Automatic Reconstruction of Fault Networks from Seismicity Catalogs: 3D Optimal Anisotropic Dynamic Clustering

We propose a new pattern recognition method that is able to reconstruct the 3D structure of the active part of a fault network using the spatial location of earthquakes. The method is a generalization of the so-called dynamic clustering method, that originally partitions a set of datapoints into clusters, using a global minimization criterion over the spatial inertia of those clusters. The new method improves on it by taking into account the full spatial inertia tensor of each cluster, in order to partition the dataset into fault-like, anisotropic clusters. Given a catalog of seismic events, the output is the optimal set of plane segments that fits the spatial structure of the data. Each plane segment is fully characterized by its location, size and orientation. The main tunable parameter is the accuracy of the earthquake localizations, which fixes the resolution, i.e. the residual variance of the fit. The resolution determines the number of fault segments needed to describe the earthquake catalog, the better the resolution, the finer the structure of the reconstructed fault segments. The algorithm reconstructs successfully the fault segments of synthetic earthquake catalogs. Applied to the real catalog constituted of a subset of the aftershocks sequence of the 28th June 1992 Landers earthquake in Southern California, the reconstructed plane segments fully agree with faults already known on geological maps, or with blind faults that appear quite obvious on longer-term catalogs. Future improvements of the method are discussed, as well as its potential use in the multi-scale study of the inner structure of fault zones

The Selection of a Hepatocyte Cell Line Susceptible to Plasmodium falciparum Sporozoite Invasion That Is Associated With Expression of Glypican-3

In vitro studies of liver stage (LS) development of the human malaria parasite Plasmodium falciparum are technically challenging; therefore, fundamental questions about hepatocyte receptors for invasion that can be targeted to prevent infection remain unanswered. To identify novel receptors and to further understand human hepatocyte susceptibility to P. falciparum sporozoite invasion, we created an optimized in vitro system by mimicking in vivo liver conditions and using the subcloned HC-04.J7 cell line that supports mean infection rates of 3–5% and early development of P. falciparum exoerythrocytic forms—a 3- to 5-fold improvement on current in vitro hepatocarcinoma models for P. falciparum invasion. We juxtaposed this invasion-susceptible cell line with an invasion-resistant cell line (HepG2) and performed comparative proteomics and RNA-seq analyses to identify host cell surface molecules and pathways important for sporozoite invasion of host cells. We identified and investigated a hepatocyte cell surface heparan sulfate proteoglycan, glypican-3, as a putative mediator of sporozoite invasion. We also noted the involvement of pathways that implicate the importance of the metabolic state of the hepatocyte in supporting LS development. Our study highlights important features of hepatocyte biology, and specifically the potential role of glypican-3, in mediating P. falciparum sporozoite invasion. Additionally, it establishes a simple in vitro system to study the LS with improved invasion efficiency. This work paves the way for the greater malaria and liver biology communities to explore fundamental questions of hepatocyte-pathogen interactions and extend the system to other human malaria parasite species, like P. vivax

Quantitative analysis and comparison of 3D morphology between viable and apoptotic MCF-7 breast cancer cells and characterization of nuclear fragmentation

Morphological changes in apoptotic cells provide essential markers for defining and detection of apoptosis as a fundamental mechanism of cell death. Among these changes, the nuclear fragmentation and condensation have been regarded as the important markers but quantitative characterization of these changes is yet to be achieved. We have acquired confocal image stacks of 206 viable and apoptotic MCF-7 cells stained by three fluorescent dyes. Three-dimensional (3D) parameters were extracted to quantify and compare their differences in morphology. To analyze nuclear fragmentation, a new method has been developed to determine clustering of nuclear voxels in the reconstructed cells due to fluorescence intensity changes in nuclei of apoptotic cells. The results of these studies reveal that the 3D morphological changes in cytoplasm and nuclear membranes in apoptotic cells provide sensitive targets for label-free detection and staging of apoptosis. Furthermore, the clustering analysis and morphological data on nuclear fragmentation are highly useful for derivation of optical cell models and simulation of diffraction images to investigate light scattering by early apoptotic cells, which can lead to future development of label-free and rapid methods of apoptosis assay based on cell morphology.Open Access Fundin

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments

Evaluating Sampling Based Hotspot Detection

Abstract. In sampling based hotspot detection, performance engineers sample the running program periodically and record the Instruction Pointer (IP) addresses at the sampling. Empirically, frequently sampled IP addresses are regarded as the hotspot of the program. The question of how well the sampled hotspot IP addresses match the real hotspot of the program is seldom studied by the researchers. In this paper, we use instrumentation tool to count how many times the sampled hotspot IP addresses are executed, and compare the real execution result with the sampled one to see how well they match. We define the normalized root mean square error, the sample coverage and the order deviation to evaluate the difference between the real execution and the sampled results. Experiment on the SPEC CPU 2006 benchmarks with various sampling periods is performed to verify the proposed evaluation measurements. Intuitively, the sampling accuracy decreases with the increase of sampling period. The experimental results reveal that the order deviation reflects the intuitive relation between the sampling accuracy and the sampling period better than the normalized root mean square error and the sample coverage. Key words: hotspot detection, sampling, accuracy, performance event counters, instrumentation