A new feature-preserving nonlinear anisotropic diffusion method for image denoising

We present a new diffusion method for noise reduction and feature preservation.Presently, denoising methods commonly use a first-order derivative to detect edges inorder to achieve a good balance between noise removal and feature preserving.However, if edges are partly lost to a certain extent or contaminated severely bynoise, these methods may not be able to detect them and thus fail to preserve variousfeatures in images. To overcome this problem, we propose a new and moresophisticated feature detector by combining first- and second-order derivatives for anonlinear anisotropic diffusion model. Numerical experiments show that the newdiffusion filter outperforms many popular filters for denoising images containingedges, blobs and ridges and textures made of these features

Secretory vesicles are preferentially targeted to areas of low molecular SNARE density

Intercellular communication is commonly mediated by the regulated fusion, or exocytosis, of vesicles with the cell surface. SNARE (soluble N-ethymaleimide sensitive factor attachment protein receptor) proteins are the catalytic core of the secretory machinery, driving vesicle and plasma membrane merger. Plasma membrane SNAREs (tSNAREs) are proposed to reside in dense clusters containing many molecules, thus providing a concentrated reservoir to promote membrane fusion. However, biophysical experiments suggest that a small number of SNAREs are sufficient to drive a single fusion event. Here we show, using molecular imaging, that the majority of tSNARE molecules are spatially separated from secretory vesicles. Furthermore, the motilities of the individual tSNAREs are constrained in membrane micro-domains, maintaining a non-random molecular distribution and limiting the maximum number of molecules encountered by secretory vesicles. Together our results provide a new model for the molecular mechanism of regulated exocytosis and demonstrate the exquisite organization of the plasma membrane at the level of individual molecular machines

An adaptive non-local means filter for denoising live-cell images and improving particle detection

Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data

Unsupervised Text Topic-Related Gene Extraction for Large Unbalanced Datasets

There is a common notion that traditional unsupervised feature extraction algorithms follow the assumption that the distribution of the different clusters in a dataset is balanced. However, feature selection is guided by the calculation of similarities among features when topic keywords are extracted from a large number of unmarked, unbalanced text datasets. As a result, the selected features cannot truly reflect the information of the original data set, which thus affects the subsequent performance of classifiers. To solve this problem, a new method of extracting unsupervised text topic-related genes is proposed in this paper. Firstly, a sample cluster group is obtained by factor analysis and a density peak algorithm, based on which the dataset is marked. Then, considering the influence of the unbalanced distribution of sample clusters on feature selection, the CHI statistical matrix feature selection method, which combines average local density and information entropy together, is used to strengthen the features of low-density small-sample clusters. Finally, a related gene extraction method based on the exploration of high-order relevance in multidimensional statistical data is described, which uses independent component analysis to enhance the generalisability of the selected features. In this way, unsupervised text topic-related genes can be extracted from large unbalanced datasets. The results of experiments suggest that the proposed method of extracting unsupervised text topic-related genes is better than existing methods in extracting text subject terms from low-density small-sample clusters, and has higher prematurity and feature dimension-reduction ability

BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

The Viscoelasticity Model of Corn Straw under the Different Moisture Contents

Viscoelastic model of corn straw, based on different moisture contents, is set up to characterise the deformation through three-point bending test. The model contains a linear elastic element, a damping element, and a nonlinear elastic element. The parameters of the model are determined according to the features of three-point bending test curve and characteristic of the model. The relationships between mechanical properties, energy absorption behavior of corn stalk, and moisture content have been, respectively, analysed. And regression analysis and curve fitting have been conducted based on various parameters and moisture contents with Matlab. These parameters provide the basis for straw crushing equipment design

Flexibly-oriented double Cdc45-MCM-GINS intermediates during eukaryotic replicative helicase maturation

The core of the eukaryotic helicase MCM is loaded as an inactive double hexamer (DH). How it is assembled into two active Cdc45-MCM-GINS (CMG) helicases remains elusive. Here, we report that at the onset of S phase, both Cdc45 and GINS are loaded as dimers onto MCM DH, resulting in formation of double CMG (d-CMG). As S phase proceeds, d-CMGs gradually mature into two single CMG-centered replisome progression complexes (RPCs). Mass spectra reveal that RPA and DNA Pol α/primase co-purify exclusively with RPCs, but not with d-CMGs. Consistently, d-CMGs are not able to catalyze either the unwinding or de novo DNA synthesis, while RPCs can do both. Using single-particle electron microscopy, we have obtained 2D class averages of d-CMGs. Compared to MCM DHs, they display heterogeneous, flexibly orientated and partially loosened conformations with changed interfaces. The dumbbell-shaped d-CMGs are mediated by Ctf4, while other types of d-CMGs are independent of Ctf4. These data suggest CMG dimers as bona fide intermediates during MCM maturation, providing an additional quality control for symmetric origin activation and bidirectional replication

High expression of transcriptional coactivator p300 correlates with aggressive features and poor prognosis of hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>It has been suggested that p300 participates in the regulation of a wide range of cell biological processes and mutation of p300 has been identified in certain types of human cancers. However, the expression dynamics of p300 in hepatocellular carcinoma (HCC) and its clinical/prognostic significance are unclear.</p> <p>Methods</p> <p>In this study, the methods of reverse transcription-polymerase chain reaction (RT-PCR), Western blotting and immunohistochemistry (IHC) were utilized to investigate protein/mRNA expression of p300 in HCCs. Receiver operating characteristic (ROC) curve analysis, spearman's rank correlation, Kaplan-Meier plots and Cox proportional hazards regression model were used to analyze the data.</p> <p>Results</p> <p>Up-regulated expression of p300 mRNA and protein was observed in the majority of HCCs by RT-PCR and Western blotting, when compared with their adjacent non-malignant liver tissues. According to the ROC curves, the cutoff score for p300 high expression was defined when more than 60% of the tumor cells were positively stained. High expression of p300 was examined in 60/123 (48.8%) of HCCs and in 8/123 (6.5%) of adjacent non-malignant liver tissues. High expression of p300 was correlated with higher AFP level, larger tumor size, multiplicity, poorer differentiation and later stage (<it>P </it>< 0.05). In univariate survival analysis, a significant association between overexpression of p300 and shortened patients' survival was found (<it>P </it>= 0.001). In different subsets of HCC patients, p300 expression was also a prognostic indicator in patients with stage II (<it>P </it>= 0.007) and stage III (<it>P </it>= 0.011). Importantly, p300 expression was evaluated as an independent prognostic factor in multivariate analysis (<it>P </it>= 0.021). Consequently, a new clinicopathologic prognostic model with three poor prognostic factors (p300 expression, AFP level and vascular invasion) was constructed. The model could significantly stratify risk (low, intermediate and high) for overall survival (<it>P </it>< 0.0001).</p> <p>Conclusions</p> <p>Our findings provide a basis for the concept that high expression of p300 in HCC may be important in the acquisition of an aggressive phenotype, suggesting that p300 overexpression, as examined by IHC, is an independent biomarker for poor prognosis of patients with HCC. The combined clinicopathologic prognostic model may become a useful tool for identifying HCC patients with different clinical outcomes.</p