System Identification Algorithm Analysis of Acupuncture Effect on Mean Blood Flux of Contralateral Hegu Acupoint

Background. Acupoints (belonging to 12 meridians) which have the same names are symmetrically distributed on the body. It has been proved that acupoints have certain biological specificities different from the normal parts of the body. However, there is little evidence that acupoints which have the same name and are located bilaterally and symmetrically have lateralized specificity. Thus, researching the lateralized specificity and the relationship between left-side and right-side acupuncture is of special importance. Methodology and Principal Findings. The mean blood flux (MBF) in both Hegu acupoints was measured by Moor full-field laser perfusion imager. With the method of system identification algorithm, the output distribution in different groups was acquired, based on different acupoint stimulation and standard signal input. It is demonstrated that after stimulation of the right Hegu acupoint by needle, the output value of MBF in contralateral Hegu acupoint was strongly amplified, while after acupuncturing the left Hegu acupoint, the output value of MBF in either side Hegu acupoint was amplified moderately. Conclusions and Significance. This paper indicates that the Hegu acupoint has lateralized specificity. After stimulating the ipsilateral Hegu acupoint, symmetry breaking will be produced in contrast to contralateral Hegu acupoint stimulation

Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition

The fault diagnosis of rotating machinery has crucial significance for the safety of modern industry, and the fault feature extraction is the key link of the diagnosis process. As an effective time-frequency method, Empirical Mode Decomposition (EMD) has been widely used in signal processing and feature extraction. However, the mode mixing phenomenon may lead to confusion in the identification of multi frequency signals and restricts the applications of EMD. In this paper, a novel method based on Multi-Differential Empirical Mode Decomposition (MDEMD) was proposed to extract the energy distribution characteristics of fault signals. Firstly, multi-order differential signals were deduced and decomposed by EMD. Then, their energy distribution characteristics were extracted and utilized to construct the feature matrix. Finally, taking the feature matrix as input, the classifiers were applied to diagnosis the existence and severity of rotating machinery faults. Simulative and practical experiments were implemented respectively, and the results demonstrated that the proposed method, i.e. MDEMD, is able to eliminate the mode mixing effectively, and the feature matrix extracted by MDEMD has high separability and universality, furthermore, the fault diagnosis based on MDEMD can be accomplished more effectively and efficiently with satisfactory accuracy

Shaft orbit identification for rotating machinery based on statistical fuzzy vector chain code and support vector machine

Shaft orbit is a significant diagnosis criterion, and its identification plays an important role in the fault diagnosis of large rotating machinery. The main difficulty of shaft orbit identification is how to extract the shape features automatically and effectively. Therefore, in this paper, a novel method named statistical fuzzy vector chain code (SFVCC) is proposed for the feature extraction of shaft orbit, which has such advantages as invariance, simple calculation and high separability. Furthermore, taking the extracted feature vectors as input, support vector machine (SVM) is utilized to identify various kinds of shaft orbits for rotating machinery. Comparative experiments are implemented, the results reveal that, compared with previous methods, the proposed method can identify the shaft orbit more effectively and efficiently with satisfactory accuracy

Shaft orbit identification for rotating machinery based on statistical fuzzy vector chain code and support vector machine

Shaft orbit is a significant diagnosis criterion, and its identification plays an important role in the fault diagnosis of large rotating machinery. The main difficulty of shaft orbit identification is how to extract the shape features automatically and effectively. Therefore, in this paper, a novel method named statistical fuzzy vector chain code (SFVCC) is proposed for the feature extraction of shaft orbit, which has such advantages as invariance, simple calculation and high separability. Furthermore, taking the extracted feature vectors as input, support vector machine (SVM) is utilized to identify various kinds of shaft orbits for rotating machinery. Comparative experiments are implemented, the results reveal that, compared with previous methods, the proposed method can identify the shaft orbit more effectively and efficiently with satisfactory accuracy

Structural Origin of Suppressed Voltage Decay in Single‐Crystalline Li‐Rich Layered Li[Li0.2_{0.2}Ni0.2_{0.2}Mn0.6_{0.6}]O2_{2} Cathodes

Lithium- and manganese-rich layered oxides (LMLOs, ≥ 250 mAh g$^{−1}$) with polycrystalline morphology always suffer from severe voltage decay upon cycling because of the anisotropic lattice strain and oxygen release induced chemo-mechanical breakdown. Herein, a Co-free single-crystalline LMLO, that is, Li[Li$_{0.2}$Ni$_{0.2}$Mn$_{0.6}$]O$_{2}$ (LLNMO-SC), is prepared via a Li$^+$/Na$^+$ ion-exchange reaction. In situ synchrotron-based X-ray diffraction (sXRD) results demonstrate that relatively small changes in lattice parameters and reduced average micro-strain are observed in LLNMO-SC compared to its polycrystalline counterpart (LLNMO-PC) during the charge–discharge process. Specifically, the as-synthesized LLNMO-SC exhibits a unit cell volume change as low as 1.1% during electrochemical cycling. Such low strain characteristics ensure a stable framework for Li-ion insertion/extraction, which considerably enhances the structural stability of LLNMO during long-term cycling. Due to these peculiar benefits, the average discharge voltage of LLNMO-SC decreases by only ≈0.2 V after 100 cycles at 28 mA g$^{-1}$ between 2.0 and 4.8 V, which is much lower than that of LLNMO-PC (≈0.5 V). Such a single-crystalline strategy offers a promising solution to constructing stable high-energy lithium-ion batteries (LIBs)

Genome-wide identification and comprehensive analysis of tubby-like protein gene family in multiple crops

IntroductionThe highly conserved tubby-like proteins (TLPs) play key roles in animal neuronal development and plant growth. The abiotic stress tolerance function of TLPs has been widely explored in plants, however, little is known about comparative studies of TLPs within crops.MethodsBioinformatic identification, phylogenetic analysis, Cis-element analysis, expression analysis, Cis-element analysis, expression analysis and so on were explored to analysis the TLP gene family of multiple crops.ResultsIn this study, a comprehensive analysis of TLP genes were carried out in seven crops to explore whether similar function of TLPs in rice could be achieved in other crops. We identified 20, 9, 14, 11, 12, 35, 14 and 13 TLP genes in Glycine max, Hordeum vulgare, Sorghum bicolor, Arabidopsis thaliana, Oryza sativa Japonica, Triticum aestivum, Setaria italic and Zea mays, respectively. All of them were divided into two groups and ten orthogroups (Ors) based on amino acids. A majority of TLP genes had two domains, tubby-like domain and F-box domain, while members of Or5 only had tubby-like domain. In addition, Or5 had more exons and shorter DNA sequences, showing that characteristics of different Ors reflected the differentiated function and feature of TLP genes in evolutionary process, and Or5 was the most different from the other Ors. Besides, we recognized 25 cis-elements in the promoter of TLP genes and explored multiple new regulation pathway of TLPs including light and hormone response. The bioinformatic and transcriptomic analysis implied the stresses induced expression and possible functional redundancy of TLP genes. We detected the expression level of 6 OsTLP genes at 1 to 6 days after seed germination in rice, and the most obvious changes in these days were appeared in OsTLP10 and OsTLP12.DiscussionCombined yeast two-hybrid system and pull down assay, we suggested that the TLP genes of Or1 may have similar function during seed germination in different species. In general, the results of comprehensive analysis of TLP gene family in multiple species provide valuable evolutionary and functional information of TLP gene family which are useful for further application and study of TLP genes

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

Integrated single-cell and bulk RNA sequencing analyses reveal a prognostic signature of cancer-associated fibroblasts in head and neck squamous cell carcinoma

Objectives: To identify a prognosis-related subtype of cancer-associated fibroblasts (CAFs) in head and neck squamous cell carcinoma (HNSCC) and comprehend its contributions to molecular characteristics, immune characteristics, and their potential benefits in immunotherapy and chemotherapy for HNSCC.Materials and Methods: We performed single-cell RNA sequencing (scRNA-seq) analysis of CAFs from the samples of HNSCC patients derived from Gene Expression Omnibus (GEO), to identify the prognosis-related subtype of CAFs. CAFs were clustered into five subtypes, and a prognosis-related subtype was identified. Univariate and multivariate cox regression analyses were performed on the cohort selected from The Cancer Genome Atlas (TCGA) to determine signature construction, which was validated in GSE65858 and GSE42743. A prognostic signature based on 4 genes was constructed, which were derived from prognosis-related CAFs. The molecular characteristics, immune characteristics as well as the predicted chemosensitivity and immunotherapeutic response in the signature-defined subgroups were analyzed subsequently.Results: The patients with higher CAF scores correlated with poor survival outcomes. Additionally, a high CAF score correlated with lower infiltration levels of many immune cells including M1 macrophages, CD8+ T cells, follicular T helper cells, monocytes, and naïve B cells. High CAF score also demonstrated different enrichment pathways, mutation genes and copy number variated genes. Furthermore, patients with high CAF scores showed lower sensitivity for chemotherapy and immunotherapy than those with low CAF scores.Conclusion: The results of our study indicate the potential of the CAF signature as a biomarker for the prognosis of HNSCC patients. Furthermore, the signature could be a prospective therapeutic target in HNSCC

Antibiotics and antibiotic resistance genes in global lakes:A review and meta-analysis

Lakes are an important source of freshwater, containing nearly 90% of the liquid surface fresh water worldwide. Long retention times in lakes mean pollutants from discharges slowly circulate around the lakes and may lead to high ecological risk for ecosystem and human health. In recent decades, antibiotics and antibiotic resistance genes (ARGs) have been regarded as emerging pollutants. The occurrence and distribution of antibiotics and ARGs in global freshwater lakes are summarized to show the pollution level of antibiotics and ARGs and to identify some of the potential risks to ecosystem and human health. Fifty-seven antibiotics were reported at least once in the studied lakes. Our meta-analysis shows that sulfamethoxazole, sulfamerazine, sulfameter, tetracycline, oxytetracycline, erythromycin, and roxithromycin were found at high concentrations in both lake water and lake sediment. There is no significant difference in the concentration of sulfonamides in lake water from China and that from other countries worldwide; however, there was a significant difference in quinolones. Erythromycin had the lowest predicted hazardous concentration for 5% of the species (HC5) and the highest ecological risk in lakes. There was no significant difference in the concentration of sulfonamide resistance genes (sul1 and sul2) in lake water and river water. There is surprisingly limited research on the role of aquatic biota in propagation of ARGs in freshwater lakes. As an environment that is susceptible to cumulative build-up of pollutants, lakes provide an important environment to study the fate of antibiotics and transport of ARGs with a broad range of niches including bacterial community, aquatic plants and animals