical Properties of Continuous Carbon Nanotube Networks Modified C f

Continuous carbon nanotube (CNT) networks were formed in Cf/SiC composites via freeze-drying method. Composites were fabricated by precursor infiltration and pyrolysis (PIP) process afterwards. The different distribution morphologies of CNTs in the preforms originating from the different CNT contents were analyzed while the influence of the distribution of CNTs was discussed in detail. Compared to composites without CNTs, the interfacial shear strength (ILSS) and the flexural strength of Cf/1%CNTs/SiC were increased by 31% and 27%, respectively, but the values of Cf/2.5%CNTs/SiC decreased as a result of lots of defects caused by excess CNTs. With the analysis of ILSS, the flexural strengths, and the fracture morphologies, CNTs effectively improved the weak interfacial strength between T700SC carbon fibers and SiC matrix

Chinese undergraduates' mental health help-seeking behavior: the health belief model

The detection rate of mental health problems among undergraduates has recently risen significantly. However, undergraduates underutilize mental health services; approximately a third only of undergraduates in need of treatment use school counseling resources. Based on a social psychological theoretical framework, the health belief model, factors of undergraduates' willingness to seek help when dealing with psychological problems were investigated. A cross-sectional online questionnaire and a snowball sampling method with 446 undergraduates investigated perceived susceptibility, perceived severity, perceived behavioral benefits, perceived barriers, self-efficacy, and cues to action to understand how students' mental health-seeking behaviors are affected. We found that perceived susceptibility (p < 0.01), perceived severity (p < 0.01), perceived benefits (p < 0.01), perceived barriers (p < 0.01), self-efficacy (p < 0.01), and cues to action (p < 0.01) significantly correlated with behavioral intention. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help. In addition, we used a bias-corrected Bootstrap approach to test the significance of the mediating effect, the mediation effect of cues to action between undergraduates' perceived susceptibility and mental health help-seeking behavior was utterly significant [mediation effect value of 0.077, with an SE value of 0.027 and a 95% CI (0.028, 0.133)]. It demonstrated that those who perceived themselves to be at high risk of developing a mental illness and who had received encouragement or counseling to seek mental health help were more likely to be motivated to seek mental health help. Multiple regression analyses indicated that self-efficacy (Z = 5.425, p < 0.01) and cues to action (Z = 6.673, p < 0.01) independently influenced behavioral intentions. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help

Visual Representation Learning with Transformer: A Sequence-to-Sequence Perspective

Visual representation learning is the key of solving various vision problems. Relying on the seminal grid structure priors, convolutional neural networks (CNNs) have been the de facto standard architectures of most deep vision models. For instance, classical semantic segmentation methods often adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated (i.e., atrous) convolutions or inserting attention modules. However, the FCN-based architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating visual representation learning generally as a sequence-to-sequence prediction task. Specifically, we deploy a pure Transformer to encode an image as a sequence of patches, without local convolution and resolution reduction. With the global context modeled in every layer of the Transformer, stronger visual representation can be learned for better tackling vision tasks. In particular, our segmentation model, termed as SEgmentation TRansformer (SETR), excels on ADE20K (50.28% mIoU, the first position in the test leaderboard on the day of submission), Pascal Context (55.83% mIoU) and reaches competitive results on Cityscapes. Further, we formulate a family of Hierarchical Local-Global (HLG) Transformers characterized by local attention within windows and global-attention across windows in a hierarchical and pyramidal architecture. Extensive experiments show that our method achieves appealing performance on a variety of visual recognition tasks (e.g., image classification, object detection and instance segmentation and semantic segmentation).Comment: Extended version of CVPR 2021 paper arXiv:2012.1584

Extreme risk induced by communities in interdependent networks

10.1038/s42005-019-0144-6Communications Physics214

Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection

Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited. In this work, we present an unconstrained dysfluency modeling (UDM) approach that addresses both transcription and detection in an automatic and hierarchical manner. UDM eliminates the need for extensive manual annotation by providing a comprehensive solution. Furthermore, we introduce a simulated dysfluent dataset called VCTK++ to enhance the capabilities of UDM in phonetic transcription. Our experimental results demonstrate the effectiveness and robustness of our proposed methods in both transcription and detection tasks.Comment: 2023 ASR

Conformal Electrodeposition of Antimicrobial Hydrogels Formed by Self-Assembled Peptide Amphiphiles

The colonization of biomedical surfaces by bacterial biofilms is concerning because these microorganisms display higher antimicrobial resistance in biofilms than in liquid cultures. Developing antimicrobial coatings that can be easily applied to medically-relevant complex-shaped objects, such as implants and surgical instruments, is an important and challenging research direction. This work reports the preparation of antibacterial surfaces via the electrodeposition of a conformal hydrogel of self-assembling cationic peptide-amphiphiles (PAs). Hydrogels of three PAs are electrodeposited: C16K2, C16K3, and C18K2, where Cn is an alkyl chain of n methylene groups and Km is an oligopeptide of m lysines. The processing variables (electrodeposition time, potential, pH, salt concentration, agitation) enable fine control of film thickness, demonstrating the flexibility of the method and allowing to unravel the mechanisms underlying electrodeposition. The electrochemically prepared hydrogels inhibit the growth of Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa in agar plates, and prevent the formation of biofilms of Acinetobacter baumannii and P. aeruginosa and the formation of A. baumannii colonies in solid media. C16K2 and C16K3 hydrogels outperform the antimicrobial activity of those of C18K2 while maintaining good compatibility with human cells

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated/atrous convolutions or inserting attention modules. However, the encoder-decoder based FCN architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer (ie, without convolution and resolution reduction) to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR). Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes. Particularly, we achieve the first position in the highly competitive ADE20K test server leaderboard on the day of submission.Comment: CVPR 2021. Project page at https://fudan-zvg.github.io/SETR

Primary lipoblastic nerve sheath tumor in an inguinal lymph node mimicking metastatic tumor: a case report and literature review

Lipoblastic nerve sheath tumors of soft tissue are characterized as schwannoma tumors that exhibit adipose tissue and lipoblast-like cells with signet-ring morphology. They have been documented to arise in various anatomic locations, including the thigh, groin, shoulder, and retroperitoneum. However, to our knowledge, this tumor has not been previously reported as a lymph node primary. We present herein the first case of a benign primary lipoblastic nerve sheath tumor arising in an inguinal lymph node in a 69-year-old man. Microscopic examination revealed a multinodular tumor comprising fascicles of spindle cells, as well as adipocytic and lipoblast-like signet-ring cell component in the context of schwannoma. Despite the presence of some bizarre cells with nuclear atypia, no obvious mitotic activity or necrosis was observed. Immunohistochemical analysis showed strong and diffuse expression of S-100, SOX10, CD56, and NSE in the spindle cells as well as in the signet-ring lipoblast-like cells and the mature adipocytes. Sequencing analysis of the neoplasm identified six non-synonymous single nucleotide variant genes, specifically NF1, BRAF, ECE1, AMPD3, CRYAB, and NPHS1, as well as four nonsense mutation genes including MRE11A, CEP290, OTOA, and ALOXE3. The patient remained alive and well with no evidence of recurrence over a period of ten-year follow-up

Design, Biological Evaluation, and Computer-Aided Analysis of Dihydrothiazepines as Selective Antichlamydial Agents

Chlamydia trachomatis (CT) causes the most prevalent sexually transmitted bacterial disease in the United States. The lack of drug selectivity is one of the main challenges of the current antichlamydial pharmacotherapy. The metabolic needs of CT are controlled, among others, by cylindrical proteases and their chaperones (e.g., ClpX). It has been shown that dihydrothiazepines can disrupt CT-ClpXP. Based on this precedent, we synthesized a dihydrothiazepine library and characterized its antichlamydial activity using a modified semi-high-throughput screening assay. Then, we demonstrated their ability to inhibit ClpX ATPase activity in vitro, supporting ClpX as a target. Further, our lead compound displayed a promising selectivity profile against CT, acceptable cytotoxicity, no mutagenic potential, and good in vitro stability. A two-dimensional quantitative structure–activity relationship (2D QSAR) model was generated as a support tool in the identification of more potent antichlamydial molecules. This study suggests dihydrothiazepines are a promising starting point for the development of new and selective antichlamydial drugs