Nonlinear Analysis of Bending GFRP Tube Concrete Member

In order to study the factors that influence anti-bending mechanical properties of GFRP tube confined concrete beams and better applied to engineering, the influence of different thickness of GFRP tube, concrete strength grade, the section form and strength of steel were analyzed using finite element analysis software ABAQUS when the beam is in compression condition in this paper. The results showed that the bearing capacity of beams was improved by increasing the GFRP tube thickness, improving the core concrete strength and increasing steel area and strength. The numerical results were in good agreement with the experimental one. The anti-bending bearing capacity formula was in good agreement with the experimental results

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio as predictive markers in hepatoblastoma

BackgroundThe neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been presented to be a prognostic indicator in several cancers. We were supposed to evaluate the prognostic role of such inflammatory markers in hepatoblastoma (HB).MethodsTotal of 101 children, diagnosed with hepatoblastoma between January 2010 and January 2018, were enrolled before treatment in the study. The clinicopathological parameters, and outcomes were collected through laboratory analyses and patient follow-up. The association between NLR, PLR, and clinicopathological characters were analyzed with Wilcoxon test, Chi-Squared test, Kaplan-Meier, Log-rank and Cox regression analyses.ResultsNLR and PLR were significantly elevated in HB patients (P < 0.001), and related to age (P < 0.001), risk stratification system (P < 0.001), and pretreatment extent of disease (P < 0.0001). NLR was significantly related to alpha-fetoprotein (P = 0.034) and lactate dehydrogenase (P = 0.026). The 3-year overall survival (OS) and event-free survival (EFS) were poor in the high-NLR group (OS: 44.3% vs. 90.3%, P < 0.0001, EFS: 38.6% vs. 80.6%, P = 0.0001). The 3-year OS and EFS were poor in the high-PLR group (OS: 49.1% vs. 68.8%, P = 0.016, EFS: 39.6% vs. 64.6%, P = 0.0117). The multivariate analysis suggested that NLR (HR: 11.359, 95% CI: 1.218–105.947; P = 0.033) and risk stratification (HR: 44.905, 95% CI: 2.458–820.36; P = 0.01), were independent predictors of OS.ConclusionOur research showed that elevated NLR and PLR were the poor prognostic factors in HB patients before treatment. The NLR was an independent prognostic factor for OS

A new chromosome-scale duck genome shows a major histocompatibility complex with several expanded multigene families

BACKGROUND: The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A virus (IAV), harbors almost all subtypes of IAVs and resists to many IAVs which cause extreme virulence in chicken and human. However, the response of duck's adaptive immune system to IAV infection is poorly characterized due to lack of a detailed gene map of the major histocompatibility complex (MHC).RESULTS: We herein reported a chromosome-scale Beijing duck assembly by integrating Nanopore, Bionano, and Hi-C data. This new reference genome SKLA1.0 covers 40 chromosomes, improves the contig N50 of the previous duck assembly with highest contiguity (ZJU1.0) of more than a 5.79-fold, surpasses the chicken and zebra finch references in sequence contiguity and contains a complete genomic map of the MHC. Our 3D MHC genomic map demonstrated that gene family arrangement in this region was primordial; however, families such as AnplMHCI, AnplMHCIIβ, AnplDMB, NKRL (NK cell receptor-like genes) and BTN underwent gene expansion events making this area complex. These gene families are distributed in two TADs and genes sharing the same TAD may work in a co-regulated model.CONCLUSIONS: These observations supported the hypothesis that duck's adaptive immunity had been optimized with expanded and diversified key immune genes which might help duck to combat influenza virus. This work provided a high-quality Beijing duck genome for biological research and shed light on new strategies for AIV control.</p

The Overseeing Mother: Revisiting the Frontal-Pose Lady in the Wu Family Shrines in Second Century China

Located in present-day Jiaxiang in Shandong province, the Wu family shrines built during the second century in the Eastern Han dynasty (25–220) were among the best-known works in Chinese art history. Although for centuries scholars have exhaustively studied the pictorial programs, the frontal-pose female image situated on the second floor of the central pavilion carved at the rear wall of the shrines has remained a question. Beginning with the woman’s eyes, this article demonstrates that the image is more than a generic portrait (“hard motif ”), but rather represents “feminine overseeing from above” (“soft motif ”). This synthetic motif combines three different earlier motifs – the frontal-pose hostess enjoying entertainment, the elevated spectator, and the Queen Mother of the West. By creatively fusing the three motifs into one unity, the Jiaxiang artists lent to the frontal-pose lady a unique power: she not only dominated the center of the composition, but also, like a divine being, commanded a unified view of the surroundings on the lofty building, hence echoing the political reality of the empress mother’s “overseeing the court” in the second century during Eastern Han dynasty

Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine

Background Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings. New method Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach. Results The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins

Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction

Epilepsy is one of the most common neurological disorders- approximately one in every 100 people worldwide are suffering from it. In this paper, a novel pattern recognition model is presented for automatic epilepsy diagnosis. Wavelet transform is investigated to decompose EEG into five EEG frequency bands which approximate to delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ) bands. Complexity based features such as permutation entropy (PE), sample entropy (SampEn), and the Hurst exponent (HE) are extracted from both the original EEG signals and each of the frequency bands. The wavelet-based methodology separates the alterations in PE, SampEn, and HE in specific frequency bands of the EEG. The effectiveness of these complexity based measures in discriminating between normal brain state and brain state during the absence of seizures is evaluated using the Extreme Learning Machine (ELM). It is discovered that although there exists no significant differences in the feature values extracted from the original EEG signals, differences can be recognized when the features are examined within specific EEG frequency bands. A genetic algorithm (GA) is developed to choose feature subsets that are effective for enhancing the recognition performance. The GA is also examined for weight alteration for both sensitivity and specificity. The results show that the abnormal EEG diagnosis rate of the model without the involvement of the genetic algorithm is 85.9%. However, the diagnosis rate of the model increases to 94.2% when the genetic algorithm is integrated as a feature selector

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Epilepsy is one of the most common neurological disorders – approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. This paper presents a novel method for automatic epileptic seizure detection. An optimized sample entropy (O-SampEn) algorithm is proposed and combined with extreme learning machine (ELM) to identify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A public dataset was utilized for evaluating the proposed method. Results show that the proposed epilepsy detection approach achieves not only high detection accuracy but also a very fast computation speed, which demonstrates its huge potential for the real-time detection of epileptic seizures

Study the Influence of Surface Morphology and Lubrication Pressure on Tribological Behavior of 316L–PTFE Friction Interface in High-Water-Based Fluid

Because of the low viscosity of high-water-based fluids, the intense wear and leakage of key friction pairs represent a bottleneck to the wide application of the high-water-based hydraulic motor in engineering machinery. In this work, based on the common characteristics of plane friction pairs, the friction experiments of a 316L stainless steel (316L)&ndash;polytetrafluoroethylene (PTFE) friction pair under various working condition were carried out by a self-designed friction experimental system with fluid lubrication. The influence of lubrication pressure and surface morphology on the 316L&ndash;PTFE friction pair was investigated both experimentally and theoretically. The experimental and numerical results indicated that increasing lubrication pressure reduced the surface wear of PTFE sample, but the leakage of 316L&ndash;PTFE friction pair also increased. It could not form an effective fluid lubrication film in the 316L&ndash;PTFE friction pair under low lubrication pressure, which caused the severe wear in friction pair interface. The smooth 316L surface could be conducive to the formation of high-water-based fluid lubrication film in 316L&ndash;PTFE friction interface. The pressure distribution of high-water-based fluid lubrication film in 316L&ndash;PTFE friction pair was also obtained in fluent. The PTFE surface was easily worn when the lubrication film in the friction pair was too thin or uneven. The friction and wear were obviously improved when the normal load was balanced by the bearing capacity of the high-water-based fluid lubrication film