Preparation of ZrB2-ZrC-SiC-ZrO2 nanopowders with in-situ grown homogeneously dispersed SiC nanowires

To explore the application of SiC nanowires (SiCnws) in ZrB2 based ceramic materials, a facile approach is reported to in situ synthesize homogeneously dispersed SiCnws in ZrB2-ZrC-SiC-ZrO2 nanopowders by pyrolyzing a B-Si-Zr containing sol precursor impregnated in polyurethane sponge. The sponge was used to provide porous skeletons for the growth of SiC nanowires and facilitate their uniform distribution in the powders. After heat-treatment of the precursor with a Si/Zr atomic ratio of 10 at 1500 °C for 2 h, ZrB2-ZrC-SiC-ZrO2 ceramic powders were obtained with an even and fine particle size of ~100 nm. The SiCnws were in a diameter of ~100 nm with a controllable length varying from tens to hundreds of microns by increasing the silicon content in the precursor. Moreover, the produced SiCnws were in high purity, and homogeneously dispersed in the hybrid nanopowders. The study can open up a feasible route to overcome the critical fabrication process in SiCnws reinforced ceramic matrix composites

Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

Due to its complexity, graph learning-based multi-modal integration and classification is one of the most challenging obstacles for disease prediction. To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning). For the problem of negative impact between modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology

Percutaneous Nephrolithotomy under Local Infiltration Anesthesia in Kneeling Prone Position for a Patient with Spinal Deformity

Urolithiasis, a common condition in patients with spinal deformity, poses a challenge to surgical procedures and anesthetic management. A 51-year-old Chinese male presented with bilateral complex renal calculi. He was also affected by severe kyphosis deformity and spinal stiffness due to ankylosing spondylitis. Dr. Li performed the percutaneous nephrolithotomy under local infiltration anesthesia with the patient in a kneeling prone position, achieving satisfactory stone clearance with no severe complications. We found this protocol safe and effective to manage kidney stones in patients with spinal deformity. Local infiltration anesthesia may benefit patients for whom epidural anesthesia and intubation anesthesia are difficult

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD.Comment: 10 pages, 14 tables, and 3 figure

New Insight into the Anti-liver Fibrosis Effect of Multitargeted Tyrosine Kinase Inhibitors: From Molecular Target to Clinical Trials

Tyrosine kinases (TKs) is a family of tyrosine protein kinases with important functions in the regulation of a broad variety of physiological cell processes. Overactivity of TK disturbs cellular homeostasis and has been linked to the development of certain diseases, including various fibrotic diseases. In regard to liver fibrosis, several TKs, such as vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR) and epidermal growth factor receptor (EGFR) kinases, have been identified as central mediators in collagen production and potential targets for anti-liver fibrosis therapies. Given the essential role of TKs during liver fibrogenesis, multitargeted inhibitors of aberrant TK activity, including sorafenib, erlotinib, imatinib, sunitinib, nilotinib, brivanib and vatalanib, have been shown to have potential for treating liver fibrosis. Beneficial effects are observed by researchers of this field using these multitargeted TK inhibitors in preclinical animal models and in patients with liver fibrosis. The present review will briefly summarize the anti-liver fibrosis effects of multitargeted TK inhibitors and molecular mechanisms

MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an important basis for AD diagnosis, can also reflect the progress of cognitive impairment, some studies have begun to apply multi-task learning methods to these two tasks. However, how to exploit feature correlation remains a challenging problem for these methods. To comprehensively address this challenge, we propose a MRI-based multi-task decoupled learning method for AD detection and MMSE score prediction. First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks. Each multi-task interaction layer contains two feature decoupling modules and one feature interaction module. Furthermore, to enhance the generalization between tasks of the features selected by the feature decoupling module, we propose the feature consistency loss constrained feature decoupling module. Finally, in order to exploit the specific distribution information of MMSE score in different groups, a distribution loss is proposed to further enhance the model performance. We evaluate our proposed method on multi-site datasets. Experimental results show that our proposed multi-task decoupled representation learning method achieves good performance, outperforming single-task learning and other existing state-of-the-art methods.Comment: 15 page

2-(3,3,4,4-Tetra­fluoro­pyrrolidin-1-yl)aniline

In the title fluorinated pyrrolidine derivative, C10H10F4N2, the dihedral angle between the best planes of the benzene and pyrrolidine rings is 62.6 (1)°. The crystal packing features inter­molecular N—H⋯F hydrogen bonds

Influence of contextual factors, technical performance and movement demands on the subjective task load associated with professional rugby league match-play

Accepted author manuscript version reprinted, by permission, from International Journal of Sports Physiology and Performance, 2021, 16(6), 763-771, https://doi.org/10.1123/ijspp.2019-0998. © Human Kinetics, Inc.Purpose: The aim of the study was to identify the association between several contextual match factors, technical performance and external movement demands on the subjective task load of elite rugby league players. Methods: Individual subjective task load, quantified using the National Aeronautics and Space Administration Task Load Index (NASA-TLX), was collected from 29 professional rugby league players from one club competing in the European Super League throughout the 2017 season. The sample consisted of 26 matches, culminating in 441 individual data points. Linear mixed-modelling was adopted to analyze the data for relationships and revealed that various combinations of contextual factors, technical performance and movement demands were associated with subjective task load. Results: Greater number of tackles (effect size correlation ± 90% CI; η2= 0.18 ±0.11), errors (η2= 0.15 ±0.08) decelerations (η2= 0.12 ±0.08), increased sprint distance (η2= 0.13 ±0.08), losing matches (η2= 0.36 ±0.08) and increased perception of effort (η2= 0.27 ±0.08) led to most likely – very likely increases in subjective total task load. The independent variables included in the final model for subjective mental demand (match outcome, time played and number of accelerations) were unclear, excluding a likely small correlation with the number of technical errors (η2= 0.10 ±0.08). Conclusions: These data provide a greater understanding of the subjective task load and their association with several contextual factors, technical performance and external movement demands during rugby league competition. Practitioners could use this detailed quantification of internal loads to inform the prescription of recovery sessions and current training practices

Circular RNAs as potential biomarkers and therapeutics for cardiovascular disease

Circular RNAs (circRNAs) are genetic regulators that were earlier considered as “junk”. In contrast to linear RNAs, they have covalently linked ends with no polyadenylated tails. CircRNAs can act as RNA-binding proteins, sequestering agents, transcriptional regulators, as well as microRNA sponges. In addition, it is reported that some selected circRNAs are transformed into functional proteins. These RNA molecules always circularize through covalent bonds, and their presence has been demonstrated across species. They are usually abundant and stable as well as evolutionarily conserved in tissues (liver, lung, stomach), saliva, exosomes, and blood. Therefore, they have been proposed as the “next big thing” in molecular biomarkers for several diseases, particularly in cancer. Recently, circRNAs have been investigated in cardiovascular diseases (CVD) and reported to play important roles in heart failure, coronary artery disease, and myocardial infarction. Here, we review the recent literature and discuss the impact and the diagnostic and prognostic values of circRNAs in CVD