Learning View-Model Joint Relevance for 3D Object Retrieval

3D object retrieval has attracted extensive research efforts and become an important task in recent years. It is noted that how to measure the relevance between 3D objects is still a difficult issue. Most of the existing methods employ just the model-based or view-based approaches, which may lead to incomplete information for 3D object representation. In this paper, we propose to jointly learn the view-model relevance among 3D objects for retrieval, in which the 3D objects are formulated in different graph structures. With the view information, the multiple views of 3D objects are employed to formulate the 3D object relationship in an object hypergraph structure. With the model data, the model-based features are extracted to construct an object graph to describe the relationship among the 3D objects. The learning on the two graphs is conducted to estimate the relevance among the 3D objects, in which the view/model graph weights can be also optimized in the learning process. This is the first work to jointly explore the view-based and model-based relevance among the 3D objects in a graph-based framework. The proposed method has been evaluated in three data sets. The experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness on retrieval accuracy of the proposed 3D object retrieval method

SigFlux: A novel network feature to evaluate the importance of proteins in signal transduction networks

BACKGROUND: Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. RESULTS: We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. CONCLUSION: SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Abstract Background Low-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. Methods In this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart. Results The statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments. Conclusion Experimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Graphical Abstract Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weight

Computational identification of rare codons of Escherichia coli based on codon pairs preference

<p>Abstract</p> <p>Background</p> <p>Codon bias is believed to play an important role in the control of gene expression. In <it>Escherichia coli</it>, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pair's preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in <it>Escherichia coli</it>.</p> <p>Results</p> <p>In <it>Escherichia coli</it>, we defined the "rare codon pairs" by calculating the frequency of occurrence of all codon pairs in coding sequences. Rare codons which are disliked in genes could make great contributions to forming rare codon pairs. Meanwhile our investigation showed that many of these rare codon pairs contain termination codons and the recognized sites of restriction enzymes. Furthermore, a new index (F_rare) was developed. Through comparison with the classical indices we found a significant negative correlation between F_rareand the indices which depend on reference datasets.</p> <p>Conclusions</p> <p>Our approach suggests that we can identify rare codons by studying the context in which a codon lies. Also, the frequency of rare codons (F_rare) could be a useful index of codon bias regardless of the lack of expression abundance information.</p

Toxicity study of oral vanadyl sulfate by NMR-based metabonomic

Vanadium compounds have been believed to be ideal drugs for diabetes biological therapy in future, but they suffer setback for the potential toxicity now. Toxicity study is necessary for vanadyl drugs development. This paper investigated the toxicity effects of vanadyl sulfate (VOSO4) oral administration in male Wistar rats using H-1 NMR-based metabonomic analysis and clinical biochemical analysis. Rat urine were collected and their H-1 NMR spectra were acquired, and then subjected to multi-variable statistical analysis. Compared to control groups, urinary excretion of lactate, TMAO, creatinine, taurine and hippurate increased following VOSO4 dosing, with concomitant decrease in the level of acetate and succinate. The dosed groups can be readily discriminated from the control groups by principle component analysis. The results showed that VOSO4 can affect energy metabolism process, interrupted intestinal microfloral metabolism, and induced liver and kidney injury. NMR-based metabonomic can offer additional information to traditional clinical chemistry in the sensitivity and specificity of results obtained

Momordica charantia suppresses inflammation and glycolysis in lipopolysaccharide-activated RAW264.7 macrophages

Macrophage activation is a key event that triggers inflammatory response. The activation is accompanied by metabolic shift such as upregulated glucose metabolism. There are accumulating evidences showing the anti-inflammatory activity of Momordica charantia. However, the effects of M. charantia on inflammatory response and glucose metabolism in activated macrophages have not been fully established. The present study aimed to examine the effect of M. charantia in modulating lipopolysaccharide (LPS)-induced inflammation and perturbed glucose metabolism in RAW264.7 murine macrophages. The results showed that LPS-induced NF-?B (p65) nuclear translocation was inhibited by M. charantia treatment. In addition, M. charantia was found to reduce the expression of inflammatory genes including IL6, TNF-a, IL1ß, COX2, iNOS, and IL10 in LPS-treated macrophages. Furthermore, the data showed that M. charantia reduced the expression of GLUT1 and HK2 genes and lactate production (-28%), resulting in suppression of glycolysis. Notably, its effect on GLUT1 gene expression was found to be independent of LPS-induced inflammation. A further experiment also indicated that the bioactivities of M. charantia may be attributed to its key bioactive compound, charantin. Taken together, the study provided supporting evidences showing the potential of M. charantia for the treatment of inflammatory disorders

(1)H NMR-based metabonomics study of urine and serum samples from diabetic db/db mice

A metabonomics approach based on high resolution (1)H NMR spectroscopy was applied to investigate the metabolite fingerprints in urine and serum samples from db/db mice of 8 weeks old, an animal model of type 2 diabetes mellitus (T2DM). Both NMR spectra and metabonomics results were discussed and the variations on related metabolic pathway were analyzed. The urinary excretions of diabetic mice have elevated levels of citrate, alanine, acetate, TMAO, hippurate, taurine, creatinine, succinate, pyruvate, glycine in addition to evident increase of glucose compared to the control ones. The metabolic variation in serum samples of db/db mice is marked by the increases of lactate, 3-hydroxybutyrate, glutamine, glutamate and choline and the decreases of leucine and valine. These results indicate that NMR-based metabonomics is an efficient approach for investigating the subtle metabolic alterations in urine and serum from diabetic mice and the findings of the characteristic metabolites would be helpful for early diagnosis and prevention of T2DM and its related complications

Comparison of PET/CT and MRI in the Diagnosis of Bone Metastasis in Prostate Cancer Patients: A Network Analysis of Diagnostic Studies

Background: Accurate diagnosis of bone metastasis status of prostate cancer (PCa) is becoming increasingly more important in guiding local and systemic treatment. Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) have increasingly been utilized globally to assess the bone metastases in PCa. Our meta-analysis was a high-volume series in which the utility of PET/CT with different radioligands was compared to MRI with different parameters in this setting. Materials and Methods: Three databases, including Medline, Embase, and Cochrane Library, were searched to retrieve original trials from their inception to August 31, 2019 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. The methodological quality of the included studies was assessed by two independent investigators utilizing Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A Bayesian network meta-analysis was performed using an arm-based model. Absolute sensitivity and specificity, relative sensitivity and specificity, diagnostic odds ratio (DOR), and superiority index, and their associated 95% confidence intervals (CI) were used to assess the diagnostic value. Results: Forty-five studies with 2,843 patients and 4,263 lesions were identified. Network meta-analysis reveals that 68Ga-labeled prostate membrane antigen (68Ga-PSMA) PET/CT has the highest superiority index (7.30) with the sensitivity of 0.91 and specificity of 0.99, followed by 18F-NaF, 11C-choline, 18F-choline, 18F-fludeoxyglucose (FDG), and 18F-fluciclovine PET/CT. The use of high magnetic field strength, multisequence, diffusion-weighted imaging (DWI), and more imaging planes will increase the diagnostic value of MRI for the detection of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT was performed in the detection of bone metastasis on patient-based level (sensitivity, 0.94 vs. 0.91; specificity, 0.94 vs. 0.96; superiority index, 4.43 vs. 4.56). Conclusions: 68Ga-PSMA PET/CT is recommended for the diagnosis of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT should be performed in the detection of bone metastasis

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

Neural network based algorithm for multi-constrained shortest path problem

Multi-Constrained Shortest Path (MCSP) selection is a fundamental problem in communication networks. Since the MCSP problem is NP-hard, there have been many efforts to develop efficient approximation algorithms and heuristics. In this paper, a new algorithm is proposed based on vectorial Autowave-Competed Neural Network which has the characteristics of parallelism and simplicity. A nonlinear cost function is defined to measure the autowaves (i.e., paths). The M-paths limited scheme, which allows no more than M autowaves can survive each time in each neuron, is adopted to reduce the computational and space complexity. And the proportional selection scheme is also adopted so that the discarded autowaves can revive with certain probability with respect to their cost functions. Those treatments ensure in theory that the proposed algorithm can find an approximate optimal path subject to multiple constraints with arbitrary accuracy in polynomial-time. Comparing experiment results showed the efficiency of the proposed algorithm