Correlated Noise: How it Breaks NMF, and What to Do About It

PubMed Central

Plis, Sergey M.; Potluru, Vamsi K.; Lane, Terran; Calhoun, Vince D.

2010-01-01

Non-negative matrix factorization (NMF) is a problem of decomposing multivariate data into a set of features and their corresponding activations. When applied to experimental data, NMF has to cope with noise, which is often highly correlated. We show that correlated noise can break the Donoho and Stodden separability conditions of a dataset and a regular NMF algorithm will fail to decompose it, even when given freedom to be able to represent the noise as a separate feature. To cope with this issue, we present an algorithm for NMF with a generalized least squares objective function (glsNMF) and derive multiplicative updates for the method together with proving their convergence. The new algorithm successfully recovers the true representation from the noisy data. Robust performance can make glsNMF a valuable tool for analyzing empirical data. PMID:23750288

Attributed community mining using joint general non-negative matrix factorization with graph Laplacian

NASA Astrophysics Data System (ADS)

Chen, Zigang; Li, Lixiang; Peng, Haipeng; Liu, Yuhong; Yang, Yixian

2018-04-01

Community mining for complex social networks with link and attribute information plays an important role according to different application needs. In this paper, based on our proposed general non-negative matrix factorization (GNMF) algorithm without dimension matching constraints in our previous work, we propose the joint GNMF with graph Laplacian (LJGNMF) to implement community mining of complex social networks with link and attribute information according to different application needs. Theoretical derivation result shows that the proposed LJGNMF is fully compatible with previous methods of integrating traditional NMF and symmetric NMF. In addition, experimental results show that the proposed LJGNMF can meet the needs of different community minings by adjusting its parameters, and the effect is better than traditional NMF in the community vertices attributes entropy.

Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Haynor, David R.; Thompson, Carol L.; Lein, Ed; Hawrylycz, Michael

2009-02-01

Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

PubMed

Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

2015-05-01

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kannan, Ramakrishnan; Ballard, Grey; Park, Haesun

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H, for the given input matrix A, such that A≈WH. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms thatmore » iteratively solves alternating non-negative least squares (NLS) subproblems for W and H. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans from few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.« less

MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization

DOE PAGES

Kannan, Ramakrishnan; Ballard, Grey; Park, Haesun

2017-10-30

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H, for the given input matrix A, such that A≈WH. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms thatmore » iteratively solves alternating non-negative least squares (NLS) subproblems for W and H. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans from few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.« less

Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech Intelligibility After Oral Surgery.

PubMed

Fu, Szu-Wei; Li, Pei-Chun; Lai, Ying-Hui; Yang, Cheng-Chien; Hsieh, Li-Chun; Tsao, Yu

2017-11-01

Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients.

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

PubMed

Dera, Dimah; Bouaynaya, Nidhal; Fathallah-Shaykh, Hassan M

2016-07-01

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

GC[Formula: see text]NMF: A Novel Matrix Factorization Framework for Gene-Phenotype Association Prediction.

PubMed

Zhang, Yaogong; Liu, Jiahui; Liu, Xiaohu; Hong, Yuxiang; Fan, Xin; Huang, Yalou; Wang, Yuan; Xie, Maoqiang

2018-04-24

Gene-phenotype association prediction can be applied to reveal the inherited basis of human diseases and facilitate drug development. Gene-phenotype associations are related to complex biological processes and influenced by various factors, such as relationship between phenotypes and that among genes. While due to sparseness of curated gene-phenotype associations and lack of integrated analysis of the joint effect of multiple factors, existing applications are limited to prediction accuracy and potential gene-phenotype association detection. In this paper, we propose a novel method by exploiting weighted graph constraint learned from hierarchical structures of phenotype data and group prior information among genes by inheriting advantages of Non-negative Matrix Factorization (NMF), called Weighted Graph Constraint and Group Centric Non-negative Matrix Factorization (GC[Formula: see text]NMF). Specifically, first we introduce the depth of parent-child relationships between two adjacent phenotypes in hierarchical phenotypic data as weighted graph constraint for a better phenotype understanding. Second, we utilize intra-group correlation among genes in a gene group as group constraint for gene understanding. Such information provides us with the intuition that genes in a group probably result in similar phenotypes. The model not only allows us to achieve a high-grade prediction performance, but also helps us to learn interpretable representation of genes and phenotypes simultaneously to facilitate future biological analysis. Experimental results on biological gene-phenotype association datasets of mouse and human demonstrate that GC[Formula: see text]NMF can obtain superior prediction accuracy and good understandability for biological explanation over other state-of-the-arts methods.

On non-negative matrix factorization algorithms for signal-dependent noise with application to electromyography data

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H where V ~ WH. It has been successfully applied in the analysis and interpretation of large-scale data arising in neuroscience, computational biology and natural language processing, among other areas. A distinctive feature of NMF is its nonnegativity constraints that allow only additive linear combinations of the data, thus enabling it to learn parts that have distinct physical representations in reality. In this paper, we describe an information-theoretic approach to NMF for signal-dependent noise based on the generalized inverse Gaussian model. Specifically, we propose three novel algorithms in this setting, each based on multiplicative updates and prove monotonicity of updates using the EM algorithm. In addition, we develop algorithm-specific measures to evaluate their goodness-of-fit on data. Our methods are demonstrated using experimental data from electromyography studies as well as simulated data in the extraction of muscle synergies, and compared with existing algorithms for signal-dependent noise. PMID:24684448

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

Recovering hidden diagonal structures via non-negative matrix factorization with multiple constraints.

PubMed

Yang, Xi; Han, Guoqiang; Cai, Hongmin; Song, Yan

2017-03-31

Revealing data with intrinsically diagonal block structures is particularly useful for analyzing groups of highly correlated variables. Earlier researches based on non-negative matrix factorization (NMF) have been shown to be effective in representing such data by decomposing the observed data into two factors, where one factor is considered to be the feature and the other the expansion loading from a linear algebra perspective. If the data are sampled from multiple independent subspaces, the loading factor would possess a diagonal structure under an ideal matrix decomposition. However, the standard NMF method and its variants have not been reported to exploit this type of data via direct estimation. To address this issue, a non-negative matrix factorization with multiple constraints model is proposed in this paper. The constraints include an sparsity norm on the feature matrix and a total variational norm on each column of the loading matrix. The proposed model is shown to be capable of efficiently recovering diagonal block structures hidden in observed samples. An efficient numerical algorithm using the alternating direction method of multipliers model is proposed for optimizing the new model. Compared with several benchmark models, the proposed method performs robustly and effectively for simulated and real biological data.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint.

PubMed

Zhou, Guoxu; Yang, Zuyuan; Xie, Shengli; Yang, Jun-Mei

2011-04-01

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

Poster — Thur Eve — 03: Application of the non-negative matrix factorization technique to [{sup 11}C]-DTBZ dynamic PET data for the early detection of Parkinson's disease

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Dong-Chang; Jans, Hans; McEwan, Sandy

2014-08-15

In this work, a class of non-negative matrix factorization (NMF) technique known as alternating non-negative least squares, combined with the projected gradient method, is used to analyze twenty-five [{sup 11}C]-DTBZ dynamic PET/CT brain data. For each subject, a two-factor model is assumed and two factors representing the striatum (factor 1) and the non-striatum (factor 2) tissues are extracted using the proposed NMF technique and commercially available factor analysis software “Pixies”. The extracted factor 1 and 2 curves represent the binding site of the radiotracer and describe the uptake and clearance of the radiotracer by soft tissues in the brain, respectively.more » The proposed NMF technique uses prior information about the dynamic data to obtain sample time-activity curves representing the striatum and the non-striatum tissues. These curves are then used for “warm” starting the optimization. Factor solutions from the two methods are compared graphically and quantitatively. In healthy subjects, radiotracer uptake by factors 1 and 2 are approximately 35–40% and 60–65%, respectively. The solutions are also used to develop a factor-based metric for the detection of early, untreated Parkinson's disease. The metric stratifies healthy subjects from suspected Parkinson's patients (based on the graphical method). The analysis shows that both techniques produce comparable results with similar computational time. The “semi-automatic” approach used by the NMF technique allows clinicians to manually set a starting condition for “warm” starting the optimization in order to facilitate control and efficient interaction with the data.« less

Practical limits on muscle synergy identification by non-negative matrix factorization in systems with mechanical constraints.

PubMed

Burkholder, Thomas J; van Antwerp, Keith W

2013-02-01

Statistical decomposition, including non-negative matrix factorization (NMF), is a convenient tool for identifying patterns of structured variability within behavioral motor programs, but it is unclear how the resolved factors relate to actual neural structures. Factors can be extracted from a uniformly sampled, low-dimension command space. In practical application, the command space is limited, either to those activations that perform some task(s) successfully or to activations induced in response to specific perturbations. NMF was applied to muscle activation patterns synthesized from low dimensional, synergy-like control modules mimicking simple task performance or feedback activation from proprioceptive signals. In the task-constrained paradigm, the accuracy of control module recovery was highly dependent on the sampled volume of control space, such that sampling even 50% of control space produced a substantial degradation in factor accuracy. In the feedback paradigm, NMF was not capable of extracting more than four control modules, even in a mechanical model with seven internal degrees of freedom. Reduced access to the low-dimensional control space imposed by physical constraints may result in substantial distortion of an existing low dimensional controller, such that neither the dimensionality nor the composition of the recovered/extracted factors match the original controller.

Peak picking NMR spectral data using non-negative matrix factorization.

PubMed

Tikole, Suhas; Jaravine, Victor; Rogov, Vladimir; Dötsch, Volker; Güntert, Peter

2014-02-11

Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap.

NMF-mGPU: non-negative matrix factorization on multi-GPU systems.

PubMed

Mejía-Roa, Edgardo; Tabas-Madrid, Daniel; Setoain, Javier; García, Carlos; Tirado, Francisco; Pascual-Montano, Alberto

2015-02-13

In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU. NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA's framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system's main memory to the GPU's memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup). Applications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used "out of the box" by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu .

Speech enhancement on smartphone voice recording

NASA Astrophysics Data System (ADS)

Tris Atmaja, Bagus; Nur Farid, Mifta; Arifianto, Dhany

2016-11-01

Speech enhancement is challenging task in audio signal processing to enhance the quality of targeted speech signal while suppress other noises. In the beginning, the speech enhancement algorithm growth rapidly from spectral subtraction, Wiener filtering, spectral amplitude MMSE estimator to Non-negative Matrix Factorization (NMF). Smartphone as revolutionary device now is being used in all aspect of life including journalism; personally and professionally. Although many smartphones have two microphones (main and rear) the only main microphone is widely used for voice recording. This is why the NMF algorithm widely used for this purpose of speech enhancement. This paper evaluate speech enhancement on smartphone voice recording by using some algorithms mentioned previously. We also extend the NMF algorithm to Kulback-Leibler NMF with supervised separation. The last algorithm shows improved result compared to others by spectrogram and PESQ score evaluation.

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks.

PubMed

Wu, Siqi; Joseph, Antony; Hammonds, Ann S; Celniker, Susan E; Yu, Bin; Frise, Erwin

2016-04-19

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set ofDrosophilaearly embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation ofDrosophilaexpression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. The performance of PP with theDrosophiladata suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

CH stretching vibration of N-methylformamide as a sensitive probe of its complexation: infrared matrix isolation and computational study.

PubMed

Sałdyka, M; Mielke, Z; Mierzwicki, K; Coussan, S; Roubin, P

2011-08-21

The complexes between trans-N-methylformamide (t-NMF) and Ar, N(2), CO, H(2)O have been studied by infrared matrix isolation spectroscopy and/or ab initio calculations. The infrared spectra of NMF/Ne, NMF/Ar and NMF/N(2)(CO,H(2)O)/Ar matrices have been measured and the effect of the complexation on the perturbation of t-NMF frequencies was analyzed. The geometries of the complexes formed between t-NMF and Ar, N(2), CO and H(2)O were optimized in two steps at the MP2/6-311++G(2d,2p) level of theory. The four structures, found for every system at this level, were reoptimized on the CP-corrected potential energy surface; both normal and CP corrected harmonic frequencies and intensities were calculated. For every optimized structure the interaction energy was partitioned according to the SAPT scheme and the topological distribution of the charge density (AIM theory) was performed. The analysis of the experimental and theoretical results indicates that the t-NMF-N(2) and CO complexes present in the matrices are stabilized by very weak N-H···N and N-H···C hydrogen bonds in which the N-H group of t-NMF serves as a proton donor. In turn, the t-NMF-H(2)O complex present in the matrix is stabilized by O-H···O(C) hydrogen bonding in which the carbonyl group of t-NMF acts as a proton acceptor. Both, the theoretical and experimental results indicate that involvement of the NH group of t-NMF in formation of very weak hydrogen bonds with the N(2) or CO molecules leads to a clearly noticeable red shift of the CH stretching wavenumber whereas engagement of the CO group as a proton acceptor triggers a blue shift of this wavenumber.

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Siqi; Joseph, Antony; Hammonds, Ann S.

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identifiedmore » 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.« less

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

DOE PAGES

Wu, Siqi; Joseph, Antony; Hammonds, Ann S.; ...

2016-04-06

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identifiedmore » 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.« less

Nonnegative matrix factorization: a blind sources separation method to extract content of fluorophores mixture media

NASA Astrophysics Data System (ADS)

Zhou, Kenneth J.; Chen, Jun

2014-03-01

The fluorophores of malignant human breast cells change their compositions that may be exposed in the fluorescence spectroscopy and blind source separation method. The content of the fluorophores mixture media such as tryptophan, collagen, elastin, NADH, and flavin were varied according to the cancer development. The native fluorescence spectra of these key fluorophores mixture media excited by the selective excitation wavelengths of 300 nm and 340 nm were analyzed using a blind source separation method: Nonnegative Matrix Factorization (NMF). The results show that the contribution from tryptophan, NADH and flavin to the fluorescence spectra of the mixture media is proportional to the content of each fluorophore. These data present a possibility that native fluorescence spectra decomposed by NMF can be used as potential native biomarkers for cancer detection evaluation of the cancer.

Peak picking NMR spectral data using non-negative matrix factorization

PubMed Central

2014-01-01

Background Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. Results To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. Conclusions Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap. PMID:24511909

Learning Microbial Community Structures with Supervised and Unsupervised Non-negative Matrix Factorization.

PubMed

Cai, Yun; Gu, Hong; Kenney, Toby

2017-08-31

Learning the structure of microbial communities is critical in understanding the different community structures and functions of microbes in distinct individuals. We view microbial communities as consisting of many subcommunities which are formed by certain groups of microbes functionally dependent on each other. The focus of this paper is on methods for extracting the subcommunities from the data, in particular Non-Negative Matrix Factorization (NMF). Our methods can be applied to both OTU data and functional metagenomic data. We apply the existing unsupervised NMF method and also develop a new supervised NMF method for extracting interpretable information from classification problems. The relevance of the subcommunities identified by NMF is demonstrated by their excellent performance for classification. Through three data examples, we demonstrate how to interpret the features identified by NMF to draw meaningful biological conclusions and discover hitherto unidentified patterns in the data. Comparing whole metagenomes of various mammals, (Muegge et al., Science 332:970-974, 2011), the biosynthesis of macrolides pathway is found in hindgut-fermenting herbivores, but not carnivores. This is consistent with results in veterinary science that macrolides should not be given to non-ruminant herbivores. For time series microbiome data from various body sites (Caporaso et al., Genome Biol 12:50, 2011), a shift in the microbial communities is identified for one individual. The shift occurs at around the same time in the tongue and gut microbiomes, indicating that the shift is a genuine biological trait, rather than an artefact of the method. For whole metagenome data from IBD patients and healthy controls (Qin et al., Nature 464:59-65, 2010), we identify differences in a number of pathways (some known, others new). NMF is a powerful tool for identifying the key features of microbial communities. These identified features can not only be used to perform difficult classification problems with a high degree of accuracy, they are also very interpretable and can lead to important biological insights into the structure of the communities. In addition, NMF is a dimension-reduction method (similar to PCA) in that it reduces the extremely complex microbial data into a low-dimensional representation, allowing a number of analyses to be performed more easily-for example, searching for temporal patterns in the microbiome. When we are interested in the differences between the structures of two groups of communities, supervised NMF provides a better way to do this, while retaining all the advantages of NMF-e.g. interpretability and a simple biological intuition.

A Comparative Study of the Application of Fluorescence Excitation-Emission Matrices Combined with Parallel Factor Analysis and Nonnegative Matrix Factorization in the Analysis of Zn Complexation by Humic Acids

PubMed Central

Boguta, Patrycja; Pieczywek, Piotr M.; Sokołowska, Zofia

2016-01-01

The main aim of this study was the application of excitation-emission fluorescence matrices (EEMs) combined with two decomposition methods: parallel factor analysis (PARAFAC) and nonnegative matrix factorization (NMF) to study the interaction mechanisms between humic acids (HAs) and Zn(II) over a wide concentration range (0–50 mg·dm−3). The influence of HA properties on Zn(II) complexation was also investigated. Stability constants, quenching degree and complexation capacity were estimated for binding sites found in raw EEM, EEM-PARAFAC and EEM-NMF data using mathematical models. A combination of EEM fluorescence analysis with one of the proposed decomposition methods enabled separation of overlapping binding sites and yielded more accurate calculations of the binding parameters. PARAFAC and NMF processing allowed finding binding sites invisible in a few raw EEM datasets as well as finding totally new maxima attributed to structures of the lowest humification. Decomposed data showed an increase in Zn complexation with an increase in humification, aromaticity and molecular weight of HAs. EEM-PARAFAC analysis also revealed that the most stable compounds were formed by structures containing the highest amounts of nitrogen. The content of oxygen-functional groups did not influence the binding parameters, mainly due to fact of higher competition of metal cation with protons. EEM spectra coupled with NMF and especially PARAFAC processing gave more adequate assessments of interactions as compared to raw EEM data and should be especially recommended for modeling of complexation processes where the fluorescence intensities (FI) changes are weak or where the processes are interfered with by the presence of other fluorophores. PMID:27782078

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chennubhotla, Chakra; Castro, Jason

2013-01-01

In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain un- clear. Here, we use non-negative matrix factorization (NMF) - a dimensionality reduction technique - to uncover structure in a panel of odor profiles, with each odor defined as a point in multi-dimensional descriptor space. The properties of NMF are favorable for the analysis of such lexical and perceptual data, and lead to a high-dimensional account of odor space. We further provide evidence that odor di- mensions apply categorically. That is, odor space is not occupied homogenously, but rather in a discrete and intrinsically clustered manner.more » We discuss the potential implications of these results for the neural coding of odors, as well as for developing classifiers on larger datasets that may be useful for predicting perceptual qualities from chemical structures.« less

Informed Source Separation of Atmospheric and Surface Signal Contributions in Shortwave Hyperspectral Imagery using Non-negative Matrix Factorization

NASA Astrophysics Data System (ADS)

Wright, L.; Coddington, O.; Pilewskie, P.

2015-12-01

Current challenges in Earth remote sensing require improved instrument spectral resolution, spectral coverage, and radiometric accuracy. Hyperspectral instruments, deployed on both aircraft and spacecraft, are a growing class of Earth observing sensors designed to meet these challenges. They collect large amounts of spectral data, allowing thorough characterization of both atmospheric and surface properties. The higher accuracy and increased spectral and spatial resolutions of new imagers require new numerical approaches for processing imagery and separating surface and atmospheric signals. One potential approach is source separation, which allows us to determine the underlying physical causes of observed changes. Improved signal separation will allow hyperspectral instruments to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. In this work, we investigate a Non-negative Matrix Factorization (NMF) method for the separation of atmospheric and land surface signal sources. NMF offers marked benefits over other commonly employed techniques, including non-negativity, which avoids physically impossible results, and adaptability, which allows the method to be tailored to hyperspectral source separation. We adapt our NMF algorithm to distinguish between contributions from different physically distinct sources by introducing constraints on spectral and spatial variability and by using library spectra to inform separation. We evaluate our NMF algorithm with simulated hyperspectral images as well as hyperspectral imagery from several instruments including, the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), NASA Hyperspectral Imager for the Coastal Ocean (HICO) and National Ecological Observatory Network (NEON) Imaging Spectrometer.

Non-negative Matrix Factorization and Co-clustering: A Promising Tool for Multi-tasks Bearing Fault Diagnosis

NASA Astrophysics Data System (ADS)

Shen, Fei; Chen, Chao; Yan, Ruqiang

2017-05-01

Classical bearing fault diagnosis methods, being designed according to one specific task, always pay attention to the effectiveness of extracted features and the final diagnostic performance. However, most of these approaches suffer from inefficiency when multiple tasks exist, especially in a real-time diagnostic scenario. A fault diagnosis method based on Non-negative Matrix Factorization (NMF) and Co-clustering strategy is proposed to overcome this limitation. Firstly, some high-dimensional matrixes are constructed using the Short-Time Fourier Transform (STFT) features, where the dimension of each matrix equals to the number of target tasks. Then, the NMF algorithm is carried out to obtain different components in each dimension direction through optimized matching, such as Euclidean distance and divergence distance. Finally, a Co-clustering technique based on information entropy is utilized to realize classification of each component. To verity the effectiveness of the proposed approach, a series of bearing data sets were analysed in this research. The tests indicated that although the diagnostic performance of single task is comparable to traditional clustering methods such as K-mean algorithm and Guassian Mixture Model, the accuracy and computational efficiency in multi-tasks fault diagnosis are improved.

Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Xu, Richard Yi Da; Luo, Xiangfeng

2018-05-01

Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

Nonnegative Matrix Factorization for Efficient Hyperspectral Image Projection

NASA Technical Reports Server (NTRS)

Iacchetta, Alexander S.; Fienup, James R.; Leisawitz, David T.; Bolcar, Matthew R.

2015-01-01

Hyperspectral imaging for remote sensing has prompted development of hyperspectral image projectors that can be used to characterize hyperspectral imaging cameras and techniques in the lab. One such emerging astronomical hyperspectral imaging technique is wide-field double-Fourier interferometry. NASA's current, state-of-the-art, Wide-field Imaging Interferometry Testbed (WIIT) uses a Calibrated Hyperspectral Image Projector (CHIP) to generate test scenes and provide a more complete understanding of wide-field double-Fourier interferometry. Given enough time, the CHIP is capable of projecting scenes with astronomically realistic spatial and spectral complexity. However, this would require a very lengthy data collection process. For accurate but time-efficient projection of complicated hyperspectral images with the CHIP, the field must be decomposed both spectrally and spatially in a way that provides a favorable trade-off between accurately projecting the hyperspectral image and the time required for data collection. We apply nonnegative matrix factorization (NMF) to decompose hyperspectral astronomical datacubes into eigenspectra and eigenimages that allow time-efficient projection with the CHIP. Included is a brief analysis of NMF parameters that affect accuracy, including the number of eigenspectra and eigenimages used to approximate the hyperspectral image to be projected. For the chosen field, the normalized mean squared synthesis error is under 0.01 with just 8 eigenspectra. NMF of hyperspectral astronomical fields better utilizes the CHIP's capabilities, providing time-efficient and accurate representations of astronomical scenes to be imaged with the WIIT.

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Qiu, Bensheng; Zhang, Xu

2017-08-01

Objective. To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. Approach. Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. Main results. Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. Significance. The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Discriminant projective non-negative matrix factorization.

PubMed

Guan, Naiyang; Zhang, Xiang; Luo, Zhigang; Tao, Dacheng; Yang, Xuejun

2013-01-01

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms.

Discriminant Projective Non-Negative Matrix Factorization

PubMed Central

Guan, Naiyang; Zhang, Xiang; Luo, Zhigang; Tao, Dacheng; Yang, Xuejun

2013-01-01

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers WT X as their coefficients, i.e., X≈WWT X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms. PMID:24376680

Characterization and discrimination of human breast cancer and normal breast tissues using resonance Raman spectroscopy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Smith, Jason; Zhang, Lin; Gao, Xin; Alfano, Robert R.

2018-02-01

Worldwide breast cancer incidence has increased by more than twenty percent in the past decade. It is also known that in that time, mortality due to the affliction has increased by fourteen percent. Using optical-based diagnostic techniques, such as Raman spectroscopy, has been explored in order to increase diagnostic accuracy in a more objective way along with significantly decreasing diagnostic wait-times. In this study, Raman spectroscopy with 532-nm excitation was used in order to incite resonance effects to enhance Stokes Raman scattering from unique biomolecular vibrational modes. Seventy-two Raman spectra (41 cancerous, 31 normal) were collected from nine breast tissue samples by performing a ten-spectra average using a 500-ms acquisition time at each acquisition location. The raw spectral data was subsequently prepared for analysis with background correction and normalization. The spectral data in the Raman Shift range of 750- 2000 cm-1 was used for analysis since the detector has highest sensitivity around in this range. The matrix decomposition technique nonnegative matrix factorization (NMF) was then performed on this processed data. The resulting leave-oneout cross-validation using two selective feature components resulted in sensitivity, specificity and accuracy of 92.6%, 100% and 96.0% respectively. The performance of NMF was also compared to that using principal component analysis (PCA), and NMF was shown be to be superior to PCA in this study. This study shows that coupling the resonance Raman spectroscopy technique with subsequent NMF decomposition method shows potential for high characterization accuracy in breast cancer detection.

A Joint Time-Frequency and Matrix Decomposition Feature Extraction Methodology for Pathological Voice Classification

NASA Astrophysics Data System (ADS)

Ghoraani, Behnaz; Krishnan, Sridhar

2009-12-01

The number of people affected by speech problems is increasing as the modern world places increasing demands on the human voice via mobile telephones, voice recognition software, and interpersonal verbal communications. In this paper, we propose a novel methodology for automatic pattern classification of pathological voices. The main contribution of this paper is extraction of meaningful and unique features using Adaptive time-frequency distribution (TFD) and nonnegative matrix factorization (NMF). We construct Adaptive TFD as an effective signal analysis domain to dynamically track the nonstationarity in the speech and utilize NMF as a matrix decomposition (MD) technique to quantify the constructed TFD. The proposed method extracts meaningful and unique features from the joint TFD of the speech, and automatically identifies and measures the abnormality of the signal. Depending on the abnormality measure of each signal, we classify the signal into normal or pathological. The proposed method is applied on the Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database which consists of 161 pathological and 51 normal speakers, and an overall classification accuracy of 98.6% was achieved.

HPC-NMF: A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kannan, Ramakrishnan; Sukumar, Sreenivas R.; Ballard, Grey M.

NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient distributed algorithms to solve the problem for big data sets. We propose a high-performance distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems formore » $$\\WW$$ and $$\\HH$$. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementation, our algorithm is also flexible: It performs well for both dense and sparse matrices, and allows the user to choose any one of the multiple algorithms for solving the updates to low rank factors $$\\WW$$ and $$\\HH$$ within the alternating iterations.« less

Contribution of non-negative matrix factorization to the classification of remote sensing images

NASA Astrophysics Data System (ADS)

Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

2008-10-01

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2017-04-01

This paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches.

MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization

PubMed Central

Mangin, Olivier; Filliat, David; ten Bosch, Louis; Oudeyer, Pierre-Yves

2015-01-01

In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across modalities (speech utterances, images and motion). We propose this computational model as an answer to the question of how some class of concepts can be learnt. In addition, the model provides a way of defining such a class of plausibly learnable concepts. We detail why the multimodal nature of perception is essential to reduce the ambiguity of learnt concepts as well as to communicate about them through speech. We then present a set of experiments that demonstrate the learning of such concepts from real non-symbolic data consisting of speech sounds, images, and motions. Finally we consider structure in perceptual signals and demonstrate that a detailed knowledge of this structure, named compositional understanding can emerge from, instead of being a prerequisite of, global understanding. An open-source implementation of the MCA-NMF learner as well as scripts and associated experimental data to reproduce the experiments are publicly available. PMID:26489021

ShiftNMFk 1.2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alexandrov, Boian S.; Vesselinov, Velimir V.; Stanev, Valentin

The ShiftNMFk1.2 code, or as we call it, GreenNMFk, represents a hybrid algorithm combining unsupervised adaptive machine learning and Green's function inverse method. GreenNMFk allows an efficient and high performance de-mixing and feature extraction of a multitude of nonnegative signals that change their shape propagating through the medium. The signals are mixed and recorded by a network of uncorrelated sensors. The code couples Non-negative Matrix Factorization (NMF) and inverse-analysis Green's functions method. GreenNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of the governing partial differential equation to identifymore » the unknown sources and their charecteristics. GreenNMF can be applied directly to any problem controlled by a known partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations. Full GreenNMFk method is a subject LANL U.S. Patent application S133364.000 August, 2017. The ShiftNMFk 1.2 version here is a toy version of this method that can work with a limited number of unknown sources (4 or less).« less

Discovering perturbation of modular structure in HIV progression by integrating multiple data sources through non-negative matrix factorization.

PubMed

Ray, Sumanta; Maulik, Ujjwal

2016-12-20

Detecting perturbation in modular structure during HIV-1 disease progression is an important step to understand stage specific infection pattern of HIV-1 virus in human cell. In this article, we proposed a novel methodology on integration of multiple biological information to identify such disruption in human gene module during different stages of HIV-1 infection. We integrate three different biological information: gene expression information, protein-protein interaction information and gene ontology information in single gene meta-module, through non negative matrix factorization (NMF). As the identified metamodules inherit those information so, detecting perturbation of these, reflects the changes in expression pattern, in PPI structure and in functional similarity of genes during the infection progression. To integrate modules of different data sources into strong meta-modules, NMF based clustering is utilized here. Perturbation in meta-modular structure is identified by investigating the topological and intramodular properties and putting rank to those meta-modules using a rank aggregation algorithm. We have also analyzed the preservation structure of significant GO terms in which the human proteins of the meta-modules participate. Moreover, we have performed an analysis to show the change of coregulation pattern of identified transcription factors (TFs) over the HIV progression stages.

Color normalization of histology slides using graph regularized sparse NMF

NASA Astrophysics Data System (ADS)

Sha, Lingdao; Schonfeld, Dan; Sethi, Amit

2017-03-01

Computer based automatic medical image processing and quantification are becoming popular in digital pathology. However, preparation of histology slides can vary widely due to differences in staining equipment, procedures and reagents, which can reduce the accuracy of algorithms that analyze their color and texture information. To re- duce the unwanted color variations, various supervised and unsupervised color normalization methods have been proposed. Compared with supervised color normalization methods, unsupervised color normalization methods have advantages of time and cost efficient and universal applicability. Most of the unsupervised color normaliza- tion methods for histology are based on stain separation. Based on the fact that stain concentration cannot be negative and different parts of the tissue absorb different stains, nonnegative matrix factorization (NMF), and particular its sparse version (SNMF), are good candidates for stain separation. However, most of the existing unsupervised color normalization method like PCA, ICA, NMF and SNMF fail to consider important information about sparse manifolds that its pixels occupy, which could potentially result in loss of texture information during color normalization. Manifold learning methods like Graph Laplacian have proven to be very effective in interpreting high-dimensional data. In this paper, we propose a novel unsupervised stain separation method called graph regularized sparse nonnegative matrix factorization (GSNMF). By considering the sparse prior of stain concentration together with manifold information from high-dimensional image data, our method shows better performance in stain color deconvolution than existing unsupervised color deconvolution methods, especially in keeping connected texture information. To utilized the texture information, we construct a nearest neighbor graph between pixels within a spatial area of an image based on their distances using heat kernal in lαβ space. The representation of a pixel in the stain density space is constrained to follow the feature distance of the pixel to pixels in the neighborhood graph. Utilizing color matrix transfer method with the stain concentrations found using our GSNMF method, the color normalization performance was also better than existing methods.

*K-means and cluster models for cancer signatures.

PubMed

Kakushadze, Zura; Yu, Willie

2017-09-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.

Population clustering based on copy number variations detected from next generation sequencing data.

PubMed

Duan, Junbo; Zhang, Ji-Gang; Wan, Mingxi; Deng, Hong-Wen; Wang, Yu-Ping

2014-08-01

Copy number variations (CNVs) can be used as significant bio-markers and next generation sequencing (NGS) provides a high resolution detection of these CNVs. But how to extract features from CNVs and further apply them to genomic studies such as population clustering have become a big challenge. In this paper, we propose a novel method for population clustering based on CNVs from NGS. First, CNVs are extracted from each sample to form a feature matrix. Then, this feature matrix is decomposed into the source matrix and weight matrix with non-negative matrix factorization (NMF). The source matrix consists of common CNVs that are shared by all the samples from the same group, and the weight matrix indicates the corresponding level of CNVs from each sample. Therefore, using NMF of CNVs one can differentiate samples from different ethnic groups, i.e. population clustering. To validate the approach, we applied it to the analysis of both simulation data and two real data set from the 1000 Genomes Project. The results on simulation data demonstrate that the proposed method can recover the true common CNVs with high quality. The results on the first real data analysis show that the proposed method can cluster two family trio with different ancestries into two ethnic groups and the results on the second real data analysis show that the proposed method can be applied to the whole-genome with large sample size consisting of multiple groups. Both results demonstrate the potential of the proposed method for population clustering.

Non-negative matrix factorisation methods for the spectral decomposition of MRS data from human brain tumours

PubMed Central

2012-01-01

Background In-vivo single voxel proton magnetic resonance spectroscopy (SV 1H-MRS), coupled with supervised pattern recognition (PR) methods, has been widely used in clinical studies of discrimination of brain tumour types and follow-up of patients bearing abnormal brain masses. SV 1H-MRS provides useful biochemical information about the metabolic state of tumours and can be performed at short (< 45 ms) or long (> 45 ms) echo time (TE), each with particular advantages. Short-TE spectra are more adequate for detecting lipids, while the long-TE provides a much flatter signal baseline in between peaks but also negative signals for metabolites such as lactate. Both, lipids and lactate, are respectively indicative of specific metabolic processes taking place. Ideally, the information provided by both TE should be of use for clinical purposes. In this study, we characterise the performance of a range of Non-negative Matrix Factorisation (NMF) methods in two respects: first, to derive sources correlated with the mean spectra of known tissue types (tumours and normal tissue); second, taking the best performing NMF method for source separation, we compare its accuracy for class assignment when using the mixing matrix directly as a basis for classification, as against using the method for dimensionality reduction (DR). For this, we used SV 1H-MRS data with positive and negative peaks, from a widely tested SV 1H-MRS human brain tumour database. Results The results reported in this paper reveal the advantage of using a recently described variant of NMF, namely Convex-NMF, as an unsupervised method of source extraction from SV1H-MRS. Most of the sources extracted in our experiments closely correspond to the mean spectra of some of the analysed tumour types. This similarity allows accurate diagnostic predictions to be made both in fully unsupervised mode and using Convex-NMF as a DR step previous to standard supervised classification. The obtained results are comparable to, or more accurate than those obtained with supervised techniques. Conclusions The unsupervised properties of Convex-NMF place this approach one step ahead of classical label-requiring supervised methods for the discrimination of brain tumour types, as it accounts for their increasingly recognised molecular subtype heterogeneity. The application of Convex-NMF in computer assisted decision support systems is expected to facilitate further improvements in the uptake of MRS-derived information by clinicians. PMID:22401579

Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng

2018-01-01

Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

PubMed

Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

2017-01-01

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Machine learning for cardiac ultrasound time series data

NASA Astrophysics Data System (ADS)

Yuan, Baichuan; Chitturi, Sathya R.; Iyer, Geoffrey; Li, Nuoyu; Xu, Xiaochuan; Zhan, Ruohan; Llerena, Rafael; Yen, Jesse T.; Bertozzi, Andrea L.

2017-03-01

We consider the problem of identifying frames in a cardiac ultrasound video associated with left ventricular chamber end-systolic (ES, contraction) and end-diastolic (ED, expansion) phases of the cardiac cycle. Our procedure involves a simple application of non-negative matrix factorization (NMF) to a series of frames of a video from a single patient. Rank-2 NMF is performed to compute two end-members. The end members are shown to be close representations of the actual heart morphology at the end of each phase of the heart function. Moreover, the entire time series can be represented as a linear combination of these two end-member states thus providing a very low dimensional representation of the time dynamics of the heart. Unlike previous work, our methods do not require any electrocardiogram (ECG) information in order to select the end-diastolic frame. Results are presented for a data set of 99 patients including both healthy and diseased examples.

A novel edge-preserving nonnegative matrix factorization method for spectral unmixing

NASA Astrophysics Data System (ADS)

Bao, Wenxing; Ma, Ruishi

2015-12-01

Spectral unmixing technique is one of the key techniques to identify and classify the material in the hyperspectral image processing. A novel robust spectral unmixing method based on nonnegative matrix factorization(NMF) is presented in this paper. This paper used an edge-preserving function as hypersurface cost function to minimize the nonnegative matrix factorization. To minimize the hypersurface cost function, we constructed the updating functions for signature matrix of end-members and abundance fraction respectively. The two functions are updated alternatively. For evaluation purpose, synthetic data and real data have been used in this paper. Synthetic data is used based on end-members from USGS digital spectral library. AVIRIS Cuprite dataset have been used as real data. The spectral angle distance (SAD) and abundance angle distance(AAD) have been used in this research for assessment the performance of proposed method. The experimental results show that this method can obtain more ideal results and good accuracy for spectral unmixing than present methods.

A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.

PubMed

Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong

2015-12-01

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

PubMed

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (p<0.001) for predicting the task being performed within each scan using artifact-cleaned components. The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy compared to the ICA and sparse coding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p<0.001). Lower classification accuracy occurred when the extracted spatial maps contained more CSF regions (p<0.001). The success of sparse coding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

Immediate and extended effects of sodium lauryl sulphate exposure on stratum corneum natural moisturizing factor.

PubMed

Hoffman, D R; Kroll, L M; Basehoar, A; Reece, B; Cunningham, C T; Koenig, D W

2014-02-01

Natural moisturizing factor (NMF) serves as the primary humectant of the stratum corneum (SC), principally comprised of hygroscopic amino acids and derivatives that absorb moisture. Barrier disruption has been shown to differentially affect the levels of specific NMF components, though the kinetics of NMF component restoration following disruption have not been examined. Here, we investigated the impact of barrier disruption caused by surfactant exposure on a subset of NMF components immediately following exposure and out to 10 days post-exposure. Volunteers wore patches containing either 1% w/v sodium lauryl sulphate (SLS) or distilled water on their forearms for 24 h. Measurements of transepidermal water loss, erythema, SC water content and a subset of SC NMF and lipid components were obtained at both sites before treatment, the day of patch removal, and 1, 2, 3, 6, and 10 days following treatment. Most measured NMF components decreased in response to SLS exposure. Exceptions were increases in lactate, ornithine and urea, and no difference in proline levels. In the days following exposure, reduced levels of several NMF components continued at the SLS site; however, all measured NMF components demonstrated equivalence to the vehicle control within 10 days. Histidine pH 7, lactate, ornithine and urea were the first to achieve levels equivalent to the vehicle control site, normalizing within 1 day after patch removal. Results imply that NMF components derived from sweat and urea cycling are least impacted by SLS exposure whereas NMF components derived from degradation of filaggrin and/or other S-100 proteins are most impacted. This implies the restoration of the processes responsible for S-100 protein processing into free amino acids takes several days to return to normal. Further examination of the enzymes involved in S-100 protein processing following barrier disruption would provide insight into the pathway(s) for NMF restoration during SC recovery. © 2013 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Dimensionality Reduction in Big Data with Nonnegative Matrix Factorization

DTIC Science & Technology

2017-06-20

appli- cations of data mining, signal processing , computer vision, bioinformatics, etc. Fun- damentally, NMF has two main purposes. First, it reduces...shape of the function becomes more spherical because ∂ 2g ∂y2i = 1, ∀i, and g(y) is convex. This part aims to make the post- processing parts more...maxStop = 0 for each thread of computation */; 3 /*Re-scaling variables*/; 4 Q = H√ diag(H)diag(H)T ; q = h√ diag(H) ; 5 /*Solving NQP: minimizingf(x

Unsupervised Bayesian linear unmixing of gene expression microarrays.

PubMed

Bazot, Cécile; Dobigeon, Nicolas; Tourneret, Jean-Yves; Zaas, Aimee K; Ginsburg, Geoffrey S; Hero, Alfred O

2013-03-19

This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor.

Local hyperspectral data multisharpening based on linear/linear-quadratic nonnegative matrix factorization by integrating lidar data

NASA Astrophysics Data System (ADS)

Benhalouche, Fatima Zohra; Karoui, Moussa Sofiane; Deville, Yannick; Ouamri, Abdelaziz

2015-10-01

In this paper, a new Spectral-Unmixing-based approach, using Nonnegative Matrix Factorization (NMF), is proposed to locally multi-sharpen hyperspectral data by integrating a Digital Surface Model (DSM) obtained from LIDAR data. In this new approach, the nature of the local mixing model is detected by using the local variance of the object elevations. The hyper/multispectral images are explored using small zones. In each zone, the variance of the object elevations is calculated from the DSM data in this zone. This variance is compared to a threshold value and the adequate linear/linearquadratic spectral unmixing technique is used in the considered zone to independently unmix hyperspectral and multispectral data, using an adequate linear/linear-quadratic NMF-based approach. The obtained spectral and spatial information thus respectively extracted from the hyper/multispectral images are then recombined in the considered zone, according to the selected mixing model. Experiments based on synthetic hyper/multispectral data are carried out to evaluate the performance of the proposed multi-sharpening approach and literature linear/linear-quadratic approaches used on the whole hyper/multispectral data. In these experiments, real DSM data are used to generate synthetic data containing linear and linear-quadratic mixed pixel zones. The DSM data are also used for locally detecting the nature of the mixing model in the proposed approach. Globally, the proposed approach yields good spatial and spectral fidelities for the multi-sharpened data and significantly outperforms the used literature methods.

Nightmare frequency is related to a propensity for mirror behaviors.

PubMed

Nielsen, Tore; Powell, Russell A; Kuiken, Don

2013-12-01

We previously reported that college students who indicated engaging in frequent dream-enacting behaviors also scored high on a new measure of mirror behaviors, which is the propensity to imitate another person's emotions or actions. Since dream-enacting behaviors are frequently the culmination of nightmares, one explanation for the observed relationship is that individuals who frequently display mirror behaviors are also prone to nightmares. We used the Mirror Behavior Questionnaire (MBQ) and self-reported frequencies of nightmares to assess this possibility. A sample of 480 students, consisting of 188 males (19.2±1.73 years) and 292 females (19.0±1.55 years) enrolled in a first-year university psychology course, participated for course credit. They completed a battery of questionnaires that included the 16-item MBQ, plus an item about nightmare frequency (NMF) in the past 30 days. NMF scores were split to create low, medium, and high NMF groups. MBQ total scores were significantly higher for female than for male subjects, but an interaction revealed that this was true only for Hi-NMF subjects. MBQ Factor 4, Motor Skill Imitation, paralleled this global interaction for females, whereas MBQ Factor 3, Sleepiness/Anger Contagion, was elevated only for Hi-NMF males. Item analyses indicated that Hi- and Med-NMF females scored higher than Lo-NMF females on the 3 items of Factor 4 that reflect voluntary imitation (imitating famous/cartoon voices, being a physically active spectator, and learning new skills by observing), as well as on 2 other items that reflect involuntary imitation (contagious yawning and self-rated empathy). Although Hi- and Lo-NMF males differed most clearly on the sleepiness item of Factor 3, all 3 items on this factor (including anger contagion and contagious yawning) are plausibly associated with perception of and response to social threat. Results provide evidence that among females nightmares are associated with voluntary and involuntary mirror behaviors during wakefulness, while among males nightmares are associated with threat-related mirror behaviors during wakefulness. They thus support the possibility that the association between mirror behaviors and dream-enacting behaviors is due to a common mirror neuron mechanism that underlies mirror behaviors and nightmares and that involves motor, rather than emotional, resonance. These results have implications for understanding the comorbidity of nightmares and other pathological symptoms such as imitative suicidal behaviors, the influence of observational learning on dissociative symptomatology, and the predominance of threat and aggression in the dream enacting behaviors of REM sleep behavior disorder. Copyright © 2013 Elsevier Inc. All rights reserved.

Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images.

PubMed

Li, Xingyu; Plataniotis, Konstantinos N

2017-01-01

In digital pathology, to address color variation and histological component colocalization in pathology images, stain decomposition is usually performed preceding spectral normalization and tissue component segmentation. This paper examines the problem of stain decomposition, which is a naturally nonnegative matrix factorization (NMF) problem in algebra, and introduces a systematical and analytical solution consisting of a circular color analysis module and an NMF-based computation module. Unlike the paradigm of existing stain decomposition algorithms where stain proportions are computed from estimated stain spectra using a matrix inverse operation directly, the introduced solution estimates stain spectra and stain depths via probabilistic reasoning individually. Since the proposed method pays extra attentions to achromatic pixels in color analysis and stain co-occurrence in pixel clustering, it achieves consistent and reliable stain decomposition with minimum decomposition residue. Particularly, aware of the periodic and angular nature of hue, we propose the use of a circular von Mises mixture model to analyze the hue distribution, and provide a complete color-based pixel soft-clustering solution to address color mixing introduced by stain overlap. This innovation combined with saturation-weighted computation makes our study effective for weak stains and broad-spectrum stains. Extensive experimentation on multiple public pathology datasets suggests that our approach outperforms state-of-the-art blind stain separation methods in terms of decomposition effectiveness.

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

NASA Technical Reports Server (NTRS)

Srivastava, Askok N.; Matthews, Bryan; Das, Santanu

2008-01-01

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

PubMed

Niegowski, Maciej; Zivanovic, Miroslav

2016-03-01

We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Distributed Unmixing of Hyperspectral Datawith Sparsity Constraint

NASA Astrophysics Data System (ADS)

Khoshsokhan, S.; Rajabi, R.; Zayyani, H.

2017-09-01

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.

A new mouse model of metabolic syndrome and associated complications

PubMed Central

Wang, Yun; Zheng, Yue; Nishina, Patsy M; Naggert, Jürgen K.

2010-01-01

Metabolic Syndrome (MS) encompasses a clustering of risk factors for cardiovascular disease, including obesity, insulin resistance, and dyslipidemia. We characterized a new mouse model carrying a dominant mutation, C57BL/6J-Nmf15/+ (B6-Nmf15/+), which develops additional complications of MS such as adipose tissue inflammation and cardiomyopathy. A backcross was used to genetically map the Nmf15 locus. Mice were examined in the CLAMS™ animal monitoring system, and dual energy X-ray absorptiometry and blood chemistry analyses were performed. Hypothalamic LepR, SOCS1 and STAT3 phosphorylation were examined. Cardiac function was assessed by Echo- and Electro Cardiography. Adipose tissue inflammation was characterized by in situ hybridization and measurement of Jun kinase activity. The Nmf15 locus mapped to distal mouse chromosome 5 with a LOD score of 13.8. Nmf15 mice developed obesity by 12 weeks of age. Plasma leptin levels were significantly elevated in pre-obese Nmf15 mice at 8 weeks of age and an attenuated STAT3 phosphorylation in the hypothalamus suggests a primary leptin resistance. Adipose tissue from Nmf15 mice showed a remarkable degree of inflammation and macrophage infiltration as indicated by expression of the F4/80 marker and increased phosphorylation of JNK1/2. Lipidosis was observed in tubular epithelial cells and glomeruli of the kidney. Nmf15 mice demonstrate both histological and pathophysiological evidence of cardiomyopathy. The Nmf15 mouse model provides a new entry point into pathways mediating leptin resistance and obesity. It is one of few models that combine many aspects of metabolic syndrome and can be useful for testing new therapeutic approaches for combating obesity complications, particularly cardiomyopathy. PMID:19398498

Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gittens, Alex; Devarakonda, Aditya; Racah, Evan

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausibility), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to 1.6TB particle physics, 2.2TB and 16TB climate modeling and 1.1TB bioimaging data. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark’s data parallel model. We perform scalingmore » experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance.« less

Speeding up the Consensus Clustering methodology for microarray data analysis

PubMed Central

2011-01-01

Background The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of Consensus (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, Consensus is a natural candidate for a speed-up. Results Since the time-precision performance of Consensus depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for Consensus. That is, the closely related algorithm FC (Fast Consensus) that would have the same precision as Consensus with a substantially better time performance. The performance of FC has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, FC turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of Consensus. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by Consensus. We have also experimented with the use of Consensus and FC in conjunction with NMF (Nonnegative Matrix Factorization), in order to identify the correct number of clusters in a dataset. Although NMF is an increasingly popular technique for biological data mining, our results are somewhat disappointing and complement quite well the state of the art about NMF, shedding further light on its merits and limitations. Conclusions In summary, FC with a parameter setting that makes it robust with respect to small and medium-sized datasets, i.e, number of items to cluster in the hundreds and number of conditions up to a thousand, seems to be the internal validation measure of choice. Moreover, the technique we have developed here can be used in other contexts, in particular for the speed-up of stability-based validation measures. PMID:21235792

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes.

PubMed

Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

2014-01-01

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

PubMed Central

Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

2014-01-01

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice. PMID:25374455

Parts-based stereoscopic image assessment by learning binocular manifold color visual properties

NASA Astrophysics Data System (ADS)

Xu, Haiyong; Yu, Mei; Luo, Ting; Zhang, Yun; Jiang, Gangyi

2016-11-01

Existing stereoscopic image quality assessment (SIQA) methods are mostly based on the luminance information, in which color information is not sufficiently considered. Actually, color is part of the important factors that affect human visual perception, and nonnegative matrix factorization (NMF) and manifold learning are in line with human visual perception. We propose an SIQA method based on learning binocular manifold color visual properties. To be more specific, in the training phase, a feature detector is created based on NMF with manifold regularization by considering color information, which not only allows parts-based manifold representation of an image, but also manifests localized color visual properties. In the quality estimation phase, visually important regions are selected by considering different human visual attention, and feature vectors are extracted by using the feature detector. Then the feature similarity index is calculated and the parts-based manifold color feature energy (PMCFE) for each view is defined based on the color feature vectors. The final quality score is obtained by considering a binocular combination based on PMCFE. The experimental results on LIVE I and LIVE Π 3-D IQA databases demonstrate that the proposed method can achieve much higher consistency with subjective evaluations than the state-of-the-art SIQA methods.

PM2.5 source apportionment in a French urban coastal site under steelworks emission influences using constrained non-negative matrix factorization receptor model.

PubMed

Kfoury, Adib; Ledoux, Frédéric; Roche, Cloé; Delmaire, Gilles; Roussel, Gilles; Courcot, Dominique

2016-02-01

The constrained weighted-non-negative matrix factorization (CW-NMF) hybrid receptor model was applied to study the influence of steelmaking activities on PM2.5 (particulate matter with equivalent aerodynamic diameter less than 2.5 μm) composition in Dunkerque, Northern France. Semi-diurnal PM2.5 samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals, water-soluble ions, and total carbon using inductively coupled plasma--atomic emission spectrometry (ICP-AES), ICP--mass spectrometry (ICP-MS), ionic chromatography and micro elemental carbon analyzer. The elemental composition shows that NO3(-), SO4(2-), NH4(+) and total carbon are the main PM2.5 constituents. Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced. The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios. Moreover Rb/Cr, Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions. The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation. Eleven source profiles with various contributions were identified: 8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities. Between them, secondary nitrates, secondary sulfates and combustion profiles give the highest contributions and account for 93% of the PM2.5 concentration. The steelwork facilities contribute in about 2% of the total PM2.5 concentration and appear to be the main source of Cr, Cu, Fe, Mn, Zn. Copyright © 2015. Published by Elsevier B.V.

Link predication based on matrix factorization by fusion of multi class organizations of the network.

PubMed

Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

2017-08-21

Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

PubMed

Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

2016-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

PubMed Central

REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

2015-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170

Quantitative assessment in thermal image segmentation for artistic objects

NASA Astrophysics Data System (ADS)

Yousefi, Bardia; Sfarra, Stefano; Maldague, Xavier P. V.

2017-07-01

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve Non-destructive Testing (NDT), Medical analysis (Computer Aid Diagnosis/Detection- CAD), Arts and Archaeology among many others. In the arts and archaeology field, infrared technology provides significant contributions in term of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard Non-Negative Matrix Factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by Non-negative least squares (NNLS) active-set algorithm (SNMF2) and eigen decomposition approaches such as Principal Component Thermography (PCT), Candid Covariance-Free Incremental Principal Component Thermography (CCIPCT) to obtain the thermal features. On one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet based data fusion combines the data of each method with PCT to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue and a fresco were analyzed using the above mentioned methods and interesting results were obtained.

Further study on the solar activity variation of daytime NmF2

NASA Astrophysics Data System (ADS)

Chen, Yiding; Liu, Libo

2010-12-01

The ionosonde observations in the East Asia-Australia sector are collected to further investigate the solar activity variation of daytime (0800 ˜ 1600 LT) NmF2. The linear increase rate of NmF2 with F10.7 at lower solar activity levels is remarkably dependent on latitude, season, and local time. The rate is largest in equinoxes (with an equinoctial asymmetry) and higher in the morning (afternoon) in local winter (summer) at geomagnetic midlatitudes; particularly, the maximum rates in local winter are obviously larger than those in local summer at northern midlatitudes. In the equatorial ionization anomaly (EIA) crest regions, the rates in equinoxes and December (June) solstice are remarkably higher than those in June (December) solstice at the northern (southern) EIA crest, and the rate grows from the morning sector to the afternoon sector. The variation trend of NmF2 with F10.7 also shows latitudinal, seasonal, and local time dependences. The saturation effect dominates in all seasons in the EIA regions; at midlatitudes, NmF2 nearly increases linearly with F10.7 in local winter so that a linear fit is a good approximation for NmF2 modeling, while the saturation effect still dominates in other seasons. The saturation effect is more significant in the afternoon, and the strongest saturation effect appears at the EIA crest latitudes in equinox afternoon. Discussions indicate that the variations of neutral atmosphere and hmF2 are responsible for the seasonal and local time dependences of the linear increase rate of NmF2 with F10.7 at midlatitudes, and the seasonal variation of neutral atmosphere is the primary reason for the seasonal dependence of the variation trend of NmF2 with F10.7, while dynamics processes are the more important factors controlling the linear increase rate and the variation trend of NmF2 with F10.7 at low latitudes. Furthermore, dynamics processes are important for the saturation effect, and the fountain effect is related to the strongest saturation effect appearing at the EIA crests.

Video based object representation and classification using multiple covariance matrices.

PubMed

Zhang, Yurong; Liu, Quan

2017-01-01

Video based object recognition and classification has been widely studied in computer vision and image processing area. One main issue of this task is to develop an effective representation for video. This problem can generally be formulated as image set representation. In this paper, we present a new method called Multiple Covariance Discriminative Learning (MCDL) for image set representation and classification problem. The core idea of MCDL is to represent an image set using multiple covariance matrices with each covariance matrix representing one cluster of images. Firstly, we use the Nonnegative Matrix Factorization (NMF) method to do image clustering within each image set, and then adopt Covariance Discriminative Learning on each cluster (subset) of images. At last, we adopt KLDA and nearest neighborhood classification method for image set classification. Promising experimental results on several datasets show the effectiveness of our MCDL method.

A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

NASA Astrophysics Data System (ADS)

Gillis, Nicolas; Luce, Robert

2018-01-01

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary. As opposed to previously proposed models, this model yields a smooth optimization problem where the sparsity is enforced through linear constraints. We show that the Euclidean projection on the polyhedron defined by these constraints can be computed efficiently, and propose a fast gradient method to solve our model. We compare our algorithm with several state-of-the-art methods on synthetic data sets and real-world hyperspectral images.

Factor models for cancer signatures

NASA Astrophysics Data System (ADS)

Kakushadze, Zura; Yu, Willie

2016-11-01

We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an ;overall; mode of somatic mutational noise. We give a prescription for factoring out this noise and source code for fixing the number of signatures. We apply nonnegative matrix factorization (NMF) to genome data aggregated by cancer subtype and filtered using our method. The resultant signatures have substantially lower variability than those from unfiltered data. Also, the computational cost of signature extraction is cut by about a factor of 10. We find 3 novel cancer signatures, including a liver cancer dominant signature (96% contribution) and a renal cell carcinoma signature (70% contribution). Our method accelerates finding new cancer signatures and improves their overall stability. Reciprocally, the methods for extracting cancer signatures could have interesting applications in quantitative finance.

Investigation on the structure of liquid N-methylformamide-dimethylsulfoxide mixtures

NASA Astrophysics Data System (ADS)

Cordeiro, João M. M.; Soper, Alan K.

2011-03-01

The structures of liquid mixtures of N-methylformamide (NMF) and dimethyl sulfoxide (DMSO) at two concentrations (80% and 50% NMF) are investigated using a combination of neutron diffraction augmented with isotopic substitution and empirical potential structure refinement simulations. The results indicate that the NMF and DMSO molecules are hydrogen-bonded to one another with a preference for NMF-DMSO hydrogen bonding, compared to the NMF-NMF ones. The liquid is orientationally structured as a consequence of these hydrogen bonds between molecules. NMF-DMSO dimers are very stable species in the bulk of the mixture. The structure of the dimers is such that the angle between the molecular dipole moments is around 60°. The NMF molecules are well solvated in DMSO with potential implications for peptides solvation in this solvent.

Community structure detection based on the neighbor node degree information

NASA Astrophysics Data System (ADS)

Tang, Li-Ying; Li, Sheng-Nan; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-11-01

Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Efficient source separation algorithms for acoustic fall detection using a microsoft kinect.

PubMed

Li, Yun; Ho, K C; Popescu, Mihail

2014-03-01

Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

Pretreatment and integrated analysis of spectral data reveal seaweed similarities based on chemical diversity.

PubMed

Wei, Feifei; Ito, Kengo; Sakata, Kenji; Date, Yasuhiro; Kikuchi, Jun

2015-03-03

Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data.

Blind colour separation of H&E stained histological images by linearly transforming the colour space.

PubMed

Celis, R; Romo, D; Romero, E

2015-12-01

Blind source separation methods aim to split information into the original sources. In histology, each dye component attempts to specifically characterize different microscopic structures. In the case of the hematoxylin-eosin stain, universally used for routine examination, quantitative analysis may often require the inspection of different morphological signatures related mainly to nuclei patterns, but also to stroma distribution. Stain separation is usually a preprocessing operation that is transversal to different applications. This paper presents a novel colour separation method that finds the hematoxylin and eosin clusters by projecting the whole (r,g,b) space to a folded surface connecting the distributions of a series of [(r-b),g] planes that divide the cloud of H&E tones. The proposed method produces density maps closer to those obtained with the colour mixing matrices set by an expert, when comparing with the density maps obtained using nonnegative matrix factorization (NMF), independent component analysis (ICA) and a state-of-the-art method. The method has outperformed three baseline methods, NMF, Macenko and ICA, in about 8%, 12% and 52% for the eosin component, whereas this was about 4%, 8% and 26% for the hematoxylin component. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Formation Mechanisms of the Spring-Autumn Asymmetry of the Midlatitudinal NmF2 under Daytime Quiet Geomagnetic Conditions at Low Solar Activity

NASA Astrophysics Data System (ADS)

Pavlov, A. V.; Pavlova, N. M.

2018-05-01

Formation mechanism of the spring-autumn asymmetry of the F2-layer peak electron number density of the midlatitudinal ionosphere, NmF2, under daytime quiet geomagnetic conditions at low solar activity are studied. We used the ionospheric parameters measured by the ionosonde and incoherent scatter radar at Millstone Hill on March 3, 2007, March 29, 2007, September 12, 2007, and September 18, 1984. The altitudinal profiles of the electron density and temperature were calculated for the studied conditions using a one-dimensional, nonstationary, ionosphere-plasmasphere theoretical model for middle geomagnetic latitudes. The study has shown that there are two main factors contributing to the formation of the observed spring-autumn asymmetry of NmF2: first, the spring-autumn variations of the plasma drift along the geomagnetic field due to the corresponding variations in the components of the neutral wind velocity, and, second, the difference between the composition of the neutral atmosphere under the spring and autumn conditions at the same values of the universal time and the ionospheric F2-layer peak altitude. The seasonal variations of the rate of O+(4S) ion production, which are associated with chemical reactions with the participation of the electronically excited ions of atomic oxygen, does not significantly affect the studied NmF2 asymmetry. The difference in the degree of influence of O+(4S) ion reactions with vibrationally excited N2 and O2 on NmF2 under spring and autumn conditions does not significantly change the spring-autumn asymmetry of NmF2.

Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

PubMed Central

Xin, Yu; Hochberg, Ephraim; Joshi, Rohit; Uzuner, Ozlem; Szolovits, Peter

2015-01-01

Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features. PMID:25862765

The Comparison Between Nmf and Ica in Pigment Mixture Identification of Ancient Chinese Paintings

NASA Astrophysics Data System (ADS)

Liu, Y.; Lyu, S.; Hou, M.; Yin, Q.

2018-04-01

Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.

An automatic search of Alzheimer patterns using a nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Giraldo, Diana L.; García-Arteaga, Juan D.; Romero, Eduardo

2013-11-01

This paper presents a fully automatic method that condenses relevant morphometric information from a database of magnetic resonance images (MR) labeled as either normal (NC) or Alzheimer's disease (AD). The proposed method generates class templates using Nonnegative Matrix Factorization (NMF) which will be used to develop an NC/AD classi cator. It then nds regions of interest (ROI) with discerning inter-class properties. by inspecting the di erence volume of the two class templates. From these templates local probability distribution functions associated to low level features such as intensities, orientation and edges within the found ROI are calculated. A sample brain volume can then be characterized by a similarity measure in the ROI to both the normal and the pathological templates. These characteristics feed a simple binary SVM classi er which, when tested with an experimental group extracted from a public brain MR dataset (OASIS), reveals an equal error rate measure which is better than the state-of-the-art tested on the same dataset (0:9 in the former and 0:8 in the latter).

Multivariate Analysis of Mixed Lipid Aggregate Phase Transitions Monitored Using Raman Spectroscopy.

PubMed

Neal, Sharon L

2018-01-01

The phase behavior of aqueous 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine (DMPC)/1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) mixtures between 8.0 ℃ and 41.0 ℃ were monitored using Raman spectroscopy. Temperature-dependent Raman matrices were assembled from series of spectra and subjected to multivariate analysis. The consensus of pseudo-rank estimation results is that seven to eight components account for the temperature-dependent changes observed in the spectra. The spectra and temperature response profiles of the mixture components were resolved by applying a variant of the non-negative matrix factorization (NMF) algorithm described by Lee and Seung (1999). The rotational ambiguity of the data matrix was reduced by augmenting the original temperature-dependent spectral matrix with its cumulative counterpart, i.e., the matrix formed by successive integration of the spectra across the temperature index (columns). Successive rounds of constrained NMF were used to isolate component spectra from a significant fluorescence background. Five major components exhibiting varying degrees of gel and liquid crystalline lipid character were resolved. Hydrogen-bonded water networks exhibiting varying degrees of organization are associated with the lipid components. Spectral parameters were computed to compare the chain conformation, packing, and hydration indicated by the resolved spectra. Based on spectral features and relative amounts of the components observed, four components reflect long chain lipid response. The fifth component could reflect the response of the short chain lipid, DHPC, but there were no definitive spectral features confirming this assignment. A minor component of uncertain assignment that exhibits a striking response to the DMPC pre-transition and chain melting transition also was recovered. While none of the spectra resolved exhibit features unequivocally attributable to a specific aggregate morphology or step in the gelation process, the results are consistent with the evolution of mixed phase bicelles (nanodisks) and small amounts of worm-like DMPC/DHPC aggregates, and perhaps DHPC micelles, at low temperature to suspensions of branched and entangled worm-like aggregates above the DMPC gel phase transition and perforated multi-lamellar aggregates at high temperature.

Characterization of a Field Portable Raman System for Rapid Chemical Identification

DTIC Science & Technology

2007-05-31

Sodium nitrate, 21% Potassium carbonate, 4% Diethanolamine lauryl sulfate , 2% Methamidophos 3 NMF 4 NMF... Sodium sulfate Y P W P 1 45.3% Detergent, 44.0% Sodium sulfate , 5.7% Benzene 2 44.0% Detergent, 42.6% Sodium sulfate , 7.5% 3- (Ethylamino)toluene 3...47.8% Detergent, 47.6% Sodium sulfate Strontium carbonate N P W P 1 NMF 2 NMF 3 NMF Strontium nitrate N P W P 1 Mixture 79%: 56% Urea nitrate,

Mutation Clusters from Cancer Exome.

PubMed

Kakushadze, Zura; Yu, Willie

2017-08-15

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development.

Mutation Clusters from Cancer Exome

PubMed Central

Kakushadze, Zura; Yu, Willie

2017-01-01

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development. PMID:28809811

Removing non-stationary noise in spectrum sensing using matrix factorization

NASA Astrophysics Data System (ADS)

van Bloem, Jan-Willem; Schiphorst, Roel; Slump, Cornelis H.

2013-12-01

Spectrum sensing is key to many applications like dynamic spectrum access (DSA) systems or telecom regulators who need to measure utilization of frequency bands. The International Telecommunication Union (ITU) recommends a 10 dB threshold above the noise to decide whether a channel is occupied or not. However, radio frequency (RF) receiver front-ends are non-ideal. This means that the obtained data is distorted with noise and imperfections from the analog front-end. As part of the front-end the automatic gain control (AGC) circuitry mainly affects the sensing performance as strong adjacent signals lift the noise level. To enhance the performance of spectrum sensing significantly we focus in this article on techniques to remove the noise caused by the AGC from the sensing data. In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis. In addition, we use live measurement results to verify the performance and to remove the effects of the AGC from the sensing data using above mentioned techniques, i.e., applied on block-wise available spectrum data. In this article it is shown that the occupancy in the industrial, scientific and medical (ISM) band, obtained by using energy detection (ITU recommended threshold), can be an overestimation of spectrum usage by 60%.

Dimension Reduction With Extreme Learning Machine.

PubMed

Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

2016-08-01

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.

Quantitative detection of settled dust over green canopy

NASA Astrophysics Data System (ADS)

Brook, Anna

2016-04-01

The main task of environmental and geoscience applications are efficient and accurate quantitative classification of earth surfaces and spatial phenomena. In the past decade, there has been a significant interest in employing hyperspectral unmixing in order to retrieve accurate quantitative information latent in hyperspectral imagery data. Recently, the ground-truth and laboratory measured spectral signatures promoted by advanced algorithms are proposed as a new path toward solving the unmixing problem of hyperspectral imagery in semi-supervised fashion. This paper suggests that the sensitivity of sparse unmixing techniques provides an ideal approach to extract and identify dust settled over/upon green vegetation canopy using hyperspectral airborne data. Atmospheric dust transports a variety of chemicals, some of which pose a risk to the ecosystem and human health (Kaskaoutis, et al., 2008). Many studies deal with the impact of dust on particulate matter (PM) and atmospheric pollution. Considering the potential impact of industrial pollutants, one of the most important considerations is the fact that suspended PM can have both a physical and a chemical impact on plants, soils, and water bodies. Not only can the particles covering surfaces cause physical distortion, but particles of diverse origin and different chemistries can also serve as chemical stressors and cause irreversible damage. Sediment dust load in an indoor environment can be spectrally assessed using reflectance spectroscopy (Chudnovsky and Ben-Dor, 2009). Small amounts of particulate pollution that may carry a signature of a forthcoming environmental hazard are of key interest when considering the effects of pollution. According to the most basic distribution dynamics, dust consists of suspended particulate matter in a fine state of subdivision that are raised and carried by wind. In this context, it is increasingly important to first, understand the distribution dynamics of pollutants, and subsequently develop dedicated tools and measures to control and monitor pollutants in the free environment. The earliest effect of settled polluted dust particles is not always reflected through poor conditions of vegetation or soils, or any visible damages. In most of the cases, it has a quite long accumulation process that graduates from a polluted condition to long-term environmental hazard. Although conducted experiments with pollutant analog powders under controlled conditions have tended to confirm the findings from field studies (Brook, 2014), a major criticism of all these experiments is their short duration. The resulting conclusion is that it is difficult, if not impossible, to determine the implications of long-term exposure to realistic concentrations of pollutants from such short-term studies. Hyperspectral remote sensing (HRS) has become a common tool for environmental and geoscience applications. HRS has promoted new opportunities for exploring a wide range of materials and evaluating a variety of natural processes due to its detailed, specific, and extensive information on spectral and spatial disseminations. Hyperspectral unmixing (HU) is the technique of presuming the category type, which constitutes the mix-pixel, and its mixing ratio (Keshava and Mustard, 2002). In general, the task of unmixing is to decompose the reflectance spectrum of each pixel into a set of endmembers or principal combined spectra and their corresponding abundances (Bioucas-Dias et al., 2012). This study suggests that the sensitivity of sparse unmixing techniques provides an ideal approach to extract and identify dust settled over/upon green vegetation canopy using hyperspectral airborne data. Among the available techniques, this study present results of seven linear and non-linear unmixing algorithms: 1) Non-negative Matrix Factorization (NMF), 2) L1 sparsity-constrained NMF (L1-NMF), 3) L1/2 sparsity-constrained NMF (L1/2-NMF), 4) Graph regularized NMF (G-NMF), 5) Structured Sparse NMF (SS-NMF), 6) Alternating Least-Square (ALS), and 2) Lin's Projected Gradient (LPG). The performance is evaluated on real hyperspectral imagery data via detailed experimental assessment. The study showed that in certain compression tasks content-adapted sparse representation is provided by state-of-the-art solutions. The NMF algorithm estimates endmembers that are used to remove spurious information. If computationally feasible, it should include interaction terms to make the model more flexible. The optimal NMF algorithms, such as ALS and LPG, are assumed to be the simplest methods that achieve the minimum error on the test set. In summary, this work shows that sediment dust can be assessed using airborne HSI data, making it a potentially powerful tool for environmental studies. References Keshava, N., Mustard, J. (2002). Spectral unmixing. IEEE Signal Process. Mag., 19(1), 44-57. Chudnovsky, A., & Ben-Dor, E. (2009). Reflectance spectroscopy as a tool for settled dust monitoring in office environment. International Journal of Environment and Waste Management, 4(1), 32-49. Brook, A. (2014). Quantitative Detection of Settled dust over Green Canopy using Sparse Unmixing of Airborne Hyperspectral Data. IEEE-Whispers 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2014, Switzerland, 4-8. Keshava, N., Mustard, J. (2002). Spectral unmixing. IEEE Signal Process. Mag., 19(1), 44-57. Bioucas-Dias et al. (2012). Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 354 -379.

Pre-storm NmF2 enhancements at middle latitudes: delusion or reality?

NASA Astrophysics Data System (ADS)

Mikhailov, A. V.; Perrone, L.

2009-03-01

A critical analysis of recent publications devoted to the NmF2 pre-storm enhancements is performed. There are no convincing arguments that the observed cases of NmF2 enhancements at middle and sub-auroral latitudes bear a relation to the following magnetic storms. In all cases considered the NmF2 pre-storm enhancements were due to previous geomagnetic storms, moderate auroral activity or they presented the class of positive quiet time events (Q-disturbances). Therefore, it is possible to conclude that there is no such an effect as the pre-storm NmF2 enhancement as a phenomenon inalienably related to the following magnetic storm. The observed nighttime NmF2 enhancements at sub-auroral latitudes may result from plasma transfer from the plasma ring area by meridional thermospheric wind. Enhanced plasmaspheric fluxes into the nighttime F2-region resulted from westward substorm-associated electric fields is another possible source of nighttime NmF2 enhancements. Daytime positive Q-disturbances occurring under very low geomagnetic activity level may be related to the dayside cusp activity.

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate which Fires Produce Ozone

NASA Technical Reports Server (NTRS)

Chatfield, Robert B.; Andreae, Meinrat O.

2016-01-01

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(burn) = delta C(tot) added to the fire plume, where C(tot) approximately equals (CO2 = CO). Mixed-effects regression can estimate pre-fire background values of C(tot) (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta, epsilon lambda tau alpha-x(sub I)/C(sub burn))I,j. MERET and "consensus" require more than emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (b scant, b abs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate Which Fires Produce Ozone

NASA Technical Reports Server (NTRS)

Chatfield, Robert B.; Andreae, Meinrat O.

2015-01-01

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(sub burn) = delta C(sub tot) added to the fire plume, where C(sub tot) approximately equals (CO2 + CO). Mixed-effects regression can estimate pre-fire background values of Ctot (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta epsilon lambda tau alpha-x(sub i)/(C(sub burn))i,j., MERET and "consensus" require more than two emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (bscat, babs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics.

A Stochastic Model for Detecting Overlapping and Hierarchical Community Structure

PubMed Central

Cao, Xiaochun; Wang, Xiao; Jin, Di; Guo, Xiaojie; Tang, Xianchao

2015-01-01

Community detection is a fundamental problem in the analysis of complex networks. Recently, many researchers have concentrated on the detection of overlapping communities, where a vertex may belong to more than one community. However, most current methods require the number (or the size) of the communities as a priori information, which is usually unavailable in real-world networks. Thus, a practical algorithm should not only find the overlapping community structure, but also automatically determine the number of communities. Furthermore, it is preferable if this method is able to reveal the hierarchical structure of networks as well. In this work, we firstly propose a generative model that employs a nonnegative matrix factorization (NMF) formulization with a l2,1 norm regularization term, balanced by a resolution parameter. The NMF has the nature that provides overlapping community structure by assigning soft membership variables to each vertex; the l2,1 regularization term is a technique of group sparsity which can automatically determine the number of communities by penalizing too many nonempty communities; and hence the resolution parameter enables us to explore the hierarchical structure of networks. Thereafter, we derive the multiplicative update rule to learn the model parameters, and offer the proof of its correctness. Finally, we test our approach on a variety of synthetic and real-world networks, and compare it with some state-of-the-art algorithms. The results validate the superior performance of our new method. PMID:25822148

Comparison between Malassezia Folliculitis and Non-Malassezia Folliculitis

PubMed Central

Song, Hyo Sang; Kim, Sue Kyung

2014-01-01

Background Among the various types of folliculitis, differentiation of Malassezia folliculitis (MF) from other forms of folliculitis is important because it is usually treated with antifungal agents. Objective We attempted to find a method to enhance the detection rate of MF, and examined the differences in the clinical manifestation between MF and non-MF (NMF). Methods We performed a retrospective study involving patients with folliculitis who were previously diagnosed with MF or NMF on the basis of serial tissue sectioning and diastase-Periodic acid-Schiff (d-PAS) staining findings. The clinical features of MF and NMF were compared. Results Among a total of 100 folliculitis patients, 20 were diagnosed with MF and 80 with NMF. Tissues from the 80 patients with NMF were sectioned serially into 10 slices and stained with hematoxylin and eosin stain; among these, 10 had many round-to-oval yeast organisms in the hair follicles that confirmed MF. Finally, d-PAS staining was used to detect the presence of yeast in the NMF slides. Notably, among the 70 d-PAS-stained samples, yeast organisms were found in 6 samples, confirming MF. As a result, the diagnosis of 16 patients changed from NMF to MF. Compared with NMF, MF showed major involvement of the trunk and low involvement of the face and legs as well as male predilection. Conclusion Physicians should consider serial sectioning and/or d-PAS staining of folliculitis lesions, particularly of those on the trunk of male patients, even if no yeast organisms are detected initially. PMID:25324652

Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation.

PubMed

Saito, Shota; Hirata, Yoshito; Sasahara, Kazutoshi; Suzuki, Hideyuki

2015-01-01

Micro-blogging services, such as Twitter, offer opportunities to analyse user behaviour. Discovering and distinguishing behavioural patterns in micro-blogging services is valuable. However, it is difficult and challenging to distinguish users, and to track the temporal development of collective attention within distinct user groups in Twitter. In this paper, we formulate this problem as tracking matrices decomposed by Nonnegative Matrix Factorisation for time-sequential matrix data, and propose a novel extension of Nonnegative Matrix Factorisation, which we refer to as Time Evolving Nonnegative Matrix Factorisation (TENMF). In our method, we describe users and words posted in some time interval by a matrix, and use several matrices as time-sequential data. Subsequently, we apply Time Evolving Nonnegative Matrix Factorisation to these time-sequential matrices. TENMF can decompose time-sequential matrices, and can track the connection among decomposed matrices, whereas previous NMF decomposes a matrix into two lower dimension matrices arbitrarily, which might lose the time-sequential connection. Our proposed method has an adequately good performance on artificial data. Moreover, we present several results and insights from experiments using real data from Twitter.

Muscle-tendon units localization and activation level analysis based on high-density surface EMG array and NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-12-01

Objective. Some skeletal muscles can be subdivided into smaller segments called muscle-tendon units (MTUs). The purpose of this paper is to propose a framework to locate the active region of the corresponding MTUs within a single skeletal muscle and to analyze the activation level varieties of different MTUs during a dynamic motion task. Approach. Biceps brachii and gastrocnemius were selected as targeted muscles and three dynamic motion tasks were designed and studied. Eight healthy male subjects participated in the data collection experiments, and 128-channel surface electromyographic (sEMG) signals were collected with a high-density sEMG electrode grid (a grid consists of 8 rows and 16 columns). Then the sEMG envelopes matrix was factorized into a matrix of weighting vectors and a matrix of time-varying coefficients by nonnegative matrix factorization algorithm. Main results. The experimental results demonstrated that the weightings vectors, which represent invariant pattern of muscle activity across all channels, could be used to estimate the location of MTUs and the time-varying coefficients could be used to depict the variation of MTUs activation level during dynamic motion task. Significance. The proposed method provides one way to analyze in-depth the functional state of MTUs during dynamic tasks and thus can be employed on multiple noteworthy sEMG-based applications such as muscle force estimation, muscle fatigue research and the control of myoelectric prostheses. This work was supported by the National Nature Science Foundation of China under Grant 61431017 and 61271138.

Simultaneous response of NmF2 and GPS-TEC to storm events at Ilorin

NASA Astrophysics Data System (ADS)

Joshua, B. W.; Adeniyi, J. O.; Oladipo, O. A.; Doherty, P. H.; Adimula, I. A.; Olawepo, A. O.; Adebiyi, S. J.

2018-06-01

A comparative study of both TEC and NmF2 variations during quiet and disturbed conditions has been investigated using simultaneous measurements from dual frequency Global Positioning System (GPS) receiver and a DPS-4 Digisonde co-located at Ilorin (Geog. Lat. 8.50°N, Long. 4.50°E, dip. - 7.9°). The results of the quiet time variations of the two parameters show some similarities as well as differences in their structures. The values of both parameters generally increase during the sunrise period attaining a peak around the noon and then decaying towards the night time. The onset time of the sunrise growth is observed to be earlier in TEC than in NmF2. The rate of decay of TEC was observed to be faster than that of the NmF2 in most cases. Also, the noon 'bite-outs', leading to the formation of pre-noon and post-noon peaks, are prominent in the NmF2 structure and was hardly noticed in TEC. Results of the variations of both TEC and NmF2 during the 5 April, 10 May and 3 August 2010 geomagnetic storm events showed a simultaneous deviations of both parameters from the quiet time behavior. The magnitude of the deviations is however most pronounced in NmF2 structure than in TEC. We also found that the enhancement observed in the two parameters during the storm events generally corresponds to decrease in hmF2.

Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion.

PubMed

Sotiras, Aristeidis; Toledo, Jon B; Gur, Raquel E; Gur, Ruben C; Satterthwaite, Theodore D; Davatzikos, Christos

2017-03-28

During adolescence, the human cortex undergoes substantial remodeling to support a rapid expansion of behavioral repertoire. Accurately quantifying these changes is a prerequisite for understanding normal brain development, as well as the neuropsychiatric disorders that emerge in this vulnerable period. Past accounts have demonstrated substantial regional heterogeneity in patterns of brain development, but frequently have been limited by small samples and analytics that do not evaluate complex multivariate imaging patterns. Capitalizing on recent advances in multivariate analysis methods, we used nonnegative matrix factorization (NMF) to uncover coordinated patterns of cortical development in a sample of 934 youths ages 8-20, who completed structural neuroimaging as part of the Philadelphia Neurodevelopmental Cohort. Patterns of structural covariance (PSCs) derived by NMF were highly reproducible over a range of resolutions, and differed markedly from common gyral-based structural atlases. Moreover, PSCs were largely symmetric and showed correspondence to specific large-scale functional networks. The level of correspondence was ordered according to their functional role and position in the evolutionary hierarchy, being high in lower-order visual and somatomotor networks and diminishing in higher-order association cortex. Furthermore, PSCs showed divergent developmental associations, with PSCs in higher-order association cortex networks showing greater changes with age than primary somatomotor and visual networks. Critically, such developmental changes within PSCs were significantly associated with the degree of evolutionary cortical expansion. Together, our findings delineate a set of structural brain networks that undergo coordinated cortical thinning during adolescence, which is in part governed by evolutionary novelty and functional specialization.

Retrieving the Quantitative Chemical Information at Nanoscale from Scanning Electron Microscope Energy Dispersive X-ray Measurements by Machine Learning

NASA Astrophysics Data System (ADS)

Jany, B. R.; Janas, A.; Krok, F.

2017-11-01

The quantitative composition of metal alloy nanowires on InSb(001) semiconductor surface and gold nanostructures on germanium surface is determined by blind source separation (BSS) machine learning (ML) method using non negative matrix factorization (NMF) from energy dispersive X-ray spectroscopy (EDX) spectrum image maps measured in a scanning electron microscope (SEM). The BSS method blindly decomposes the collected EDX spectrum image into three source components, which correspond directly to the X-ray signals coming from the supported metal nanostructures, bulk semiconductor signal and carbon background. The recovered quantitative composition is validated by detailed Monte Carlo simulations and is confirmed by separate cross-sectional TEM EDX measurements of the nanostructures. This shows that SEM EDX measurements together with machine learning blind source separation processing could be successfully used for the nanostructures quantitative chemical composition determination.

Morphology of the winter anomaly in NmF2 and Total Electron Content

NASA Astrophysics Data System (ADS)

Yasyukevich, Yury; Ratovsky, Konstantin; Yasyukevich, Anna; Klimenko, Maksim; Klimenko, Vladimir; Chirik, Nikolay

2017-04-01

We analyzed the winter anomaly manifestation in the F2 peak electron density (NmF2) and Total Electron Content (TEC) based on the observation data and model calculation results. For the analysis we used 1998-2015 TEC Global Ionospheric Maps (GIM) and NmF2 ground-based ionosonde observation data from and COSMIC, CHAMP and GRACE radio occultation data. We used Global Self-consistent Model of the Thermosphere, Ionosphere, and Protonosphere (GSM TIP) and International Reference Ionosphere model (IRI-2012). Based on the observation data and model calculation results we constructed the maps of the winter anomaly intensity in TEC and NmF2 for the different solar and geomagnetic activity levels. The winter anomaly intensity was found to be higher in NmF2 than in TEC according to both observation and modeling. In this report we show the similarity and difference in winter anomaly as revealed in experimental data and model results.

Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

PubMed

Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang

2015-01-01

RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.

Causes of the mid-latitudinal daytime NmF2 semi-annual anomaly at solar minimum

NASA Astrophysics Data System (ADS)

Pavlov, A. V.

2018-04-01

Ionospheric ionosonde and radar observations and theoretical calculations of the F2-layer peak altitude, hmF2, and number density, NmF2, over Millstone Hill during winter, spring, summer, and autumn geomagnetically quiet time periods at low solar activity are used to study the causes of the observed daytime NmF2 semi-annual anomaly. It follows from the model simulations that this anomalous phenomenon arises in the ionosphere mainly as a result of seasonal variations of the following atmospheric parameters: (1) the plasma drift along geomagnetic field lines due to corresponding changes in neutral wind components, (2) temperature and number densities of the neutral atmosphere, and (3) an optical thickness of the atmosphere caused by the dependence of the solar zenith angle on the day of the year for the same solar local time. Seasonal variations of the production rate unexcited O+ ions due to chemical reactions involving electronically excited O+ ions contribute to the formation of the NmF2 semi-annual anomaly during the predominant part of the existence time of this anomalous phenomenon. However, these seasonal variations are not significant, and this mechanism should be considered only as an additional source of the NmF2 semi-annual anomaly during its time of existence. The reactions of unexcited O+ ions with vibrationally excited N2 and O2 cause only weak changes of NmF2 and these changes are close in magnitude at a given solar local time during the winter, spring, summer, and autumn daytime conditions under consideration. Ignoring these reactions cannot produce a significant impact on the formation of the NmF2 semi-annual anomaly.

Development of a Real Time Sparse Non-Negative Matrix Factorization Module for Cochlear Implants by Using xPC Target

PubMed Central

Hu, Hongmei; Krasoulis, Agamemnon; Lutman, Mark; Bleeck, Stefan

2013-01-01

Cochlear implants (CIS) require efficient speech processing to maximize information transmission to the brain, especially in noise. A novel CI processing strategy was proposed in our previous studies, in which sparsity-constrained non-negative matrix factorization (NMF) was applied to the envelope matrix in order to improve the CI performance in noisy environments. It showed that the algorithm needs to be adaptive, rather than fixed, in order to adjust to acoustical conditions and individual characteristics. Here, we explore the benefit of a system that allows the user to adjust the signal processing in real time according to their individual listening needs and their individual hearing capabilities. In this system, which is based on MATLAB®, SIMULINK® and the xPC Target™ environment, the input/outupt (I/O) boards are interfaced between the SIMULINK blocks and the CI stimulation system, such that the output can be controlled successfully in the manner of a hardware-in-the-loop (HIL) simulation, hence offering a convenient way to implement a real time signal processing module that does not require any low level language. The sparsity constrained parameter of the algorithm was adapted online subjectively during an experiment with normal-hearing subjects and noise vocoded speech simulation. Results show that subjects chose different parameter values according to their own intelligibility preferences, indicating that adaptive real time algorithms are beneficial to fully explore subjective preferences. We conclude that the adaptive real time systems are beneficial for the experimental design, and such systems allow one to conduct psychophysical experiments with high ecological validity. PMID:24129021

Development of a real time sparse non-negative matrix factorization module for cochlear implants by using xPC target.

PubMed

Hu, Hongmei; Krasoulis, Agamemnon; Lutman, Mark; Bleeck, Stefan

2013-10-14

Cochlear implants (CIs) require efficient speech processing to maximize information transmission to the brain, especially in noise. A novel CI processing strategy was proposed in our previous studies, in which sparsity-constrained non-negative matrix factorization (NMF) was applied to the envelope matrix in order to improve the CI performance in noisy environments. It showed that the algorithm needs to be adaptive, rather than fixed, in order to adjust to acoustical conditions and individual characteristics. Here, we explore the benefit of a system that allows the user to adjust the signal processing in real time according to their individual listening needs and their individual hearing capabilities. In this system, which is based on MATLAB®, SIMULINK® and the xPC Target™ environment, the input/outupt (I/O) boards are interfaced between the SIMULINK blocks and the CI stimulation system, such that the output can be controlled successfully in the manner of a hardware-in-the-loop (HIL) simulation, hence offering a convenient way to implement a real time signal processing module that does not require any low level language. The sparsity constrained parameter of the algorithm was adapted online subjectively during an experiment with normal-hearing subjects and noise vocoded speech simulation. Results show that subjects chose different parameter values according to their own intelligibility preferences, indicating that adaptive real time algorithms are beneficial to fully explore subjective preferences. We conclude that the adaptive real time systems are beneficial for the experimental design, and such systems allow one to conduct psychophysical experiments with high ecological validity.

Limited-memory fast gradient descent method for graph regularized nonnegative matrix factorization.

PubMed

Guan, Naiyang; Wei, Lei; Luo, Zhigang; Tao, Dacheng

2013-01-01

Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix X[Symbol:see text]R(m x n) to the product of two lower-rank nonnegative factor matrices, i.e.,W[Symbol:see text]R(m x r) and H[Symbol:see text]R(r x n) (r < min {m,n}) and aims to preserve the local geometric structure of the dataset by minimizing squared Euclidean distance or Kullback-Leibler (KL) divergence between X and WH. The multiplicative update rule (MUR) is usually applied to optimize GNMF, but it suffers from the drawback of slow-convergence because it intrinsically advances one step along the rescaled negative gradient direction with a non-optimal step size. Recently, a multiple step-sizes fast gradient descent (MFGD) method has been proposed for optimizing NMF which accelerates MUR by searching the optimal step-size along the rescaled negative gradient direction with Newton's method. However, the computational cost of MFGD is high because 1) the high-dimensional Hessian matrix is dense and costs too much memory; and 2) the Hessian inverse operator and its multiplication with gradient cost too much time. To overcome these deficiencies of MFGD, we propose an efficient limited-memory FGD (L-FGD) method for optimizing GNMF. In particular, we apply the limited-memory BFGS (L-BFGS) method to directly approximate the multiplication of the inverse Hessian and the gradient for searching the optimal step size in MFGD. The preliminary results on real-world datasets show that L-FGD is more efficient than both MFGD and MUR. To evaluate the effectiveness of L-FGD, we validate its clustering performance for optimizing KL-divergence based GNMF on two popular face image datasets including ORL and PIE and two text corpora including Reuters and TDT2. The experimental results confirm the effectiveness of L-FGD by comparing it with the representative GNMF solvers.

A hybrid neutron diffraction and computer simulation study on the solvation of N-methylformamide in dimethylsulfoxide

NASA Astrophysics Data System (ADS)

Cordeiro, João M. M.; Soper, Alan K.

2013-01-01

The solvation of N-methylformamide (NMF) by dimethylsulfoxide (DMSO) in a 20% NMF/DMSO liquid mixture is investigated using a combination of neutron diffraction augmented with isotopic substitution and Monte Carlo simulations. The aim is to investigate the solute-solvent interactions and the structure of the solution. The results point to the formation of a hydrogen bond (H-bond) between the H bonded to the N of the amine group of NMF and the O of DMSO particularly strong when compared with other H-bonded liquids. Moreover, a second cooperative H-bond is identified with the S atom of DMSO. As a consequence of these H-bonds, molecules of NMF and DMSO are rather rigidly connected, establishing very stable dimmers in the mixture and very well organized first and second solvation shells.

Sweat Facilitated Amino Acid Losses in Male Athletes during Exercise at 32-34°C.

PubMed

Dunstan, R Hugh; Sparkes, Diane L; Dascombe, Benjamin J; Macdonald, Margaret M; Evans, Craig A; Stevens, Christopher J; Crompton, Marcus J; Gottfries, Johan; Franks, Jesse; Murphy, Grace; Wood, Ryan; Roberts, Timothy K

2016-01-01

Sweat contains amino acids and electrolytes derived from plasma and athletes can lose 1-2L of sweat per hour during exercise. Sweat may also contain contributions of amino acids as well as urea, sodium and potassium from the natural moisturizing factors (NMF) produced in the stratum corneum. In preliminary experiments, one participant was tested on three separate occasions to compare sweat composition with surface water washings from the same area of skin to assess contributions from NMF. Two participants performed a 40 minute self-paced cycle session with sweat collected from cleansed skin at regular intervals to assess the contributions to the sweat load from NMF over the period of exercise. The main study investigated sweat amino acid composition collected from nineteen male athletes following standardised endurance exercise regimes at 32-34°C and 20-30% RH. Plasma was also collected from ten of the athletes to compare sweat and plasma composition of amino acids. The amino acid profiles of the skin washings were similar to the sweat, suggesting that the NMF could contribute certain amino acids into sweat. Since the sweat collected from athletes contained some amino acid contributions from the skin, this fluid was subsequently referred to as "faux" sweat. Samples taken over 40 minutes of exercise showed that these contributions diminished over time and were minimal at 35 minutes. In the main study, the faux sweat samples collected from the athletes with minimal NMF contributions, were characterised by relatively high levels of serine, histidine, ornithine, glycine and alanine compared with the corresponding levels measured in the plasma. Aspartic acid was detected in faux sweat but not in the plasma. Glutamine and proline were lower in the faux sweat than plasma in all the athletes. Three phenotypic groups of athletes were defined based on faux sweat volumes and composition profiles of amino acids with varying relative abundances of histidine, serine, glycine and ornithine. It was concluded that for some individuals, faux sweat resulting from exercise at 32-34°C and 20-30% RH posed a potentially significant source of amino acid loss.

Sweat Facilitated Amino Acid Losses in Male Athletes during Exercise at 32-34°C

PubMed Central

Dunstan, R. Hugh; Sparkes, Diane L.; Dascombe, Benjamin J.; Macdonald, Margaret M.; Evans, Craig A.; Stevens, Christopher J.; Crompton, Marcus J.; Gottfries, Johan; Franks, Jesse; Murphy, Grace; Wood, Ryan; Roberts, Timothy K.

2016-01-01

Sweat contains amino acids and electrolytes derived from plasma and athletes can lose 1-2L of sweat per hour during exercise. Sweat may also contain contributions of amino acids as well as urea, sodium and potassium from the natural moisturizing factors (NMF) produced in the stratum corneum. In preliminary experiments, one participant was tested on three separate occasions to compare sweat composition with surface water washings from the same area of skin to assess contributions from NMF. Two participants performed a 40 minute self-paced cycle session with sweat collected from cleansed skin at regular intervals to assess the contributions to the sweat load from NMF over the period of exercise. The main study investigated sweat amino acid composition collected from nineteen male athletes following standardised endurance exercise regimes at 32–34°C and 20–30% RH. Plasma was also collected from ten of the athletes to compare sweat and plasma composition of amino acids. The amino acid profiles of the skin washings were similar to the sweat, suggesting that the NMF could contribute certain amino acids into sweat. Since the sweat collected from athletes contained some amino acid contributions from the skin, this fluid was subsequently referred to as “faux” sweat. Samples taken over 40 minutes of exercise showed that these contributions diminished over time and were minimal at 35 minutes. In the main study, the faux sweat samples collected from the athletes with minimal NMF contributions, were characterised by relatively high levels of serine, histidine, ornithine, glycine and alanine compared with the corresponding levels measured in the plasma. Aspartic acid was detected in faux sweat but not in the plasma. Glutamine and proline were lower in the faux sweat than plasma in all the athletes. Three phenotypic groups of athletes were defined based on faux sweat volumes and composition profiles of amino acids with varying relative abundances of histidine, serine, glycine and ornithine. It was concluded that for some individuals, faux sweat resulting from exercise at 32–34°C and 20–30% RH posed a potentially significant source of amino acid loss. PMID:27936120

Nmf9 Encodes a Highly Conserved Protein Important to Neurological Function in Mice and Flies.

PubMed

Zhang, Shuxiao; Ross, Kevin D; Seidner, Glen A; Gorman, Michael R; Poon, Tiffany H; Wang, Xiaobo; Keithley, Elizabeth M; Lee, Patricia N; Martindale, Mark Q; Joiner, William J; Hamilton, Bruce A

2015-07-01

Many protein-coding genes identified by genome sequencing remain without functional annotation or biological context. Here we define a novel protein-coding gene, Nmf9, based on a forward genetic screen for neurological function. ENU-induced and genome-edited null mutations in mice produce deficits in vestibular function, fear learning and circadian behavior, which correlated with Nmf9 expression in inner ear, amygdala, and suprachiasmatic nuclei. Homologous genes from unicellular organisms and invertebrate animals predict interactions with small GTPases, but the corresponding domains are absent in mammalian Nmf9. Intriguingly, homozygotes for null mutations in the Drosophila homolog, CG45058, show profound locomotor defects and premature death, while heterozygotes show striking effects on sleep and activity phenotypes. These results link a novel gene orthology group to discrete neurological functions, and show conserved requirement across wide phylogenetic distance and domain level structural changes.

A novel sampling method for identification of endogenous skin surface compounds by use of DART-MS and MALDI-MS.

PubMed

Mess, Aylin; Enthaler, Bernd; Fischer, Markus; Rapp, Claudius; Pruns, Julia K; Vietzke, Jens-Peter

2013-01-15

Identification of endogenous skin surface compounds is an intriguing challenge in comparative skin investigations. Notably, this short communication is focused on the analysis of small molecules, e.g. natural moisturizing factor (NMF) components and lipids, using a novel sampling method with DIP-it samplers for non-invasive examination of the human skin surface. As a result, extraction of analytes directly from the skin surface by use of various solvents can be replaced with the mentioned procedure. Screening of measureable compounds is achieved by direct analysis in real time mass spectrometry (DART-MS) without further sample preparation. Results are supplemented by dissolving analytes from the DIP-it samplers by use of different solvents, and subsequent matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) measurements. An interesting comparison of the mentioned MS techniques for determination of skin surface compounds in the mass range of 50-1000 Da is presented. Copyright © 2012 Elsevier B.V. All rights reserved.

Blind decomposition of Herschel-HIFI spectral maps of the NGC 7023 nebula

NASA Astrophysics Data System (ADS)

Berné, O.; Joblin, C.; Deville, Y.; Pilleri, P.; Pety, J.; Teyssier, D.; Gerin, M.; Fuente, A.

2012-12-01

Large spatial-spectral surveys are more and more common in astronomy. This calls for the need of new methods to analyze such mega- to giga-pixel data-cubes. In this paper we present a method to decompose such observations into a limited and comprehensive set of components. The original data can then be interpreted in terms of linear combinations of these components. The method uses non-negative matrix factorization (NMF) to extract latent spectral end-members in the data. The number of needed end-members is estimated based on the level of noise in the data. A Monte-Carlo scheme is adopted to estimate the optimal end-members, and their standard deviations. Finally, the maps of linear coefficients are reconstructed using non-negative least squares. We apply this method to a set of hyperspectral data of the NGC 7023 nebula, obtained recently with the HIFI instrument onboard the Herschel space observatory, and provide a first interpretation of the results in terms of 3-dimensional dynamical structure of the region.

NmF2 and hmF2 measurements at 95° E and 127° E around the EIA northern crest during 2010-2014

NASA Astrophysics Data System (ADS)

Kalita, Bitap Raj; Bhuyan, Pradip Kumar; Yoshikawa, Akimasa

2015-11-01

The characteristics of the F2 layer parameters NmF2 and hmF2 over Dibrugarh (27.5° N, 95° E, 17° N geomagnetic, 43° dip) measured by a Canadian Advanced Digital Ionosonde (CADI) for the period of August 2010 to July 2014 are reported for the first time from this low mid-latitude station lying within the daytime peak of the longitudinal wave number 4 structure of equatorial anomaly (EIA) around the northern edge of anomaly crest. Equinoctial asymmetry is clearly observed at all solar activity levels whereas the midday winter anomaly is observed only during high solar activity years and disappears during the temporary dip in solar activity in 2013 but forenoon winter anomaly can be observed even at moderate solar activity. The NmF2/hmF2 variations over Dibrugarh are compared with that of Okinawa (26.5° N, 127° E, 17° N geomagnetic), and the eastward propagation speed of the wave number 4 longitudinal structure from 95° E to 127° E is estimated. The speed is found to be close to the theoretical speed of the wave number 4 (WN4) structure. The correlation of daily NmF2 over Dibrugarh and Okinawa with solar activity exhibits diurnal and seasonal variations. The highest correlation in daytime is observed during the forenoon hours in equinox. The correlation of daily NmF2 (linear or non-linear) with solar activity exhibits diurnal variation. A tendency for amplification with solar activity is observed in the forenoon and late evening period of March equinox and the postsunset period of December solstice. NmF2 saturation effect is observed only in the midday period of equinox. Non-linear variation of neutral composition at higher altitudes and variation of recombination rates with solar activity via temperature dependence may be related to the non-linear trend. The noon time maximum NmF2 over Dibrugarh exhibits better correlation with equatorial electrojet (EEJ) than with solar activity and, therefore, new low-latitude NmF2 index is proposed taking both solar activity and EEJ strength into account.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

PubMed

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

Simulated East-west differences in F-region peak electron density at Far East mid-latitude region

NASA Astrophysics Data System (ADS)

Ren, Zhipeng; Zhao, Biqiang; Wan, Weixing; Liu, Libo

2017-04-01

In the present work, using Three-Dimensional Theoretical Ionospheric Model of the Earth in Institute of Geology and Geophysics, Chinese Academy of Sciences (TIME3D-IGGCAS), we simulated the east-west differences in Fregion peak electron density (NmF2) at Far East mid-latitude region.We found that, after removing the longitudinal variations of neutral parameters, TIME3D-IGGCAS can better represent the observed relative east-west difference (Rew) features. Rew is mainly negative (West NmF2 > East NmF2) at noon and positive (East NmF2 >West NmF2) at evening-night. The magnitude of daytime negative Rew is weak at local winter and strong at local summer, and the daytime Rew show two negative peaks around two equinoxes. With the increasing of solar flux level, the magnitude of Rew mainly become larger, and two daytime negative peaks slight shifts to June Solstice. With the decreasing of geographical latitude, Rew mainly become positive, and two daytime negative peaks slight shifts to June Solstice. Our simulation also suggested that the thermospheric zonal wind combined with the geomagnetic field configuration play a pivotal role in the formation of the ionospheric east-west differences at Far East midlatitude region.

Simulated East-west differences in F-region peak electron density at Far East mid-latitude region

NASA Astrophysics Data System (ADS)

Ren, Z.; Wan, W.

2017-12-01

In the present work, using Three-Dimensional Theoretical Ionospheric Model of the Earth in Institute of Geology and Geophysics, Chinese Academy of Sciences (TIME3D-IGGCAS), we simulated the east-west differences in Fregion peak electron density (NmF2) at Far East mid-latitude region.We found that, after removing the longitudinal variations of neutral parameters, TIME3D-IGGCAS can better represent the observed relative east-west difference (Rew) features. Rew is mainly negative (West NmF2 > East NmF2) at noon and positive (East NmF2 >West NmF2) at evening-night. The magnitude of daytime negative Rew is weak at local winter and strong at local summer, and the daytime Rew show two negative peaks around two equinoxes. With the increasing of solar flux level, the magnitude of Rew mainly become larger, and two daytime negative peaks slight shifts to June Solstice. With the decreasing of geographical latitude, Rew mainly become positive, and two daytime negative peaks slight shifts to June Solstice. Our simulation also suggested that the thermospheric zonal wind combined with the geomagnetic field configuration play a pivotal role in the formation of the ionospheric east-west differences at Far East midlatitude region.

Quantifications of Geomagnetic Storm Impact on TEC and NmF2 during 2013 Mar. event

NASA Astrophysics Data System (ADS)

Shim, J. S.; Tsagouri, I.; Goncharenko, L. P.; Mays, M. L.; Taktakishvili, A.; Rastaetter, L.; Kuznetsova, M. M.

2016-12-01

We investigate the ionospheric response to 2013 Mar. geomagnetic storm event using GPS TEC, ISR and ionosonde observations in North American sector. In order to quantify variations of TEC and NmF2 (or foF2) due to the storm, we remove the background quiet-time values (e.g., TEC of one day prior to the storm, NmF2 median and average of five quietest days for 30 days prior to the storm). In addition, in order to assess modeling capability of reproducing storm impacts on TEC and NmF2, we compare the observations with various model simulations, which are obtained from empirical, physics-based, and data assimilation models. Further, we investigate how uncertainty in the interplanetary magnetic field (IMF) impacts on TEC and NmF2 during the geomagnetic storm event. For this uncertainty study, we use a physics-based coupled ionosphere-thermosphere model, CTIPe, and solar wind parameters obtained from ensemble of WSA-ENLIL+Cone model simulations. This study has been supported by the Community Coordinated Modeling Center (CCMC) at the Goddard Space Flight Center. Model outputs and observational data used for the study will be permanently posted at the CCMC website (http://ccmc.gsfc.nasa.gov) for the space science communities to use.

COMPADRE: an R and web resource for pathway activity analysis by component decompositions.

PubMed

Ramos-Rodriguez, Roberto-Rafael; Cuevas-Diaz-Duran, Raquel; Falciani, Francesco; Tamez-Peña, Jose-Gerardo; Trevino, Victor

2012-10-15

The analysis of biological networks has become essential to study functional genomic data. Compadre is a tool to estimate pathway/gene sets activity indexes using sub-matrix decompositions for biological networks analyses. The Compadre pipeline also includes one of the direct uses of activity indexes to detect altered gene sets. For this, the gene expression sub-matrix of a gene set is decomposed into components, which are used to test differences between groups of samples. This procedure is performed with and without differentially expressed genes to decrease false calls. During this process, Compadre also performs an over-representation test. Compadre already implements four decomposition methods [principal component analysis (PCA), Isomaps, independent component analysis (ICA) and non-negative matrix factorization (NMF)], six statistical tests (t- and f-test, SAM, Kruskal-Wallis, Welch and Brown-Forsythe), several gene sets (KEGG, BioCarta, Reactome, GO and MsigDB) and can be easily expanded. Our simulation results shown in Supplementary Information suggest that Compadre detects more pathways than over-representation tools like David, Babelomics and Webgestalt and less false positives than PLAGE. The output is composed of results from decomposition and over-representation analyses providing a more complete biological picture. Examples provided in Supplementary Information show the utility, versatility and simplicity of Compadre for analyses of biological networks. Compadre is freely available at http://bioinformatica.mty.itesm.mx:8080/compadre. The R package is also available at https://sourceforge.net/p/compadre.

Ionospheric Peak Electron Density and Performance Evaluation of IRI-CCIR Near Magnetic Equator in Africa During Two Extreme Solar Activities

NASA Astrophysics Data System (ADS)

Adebesin, B. O.; Rabiu, A. B.; Obrou, O. K.; Adeniyi, J. O.

2018-03-01

The F2 layer peak electron density (NmF2) was investigated over Korhogo (Geomagnetic: 1.26°S, 67.38°E), a station near the magnetic equator in the African sector. Data for 1996 and 2000 were, respectively, categorized into low solar quiet and disturbed and high solar quiet and disturbed. NmF2 prenoon peak was higher than the postnoon peak during high solar activity irrespective of magnetic activity condition, while the postnoon peak was higher for low solar activity. Higher NmF2 peak amplitude characterizes disturbed magnetic activity than quiet magnetic condition for any solar activity. The maximum peaks appeared in equinox. June solstice noontime bite out lagged other seasons by 1-2 h. For any condition of solar and magnetic activities, the daytime NmF2 percentage variability (%VR) measured by the relative standard deviation maximizes/minimizes in June solstice/equinox. Daytime variability increases with increasing magnetic activity. The highest peak in the morning time NmF2 variability occurs in equinox, while the highest evening/nighttime variability appeared in June solstice for all solar/magnetic conditions. The nighttime annual variability amplitude is higher during disturbed than quiet condition regardless of solar activity period. At daytime, variability is similar for all conditions of solar activities. NmF2 at Korhogo is well represented on the International Reference Ionosphere-International Radio Consultative Committee (IRI-CCIR) option. The model/observation relationship performed best between local midnight and postmidnight period (00-08 LT). The noontime trough characteristics is not prominent in the IRI pattern during high solar activity but evident during low solar conditions when compared with Korhogo observations. The Nash-Sutcliffe coefficients revealed better model performance during disturbed activities.

IRI STORM validation over Europe

NASA Astrophysics Data System (ADS)

Haralambous, Haris; Vryonides, Photos; Demetrescu, Crişan; Dobrică, Venera; Maris, Georgeta; Ionescu, Diana

2014-05-01

The International Reference Ionosphere (IRI) model includes an empirical Storm-Time Ionospheric Correction Model (STORM) extension to account for storm-time changes of the F layer peak electron density (NmF2) during increased geomagnetic activity. This model extension is driven by past history values of the geomagnetic index ap (The magnetic index applied is the integral of ap over the previous 33 hours with a weighting function deduced from physically based modeling) and it adjusts the quiet-time F layer peak electron density (NmF2) to account for storm-time changes in the ionosphere. In this investigation manually scaled hourly values of NmF2 measured during the main and recovery phases of selected storms for the maximum solar activity period of the current solar cycle are compared with the predicted IRI-2012 NmF2 over European ionospheric stations using the STORM model option. Based on the comparison a subsequent performance evaluation of the STORM option during this period is quantified.

Ionospheric tomography over South Africa: Comparison of MIDAS and ionosondes measurements

NASA Astrophysics Data System (ADS)

Giday, Nigussie M.; Katamzi, Zama T.; McKinnell, Lee-Anne

2016-01-01

This paper aims to show the results of an ionospheric tomography algorithm called Multi-Instrument Data Analysis System (MIDAS) over the South African region. Recorded data from a network of 49-53 Global Positioning System (GPS) receivers over the South African region was used as input for the inversion. The inversion was made for April, July, October and December representing the four distinct seasons (Autumn, Winter, Spring and Summer respectively) of the year 2012. MIDAS reconstructions were validated by comparing maximum electron density of the F2 layer (NmF2) and peak height (hmF2) values predicted by MIDAS to those derived from three South African ionosonde measurements. The diurnal and seasonal trends of the MIDAS NmF2 values were in good agreement with the respective NmF2 values derived from the ionosondes. In addition, good agreement was found between the two measurements with minimum and maximum coefficients of determination (r2) between 0.84 and 0.96 in all the stations and validation days. The seasonal trend of the NmF2 values over the South Africa region has been reproduced using this inversion which was in good agreement with the ionosonde measurements. Moreover, a comparison of the International Reference Ionosphere (IRI-2012) model NmF2 values with the respective ionosonde derived NmF2 values showed to have higher deviation than a similar comparison between the MIDAS reconstruction and the ionosonde measurements. However, the monthly averaged hmF2 values derived from IRI 2012 model showed better agreement than the respective MIDAS reconstructed hmF2 values compared with the ionosonde derived hmF2 values.The performance of the MIDAS reconstruction was observed to deteriorate with increased geomagnetic conditions. MIDAS reconstructed electron density were slightly elevated during three storm periods studied (24 April, 15 July and 8 October) which was in good agreement with the ionosonde measurements.

Ionospheric climatology at Africa EIA trough stations during descending phase of sunspot cycle 22

NASA Astrophysics Data System (ADS)

Adebesin, B. O.; Rabiu, A. B.; Bolaji, O. S.; Adeniyi, J. O.; Amory-Mazaudier, C.

2018-07-01

The African equatorial ionospheric climatology during the descending phase of sunspot-cycle 22 (spanning 1992-1996) was investigated using 3 ionosondes located at Dakar (14.70 N, 342.60 E), Ouagadougou (12.420 N, 358.60 E), and Korhogo (9.510 N, 354.40 E). The variations in the virtual height of the F-layer (h'F), maximum electron density (NmF2), vertical plasma drift (Vp) and zonal electric field (Ey) were presented. Significant decrease in the NmF2 amplitude compared to h'F in all of the stations during the descending period is obvious. While NmF2 magnitude maximizes/minimizes during the E-seasons/J-season, h'F attained highest/lowest altitude in J-season/D-season for all stations. D-season anomaly was evident in NmF2 at all stations. For any season, the intensity (Ibt) of NmF2 noon-bite-out is highest at Dakar owning to fountain effect and maximizes in March-E season. Stations across the EIA trough show nearly coherence ionospheric climatology characteristics whose difference is of latitudinal origin. Hemispheric dependence in NmF2 is obvious, with difference more significant during high-solar activity and closes with decreasing solar activity. The variability in the plasma drift during the entire phase is suggested to emanate from solar flux variations, and additionally from enhanced leakage of electric fields from high-to low-latitudes. Existing African regional model of evening/nightttime pre-reversal plasma drift/sunspot number (PREpeak/R) relationship compares well with experimental observations at all stations with slight over-estimation. The correlation/root-mean-square-deviation (RMSdev) pair between the model and observed Vp during the descending phase recorded 94.9%/0.756, 92.4%/1.526, and 79.1%/3.612 at Korhogo, Ouagadougou and Dakar respectively. The Ey/h'F and Ey/NmF2 relationships suggest that zonal electric field is more active in the lifting of h'F and suppression of NmF2 during high- and moderate-solar activities when compared with low-solar activity. This is the first work to show higher bite-out at the equatorial northern-station (Dakar) than southern-station (Korhogo) using ionosonde data.

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals

PubMed Central

Iliev, Filip L.; Stanev, Valentin G.; Vesselinov, Velimir V.

2018-01-01

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found. PMID:29518126

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

PubMed

Iliev, Filip L; Stanev, Valentin G; Vesselinov, Velimir V; Alexandrov, Boian S

2018-01-01

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the mixtures are linear combinations of signals with delays is less explored. Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well. To address this problem, in this paper, we present a new method based on Nonnegative Matrix Factorization (NMF) that is capable of identifying: (a) the unknown number of the sources, (b) the delays and speed of propagation of the signals, and (c) the locations of the sources. Our method can be used to decompose records of mixtures of signals with delays emitted by an unknown number of sources in a nondispersive medium, based only on recorded data. This is the case, for example, when electromagnetic signals from multiple antennas are received asynchronously; or mixtures of acoustic or seismic signals recorded by sensors located at different positions; or when a shift in frequency is induced by the Doppler effect. By applying our method to synthetic datasets, we demonstrate its ability to identify the unknown number of sources as well as the waveforms, the delays, and the strengths of the signals. Using Bayesian analysis, we also evaluate estimation uncertainties and identify the region of likelihood where the positions of the sources can be found.

Source apportionment of hydrocarbons measured in the Eagle Ford shale

NASA Astrophysics Data System (ADS)

Roest, G. S.; Schade, G. W.

2016-12-01

The rapid development of unconventional oil and gas in the US has led to hydrocarbon emissions that are yet to be accurately quantified. Emissions from the Eagle Ford Shale in southern Texas, one of the most productive shale plays in the U.S., have received little attention due to a sparse air quality monitoring network, thereby limiting studies of air quality within the region. We use hourly atmospheric hydrocarbon and meteorological data from three locations in the Eagle Ford Shale to assess their sources. Data are available from the Texas commission of environmental quality (TCEQ) air quality monitors in Floresville, a small town southeast of San Antonio and just north of the shale area; and Karnes city, a midsize rural city in the center of the shale. Our own measurements were carried out at a private ranch in rural Dimmit County in southern Texas from April to November of 2015. Air quality monitor data from the TCEQ were selected for the same time period. Non-negative matrix factorization in R (package NMF) was used to determine likely sources and their contributions above background. While the TCEQ monitor data consisted mostly of hydrocarbons, our own data include both CO, CO2, O3, and NOx. We find that rural Dimmit County hydrocarbons are dominated by oil and gas development sources, while central shale hydrocarbons at the TCEQ monitoring sites have a mix of sources including car traffic. However, oil and gas sources also dominate hydrocarbons at Floresville and Karnes City. Toxic benzene is nearly exclusively due to oil and gas development sources, including flaring, which NMF identifies as a major hydrocarbon source in Karnes City. Other major sources include emissions of light weight alkanes (C2-C5) from raw natural gas emissions and a larger set of alkanes (C2-C10) from oil sources, including liquid storage tanks.

Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA)

NASA Astrophysics Data System (ADS)

Zhang, Chengye; Qin, Qiming; Zhang, Tianyuan; Sun, Yuanheng; Chen, Chao

2017-04-01

This study proposed a novel method to extract endmembers from hyperspectral image based on discrete firefly algorithm (EE-DFA). Endmembers are the input of many spectral unmixing algorithms. Hence, in this paper, endmember extraction from hyperspectral image is regarded as a combinational optimization problem to get best spectral unmixing results, which can be solved by the discrete firefly algorithm. Two series of experiments were conducted on the synthetic hyperspectral datasets with different SNR and the AVIRIS Cuprite dataset, respectively. The experimental results were compared with the endmembers extracted by four popular methods: the sequential maximum angle convex cone (SMACC), N-FINDR, Vertex Component Analysis (VCA), and Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF). What's more, the effect of the parameters in the proposed method was tested on both synthetic hyperspectral datasets and AVIRIS Cuprite dataset, and the recommended parameters setting was proposed. The results in this study demonstrated that the proposed EE-DFA method showed better performance than the existing popular methods. Moreover, EE-DFA is robust under different SNR conditions.

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

PubMed

Donnelly-Kehoe, Patricio Andres; Pascariello, Guido Orlando; Gómez, Juan Carlos

2018-05-15

We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features. Copyright © 2017 Elsevier B.V. All rights reserved.

Characterization of the non-metal fraction of the processed waste printed circuit boards.

PubMed

Kumar, Amit; Holuszko, Maria E; Janke, Travis

2018-05-01

Electronic waste is one the fastest growing waste streams in the world and waste printed circuit boards (PCB) are the most valuable part of this stream due to the presence of gold, silver, copper, and palladium. The metal present in PCBs is mostly recovered for the market value whereas the nonmetal fractions are often ignored. This research explored the characteristics of the non-metal fraction (NMF) obtained after the processing of milled waste PCBs with a focus on responsible end-of-life solutions, in the form of non-hazardous landfilling or incineration. The NMF was characterized using sizing, assaying, loss on ignition, calorific value measurement, and thermogravimetric analysis (TGA). The result showed that the metal content in the NMF increased with decrease in the particle size for most of the metals except antimony and the result from loss on ignition (LOI) also showed that over 50% of the coarser fraction represented organic matter compared to less than 30% for the finest fraction. The study also showed that after the recovery of metals from the waste PCBs, landfill leaching for most of the metal is reduced below the environmental limits, with lead being the only exception. The lead leachate concentration of 18 mg/L was observed, which requires further treatment prior to landfilling. With an energy value of 16 GJ/t, the NMF could provide high energy recovery if incinerated but 194 mg/kg of hazardous flame retardants present in the NMF might be released if the combustion process is not closely monitored. Copyright © 2018 Elsevier Ltd. All rights reserved.

FINAL REPORT (MILESTONE DATE 9/30/11) FOR SUBCONTRACT NO. B594099 NUMERICAL METHODS FOR LARGE-SCALE DATA FACTORIZATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Sterck, H

2011-10-18

The following work has been performed by PI Hans De Sterck and graduate student Manda Winlaw for the required tasks 1-5 (as listed in the Statement of Work). Graduate student Manda Winlaw has visited LLNL January 31-March 11, 2011 and May 23-August 19, 2010, working with Van Henson and Mike O'Hara on non-negative matrix factorizations (NMF). She has investigated the dense subgraph clustering algorithm from 'Finding Dense Subgraphs for Sparse Undirected, Directed, and Bipartite Graphs' by Chen and Saad, testing this method on several term-document matrices and adapting it to cluster based on the rank of the subgraphs instead ofmore » the density. Manda Winlaw was awarded a first prize in the annual LLNL summer student poster competition for a poster on her NMF research. PI Hans De Sterck has developed a new adaptive algebraic multigrid algorithm for computing a few dominant or minimal singular triplets of sparse rectangular matrices. This work builds on adaptive algebraic multigrid methods that were further developed by the PI and collaborators (including Sanders and Henson) for Markov chains. The method also combines and extends existing multigrid algorithms for the symmetric eigenproblem. The PI has visited LLNL February 22-25, 2011, and has given a CASC seminar 'Algebraic Multigrid for the Singular Value Problem' on this work on February 23, 2011. During his visit, he has discussed this work and related topics with Van Henson, Geoffrey Sanders, Panayot Vassilevski, and others. He has tested the algorithm on PDE matrices and on a term-document matrix, with promising initial results. Manda Winlaw has also started to work, with O'Hara, on estimating probability distributions over undirected graph edges. The goal is to estimate probabilistic models from sets of undirected graph edges for the purpose of prediction, anomaly detection and support to supervised learning. Graduate student Manda Winlaw is writing a paper on the results obtained with O'Hara which will be submitted some time later in 2011 to a data mining conference. PI Hans De Sterck has developed a new optimization algorithm for canonical tensor approximation, formulating an extension of the nonlinear GMRES method to optimization problems. Numerical results for tensors with up to 8 modes show that this new method is efficient for sparse and dense tensors. He has written a paper on this which has been submitted to the SIAM Journal on Scientific Computing. PI Hans De Sterck has further developed his new optimization algorithm for canonical tensor approximation, formulating an extension in terms of steepest-descent preconditioning, which makes the approach generally applicable for nonlinear optimization. He has written a paper on this extension which has been submitted to Numerical Linear Algebra with Applications.« less

Specificity of gap junction communication among human mammary cells and connexin transfectants in culture

PubMed Central

1993-01-01

In a previous paper (Lee et al., 1992), it was shown that normal human mammary epithelial cells (NMEC) express two connexin genes, Cx26 and Cx43, whereas neither gene is transcribed in a series of mammary tumor cell lines (TMEC). In this paper it is shown that normal human mammary fibroblasts (NMF) communicate and express Cx43 mRNA and protein. Transfection of either Cx26 or Cx43 genes into a tumor line, 21MT-2, induced the expression of the corresponding mRNAs and proteins as well as communication via gap junctions (GJs), although immunofluorescence demonstrated that the majority of Cx26 and Cx43 proteins present in transfected TMEC was largely cytoplasmic. Immunoblotting demonstrated that NMEC, NMF, and transfected TMEC each displayed a unique pattern of posttranslationally modified forms of Cx43 protein. The role of different connexins in regulating gap junction intercellular communication (GJIC) was examined using a novel two-dye method to assess homologous and heterologous communication quantitatively. The recipient cell population was prestained with a permanent non-toxic lipophilic dye that binds to membranes irreversibly (PKH26, Zynaxis); and the donor population is treated with a GJ-permeable dye Calcein, a derivative of fluorescein diacetate (Molecular Probes). After mixing the two cell populations under conditions promoting GJ formation, cells were analyzed by flow cytometry to determine the percentage of cells containing both dyes. It is shown here that Cx26 and Cx43 transfectants display strong homologous communication, as do NMEC and NMF. Furthermore, NMEC mixed with NMF communicate efficiently, Cx26 transfectants communicate with NMEC but not with NMF, and Cx43 transfectants communicate with NMF. Communication between Cx26 TMEC transfectants and NMEC was asymetrical with preferential movement of calcein from TMEC to NMEC. Despite the presence of Cx43 as well as Cx26 encoded proteins in the GJs of NMEC, few Cx43 transfectants communicated with NMEC. No heterologous GJIC was observed between Cx26- and Cx43-transfected TMEC suggesting that heterotypic GJs do not form or that Cx26/Cx43 channels do not permit dye transfer. PMID:8391000

Variation of hmF2 and NmF2 deduced from DPS-4 over Multan (Pakistan) and their comparisons with IRI-2012 & IRI-2016 during the deep solar minimum between cycles 23 & 24

NASA Astrophysics Data System (ADS)

Ameen, Muhammad Ayyaz; Khursheed, Haqqa; Jabbar, Mehak Abdul; Ali, Muneeza Salman; Chishtie, Farrukh

2018-04-01

We report the results of ionospheric measurements from DPS-4 installed at Multan (Geog coord. 30.18°N, 71.48°E, dip 47.4°). The variations in F2-layer maximum electron density NmF2 and its peak height hmF2 are studied during the deep solar minimum between cycles 23 & 24 i.e 2008-2009 with comparisons conducted with the International Reference Ionosphere (IRI) versions 2012 & 2016. We find that the hmF2 observations peak around the pre-sunrise and sunrise hours depending on the month. Seasonally, the daytime variation of NmF2 is higher in the Equinox and Summer, while daytime hmF2 are slightly higher in the Equinox and Winter. High values of hmF2 around midnight are caused by an increase of upward drifts produced by meridional winds. The ionosphere over Multan, which lies at the verge of low and mid latitude, is affected by both E × B drifts and thermospheric winds as evident from mid-night peaks and near-sunrise dips in hmF2. The results of the comparison of the observed NmF2 and hmF2 for the year 2008-2009 with the IRI-2012 (both NmF2 and hmF2) and IRI-2016 (only hmF2) estimates indicate that for NmF2, IRI-2012 with Consultative Committee International Radio (CCIR) option produces values in better agreement with observed data. Whereas, for hmF2, IRI-2016 with both International Union of Radio Science (URSI) and CCIR SHU-2015 options, predicts well for nighttime hours throughout the year. However, the IRI-2012 with CCIR option produces better agreement with data during daytime hours. Furthermore, IRI-2012 with CCIR option gives better results during Equinox months, whereas, IRI-2016 with both URSI and CCIR SHU-2015 options predict well for Winter and Summer.

Pure endmember extraction using robust kernel archetypoid analysis for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Yang, Gang; Wu, Ke; Li, Weiyue; Zhang, Dianfa

2017-09-01

A robust kernel archetypoid analysis (RKADA) method is proposed to extract pure endmembers from hyperspectral imagery (HSI). The RKADA assumes that each pixel is a sparse linear mixture of all endmembers and each endmember corresponds to a real pixel in the image scene. First, it improves the re8gular archetypal analysis with a new binary sparse constraint, and the adoption of the kernel function constructs the principal convex hull in an infinite Hilbert space and enlarges the divergences between pairwise pixels. Second, the RKADA transfers the pure endmember extraction problem into an optimization problem by minimizing residual errors with the Huber loss function. The Huber loss function reduces the effects from big noises and outliers in the convergence procedure of RKADA and enhances the robustness of the optimization function. Third, the random kernel sinks for fast kernel matrix approximation and the two-stage algorithm for optimizing initial pure endmembers are utilized to improve its computational efficiency in realistic implementations of RKADA, respectively. The optimization equation of RKADA is solved by using the block coordinate descend scheme and the desired pure endmembers are finally obtained. Six state-of-the-art pure endmember extraction methods are employed to make comparisons with the RKADA on both synthetic and real Cuprite HSI datasets, including three geometrical algorithms vertex component analysis (VCA), alternative volume maximization (AVMAX) and orthogonal subspace projection (OSP), and three matrix factorization algorithms the preconditioning for successive projection algorithm (PreSPA), hierarchical clustering based on rank-two nonnegative matrix factorization (H2NMF) and self-dictionary multiple measurement vector (SDMMV). Experimental results show that the RKADA outperforms all the six methods in terms of spectral angle distance (SAD) and root-mean-square-error (RMSE). Moreover, the RKADA has short computational times in offline operations and shows significant improvement in identifying pure endmembers for ground objects with smaller spectrum differences. Therefore, the RKADA could be an alternative for pure endmember extraction from hyperspectral images.

Preferential solvation of Brooker's merocyanine in binary solvent mixtures composed of formamides and hydroxylic solvents.

PubMed

Bevilaqua, Tharly; da Silva, Domingas C; Machado, Vanderlei G

2004-03-01

The ET polarity values of 4-[(1-methyl-4(1H)-pyridinylidene)-ethylidene]-2,5-cyclohexadien-1-one (Brooker's merocyanine) were collected in mixed-solvent systems comprising a formamide [N,N-dimethylformamide (DMF), N-methylformamide (NMF) or formamide (FA)] and a hydroxylic (water, methanol, ethanol, propan-2-ol or butan-1-ol) solvent. Binary mixtures involving DMF and the other formamides (NMF and FA) as well as NMF and FA were also studied. These data were employed in the investigation of the preferential solvation (PS) of the probe. Each solvent system was analyzed in terms of both solute-solvent and solvent-solvent interactions. These latter interactions were responsible for the synergism observed in many binary mixtures. This synergistic behaviour was observed for DMF-propan-2-ol, DMF-butan-1-ol, FA-methanol, FA-ethanol and for the mixtures of the alcohols with NMF. All data were successfully fitted to a model based on solvent-exchange equilibria, which allowed the separation of the different contributions of the solvent species in the solvation shell of the dye. The results suggest that both hydrogen bonding and solvophobic interactions contribute to the formation of the solvent complexes responsible for the observed synergistic effects in the PS of the dye.

Efficacy of a Cream Containing Ceramides and Magnesium in the Treatment of Mild to Moderate Atopic Dermatitis: A Randomized, Double-blind, Emollient- and Hydrocortisone-controlled Trial.

PubMed

Koppes, Sjors A; Charles, Frank; Lammers, Laureen; Frings-Dresen, Monique; Kezic, Sanja; Rustemeyer, Thomas

2016-11-02

The aim of this randomized controlled trial was to assess the efficacy of a cream containing ceramides and magnesium (Cer-Mg) in the treatment of mild to moderate atopic dermatitis and to compare it with hydrocortisone and a commonly used emollient (unguentum leniens; cold cream). A total of 100 patients, randomized into 2 groups, were treated for 6 weeks simultaneously (left vs. right side of the body) with either Cer-Mg and hydrocortisone (group I) or Cer-Mg and emollient (group II). The primary outcome was a reduction in severity of lesions as assessed by (local) SCORAD (SCORing Atopic Dermatitis). Levels of trans-epidermal water loss (TEWL), skin hydration, and natural moisturizing factors (NMF) were then measured. After 6 weeks, group I showed comparable significant improvement in SCORAD and TEWL, while in group II, the decrease in SCORAD and TEWL was significantly greater after Cer-Mg compared with emollient. Finally, Cer-Mg cream was more effective in improving skin hydration and maintenance of levels of NMF than hydrocortisone and emollient.

A novel mouse model of Niemann-Pick type C disease carrying a D1005G-Npc1 mutation comparable to commonly observed human mutations.

PubMed

Maue, Robert A; Burgess, Robert W; Wang, Bing; Wooley, Christine M; Seburn, Kevin L; Vanier, Marie T; Rogers, Maximillian A; Chang, Catherine C; Chang, Ta-Yuan; Harris, Brent T; Graber, David J; Penatti, Carlos A A; Porter, Donna M; Szwergold, Benjamin S; Henderson, Leslie P; Totenhagen, John W; Trouard, Theodore P; Borbon, Ivan A; Erickson, Robert P

2012-02-15

We have identified a point mutation in Npc1 that creates a novel mouse model (Npc1(nmf164)) of Niemann-Pick type C1 (NPC) disease: a single nucleotide change (A to G at cDNA bp 3163) that results in an aspartate to glycine change at position 1005 (D1005G). This change is in the cysteine-rich luminal loop of the NPC1 protein and is highly similar to commonly occurring human mutations. Genetic and molecular biological analyses, including sequencing the Npc1(spm) allele and identifying a truncating mutation, confirm that the mutation in Npc1(nmf164) mice is distinct from those in other existing mouse models of NPC disease (Npc1(nih), Npc1(spm)). Analyses of lifespan, body and spleen weight, gait and other motor activities, as well as acoustic startle responses all reveal a more slowly developing phenotype in Npc1(nmf164) mutant mice than in mice with the null mutations (Npc1(nih), Npc1(spm)). Although Npc1 mRNA levels appear relatively normal, Npc1(nmf164) brain and liver display dramatic reductions in Npc1 protein, as well as abnormal cholesterol metabolism and altered glycolipid expression. Furthermore, histological analyses of liver, spleen, hippocampus, cortex and cerebellum reveal abnormal cholesterol accumulation, glial activation and Purkinje cell loss at a slower rate than in the Npc1(nih) mouse model. Magnetic resonance imaging studies also reveal significantly less demyelination/dysmyelination than in the null alleles. Thus, although prior mouse models may correspond to the severe infantile onset forms of NPC disease, Npc1(nmf164) mice offer many advantages as a model for the late-onset, more slowly progressing forms of NPC disease that comprise the large majority of human cases.

A novel mouse model of Niemann–Pick type C disease carrying a D1005G-Npc1 mutation comparable to commonly observed human mutations

PubMed Central

Maue, Robert A.; Burgess, Robert W.; Wang, Bing; Wooley, Christine M.; Seburn, Kevin L.; Vanier, Marie T.; Rogers, Maximillian A.; Chang, Catherine C.; Chang, Ta-Yuan; Harris, Brent T.; Graber, David J.; Penatti, Carlos A.A.; Porter, Donna M.; Szwergold, Benjamin S.; Henderson, Leslie P.; Totenhagen, John W.; Trouard, Theodore P.; Borbon, Ivan A.; Erickson, Robert P.

2012-01-01

We have identified a point mutation in Npc1 that creates a novel mouse model (Npc1nmf164) of Niemann–Pick type C1 (NPC) disease: a single nucleotide change (A to G at cDNA bp 3163) that results in an aspartate to glycine change at position 1005 (D1005G). This change is in the cysteine-rich luminal loop of the NPC1 protein and is highly similar to commonly occurring human mutations. Genetic and molecular biological analyses, including sequencing the Npc1spm allele and identifying a truncating mutation, confirm that the mutation in Npc1nmf164 mice is distinct from those in other existing mouse models of NPC disease (Npc1nih, Npc1spm). Analyses of lifespan, body and spleen weight, gait and other motor activities, as well as acoustic startle responses all reveal a more slowly developing phenotype in Npc1nmf164 mutant mice than in mice with the null mutations (Npc1nih, Npc1spm). Although Npc1 mRNA levels appear relatively normal, Npc1nmf164 brain and liver display dramatic reductions in Npc1 protein, as well as abnormal cholesterol metabolism and altered glycolipid expression. Furthermore, histological analyses of liver, spleen, hippocampus, cortex and cerebellum reveal abnormal cholesterol accumulation, glial activation and Purkinje cell loss at a slower rate than in the Npc1nih mouse model. Magnetic resonance imaging studies also reveal significantly less demyelination/dysmyelination than in the null alleles. Thus, although prior mouse models may correspond to the severe infantile onset forms of NPC disease, Npc1nmf164 mice offer many advantages as a model for the late-onset, more slowly progressing forms of NPC disease that comprise the large majority of human cases. PMID:22048958

Inferring Aggregated Functional Traits from Metagenomic Data Using Constrained Non-negative Matrix Factorization: Application to Fiber Degradation in the Human Gut Microbiota.

PubMed

Raguideau, Sébastien; Plancade, Sandra; Pons, Nicolas; Leclerc, Marion; Laroche, Béatrice

2016-12-01

Whole Genome Shotgun (WGS) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems, both from the taxonomic and functional point of view. Gene inventories of otherwise uncultured microbial communities make the direct functional profiling of microbial communities possible. The concept of community aggregated trait has been adapted from environmental and plant functional ecology to the framework of microbial ecology. Community aggregated traits are quantified from WGS data by computing the abundance of relevant marker genes. They can be used to study key processes at the ecosystem level and correlate environmental factors and ecosystem functions. In this paper we propose a novel model based approach to infer combinations of aggregated traits characterizing specific ecosystemic metabolic processes. We formulate a model of these Combined Aggregated Functional Traits (CAFTs) accounting for a hierarchical structure of genes, which are associated on microbial genomes, further linked at the ecosystem level by complex co-occurrences or interactions. The model is completed with constraints specifically designed to exploit available genomic information, in order to favor biologically relevant CAFTs. The CAFTs structure, as well as their intensity in the ecosystem, is obtained by solving a constrained Non-negative Matrix Factorization (NMF) problem. We developed a multicriteria selection procedure for the number of CAFTs. We illustrated our method on the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota. We used 1408 samples of gene abundances from several high-throughput sequencing projects and found that four CAFTs only were needed to represent the fiber degradation potential. This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data. Our method is generic and can be applied to other metabolic processes in the gut or in other ecosystems.

Specific Changes of Exocarp and Mesocarp Occurring during Softening Differently Affect Firmness in Melting (MF) and Non Melting Flesh (NMF) Fruits

PubMed Central

Onelli, E.; Ghiani, A.; Gentili, R.; Serra, S.; Musacchi, S.; Citterio, S.

2015-01-01

Melting (MF) and non melting flesh (NMF) peaches differ in their final texture and firmness. Their specific characteristics are achieved by softening process and directly dictate fruit shelf life and quality. Softening is influenced by various mechanisms including cell wall reorganization and water loss. In this work, the biomechanical properties of MF Spring Crest’s and NMF Oro A’s exocarp and mesocarp along with the amount and localization of hydroxycinnamic acids and flavonoids were investigated during fruit ripening and post-harvest. The objective was to better understand the role played by water loss and cell wall reorganization in peach softening. Results showed that in ripe Spring Crest, where both cell turgor loss and cell wall dismantling occurred, mesocarp had a little role in the fruit reaction to compression and probe penetration response was almost exclusively ascribed to the epidermis which functioned as a mechanical support to the pulp. In ripe Oro A’s fruit, where cell wall disassembly did not occur and the loss of cell turgor was observed only in mesocarp, the contribution of exocarp to fruit firmness was consistent but relatively lower than that of mesocarp, suggesting that in addition to cell turgor, the integrity of cell wall played a key role in maintaining NMF fruit firmness. The analysis of phenols suggested that permeability and firmness of epidermis were associated with the presence of flavonoids and hydroxycinnamic acids. PMID:26709823

Annual and semiannual variations in the ionospheric F2-layer: II. Physical discussion

NASA Astrophysics Data System (ADS)

Rishbeth, H.; Müller-Wodarg, I. C. F.; Zou, L.; Fuller-Rowell, T. J.; Millward, G. H.; Moffett, R. J.; Idenden, D. W.; Aylward, A. D.

2000-08-01

The companion paper by Zou et al. shows that the annual and semiannual variations in the peak F2-layer electron density (NmF2) at midlatitudes can be reproduced by a coupled thermosphere-ionosphere computational model (CTIP), without recourse to external influences such as the solar wind, or waves and tides originating in the lower atmosphere. The present work discusses the physics in greater detail. It shows that noon NmF2 is closely related to the ambient atomic/molecular concentration ratio, and suggests that the variations of NmF2 with geographic and magnetic longitude are largely due to the geometry of the auroral ovals. It also concludes that electric fields play no important part in the dynamics of the midlatitude thermosphere. Our modelling leads to the following picture of the global three-dimensional thermospheric circulation which, as envisaged by Duncan, is the key to explaining the F2-layer variations. At solstice, the almost continuous solar input at high summer latitudes drives a prevailing summer-to-winter wind, with upwelling at low latitudes and throughout most of the summer hemisphere, and a zone of downwelling in the winter hemisphere, just equatorward of the auroral oval. These motions affect thermospheric composition more than do the alternating day/night (up-and-down) motions at equinox. As a result, the thermosphere as a whole is more molecular at solstice than at equinox. Taken in conjunction with the well-known relation of F2-layer electron density to the atomic/molecular ratio in the neutral air, this explains the F2-layer semiannual effect in NmF2 that prevails at low and middle latitudes. At higher midlatitudes, the seasonal behaviour depends on the geographic latitude of the winter downwelling zone, though the effect of the composition changes is modified by the large solar zenith angle at midwinter. The zenith angle effect is especially important in longitudes far from the magnetic poles. Here, the downwelling occurs at high geographic latitudes, where the zenith angle effect becomes overwhelming and causes a midwinter depression of electron density, despite the enhanced atomic/molecular ratio. This leads to a semiannual variation of NmF2. A different situation exists in winter at longitudes near the magnetic poles, where the downwelling occurs at relatively low geographic latitudes so that solar radiation is strong enough to produce large values of NmF2. This circulation-driven mechanism provides a reasonably complete explanation of the observed pattern of F2 layer annual and semiannual quiet-day variations.

Hydrogen bonding donation of N-methylformamide with dimethylsulfoxide and water

NASA Astrophysics Data System (ADS)

Borges, Alexandre; Cordeiro, João M. M.

2013-04-01

20% N-methylformamide (NMF) mixtures with water and with dimethylsulfoxide (DMSO) have been studied. A comparison between the hydrogen bonding (H-bond) donation of N-methylformamide with both solvents in the mixtures is presented. Results of radial distribution functions, pair distribution energies, molecular dipole moment correlation, and geometry of the H-bonded species in each case are shown. The results indicate that the NMF - solvent H-bond is significantly stronger with DMSO than with water. The solvation shell is best organized in the DMSO mixture than in the aqueous one.

Elastomeric Cellular Structure Enhanced by Compressible Liquid Filler

NASA Astrophysics Data System (ADS)

Sun, Yueting; Xu, Xiaoqing; Xu, Chengliang; Qiao, Yu; Li, Yibing

2016-05-01

Elastomeric cellular structures provide a promising solution for energy absorption. Their flexible and resilient nature is particularly relevant to protection of human bodies. Herein we develop an elastomeric cellular structure filled with nanoporous material functionalized (NMF) liquid. Due to the nanoscale infiltration in NMF liquid and its interaction with cell walls, the cellular structure has a much enhanced mechanical performance, in terms of loading capacity and energy absorption density. Moreover, it is validated that the structure is highly compressible and self-restoring. Its hyper-viscoelastic characteristics are elucidated.

Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.

PubMed

Yu, Hui; Mao, Kui-Tao; Shi, Jian-Yu; Huang, Hua; Chen, Zhi; Dong, Kai; Yiu, Siu-Ming

2018-04-11

Drug-drug interactions (DDIs) always cause unexpected and even adverse drug reactions. It is important to identify DDIs before drugs are used in the market. However, preclinical identification of DDIs requires much money and time. Computational approaches have exhibited their abilities to predict potential DDIs on a large scale by utilizing pre-market drug properties (e.g. chemical structure). Nevertheless, none of them can predict two comprehensive types of DDIs, including enhancive and degressive DDIs, which increases and decreases the behaviors of the interacting drugs respectively. There is a lack of systematic analysis on the structural relationship among known DDIs. Revealing such a relationship is very important, because it is able to help understand how DDIs occur. Both the prediction of comprehensive DDIs and the discovery of structural relationship among them play an important guidance when making a co-prescription. In this work, treating a set of comprehensive DDIs as a signed network, we design a novel model (DDINMF) for the prediction of enhancive and degressive DDIs based on semi-nonnegative matrix factorization. Inspiringly, DDINMF achieves the conventional DDI prediction (AUROC = 0.872 and AUPR = 0.605) and the comprehensive DDI prediction (AUROC = 0.796 and AUPR = 0.579). Compared with two state-of-the-art approaches, DDINMF shows it superiority. Finally, representing DDIs as a binary network and a signed network respectively, an analysis based on NMF reveals crucial knowledge hidden among DDIs. Our approach is able to predict not only conventional binary DDIs but also comprehensive DDIs. More importantly, it reveals several key points about the DDI network: (1) both binary and signed networks show fairly clear clusters, in which both drug degree and the difference between positive degree and negative degree show significant distribution; (2) the drugs having large degrees tend to have a larger difference between positive degree and negative degree; (3) though the binary DDI network contains no information about enhancive and degressive DDIs at all, it implies some of their relationship in the comprehensive DDI matrix; (4) the occurrence of signs indicating enhancive and degressive DDIs is not random because the comprehensive DDI network is equipped with a structural balance.

The quiet and disturbed time performance of the IRI 2012 within 90°-130°E longitude sector during solar cycle 24

NASA Astrophysics Data System (ADS)

Bhuyan, Pradip; Yokoyama, Tatsuhiro; Supnithi, Pornchai; Kalita, Bitap Raj; Wang, Kehe; Komolmis, Tharadol; Yatini, Clara

2016-07-01

The performance of the IRI 2012 model is examined for the double peaked solar cycle 24 in the low latitude region of 90-130oE longitude in the context of the global longitudinal wave number four structure (WN4). The monthly mean values of the foF2 and the hmF2(if available) measurements at low and low mid-latitude stations Dibrugarh (27.5°N, 95°E), Hainan (19.2°N,109.7°E),Okinawa (26.5°N,128°E) and Cocos Island (12.2°S,96.8°E) during quiet times and Dibrugarh (27.5°N, 95°E), Chiang Mai (18.76°N,98.93°E), Chumphon (10.72°N,99.37°E), Kototabang (0.2°S,100.32°E) and Cocos Island (12.2°S,96.8°E ) during the disturbed days of a severe geomagnetic storm are investigated. These stations are located under the strongest peak of the longitudinal WN4 structure in NmF2 along 90-130°E longitudes. The IRI is quite successful in predicting the seasonal averages of NmF2 over this region except in the equinox afternoon period where IRI underestimates the NmF2 in low latitudes. When the monthly mean measured data is compared with IRI, the difference between the IRI model predictions and the measurements are found to follow a systematic pattern. The IRI-2012 with CCIR options slightly underestimates foF2 over Dibrugarh in day time and overestimates in the night time. The amount of underestimation varies from month to month and also depends on the solar activity levels. The IRI also underestimated the day time hmF2 and overestimated the night time hmF2 over Dibrugarh. In case of Hainan, the IRI overestimates the NmF2 in the equinox months and generally in the afternoon to post sunset period. The model values are closer in the solstice than in the equinox. In Okinawa, the trend reverses and the IRI overestimates the NmF2 in the day time and underestimates in the night time. The IRI overestimated the day time hmF2 and underestimated the night time hmF2 over Okinawa. In case of Cocos Island which lies almost on the EIA anomaly region of the southern hemisphere, IRI underestimates the peak day time NmF2 in most months. The measurements are closer to the model values in the forenoon period and in the low solar activity period. The striking feature across all the stations is the IRI underestimation of the post sunset enhancement of NmF2 in low latitudes. During the severe geomagnetic storm of 17-18 March, 2015, the IRI was not able to replicate the inhibition of EIA on the next day i.e. 18 March as observed in the 100°E longitude.

3-DIMENSIONAL Optoelectronic

NASA Astrophysics Data System (ADS)

Krishnamoorthy, Ashok Venketaraman

This thesis covers the design, analysis, optimization, and implementation of optoelectronic (N,M,F) networks. (N,M,F) networks are generic space-division networks that are well suited to implementation using optoelectronic integrated circuits and free-space optical interconnects. An (N,M,F) networks consists of N input channels each having a fanout F_{rm o}, M output channels each having a fanin F_{rm i}, and Log_{rm K}(N/F) stages of K x K switches. The functionality of the fanout, switching, and fanin stages depends on the specific application. Three applications of optoelectronic (N,M,F) networks are considered. The first is an optoelectronic (N,1,1) content -addressable memory system that achieves associative recall on two-dimensional images retrieved from a parallel-access optical memory. The design and simulation of the associative memory are discussed, and an experimental emulation of a prototype system using images from a parallel-readout optical disk is presented. The system design provides superior performance to existing electronic content-addressable memory chips in terms of capacity and search rate, and uses readily available optical disk and VLSI technologies. Next, a scalable optoelectronic (N,M,F) neural network that uses free-space holographic optical interconnects is presented. The neural architecture minimizes the number of optical transmitters needed, and provides accurate electronic fanin with low signal skew, and dendritic-type fan-in processing capability in a compact layout. Optimal data-encoding methods and circuit techniques are discussed. The implementation of an prototype optoelectronic neural system, and its application to a simple recognition task is demonstrated. Finally, the design, analysis, and optimization of a (N,N,F) self-routing, packet-switched multistage interconnection network is described. The network is suitable for parallel computing and broadband switching applications. The tradeoff between optical and electronic interconnects is examined quantitatively by varying the electronic switch size K. The performance of the (N,N,F) network versus the fanning parameter F, is also analyzed. It is shown that the optoelectronic (N,N,F) networks provide a range of performance-cost alternatives, and offer superior performance-per-cost to fully electronic switching networks and to previous networks designs.

Impact of neuromuscular fatigue on match exercise intensity and performance in elite Australian football.

PubMed

Mooney, Mitchell G; Cormack, Stuart; Oʼbrien, Brendan J; Morgan, William M; McGuigan, Mike

2013-01-01

This study aimed to quantify the influence of neuromuscular fatigue (NMF) via flight time to contraction time ratio (FT:CT) obtained from a countermovement jump (CMJ) on the relationships between yo-yo intermittent recovery (level 2) test (yo-yo IR2), match exercise intensity (high-intensity running [HIR] m·min(-1) and Load·min(-1)) and Australian football (AF) performance. Thirty-seven data sets were collected from 17 different players across 22 elite AF matches. Each data set comprised an athlete's yo-yo IR2 score before the start of the season, match exercise intensity via global positioning system and on-field performance rated by coaches' votes and number of ball disposals. Each data set was categorized as normal (>92% baseline FT:CT, n = 20) or fatigued (<92% baseline FT:CT, n = 17) from a single CMJ performed 96 hours after the previous match. Moderation-mediation analysis was completed with yo-yo IR2 (independent variable), match exercise intensity (mediator), and AF performance (dependent variable) with NMF status as the conditional variable. Isolated interactions between variables were analyzed by Pearson's correlation and effect size statistics. The Yo-yo IR2 score showed an indirect influence on the number of ball disposals via HIR m·min(-1) regardless of NMF status (normal FT:CT indirect effect = 0.019, p < 0.1, reduced FT:CT indirect effect = 0.022, p < 0.1). However, the yo-yo IR2 score only influenced coaches' votes via Load·min(-1) in the nonfatigued state (normal: FT:CT indirect effect = 0.007, p <0.1, reduced: FT:CT indirect effect = -0.001, p > 0.1). In isolation, NMF status also reduces relationships between yo-yo IR2 and load·min(-1), yo-yo IR2 and coaches votes, Load·min(-1) and coaches' votes (Δr > 0.1). Routinely testing yo-yo IR2 capacity, NMF via FT:CT and monitoring Load·min(-1) in conjunction with HIR m·min(-1) as exercise intensity measures in elite AF is recommended.

NmF2 Morphology during four-classes of solar and magnetic activity conditions at an African station around the EIA trough and comparison with IRI-2016 Map

NASA Astrophysics Data System (ADS)

Adebesin, B.; Rabiu, B.; Obrou, O. K.

2017-12-01

Better understanding of the electrodynamics between parameters used in describing the ionospheric layer and their solar and geomagnetic influences goes a long way in furthering the expansion of space weather knowledge. Telecommunication and scientific radar launch activities can however be interrupted either on a larger/smaller scales by geomagnetic activities which is susceptible to changes in solar activity and effects. Consequently, the ionospheric NmF2 electrodynamics was investigated for a station near the magnetic dip in the African sector (Korhogo, Geomagnetic: -1.26°N, 67.38°E). Data covering years 1996 and 2000 were investigated for four categories of magnetic and solar activities viz (i) F10.7 < 85 sfu, ap ≤ 7 nT (low solar quiet, LSQ); (ii) F10.7 < 85 sfu, ap > 7 nT (low solar disturbed, LSD); (iii) F10.7 > 150 sfu, ap ≤ 7 nT (high solar quiet, HSQ); and (iv) F10.7 > 150 sfu, ap > 7 nT (high solar disturbed, HSD). NmF2 revealed a pre-noon peak higher than the post-noon peak during high solar activity irrespective of magnetic activity condition and overturned during low solar activity. Higher NmF2 peak amplitude however characterise disturbed magnetic activity than quiet magnetic condition for any solar activity. The maximum pre-/post-noon peaks appeared in equinox season. June solstice noon-time bite out lagged other seasons by 1-2 h. Daytime variability increases with increasing magnetic activity. Equinox/June solstice recorded the highest pre-sunrise/post-sunset peak variability magnitudes with the lowest emerging in June solstice/equinox for all solar and magnetic conditions. The nighttime annual variability amplitude is higher during disturbed than quiet condition regardless of solar activity period; while the range is similar for daytime observations. The noon-time trough characteristics is not significant in the IRI NmF2 pattern during high solar activity but evident during low solar conditions. IRI-2016 map performed best during disturbed activity conditions especially for F10.7 < 85 sfu, ap > 7 nT condition.

Barrier function and natural moisturizing factor levels after cumulative exposure to a fruit-derived organic acid and a detergent: different outcomes in atopic and healthy skin and relevance for occupational contact dermatitis in the food industry.

PubMed

Angelova-Fischer, Irena; Hoek, Anne-Karin; Dapic, Irena; Jakasa, Ivone; Kezic, Sanja; Fischer, Tobias W; Zillikens, Detlef

2015-12-01

Fruit-derived organic compounds and detergents are relevant exposure factors for occupational contact dermatitis in the food industry. Although individuals with atopic dermatitis (AD) are at risk for development of occupational contact dermatitis, there have been no controlled studies on the effects of repeated exposure to multiple irritants, relevant for the food industry, in atopic skin. The aim of the study was to investigate the outcomes of repeated exposure to a fruit-derived organic acid and a detergent in AD compared to healthy volunteers. The volunteers were exposed to 2.0% acetic acid (AcA) and/or 0.5% sodium lauryl sulfate (SLS) in controlled tandem repeated irritation test. The outcomes were assessed by measurements of erythema, transepidermal water loss (TEWL) and natural moisturizing factor (NMF) levels. In the AD volunteers, repeated AcA exposure led to barrier disruption and significant TEWL increase; no significant differences after the same exposure in the healthy controls were found. Repeated exposure to SLS and the irritant tandems enhanced the reactions and resulted in a significantly higher increase in TEWL in the AD compared to the control group. Cumulative irritant exposure reduced the NMF levels in both groups. Differences in the severity of irritant-induced barrier impairment in atopic individuals contribute to the risk for occupational contact dermatitis in result of multiple exposures to food-derived irritants and detergents. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Detection of goal events in soccer videos

NASA Astrophysics Data System (ADS)

Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

2005-01-01

In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

Statistical behavior of the longitudinal variations of daytime electron density in the topside ionosphere at middle latitudes

NASA Astrophysics Data System (ADS)

Su, Fanfan; Wang, Wenbin; Burns, Alan G.; Yue, Xinan; Zhu, Fuying; Lin, Jian

2016-11-01

Electron density in the topside ionosphere has significant variations with latitude, longitude, altitude, local time, season, and solar cycle. This paper focuses on the global and seasonal features of longitudinal structures of daytime topside electron density (Ne) at middle latitudes and their possible causes. We used in situ Ne measured by DEMETER and F2 layer peak height (hmF2) and peak density (NmF2) from COSMIC. The longitudinal variations of the daytime topside Ne show a wave number 2-type structure in the Northern Hemisphere, whereas those in the Southern Hemisphere are dominated by a wave number 1 structure and are much larger than those in the Northern Hemisphere. The patterns around December solstice (DS) in the Northern Hemisphere (winter) are different from other seasons, whereas the patterns in the Southern Hemisphere are similar in each season. Around March equinox (ME), June solstice (JS), and September equinox (SE) in the Northern Hemisphere and around ME, SE, and DS in the Southern Hemisphere, the longitudinal variations of topside Ne have similar patterns to hmF2. Around JS in the Southern Hemisphere (winter), the topside Ne has similar patterns to NmF2 and hmF2 does not change much with longitude. Thus, the topside variations may be explained intuitively in terms of hmF2 and NmF2. This approach works reasonably well in most of the situations except in the northern winter in the topside not too far from the F2 peak. In this sense, understanding variations in hmF2 and NmF2 becomes an important and relevant subject for this topside ionospheric study.

Muscle synergies obtained from comprehensive mapping of the primary motor cortex forelimb representation using high-frequency, long-duration ICMS.

PubMed

Amundsen Huffmaster, Sommer L; Van Acker, Gustaf M; Luchies, Carl W; Cheney, Paul D

2017-07-01

Simplifying neuromuscular control for movement has previously been explored by extracting muscle synergies from voluntary movement electromyography (EMG) patterns. The purpose of this study was to investigate muscle synergies represented in EMG recordings associated with direct electrical stimulation of single sites in primary motor cortex (M1). We applied single-electrode high-frequency, long-duration intracortical microstimulation (HFLD-ICMS) to the forelimb region of M1 in two rhesus macaques using parameters previously found to produce forelimb movements to stable spatial end points (90-150 Hz, 90-150 μA, 1,000-ms stimulus train lengths). To develop a comprehensive representation of cortical output, stimulation was applied systematically across the full extent of M1. We recorded EMG activity from 24 forelimb muscles together with movement kinematics. Nonnegative matrix factorization (NMF) was applied to the mean stimulus-evoked EMG, and the weighting coefficients associated with each synergy were mapped to the cortical location of the stimulating electrode. Synergies were found for three data sets including 1 ) all stimulated sites in the cortex, 2 ) a subset of sites that produced stable movement end points, and 3 ) EMG activity associated with voluntary reaching. Two or three synergies accounted for 90% of the overall variation in voluntary movement EMG whereas four or five synergies were needed for HFLD-ICMS-evoked EMG data sets. Maps of the weighting coefficients from the full HFLD-ICMS data set show limited regional areas of higher activation for particular synergies. Our results demonstrate fundamental NMF-based muscle synergies in the collective M1 output, but whether and how the central nervous system might coordinate movements using these synergies remains unclear. NEW & NOTEWORTHY While muscle synergies have been investigated in various muscle activity sets, it is unclear whether and how synergies may be organized in the cortex. We have investigated muscle synergies resulting from high-frequency, long-duration intracortical microstimulation (HFLD-ICMS) applied throughout M1. We compared HFLD-ICMS synergies to synergies from voluntary movement. While synergies can be identified from M1 stimulation, they are not clearly related to voluntary movement synergies and do not show an orderly topographic organization across M1. Copyright © 2017 the American Physiological Society.

Toward environmentally-benign utilization of nonmetallic fraction of waste printed circuit boards as modifier and precursor.

PubMed

Hadi, Pejman; Ning, Chao; Ouyang, Weiyi; Xu, Meng; Lin, Carol S K; McKay, Gordon

2015-01-01

Electronic waste, including printed circuit boards, is growing at an alarming rate due to the accelerated technological progress and the shorter lifespan of the electronic equipment. In the past decades, due to the lack of proper economic and environmentally-benign recycling technologies, a major fraction of e-waste generated was either destined to landfills or incinerated with the sole intention of its disposal disregarding the toxic nature of this waste. Recently, with the increasing public awareness over their environment and health issues and with the enaction of more stringent regulations, environmentally-benign recycling has been driven to be an alternative option partially replacing the traditional eco-unfriendly disposal methods. One of the most favorable green technologies has been the mechanical separation of the metallic and nonmetallic fraction of the waste printed circuit boards. Although metallic fraction, as the most profitable component, is used to generate the revenue of the separation process, the nonmetallic fraction (NMF) has been left isolated. Herein, the recent developments in the application of NMF have been comprehensively reviewed and an eco-friendly emerging usage of NMF as a value-added material for sustainable remediation has been introduced. Copyright © 2014 Elsevier Ltd. All rights reserved.

A pilot study of direct delivery of hydroxypropyl-beta-cyclodextrin to the lung by the nasal route in a mouse model of Niemann-Pick C1 disease: motor performance is unaltered and lung disease is worsened.

PubMed

Erickson, Robert P; Deutsch, Gail; Patil, Ruturaj

2018-05-01

We have tested the efficacy of hydroxypropyl-beta-cyclodextrin (HPBCD) delivered by the nasal route in the mouse model of juvenile Niemann-Pick C1 disease (NPC1), as pulmonary disease has not responded to systemic therapy with this drug. Since mice have no gag reflex, coating of the nasal cavity, with possible access to the brain, would be followed by delivery of HPBCD to the lung. While foamy macrophages, containing stored cholesterol, were found in the Npc1 nmf164 homozygous mice, a marked inflammatory response was found with inhaled HPBCD, both in mutant and wild-type animals. Slight inflammation also occasionally occurred with saline inhalation. There was no difference between the saline-treated, HPBCD-treated, and untreated Npc1 nmf164 homozygous mice for weight, balance beam performance, or coat hanger performance. Interestingly, there was a trend to longer survival in the HPBCD-treated Npc1 nmf164 homozygous mice, which, when combined with the survival times of the saline-treated survivals (each of which was not different), became significant.

SAMI3_ICON: Model of the Ionosphere/Plasmasphere System

NASA Astrophysics Data System (ADS)

Huba, J. D.; Maute, A.; Crowley, G.

2017-10-01

The NRL ionosphere/plasmasphere model SAMI3 has been modified to support the NASA ICON mission. Specifically, SAMI3_ICON has been modified to import the thermospheric composition, temperature, and winds from TIEGCM-ICON and the high-latitude potential from AMIE data. The codes will be run on a daily basis during the ICON mission to provide ionosphere and thermosphere properties to the science community. SAMI3_ICON will provide ionospheric and plasmaspheric parameters such as the electron and ion densities, temperatures, and velocities, as well as the total electron content (TEC), peak ionospheric electron density (NmF2) and height of the F layer at NmF2 (hmF2).

Contaminant source identification using semi-supervised machine learning

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir V.; Alexandrov, Boian S.; O'Malley, Daniel

2018-05-01

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

Contaminant source identification using semi-supervised machine learning

DOE PAGES

Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

2017-11-08

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may needmore » to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).« less

Contaminant source identification using semi-supervised machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vesselinov, Velimir Valentinov; Alexandrov, Boian S.; O’Malley, Dan

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may needmore » to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).« less

Digital Protocol for Chemical Analysis at Ultralow Concentrations by Surface-Enhanced Raman Scattering.

PubMed

de Albuquerque, Carlos Diego L; Sobral-Filho, Regivaldo G; Poppi, Ronei J; Brolo, Alexandre G

2018-01-16

Single molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to revolutionize quantitative analysis at ultralow concentrations (less than 1 nM). However, there are no established protocols to generalize the application of this technique in analytical chemistry. Here, a protocol for quantification at ultralow concentrations using SM-SERS is proposed. The approach aims to take advantage of the stochastic nature of the single-molecule regime to achieved lower limits of quantification (LOQ). Two emerging contaminants commonly found in aquatic environments, enrofloxacin (ENRO) and ciprofloxacin (CIPRO), were chosen as nonresonant molecular probes. The methodology involves a multivariate resolution curve fitting known as non-negative matrix factorization with alternating least-squares algorithm (NMF-ALS) to solve spectral overlaps. The key element of the quantification is to realize that, under SM-SERS conditions, the Raman intensity generated by a molecule adsorbed on a "hotspot" can be digitalized. Therefore, the number of SERS event counts (rather than SERS intensities) was shown to be proportional to the solution concentration. This allowed the determination of both ENRO and CIPRO with high accuracy and precision even at ultralow concentrations regime. The LOQ for both ENRO and CIPRO were achieved at 2.8 pM. The digital SERS protocol, suggested here, is a roadmap for the implementation of SM-SERS as a routine tool for quantification at ultralow concentrations.

Preparation of hierarchical porous carbon from waste printed circuit boards for high performance electric double-layer capacitors

NASA Astrophysics Data System (ADS)

Du, Xuan; Wang, Li; Zhao, Wei; Wang, Yi; Qi, Tao; Li, Chang Ming

2016-08-01

Renewable clean energy and resources recycling have become inevitable choices to solve worldwide energy shortages and environmental pollution problems. It is a great challenge to recycle tons of waste printed circuit boards (PCB) produced every year for clean environment while creating values. In this work, low cost, high quality activated carbons (ACs) were synthesized from non-metallic fractions (NMF) of waste PCB to offer a great potential for applications of electrochemical double-layer capacitors (EDLCs). After recovering metal from waste PCB, hierarchical porous carbons were produced from NMF by carbonization and activation processes. The experimental results exhibit that some pores were formed after carbonization due to the escape of impurity atoms introduced by additives in NMF. Then the pore structure was further tailored by adjusting the activation parameters. Roles of micropores and non-micropores in charge storage were investigated when the hierarchical porous carbons were applied as electrode of EDLCs. The highest specific capacitance of 210 F g-1 (at 50 mA g-1) and excellent rate capability were achieved when the ACs possessing a proper micropores/non-micropores ratio. This work not only provides a promising method to recycle PCB, but also investigates the structure tailoring arts for a rational hierarchical porous structure in energy storage/conversion.

Amide-induced phase separation of hexafluoroisopropanol-water mixtures depending on the hydrophobicity of amides.

PubMed

Takamuku, Toshiyuki; Wada, Hiroshi; Kawatoko, Chiemi; Shimomura, Takuya; Kanzaki, Ryo; Takeuchi, Munetaka

2012-06-21

Amide-induced phase separation of hexafluoro-2-propanol (HFIP)-water mixtures has been investigated to elucidate solvation properties of the mixtures by means of small-angle neutron scattering (SANS), (1)H and (13)C NMR, and molecular dynamics (MD) simulation. The amides included N-methylformamide (NMF), N-methylacetamide (NMA), and N-methylpropionamide (NMP). The phase diagrams of amide-HFIP-water ternary systems at 298 K showed that phase separation occurs in a closed-loop area of compositions as well as an N,N-dimethylformamide (DMF) system previously reported. The phase separation area becomes wider as the hydrophobicity of amides increases in the order of NMF < NMA < DMF < NMP. Thus, the evolution of HFIP clusters around amides due to the hydrophobic interaction gives rise to phase separation of the mixtures. In contrast, the disruption of HFIP clusters causes the recovery of the homogeneity of the ternary systems. The present results showed that HFIP clusters are evolved with increasing amide content to the lower phase separation concentration in the same mechanism among the four amide systems. However, the disruption of HFIP clusters in the NMP and DMF systems with further increasing amide content to the upper phase separation concentration occurs in a different way from those in the NMF and NMA systems.

Evidences for Cooperative Resonance-Assisted Hydrogen Bonds in Protein Secondary Structure Analogs

NASA Astrophysics Data System (ADS)

Zhou, Yu; Deng, Geng; Zheng, Yan-Zhen; Xu, Jing; Ashraf, Hamad; Yu, Zhi-Wu

2016-11-01

Cooperative behaviors of the hydrogen bonding networks in proteins have been discovered for a long time. The structural origin of this cooperativity, however, is still under debate. Here we report a new investigation combining excess infrared spectroscopy and density functional theory calculation on peptide analogs, represented by N-methylformamide (NMF) and N-methylacetamide (NMA). Interestingly, addition of the strong hydrogen bond acceptor, dimethyl sulfoxide, to the pure analogs caused opposite effects, namely red- and blue-shift of the N-H stretching infrared absorption in NMF and NMA, respectively. The contradiction can be reconciled by the marked lowering of the energy levels of the self-associates between NMA molecules due to a cooperative effect of the hydrogen bonds. On the contrary, NMF molecules cannot form long-chain cooperative hydrogen bonds because they tend to form dimers. Even more interestingly, we found excellent linear relationships between changes on bond orders of N-H/N-C/C = O and the hydrogen bond energy gains upon the formation of hydrogen bonding multimers in NMA, suggesting strongly that the cooperativity originates from resonance-assisted hydrogen bonds. Our findings provide insights on the structures of proteins and may also shed lights on the rational design of novel molecular recognition systems.

An Artificial Neural Network-Based Ionospheric Model to Predict NmF2 and hmF2 Using Long-Term Data Set of FORMOSAT-3/COSMIC Radio Occultation Observations: Preliminary Results

NASA Astrophysics Data System (ADS)

Sai Gowtam, V.; Tulasi Ram, S.

2017-11-01

Artificial Neural Networks (ANNs) are known to be capable of solving linear as well as highly nonlinear problems. Using the long-term and high-quality data set of Formosa Satellite-3/Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC, in short F3/C) from 2006 to 2015, an ANN-based two-dimensional (2-D) Ionospheric Model (ANNIM) is developed to predict the ionospheric peak parameters, such as NmF2 and hmF2. In this pilot study, the ANNIM results are compared with the original F3/C data, GRACE (Gravity Recovery and Climate Experiment) observations as well as International Reference Ionosphere (IRI)-2016 model to assess the learning efficiency of the neural networks used in the model. The ANNIM could well predict the NmF2 (hmF2) values with RMS errors of 1.87 × 105 el/cm3 (27.9 km) with respect to actual F3/C; and 2.98 × 105 el/cm3 (40.18 km) with respect to independent GRACE data. Further, the ANNIM predictions found to be as good as IRI-2016 model with a slightly smaller RMS error when compared to independent GRACE data. The ANNIM has successfully reproduced the local time, latitude, longitude, and seasonal variations with errors ranging 15-25% for NmF2 and 10-15% for hmF2 compared to actual F3/C data, except the postsunset enhancement in hmF2. Further, the ANNIM has also captured the global-scale ionospheric phenomena such as ionospheric annual anomaly, Weddell Sea Anomaly, and the midlatitude summer nighttime anomaly. Compared to IRI-2016 model, the ANNIM is found to have better represented the fine longitudinal structures and the midlatitude summer nighttime enhancements in both the hemispheres.

Longitudinal Differences in the Low-latitude Ionosphere and in the Ionospheric Variability

NASA Astrophysics Data System (ADS)

Goncharenko, L. P.; Zhang, S.; Liu, H.; Tsugawa, T.; Batista, I. S.; Reinisch, B. W.

2017-12-01

Analysis of longitudinal differences in ionospheric parameters can illuminate variety of mechanisms responsible for ionospheric variability. In this study, we aim to 1) quantitatively describe major features of longitudinal differences in peak electron density in the low-latitude ionosphere; 2) examine differences in ionospheric variability at different longitude sectors, and 3) illustrate longitudinal differences in ionospheric response to a large disturbance event, sudden stratospheric warming of 2016. We examine NmF2 observations by a network of ionosondes in the American (30-80W) and Asian (110-170E) longitudinal sectors. Selected instruments are located in the vicinity of EIA troughs (Jicamarca, Sao Luis, Guam, Kwajalein), northern and southern crests of EIA (Boa Vista, Tucuman, Cachoeira Paulista, Okinawa), and beyond EIA crests (Ramey, Yamagawa, Kokubunji). To examine main ionospheric features at each location, we use long-term datasets collected at each site to construct empirical models that describe variations in NmF2 as a function of local time, season, solar flux, and geomagnetic activity. This set of empirical models can be used to accurately describe background ionospheric behavior and serve as a set of observational benchmarks for global circulation models. It reveals, for example, higher NmF2 in the EIA trough in the Asian sector as compared to the American sector. Further, we quantitatively describe variability in NmF2 as a difference between local observations and local empirical model, and find that American sector's EIA trough has overall higher variability that maximizes for all local times during wintertime, while Asian sector trough variability does not change significantly with season. Additionally, local empirical models are used to isolate ionospheric features resulting from dynamical disturbances of different origin (e.g. geomagnetic storms, convective activity, sudden stratospheric warming events, etc.). We illustrate this approach with the case of sudden stratospheric warming of 2016.

Effect of Standardized Boesenbergia pandurata Extract and Its Active Compound Panduratin A on Skin Hydration and Barrier Function in Human Epidermal Keratinocytes.

PubMed

Woo, Seon Wook; Rhim, Dong-Bin; Kim, Changhee; Hwang, Jae-Kwan

2015-03-01

The skin plays a key role in protecting the body from the environment and from water loss. Cornified envelope (CE) and natural moisturizing factor (NMF) are considered as the primary regulators of skin hydration and barrier function. The CE prevents loss of water from the body and is formed by cross-linking of several proteins. Among these proteins, filaggrin is an important protein because NMF is produced by the degradation of filaggrin. Proteases, including matriptase and prostasin, stimulate the generation of filaggrin from profilaggrin and caspase-14 plays a role in the degradation of filaggrin. This study elucidated the effects of an ethanol extract of Boesenbergia pandurata (Roxb.) Schltr., known as fingerroot, and its active compound panduratin A on CE formation and filaggrin processing in HaCaT, human epidermal keratinocytes. B. pandurata extract (BPE) and panduratin A significantly stimulated not only CE formation but also the expression of CE proteins, such as loricrin, involucrin, and transglutaminase, which were associated with PPARα expression. The mRNA and protein levels of filaggrin and filaggrin-related enzymes, such as matriptase, prostasin, and caspase-14 were also up-regulated by BPE and panduratin A treatment. These results suggest that BPE and panduratin A are potential nutraceuticals which can enhance skin hydration and barrier function based on their CE formation and filaggrin processing.

Evaluating abdominal core muscle fatigue: Assessment of the validity and reliability of the prone bridging test.

PubMed

De Blaiser, C; De Ridder, R; Willems, T; Danneels, L; Vanden Bossche, L; Palmans, T; Roosen, P

2018-02-01

The aims of this study were to research the amplitude and median frequency characteristics of selected abdominal, back, and hip muscles of healthy subjects during a prone bridging endurance test, based on surface electromyography (sEMG), (a) to determine if the prone bridging test is a valid field test to measure abdominal muscle fatigue, and (b) to evaluate if the current method of administrating the prone bridging test is reliable. Thirty healthy subjects participated in this experiment. The sEMG activity of seven abdominal, back, and hip muscles was bilaterally measured. Normalized median frequencies were computed from the EMG power spectra. The prone bridging tests were repeated on separate days to evaluate inter and intratester reliability. Significant differences in normalized median frequency slope (NMF slope ) values between several abdominal, back, and hip muscles could be demonstrated. Moderate-to-high correlation coefficients were shown between NMF slope values and endurance time. Multiple backward linear regression revealed that the test endurance time could only be significantly predicted by the NMF slope of the rectus abdominis. Statistical analysis showed excellent reliability (ICC=0.87-0.89). The findings of this study support the validity and reliability of the prone bridging test for evaluating abdominal muscle fatigue. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Profiles of Ionospheric Storm-enhanced Density during the 17 March 2015 Great Storm

NASA Astrophysics Data System (ADS)

Liu, J.; Wang, W.; Burns, A. G.; Yue, X.; Zhang, S.; Zhang, Y.

2015-12-01

Ionospheric F2 region peak densities (NmF2) are expected to show a positive phase correlation with total electron content (TEC), and electron density is expected to have an anti-correlation with electron temperature near the ionospheric F2 peak. However, we show that, during the 17 March 2015 great storm, TEC and F2 region electron density peak height (hmF2) over Millstone Hill increased, but the F2 region electron density peak (NmF2) decreased significantly during the storm-enhanced density (SED) phase of the storm compared with the quiet-time ionosphere. This SED occurred where there was a negative ionospheric storm near the F2 peak and below it. The weak ionosphere below the F2 peak resulted in much reduced downward heat conduction for the electrons, trapping the heat in the topside. This, in turn, increased the topside scale height, so that, even though electron densities at the F2 peak were depleted, TEC increased in the SED. The depletion in NmF2 was probably caused by an increase in the density of the molecular neutrals, resulting in enhanced recombination. In addition, the storm-time topside ionospheric electron density profile was much closer to diffusive equilibrium than non-storm time profile because of less daytime plasma flow from the ionosphere to the plasmasphere.

Model-free data analysis for source separation based on Non-Negative Matrix Factorization and k-means clustering (NMFk)

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Alexandrov, B.

2014-12-01

The identification of the physical sources causing spatial and temporal fluctuations of state variables such as river stage levels and aquifer hydraulic heads is challenging. The fluctuations can be caused by variations in natural and anthropogenic sources such as precipitation events, infiltration, groundwater pumping, barometric pressures, etc. The source identification and separation can be crucial for conceptualization of the hydrological conditions and characterization of system properties. If the original signals that cause the observed state-variable transients can be successfully "unmixed", decoupled physics models may then be applied to analyze the propagation of each signal independently. We propose a new model-free inverse analysis of transient data based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS) coupled with k-means clustering algorithm, which we call NMFk. NMFk is capable of identifying a set of unique sources from a set of experimentally measured mixed signals, without any information about the sources, their transients, and the physical mechanisms and properties controlling the signal propagation through the system. A classical BSS conundrum is the so-called "cocktail-party" problem where several microphones are recording the sounds in a ballroom (music, conversations, noise, etc.). Each of the microphones is recording a mixture of the sounds. The goal of BSS is to "unmix'" and reconstruct the original sounds from the microphone records. Similarly to the "cocktail-party" problem, our model-freee analysis only requires information about state-variable transients at a number of observation points, m, where m > r, and r is the number of unknown unique sources causing the observed fluctuations. We apply the analysis on a dataset from the Los Alamos National Laboratory (LANL) site. We identify and estimate the impact and sources are barometric pressure and water-supply pumping effects. We also estimate the location of the water-supply pumping wells based on the available data. The possible applications of the NMFk algorithm are not limited to hydrology problems; NMFk can be applied to any problem where temporal system behavior is observed at multiple locations and an unknown number of physical sources are causing these fluctuations.

Transcriptional response to hypoxic stress in melanoma and prognostic potential of GBE1 and BNIP3.

PubMed

Buart, Stéphanie; Terry, Stéphane; Noman, Muhammad Z; Lanoy, Emilie; Boutros, Céline; Fogel, Paul; Dessen, Philippe; Meurice, Guillaume; Gaston-Mathé, Yann; Vielh, Philippe; Roy, Séverine; Routier, Emilie; Marty, Virginie; Ferlicot, Sophie; Legrès, Luc; Bouchtaoui, Morad El; Kamsu-Kom, Nyam; Muret, Jane; Deutsch, Eric; Eggermont, Alexander; Soria, Jean-Charles; Robert, Caroline; Chouaib, Salem

2017-12-12

Gradients of hypoxia occur in most solid tumors and cells found in hypoxic regions are associated with the most aggressive and therapy-resistant fractions of the tumor. Despite the ubiquity and importance of hypoxia responses, little is known about the variation in the global transcriptional response to hypoxia in melanoma. Using microarray technology, whole genome gene expression profiling was first performed on established melanoma cell lines. From gene set enrichment analyses, we derived a robust 35 probes signature (hypomel for HYPOxia MELanoma) associated with hypoxia-response pathways, including 26 genes up regulated, and 9 genes down regulated. The microarray data were validated by RT-qPCR for the 35 transcripts. We then validated the signature in hypoxic zones from 8 patient specimens using laser microdissection or macrodissection of Formalin fixed-paraffin-embedded (FFPE) material, followed with RT-qPCR. Moreover, a similar hypoxia-associated gene expression profile was observed using NanoString technology to analyze RNAs from FFPE melanoma tissues of a cohort of 19 patients treated with anti-PD1. Analysis of NanoString data from validation sets using Non-Negative Matrix Factorization (NMF) analysis (26 genes up regulated in hypoxia) and dual clustering (samples and genes) further revealed that the increased level of BNIP3 (Bcl-2 adenovirus E1B 19 kDa-interacting protein 3)/GBE1 (glycogen branching enzyme1) differential pair correlates with the lack of response of melanoma patients to anti-PD1 (pembrolizumab) immunotherapy. These studies suggest that through elevated glycogenic flux and induction of autophagy, hypoxia is a critical molecular program that could be considered as a prognostic factor for melanoma.

Learning new gait patterns: Exploratory muscle activity during motor learning is not predicted by motor modules

PubMed Central

Ranganathan, Rajiv; Krishnan, Chandramouli; Dhaher, Yasin Y.; Rymer, William Z.

2018-01-01

The motor module hypothesis in motor control proposes that the nervous system can simplify the problem of controlling a large number of muscles in human movement by grouping muscles into a smaller number of modules. Here, we tested one prediction of the modular organization hypothesis by examining whether there is preferential exploration along these motor modules during the learning of a new gait pattern. Healthy college-aged participants learned a new gait pattern which required increased hip and knee flexion during the swing phase while walking in a lower-extremity robot (Lokomat). The new gait pattern was displayed as a foot trajectory in the sagittal plane and participants attempted to match their foot trajectory to this template. We recorded EMG from 8 lower-extremity muscles and we extracted motor modules during both baseline walking and target-tracking using non-negative matrix factorization (NMF). Results showed increased trajectory variability in the first block of learning, indicating that participants were engaged in exploratory behavior. Critically, when we examined the muscle activity during this exploratory phase, we found that the composition of motor modules changed significantly within the first few strides of attempting the new gait pattern. The lack of persistence of the motor modules under even short time scales suggests that motor modules extracted during locomotion may be more indicative of correlated muscle activity induced by the task constraints of walking, rather than reflecting a modular control strategy. PMID:26916510

Glycerol and urea can be used to increase skin permeability in reduced hydration conditions.

PubMed

Björklund, Sebastian; Engblom, Johan; Thuresson, Krister; Sparr, Emma

2013-12-18

The natural moisturizing factor (NMF) is a group of hygroscopic molecules that is naturally present in skin and protects from severe drying. Glycerol and urea are two examples of NMF components that are also used in skin care applications. In the present study, we investigate the influence of glycerol and urea on the permeability of a model drug (metronidazole, Mz) across excised pig skin membranes at different hydrating conditions. The degree of skin hydration is regulated by the gradient in water activity across the membrane, which in turn depends on the water activity of the formulation in contact with the skin membrane. Here, we determine the water activity of all formulations employed using an isothermal calorimetric method. Thus, the gradient in water activity is controlled by a novel experimental set-up with well-defined boundary conditions on both sides of the skin membrane. The results demonstrate that glycerol and urea can retain high steady state flux of Mz across skin membranes at dehydrating conditions, which otherwise would decrease the permeability due to dehydration. X-ray diffraction measurements are performed to give insight into the effects of glycerol and urea on SC molecular organization. The novel steady state flux results can be related to the observation that water, glycerol, and urea all affect the structural features of the SC molecular components in a similar manner. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Chemical pyrolysis of E-waste plastics: Char characterization.

PubMed

Shen, Yafei; Chen, Xingming; Ge, Xinlei; Chen, Mindong

2018-05-15

This work studied the disposal of the non-metallic fraction from waste printed circuit board (NMF-WPCB) via the chemical pretreatments followed by pyrolysis. As a main heavy metal, the metallic Cu could be significantly removed by 92.4% using the HCl leaching process. Subsequently, the organic-Br in the brominated flame retardants (BFRs) plastics could be converted into HBr by pyrolysis. The alkali pretreatment was benefit for the Br fixation in the solid char. The Br fixation efficiency could reach up to 53.6% by the NaOH pretreatment followed by the pyrolysis process. The formed HBr could react with NaOH/KOH to generate the stabilized NaBr/KBr. Therefore, the integrated chemical pretreatment could be used for the eco-friendly disposal of the NMF-WPCB via pyrolysis. Copyright © 2018 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadi, Pejman; Ning, Chao; Ouyang, Weiyi

Highlights: • Environmental impacts of electronic waste and specifically waste printed circuit boards. • Review of the recycling techniques of waste printed circuit boards. • Advantages of physico-mechanical recycling techniques over chemical methods. • Utilization of nonmetallic fraction of waste printed circuit boards as modifier/filler. • Recent advances in the use of nonmetallic fraction of waste printed circuit boards as precursor. - Abstract: Electronic waste, including printed circuit boards, is growing at an alarming rate due to the accelerated technological progress and the shorter lifespan of the electronic equipment. In the past decades, due to the lack of proper economicmore » and environmentally-benign recycling technologies, a major fraction of e-waste generated was either destined to landfills or incinerated with the sole intention of its disposal disregarding the toxic nature of this waste. Recently, with the increasing public awareness over their environment and health issues and with the enaction of more stringent regulations, environmentally-benign recycling has been driven to be an alternative option partially replacing the traditional eco-unfriendly disposal methods. One of the most favorable green technologies has been the mechanical separation of the metallic and nonmetallic fraction of the waste printed circuit boards. Although metallic fraction, as the most profitable component, is used to generate the revenue of the separation process, the nonmetallic fraction (NMF) has been left isolated. Herein, the recent developments in the application of NMF have been comprehensively reviewed and an eco-friendly emerging usage of NMF as a value-added material for sustainable remediation has been introduced.« less

Short-term variability in the ionosphere due to the nonlinear interaction between the 6 day wave and migrating tides

NASA Astrophysics Data System (ADS)

Gan, Quan; Oberheide, Jens; Yue, Jia; Wang, Wenbin

2017-08-01

Using the thermosphere-ionosphere-mesosphere electrodynamics general circulation model simulations, we investigate the short-term ionospheric variability due to the child waves and altered tides produced by the nonlinear interaction between the 6 day wave and migrating tides. Via the Fourier spectral diagnostics and least squares fittings, the [21 h, W2] and [13 h, W1] child waves, generated by the interaction of the 6 day wave with the DW1 and SW2, respectively, are found to play the leading roles on the subdiurnal variability (e.g., ±10 m/s in the ion drift and 50% in the NmF2) in the F region vertical ion drift changes through the dynamo modulation induced by the low-latitude zonal wind and the meridional wind at higher latitudes. The relatively minor contribution of the [11 h, W3] child wave is explicit as well. Although the [29 h, W0] child wave has the largest magnitude in the E region, its effect is totally absent in the vertical ion drift due to the zonally uniform structure. But the [29 h, W0] child wave shows up in the NmF2. It is found that the NmF2 short-term variability is attributed to the wave modulations on both E region dynamo and in situ F region composition. Also, the altered migrating tides due to the interaction will not contribute to the ionospheric changes significantly.

Use of a nictitating membrane flap for treatment of feline acute corneal hydrops-21 eyes.

PubMed

Pederson, Samantha L; Pizzirani, Stefano; Andrew, Stacy E; Pate, Diana O; Stine, Jessica M; Michau, Tammy M

2016-07-01

To evaluate the effectiveness of the use of a nictitating membrane flap (NMF) as therapy in 19 cats (21 eyes) affected with feline acute corneal hydrops (FACH). Medical records from 19 cats diagnosed with FACH and treated with a NMF were retrospectively evaluated. Information was collected from multiple veterinary hospitals and included signalment, medical history, therapy, and ocular outcome. Breeds included 13 Domestic Shorthairs, 2 Exotic Shorthairs, 2 Maine Coons, 1 Persian, and 1 Domestic Medium Hair. Two cats were bilaterally affected. Median age of cats was 3.2 years (range 0.26-15 years). Eleven patients were spayed females, 6 were neutered males, and 2 were intact males. Topical steroids were previously administered in 5 (23.8%) eyes; oral steroids were previously administered in 7 cats (36.8% of patients); three patients received both oral and topical steroids. Thirteen of 21 (61.9%) eyes had a history of ocular disease including ulcerative and nonulcerative keratitis, anterior uveitis, corneal sequestrum, conjunctivitis, and glaucoma. Median duration of NMF was 15 days (range 6-30 days). Follow-up ranged from 12 to 1601 days (median 169 days). Corneal perforation occurred in 1 (4.7%) eye and was successfully repaired. One lesion (4.7%) in a diabetic patient did not resolve. Nineteen of the treated eyes (90.5%) resolved with no complications. A nictitating membrane flap successfully treated 90.5% of FACH eyes (89.5% of patients). © 2016 American College of Veterinary Ophthalmologists.

USCG HF SITOR

Science.gov Websites

broadcasts from Boston sharing the same transmitters. See table below for station locations and schedules meteorological observations. Boston(NMF) HF SITOR (NBDP) Broadcast Schedule 6314, 8416.5, 12579 kHz 0140Z3 8416.5

Waste Printed Circuit Board (PCB) Recycling Techniques.

PubMed

Ning, Chao; Lin, Carol Sze Ki; Hui, David Chi Wai; McKay, Gordon

2017-04-01

With the development of technologies and the change of consumer attitudes, the amount of waste electrical and electronic equipment (WEEE) is increasing annually. As the core part of WEEE, the waste printed circuit board (WPCB) is a dangerous waste but at the same time a rich resource for various kinds of materials. In this work, various WPCB treatment methods as well as WPCB recycling techniques divided into direct treatment (landfill and incineration), primitive recycling technology (pyrometallurgy, hydrometallurgy, biometallurgy and primitive full recovery of NMF-non metallic fraction), and advanced recycling technology (mechanical separation, direct use and modification of NMF) are reviewed and analyzed based on their advantages and disadvantages. Also, the evaluation criteria are discussed including economic, environmental, and gate-to-market ability. This review indicates the future research direction of WPCB recycling should focus on a combination of several techniques or in series recycling to maximize the benefits of process.

Magnetosphere-Ionosphere-Thermosphere Response to Quasi-periodic Oscillations in Solar Wind Driving Conditions

NASA Astrophysics Data System (ADS)

Liu, J.; Wang, W.; Zhang, B.; Huang, C.

2017-12-01

Periodical oscillations with periods of several tens of minutes to several hours are commonly seen in the Alfven wave embedded in the solar wind. It is yet to be known how the solar wind oscillation frequency modulates the solar wind-magnetosphere-ionosphere coupled system. Utilizing the Coupled Magnetosphere-Ionosphere-Thermosphere Model (CMIT), we analyzed the magnetosphere-ionosphere-thermosphere system response to IMF Bz oscillation with periods of 10, 30, and 60 minutes from the perspective of energy budget and electrodynamic coupling processes. Our results indicate that solar wind energy coupling efficiency depends on IMF Bz oscillation frequency; energy coupling efficiency, represented by the ratio between globally integrated Joule heating and Epsilon function, is higher for lower frequency IMF Bz oscillation. Ionospheric Joule heating dissipation not only depends on the direct solar wind driven process but also is affected by the intrinsic nature of magnetosphere (i.e. loading-unloading process). In addition, ionosphere acts as a low-pass filter and tends to filter out very high-frequency solar wind oscillation (i.e. shorter than 10 minutes). Ionosphere vertical ion drift is most sensitive to IMF Bz oscillation compared to hmF2, and NmF2, while NmF2 is less sensitive. This can account for not synchronized NmF2 and hmF2 response to penetration electric fields in association with fast solar wind changes. This research highlights the critical role of IMF Bz oscillation frequency in constructing energy coupling function and understanding electrodynamic processes in the coupled solar wind-magnetosphere-ionosphere system.

A global picture of ionospheric slab thickness derived from GIM TEC and COSMIC radio occultation observations

NASA Astrophysics Data System (ADS)

Huang, He; Liu, Libo; Chen, Yiding; Le, Huijun; Wan, Weixing

2016-01-01

The ionospheric equivalent slab thickness (EST), defined as the ratio of total electron content (TEC) to F2 layer peak electron density (NmF2), describes the thickness of the ionospheric profile. In this study, we retrieve EST from TEC data obtained from Global Ionospheric Map (GIM) and NmF2 retrieved from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) ionospheric radio occultation data. The diurnal, seasonal, and solar activity variations of global EST are analyzed as the excellent spatial coverage of GIM and COSMIC data. During solstices, daytime EST in the summer hemisphere is larger than that in the winter hemisphere, except in some high-latitude regions, and the reverse is true for the nighttime EST. The peaks of EST often appear at 0400 local time. The presunrise enhancement in EST appears in all seasons, while the postsunset enhancement in EST is not readily observed in equinox. Both enhancements are attributed to the more remarkable electron density decay of NmF2 compared to that of TEC. The dependence of EST on solar activity is related to the inconsistent solar activity dependences of electron density at different altitudes. Furthermore, it is interesting that EST is enhanced from 0° to 120°E in longitude and 30° to 75°S in latitude during nighttime, just to the east of Weddell Sea Anomaly, during equinox and the Southern Hemisphere summer. This phenomenon is supposed to be related to the effects of geomagnetic declination-related plasma vertical drifts.

Lowering relative humidity level increases epidermal protein deimination and drives human filaggrin breakdown.

PubMed

Cau, Laura; Pendaries, Valérie; Lhuillier, Emeline; Thompson, Paul R; Serre, Guy; Takahara, Hidenari; Méchin, Marie-Claire; Simon, Michel

2017-05-01

Deimination (also known as citrullination), the conversion of arginine in a protein to citrulline, is catalyzed by a family of enzymes called peptidylarginine deiminases (PADs). Three PADs are expressed in the epidermis, one of their targets being filaggrin. Filaggrin plays a central role in atopic dermatitis and is a key protein for the epidermal barrier. It aggregates keratins and is cross-linked to cornified envelopes. Following its deimination, it is totally degraded to release free amino acids, contributing to the natural moisturizing factor (NMF). The mechanisms controlling this multistep catabolism in human are unknown. To test whether external humidity plays a role, and investigate the molecular mechanisms involved. Specimens of reconstructed human epidermis (RHEs) produced in humid or dry conditions (>95% or 30-50% relative humidity) were compared. RHEs produced in the dry condition presented structural changes, including a thicker stratum corneum and a larger amount of keratohyalin granules. The transepidermal water loss and the stratum corneum pH were decreased whereas the quantity of NMF was greater. This highly suggested that filaggrin proteolysis was up-regulated. The expression/activity of the proteases involved in filaggrin breakdown did not increase while PAD1 expression and the deimination rate of proteins, including filaggrin, were drastically enhanced. Partial inhibition of PADs with Cl-amidine reversed the effect of dryness on filaggrin breakdown. These results demonstrate the importance of external humidity in the control of human filaggrin metabolism, and suggest that deimination plays a major role in this regulation. Copyright © 2017 Japanese Society for Investigative Dermatology. All rights reserved.

Changes in hydration of the stratum corneum are the most suitable indicator to evaluate the irritation of surfactants on the skin.

PubMed

Fujimura, T; Shimotoyodome, Y; Nishijima, T; Sugata, K; Taguchi, H; Moriwaki, S

2017-02-01

Irritancy levels of surfactants on human skin have not been clarified completely. The relationships between skin damage and changes of skin properties caused by various surfactants were investigated using non-invasive measurements. Aqueous solutions of seven kinds of anionic, non-ionic, and amphoteric surfactants were exposed to the inside of forearm skin of 20 human subjects in two separate studies using the cup method. Hydration of the stratum corneum (SC), transepidermal water loss (TEWL), pH, skin surface roughness, and contents of the SC were measured before and after one exposure and after five and nine consecutive exposures to various surfactants. The discontinuation ratio of subjects for testing in each surfactant was determined by skin irritation symptoms and was defined as the degree of skin damage. Significant changes were observed only in hydration, TEWL, and natural moisturizing factors (NMF) content in the SC following surfactant exposure. A significant correlation was observed between the discontinuation ratio of each surfactant and the changes of hydration, TEWL, and NMF. Especially, the change of SC hydration showed an excellent correlation with the discontinuation ratio both for single (r = 0.942, P < 0.001) and for chronic exposures (r = 0.934, P < 0.001). Our results indicate that the change of hydration of the SC is equivalent to the skin damage caused by surfactants, and therefore is the most suitable indicator to evaluate the irritation of surfactants on the skin. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Bearing performance degradation assessment based on time-frequency code features and SOM network

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

2017-04-01

Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data.

CEDAR Electrodynamics Thermosphere Ionosphere (ETI) Challenge for Systematic Assessment of Ionosphere/Thermosphere Models: NmF2, hmF2, and Vertical Drift Using Ground-Based Observations

NASA Technical Reports Server (NTRS)

Shim, J. S.; Kuznetsova, M.; Rastatter, L.; Hesse, M.; Bilitza, D.; Butala, M.; Codrescu, M.; Emery, B.; Foster, B.; Fuller-Rowell, T.;

The effect of physical contact on changes in fatigue markers following rugby union field-based training.

PubMed

Roe, Gregory; Darrall-Jones, Joshua; Till, Kevin; Phibbs, Padraic; Read, Dale; Weakley, Jonathon; Rock, Andrew; Jones, Ben

2017-07-01

Repeated physical contact in rugby union is thought to contribute to post-match fatigue; however, no evidence exists on the effect of contact activity during field-based training on fatigue responses. Therefore, the purpose of this study was to examine the effect of contact during training on fatigue markers in rugby union players. Twenty academy rugby union players participated in the cross-over study. The magnitude of change in upper- and lower-body neuromuscular function (NMF), whole blood creatine kinase concentration [CK] and perception of well-being was assessed pre-training (baseline), immediately and 24 h post-training following contact and non-contact, field-based training. Training load was measured using mean heart rate, session rating of perceived exertion (sRPE) and microtechnology (Catapult Optimeye S5). The inclusion of contact during field-based training almost certainly increased mean heart rate (9.7; ±3.9%) and sRPE (42; ±29.2%) and resulted in likely and very likely greater decreases in upper-body NMF (-7.3; ±4.7% versus 2.7; ±5.9%) and perception of well-being (-8.0; ±4.8% versus  -3.4; ±2.2%) 24 h post-training, respectively, and almost certainly greater elevations in [CK] (88.2; ±40.7% versus 3.7; ±8%). The exclusion of contact from field-based training almost certainly increased running intensity (19.8; ±5%) and distance (27.5; ±5.3%), resulting in possibly greater decreases in lower-body NMF (-5.6; ±5.2% versus 2.3; ±2.4%). Practitioners should be aware of the different demands and fatigue responses of contact and non-contact, field-based training and can use this information to appropriately schedule such training in the weekly microcycle.

Normalization of Hepatic Homeostasis in the Npc1nmf164 Mouse Model of Niemann-Pick Type C Disease Treated with the Histone Deacetylase Inhibitor Vorinostat*

PubMed Central

Munkacsi, Andrew B.; Hammond, Natalie; Schneider, Remy T.; Senanayake, Dinindu S.; Higaki, Katsumi; Lagutin, Kirill; Bloor, Stephen J.; Ory, Daniel S.; Maue, Robert A.; Chen, Fannie W.; Hernandez-Ono, Antonio; Dahlson, Nicole; Repa, Joyce J.; Ginsberg, Henry N.; Ioannou, Yiannis A.; Sturley, Stephen L.

2017-01-01

Niemann-Pick type C (NP-C) disease is a fatal genetic lipidosis for which there is no Food and Drug Administration (FDA)-approved therapy. Vorinostat, an FDA-approved inhibitor of histone deacetylases, ameliorates lysosomal lipid accumulation in cultured NP-C patient fibroblasts. To assess the therapeutic potential of histone deacetylase inhibition, we pursued these in vitro observations in two murine models of NP-C disease. Npc1nmf164 mice, which express a missense mutation in the Npc1 gene, were treated intraperitoneally, from weaning, with the maximum tolerated dose of vorinostat (150 mg/kg, 5 days/week). Disease progression was measured via gene expression, liver function and pathology, serum and tissue lipid levels, body weight, and life span. Transcriptome analyses of treated livers indicated multiple changes consistent with reversal of liver dysfunction that typifies NP-C disease. Significant improvements in liver pathology and function were achieved by this treatment regimen; however, NPC1 protein maturation and levels, disease progression, weight loss, and animal morbidity were not detectably altered. Vorinostat concentrations were >200 μm in the plasma compartment of treated animals but were almost 100-fold lower in brain tissue. Apolipoprotein B metabolism and the expression of key components of lipid homeostasis in primary hepatocytes from null (Npc1−/−) and missense (Npc1nmf164) mutant mice were altered by vorinostat treatment, consistent with a response by these cells independent of the status of the Npc1 locus. These results suggest that HDAC inhibitors have utility to treat visceral NP-C disease. However, it is clear that improved blood-brain barrier penetration will be required to alleviate the neurological symptoms of human NP-C disease. PMID:28031458

Ionospheric response to the 17-18 March 2015 geomagnetic storm as seen from multiple TEC and NmF2 measurements along 100°E

NASA Astrophysics Data System (ADS)

Bhuyan, Pradip; Yokoyama, Tatsuhiro; Kalita, Bitap Raj; Seemala, G. K.; Hazarika, Rumajyoti; Komolmis, Tharadol; Yatini, Clara; Chakrabarty, Dibyendu; Supnithi, Pornchai

2016-07-01

The response of the ionosphere along 100°E to the strong geomagnetic storm of 17-18 March 2015 has been investigated combining TEC and NmF2 data from multiple stations spanning low latitudes in the northern and southern hemispheres to the equator. The GPS TEC data measured over Dibrugarh (27.4°N, 95°E), Kohima (25.6°N, 94.1°E) and Ahmedabad (23.0°N, 72.5°E) and NmF2 measured along a chain of ionosonde stations Dibrugarh (27.5°N, 95°E), Chiang Mai (18.76ºN, 98.93ºE), Chumphon (10.72ºN,99.37ºE), Kototabang (0.2ºS,100.32ºE) and Cocos Island (12.2ºS,96.8ºE ) were used to examine the signature of the storm around the low-mid latitude ionosphere in this sector. Nearly similar TEC variation has been observed over Dibrugarh and Kohima located at the northern edge of the EIA. The maximum TEC on 18 March over Dibrugarh and Kohima was reduced by more than ~80 TECU compared to that on the geomagnetically quiet day of 16 March 2015. In contrast to the substantial reduction in TEC over ~100°E TEC from the ~75°E longitude station Ahmedabad showed insignificant variations on the same day. Strong reduction in NmF2 at the crest of the anomaly in both northern and southern hemisphere (Dibrugarh, Ching Mai and Cocos Island) and enhancement near the equator (Cumphon and Kototbang) has been observed. The O/N2 ratio as obtained from the TIMED/GUVI reduced substantially along 100°E on 18 March compared to other longitude sectors. Equatorward meridional winds depleted the ionization at the crest region and enhanced the same near the equator. No L band scintillation was observed in the evening of 17 March at Dibrugarh and Kohima indicating absence of F region irregularity along this longitude while strong scintillations were observed at 75°E. The reversal of the IMF Bz from southward to northward direction in the dusk to evening sector inhibited the growth of the irregularity due to reversal of the PPEF at 100°E while the PPEF favoured generation and growth of Spread F at 75°E.

Task-discriminative space-by-time factorization of muscle activity

PubMed Central

Delis, Ioannis; Panzeri, Stefano; Pozzo, Thierry; Berret, Bastien

2015-01-01

Movement generation has been hypothesized to rely on a modular organization of muscle activity. Crucial to this hypothesis is the ability to perform reliably a variety of motor tasks by recruiting a limited set of modules and combining them in a task-dependent manner. Thus far, existing algorithms that extract putative modules of muscle activations, such as Non-negative Matrix Factorization (NMF), identify modular decompositions that maximize the reconstruction of the recorded EMG data. Typically, the functional role of the decompositions, i.e., task accomplishment, is only assessed a posteriori. However, as motor actions are defined in task space, we suggest that motor modules should be computed in task space too. In this study, we propose a new module extraction algorithm, named DsNM3F, that uses task information during the module identification process. DsNM3F extends our previous space-by-time decomposition method (the so-called sNM3F algorithm, which could assess task performance only after having computed modules) to identify modules gauging between two complementary objectives: reconstruction of the original data and reliable discrimination of the performed tasks. We show that DsNM3F recovers the task dependence of module activations more accurately than sNM3F. We also apply it to electromyographic signals recorded during performance of a variety of arm pointing tasks and identify spatial and temporal modules of muscle activity that are highly consistent with previous studies. DsNM3F achieves perfect task categorization without significant loss in data approximation when task information is available and generalizes as well as sNM3F when applied to new data. These findings suggest that the space-by-time decomposition of muscle activity finds robust task-discriminating modular representations of muscle activity and that the insertion of task discrimination objectives is useful for describing the task modulation of module recruitment. PMID:26217213

Task-discriminative space-by-time factorization of muscle activity.

PubMed

Delis, Ioannis; Panzeri, Stefano; Pozzo, Thierry; Berret, Bastien

2015-01-01

Movement generation has been hypothesized to rely on a modular organization of muscle activity. Crucial to this hypothesis is the ability to perform reliably a variety of motor tasks by recruiting a limited set of modules and combining them in a task-dependent manner. Thus far, existing algorithms that extract putative modules of muscle activations, such as Non-negative Matrix Factorization (NMF), identify modular decompositions that maximize the reconstruction of the recorded EMG data. Typically, the functional role of the decompositions, i.e., task accomplishment, is only assessed a posteriori. However, as motor actions are defined in task space, we suggest that motor modules should be computed in task space too. In this study, we propose a new module extraction algorithm, named DsNM3F, that uses task information during the module identification process. DsNM3F extends our previous space-by-time decomposition method (the so-called sNM3F algorithm, which could assess task performance only after having computed modules) to identify modules gauging between two complementary objectives: reconstruction of the original data and reliable discrimination of the performed tasks. We show that DsNM3F recovers the task dependence of module activations more accurately than sNM3F. We also apply it to electromyographic signals recorded during performance of a variety of arm pointing tasks and identify spatial and temporal modules of muscle activity that are highly consistent with previous studies. DsNM3F achieves perfect task categorization without significant loss in data approximation when task information is available and generalizes as well as sNM3F when applied to new data. These findings suggest that the space-by-time decomposition of muscle activity finds robust task-discriminating modular representations of muscle activity and that the insertion of task discrimination objectives is useful for describing the task modulation of module recruitment.

Uncertainty quantification and experimental design based on unsupervised machine learning identification of contaminant sources and groundwater types using hydrogeochemical data

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.

2017-12-01

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical species. Numerous geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. As a result, these types of model analyses are typically extremely challenging. Here, we demonstrate a new contaminant source identification approach that performs decomposition of the observation mixtures based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. We also demonstrate how NMFk can be extended to perform uncertainty quantification and experimental design related to real-world site characterization. The NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios). The NMFk algorithm has been extensively tested on synthetic datasets; NMFk analyses have been actively performed on real-world data collected at the Los Alamos National Laboratory (LANL) groundwater sites related to Chromium and RDX contamination.

Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

2017-04-01

Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

Characteristics and source apportionment of fine haze aerosol in Beijing during the winter of 2013

NASA Astrophysics Data System (ADS)

Shang, Xiaona; Zhang, Kai; Meng, Fan; Wang, Shihao; Lee, Meehye; Suh, Inseon; Kim, Daigon; Jeon, Kwonho; Park, Hyunju; Wang, Xuezhong; Zhao, Yuxi

2018-02-01

For PM2.5 filter samples collected daily at the Chinese Research Academy of Environmental Sciences (Beijing, China) from December of 2013 to February of 2014 (the winter period), chemical characteristics and sources were investigated with an emphasis on haze events in different alert levels. During the 3 months, the average PM2.5 concentration was 89 µg m-3, exceeding the Chinese national standard of 75 µg m-3 in 24  h. The maximum PM2.5 concentration was 307 µg m-3, which characterizes developed-type pollution (PM2.5 / PM10>0.5) in the World Health Organization criteria. PM2.5 was dominated by SO42-, NO3-, and pseudo-carbonaceous compounds with obvious differences in concentrations and proportions between non-haze and haze episodes. The non-negative matrix factorization (NMF) analysis provided reasonable PM2.5 source profiles, by which five sources were identified: soil dust, traffic emission, biomass combustion, industrial emission, and coal combustion accounting for 13, 22, 12, 28, and 25  % of the total, respectively. The dust impact increased with northwesterlies during non-haze periods and decreased under stagnant conditions during haze periods. A blue alert of heavy air pollution was characterized by the greatest contribution from industrial emissions (61  %). During the Chinese Lantern Festival, an orange alert was issued and biomass combustion was found to be the major source owing to firework explosions. Red-alert haze was almost equally contributed by local traffic and transported coal combustion emissions from the vicinity of Beijing (approximately 40  % each) that was distinguished by the highest levels of NO3- and SO42-, respectively. This study also reveals that the severity and source of haze are largely dependent on meteorological conditions.

Discovering semantic features in the literature: a foundation for building functional associations

PubMed Central

Chagoyen, Monica; Carmona-Saez, Pedro; Shatkay, Hagit; Carazo, Jose M; Pascual-Montano, Alberto

2006-01-01

Background Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research. Results We present a method to create literature profiles for large sets of genes or proteins based on common semantic features extracted from a corpus of relevant documents. These profiles can be used to establish pair-wise similarities among genes, utilized in gene/protein classification or can be even combined with experimental measurements. Semantic features can be used by researchers to facilitate the understanding of the commonalities indicated by experimental results. Our approach is based on non-negative matrix factorization (NMF), a machine-learning algorithm for data analysis, capable of identifying local patterns that characterize a subset of the data. The literature is thus used to establish putative relationships among subsets of genes or proteins and to provide coherent justification for this clustering into subsets. We demonstrate the utility of the method by applying it to two independent and vastly different sets of genes. Conclusion The presented method can create literature profiles from documents relevant to sets of genes. The representation of genes as additive linear combinations of semantic features allows for the exploration of functional associations as well as for clustering, suggesting a valuable methodology for the validation and interpretation of high-throughput experimental data. PMID:16438716

Time reversal optical tomography and decomposition methods for detection and localization of targets in highly scattering turbid media

NASA Astrophysics Data System (ADS)

Wu, Binlin

New near-infrared (NIR) diffuse optical tomography (DOT) approaches were developed to detect, locate, and image small targets embedded in highly scattering turbid media. The first approach, referred to as time reversal optical tomography (TROT), is based on time reversal (TR) imaging and multiple signal classification (MUSIC). The second approach uses decomposition methods of non-negative matrix factorization (NMF) and principal component analysis (PCA) commonly used in blind source separation (BSS) problems, and compare the outcomes with that of optical imaging using independent component analysis (OPTICA). The goal is to develop a safe, affordable, noninvasive imaging modality for detection and characterization of breast tumors in early growth stages when those are more amenable to treatment. The efficacy of the approaches was tested using simulated data, and experiments involving model media and absorptive, scattering, and fluorescent targets, as well as, "realistic human breast model" composed of ex vivo breast tissues with embedded tumors. The experimental arrangements realized continuous wave (CW) multi-source probing of samples and multi-detector acquisition of diffusely transmitted signal in rectangular slab geometry. A data matrix was generated using the perturbation in the transmitted light intensity distribution due to the presence of absorptive or scattering targets. For fluorescent targets the data matrix was generated using the diffusely transmitted fluorescence signal distribution from the targets. The data matrix was analyzed using different approaches to detect and characterize the targets. The salient features of the approaches include ability to: (a) detect small targets; (b) provide three-dimensional location of the targets with high accuracy (~within a millimeter or 2); and (c) assess optical strength of the targets. The approaches are less computation intensive and consequently are faster than other inverse image reconstruction methods that attempt to reconstruct the optical properties of every voxel of the sample volume. The location of a target was estimated to be the weighted center of the optical property of the target. Consequently, the locations of small targets were better specified than those of the extended targets. It was more difficult to retrieve the size and shape of a target. The fluorescent measurements seemed to provide better accuracy than the transillumination measurements. In the case of ex vivo detection of tumors embedded in human breast tissue, measurements using multiple wavelengths provided more robust results, and helped suppress artifacts (false positives) than that from single wavelength measurements. The ability to detect and locate small targets, speedier reconstruction, combined with fluorophore-specific multi-wavelength probing has the potential to make these approaches suitable for breast cancer detection and diagnosis.

Eagle Ford Shale BTEX and NOx concentrations are dominated by oil and gas industry emissions

NASA Astrophysics Data System (ADS)

Schade, G. W.; Roest, G. S.

2017-12-01

US shale oil and gas exploration has been identified as a major source of greenhouse gases and non-methane hydrocarbon (NMHC) emissions to the atmosphere. Here, we present a detailed analysis of 2015 air quality data acquired by the Texas Commission on Environmental Quality (TCEQ) at an air quality monitoring station in Karnes County, TX, central to Texas' Eagle Ford shale area. Data include time series of hourly measured NMHCs, nitrogen oxides (NOx), and hydrogen sulfide (H2S) alongside meteorological measurements. The monitor was located in Karnes City, and thus affected by various anthropogenic emissions, including traffic and oil and gas exploration sources. Highest mixing ratios measured in 2015 included nearly 1 ppm ethane, 0.8 ppm propane, alongside 4 ppb benzene. A least-squares minimization non-negative matrix factorization (NMF) analysis, tested with prior data analyzed using standard PMF-2 software, showed six major emission sources: an evaporative and fugitive source, a flaring source, a traffic source, an oil field source, a diesel source, and an industrial manufacturing source, together accounting for more than 95% of data set variability, and interpreted using NMHC composition and meteorological data. Factor scores strongly suggest that NOx emissions are dominated by flaring and associated sources, such as diesel compressor engines, likely at midstream facilities, while traffic in this rural area is a minor NOx source. The results support, but exceed existing 2012 emission inventories estimating that local traffic emitted seven times fewer NOx than oil and gas exploration sources in the county. Sources of air toxics such as the BTEX compounds are also dominated by oil and gas exploration sources, but are more equally distributed between the associated factors. Benzene abundance is only 20-40% associated with traffic sources, and may thus be 2.5-5 times higher now than prior to the shale boom in this area. Although the monitor was located relatively far from oil and gas exploration sources, these results suggest that exposure to air toxics in this rural population has likely increased manifold since the start of the regional shale boom in 2008.

Nonmotor fluctuations: phenotypes, pathophysiology, management, and open issues.

PubMed

Classen, Joseph; Koschel, Jiri; Oehlwein, Christian; Seppi, Klaus; Urban, Peter; Winkler, Christian; Wüllner, Ullrich; Storch, Alexander

2017-08-01

Parkinson's disease (PD) is a neurodegenerative multisystem disorder characterized by progressive motor symptoms such as bradykinesia, tremor and muscle rigidity. Over the course of the disease, numerous non-motor symptoms, sometimes preceding the onset of motor symptoms, significantly impair patients' quality of life. The significance of non-motor symptoms may outweigh the burden through progressive motor incapacity, especially in later stages of the disease. The advanced stage of the disease is characterized by motor complications such as fluctuations and dyskinesias induced by the long-term application of levodopa therapy. In recent years, it became evident that various non-motor symptoms such as psychiatric symptoms, fatigue and pain also show fluctuations after chronic levodopa therapy (named non-motor fluctuations or NMFs). Although NMFs have moved into the focus of interest, current national guidelines on the treatment of PD may refer to non-motor symptoms and their management, but do not mention NMF, and do not contain recommendations on their management. The present article summarizes major issues related to NMF including clinical phenomenology and pathophysiology, and outlines a number of open issues and topics for future research.

Potentiation of kinin analogues by ramiprilat is exclusively related to their degradation.

PubMed

Dendorfer, A; Reibetamann, S; Wolfrum, S; Raasch, W; Dominiak, P

2001-07-01

The potentiation of kinin actions represents a cardioprotective property of ACE inhibitors. Although a clear contribution to this effect is related to the inhibition of bradykinin (BK) breakdown, the high efficacy of potentiation and the ability of ACE inhibitors to provoke a B(2)-receptor-mediated response even after receptor desensitization has also triggered hypotheses concerning additional mechanisms of kinin potentiation. The application of kinin analogues with enhanced metabolic stability for the demonstration of degradation-independent mechanisms of potentiation, however, has yielded inconsistent results. Therefore, the relation between the susceptibility of B(2)-agonists to ACE and the potentiation of their actions by ACE inhibitors was investigated with the use of minimally modified kinin derivatives that varied in their degree of ACE resistance. The B(2)-agonists BK, D-Arg-[Hyp(3)]-BK, [Hyp,(3) Tyr(Me)(8)]-BK, [DeltaPhe(5)]-BK, [D-NMF(7)]-BK, and [Phe(8)psi(CH(2)-NH)Arg(9)]-BK were tested for degradation by purified rabbit ACE and for their potency in contracting the endothelium-denuded rabbit jugular vein in the absence and presence of ramiprilat. Purified ACE degraded D-Arg-[Hyp(3)]-BK and [Hyp,(3) Tyr(Me)(8)]-BK at 81% and 71% of BK degradation activity, respectively, whereas other peptides were highly ([DeltaPhe(5)]-BK) or completely ([D-NMF(7)]-BK, [Phe(8)psi(CH(2)-NH)Arg(9)]-BK) resistant. The EC(50) of BK-induced venoconstriction (1.15+/-0.2 nmol/L) was reduced by a factor of 5.7 in the presence of ramiprilat. Likewise, D-Arg-[Hyp(3)]-BK and [Hyp,(3) Tyr(Me)(8)]-BK were both significantly potentiated by a factor of 4.4, whereas the activities of the other agonists were not affected. Ramiprilat exerted no influence on the maximum contraction induced by any of the agonists. It is concluded that the potentiation of kinin analogues during ACE inhibition correlates quantitatively with the susceptibility of each substance to degradation by ACE. As such, no evidence of degradation-independent potentiating actions of ACE inhibitors could be obtained.

Ionospheric variations during sudden stratospheric warming in the high- and mid-latitude regions

NASA Astrophysics Data System (ADS)

Yasyukevich, Anna; Voeykov, Sergey; Mylnikova, Anna

2017-04-01

The ionospheric dynamic in the high- and middle-latitude regions during the periods of sudden stratospheric warmings (SSW) was studied by using the international network of phase dual-frequency GPS/GLONASS receivers and the vertical sounding data. Twelve SSW events that occurred in the Northern Hemisphere 2006 through 2013 were considered. In order to identify the possible response of the ionosphere to SSW events, we carried out the analysis of the total electron (TEC) and the F2-layer maximum electron density (NmF2) deviations from the background level. We have also studied changes of the level of total electron content (TEC) wave-like variations characterized by a special index WTEC. The index reflects the intensity of medium- and large-scale traveling ionospheric disturbances. The dynamics of the high- and middle-latitude ionosphere at the points near the SSW areas was found to differ from the regular. For a large number of events, it is shown that, despite quiet geomagnetic conditions, a noticeable decrease in the NmF2 and TEC values (by 5-10% relative to the background level) is observed during the SSW evolution and maximum stages. On the contrary, for 10-20 days after the SSW maxima, NmF2 and TEC significantly exceed the monthly averaged values. Moreover, these electron density changes are observed for both strong and weak stratospheric warmings, and are recorded mainly during daytime. The observed SSW effects in the polar and mid-latitude ionosphere are assumed to be probably associated with the changes in the neutral composition at the thermospheric heights that affect the F2-layer electron density. The study is supported by the Russian Foundation for Basic Research under Grant No. 16-35-60018, as well as by the RF President Grant of Public Support for RF Leading Scientific Schools (NSh-6894.2016.5).

Normalization of Hepatic Homeostasis in the Npc1nmf164 Mouse Model of Niemann-Pick Type C Disease Treated with the Histone Deacetylase Inhibitor Vorinostat.

PubMed

Munkacsi, Andrew B; Hammond, Natalie; Schneider, Remy T; Senanayake, Dinindu S; Higaki, Katsumi; Lagutin, Kirill; Bloor, Stephen J; Ory, Daniel S; Maue, Robert A; Chen, Fannie W; Hernandez-Ono, Antonio; Dahlson, Nicole; Repa, Joyce J; Ginsberg, Henry N; Ioannou, Yiannis A; Sturley, Stephen L

2017-03-17

Niemann-Pick type C (NP-C) disease is a fatal genetic lipidosis for which there is no Food and Drug Administration (FDA)-approved therapy. Vorinostat, an FDA-approved inhibitor of histone deacetylases, ameliorates lysosomal lipid accumulation in cultured NP-C patient fibroblasts. To assess the therapeutic potential of histone deacetylase inhibition, we pursued these in vitro observations in two murine models of NP-C disease. Npc1 nmf164 mice, which express a missense mutation in the Npc1 gene, were treated intraperitoneally, from weaning, with the maximum tolerated dose of vorinostat (150 mg/kg, 5 days/week). Disease progression was measured via gene expression, liver function and pathology, serum and tissue lipid levels, body weight, and life span. Transcriptome analyses of treated livers indicated multiple changes consistent with reversal of liver dysfunction that typifies NP-C disease. Significant improvements in liver pathology and function were achieved by this treatment regimen; however, NPC1 protein maturation and levels, disease progression, weight loss, and animal morbidity were not detectably altered. Vorinostat concentrations were >200 μm in the plasma compartment of treated animals but were almost 100-fold lower in brain tissue. Apolipoprotein B metabolism and the expression of key components of lipid homeostasis in primary hepatocytes from null ( Npc1 -/- ) and missense ( Npc1 nmf164 ) mutant mice were altered by vorinostat treatment, consistent with a response by these cells independent of the status of the Npc1 locus. These results suggest that HDAC inhibitors have utility to treat visceral NP-C disease. However, it is clear that improved blood-brain barrier penetration will be required to alleviate the neurological symptoms of human NP-C disease. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Neuromuscular Consequences of an Extreme Mountain Ultra-Marathon

PubMed Central

Millet, Guillaume Y.; Tomazin, Katja; Verges, Samuel; Vincent, Christopher; Bonnefoy, Régis; Boisson, Renée-Claude; Gergelé, Laurent; Féasson, Léonard; Martin, Vincent

2011-01-01

We investigated the physiological consequences of one of the most extreme exercises realized by humans in race conditions: a 166-km mountain ultra-marathon (MUM) with 9500 m of positive and negative elevation change. For this purpose, (i) the fatigue induced by the MUM and (ii) the recovery processes over two weeks were assessed. Evaluation of neuromuscular function (NMF) and blood markers of muscle damage and inflammation were performed before and immediately following (n = 22), and 2, 5, 9 and 16 days after the MUM (n = 11) in experienced ultra-marathon runners. Large maximal voluntary contraction decreases occurred after MUM (−35% [95% CI: −28 to −42%] and −39% [95% CI: −32 to −46%] for KE and PF, respectively), with alteration of maximal voluntary activation, mainly for KE (−19% [95% CI: −7 to −32%]). Significant modifications in markers of muscle damage and inflammation were observed after the MUM as suggested by the large changes in creatine kinase (from 144±94 to 13,633±12,626 UI L−1), myoglobin (from 32±22 to 1,432±1,209 µg L−1), and C-Reactive Protein (from <2.0 to 37.7±26.5 mg L−1). Moderate to large reductions in maximal compound muscle action potential amplitude, high-frequency doublet force, and low frequency fatigue (index of excitation-contraction coupling alteration) were also observed for both muscle groups. Sixteen days after MUM, NMF had returned to initial values, with most of the recovery process occurring within 9 days of the race. These findings suggest that the large alterations in NMF after an ultra-marathon race are multi-factorial, including failure of excitation-contraction coupling, which has never been described after prolonged running. It is also concluded that as early as two weeks after such an extreme running exercise, maximal force capacities have returned to baseline. PMID:21364944

The use of biomarkers of exposure of N,N-dimethylformamide in health risk assessment and occupational hygiene in the polyacrylic fibre industry

PubMed Central

Kafferlein, H; Ferstl, C; Burkhart-Reichl, A; Hennebruder, K; Drexler, H; Bruning, T; Angerer, J

2005-01-01

Background: N,N-dimethylformamide (DMF) was recently prioritised for field studies by the National Toxicology Program based on the potency of its reproductive toxic effects. Aims: To measure accurately exposure to DMF in occupational settings. Methods: In 35 healthy workers employed in the polyacrylic fibre industry, N-methylformamide (NMF) and N-acetyl-S-(N-methylcarbamoyl)cysteine (AMCC) in urine, and N-methylcarbamoylated haemoglobin (NMHb) in blood were measured. Workplace documentation and questionnaire information were used to categorise workers in groups exposed to low, medium, and high concentrations of DMF. Results: All three biomarkers can be used to identify occupational exposure to DMF. However, only the analysis of NMHb could accurately distinguish between workers exposed to different concentrations of DMF. The median concentrations were determined to be 55.1, 122.8, and 152.6 nmol/g globin in workers exposed to low, medium, and high concentrations of DMF, respectively. It was possible by the use of NMHb to identify all working tasks with increased exposure to DMF. While fibre crimpers were found to be least exposed to DMF, persons washing, dyeing, or towing the fibres were found to be highly exposed to DMF. In addition, NMHb measurements were capable of uncovering working tasks, which previously were not associated with increased exposure to DMF; for example, the person preparing the fibre forming solution. Conclusions: Measurement of NMHb in blood is recommended rather than measurement of NMF and AMCC in urine to accurately assess exposure to DMF in health risk assessment. However, NMF and AMCC are useful biomarkers for occupational hygiene intervention. Further investigations regarding toxicity of DMF should focus on highly exposed persons in the polyacrylic fibre industry. Additional measurements in occupational settings other than the polyacrylic fibre industry are also recommended, since the population at risk and the production volume of DMF are high. PMID:15837855

Nixtamalized flour from quality protein maize (Zea mays L). optimization of alkaline processing.

PubMed

Milán-Carrillo, J; Gutiérrez-Dorado, R; Cuevas-Rodríguez, E O; Garzón-Tiznado, J A; Reyes-Moreno, C

2004-01-01

Quality of maize proteins is poor, they are deficient in the essential amino acids lysine and tryptophan. Recently, in Mexico were successfully developed nutritionally improved 26 new hybrids and cultivars called quality protein maize (QPM) which contain greater amounts of lysine and tryptophan. Alkaline cooking of maize with lime (nixtamalization) is the first step for producing several maize products (masa, tortillas, flours, snacks). Processors adjust nixtamalization variables based on experience. The objective of this work was to determine the best combination of nixtamalization process variables for producing nixtamalized maize flour (NMF) from QPM V-537 variety. Nixtamalization conditions were selected from factorial combinations of process variables: nixtamalization time (NT, 20-85 min), lime concentration (LC, 3.3-6.7 g Ca(OH)2/l, in distilled water), and steep time (ST, 8-16 hours). Nixtamalization temperature and ratio of grain to cooking medium were 85 degrees C and 1:3 (w/v), respectively. At the end of each cooking treatment the steeping started for the required time. Steeping was finished by draining the cooking liquor (nejayote). Nixtamal (alkaline-cooked maize kernels) was washed with running tap water. Wet nixtamal was dried (24 hours, 55 degrees C) and milled to pass through 80-US mesh screen to obtain NMF. Response surface methodology (RSM) was applied as optimization technique, over four response variables: In vitro protein digestibility (PD), total color difference (deltaE), water absorption index (WAI), and pH. Predictive models for response variables were developed as a function of process variables. Conventional graphical method was applied to obtain maximum PD, WAI and minimum deltaE, pH. Contour plots of each of the response variables were utilized applying superposition surface methodology, to obtain three contour plots for observation and selection of best combination of NT (31 min), LC (5.4 g Ca(OH)2/l), and ST (8.1 hours) for producing optimized NMF from QPM.

Structural and spectroscopic investigation of the N-methylformamide-water (NMF···3H2O) complex

NASA Astrophysics Data System (ADS)

Hammami, F.; Ghalla, H.; Chebaane, A.; Nasr, S.

2015-01-01

In this work, theoretical studies on the structure, molecular properties, hydrogen bonding, and vibrational spectra of the N-methylformamide-water (NMF...3H2O) complex will be presented. The molecular geometry was optimised by using Hartree-Fock (HF), second Møller-Plesset (MP2), and density functional theory methods with different basis sets. The harmonic vibrational frequencies are computed by using the B3LYP method with 6-311++G(d,p) as a basis set and then scaled with a suitable scale factor to yield good coherence with the observed values. The temperature dependence of various thermodynamic functions (heat capacity, entropy, and enthalpy changes) was also studied. A detailed analysis of the nature of the hydrogen bonding, using natural bond orbital (NBO) and topological atoms in molecules theory, has been reported.

75 FR 5950 - Fisheries of the Caribbean, Gulf of Mexico, and South Atlantic; Snapper and Grouper Off the...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-05

... and Management Act (16 U.S.C. 1801 et seq.), and regulations at 50 CFR 600.745(b) concerning exempted... NMF4540135). The research is intended to involve commercial fishermen in the collection of fundamental... management and regulatory options. The proposed collection for scientific research involves activities...

Moisture-Stable Zn(II) Metal-Organic Framework as a Multifunctional Platform for Highly Efficient CO2 Capture and Nitro Pollutant Vapor Detection.

PubMed

Chen, Di-Ming; Tian, Jia-Yue; Chen, Min; Liu, Chun-Sen; Du, Miao

2016-07-20

A moisture-stable three-dimensional (3D) metal-organic framework (MOF), {(Me2NH2)[Zn2(bpydb)2(ATZ)](DMA)(NMF)2}n (1, where bpydb = 4,4'-(4,4'-bipyridine-2,6-diyl)dibenzoate, ATZ = deprotonated 5-aminotetrazole, DMA = N,N-dimethylacetamide, and NMF = N-methylformamide), with uncoordinated N-donor sites and charged framework skeleton was fabricated. This MOF exhibits interesting structural dynamic upon CO2 sorption at 195 K and high CO2/N2 (127) and CO2/CH4 (131) sorption selectivity at 298 K and 1 bar. Particularly, its CO2/CH4 selectivity is among the highest MOFs for selective CO2 separation. The results of Grand Canonical Monte Carlo (GCMC) simulation indicate that the polar framework contributes to the strong framework-CO2 binding at zero loading, and the tetrazole pillar contributes to the high CO2 uptake capacity at high loading. Furthermore, the solvent-responsive luminescent properties of 1 indicate that it could be utilized as a fluorescent sensor to detect trace amounts of nitrobenzene in both solvent and vapor systems.

The development of peripheral fatigue and short-term recovery during self-paced high-intensity exercise

PubMed Central

Froyd, Christian; Millet, Guillaume Y; Noakes, Timothy D

2013-01-01

The time course of muscular fatigue that develops during and after an intense bout of self-paced dynamic exercise was characterized by using different forms of electrical stimulation (ES) of the exercising muscles. Ten active subjects performed a time trial (TT) involving repetitive concentric extension/flexion of the right knee using a Biodex dynamometer. Neuromuscular function (NMF), including ES and a 5 s maximal isometric voluntary contraction (MVC), was assessed before the start of the TT and immediately (<5 s) after each 20% of the TT had been completed, as well as 1, 2, 4 and 8 min after TT termination. The TT time was 347 ± 98 s. MVCs were 52% of baseline values at TT termination. Torque responses from ES were reduced to 33–68% of baseline using different methods of stimulation, suggesting that the extent to which peripheral fatigue is documented during exercise depends upon NMF assessment methodology. The major changes in muscle function occurred within the first 40% of exercise. Significant recovery in skeletal muscle function occurs within the first 1–2 min after exercise, showing that previous studies may have underestimated the extent to which peripheral fatigue develops during exercise. PMID:23230235

Modeling the behavior of ionosphere above Millstone Hill during the September 21-27, 1998 storm

NASA Astrophysics Data System (ADS)

Lei, Jiuhou; Liu, Libo; Wan, Weixing; Zhang, Shun-Rong

2004-08-01

A theoretical ionospheric model is employed to investigate the ionospheric behavior as observed by the incoherent-scatter radar (ISR) at Millstone Hill during the September 21-27, 1998 storm. The observed NmF2 presented a significant negative phase on September 25, and a G condition (hmF2<200km) was also observed. The model results based on the standard input parameters (climatological model values) are in good agreement with the observed electron densities under quiet conditions, but there are large discrepancies during disturbed periods. The exospheric temperature Tex, neutral winds, atomic oxygen density [O] and molecular nitrogen density [N2], and solar flux are inferred from the ISR ion temperature profiles and from the electron density profiles. Our calculated results show that the maximum Tex is higher than 1700K, and an averaged decrease in [O] is a factor of 2.2 and an increase in [N2] at 300km is about 1.8 times for the disturbed day, September 25, relative to the quiet day level. Therefore, the large change of [N2]/[O] ratio gives a good explanation for the negative phase at Millstone Hill during this storm. Furthermore, at the disturbed nighttime the observations show a strong NmF2 decrease, accompanied by a significant hmF2 increase after the sudden storm commencement (SSC). Simulations are carried out based on the inferred Tex. It is found that the uplift of F2 layer during the period from sunset to post-midnight is mainly associated with the large equatorward winds, and a second rise in hmF2 after midnight results from the depleted Ne in the bottom-side of F2 layer due to the increased recombination, while the ``midnight collapse'' of hmF2 is attributed to the large-scale traveling atmospheric disturbances.

Improving PTSD Symptoms and Preventing Progression of Subclinical PTSD to an Overt Disorder by Treating Comorbid OSA With CPAP.

PubMed

Ullah, M I; Campbell, Douglas G; Bhagat, Rajesh; Lyons, Judith A; Tamanna, Sadeka

2017-10-15

Obstructive sleep apnea (OSA) and posttraumatic stress disorder (PTSD) are common in United States veterans. These conditions often coexist and symptoms overlap. Previous studies reported improvement in PTSD symptoms with continuous positive airway pressure (CPAP) therapy for comorbid OSA but its effect has not been assessed in a non-PTSD cohort. We have prospectively assessed the effect of CPAP therapy on clinical symptom improvement as a function of CPAP compliance levels among PTSD and non-PTSD veterans. Veterans in whom OSA was newly diagnosed were enrolled in our study (n = 192). Assignment to PTSD and non-PTSD cohorts was determined by chart review. Each patient completed the military version of the PTSD Checklist (PCL), Epworth Sleepiness Scale (ESS), and reported nightmare frequency (NMF) at baseline and 6 months after CPAP therapy. CPAP adherence was objectively documented from machine compliance data. We had complete data for 177 veterans (PTSD n = 59, non-PTSD n = 118) for analysis. The mean ages were 51.24 years in the PTSD cohort and 52.36 years in the non-PTSD cohort ( P = .30). In the PTSD cohort, the mean total PCL score (baseline = 66.06, post-CPAP = 61.27, P = .004, d = -0.34) and NMF (baseline = 4.61, post-CPAP = 1.49, P = .0001, d = -0.51) decreased after 6 months of CPAP treatment. Linear regression analysis showed that the CPAP compliance was the only significant predictor for these changes among veterans with PTSD (PCL score: P = .033, R 2 = .65; NMF; P = .03, R 2 = .61). Further analysis by CPAP compliance quartiles in this cohort (Q1 = 0% to 25%, Q2 = 26% to 50%, Q3 = 51% to 75%, Q4 > 75%) revealed that mean total PCL score declined in Q2 (change = -3.91, P = .045, d = 0.43), Q3 (change = -6.6, P = .002, d = 0.59), and Q4 (change = -7.94, P = .037, d = 0.49). In the non-PTSD cohort, the PCL score increased despite CPAP therapy in lower CPAP compliance quartiles Q1 (change = 8.71, P = .0001, d = 0.46) and Q2 (change = 4.51, P = .046, d = 0.27). With higher CPAP compliance (in Q3 and Q4) in this cohort, the mean total PCL scores slightly improved with CPAP but they were not statistically significant ( P > .05). CPAP treatment reduces total PCL score and NMF in veterans with PTSD and OSA. Those with overt PTSD respond to even lower CPAP compliance, whereas non-PTSD patients require higher compliance to achieve any symptom improvement. Poor CPAP compliance results in increased PCL score in non-PTSD veterans and may lead to overt PTSD if the OSA remains undertreated. A commentary on this article appears in this issue on page 1121. © 2017 American Academy of Sleep Medicine

About a global model of the equivalent slab thickness of the ionosphere

NASA Astrophysics Data System (ADS)

Maltseva, Olga; Mozhaeva, Natalya

2016-07-01

Use of a median of an equivalent slab thickness of the ionosphere τ(med) is the simplest case of assimilation of the total electron content TEC. To use τ(med) on a global scale it is necessary to have its model. Some variants are possible: (1) construction of superficial function of kriging type using values of τ(med) in several points, (2) the NGM model which can be constructed on the basis of two empirical Neustrelitz models for TEC and NmF2, (3) the IRI-Plas model. Construction of a model with use of τ(med) values is difficult because of the large variability of values (in particular, a strong pre-sunrise peak at some latitudes). Testing of models NGM and IRI-Plas shows that they not always provide satisfactory results in that or another region of globe. Besides, they are not pure empirical models. In the present work, an attempt is done to use two-parameter model on a basis of hyperbolic dependence of τ(med) from NmF2 (τ(hyp) =b0+b1/NmF2) and approximation of coefficient K(τ) = τ(med)/τ(IRI) in a latitudinal course. On an example of March 2015 when there was a great number of ionosonde data, coefficients b0 and b1 were modeled. Results are presented for two regions Lat1 and Lat2. Area Lat1 contains 13 stations, basically, on the American continent of northern and southern hemispheres. Area Lat2 contains 20 stations of the European, Siberian and Southeast regions. Certain advantage of use of coefficients K(τ) can be that in its numerator there is a magnitude of τ(IRI), having a global character, and a small variation of K(τ) in zones with close longitudes. Difference is a model construction at each hour. Degree of coincidence is better to illustrate on circular diagrams. Models were tested by elimination of one of stations and definition of deviations of calculated foF2 from experimental values. Authors thank Southern Federal University for support by grant #213.01-11/2014-22.

Effects of 27-day averaged tidal forcing on the thermosphere-ionosphere as examined by the TIEGCM

NASA Astrophysics Data System (ADS)

Maute, A. I.; Forbes, J. M.; Hagan, M. E.

2016-12-01

The variability of the ionosphere and thermosphere is influenced by solar and geomagnetic forcing and by lower atmosphere coupling. During the last solar minimum low- and mid-latitude ionospheric observations have shown strong longitudinal signals which are associated with upward propagating tides. Progress has been made in explaining observed ionospheric and thermospheric variations by investigating possible coupling mechanisms e.g., wind dynamo, propagation of tides into the upper thermosphere, global circulation changes, and compositional effects. To fully understand the vertical coupling a comprehensive set of simultaneous measurements of key quantities is missing. The Ionospheric Connection (ICON) explorer will provide such a data set and the data interpretation will be supported by numerical modeling to investigate the lower to upper atmosphere coupling. Due to ICON's orbit, 27 days of measurements are needed to cover all longitudes and local times and to be able to derive tidal components. In this presentation we employ the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) to evaluate the influence of the 27-day processing window on the ionosphere and thermosphere state. Specifically, we compare TIEGCM simulations that are forced at its 97 km lower boundary by daily tidal fields from 2009 MERRA-forced TIME-GCM output [Häusler et al., 2015], and by the corresponding 27-day mean tidal fields. Apart from the expected reduced day-to-day variability when using 27-day averaged tidal forcing, the simulations indicate net NmF2 changes at low latitudes, which vary with season. First results indicate that compositional effects may influence the Nmf2 modifications. We will quantify the effect of using a 27-day averaged diurnal tidal forcing versus daily ones on the equatorial vertical drift, low and mid-latitude NmF2 and hmF2, global circulation, and composition. The possible causes for the simulated changes will be examined. The result of this study will be important for the comparison of the ICON observations with the accompanying ICON-TIEGCM simulations and guide the model-data interpretation.

Global features of ionospheric slab thickness derived from JPL TEC and COSMIC observations

NASA Astrophysics Data System (ADS)

Huang, He; Liu, Libo

2016-04-01

The ionospheric equivalent slab thickness (EST) is the ratio of total electron content (TEC) to F2-layer peak electron density (NmF2), describing the thickness of the ionospheric profile. In this study, we retrieve EST from Jet Propulsion Laboratory (JPL) TEC data and NmF2 retrieved from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) ionospheric radio occultation data. The diurnal, seasonal and solar activity variations of global EST are analyzed as the excellent spatial coverage of JPL TEC and COSMIC data. During solstices, daytime EST in the summer hemisphere is larger than that in the winter hemisphere, except in some high-latitude regions; and the reverse is true for the nighttime EST. The peaks of EST often appear at 0400 local time. The pre-sunrise enhancement in EST appears in all seasons, while the post-sunset enhancement in EST is not readily observed in equinox. The dependence of EST on solar activity is very complicated. Furthermore, an interesting phenomenon is found that EST is enhanced from 0° to 120° E in longitude and 30° to 75° S in latitude during nighttime, just to the east of Weddell Sea Anomaly, during equinox and southern hemisphere summer.

Variations in Ionospheric Peak Electron Density During Sudden Stratospheric Warmings in the Arctic Region

NASA Astrophysics Data System (ADS)

Yasyukevich, A. S.

2018-04-01

The focus of the paper is the ionospheric disturbances during sudden stratospheric warming (SSW) events in the Arctic region. This study examines the ionospheric behavior during 12 SSW events, which occurred in the Northern Hemisphere over 2006-2013, based on vertical sounding data from DPS-4 ionosonde located in Norilsk (88.0°E, 69.2°N). Most of the addressed events show that despite generally quiet geomagnetic conditions, notable changes in the ionospheric behavior are observed during SSWs. During the SSW evolution and peak phases, there is a daytime decrease in NmF2 values at 10-20% relative to background level. After the SSW maxima, in contrast, midday NmF2 surpasses the average monthly values for 10-20 days. These changes in the electron density are observed for both strong and weak stratospheric warmings occurring at midwinter. The revealed SSW effects in the polar ionosphere are assumed to be associated with changes in the thermospheric neutral composition, affecting the F2-layer electron density. Analysis of the Global Ultraviolet Imager data revealed the positive variations in the O/N2 ratio within the thermosphere during SSW peak and recovery periods. Probable mechanisms for SSW impact on the state of the high-latitude neutral thermosphere and ionosphere are discussed.

Capturing Change: Creating a Template to Examine the Educational Experiences and Outcomes of the AONE Foundation Nurse Manager Fellowship.

PubMed

Mackoff, Barbara L; Meadows, Mary T; Nash, Alice

2017-03-01

The aim of the study is to create a mixed-methods evaluation template to examine the educational experiences and outcomes of participants in the Nurse Manager Fellowship (NMF) sponsored by the American Organization of Nurse Executives (AONE) Foundation. The focus was to capture change as reported by the nurse manager (NM) fellows and the senior leaders who sponsored them and to gain access to the participants' lived experiences as leadership learners. The AONE Foundation's NMF conducts a yearlong professional development program with a cohort of 30 fellows who meet 4 times a year in face-to-face sessions and complete a capstone project. Four data collection methods were used. Participants completed 2 quantitative leadership program outcome surveys, as well as 1 qualitative measure to focus on self-perceived change outcomes. Their sponsors completed a qualitative perception of change measure. The participants' reflections, self-reports, and the sponsor observations capture impactful changes in the NM fellows' increases in knowledge and application in the spheres of self, organization, and community. The enhancement of the participants' self-identification as leaders was also demonstrated. The variety of data collection methods suggests both distinct choices in creating future evaluation templates for the fellowship and approaches that might be adapted by other organizations.

Day-to-day ionospheric variability due to lower atmosphere perturbations

NASA Astrophysics Data System (ADS)

Liu, H.; Yudin, V. A.; Roble, R. G.

2013-12-01

Ionospheric day-to-day variability is a ubiquitous feature, even in the absence of appreciable geomagnetic activities. Although meteorological perturbations have been recognized as an important source of the variability, it is not well represented in previous modeling studies, and the mechanism is not well understood. This study demonstrates that TIME-GCM (Thermosphere-Ionosphere-Mesosphere-Electrodynamics General Circulation Model) constrained in the stratosphere and mesosphere by the hourly Whole Atmosphere Community Climate Model (WACCM) simulations is capable of reproducing observed features of day-to-day variability in the thermosphere-ionosphere. Realistic weather patterns in the lower atmosphere in WACCM was specified by Modern Era Retrospective reanalysis for Research and Application (MERRA). The day-to-day variations in mean zonal wind, migrating and non-migrating tides in the thermosphere, vertical and zonal ExB drifts, and ionosphere F2 layer peak electron density (NmF2) are examined. The standard deviations of the drifts and NmF2 display local time and longitudinal dependence that compare favorably with observations. Their magnitudes are 50% or more of those from observations. The day-to-day thermosphere and ionosphere variability in the model is primarily caused by the perturbations originated in lower atmosphere, since the model simulation is under constant solar minimum and low geomagnetic conditions.

Microbial community pattern detection in human body habitats via ensemble clustering framework.

PubMed

Yang, Peng; Su, Xiaoquan; Ou-Yang, Le; Chua, Hon-Nian; Li, Xiao-Li; Ning, Kang

2014-01-01

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.

Microbial community pattern detection in human body habitats via ensemble clustering framework

PubMed Central

2014-01-01

Background The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. Results To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. Conclusions In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome. PMID:25521415

Feedback of mechanical effectiveness induces adaptations in motor modules during cycling

PubMed Central

De Marchis, Cristiano; Schmid, Maurizio; Bibbo, Daniele; Castronovo, Anna Margherita; D'Alessio, Tommaso; Conforto, Silvia

2013-01-01

Recent studies have reported evidence that the motor system may rely on a modular organization, even if this behavior has yet to be confirmed during motor adaptation. The aim of the present study is to investigate the modular motor control mechanisms underlying the execution of pedaling by untrained subjects in different biomechanical conditions. We use the muscle synergies framework to characterize the muscle coordination of 11 subjects pedaling under two different conditions. The first one consists of a pedaling exercise with a strategy freely chosen by the subjects (Preferred Pedaling Technique, PPT), while the second condition constrains the gesture by means of a real time visual feedback of mechanical effectiveness (Effective Pedaling Technique, EPT). Pedal forces, recorded using a pair of instrumented pedals, were used to calculate the Index of Effectiveness (IE). EMG signals were recorded from eight muscles of the dominant leg and Non-negative Matrix Factorization (NMF) was applied for the extraction of muscle synergies. All the synergy vectors, extracted cycle by cycle for each subject, were pooled across subjects and conditions and underwent a 2-dimensional Sammon's non-linear mapping. Seven representative clusters were identified on the Sammon's projection, and the corresponding eight-dimensional synergy vectors were used to reconstruct the repertoire of muscle activation for all subjects and all pedaling conditions (VAF > 0.8 for each individual muscle pattern). Only 5 out of the 7 identified modules were used by the subjects during the PPT pedaling condition, while 2 additional modules were found specific for the pedaling condition EPT. The temporal recruitment of three identified modules was highly correlated with IE. The structure of the identified modules was found similar to that extracted in other studies of human walking, partly confirming the existence of shared and task specific muscle synergies, and providing further evidence on the modularity of the motor system. PMID:23616763

A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations

PubMed Central

Bollegala, Danushka; Kontonatsios, Georgios; Ananiadou, Sophia

2015-01-01

Bilingual dictionaries for technical terms such as biomedical terms are an important resource for machine translation systems as well as for humans who would like to understand a concept described in a foreign language. Often a biomedical term is first proposed in English and later it is manually translated to other languages. Despite the fact that there are large monolingual lexicons of biomedical terms, only a fraction of those term lexicons are translated to other languages. Manually compiling large-scale bilingual dictionaries for technical domains is a challenging task because it is difficult to find a sufficiently large number of bilingual experts. We propose a cross-lingual similarity measure for detecting most similar translation candidates for a biomedical term specified in one language (source) from another language (target). Specifically, a biomedical term in a language is represented using two types of features: (a) intrinsic features that consist of character n-grams extracted from the term under consideration, and (b) extrinsic features that consist of unigrams and bigrams extracted from the contextual windows surrounding the term under consideration. We propose a cross-lingual similarity measure using each of those feature types. First, to reduce the dimensionality of the feature space in each language, we propose prototype vector projection (PVP)—a non-negative lower-dimensional vector projection method. Second, we propose a method to learn a mapping between the feature spaces in the source and target language using partial least squares regression (PLSR). The proposed method requires only a small number of training instances to learn a cross-lingual similarity measure. The proposed PVP method outperforms popular dimensionality reduction methods such as the singular value decomposition (SVD) and non-negative matrix factorization (NMF) in a nearest neighbor prediction task. Moreover, our experimental results covering several language pairs such as English–French, English–Spanish, English–Greek, and English–Japanese show that the proposed method outperforms several other feature projection methods in biomedical term translation prediction tasks. PMID:26030738

Comparison of COSMIC RO Data with European Digisondes and GPS TEC measurements

NASA Astrophysics Data System (ADS)

Zakharenkova, Irina; Krypiak-Gregorczyk, Anna; Shagimuratov, Irk; Krankowski, Andrzej; Lagovsky, Anatoly

FormoSat-3/COSMIC now provides unprecedented global coverage of GPS occultations mea-surements, each of which yields the ionosphere electron density information with high vertical resolution. However systematic validation work is still needed before using the powerful RO technique for sounding the ionosphere on a routine basis. In the given study electron density profiles retrieved from the Formosat-3/COSMIC RO measurements were compared with differ-ent kinds of ground-based observations. We used the ionospheric data recorded by European digisondes of DIAS network (Rome, Ebro, Arenosillo, Athens, Chilton, Pruhonice and Julius-ruh) for temporal interval of 2007-2009 and compare these ground measured data with the GPS COSMIC RO ionospheric profiles. It was revealed that in general the form of COSMIC profile in the bottom side is in a good agreement with ionosonde profiles, the heights of the peak density value are also good comparable. Special attention was focused to the question of the topside part of electron density profile. Practically for all analyzed cases there are observed the understated values of electron density in the topside part of the ionosonde profiles in compare with RO profiles. As the topside ionosonde profile is obtained by fitting a model to the peak electron density value, the COSMIC radio occultation measurements can make an important contribution to the investigation of the topside part of the ionosphere. In order to assess the ac-curacy of the COSMIC ionospheric electron density retrievals, coincidences of ionosonde data with COSMIC NmF2 values have been examined. NmF2 was calculated from the observed critical plasma frequency foF2 of the F2 layer. Values of foF2 have been scaled manually from ionograms for all considered time-location cases to avoid the evident risks related with using of the autoscaled data. The created scatter plots show a high degree of correlation between two independent estimates of NmF2. Also it was analyzed the variation of NmF2 for the considered seasons depending on day-time and night-time conditions. Also it was analyzed the total elec-tron content values calculated for the nearest ground-based GPS stations located in European region. To compare GPS TEC with RO and ionosondes' data these profiles were integrated. In general bottom parts of COSMIC and ionosondes' data are in a rather good agreement while the topside can be varied greatly that is the evidence of difference in the topside parts of these profiles. GPS TEC values are greater than COSMIC and ionosondes' data as TEC contains IEC and PEC. This procedure can be useful to estimate the impact of PEC into TEC. Results of the given comparisons can be important to validate the reliability of the COSMIC iono-spheric observations using the RO technique. We acknowledge the Taiwan's National Space Organization (NSPO) and the University Corporation for Atmospheric Research (UCAR) for providing the COSMIC Data. We are grateful to European Digital Upper Atmosphere Server (DIAS) for providing the ionosondes' products and to International GNSS Service (IGS) for GPS Data.

Improving PTSD Symptoms and Preventing Progression of Subclinical PTSD to an Overt Disorder by Treating Comorbid OSA With CPAP

PubMed Central

Ullah, M. I.; Campbell, Douglas G.; Bhagat, Rajesh; Lyons, Judith A.; Tamanna, Sadeka

2017-01-01

Study Objectives: Obstructive sleep apnea (OSA) and posttraumatic stress disorder (PTSD) are common in United States veterans. These conditions often coexist and symptoms overlap. Previous studies reported improvement in PTSD symptoms with continuous positive airway pressure (CPAP) therapy for comorbid OSA but its effect has not been assessed in a non-PTSD cohort. We have prospectively assessed the effect of CPAP therapy on clinical symptom improvement as a function of CPAP compliance levels among PTSD and non-PTSD veterans. Methods: Veterans in whom OSA was newly diagnosed were enrolled in our study (n = 192). Assignment to PTSD and non-PTSD cohorts was determined by chart review. Each patient completed the military version of the PTSD Checklist (PCL), Epworth Sleepiness Scale (ESS), and reported nightmare frequency (NMF) at baseline and 6 months after CPAP therapy. CPAP adherence was objectively documented from machine compliance data. Results: We had complete data for 177 veterans (PTSD n = 59, non-PTSD n = 118) for analysis. The mean ages were 51.24 years in the PTSD cohort and 52.36 years in the non-PTSD cohort (P = .30). In the PTSD cohort, the mean total PCL score (baseline = 66.06, post-CPAP = 61.27, P = .004, d = −0.34) and NMF (baseline = 4.61, post-CPAP = 1.49, P = .0001, d = −0.51) decreased after 6 months of CPAP treatment. Linear regression analysis showed that the CPAP compliance was the only significant predictor for these changes among veterans with PTSD (PCL score: P = .033, R2 = .65; NMF; P = .03, R2 = .61). Further analysis by CPAP compliance quartiles in this cohort (Q1 = 0% to 25%, Q2 = 26% to 50%, Q3 = 51% to 75%, Q4 > 75%) revealed that mean total PCL score declined in Q2 (change = −3.91, P = .045, d = 0.43), Q3 (change = −6.6, P = .002, d = 0.59), and Q4 (change = −7.94, P = .037, d = 0.49). In the non-PTSD cohort, the PCL score increased despite CPAP therapy in lower CPAP compliance quartiles Q1 (change = 8.71, P = .0001, d = 0.46) and Q2 (change = 4.51, P = .046, d = 0.27). With higher CPAP compliance (in Q3 and Q4) in this cohort, the mean total PCL scores slightly improved with CPAP but they were not statistically significant (P > .05). Conclusions: CPAP treatment reduces total PCL score and NMF in veterans with PTSD and OSA. Those with overt PTSD respond to even lower CPAP compliance, whereas non-PTSD patients require higher compliance to achieve any symptom improvement. Poor CPAP compliance results in increased PCL score in non-PTSD veterans and may lead to overt PTSD if the OSA remains undertreated. Commentary: A commentary on this article appears in this issue on page 1121. Citation: Ullah MI, Campbell DG, Bhagat R, Lyons JA, Tamanna S. Improving PTSD symptoms and preventing progression of subclinical PTSD to an overt disorder by treating comorbid OSA with CPAP. J Clin Sleep Med. 2017;13(10):1191–1198. PMID:28859723

An Investigation of Variable Time Interval K-like Geomagnetic Indices

DTIC Science & Technology

1999-12-16

ionosphere system. The index is widely used to drive empirical models of auroral particle precipitation, high-latitude convection patterns...81 35 Kp use in TDIM simulations 86 36 Fredericksburg magnetic disturbance for 28 July 1990 88 Xlll 37 The Heppner-Maynard convection patterns used...in our TDIM simulations 90 38 High-latitude electron density difference histograms for 0500 UT on 28 July 1990 95 39 High-latitude NmF2 percent

Approximate method of variational Bayesian matrix factorization/completion with sparse prior

NASA Astrophysics Data System (ADS)

Kawasumi, Ryota; Takeda, Koujin

2018-05-01

We derive the analytical expression of a matrix factorization/completion solution by the variational Bayes method, under the assumption that the observed matrix is originally the product of low-rank, dense and sparse matrices with additive noise. We assume the prior of a sparse matrix is a Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for the derivation of a matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of a sparse matrix reconstruction in matrix factorization, and completion of a missing matrix element in matrix completion.

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Sensitivity of Ionosphere/Thermosphere to different high-latitude drivers

NASA Astrophysics Data System (ADS)

Shim, J.; Kuznetsova, M. M.; Rastaetter, L.; Swindell, M.; Codrescu, M.; Emery, B. A.; Foerster, M.; Foster, B.; Fuller-Rowell, T. J.; Mannucci, A. J.; Pi, X.; Prokhorov, B.; Ridley, A. J.; Coster, A. J.; Goncharenko, L. P.; Lomidze, L.; Scherliess, L.; Crowley, G.

2013-12-01

We compared Ionosphere/Thermosphere (IT) parameters, which were obtained using different models for the high-latitude ionospheric electric potential (e.g., Weimer 2005, AMIE (assimilative mapping of ionospheric electrodynamics) and global magnetosphere models (e.g. Space Weather Modeling Framework)) and particle precipitation (e.g., Fuller-Rowell & Evans, Roble & Ridley, and SWMF). For this study, the physical parameters such as Total Electron Content (TEC), NmF2 and hmF2, and electron and neutral densities at the CHAMP satellite track are considered. In addition, we compared the modeled physical parameters with observed data including ground-based GPS TEC measurements, NmF2 and hmF2 from COSMIC LEO satellites in the selected 5 degree eight longitude sectors, and Ne and neutral density measured by the CHAMP satellite. We quantified the performance of the models using skill scores. Furthermore, the skill scores are obtained for three latitude regions (low, middle and high latitudes) in order to investigate latitudinal dependence of the models' performance. This study is supported by the Community Coordinated Modeling Center (CCMC) at the Goddard Space Flight Center. The CCMC converted ionosphere drivers from a variety of sources and developed an interpolation tool that can be employed by any modelers for easy driver swapping. Model outputs and observational data used for the study will be permanently posted at the CCMC website (http://ccmc.gsfc.nasa.gov) as a resource for the space science communities to use.

Combinatorial investigation of rare-earth free permanent magnets

NASA Astrophysics Data System (ADS)

Fackler, Sean Wu

The combinatorial high throughput method allows one to rapidly study a large number of samples with systematically changing parameters. We apply this method to study Fe-Co-V alloys as alternatives to rare-earth permanent magnets. Rare-earth permanent magnets derive their unmatched magnetic properties from the hybridization of Fe and Co with the f-orbitals of rare-earth elements, which have strong spin-orbit coupling. It is predicted that Fe and Co may also have strong hybridization with 4d and 5d refractory transition metals with strong spin-orbit coupling. Refractory transition metals like V also have the desirable property of high temperature stability, which is important for permanent magnet applications in traction motors. In this work, we focus on the role of crystal structure, composition, and secondary phases in the origin of competitive permanent magnetic properties of a particular Fe-Co-V alloy. Fe38Co52V10, compositions are known as Vicalloys. Fe-CoV composition spreads were sputtered onto three-inch silicon wafers and patterned into discrete sample pads forming a combinatorial library. We employed highthroughput screening methods using synchrotron X-rays, wavelength dispersive spectroscopy, and magneto-optical Kerr effect (MOKE) to rapidly screen crystal structure, composition, and magnetic properties, respectively. We found that in-plane magnetic coercive fields of our Vicalloy thin films agree with known bulk values (300 G), but found a remarkable eight times increase of the out-of-plane coercive fields (˜2,500 G). To explain this, we measured the switching fields between in-plane and out-of-plane thin film directions which revealed that the Kondorsky model of 180° domain wall reversal was responsible for Vicalloy's enhanced out-of-plane coercive field and possibly its permanent magnetic properties. The Kondorsky model suggests that domain-wall pinning is the origin of Vicalloy's permanent magnetic properties, in contrast to strain, shape, or crystalline anisotropy mechanisms suggested in the literature. We also studied the thickness dependence of an Fe70Co30- V thin film library to consider the unique effects of our thin film libraries which are not found in bulk samples. We present results of data mining of synchrotron X-ray diffraction data using non-negative matrix factorization (NMF). NMF can automatically identify pure crystal phases that make up an unknown phase mixture. We found a strong correlation between magnetic properties and crystal phase quantity using this valuable visualization. In addition to the combinatorial study, this dissertation includes a study of strain controlled properties of magnetic thin films for future applications in random access memories. We investigated the local coupling between dense magnetic stripe domains in transcritical Permalloy (tPy) thin films and ferroelectric domains of BaTiO3 single crystals in a tPy/BaTiO3 heterostructure. Two distinct changes in the magnetic stripe domains of tPy were observed from the magnetic force microscopy images after cooling the heterostructure from above the ferroelectric Curie temperature of BaTiO3 (120°C) to room temperature. First, an abrupt break in the magnetic stripe domain direction was found at the ferroelectric a-c-domain boundaries due to an induced change in in-plane magnetic anisotropy. Second, the magnetic stripe domain period increased when coupled to a ferroelectric a-domain due to a change in out-of-plane magnetic anisotropy. Micromagnetic simulations reveal that local magnetic anisotropy energy from inverse magnetostriction is conserved between in-plane and out-of-plane components.

A DEIM Induced CUR Factorization

DTIC Science & Technology

2015-09-18

CUR approximate matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). For a given matrix A, such a factorization provides a...CUR approximations based on leverage scores. 1 Introduction This work presents a new CUR matrix factorization based upon the Discrete Empirical...SUPPLEMENTARY NOTES 14. ABSTRACT We derive a CUR approximate matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). For a given

The role of decreased levels of Niemann-Pick C1 intracellular cholesterol transport on obesity is reversed in the C57BL/6J, metabolic syndrome mouse strain: a metabolic or an inflammatory effect?

PubMed

Borbon, Ivan; Campbell, Erin; Ke, Wangjing; Erickson, Robert P

2012-08-01

We have previously shown that decreased dosage of Niemann-Pick C1 (Npc1) protein, caused by heterozygosity at the null mutation, Npc1 (nih), locus, causes altered lipid metabolism in mice. When studied on the "lean" BALB/cJ genetic background, the decreased protein was associated with no weight changes in either males or females when on a regular diet but increased weights and adiposity when on a high fat diet Jelinek et al. (Obesity 18: 1457-1459, 2010, Gene 491:128-134, 2012). When the heterozygotes were studied on a mixed C57BL/6J, BALB/cJ background, increased weight and adiposity were also found on a regular diet (sexes pooled Jelinek et al. [Hum Molec Genet 20:312-321, 2011]). We find somewhat different results when the hypomorphic Npc1 mutation, Npc1 (nmf164), is studied on a pure C57BL/6J, "metabolic syndrome" genetic background with male, but not female, heterozygotes having lower weights on the regular diet. The result does not seem to be due to the difference in the two mutations as heterozygous Npc1 (nmf164) mice on the BALB/cJ background acted like the null mutant heterozygotes. Studies of glucose tolerance, liver enzymes, liver triglycerides and fat deposition, and adipose tissue caveolin 1 levels did not disclose reasons for these differing results.

Effect of chemical pretreatment on pyrolysis of non-metallic fraction recycled from waste printed circuit boards.

PubMed

Shen, Yafei

2018-06-01

The non-metallic fraction from waste printed circuit boards (NMF-WPCB) generally consists of plastics with high content of Br, glass fibers and metals (e.g. Cu), which are normally difficult to dispose. This work aims to study the chemical pretreatments by using alkalis, acids and alkali-earth-metal salts on pyrolysis of NMF-WPCB. Char (60-79%) and volatile matter (21-40%) can be produced via the pyrolysis process. In particular, the ash content can reach up to 42-56%, which was attributed to the high content of glass fibers and other minerals. Copper (Cu, 2.5%), calcium (Ca, 28.7%), and aluminum (Al, 6.9%) were the main metal constituents. Meanwhile, silicon (Si, 28.3%) and bromine (Br, 26.4%) were the predominant non-metallic constituents. The heavy metals such as Cu were significantly reduced by 92.4% with the acid (i.e. HCl) pretreatment. It has been proved that the organic Br in the plastics (e.g. BFR) can be transformed into HBr via the pyrolysis process at relatively high temperature. It was noteworthy that the alkali pretreatment was more benefit for the Br fixation in the solid char. Particularly, the Br fixation efficiency can reach up to 53.6% by the sodium hydroxide (NaOH) pretreatment with the pyrolysis process. The formed HBr can react with NaOH to generate NaBr. Copyright © 2018 Elsevier Ltd. All rights reserved.

Identification of potential genomic biomarkers of hepatotoxicity caused by reactive metabolites of N-methylformamide: Application of stable isotope labeled compounds in toxicogenomic studies.

PubMed

Mutlib, Abdul; Jiang, Ping; Atherton, Jim; Obert, Leslie; Kostrubsky, Seva; Madore, Steven; Nelson, Sidney

2006-10-01

The inability to predict if a metabolically bioactivated compound will cause toxicity in later stages of drug development or post-marketing is of serious concern. One approach for improving the predictive success of compound toxicity has been to compare the gene expression profile in preclinical models dosed with novel compounds to a gene expression database generated from compounds with known toxicity. While this guilt-by-association approach can be useful, it is often difficult to elucidate gene expression changes that may be related to the generation of reactive metabolites. In an effort to address this issue, we compared the gene expression profiles obtained from animals treated with a soft-electrophile-producing hepatotoxic compound against corresponding deuterium labeled analogues resistant to metabolic processing. Our aim was to identify a subset of potential biomarker genes for hepatotoxicity caused by soft-electrophile-producing compounds. The current study utilized a known hepatotoxic compound N-methylformamide (NMF) and its two analogues labeled with deuterium at different positions to block metabolic oxidation at the formyl (d(1)) and methyl (d(3)) moieties. Groups of mice were dosed with each compound, and their livers were harvested at different time intervals. RNA was prepared and analyzed on Affymetrix GeneChip arrays. RNA transcripts showing statistically significant changes were identified, and selected changes were confirmed using TaqMan RT-PCR. Serum clinical chemistry and histopathologic evaluations were performed on selected samples as well. The data set generated from the different groups of animals enabled us to determine which gene expression changes were attributed to the bioactivating pathway. We were able to selectively modulate the metabolism of NMF by labeling various positions of the molecule with a stable isotope, allowing us to monitor gene changes specifically due to a particular metabolic pathway. Two groups of genes were identified, which were associated with the metabolism of a certain part of the NMF molecule. The metabolic pathway leading to the production of reactive methyl isocyanate resulted in distinct expression patterns that correlated with histopathologic findings. There was a clear correlation between the expression of certain genes involved in the cell cycle/apoptosis and inflammatory pathways and the presence of reactive metabolite. These genes may serve as potential genomic biomarkers of hepatotoxicity induced by soft-electrophile-producing compounds. However, the robustness of these potential genomic biomarkers will need to be validated using other hepatotoxicants (both soft- and hard-electrophile-producing agents) and compounds known to cause idiosyncratic liver toxicity before being adopted into the drug discovery screening process.

EPA Positive Matrix Factorization (PMF) 3.0 Fundamentals & User Guide

EPA Science Inventory

Positive matrix factorization (PMF) is a multivariate factor analysis tool that decomposes a matrix of ambient data into two matrices - factor contributions and factor profiles - which then need to be interpreted by an analyst as to what source types are represented using measure...

Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration

NASA Astrophysics Data System (ADS)

Zhou, Jian; Qi, Jinyi

2011-03-01

Statistically based iterative approaches for image reconstruction have gained much attention in medical imaging. An accurate system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction. However, an accurate system matrix is often associated with high computational cost and huge storage requirement. Here we present a method to address this problem by using sparse matrix factorization and parallel computing on a graphic processing unit (GPU).We factor the accurate system matrix into three sparse matrices: a sinogram blurring matrix, a geometric projection matrix, and an image blurring matrix. The sinogram blurring matrix models the detector response. The geometric projection matrix is based on a simple line integral model. The image blurring matrix is to compensate for the line-of-response (LOR) degradation due to the simplified geometric projection matrix. The geometric projection matrix is precomputed, while the sinogram and image blurring matrices are estimated by minimizing the difference between the factored system matrix and the original system matrix. The resulting factored system matrix has much less number of nonzero elements than the original system matrix and thus substantially reduces the storage and computation cost. The smaller size also allows an efficient implement of the forward and back projectors on GPUs, which have limited amount of memory. Our simulation studies show that the proposed method can dramatically reduce the computation cost of high-resolution iterative image reconstruction. The proposed technique is applicable to image reconstruction for different imaging modalities, including x-ray CT, PET, and SPECT.

Regular approximate factorization of a class of matrix-function with an unstable set of partial indices

PubMed Central

Rogosin, S.

2018-01-01

From the classic work of Gohberg & Krein (1958 Uspekhi Mat. Nauk. XIII, 3–72. (Russian).), it is well known that the set of partial indices of a non-singular matrix function may change depending on the properties of the original matrix. More precisely, it was shown that if the difference between the largest and the smallest partial indices is larger than unity then, in any neighbourhood of the original matrix function, there exists another matrix function possessing a different set of partial indices. As a result, the factorization of matrix functions, being an extremely difficult process itself even in the case of the canonical factorization, remains unresolvable or even questionable in the case of a non-stable set of partial indices. Such a situation, in turn, has became an unavoidable obstacle to the application of the factorization technique. This paper sets out to answer a less ambitious question than that of effective factorizing matrix functions with non-stable sets of partial indices, and instead focuses on determining the conditions which, when having known factorization of the limiting matrix function, allow to construct another family of matrix functions with the same origin that preserves the non-stable partial indices and is close to the original set of the matrix functions. PMID:29434502

Regular approximate factorization of a class of matrix-function with an unstable set of partial indices.

PubMed

Mishuris, G; Rogosin, S

2018-01-01

From the classic work of Gohberg & Krein (1958 Uspekhi Mat. Nauk. XIII , 3-72. (Russian).), it is well known that the set of partial indices of a non-singular matrix function may change depending on the properties of the original matrix. More precisely, it was shown that if the difference between the largest and the smallest partial indices is larger than unity then, in any neighbourhood of the original matrix function, there exists another matrix function possessing a different set of partial indices. As a result, the factorization of matrix functions, being an extremely difficult process itself even in the case of the canonical factorization, remains unresolvable or even questionable in the case of a non-stable set of partial indices. Such a situation, in turn, has became an unavoidable obstacle to the application of the factorization technique. This paper sets out to answer a less ambitious question than that of effective factorizing matrix functions with non-stable sets of partial indices, and instead focuses on determining the conditions which, when having known factorization of the limiting matrix function, allow to construct another family of matrix functions with the same origin that preserves the non-stable partial indices and is close to the original set of the matrix functions.

Evaluation of algorithm methods for fluorescence spectra of cancerous and normal human tissues

NASA Astrophysics Data System (ADS)

Pu, Yang; Wang, Wubao; Alfano, Robert R.

2016-03-01

The paper focus on the various algorithms on to unravel the fluorescence spectra by unmixing methods to identify cancerous and normal human tissues from the measured fluorescence spectroscopy. The biochemical or morphologic changes that cause fluorescence spectra variations would appear earlier than the histological approach; therefore, fluorescence spectroscopy holds a great promise as clinical tool for diagnosing early stage of carcinomas and other deceases for in vivo use. The method can further identify tissue biomarkers by decomposing the spectral contributions of different fluorescent molecules of interest. In this work, we investigate the performance of blind source un-mixing methods (backward model) and spectral fitting approaches (forward model) in decomposing the contributions of key fluorescent molecules from the tissue mixture background when certain selected excitation wavelength is applied. Pairs of adenocarcinoma as well as normal tissues confirmed by pathologist were excited by selective wavelength of 340 nm. The emission spectra of resected fresh tissue were used to evaluate the relative changes of collagen, reduced nicotinamide adenine dinucleotide (NADH), and Flavin by various spectral un-mixing methods. Two categories of algorithms: forward methods and Blind Source Separation [such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and Nonnegative Matrix Factorization (NMF)] will be introduced and evaluated. The purpose of the spectral analysis is to discard the redundant information which conceals the difference between these two types of tissues, but keep their diagnostically significance. The facts predicted by different methods were compared to the gold standard of histopathology. The results indicate that these key fluorophores within tissue, e.g. tryptophan, collagen, and NADH, and flavin, show differences of relative contents of fluorophores among different types of human cancer and normal tissues. The sensitivity, specificity, and receiver operating characteristic (ROC) are finally employed as the criteria to evaluate the efficacy of these methods in cancer detection. The underlying physical and biological basis for these optical approaches will be discussed with examples. This ex vivo preliminary trial demonstrates that these different criteria from different methods can distinguish carcinoma from normal tissues with good sensitivity and specificity while among them, we found that ICA appears to be the superior method in predication accuracy.

Scale-Free Nonparametric Factor Analysis: A User-Friendly Introduction with Concrete Heuristic Examples.

ERIC Educational Resources Information Center

Mittag, Kathleen Cage

Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…

Computing row and column counts for sparse QR and LU factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, John R.; Li, Xiaoye S.; Ng, Esmond G.

2001-01-01

We present algorithms to determine the number of nonzeros in each row and column of the factors of a sparse matrix, for both the QR factorization and the LU factorization with partial pivoting. The algorithms use only the nonzero structure of the input matrix, and run in time nearly linear in the number of nonzeros in that matrix. They may be used to set up data structures or schedule parallel operations in advance of the numerical factorization. The row and column counts we compute are upper bounds on the actual counts. If the input matrix is strong Hall and theremore » is no coincidental numerical cancellation, the counts are exact for QR factorization and are the tightest bounds possible for LU factorization. These algorithms are based on our earlier work on computing row and column counts for sparse Cholesky factorization, plus an efficient method to compute the column elimination tree of a sparse matrix without explicitly forming the product of the matrix and its transpose.« less

Variation in the activities of late stage filaggrin processing enzymes, calpain-1 and bleomycin hydrolase, together with pyrrolidone carboxylic acid levels, corneocyte phenotypes and plasmin activities in non-sun-exposed and sun-exposed facial stratum corneum of different ethnicities.

PubMed

Raj, N; Voegeli, R; Rawlings, A V; Summers, B; Munday, M R; Lane, M E

2016-12-01

Knowledge of the ethnic differences and effects of photodamage on the relative amounts of natural moisturizing factor (NMF) together with filaggrin processing enzymes in facial stratum corneum is limited. Our aim was to characterize the activities of calpain-1 (C-1), bleomycin hydrolase (BH) and the levels of pyrrolidone carboxylic acid (PCA) as a marker for total NMF levels and to relate them to plasmin activities and corneocyte maturation. Enzyme activities, PCA levels and corneocyte maturation were determined from facial tape strippings of photoexposed cheek and photoprotected post-auricular areas (PA) of healthy Caucasian (C), Black African (BA) and albino African (AA) female subjects living in South Africa. PCA concentration levels were of the order AA > BA > C subjects, and the highest activities of BH were present in the AA subjects. BH activities were greater on the photoexposed sites for the BA and C subjects, but they were only numerically elevated in the AA subjects. Photoprotected sites had an increase in C-1 activity in pigmented groups (C and BA), whereas in the AA subjects, the opposite was measured. Plasmin activities were greater on the cheek compared with the PA site for the AA and C subjects, but the activity was low in the BA subjects. In both test sites, the AA, but not the BA and C subjects, had smaller, parakeratotic and less mature corneocytes. Variation in PCA levels has been found for different ethnic groups in this study (AA > BA > C subjects). The values in the AA subjects are surprising as one might expect that the lack of pigmentation, and thereby increased photodamage, might lead to lower levels. Increased BH, but not C-1 activity, was observed in the AA subjects indicating that BH is associated with PCA production to a greater extent. Surprisingly, corneocyte maturation is still impaired with elevated PCA levels in AA subjects. The higher levels of plasmin and BH activities on the cheeks, especially for AA and C subjects, suggest that they can be used as markers for epidermal photodamage. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Efficient system modeling for a small animal PET scanner with tapered DOI detectors.

PubMed

Zhang, Mengxi; Zhou, Jian; Yang, Yongfeng; Rodríguez-Villafuerte, Mercedes; Qi, Jinyi

2016-01-21

A prototype small animal positron emission tomography (PET) scanner for mouse brain imaging has been developed at UC Davis. The new scanner uses tapered detector arrays with depth of interaction (DOI) measurement. In this paper, we present an efficient system model for the tapered PET scanner using matrix factorization and a virtual scanner geometry. The factored system matrix mainly consists of two components: a sinogram blurring matrix and a geometrical matrix. The geometric matrix is based on a virtual scanner geometry. The sinogram blurring matrix is estimated by matrix factorization. We investigate the performance of different virtual scanner geometries. Both simulation study and real data experiments are performed in the fully 3D mode to study the image quality under different system models. The results indicate that the proposed matrix factorization can maintain image quality while substantially reduce the image reconstruction time and system matrix storage cost. The proposed method can be also applied to other PET scanners with DOI measurement.

A Note on the Factor Analysis of Partial Covariance Matrices

ERIC Educational Resources Information Center

McDonald, Roderick P.

1978-01-01

The relationship between the factor structure of a convariance matrix and the factor structure of a partial convariance matrix when one or more variables are partialled out of the original matrix is given in this brief note. (JKS)

Bayesian Factor Analysis When Only a Sample Covariance Matrix Is Available

ERIC Educational Resources Information Center

Hayashi, Kentaro; Arav, Marina

2006-01-01

In traditional factor analysis, the variance-covariance matrix or the correlation matrix has often been a form of inputting data. In contrast, in Bayesian factor analysis, the entire data set is typically required to compute the posterior estimates, such as Bayes factor loadings and Bayes unique variances. We propose a simple method for computing…

Algorithms for Solvents and Spectral Factors of Matrix Polynomials

DTIC Science & Technology

1981-01-01

spectral factors of matrix polynomials LEANG S. SHIEHt, YIH T. TSAYt and NORMAN P. COLEMANt A generalized Newton method , based on the contracted gradient...of a matrix poly- nomial, is derived for solving the right (left) solvents and spectral factors of matrix polynomials. Two methods of selecting initial...estimates for rapid convergence of the newly developed numerical method are proposed. Also, new algorithms for solving complete sets of the right

Porcine Burn Shock - Development of a Reliable Model and Response to Sodium, Water, and Plasma Loads Administered for Resuscitation

DTIC Science & Technology

1973-06-01

nm.ddt. inital, Diet n*Mf) Thomas L. Wachtel, M.D. G. R. McCahan, Jr., D.V.M. 0 REPORT CATS 70. TOTAL No. Or PAGE Nb O. or mrs June 1973 - w 78 0. CON...observations of caloric uptake of pigskin, rise in temperature at the dermis-fat interface as a function of both time and skin surface temperature and an...of Iso-, Hypo - and Hypertonic Sodium Solutions in the Treatment of Burn Shock in Mice," Surgery, 57: 698-704, May 1965. 24. Rosenthal, S. M

Multi-instrument Observations of Storm Enhanced Density (SED) During the Oct. 24-25 2011 Storm: Implications for SED Formation Processes (Invited)

NASA Astrophysics Data System (ADS)

Zou, S.; Ridley, A. J.; Moldwin, M.; Nicolls, M. J.; Coster, A. J.; Thomas, E. G.; Ruohoniemi, J.

2013-12-01

Ionospheric density often exhibits significant variations, which affect the propagation of radio signals that pass through or are reflected by the ionosphere. One example of these effects is the loss of phase lock and range errors in Global Navigation Satellite Systems (GNSS) signals. Because our modern society increasingly relies on ground-to-ground and ground-to-space communications and navigation, understanding the sources of the ionospheric density variability and monitoring its dynamics during space weather events has great importance. Storm-enhanced density (SED) is one of the most prominent ionospheric density structures that can have significant space weather impact. We present multi-instrument observations of a SED event during the Oct. 24-25, 2011 intense geomagnetic storm. Formation and the subsequent evolution of the SED and the mid-latitude trough are revealed by global GPS vertical total electron content (VTEC) maps. In addition, we present high time resolution Poker Flat Incoherent Scatter Radar (PFISR) observations of ionospheric properties within the SED. The SED structure observed by PFISR is found to consist of two parts with different properties. Both parts are characterized by elevated ionospheric peak height (HmF2) and TEC, compared to quiet time values. However, the two parts of the SED have different characteristics in the electron temperature (Te), the F-region peak density (NmF2) and convection flows. The first part of the SED is associated with enhanced Te in the lower F region and reduced Te in the upper F region, and is collocated with northward convection flows. The NmF2 was lower than quiet time values. The second part of the SED is associated with significantly increased NmF2, elevated Te at all altitudes, and is located near the equatorward boundary of large northwestward flow, which is probably subauroral polarization stream (SAPS). Based on these observations, we suggest that the mechanisms responsible for the formation of the two parts of the SED are different. The first part is due to equatorward expansion of the convection pattern and the projection of northward convection flows in the vertical direction, which lifts the ionospheric plasma to higher altitudes and thus reduces the loss rate of plasma recombination. The formation mechanism of the second part appears more complex. Besides equatorward expansion of the convection pattern and large upward flows, evidence of other mechanisms, including horizontal advection due to SAPS flows, energetic particle precipitation, and enhanced thermospheric wind in the topside ionosphere, is also present in the observations. Our estimates show that contribution from precipitating energetic protons accounts for at most ~10% of the total F-region density. The thermospheric wind also plays a minor role in this case.

Response of the Ionospheric F-region in the Latin American Sector During the Intense Geomagnetic Storm of 21-22 January 2005

NASA Astrophysics Data System (ADS)

Sahai, Y.; Fagundes, P. R.; de Jesus, R.; de Abreu, A. J.; Crowley, G.; Pillat, V. G.; Guarnieri, F. L.; Abalde, J. R.; Bittencourt, J. A.

2009-12-01

Ionospheric storms are closely associated with geomagnetic storms and are an extreme example of space weather events. The response of the ionosphere to storms is rather complicated. In the present investigation, we have studied the response of the ionospheric F-region in the Latin American sector during the intense geomagnetic storm of 21-22 January 2005 (with storm sudden commencement (SSC) at 1712 UT on 21 January). This geomagnetic storm is anomalous (minimum Dst reached -105 nT at 0700 UT on 22 January) because the main phase occurred during the northward excursion of the Bz component of interplanetary magnetic fields (IMFs). The monthly mean F10.7 solar flux for the month of January 2005 was 99.0 sfu. The ionospheric F-region parameters observed at Ramey (18.5 N, 67.1 W; RAM), Puerto Rico, Jicamarca (12.0 S, 76.8 W; JIC), Peru, Manaus (2.9 S, 60.0 W; MAN), and São José dos Campos (23.2 S, 45.9 W; SJC), Brazil, during 21-22 January (geomagnetically disturbed) and 25 January (geomagnetically quiet) have been analyzed. Both JIC and MAN, the equatorial stations, show unusually rapid uplifting of the F-region peak heights(hpF2/hmF2) and a decrease in the NmF2 coincident with the time of SSC. At both RAM and SJC an uplifting of the F-region peak height is observed at about 2000 UT. The low-latitude station SJC shows a coincident decrease in NmF2 with the uplifting, whereas the mid-latitude station RAM shows a decrease in NmF2 earlier than the uplifting. Also, the observed variations in the F-region ionospheric parameters are compared with the TIMEGCM model run for 21-22 January and the model results show both similarities and differences from the observed results. Average GPS-TEC (21-22 and 25 January) and phase fluctuations (21, 22, 25, 26 January) observed at Belem (1.5 S, 48.5 W; BELE), Brasilia (15.9 S, 47.9 W; BRAZ), Presidente Prudente (22.3o S, 51.4 W; UEPP), and Porto Alegre (30.1 S, 51.1 W; POAL), Brazil, are also presented. These GPS stations belong to the RBMC/IBGE network of Brazil. Few hours after the onset of the storm, large enhancements in VTEC between 2000 and 2400 UT on 21 January was observed at all the stations. However, the increase in VTEC was greater at the near equatorial station and enhancements in VTEC decreased with latitude. No phase fluctuations were observed during the pre-reversal time during the geomagnetic disturbance (21 January).

Angiogenic Type I Collagen Extracellular Matrix Integrated with Recombinant Bacteriophages Displaying Vascular Endothelial Growth Factors.

PubMed

Yoon, Junghyo; Korkmaz Zirpel, Nuriye; Park, Hyun-Ji; Han, Sewoon; Hwang, Kyung Hoon; Shin, Jisoo; Cho, Seung-Woo; Nam, Chang-Hoon; Chung, Seok

2016-01-21

Here, a growth-factor-integrated natural extracellular matrix of type I collagen is presented that induces angiogenesis. The developed matrix adapts type I collagen nanofibers integrated with synthetic colloidal particles of recombinant bacteriophages that display vascular endothelial growth factor (VEGF). The integration is achieved during or after gelation of the type I collagen and the matrix enables spatial delivery of VEGF into a desired region. Endothelial cells that contact the VEGF are found to invade into the matrix to form tube-like structures both in vitro and in vivo, proving the angiogenic potential of the matrix. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Background recovery via motion-based robust principal component analysis with matrix factorization

NASA Astrophysics Data System (ADS)

Pan, Peng; Wang, Yongli; Zhou, Mingyuan; Sun, Zhipeng; He, Guoping

2018-03-01

Background recovery is a key technique in video analysis, but it still suffers from many challenges, such as camouflage, lighting changes, and diverse types of image noise. Robust principal component analysis (RPCA), which aims to recover a low-rank matrix and a sparse matrix, is a general framework for background recovery. The nuclear norm is widely used as a convex surrogate for the rank function in RPCA, which requires computing the singular value decomposition (SVD), a task that is increasingly costly as matrix sizes and ranks increase. However, matrix factorization greatly reduces the dimension of the matrix for which the SVD must be computed. Motion information has been shown to improve low-rank matrix recovery in RPCA, but this method still finds it difficult to handle original video data sets because of its batch-mode formulation and implementation. Hence, in this paper, we propose a motion-assisted RPCA model with matrix factorization (FM-RPCA) for background recovery. Moreover, an efficient linear alternating direction method of multipliers with a matrix factorization (FL-ADM) algorithm is designed for solving the proposed FM-RPCA model. Experimental results illustrate that the method provides stable results and is more efficient than the current state-of-the-art algorithms.

Differential effect of extracellular matrix derived from papillary and reticular fibroblasts on epidermal development in vitro.

PubMed

Janson, David; Rietveld, Marion; Mahé, Christian; Saintigny, Gaëlle; El Ghalbzouri, Abdoelwaheb

2017-06-01

Papillary and reticular fibroblasts have different effects on keratinocyte proliferation and differentiation. The aim of this study was to investigate whether these effects are caused by differential secretion of soluble factors or by differential generation of extracellular matrix from papillary and reticular fibroblasts. To study the effect of soluble factors, keratinocyte monolayer cultures were grown in papillary or reticular fibroblast-conditioned medium. To study the effect of extracellular matrix, keratinocytes were grown on papillary or reticular-derived matrix. Conditioned medium from papillary or reticular fibroblasts did not differentially affect keratinocyte viability or epidermal development. However, keratinocyte viability was increased when grown on matrix derived from papillary, compared with reticular, fibroblasts. In addition, the longevity of the epidermis was increased when cultured on papillary fibroblast-derived matrix skin equivalents compared with reticular-derived matrix skin equivalents. The findings indicate that the matrix secreted by papillary and reticular fibroblasts is the main causal factor to account for the differences in keratinocyte growth and viability observed in our study. Differences in response to soluble factors between both populations were less significant. Matrix components specific to the papillary dermis may account for the preferential growth of keratinocytes on papillary dermis.

Targeting extracellular matrix remodeling in disease: Could resveratrol be a potential candidate?

PubMed

Agarwal, Renu; Agarwal, Puneet

2017-02-01

Disturbances of extracellular matrix homeostasis are associated with a number of pathological conditions. The ability of extracellular matrix to provide contextual information and hence control the individual or collective cellular behavior is increasingly being recognized. Hence, newer therapeutic approaches targeting extracellular matrix remodeling are widely investigated. We reviewed the current literature showing the effects of resveratrol on various aspects of extracellular matrix remodeling. This review presents a summary of the effects of resveratrol on extracellular matrix deposition and breakdown. Mechanisms of action of resveratrol in extracellular matrix deposition involving growth factors and their signaling pathways are discussed. Involvement of phosphoinositol-3-kinase/Akt and mitogen-activated protein kinase pathways and role of transcription factors and sirtuins on the effects of resveratrol on extracellular matrix homeostasis are summarized. It is evident from the literature presented in this review that resveratrol has significant effects on both the synthesis and breakdown of extracellular matrix. The major molecular targets of the action of resveratrol are growth factors and their signaling pathways, phosphoinositol-3-kinase/Akt and mitogen-activated protein kinase pathways, transcription factors, and SIRT-1. The effects of resveratrol on extracellular matrix and the molecular targets appear to be related to experimental models, experimental environment as well as the doses.

Targeting extracellular matrix remodeling in disease: Could resveratrol be a potential candidate?

PubMed Central

Agarwal, Puneet

2016-01-01

Disturbances of extracellular matrix homeostasis are associated with a number of pathological conditions. The ability of extracellular matrix to provide contextual information and hence control the individual or collective cellular behavior is increasingly being recognized. Hence, newer therapeutic approaches targeting extracellular matrix remodeling are widely investigated. We reviewed the current literature showing the effects of resveratrol on various aspects of extracellular matrix remodeling. This review presents a summary of the effects of resveratrol on extracellular matrix deposition and breakdown. Mechanisms of action of resveratrol in extracellular matrix deposition involving growth factors and their signaling pathways are discussed. Involvement of phosphoinositol-3-kinase/Akt and mitogen-activated protein kinase pathways and role of transcription factors and sirtuins on the effects of resveratrol on extracellular matrix homeostasis are summarized. It is evident from the literature presented in this review that resveratrol has significant effects on both the synthesis and breakdown of extracellular matrix. The major molecular targets of the action of resveratrol are growth factors and their signaling pathways, phosphoinositol-3-kinase/Akt and mitogen-activated protein kinase pathways, transcription factors, and SIRT-1. The effects of resveratrol on extracellular matrix and the molecular targets appear to be related to experimental models, experimental environment as well as the doses. PMID:27798117

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Effect of enhanced x-ray flux on the ionosphere over Cyprus during solar flares

NASA Astrophysics Data System (ADS)

Mostafa, Md. Golam; Haralambous, Haris

2015-06-01

In this work we study the effect of solar flares on the ionosphere over Cyprus. Solar flares are impulsive solar activity events usually coupled with Coronal Mass Ejection (CME). The arrival and the subsequent impact of solar flares on geospace, following an eruption on the Sun's surface is almost immediate (around 9 min) whereas the impact of CMEs is rather delayed (2-3 days) as the former is based on X-ray radiation whereas the latter phenomenon is related with particles and magnetic fields travelling at lower speeds via the Solar Wind. The penetration of X-rays down to the Dregion following such an event enhances the electron density. This increase can be monitored by ionosondes, which measure the electron density up to the maximum electron density NmF2. The significance of this increase lies on the increase of signal absorption causing limited window of operating frequencies for HF communications. In this study the effect of enhanced X-ray flux on the ionosphere over Cyprus during solar flares has been investigated. To establish the correlation and extent of impact on different layers, data of X-ray intensity from Geostationary Operational Environmental Satellite (GOES) and ionospheric characteristics (D & F layer) over Nicosia station (35° N, 33° E) were examined for all solar flares during the period 2011-2014. The analysis revealed a positive and good correlation between frequency of minimum reflection, fmin and X-ray intensity for D layer demonstrating that X-rays play a dominant role in the ionization of lower ionosphere. Hence, X-ray flux can be used as a good proxy for studying the solar flare effects on lower ionosphere. The correlation coefficient between maximum electron density of F layer, NmF2 and X-ray intensity was found to be poor.

First Results From the Ionospheric Extension of WACCM-X During the Deep Solar Minimum Year of 2008

NASA Astrophysics Data System (ADS)

Liu, Jing; Liu, Hanli; Wang, Wenbin; Burns, Alan G.; Wu, Qian; Gan, Quan; Solomon, Stanley C.; Marsh, Daniel R.; Qian, Liying; Lu, Gang; Pedatella, Nicholas M.; McInerney, Joe M.; Russell, James M.; Schreiner, William S.

2018-02-01

New ionosphere and electrodynamics modules have been incorporated in the thermosphere and ionosphere eXtension of the Whole Atmosphere Community Climate Model (WACCM-X), in order to self-consistently simulate the coupled atmosphere-ionosphere system. The first specified dynamics WACCM-X v.2.0 results are compared with several data sets, and with the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), during the deep solar minimum year. Comparisons with Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite of temperature and zonal wind in the lower thermosphere show that WACCM-X reproduces the seasonal variability of tides remarkably well, including the migrating diurnal and semidiurnal components and the nonmigrating diurnal eastward propagating zonal wavenumber 3 component. There is overall agreement between WACCM-X, TIE-GCM, and vertical drifts observed by the Communication/Navigation Outage Forecast System (C/NOFS) satellite over the magnetic equator, but apparent discrepancies also exist. Both model results are dominated by diurnal variations, while C/NOFS observed vertical plasma drifts exhibit strong temporal variations. The climatological features of ionospheric peak densities and heights (NmF2 and hmF2) from WACCM-X are in general agreement with the results derived from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) data, although the WACCM-X predicted NmF2 values are smaller, and the equatorial ionization anomaly crests are closer to the magnetic equator compared to COSMIC and ionosonde observations. This may result from the excessive mixing in the lower thermosphere due to the gravity wave parameterization. These data-model comparisons demonstrate that WACCM-X can capture the dynamic behavior of the coupled atmosphere and ionosphere in a climatological sense.

Anomalous night-time peaks in diurnal variations of NmF2 close to the geomagnetic equator: A statistical study

NASA Astrophysics Data System (ADS)

Pavlov, A. V.; Pavlova, N. M.

2007-11-01

We present a study of anomalous night-time NmF2 peaks, ANNPs, observed by the La Paz, Natal, Djibouti, Kodaikanal, Madras, Manila, Talara, and Huancayo Jicamarca ionosonde stations close to the geomagnetic equator. It is shown for the first time that the probabilities of occurrence of the first and second ANNPs depend on the geomagnetic longitude, and there is a longitude sector close to 110° geomagnetic longitude where the first and second ANNPs occur less frequently in comparison with the longitude regions located close to and below about 34° geomagnetic longitude and close to and above about 144° geomagnetic longitude. The found frequencies of occurrence of the ANNPs increase with increasing solar activity, except of the Djibouti and Kodaikanal ionosonde stations, where the probability of the first ANNP occurrence is found to decrease with increasing solar activity from low to moderate solar activity, and except of the Natal ionosonde station, where the frequencies of occurrence of the first and second ANNPs decrease with increasing solar activity from moderate to high solar activity. We found that the occurrence probabilities of ANNPs during geomagnetically disturbed conditions are greater than those during geomagnetically quiet conditions. The ANNP probabilities are largest in summer and are lowest in winter for the La-Paz, Talara, and Huancayo Jicamarca sounders. These probabilities are lowest in summer for the Djibouti, Madras, and Manila ionosonde stations, and in spring for the Kodaikanal sounder. The maximums in the probabilities are found to be in autumn for the Djibouti, Madras, and Manila ionosonde stations, and in winter for the Kodaikanal sounder.

A model-assisted radio occultation data inversion method based on data ingestion into NeQuick

NASA Astrophysics Data System (ADS)

Shaikh, M. M.; Nava, B.; Kashcheyev, A.

2017-01-01

Inverse Abel transform is the most common method to invert radio occultation (RO) data in the ionosphere and it is based on the assumption of the spherical symmetry for the electron density distribution in the vicinity of an occultation event. It is understood that this 'spherical symmetry hypothesis' could fail, above all, in the presence of strong horizontal electron density gradients. As a consequence, in some cases wrong electron density profiles could be obtained. In this work, in order to incorporate the knowledge of horizontal gradients, we have suggested an inversion technique based on the adaption of the empirical ionospheric model, NeQuick2, to RO-derived TEC. The method relies on the minimization of a cost function involving experimental and model-derived TEC data to determine NeQuick2 input parameters (effective local ionization parameters) at specific locations and times. These parameters are then used to obtain the electron density profile along the tangent point (TP) positions associated with the relevant RO event using NeQuick2. The main focus of our research has been laid on the mitigation of spherical symmetry effects from RO data inversion without using external data such as data from global ionospheric maps (GIM). By using RO data from Constellation Observing System for Meteorology Ionosphere and Climate (FORMOSAT-3/COSMIC) mission and manually scaled peak density data from a network of ionosondes along Asian and American longitudinal sectors, we have obtained a global improvement of 5% with 7% in Asian longitudinal sector (considering the data used in this work), in the retrieval of peak electron density (NmF2) with model-assisted inversion as compared to the Abel inversion. Mean errors of NmF2 in Asian longitudinal sector are calculated to be much higher compared to American sector.

North-south components of the annual asymmetry in the ionosphere

NASA Astrophysics Data System (ADS)

Gulyaeva, T. L.; Arikan, F.; Hernandez-Pajares, M.; Veselovsky, I. S.

2014-07-01

A retrospective study of the asymmetry in the ionosphere during the solstices is made using the different geospace parameters in the North and South magnetic hemispheres. Data of total electron content (TEC) and global electron content (GEC) produced from global ionospheric maps, GIM-TEC for 1999-2013, the ionospheric electron content (IEC) measured by TOPEX-Jason 1 and 2 satellites for 2001-2012, the F2 layer critical frequency and peak height measured on board ISIS 1, ISIS 2, and IK19 satellites during 1969-1982, and the earthquakes M5+ occurrences for 1999-2013 are analyzed. Annual asymmetry is observed with GEC and IEC for the years of observation with asymmetry index, AI, showing January > July excess from 0.02 to 0.25. The coincident pattern of January-to-July asymmetry ratio of TEC and IEC colocated along the magnetic longitude sector of 270° ± 5°E in the Pacific Ocean is obtained varying with local time and magnetic latitude. The sea/land differences in the F2 layer peak electron density, NmF2, and the peak height, hmF2, gathered with topside sounding data exhibit tilted ionosphere along the seashores with denser electron population at greater peak heights over the sea. The topside peak electron density NmF2, TEC, IEC, and the hemisphere part of GEC are dominant in the South hemisphere which resembles the pattern for seismic activity with dominant earthquake occurrence in the South magnetic hemisphere. Though the study is made for the hemispheric and annual asymmetry during solstices in the ionosphere, the conclusions seem valid for other aspects of seismic-ionospheric associations with tectonic plate boundaries representing zones of enhanced risk for space weather.

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

PubMed Central

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time. PMID:26270539

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.

PubMed

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

2015-01-01

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.

Synthetic Division and Matrix Factorization

ERIC Educational Resources Information Center

Barabe, Samuel; Dubeau, Franc

2007-01-01

Synthetic division is viewed as a change of basis for polynomials written under the Newton form. Then, the transition matrices obtained from a sequence of changes of basis are used to factorize the inverse of a bidiagonal matrix or a block bidiagonal matrix.

Fuzzy Mathematical Models To Remove Poverty Of Gypsies In Tamilnadu

NASA Astrophysics Data System (ADS)

Chandrasekaran, A. D.; Ramkumar, C.; Siva, E. P.; Balaji, N.

2018-04-01

In the society there are several poor people are living. One of the sympathetic poor people is gypsies. They are moving from one place to another place towards survive of life because of not having any permanent place to live. In this paper we have interviewed 895 gypsies in Tamilnadu using a linguistic questionnaire. As the problems faced by them to improve their life at large involve so much of feeling, uncertainties and unpredictabilitys. I felt that it deem fit to use fuzzy theory in general and fuzzy matrix in particular. Fuzzy matrix is the best suitable tool where the data is an unsupervised one. Further the fuzzy matrix is so powerful to identify the main development factor of gypsies.This paper has three sections. In section one the method of application of CEFD matrix. In section two, we describe the development factors of gypsies. In section three, we apply these factors to the CEFD matrix and derive our conclusions. Key words: RD matrix, AFD matrix, CEFD matrix.

Uncertainty of relative sensitivity factors in glow discharge mass spectrometry

NASA Astrophysics Data System (ADS)

Meija, Juris; Methven, Brad; Sturgeon, Ralph E.

2017-10-01

The concept of the relative sensitivity factors required for the correction of the measured ion beam ratios in pin-cell glow discharge mass spectrometry is examined in detail. We propose a data-driven model for predicting the relative response factors, which relies on a non-linear least squares adjustment and analyte/matrix interchangeability phenomena. The model provides a self-consistent set of response factors for any analyte/matrix combination of any element that appears as either an analyte or matrix in at least one known response factor.

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2013-09-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

On the Relations among Regular, Equal Unique Variances, and Image Factor Analysis Models.

ERIC Educational Resources Information Center

Hayashi, Kentaro; Bentler, Peter M.

2000-01-01

Investigated the conditions under which the matrix of factor loadings from the factor analysis model with equal unique variances will give a good approximation to the matrix of factor loadings from the regular factor analysis model. Extends the results to the image factor analysis model. Discusses implications for practice. (SLD)

Spatial operator factorization and inversion of the manipulator mass matrix

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo; Kreutz-Delgado, Kenneth

1992-01-01

This paper advances two linear operator factorizations of the manipulator mass matrix. Embedded in the factorizations are many of the techniques that are regarded as very efficient computational solutions to inverse and forward dynamics problems. The operator factorizations provide a high-level architectural understanding of the mass matrix and its inverse, which is not visible in the detailed algorithms. They also lead to a new approach to the development of computer programs or organize complexity in robot dynamics.

EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide

EPA Science Inventory

PMF is a multivariate factor analysis tool that decomposes a matrix of speciated sample data into two matrices: factor contributions (G) and factor profiles (F). These factor profiles need to be interpreted by the user to identify the source types that may be contributing to the ...

A Study of the Critical Factors Controlling the Synthesis of Ceramic Matrix Composites from Preceramic Polymers.

DTIC Science & Technology

1988-04-15

physical properties of a polycarbosilane preceramic polymer as a function of temperature to derive synthesis methodology for SiC matrix composites , (2...investigate the role of interface modification in creating tough carbon fiber reinforced SiC matrix composites . RESEARCH PROGRESS Preceramic Polymer ...Classfication) A STUDY OF THE CRITICAL FACTORS CONTROLLING THE SYNTHESIS OF CERAMIC MATRIX COMPOSITES FROM PRECERAMIC POLYMERS 12. PERSONAL AUTHOR(S

Real-time reconstruction of topside ionosphere scale height from coordinated GPS-TEC and ionosonde observations

NASA Astrophysics Data System (ADS)

Gulyaeva, Tamara; Poustovalova, Ljubov

The International Reference Ionosphere model extended to the plasmasphere, IRI-Plas, has been recently updated for assimilation of total electron content, TEC, derived from observations with Global Navigation Satellite System, GNSS. The ionosonde products of the F2 layer peak density (NmF2) and height (hmF2) ensure true electron density maximum at the F2 peak. The daily solar and magnetic indices used by IRI-Plas code are compiled in data files including the 3-hour ap and kp magnetic index from 1958 onward, 12-monthly smoothed sunspot number R12 and Global Electron Content GEC12, daily solar radio flux F10.7 and daily sunspot number Ri. The 3-h ap-index is available in Real Time, RT, mode from GFZ, Potsdam, Germany, daily update of F10.7 is provided by Space Weather Canada service, and daily estimated international sunspot number Ri is provided by Solar Influences Data Analysis Center, SIDC, Belgium. For IRI-Plas-RT operation in regime of the daily update and prediction of the F2 layer peak parameters, the proxy kp and ap forecast for 3 to 24 hours ahead based on data for preceding 12 hours is applied online at http://www.izmiran.ru/services/iweather/. The topside electron density profile of IRI-Plas code is expressed with complementary half-peak density anchor height above hmF2 which corresponds to transition O+/H+ height. The present investigation is focused on reconstruction of topside ionosphere scale height using vertical total electron content (TEC) data derived from the Global Positioning System GPS observations and the ionosonde derived F2 layer peak parameters from 25 observatories ingested into IRI-Plas model. GPS-TEC and ionosonde measurements at solar maximum (September, 2002, and October, 2003) for quiet, positively disturbed, and negatively disturbed days of the month are used to obtain the topside scale height, Htop, representing the range of altitudes from hmF2 to the height where NmF2 decay by e times occurs. Mapping of the F2 layer peak parameters and TEC allows interpolate these parameters at coordinated grid sites from independent GPS receivers and ionosondes data. Exponential scale height Htop exceeds scale height HT of the α-Chapman layer by 3 times - the latter refers to a narrow altitude range from hmF2 to the height of 1.2 times decay of NmF2. While typical quiet daytime value of the topside scale height is around 200 km, it can be enhanced by 2-3 times during the negative phase of the ionospheric storm as it is captured by IRI-Plas-RT model ingesting the F2 peak and TEC data. This study is supported by the joint grant of RFBR 13-02-91370-CT_a and TUBITAK 112E568.

Environmental history of Lake Hovsgul from physical interpretation of remanent magnetization endmember analysis

NASA Astrophysics Data System (ADS)

Kosareva, Lina; Fabian, Karl; Shcherbakov, Valera; Nurgaliev, Danis

2016-04-01

The environmental history of Lake Hovsgul (Mongolia) is studied based on magnetic measurements of the core KDP-01. The drill hole reached a maximum depth of 53 m, from which sediment cores with a total length of 48 m were recovered. Coring gaps are due to the applied drilling technology. Following the approach by Heslop and Dillon, 2007, we develop the way of decomposition of the total magnetic fraction of a sample into not virtual but real three distinctive mineralogical components. For this, we first apply the end-member non-negative matrix factorization (NMF) modeling for the unmixing magnetic remanence curves. Having these results in hands, we decompose the hysteresis loops, backfield and strong field thermomagnetic curves into the components which now can be interpreted as certain mineralogical fractions. The likely interpretation of the components obtained is as follows. The soft component is represented by a coarse grained magnetite fraction as it typically results from terrigenous influx via fluvial transport. The second component is presented by a sharply defined magnetite grain size fraction in the 30-100 nm range that in lake environments is related to magnetosome chains of magnetotactic bacteria. It apparently covaries with a diamagnetic mineral, most likely carbonate. This indicates a link to organic authigenic fractions and fits to biogenic magnetite from magnetotactic bacteria. The third component also has a very high coercivity around 85 mT and is identified as a mixture of biogenic and abiotic greigite common in suboxic/anoxic sediments. The results of such the combined study are used to infer information on paleoclimatic and paleogeography conditions around the lake Hovsgul's area for the period of the last million years. A correlation between the outbursts of biogenic magnetite and greigite content with warm periods is found. Within some parts of the core the dominance of greigite contribution into magnetic signal is observed which we link to onset of icy anoxic environmental conditions. The work was carried out according to the Russian Government's Program of Competitive Growth of Kazan Federal University, supported by the grant provided to the Kazan State University for performing the state program in the field of scientific research, and partially supported by the Russian Foundation for Basic research (grant №. 14_05_00785).

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

An Efficient Scheme for Updating Sparse Cholesky Factors

NASA Technical Reports Server (NTRS)

Raghavan, Padma

2002-01-01

Raghavan had earlier developed the software package DCSPACK which can be used for solving sparse linear systems where the coefficient matrix is symmetric and positive definite (this project was not funded by NASA but by agencies such as NSF). DSCPACK-S is the serial code and DSCPACK-P is a parallel implementation suitable for multiprocessors or networks-of-workstations with message passing using MCI. The main algorithm used is the Cholesky factorization of a sparse symmetric positive positive definite matrix A = LL(T). The code can also compute the factorization A = LDL(T). The complexity of the software arises from several factors relating to the sparsity of the matrix A. A sparse N x N matrix A has typically less that cN nonzeroes where c is a small constant. If the matrix were dense, it would have O(N2) nonzeroes. The most complicated part of such sparse Cholesky factorization relates to fill-in, i.e., zeroes in the original matrix that become nonzeroes in the factor L. An efficient implementation depends to a large extent on complex data structures and on techniques from graph theory to reduce, identify, and manage fill. DSCPACK is based on an efficient multifrontal implementation with fill-managing algorithms and implementation arising from earlier research by Raghavan and others. Sparse Cholesky factorization is typically a four step process: (1) ordering to compute a fill-reducing numbering, (2) symbolic factorization to determine the nonzero structure of L, (3) numeric factorization to compute L, and, (4) triangular solution to solve L(T)x = y and Ly = b. The first two steps are symbolic and are performed using the graph of the matrix. The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor. The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution rates (operations/second) that are close to the peak rates for dense matrix computations. Currently, EPISCOPACY is being used in an application at NASA directed by J. Newman and M. James. We propose the implementation of efficient schemes for updating the LL(T) or LDL(T) factors computed in DSCPACK-S to meet the computational requirements of their project. A brief description is provided in the next section.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

Technique for information retrieval using enhanced latent semantic analysis generating rank approximation matrix by factorizing the weighted morpheme-by-document matrix

DOEpatents

Chew, Peter A; Bader, Brett W

2012-10-16

A technique for information retrieval includes parsing a corpus to identify a number of wordform instances within each document of the corpus. A weighted morpheme-by-document matrix is generated based at least in part on the number of wordform instances within each document of the corpus and based at least in part on a weighting function. The weighted morpheme-by-document matrix separately enumerates instances of stems and affixes. Additionally or alternatively, a term-by-term alignment matrix may be generated based at least in part on the number of wordform instances within each document of the corpus. At least one lower rank approximation matrix is generated by factorizing the weighted morpheme-by-document matrix and/or the term-by-term alignment matrix.

Designing Feature and Data Parallel Stochastic Coordinate Descent Method forMatrix and Tensor Factorization

DTIC Science & Technology

2016-05-11

AFRL-AFOSR-JP-TR-2016-0046 Designing Feature and Data Parallel Stochastic Coordinate Descent Method for Matrix and Tensor Factorization U Kang Korea...maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or   any other aspect...Designing Feature and Data Parallel Stochastic Coordinate Descent Method for Matrix and Tensor Factorization 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA2386

Adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization

NASA Astrophysics Data System (ADS)

Zhang, Tianzhen; Wang, Xiumei; Gao, Xinbo

2018-04-01

Nowadays, several datasets are demonstrated by multi-view, which usually include shared and complementary information. Multi-view clustering methods integrate the information of multi-view to obtain better clustering results. Nonnegative matrix factorization has become an essential and popular tool in clustering methods because of its interpretation. However, existing nonnegative matrix factorization based multi-view clustering algorithms do not consider the disagreement between views and neglects the fact that different views will have different contributions to the data distribution. In this paper, we propose a new multi-view clustering method, named adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization. The proposed algorithm can obtain the parts-based representation of multi-view data by nonnegative matrix factorization. Then, pairwise co-regularization is used to measure the disagreement between views. There is only one parameter to auto learning the weight values according to the contribution of each view to data distribution. Experimental results show that the proposed algorithm outperforms several state-of-the-arts algorithms for multi-view clustering.

Clonorchis sinensis excretory-secretory products regulate migration and invasion in cholangiocarcinoma cells via extracellular signal-regulated kinase 1/2/nuclear factor-κB-dependent matrix metalloproteinase-9 expression.

PubMed

Pak, Jhang Ho; Shin, Jimin; Song, In-Sung; Shim, Sungbo; Jang, Sung-Wuk

2017-01-01

Matrix metalloproteinase-9 plays an important role in the invasion and metastasis of various types of cancer cells. We have previously reported that excretory-secretory products from Clonorchis sinensis increases matrix metalloproteinase-9 expression. However, the regulatory mechanisms through which matrix metalloproteinase-9 expression affects cholangiocarcinoma development remain unclear. In the current study, we examined the potential role of excretory-secretory products in regulating the migration and invasion of various cholangiocarcinoma cell lines. We demonstrated that excretory-secretory products significantly induced matrix metalloproteinase-9 expression and activity in a concentration-dependent manner. Reporter gene and chromatin immunoprecipitation assays showed that excretory-secretory products induced matrix metalloproteinase-9 expression by enhancing the activity of nuclear factor-kappa B. Moreover, excretory-secretory products induced the degradation and phosphorylation of IκBα and stimulated nuclear factor-kappa B p65 nuclear translocation, which was regulated by extracellular signal-regulated kinase 1/2. Taken together, our findings indicated that the excretory-secretory product-dependent enhancement of matrix metalloproteinase-9 activity and subsequent induction of IκBα and nuclear factor-kappa B activities may contribute to the progression of cholangiocarcinoma. Copyright © 2016 Australian Society for Parasitology. Published by Elsevier Ltd. All rights reserved.

A tight and explicit representation of Q in sparse QR factorization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ng, E.G.; Peyton, B.W.

1992-05-01

In QR factorization of a sparse m{times}n matrix A (m {ge} n) the orthogonal factor Q is often stored implicitly as a lower trapezoidal matrix H known as the Householder matrix. This paper presents a simple characterization of the row structure of Q, which could be used as the basis for a sparse data structure that can store Q explicitly. The new characterization is a simple extension of a well known row-oriented characterization of the structure of H. Hare, Johnson, Olesky, and van den Driessche have recently provided a complete sparsity analysis of the QR factorization. Let U be themore » matrix consisting of the first n columns of Q. Using results from, we show that the data structures for H and U resulting from our characterizations are tight when A is a strong Hall matrix. We also show that H and the lower trapezoidal part of U have the same sparsity characterization when A is strong Hall. We then show that this characterization can be extended to any weak Hall matrix that has been permuted into block upper triangular form. Finally, we show that permuting to block triangular form never increases the fill incurred during the factorization.« less

Normalization Of Thermal-Radiation Form-Factor Matrix

NASA Technical Reports Server (NTRS)

Tsuyuki, Glenn T.

1994-01-01

Report describes algorithm that adjusts form-factor matrix in TRASYS computer program, which calculates intraspacecraft radiative interchange among various surfaces and environmental heat loading from sources such as sun.

Quantitative evaluation of the matrix effect in bioanalytical methods based on LC-MS: A comparison of two approaches.

PubMed

Rudzki, Piotr J; Gniazdowska, Elżbieta; Buś-Kwaśnik, Katarzyna

2018-06-05

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful tool for studying pharmacokinetics and toxicokinetics. Reliable bioanalysis requires the characterization of the matrix effect, i.e. influence of the endogenous or exogenous compounds on the analyte signal intensity. We have compared two methods for the quantitation of matrix effect. The CVs(%) of internal standard normalized matrix factors recommended by the European Medicines Agency were evaluated against internal standard normalized relative matrix effects derived from Matuszewski et al. (2003). Both methods use post-extraction spiked samples, but matrix factors require also neat solutions. We have tested both approaches using analytes of diverse chemical structures. The study did not reveal relevant differences in the results obtained with both calculation methods. After normalization with the internal standard, the CV(%) of the matrix factor was on average 0.5% higher than the corresponding relative matrix effect. The method adopted by the European Medicines Agency seems to be slightly more conservative in the analyzed datasets. Nine analytes of different structures enabled a general overview of the problem, still, further studies are encouraged to confirm our observations. Copyright © 2018 Elsevier B.V. All rights reserved.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS

PubMed Central

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790

Block LU factorization

NASA Technical Reports Server (NTRS)

Demmel, James W.; Higham, Nicholas J.; Schreiber, Robert S.

1992-01-01

Many of the currently popular 'block algorithms' are scalar algorithms in which the operations have been grouped and reordered into matrix operations. One genuine block algorithm in practical use is block LU factorization, and this has recently been shown by Demmel and Higham to be unstable in general. It is shown here that block LU factorization is stable if A is block diagonally dominant by columns. Moreover, for a general matrix the level of instability in block LU factorization can be founded in terms of the condition number kappa(A) and the growth factor for Gaussian elimination without pivoting. A consequence is that block LU factorization is stable for a matrix A that is symmetric positive definite or point diagonally dominant by rows or columns as long as A is well-conditioned.

Recursive flexible multibody system dynamics using spatial operators

NASA Technical Reports Server (NTRS)

Jain, A.; Rodriguez, G.

1992-01-01

This paper uses spatial operators to develop new spatially recursive dynamics algorithms for flexible multibody systems. The operator description of the dynamics is identical to that for rigid multibody systems. Assumed-mode models are used for the deformation of each individual body. The algorithms are based on two spatial operator factorizations of the system mass matrix. The first (Newton-Euler) factorization of the mass matrix leads to recursive algorithms for the inverse dynamics, mass matrix evaluation, and composite-body forward dynamics for the systems. The second (innovations) factorization of the mass matrix, leads to an operator expression for the mass matrix inverse and to a recursive articulated-body forward dynamics algorithm. The primary focus is on serial chains, but extensions to general topologies are also described. A comparison of computational costs shows that the articulated-body, forward dynamics algorithm is much more efficient than the composite-body algorithm for most flexible multibody systems.

Direct Solve of Electrically Large Integral Equations for Problem Sizes to 1M Unknowns

NASA Technical Reports Server (NTRS)

Shaeffer, John

2008-01-01

Matrix methods for solving integral equations via direct solve LU factorization are presently limited to weeks to months of very expensive supercomputer time for problems sizes of several hundred thousand unknowns. This report presents matrix LU factor solutions for electromagnetic scattering problems for problem sizes to one million unknowns with thousands of right hand sides that run in mere days on PC level hardware. This EM solution is accomplished by utilizing the numerical low rank nature of spatially blocked unknowns using the Adaptive Cross Approximation for compressing the rank deficient blocks of the system Z matrix, the L and U factors, the right hand side forcing function and the final current solution. This compressed matrix solution is applied to a frequency domain EM solution of Maxwell's equations using standard Method of Moments approach. Compressed matrix storage and operations count leads to orders of magnitude reduction in memory and run time.

Application of fiber bridging models to fatigue crack growth in unidirectional titanium matrix composites

NASA Technical Reports Server (NTRS)

Bakuckas, J. G., Jr.; Johnson, W. S.

1992-01-01

Several fiber bridging models were reviewed and applied to study the matrix fatigue crack growth behavior in center notched (0)(sub 8) SCS-6/Ti-15-3 and (0)(sub 4) SCS-6/Ti-6Al-4V laminates. Observations revealed that fatigue damage consisted primarily of matrix cracks and fiber matrix interfacial failure in the (0)(sub 8) SCS-6/Ti-15-3 laminates. Fiber-matrix interface failure included fracture of the brittle reaction zone and cracking between the two carbon rich fiber coatings. Intact fibers in the wake of the matrix cracks reduce the stress intensity factor range. Thus, an applied stress intensity factor range is inappropriate to characterize matrix crack growth behavior. Fiber bridging models were used to determine the matrix stress intensity factor range in titanium metal matrix composites. In these models, the fibers in the wake of the crack are idealized as a closure pressure. An unknown constant frictional shear stress is assumed to act along the debond or slip length of the bridging fibers. The frictional shear stress was used as a curve fitting parameter to available data (crack growth data, crack opening displacement data, and debond length data). Large variations in the frictional shear stress required to fit the experimental data indicate that the fiber bridging models in their present form lack predictive capabilities. However, these models provide an efficient and relatively simple engineering method for conducting parametric studies of the matrix growth behavior based on constituent properties.

Anomalous night-time peaks in diurnal variations of NmF2 close to the geomagnetic equator: a statistical study

NASA Astrophysics Data System (ADS)

Pavlov, Anatoli

We present a study of anomalous night-time NmF2 peaks, ANNPs, observed by the La Paz, Natal, Djibouti, Kodaikanal, Madras, Manila, Talara, and Huancayo-Jicamarca ionosonde stations close to the geomagnetic equator. It is shown that the probabilities of occurrence of the first and second ANNPs depend on the geomagnetic longitude, and there is a longitude sector close to 110° geomagnetic longitude where the first and second ANNPs occur less frequently in comparisons with the longitude regions located close to and below about 34° geomagnetic longitude and close to and above about 144° geomagnetic longitude. The found frequencies of occurrence of the ANNPs increase with increasing solar activity, except of the Djibouti and Kodaikanal ionosonde stations, where the probability of the first ANNP occurrence is found to decrease with increasing solar activity from low (F10.7<100) to moderate (100≤F10.7≤170) solar activity, and except of the Natal ionosonde station, where the frequencies of occurrence of the first and second ANNPs decrease with increasing solar activity from moderate to high (F10.7>170) solar activity. We found that the occurrence probabilities of ANNPs during geomagnetically disturbed conditions are greater than those during geomagnetically quiet conditions. The calculated values of these probabilities have pronounced maximums in June (La-Paz and Talara) and in July (Huancayo-Jicamarca) at the ionosonde stations located in the southern geographic hemisphere. The first ANNP is least frequently observed in January (La-Paz, Talara, and Huancayo-Jicamarca), and the second ANNP is least frequently measured in January (La-Paz and Huancayo-Jicamarca) and in December (Talara). In the northern geographic hemisphere, the studied probabilities are lowest in June (Djibouti and Madras), in July (Manila), and in April (Kodaikanal). The maximums in the probabilities of occurrence of the first and second ANNPs are found to be in September (Djibouti), in October (Madras), in November (Manila), and in December (Kodaikanal).

Positive Matrix Factorization Model for environmental data analyses

EPA Pesticide Factsheets

Positive Matrix Factorization is a receptor model developed by EPA to provide scientific support for current ambient air quality standards and implement those standards by identifying and quantifying the relative contributions of air pollution sources.

Difficulty Factors, Distribution Effects, and the Least Squares Simplex Data Matrix Solution

ERIC Educational Resources Information Center

Ten Berge, Jos M. F.

1972-01-01

In the present article it is argued that the Least Squares Simplex Data Matrix Solution does not deal adequately with difficulty factors inasmuch as the theoretical foundation is insufficient. (Author/CB)

Factors affecting fixation of heavy metals in solidified/stabilized matrix: a review.

PubMed

Malviya, Rachana; Chaudhary, Rubina

2010-07-01

In this paper, an effort has been made to understand the factors, which affect fixation of heavy metals in solidified/stabilized matrix. Various aspects related to the solidification/stabilization of different heavy metals (Ar, Ba, Cu, Cr, Pb, Zn, Hg) are reviewed. A comparative study of different binders for the fixation of each metal has also been carried out to suggest the most suitable binder, pretreatment required for the metal. Valence, speciation, pH and other factors are also considered while reviewing metal retention capacity of different matrix.

Fiber pushout and interfacial shear in metal-matrix composites

NASA Technical Reports Server (NTRS)

Koss, Donald A.; Hellmann, John R.; Kallas, M. N.

1993-01-01

Recent thin-slice pushout tests have suggested that MMC matrix-fiber interface failure processes depend not only on such intrinsic factors as bond strength and toughness, and matrix plasticity, but such extrinsic factors as specimen configuration, thermally-induced residual stresses, and the mechanics associated with a given test. After detailing the contrasts in fiber-pullout and fiber-pushout mechanics, attention is given to selected aspects of thin-slice fiber pushout behavior illustrative of the physical nature of interfacial shear response and its dependence on both intrinsic and extrinsic factors.

Bottom side profiles for two close stations at the southern crest of the EIA: Differences and comparison with IRI-2012 and NeQuick2 for low and high solar activity

NASA Astrophysics Data System (ADS)

Perna, L.; Venkatesh, K.; Pillat, V. G.; Pezzopane, M.; Fagundes, P. R.; Ezquer, R. G.; Cabrera, M. A.

2018-01-01

Bottom side electron density profiles for two stations at the southern crest of the Equatorial Ionization Anomaly (EIA), São José dos Campos (23.1°S, 314.5°E, dip latitude 19.8°S; Brazil) and Tucumán (26.9°S, 294.6°E, dip latitude 14.0°S; Argentina), located at similar latitude and separated by only 20° in longitude, have been compared during equinoctial, winter and summer months under low (year 2008, minimum of the solar cycle 23/24) and high solar activity (years 2013-2014, maximum of the solar cycle 24) conditions. An analysis of parameters describing the bottom side part of the electron density profile, namely the peak electron density NmF2, the height hmF2 at which it is reached, the thickness parameter B0 and the shape parameter B1, is carried out. Further, a comparison of bottom side profiles and F-layer parameters with the corresponding outputs of IRI-2012 and NeQuick2 models is also reported. The variations of NmF2 at both stations reveal the absence of semi-annual anomaly for low solar activity (LSA), evidencing the anomalous activity of the last solar minimum, while those related to hmF2 show an uplift of the ionosphere for high solar activity (HSA). As expected, the EIA is particularly visible at both stations during equinox for HSA, when its strength is at maximum in the South American sector. Despite the similar latitude of the two stations upon the southern crest of the EIA, the anomaly effect is more pronounced at Tucumán than at São José dos Campos. The differences encountered between these very close stations suggest that in this sector relevant longitudinal-dependent variations could occur, with the longitudinal gradient of the Equatorial Electrojet that plays a key role to explain such differences together with the 5.8° separation in dip latitude between the two ionosondes. Furthermore at Tucumán, the daily peak value of NmF2 around 21:00 LT during equinox for HSA is in temporal coincidence with an impulsive enhancement of hmF2, showing a kind of "elastic rebound" under the action of the EIA. IRI-2012 and NeQuick2 bottom side profiles show significant deviations from ionosonde observations. In particular, both models provide a clear underestimation of the EIA strength at both stations, with more pronounced differences for Tucumán. Large discrepancies are obtained for the parameter hmF2 for HSA during daytime at São José dos Campos, where clear underestimations made by both models are observed. The shape parameter B0 is quite well described by the IRI-2012 model, with very good agreement in particular during equinox for both stations for both LSA and HSA. On the contrary, the two models show poor agreements with ionosonde data concerning the shape parameter B1.

Classification and identification of molecules through factor analysis method based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Jianglou; Liu, Jinsong; Wang, Kejia; Yang, Zhengang; Liu, Xiaming

2018-06-01

By means of factor analysis approach, a method of molecule classification is built based on the measured terahertz absorption spectra of the molecules. A data matrix can be obtained by sampling the absorption spectra at different frequency points. The data matrix is then decomposed into the product of two matrices: a weight matrix and a characteristic matrix. By using the K-means clustering to deal with the weight matrix, these molecules can be classified. A group of samples (spirobenzopyran, indole, styrene derivatives and inorganic salts) has been prepared, and measured via a terahertz time-domain spectrometer. These samples are classified with 75% accuracy compared to that directly classified via their molecular formulas.

Study on the Preparation Process and Influential Factors of Large Area Environment-friendly Molten Carbonate Fuel Cell Matrix

NASA Astrophysics Data System (ADS)

Zhang, Ruiyun; Xu, Shisen; Cheng, Jian; Wang, Hongjian; Ren, Yongqiang

2017-07-01

Low-cost and high-performance matrix materials used in mass production of molten carbonate fuel cell (MCFC) were prepared by automatic casting machine with α-LiAlO2 powder material synthesized by gel-solid method, and distilled water as solvent. The single cell was assembled for generating test, and the good performance of the matrix was verified. The paper analyzed the factors affecting aqueous tape casting matrix preparation, such as solvent content, dispersant content, milling time, blade height and casting machine running speed, providing a solid basis for the mass production of large area environment-friendly matrix used in molten carbonate fuel cell.

A fast, preconditioned conjugate gradient Toeplitz solver

NASA Technical Reports Server (NTRS)

Pan, Victor; Schrieber, Robert

1989-01-01

A simple factorization is given of an arbitrary hermitian, positive definite matrix in which the factors are well-conditioned, hermitian, and positive definite. In fact, given knowledge of the extreme eigenvalues of the original matrix A, an optimal improvement can be achieved, making the condition numbers of each of the two factors equal to the square root of the condition number of A. This technique is to applied to the solution of hermitian, positive definite Toeplitz systems. Large linear systems with hermitian, positive definite Toeplitz matrices arise in some signal processing applications. A stable fast algorithm is given for solving these systems that is based on the preconditioned conjugate gradient method. The algorithm exploits Toeplitz structure to reduce the cost of an iteration to O(n log n) by applying the fast Fourier Transform to compute matrix-vector products. Matrix factorization is used as a preconditioner.

Factor Covariance Analysis in Subgroups.

ERIC Educational Resources Information Center

Pennell, Roger

The problem considered is that of an investigator sampling two or more correlation matrices and desiring to fit a model where a factor pattern matrix is assumed to be identical across samples and we need to estimate only the factor covariance matrix and the unique variance for each sample. A flexible, least squares solution is worked out and…

Field-scale effective matrix diffusion coefficient for fractured rock: results from literature survey.

PubMed

Zhou, Quanlin; Liu, Hui-Hai; Molz, Fred J; Zhang, Yingqi; Bodvarsson, Gudmundur S

2007-08-15

Matrix diffusion is an important mechanism for solute transport in fractured rock. We recently conducted a literature survey on the effective matrix diffusion coefficient, D(m)(e), a key parameter for describing matrix diffusion processes at the field scale. Forty field tracer tests at 15 fractured geologic sites were surveyed and selected for the study, based on data availability and quality. Field-scale D(m)(e) values were calculated, either directly using data reported in the literature, or by reanalyzing the corresponding field tracer tests. The reanalysis was conducted for the selected tracer tests using analytic or semi-analytic solutions for tracer transport in linear, radial, or interwell flow fields. Surveyed data show that the scale factor of the effective matrix diffusion coefficient (defined as the ratio of D(m)(e) to the lab-scale matrix diffusion coefficient, D(m), of the same tracer) is generally larger than one, indicating that the effective matrix diffusion coefficient in the field is comparatively larger than the matrix diffusion coefficient at the rock-core scale. This larger value can be attributed to the many mass-transfer processes at different scales in naturally heterogeneous, fractured rock systems. Furthermore, we observed a moderate, on average trend toward systematic increase in the scale factor with observation scale. This trend suggests that the effective matrix diffusion coefficient is likely to be statistically scale-dependent. The scale-factor value ranges from 0.5 to 884 for observation scales from 5 to 2000 m. At a given scale, the scale factor varies by two orders of magnitude, reflecting the influence of differing degrees of fractured rock heterogeneity at different geologic sites. In addition, the surveyed data indicate that field-scale longitudinal dispersivity generally increases with observation scale, which is consistent with previous studies. The scale-dependent field-scale matrix diffusion coefficient (and dispersivity) may have significant implications for assessing long-term, large-scale radionuclide and contaminant transport events in fractured rock, both for nuclear waste disposal and contaminant remediation.

A real time spectrum to dose conversion system

NASA Technical Reports Server (NTRS)

Farmer, B. J.; Johnson, J. H.; Bagwell, R. G.

1972-01-01

A system has been developed which permits the determination of dose in real time or near real time directly from the pulse-height output of a radiation spectrometer. The technique involves the use of the resolution matrix of a spectrometer, the radiation energy-to-dose conversion function, and the geometrical factors, although the order of matrix operations is reversed. The new technique yields a result which is mathematically identical to the standard method while requiring no matrix manipulations or resolution matrix storage in the remote computer. It utilizes only a single function for each type dose required and each geometric factor involved.

Factors associated with continuance commitment to FAA matrix teams.

DOT National Transportation Integrated Search

1993-11-01

Several organizations within the FAA employ matrix teams to achieve cross-functional coordination. Matrix team members typically represent different organizational functions required for project accomplishment (e.g., research and development, enginee...

Matrix Theory of Small Oscillations

ERIC Educational Resources Information Center

Chavda, L. K.

1978-01-01

A complete matrix formulation of the theory of small oscillations is presented. Simple analytic solutions involving matrix functions are found which clearly exhibit the transients, the damping factors, the Breit-Wigner form for resonances, etc. (BB)

Proposed framework for thermomechanical life modeling of metal matrix composites

NASA Technical Reports Server (NTRS)

Halford, Gary R.; Lerch, Bradley A.; Saltsman, James F.

1993-01-01

The framework of a mechanics of materials model is proposed for thermomechanical fatigue (TMF) life prediction of unidirectional, continuous-fiber metal matrix composites (MMC's). Axially loaded MMC test samples are analyzed as structural components whose fatigue lives are governed by local stress-strain conditions resulting from combined interactions of the matrix, interfacial layer, and fiber constituents. The metallic matrix is identified as the vehicle for tracking fatigue crack initiation and propagation. The proposed framework has three major elements. First, TMF flow and failure characteristics of in situ matrix material are approximated from tests of unreinforced matrix material, and matrix TMF life prediction equations are numerically calibrated. The macrocrack initiation fatigue life of the matrix material is divided into microcrack initiation and microcrack propagation phases. Second, the influencing factors created by the presence of fibers and interfaces are analyzed, characterized, and documented in equation form. Some of the influences act on the microcrack initiation portion of the matrix fatigue life, others on the microcrack propagation life, while some affect both. Influencing factors include coefficient of thermal expansion mismatch strains, residual (mean) stresses, multiaxial stress states, off-axis fibers, internal stress concentrations, multiple initiation sites, nonuniform fiber spacing, fiber debonding, interfacial layers and cracking, fractured fibers, fiber deflections of crack fronts, fiber bridging of matrix cracks, and internal oxidation along internal interfaces. Equations exist for some, but not all, of the currently identified influencing factors. The third element is the inclusion of overriding influences such as maximum tensile strain limits of brittle fibers that could cause local fractures and ensuing catastrophic failure of surrounding matrix material. Some experimental data exist for assessing the plausibility of the proposed framework.

Positive semidefinite tensor factorizations of the two-electron integral matrix for low-scaling ab initio electronic structure.

PubMed

Hoy, Erik P; Mazziotti, David A

2015-08-14

Tensor factorization of the 2-electron integral matrix is a well-known technique for reducing the computational scaling of ab initio electronic structure methods toward that of Hartree-Fock and density functional theories. The simplest factorization that maintains the positive semidefinite character of the 2-electron integral matrix is the Cholesky factorization. In this paper, we introduce a family of positive semidefinite factorizations that generalize the Cholesky factorization. Using an implementation of the factorization within the parametric 2-RDM method [D. A. Mazziotti, Phys. Rev. Lett. 101, 253002 (2008)], we study several inorganic molecules, alkane chains, and potential energy curves and find that this generalized factorization retains the accuracy and size extensivity of the Cholesky factorization, even in the presence of multi-reference correlation. The generalized family of positive semidefinite factorizations has potential applications to low-scaling ab initio electronic structure methods that treat electron correlation with a computational cost approaching that of the Hartree-Fock method or density functional theory.

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Higher impact of female than male migration on population structure in large mammals.

PubMed

Tiedemann, R; Hardy, O; Vekemans, X; Milinkovitch, M C

2000-08-01

We simulated large mammal populations using an individual-based stochastic model under various sex-specific migration schemes and life history parameters from the blue whale and the Asian elephant. Our model predicts that genetic structure at nuclear loci is significantly more influenced by female than by male migration. We identified requisite comigration of mother and offspring during gravidity and lactation as the primary cause of this phenomenon. In addition, our model predicts that the common assumption that geographical patterns of mitochondrial DNA (mtDNA) could be translated into female migration rates (Nmf) will cause biased estimates of maternal gene flow when extensive male migration occurs and male mtDNA haplotypes are included in the analysis.

New Factorization Techniques and Fast Serial and Parrallel Algorithms for Operational Space Control of Robot Manipulators

NASA Technical Reports Server (NTRS)

Fijany, Amir; Djouani, Karim; Fried, George; Pontnau, Jean

1997-01-01

In this paper a new factorization technique for computation of inverse of mass matrix, and the operational space mass matrix, as arising in implementation of the operational space control scheme, is presented.

Optical factors determined by the T-matrix method in turbidity measurement of absolute coagulation rate constants.

PubMed

Xu, Shenghua; Liu, Jie; Sun, Zhiwei

2006-12-01

Turbidity measurement for the absolute coagulation rate constants of suspensions has been extensively adopted because of its simplicity and easy implementation. A key factor in deriving the rate constant from experimental data is how to theoretically evaluate the so-called optical factor involved in calculating the extinction cross section of doublets formed during aggregation. In a previous paper, we have shown that compared with other theoretical approaches, the T-matrix method provides a robust solution to this problem and is effective in extending the applicability range of the turbidity methodology, as well as increasing measurement accuracy. This paper will provide a more comprehensive discussion of the physical insight for using the T-matrix method in turbidity measurement and associated technical details. In particular, the importance of ensuring the correct value for the refractive indices for colloidal particles and the surrounding medium used in the calculation is addressed, because the indices generally vary with the wavelength of the incident light. The comparison of calculated results with experiments shows that the T-matrix method can correctly calculate optical factors even for large particles, whereas other existing theories cannot. In addition, the data of the optical factor calculated by the T-matrix method for a range of particle radii and incident light wavelengths are listed.

Trust in Leadership DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

Item Corrected Item- Total Correlation Cronbach’s Alpha if Item Deleted Four-point Scale Items I can depend on my immediate supervisor to meet...1974) were used to assess the fit between the data and the factor. The BTS hypothesizes that the correlation matrix is an identity matrix. The...to reject the null hypothesis that the correlation matrix is an identity, and to conclude that the factor analysis is an appropriate method to

Estimating Depolarization with the Jones Matrix Quality Factor

NASA Astrophysics Data System (ADS)

Hilfiker, James N.; Hale, Jeffrey S.; Herzinger, Craig M.; Tiwald, Tom; Hong, Nina; Schöche, Stefan; Arwin, Hans

2017-11-01

Mueller matrix (MM) measurements offer the ability to quantify the depolarization capability of a sample. Depolarization can be estimated using terms such as the depolarization index or the average degree of polarization. However, these calculations require measurement of the complete MM. We propose an alternate depolarization metric, termed the Jones matrix quality factor, QJM, which does not require the complete MM. This metric provides a measure of how close, in a least-squares sense, a Jones matrix can be found to the measured Mueller matrix. We demonstrate and compare the use of QJM to other traditional calculations of depolarization for both isotropic and anisotropic depolarizing samples; including non-uniform coatings, anisotropic crystal substrates, and beetle cuticles that exhibit both depolarization and circular diattenuation.

Study on the Algorithm of Judgment Matrix in Analytic Hierarchy Process

NASA Astrophysics Data System (ADS)

Lu, Zhiyong; Qin, Futong; Jin, Yican

2017-10-01

A new algorithm is proposed for the non-consistent judgment matrix in AHP. A primary judgment matrix is generated firstly through pre-ordering the targeted factor set, and a compared matrix is built through the top integral function. Then a relative error matrix is created by comparing the compared matrix with the primary judgment matrix which is regulated under the control of the relative error matrix and the dissimilar degree of the matrix step by step. Lastly, the targeted judgment matrix is generated to satisfy the requirement of consistence and the least dissimilar degree. The feasibility and validity of the proposed method are verified by simulation results.

The nuclear matrix protein NMP-1 is the transcription factor YY1.

PubMed Central

Guo, B; Odgren, P R; van Wijnen, A J; Last, T J; Nickerson, J; Penman, S; Lian, J B; Stein, J L; Stein, G S

1995-01-01

NMP-1 was initially identified as a nuclear matrix-associated DNA-binding factor that exhibits sequence-specific recognition for the site IV regulatory element of a histone H4 gene. This distal promoter domain is a nuclear matrix interaction site. In the present study, we show that NMP-1 is the multifunctional transcription factor YY1. Gel-shift and Western blot analyses demonstrate that NMP-1 is immunoreactive with YY1 antibody. Furthermore, purified YY1 protein specifically recognizes site IV and reconstitutes the NMP-1 complex. Western blot and gel-shift analyses indicate that YY1 is present within the nuclear matrix. In situ immunofluorescence studies show that a significant fraction of YY1 is localized in the nuclear matrix, principally but not exclusively associated with residual nucleoli. Our results confirm that NMP-1/YY1 is a ubiquitous protein that is present in both human cells and in rat osteosarcoma ROS 17/2.8 cells. The finding that NMP-1 is identical to YY1 suggests that this transcriptional regulator may mediate gene-matrix interactions. Our results are consistent with the concept that the nuclear matrix may functionally compartmentalize the eukaryotic nucleus to support regulation of gene expression. Images Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 PMID:7479833

A Deep Stochastic Model for Detecting Community in Complex Networks

NASA Astrophysics Data System (ADS)

Fu, Jingcheng; Wu, Jianliang

2017-01-01

Discovering community structures is an important step to understanding the structure and dynamics of real-world networks in social science, biology and technology. In this paper, we develop a deep stochastic model based on non-negative matrix factorization to identify communities, in which there are two sets of parameters. One is the community membership matrix, of which the elements in a row correspond to the probabilities of the given node belongs to each of the given number of communities in our model, another is the community-community connection matrix, of which the element in the i-th row and j-th column represents the probability of there being an edge between a randomly chosen node from the i-th community and a randomly chosen node from the j-th community. The parameters can be evaluated by an efficient updating rule, and its convergence can be guaranteed. The community-community connection matrix in our model is more precise than the community-community connection matrix in traditional non-negative matrix factorization methods. Furthermore, the method called symmetric nonnegative matrix factorization, is a special case of our model. Finally, based on the experiments on both synthetic and real-world networks data, it can be demonstrated that our algorithm is highly effective in detecting communities.

THE U.S. ENVIRONMENTAL PROTECTION AGENCY VERSION OF POSITIVE MATRIX FACTORIZATION

EPA Science Inventory

The abstract describes some of the special features of the EPA's version of Positive Matrix Factorization that is freely distributed. Features include descriptions of the Graphical User Interface, an approach for estimating errors in the modeled solutions, and future development...

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Postconcussive Symptoms in OEF-OIF Veterans: Factor Structure and Impact of Posttraumatic Stress

DTIC Science & Technology

2009-06-03

correlations between NSI full items are presented in Appendix A. Visual inspection of the correlation matrix, the Kaiser - Meyer - Olkin coefficient of .92, and...Spearman rho correlations between NSI residuals are pre- sented in Appendix B. Again, visual inspection of the correla- tion matrix, the Kaiser - Meyer ... Olkin coefficient of .83, and Bartlett’s test of sphericity (x2 5 1,936.0, p , .01) suggested that the matrix could be factored. Principal-components

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

PubMed Central

Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou

2013-01-01

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix. PMID:23858479

RECEPTOR MODELING OF AMBIENT PARTICULATE MATTER DATA USING POSITIVE MATRIX FACTORIZATION REVIEW OF EXISTING METHODS

EPA Science Inventory

Methods for apportioning sources of ambient particulate matter (PM) using the positive matrix factorization (PMF) algorithm are reviewed. Numerous procedural decisions must be made and algorithmic parameters selected when analyzing PM data with PMF. However, few publications docu...

Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.

PubMed

Zitnik, Marinka; Zupan, Blaž

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.

Structure of collagen-glycosaminoglycan matrix and the influence to its integrity and stability.

PubMed

Bi, Yuying; Patra, Prabir; Faezipour, Miad

2014-01-01

Glycosaminoglycan (GAG) is a chain-like disaccharide that is linked to polypeptide core to connect two collagen fibrils/fibers and provide the intermolecular force in Collagen-GAG matrix (C-G matrix). Thus, the distribution of GAG in C-G matrix contributes to the integrity and mechanical properties of the matrix and related tissue. This paper analyzes the transverse isotropic distribution of GAG in C-G matrix. The angle of GAGs related to collagen fibrils is used as parameters to qualify the GAGs isotropic characteristic in both 3D and 2D rendering. Statistical results included that over one third of GAGs were perpendicular directed to collagen fibril with symmetrical distribution for both 3D matrix and 2D plane cross through collagen fibrils. The three factors tested in this paper: collagen radius, collagen distribution, and GAGs density, were not statistically significant for the strength of Collagen-GAG matrix in 3D rendering. However in 2D rendering, a significant factor found was the radius of collagen in matrix for the GAGs directed to orthogonal plane of Collagen-GAG matrix. Between two cross-section selected from Collagen-GAG matrix model, the plane cross through collagen fibrils was symmetrically distributed but the total percentage of perpendicular directed GAG was deducted by decreasing collagen radius. There were some symmetry features of GAGs angle distribution in selected 2D plane that passed through space between collagen fibrils, but most models showed multiple peaks in GAGs angle distribution. With less GAGs directed to perpendicular of collagen fibril, strength in collagen cross-section weakened. Collagen distribution was also a factor that influences GAGs angle distribution in 2D rendering. True hexagonal collagen packaging is reported in this paper to have less strength at collagen cross-section compared to quasi-hexagonal collagen arrangement. In this work focus is on GAGs matrix within the collagen and its relevance to anisotropy.

Selection of representative embankments based on rough set - fuzzy clustering method

NASA Astrophysics Data System (ADS)

Bin, Ou; Lin, Zhi-xiang; Fu, Shu-yan; Gao, Sheng-song

2018-02-01

The premise condition of comprehensive evaluation of embankment safety is selection of representative unit embankment, on the basis of dividing the unit levee the influencing factors and classification of the unit embankment are drafted.Based on the rough set-fuzzy clustering, the influence factors of the unit embankment are measured by quantitative and qualitative indexes.Construct to fuzzy similarity matrix of standard embankment then calculate fuzzy equivalent matrix of fuzzy similarity matrix by square method. By setting the threshold of the fuzzy equivalence matrix, the unit embankment is clustered, and the representative unit embankment is selected from the classification of the embankment.

3D tensor-based blind multispectral image decomposition for tumor demarcation

NASA Astrophysics Data System (ADS)

Kopriva, Ivica; Peršin, Antun

2010-03-01

Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

Pattern identification in time-course gene expression data with the CoGAPS matrix factorization.

PubMed

Fertig, Elana J; Stein-O'Brien, Genevieve; Jaffe, Andrew; Colantuoni, Carlo

2014-01-01

Patterns in time-course gene expression data can represent the biological processes that are active over the measured time period. However, the orthogonality constraint in standard pattern-finding algorithms, including notably principal components analysis (PCA), confounds expression changes resulting from simultaneous, non-orthogonal biological processes. Previously, we have shown that Markov chain Monte Carlo nonnegative matrix factorization algorithms are particularly adept at distinguishing such concurrent patterns. One such matrix factorization is implemented in the software package CoGAPS. We describe the application of this software and several technical considerations for identification of age-related patterns in a public, prefrontal cortex gene expression dataset.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

Factorization-based texture segmentation

DOE PAGES

Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

2015-06-17

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histogramsmore » to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.« less

Weighted graph based ordering techniques for preconditioned conjugate gradient methods

NASA Technical Reports Server (NTRS)

Clift, Simon S.; Tang, Wei-Pai

1994-01-01

We describe the basis of a matrix ordering heuristic for improving the incomplete factorization used in preconditioned conjugate gradient techniques applied to anisotropic PDE's. Several new matrix ordering techniques, derived from well-known algorithms in combinatorial graph theory, which attempt to implement this heuristic, are described. These ordering techniques are tested against a number of matrices arising from linear anisotropic PDE's, and compared with other matrix ordering techniques. A variation of RCM is shown to generally improve the quality of incomplete factorization preconditioners.

Growth factor transgenes interactively regulate articular chondrocytes.

PubMed

Shi, Shuiliang; Mercer, Scott; Eckert, George J; Trippel, Stephen B

2013-04-01

Adult articular chondrocytes lack an effective repair response to correct damage from injury or osteoarthritis. Polypeptide growth factors that stimulate articular chondrocyte proliferation and cartilage matrix synthesis may augment this response. Gene transfer is a promising approach to delivering such factors. Multiple growth factor genes regulate these cell functions, but multiple growth factor gene transfer remains unexplored. We tested the hypothesis that multiple growth factor gene transfer selectively modulates articular chondrocyte proliferation and matrix synthesis. We tested the hypothesis by delivering combinations of the transgenes encoding insulin-like growth factor I (IGF-I), fibroblast growth factor-2 (FGF-2), transforming growth factor beta1 (TGF-β1), bone morphogenetic protein-2 (BMP-2), and bone morphogenetic protien-7 (BMP-7) to articular chondrocytes and measured changes in the production of DNA, glycosaminoglycan, and collagen. The transgenes differentially regulated all these chondrocyte activities. In concert, the transgenes interacted to generate widely divergent responses from the cells. These interactions ranged from inhibitory to synergistic. The transgene pair encoding IGF-I and FGF-2 maximized cell proliferation. The three-transgene group encoding IGF-I, BMP-2, and BMP-7 maximized matrix production and also optimized the balance between cell proliferation and matrix production. These data demonstrate an approach to articular chondrocyte regulation that may be tailored to stimulate specific cell functions, and suggest that certain growth factor gene combinations have potential value for cell-based articular cartilage repair. Copyright © 2012 Wiley Periodicals, Inc.

Leukocyte- and platelet-rich fibrin (L-PRF) for long-term delivery of growth factor in rotator cuff repair: review, preliminary results and future directions.

PubMed

Zumstein, Matthias A; Berger, Simon; Schober, Martin; Boileau, Pascal; Nyffeler, Richard W; Horn, Michael; Dahinden, Clemens A

2012-06-01

Surgical repair of the rotator cuff repair is one of the most common procedures in orthopedic surgery. Despite it being the focus of much research, the physiological tendon-bone insertion is not recreated following repair and there is an anatomic non-healing rate of up to 94%. During the healing phase, several growth factors are upregulated that induce cellular proliferation and matrix deposition. Subsequently, this provisional matrix is replaced by the definitive matrix. Leukocyte- and platelet-rich fibrin (L-PRF) contain growth factors and has a stable dense fibrin matrix. Therefore, use of LPRF in rotator cuff repair is theoretically attractive. The aim of the present study was to determine 1) the optimal protocol to achieve the highest leukocyte content; 2) whether L-PRF releases growth factors in a sustained manner over 28 days; 3) whether standard/gelatinous or dry/compressed matrix preparation methods result in higher growth factor concentrations. 1) The standard L-PRF centrifugation protocol with 400 x g showed the highest concentration of platelets and leukocytes. 2) The L-PRF clots cultured in medium showed a continuous slow release with an increase in the absolute release of growth factors TGF-β1, VEGF and MPO in the first 7 days, and for IGF1, PDGF-AB and platelet activity (PF4=CXCL4) in the first 8 hours, followed by a decrease to close to zero at 28 days. Significantly higher levels of growth factor were expressed relative to the control values of normal blood at each culture time point. 3) Except for MPO and the TGFβ-1, there was always a tendency towards higher release of growth factors (i.e., CXCL4, IGF-1, PDGF-AB, and VEGF) in the standard/gelatinous- compared to the dry/compressed group. L-PRF in its optimal standard/gelatinous-type matrix can store and deliver locally specific healing growth factors for up to 28 days and may be a useful adjunct in rotator cuff repair.

Factor Analysis by Generalized Least Squares.

ERIC Educational Resources Information Center

Joreskog, Karl G.; Goldberger, Arthur S.

Aitkin's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are scale free and asymptotically efficient. The estimates are computed by a rapidly…

EFFECT OF GROWTH FACTOR-FIBRONECTIN MATRIX INTERACTION ON RAT TYPE II CELL ADHESION AND DNA SYTHESIS

EPA Science Inventory

ABSTRACT

Type II cells attach, migrate and proliferate on a provisional fibronectin-rich matrix during alveolar wall repair after lung injury. The combination of cell-substratum interactions via integrin receptors and exposure to local growth factors are likely to initiat...

UDU/T/ covariance factorization for Kalman filtering

NASA Technical Reports Server (NTRS)

Thornton, C. L.; Bierman, G. J.

1980-01-01

There has been strong motivation to produce numerically stable formulations of the Kalman filter algorithms because it has long been known that the original discrete-time Kalman formulas are numerically unreliable. Numerical instability can be avoided by propagating certain factors of the estimate error covariance matrix rather than the covariance matrix itself. This paper documents filter algorithms that correspond to the covariance factorization P = UDU(T), where U is a unit upper triangular matrix and D is diagonal. Emphasis is on computational efficiency and numerical stability, since these properties are of key importance in real-time filter applications. The history of square-root and U-D covariance filters is reviewed. Simple examples are given to illustrate the numerical inadequacy of the Kalman covariance filter algorithms; these examples show how factorization techniques can give improved computational reliability.

Factorization and fitting of molecular scattering information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldflam, R.; Kouri, D.J.; Green, S.

1977-12-15

The factorization of cross sections of various kinds resulting from the infinite order sudden approximation is considered in detail. Unlike the earlier study of Goldflam, Green, and Kouri, we base the present analysis on the factored IOS T-matrix rather than on the S-matrix. This enables us to obtain somewhat simpler expressions. For example, we show that the factored IOS approximation to the Arthurs--Dalgarno T-matrix involves products of dynamical coefficients T/sup L//sub l/ and Percival--Seaton coefficients f/sub L/(jlvertical-barj/sub 0/l/sub 0/vertical-barJ). It is shown that an optical theorem exists for the T/sub l//sup L/ dynamical coefficients of the T-matrix. The differential scatteringmore » amplitudes are shown to factor into dynamical coefficients q/sub L/(chi) times spectroscopic factors that are independent of the dynamics (potential). Then a generalized form of the Parker--Pack result for ..sigma../sub j/(dsigma/dR)(j/sub 0/..-->..j) is derived. It is also shown that the IOS approximation for (dsigma/dR)(j/sub 0/..-->..j) factors into sums of spectroscopic coefficients times the differential cross sections out of j/sub 0/=0. The IOS integral cross sections factor into spectroscopic coefficients times the integral cross sections out of j/sub 0/=0. The factored IOS general phenomenological cross sections are rederived using the T-matrix approach and are shown to equal sums of Percival--Seaton coefficients timesthe inelastic integral cross section out of initial rotor state j/sub 0/ = 0. This suggests that experimental measurements of line shapes and/or NMR spin--lattice relaxation can be used to directly give inelastic state-to-state degeneracy averaged integral cross sections whenever the IOS is a good approximation. Factored IOS expressions for viscosity and diffusion are derived and shown to potentially yield additional information beyond that contained in line shapes.« less

Human umbilical cord derived matrix: A scaffold suitable for tissue engineering application.

PubMed

Dan, Pan; Velot, Émilie; Mesure, Benjamin; Groshenry, Guillaume; Bacharouche, Jalal; Decot, Véronique; Menu, Patrick

2017-01-01

Human tissue derived natural extracellular matrix (ECM) has great potential in tissue engineering. We sought to isolate extracellular matrix derived from human umbilical cord and test its potential in tissue engineering. An enzymatic method was applied to isolate and solubilized complete human umbilical cord derived matrix (hUCM). The obtained solution was analyzed for growth factors, collagen and residual DNA contents, then used to coat 2D and 3D surfaces for cell culture application. The hUCM was successfully isolated with trypsin digestion to acquire a solution containing various growth factors and collagen but no residual DNA. This hUCM solution can form a coating on 2D and 3D substrates suitable cell culture. We developed a new matrix derived from human source that can be further used in tissue engineering.

Strides made in understanding space weather at Earth

NASA Astrophysics Data System (ADS)

Buonsanto, M. J.; Fuller-Rowell, T. J.

Disturbances on the Sun can produce dramatic effects in the space environment surrounding the Earth. Energetic particle effects become more intense and pose a hazard to astronauts and damage spacecraft electronics; satellite lifetimes are shortened by increased atmospheric drag, and communications and navigation are disrupted by the changing plasma environment.“Space weather” has become the modern idiom for these effects, and periods of high activity are called geomagnetic storms. During a storm the ionosphere can be severely altered. A typical episode may reveal either a large decrease (negative phase) or increase (positive phase) in the normal daily peak ion density (NmF2) or total electron content (TEC). These changes in ion density are sometimes called ionospheric storms, and often persist for more than a day after a period of high geomagnetic activity.

A Damage Assessment Model for Surface Engagement for Missile and Gunfire.

DTIC Science & Technology

1982-03-01

characteristics make war games perhaps the only medium available, short of war, efficient enough for evalua- ting and examining command decisions at every level ...NP4-(NNNc enmW)" 1000 04 if2=A as O4 920331:91 52 *w:( 0n 0 % %*~ L 00000 0 t t h. 0 Cc SLLM v-%aeVI%0% 0C* 0 0% % 0 m % %0 4b. 0 0 I4qNenc 0 __ 0 4r...cDUnh 04 %W H 11 ACUE40% %N-o 0 0 090 hiit t (MCD hit c-4 m-N’-o m tI i 10 0t i4Nt a6 HLMm0~ *as oil wa a to 910 164 mcq %%% c 𔃺.~ i Nmf’ImP*2 v it

Implementation of thermal residual stresses in the analysis of fiber bridged matrix crack growth in titanium matrix composites

NASA Technical Reports Server (NTRS)

Bakuckas, John G., Jr.; Johnson, W. Steven

1994-01-01

In this research, thermal residual stresses were incorporated in an analysis of fiber-bridged matrix cracks in unidirectional and cross-ply titanium matrix composites (TMC) containing center holes or center notches. Two TMC were investigated, namely, SCS-6/Timelal-21S laminates. Experimentally, matrix crack initiation and growth were monitored during tension-tension fatigue tests conducted at room temperature and at an elevated temperature of 200 C. Analytically, thermal residual stresses were included in a fiber bridging (FB) model. The local R-ratio and stress-intensity factor in the matrix due to thermal and mechanical loadings were calculated and used to evaluate the matrix crack growth behavior in the two materials studied. The frictional shear stress term, tau, assumed in this model was used as a curve-fitting parameter to matrix crack growth data. The scatter band in the values of tau used to fit the matrix crack growth data was significantly reduced when thermal residual stresses were included in the fiber bridging analysis. For a given material system, lay-up and temperature, a single value of tau was sufficient to analyze the crack growth data. It was revealed in this study that thermal residual stresses are an important factor overlooked in the original FB models.

Receptor control in mesenchymal stem cell engineering

NASA Astrophysics Data System (ADS)

Dalby, Matthew J.; García, Andrés J.; Salmeron-Sanchez, Manuel

2018-03-01

Materials science offers a powerful tool to control mesenchymal stem cell (MSC) growth and differentiation into functional phenotypes. A complex interplay between the extracellular matrix and growth factors guides MSC phenotypes in vivo. In this Review, we discuss materials-based bioengineering approaches to direct MSC fate in vitro and in vivo, mimicking cell-matrix-growth factor crosstalk. We first scrutinize MSC-matrix interactions and how the properties of a material can be tailored to support MSC growth and differentiation in vitro, with an emphasis on MSC self-renewal mechanisms. We then highlight important growth factor signalling pathways and investigate various materials-based strategies for growth factor presentation and delivery. Integrin-growth factor crosstalk in the context of MSC engineering is introduced, and bioinspired material designs with the potential to control the MSC niche phenotype are considered. Finally, we summarize important milestones on the road to MSC engineering for regenerative medicine.

Constrained low-rank matrix estimation: phase transitions, approximate message passing and applications

NASA Astrophysics Data System (ADS)

Lesieur, Thibault; Krzakala, Florent; Zdeborová, Lenka

2017-07-01

This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on Information Theory Proc. pp 1635-9 and 2015 53rd Annual Allerton Conf. on Communication, Control and Computing (IEEE) pp 680-7) on low-rank matrix estimation in the presence of constraints on the factors into which the matrix is factorized. Low-rank matrix factorization is one of the basic methods used in data analysis for unsupervised learning of relevant features and other types of dimensionality reduction. We present a framework to study the constrained low-rank matrix estimation for a general prior on the factors, and a general output channel through which the matrix is observed. We draw a parallel with the study of vector-spin glass models—presenting a unifying way to study a number of problems considered previously in separate statistical physics works. We present a number of applications for the problem in data analysis. We derive in detail a general form of the low-rank approximate message passing (Low-RAMP) algorithm, that is known in statistical physics as the TAP equations. We thus unify the derivation of the TAP equations for models as different as the Sherrington-Kirkpatrick model, the restricted Boltzmann machine, the Hopfield model or vector (xy, Heisenberg and other) spin glasses. The state evolution of the Low-RAMP algorithm is also derived, and is equivalent to the replica symmetric solution for the large class of vector-spin glass models. In the section devoted to result we study in detail phase diagrams and phase transitions for the Bayes-optimal inference in low-rank matrix estimation. We present a typology of phase transitions and their relation to performance of algorithms such as the Low-RAMP or commonly used spectral methods.

A plasma-based biomatrix mixed with endothelial progenitor cells and keratinocytes promotes matrix formation, angiogenesis, and reepithelialization in full-thickness wounds.

PubMed

Vermeulen, Pieter; Dickens, Stijn; Degezelle, Karlien; Van den Berge, Stefaan; Hendrickx, Benoit; Vranckx, Jan Jeroen

2009-07-01

In search of an autologous vascularized skin substitute, we treated full-thickness wounds (FTWs) with autologous platelet-rich plasma gel (APG) in which we embedded endothelial progenitor cells (EPCs) and basal cell keratinocytes (KCs). We cultivated autologous KCs in low-serum conditions and expanded autologous EPCs from venous blood. FTWs (n = 55) were created on the backs of four pigs, covered with wound chambers, and randomly assigned to the following treatments: (1) APG, (2) APG + KCs, (3) APG + EPCs, (4) APG + KCs + EPCs, and (5) saline. All wounds were biopsied to measure neovascularization (lectin Bandeiraea Simplicifolia-1 (BS-1), alpha smooth muscle actin [alphaSMA], and membrane type 1 matrix metalloproteinase (MT1-MMP)), matrix deposition (fibronectin, collagen type I/III, and alphavbeta3), and reepithelialization. Wound fluids were analyzed for protein expression. All APG-treated wounds showed more vascular structures (p < 0.001), and the addition of EPCs further improved neovascularization, as confirmed by higher lectin, alphaSMA, and MT1-MMP. APG groups had higher collagen I/III (p < 0.05), alphavbeta3, and fibronectin content (p < 0.001), and they exhibited higher concentrations of platelet-derived growth factor subunit bb, basic fibroblast growth factor, hepatocyte growth factor, insulin growth factor-1, transforming growth factor-beta1 and -beta3, matrix metalloproteinase-1 and -z9, and tissue-inhibiting matrix metalloproteinase-1 and -2. Applying APG + KCs resulted in the highest reepithelialization rates (p < 0.001). No differences were found for wound contraction by planimetry. In this porcine FTW model, APG acts as a supportive biomatrix that, along with the embedded cells, improves extracellular matrix organization, promotes angiogenesis, and accelerates reepithelialization.

Quantification of various growth factors in different demineralized bone matrix preparations.

PubMed

Wildemann, B; Kadow-Romacker, A; Haas, N P; Schmidmaier, G

2007-05-01

Besides autografts, allografts, and synthetic materials, demineralized bone matrix (DBM) is used for bone defect filling and treatment of non-unions. Different DBM formulations are introduced in clinic since years. However, little is known about the presents and quantities of growth factors in DBM. Aim of the present study was the quantification of eight growth factors important for bone healing in three different "off the shelf" DBM formulations, which are already in human use: DBX putty, Grafton DBM putty, and AlloMatrix putty. All three DBM formulations are produced from human donor tissue but they differ in the substitutes added. From each of the three products 10 different lots were analyzed. Protein was extracted from the samples with Guanidine HCL/EDTA method and human ELISA kits were used for growth factor quantification. Differences between the three different products were seen in total protein contend and the absolute growth factor values but also a large variability between the different lots was found. The order of the growth factors, however, is almost comparable between the materials. In the three investigated materials FGF basic and BMP-4 were not detectable in any analyzed sample. BMP-2 revealed the highest concentration extractable from the samples with approximately 3.6 microg/g tissue without a significant difference between the three DBM formulations. In DBX putty significantly more TGF-beta1 and FGFa were measurable compared to the two other DBMs. IGF-I revealed the significantly highest value in the AlloMatrix and PDGF in Grafton. No differences were accessed for VEGF. Due to the differences in the growth factor concentration between the individual samples, independently from the product formulation, further analyzes are required to optimize the clinical outcome of the used demineralized bone matrix. Copyright 2006 Wiley Periodicals, Inc.

Rephasing invariants of the Cabibbo-Kobayashi- Maskawa matrix

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pérez R, H.; Kielanowski, P., E-mail: kiel@fis.cinvestav.mx; Juárez W, S. R., E-mail: rebeca@esfm.ipn.mx

2016-03-15

The paper is motivated by the importance of the rephasing invariance of the CKM (Cabibbo-Kobayashi-Maskawa) matrix observables. These observables appear in the discussion of the CP violation in the standard model (Jarlskog invariant) and also in the renormalization group equations for the quark Yukawa couplings. Our discussion is based on the general phase invariant monomials built out of the CKM matrix elements and their conjugates. We show that there exist 30 fundamental phase invariant monomials and 18 of them are a product of 4 CKM matrix elements and 12 are a product of 6 CKM matrix elements. In the mainmore » theorem we show that a general rephasing invariant monomial can be expressed as a product of at most five factors: four of them are fundamental phase invariant monomials and the fifth factor consists of powers of squares of absolute values of the CKM matrix elements. We also show that the imaginary part of any rephasing invariant monomial is proportional to the Jarlskog’s invariant J or is 0.« less

Multichannel Compressive Sensing MRI Using Noiselet Encoding

PubMed Central

Pawar, Kamlesh; Egan, Gary; Zhang, Jingxin

2015-01-01

The incoherence between measurement and sparsifying transform matrices and the restricted isometry property (RIP) of measurement matrix are two of the key factors in determining the performance of compressive sensing (CS). In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix. However, the incoherence between the randomly under-sampled Fourier matrix and the wavelet matrix is not optimal, which can deteriorate the performance of CS-MRI. Using the mathematical result that noiselets are maximally incoherent with wavelets, this paper introduces the noiselet unitary bases as the measurement matrix to improve the incoherence and RIP in CS-MRI. Based on an empirical RIP analysis that compares the multichannel noiselet and multichannel Fourier measurement matrices in CS-MRI, we propose a multichannel compressive sensing (MCS) framework to take the advantage of multichannel data acquisition used in MRI scanners. Simulations are presented in the MCS framework to compare the performance of noiselet encoding reconstructions and Fourier encoding reconstructions at different acceleration factors. The comparisons indicate that multichannel noiselet measurement matrix has better RIP than that of its Fourier counterpart, and that noiselet encoded MCS-MRI outperforms Fourier encoded MCS-MRI in preserving image resolution and can achieve higher acceleration factors. To demonstrate the feasibility of the proposed noiselet encoding scheme, a pulse sequences with tailored spatially selective RF excitation pulses was designed and implemented on a 3T scanner to acquire the data in the noiselet domain from a phantom and a human brain. The results indicate that noislet encoding preserves image resolution better than Fouirer encoding. PMID:25965548

Constructing the tree-level Yang-Mills S-matrix using complex factorization

NASA Astrophysics Data System (ADS)

Schuster, Philip C.; Toro, Natalia

2009-06-01

A remarkable connection between BCFW recursion relations and constraints on the S-matrix was made by Benincasa and Cachazo in 0705.4305, who noted that mutual consistency of different BCFW constructions of four-particle amplitudes generates non-trivial (but familiar) constraints on three-particle coupling constants — these include gauge invariance, the equivalence principle, and the lack of non-trivial couplings for spins > 2. These constraints can also be derived with weaker assumptions, by demanding the existence of four-point amplitudes that factorize properly in all unitarity limits with complex momenta. From this starting point, we show that the BCFW prescription can be interpreted as an algorithm for fully constructing a tree-level S-matrix, and that complex factorization of general BCFW amplitudes follows from the factorization of four-particle amplitudes. The allowed set of BCFW deformations is identified, formulated entirely as a statement on the three-particle sector, and using only complex factorization as a guide. Consequently, our analysis based on the physical consistency of the S-matrix is entirely independent of field theory. We analyze the case of pure Yang-Mills, and outline a proof for gravity. For Yang-Mills, we also show that the well-known scaling behavior of BCFW-deformed amplitudes at large z is a simple consequence of factorization. For gravity, factorization in certain channels requires asymptotic behavior ~ 1/z2.

MATRIX FACTORIZATION-BASED DATA FUSION FOR GENE FUNCTION PREDICTION IN BAKER’S YEAST AND SLIME MOLD

PubMed Central

ŽITNIK, MARINKA; ZUPAN, BLAŽ

2014-01-01

The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker’s yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps. PMID:24297565

The Incremental Multiresolution Matrix Factorization Algorithm

PubMed Central

Ithapu, Vamsi K.; Kondor, Risi; Johnson, Sterling C.; Singh, Vikas

2017-01-01

Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices – an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct “global” factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision. PMID:29416293

An inflammation-responsive transcription factor in the pathophysiology of osteoarthritis.

PubMed

Ray, Alpana; Ray, Bimal K

2008-01-01

A number of risk factors including biomechanical stress on the articular cartilage imposed by joint overloading due to obesity, repetitive damage of the joint tissues by injury of the menisci and ligaments, and abnormal joint alignment play a significant role in the onset of osteoarthritis (OA). Genetic predisposition can also lead to the formation of defective cartilage matrix because of abnormal gene expression in the cartilage-specific cells. Another important biochemical event in OA is the consequence of inflammation. It has been shown that synovial inflammation triggers the synthesis of biological stimuli such as cytokines and growth factors which subsequently reach the chondrocyte cells of the articular cartilage activating inflammatory events in the chondrocytes leading to cartilage destruction. In addition to cartilage degradation, hypertrophy of the subchondral bone and osteophyte formation at the joint margins also takes place in OA. Both processes involve abnormal expression of a number of genes including matrix metalloproteinases (MMPs) for cartilage degradation and those associated with bone formation during osteophyte development. To address how diverse groups of genes are activated in OA chondrocyte, we have studied their induction mechanism. We present evidence for abundant expression of an inflammation-responsive transcription factor, SAF-1, in moderate to severely damaged OA cartilage tissues. In contrast, cells in normal cartilage matrix contain very low level of SAF-1 protein. SAF-1 is identified as a major regulator of increased synthesis of MMP-1 and -9 and pro-angiogenic factor, vascular endothelial growth factor (VEGF). While VEGF by stimulating angiogenesis plays a key role in new bone formation in osteophyte, increase of MMP-1 and -9 is instrumental for cartilage erosion in the pathogenesis of OA. Increased expression in degenerated cartilage matrix and in the osteophytes indicate for a key regulatory role of SAF-1 in directing catabolic matrix degrading and anabolic matrix regenerating activities.

Physiological Ranges of Matrix Rigidity Modulate Primary Mouse Hepatocyte Function In Part Through Hepatocyte Nuclear Factor 4 Alpha

PubMed Central

Desai, Seema S.; Tung, Jason C.; Zhou, Vivian X.; Grenert, James P.; Malato, Yann; Rezvani, Milad; Español-Suñer, Regina; Willenbring, Holger; Weaver, Valerie M.; Chang, Tammy T.

2016-01-01

Matrix rigidity has important effects on cell behavior and is increased during liver fibrosis; however, its effect on primary hepatocyte function is unknown. We hypothesized that increased matrix rigidity in fibrotic livers would activate mechanotransduction in hepatocytes and lead to inhibition of hepatic-specific functions. To determine the physiologically relevant ranges of matrix stiffness at the cellular level, we performed detailed atomic force microscopy analysis across liver lobules from normal and fibrotic livers. We determined that normal liver matrix stiffness was around 150Pa and increased to 1–6kPa in areas near fibrillar collagen deposition in fibrotic livers. In vitro culture of primary hepatocytes on collagen matrix of tunable rigidity demonstrated that fibrotic levels of matrix stiffness had profound effects on cytoskeletal tension and significantly inhibited hepatocyte-specific functions. Normal liver stiffness maintained functional gene regulation by hepatocyte nuclear factor 4 alpha (HNF4α) whereas fibrotic matrix stiffness inhibited the HNF4α transcriptional network. Fibrotic levels of matrix stiffness activated mechanotransduction in primary hepatocytes through focal adhesion kinase (FAK). In addition, blockade of the Rho/Rho-associated protein kinase (ROCK) pathway rescued HNF4α expression from hepatocytes cultured on stiff matrix. Conclusion Fibrotic levels of matrix stiffness significantly inhibit hepatocyte-specific functions in part by inhibiting the HNF4α transcriptional network mediated through the Rho/ROCK pathway. Increased appreciation of the role of matrix rigidity in modulating hepatocyte function will advance our understanding of the mechanisms of hepatocyte dysfunction in liver cirrhosis and spur development of novel treatments for chronic liver disease. PMID:26755329

Thermal form-factor approach to dynamical correlation functions of integrable lattice models

NASA Astrophysics Data System (ADS)

Göhmann, Frank; Karbach, Michael; Klümper, Andreas; Kozlowski, Karol K.; Suzuki, Junji

2017-11-01

We propose a method for calculating dynamical correlation functions at finite temperature in integrable lattice models of Yang-Baxter type. The method is based on an expansion of the correlation functions as a series over matrix elements of a time-dependent quantum transfer matrix rather than the Hamiltonian. In the infinite Trotter-number limit the matrix elements become time independent and turn into the thermal form factors studied previously in the context of static correlation functions. We make this explicit with the example of the XXZ model. We show how the form factors can be summed utilizing certain auxiliary functions solving finite sets of nonlinear integral equations. The case of the XX model is worked out in more detail leading to a novel form-factor series representation of the dynamical transverse two-point function.

[Preparation of hydrophilic matrix sustained release tablets of total lactones from Andrographis paniculata and study on its in vitro release mechanism].

PubMed

Xu, Fang-Fang; Shi, Wei; Zhang, Hui; Guo, Qing-Ming; Wang Zhen-Zhong; Bi, Yu-An; Wang, Zhi-Min; Xiao, Wei

2015-01-01

In this study, hydrophilic matrix sustained release tablets of total lactones from Andrographis paniculata were prepared and the in vitro release behavior were also evaluated. The optimal prescription was achieved by studying the main factor of the type and amount of hydroxypropyl methylcellulose (HPMC) using single factor test and evaluating through cumulative release of three lactones. No burst drug release from the obtained matrix tablets was observed. Drug release sustained to 14 h. The release mechanism of three lactones from A. paniculata was accessed by zero-order, first-order, Higuchi and Peppas equation. The release behavior of total lactones from A. paniculata was better agreed with Higuchi model and the drug release from the tablets was controlled by degradation of the matrix. The preparation of hydrophilic matrix sustained release tablets of total lactones from A. paniculata with good performance of drug release was simple.

A global scale picture of ionospheric peak electron density changes during geomagnetic storms

NASA Astrophysics Data System (ADS)

Kumar, Vickal V.; Parkinson, Murray L.

2017-04-01

Changes in ionospheric plasma densities can affect society more than ever because of our increasing reliance on communication, surveillance, navigation, and timing technology. Models struggle to predict changes in ionospheric densities at nearly all temporal and spatial scales, especially during geomagnetic storms. Here we combine a 50 year (1965-2015) geomagnetic disturbance storm time (Dst) index with plasma density measurements from a worldwide network of 132 vertical incidence ionosondes to develop a picture of global scale changes in peak plasma density due to geomagnetic storms. Vertical incidence ionosondes provide measurements of the critical frequency of the ionospheric F2 layer (foF2), a direct measure of the peak electron density (NmF2) of the ionosphere. By dissecting the NmF2 perturbations with respect to the local time at storm onset, season, and storm intensity, it is found that (i) the storm-associated depletions (negative storm effects) and enhancements (positive storm effects) are driven by different but related physical mechanisms, and (ii) the depletion mechanism tends to dominate over the enhancement mechanism. The negative storm effects, which are detrimental to HF radio links, are found to start immediately after geomagnetic storm onset in the nightside high-latitude ionosphere. The depletions in the dayside high-latitude ionosphere are delayed by a few hours. The equatorward expansion of negative storm effects is found to be regulated by storm intensity (farthest equatorward and deepest during intense storms), season (largest in summer), and time of day (generally deeper on the nightside). In contrast, positive storm effects typically occur on the dayside midlatitude and low-latitude ionospheric regions when the storms are in the main phase, regardless of the season. Closer to the magnetic equator, moderate density enhancements last up to 40 h during the recovery phase of equinox storms, regardless of the local time. Strikingly, high-latitude plasma densities are moderately enhanced for up to 60 h prior to the actual onset of storms during the equinoxes and summer; a potential precursor of a geomagnetic storm.

Alterations of Neuromuscular Function after the World's Most Challenging Mountain Ultra-Marathon

PubMed Central

Saugy, Jonas; Place, Nicolas; Millet, Guillaume Y.; Degache, Francis; Schena, Federico; Millet, Grégoire P.

2013-01-01

We investigated the physiological consequences of the most challenging mountain ultra-marathon (MUM) in the world: a 330-km trail run with 24000 m of positive and negative elevation change. Neuromuscular fatigue (NMF) was assessed before (Pre-), during (Mid-) and after (Post-) the MUM in experienced ultra-marathon runners (n = 15; finish time  = 122.43 hours ±17.21 hours) and in Pre- and Post- in a control group with a similar level of sleep deprivation (n = 8). Blood markers of muscle inflammation and damage were analyzed at Pre- and Post-. Mean ± SD maximal voluntary contraction force declined significantly at Mid- (−13±17% and −10±16%, P<0.05 for knee extensor, KE, and plantar flexor muscles, PF, respectively), and further decreased at Post- (−24±13% and −26±19%, P<0.01) with alteration of the central activation ratio (−24±24% and −28±34% between Pre- and Post-, P<0.05) in runners whereas these parameters did not change in the control group. Peripheral NMF markers such as 100 Hz doublet (KE: −18±18% and PF: −20±15%, P<0.01) and peak twitch (KE: −33±12%, P<0.001 and PF: −19±14%, P<0.01) were also altered in runners but not in controls. Post-MUM blood concentrations of creatine kinase (3719±3045 Ul·1), lactate dehydrogenase (1145±511 UI·L−1), C-Reactive Protein (13.1±7.5 mg·L−1) and myoglobin (449.3±338.2 µg·L−1) were higher (P<0.001) than at Pre- in runners but not in controls. Our findings revealed less neuromuscular fatigue, muscle damage and inflammation than in shorter MUMs. In conclusion, paradoxically, such extreme exercise seems to induce a relative muscle preservation process due likely to a protective anticipatory pacing strategy during the first half of MUM and sleep deprivation in the second half. PMID:23840345

MATRIX METALLOPROTEINS (MMP)-MEDIATED PHOSPHORYLATION OF THE EPIDERMAL GROWTH FACTOR RECEPTOR (EGFR) IN HUMAN AIRWAY EPITHELIAL CELLS (HAEC) EXPOSED TO ZINC (ZN)

EPA Science Inventory

Matrix Metalloproteinase (MMP)-Mediated Phosphorylation of The Epidermal Growth Factor Receptor (EGFR) in Human Airway Epithelial Cells (HAEC) Exposed to Zinc (Zn)
Weidong Wu, James M. Samet, Robert Silbajoris, Lisa A. Dailey, Lee M. Graves, and Philip A. Bromberg
Center fo...

A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets

ERIC Educational Resources Information Center

Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars

2015-01-01

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL),…

SOURCE APPORTIONMENT OF PM 2.5 AND CARBON IN SEATTLE USING CHEMICAL MASS BALANCE AND POSITIVE MATRIX FACTORIZATION

EPA Science Inventory

Three years of PM2.5 speciated data were collected and chemically analyzed using the IMPROVE protocol at the Beacon Hill site in Seattle. The data were analyzed by the Chemical Mass Balance Version 8 (CMB8) and Positive Matrix Factorization (PMF) source apportionment models. T...

In Spite of Indeterminacy Many Common Factor Score Estimates Yield an Identical Reproduced Covariance Matrix

ERIC Educational Resources Information Center

Beauducel, Andre

2007-01-01

It was investigated whether commonly used factor score estimates lead to the same reproduced covariance matrix of observed variables. This was achieved by means of Schonemann and Steiger's (1976) regression component analysis, since it is possible to compute the reproduced covariance matrices of the regression components corresponding to different…

Estimating gene function with least squares nonnegative matrix factorization.

PubMed

Wang, Guoli; Ochs, Michael F

2007-01-01

Nonnegative matrix factorization is a machine learning algorithm that has extracted information from data in a number of fields, including imaging and spectral analysis, text mining, and microarray data analysis. One limitation with the method for linking genes through microarray data in order to estimate gene function is the high variance observed in transcription levels between different genes. Least squares nonnegative matrix factorization uses estimates of the uncertainties on the mRNA levels for each gene in each condition, to guide the algorithm to a local minimum in normalized chi2, rather than a Euclidean distance or divergence between the reconstructed data and the data itself. Herein, application of this method to microarray data is demonstrated in order to predict gene function.

Endothelial cell-derived matrix promotes the metabolic functional maturation of hepatocyte via integrin-Src signalling.

PubMed

Guo, Xinyue; Li, Weihong; Ma, Minghui; Lu, Xin; Zhang, Haiyan

2017-11-01

The extracellular matrix (ECM) microenvironment is involved in the regulation of hepatocyte phenotype and function. Recently, the cell-derived extracellular matrix has been proposed to represent the bioactive and biocompatible materials of the native ECM. Here, we show that the endothelial cell-derived matrix (EC matrix) promotes the metabolic maturation of human adipose stem cell-derived hepatocyte-like cells (hASC-HLCs) through the activation of the transcription factor forkhead box protein A2 (FOXA2) and the nuclear receptors hepatocyte nuclear factor 4 alpha (HNF4α) and pregnane X receptor (PXR). Reducing the fibronectin content in the EC matrix or silencing the expression of α5 integrin in the hASC-HLCs inhibited the effect of the EC matrix on Src phosphorylation and hepatocyte maturation. The inhibition of Src phosphorylation using the inhibitor PP2 or silencing the expression of Src in hASC-HLCs also attenuated the up-regulation of the metabolic function of hASC-HLCs in a nuclear receptor-dependent manner. These data elucidate integrin-Src signalling linking the extrinsic EC matrix signals and metabolic functional maturation of hepatocyte. This study provides a model for studying the interaction between hepatocytes and non-parenchymal cell-derived matrix. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Extending injury prevention methodology to chemical terrorism preparedness: the Haddon Matrix and sarin.

PubMed

Varney, Shawn; Hirshon, Jon Mark; Dischinger, Patricia; Mackenzie, Colin

2006-01-01

The Haddon Matrix offers a classic epidemiological model for studying injury prevention. This methodology places the public health concepts of agent, host, and environment within the three sequential phases of an injury-producing incident-pre-event, event, and postevent. This study uses this methodology to illustrate how it could be applied in systematically preparing for a mass casualty disaster such as an unconventional sarin attack in a major urban setting. Nineteen city, state, federal, and military agencies responded to the Haddon Matrix chemical terrorism preparedness exercise and offered feedback in the data review session. Four injury prevention strategies (education, engineering, enforcement, and economics) were applied to the individual factors and event phases of the Haddon Matrix. The majority of factors identified in all phases were modifiable, primarily through educational interventions focused on individual healthcare providers and first responders. The Haddon Matrix provides a viable means of studying an unconventional problem, allowing for the identification of modifiable factors to decrease the type and severity of injuries following a mass casualty disaster such as a sarin release. This strategy could be successfully incorporated into disaster planning for other weapons attacks that could potentially cause mass casualties.

Matrix Metallopeptidase 14 Plays an Important Role in Regulating Tumorigenic Gene Expression and Invasion Ability of HeLa Cells.

PubMed

Zhang, Ying-Hui; Wang, Juan-Juan; Li, Min; Zheng, Han-Xi; Xu, Lan; Chen, You-Guo

2016-03-01

The objectives of this study were to investigate the functional effect of matrix metallopeptidase 14 (MMP14) on cell invasion in cervical cancer cells (HeLa line) and to study the underlying molecular mechanisms. Expression vector of short hairpin RNA targeting MMP14 was treated in HeLa cells, and then, transfection efficiency was verified by a florescence microscope. Transwell assay was used to investigate cell invasion ability in HeLa cells. Quantitative polymerase chain reaction and Western blotting analysis were used to detect the expression of MMP14 and relative factors in messenger RNA and protein levels, respectively. Matrix metallopeptidase 14 short hairpin RNA expression vector transfection obviously decreased MMP14 expression in messenger RNA and protein levels. Down-regulation of MMP14 suppressed invasion ability of HeLa cells and reduced transforming growth factor β1 and vascular endothelial growth factor B expressions. Furthermore, MMP14 knockdown decreased bone sialoprotein and enhanced forkhead box protein L2 expression in both RNA and protein levels. Matrix metallopeptidase 14 plays an important role in regulating invasion of HeLa cells. Matrix metallopeptidase 14 knockdown contributes to attenuating the malignant phenotype of cervical cancer cell.

Dimensions of postconcussive symptoms in children with mild traumatic brain injuries.

PubMed

Ayr, Lauren K; Yeates, Keith Owen; Taylor, H Gerry; Browne, Michael

2009-01-01

The dimensions of postconcussive symptoms (PCS) were examined in a prospective, longitudinal study of 186 8 to 15 year old children with mild traumatic brain injuries (TBI). Parents and children completed a 50-item questionnaire within 2 weeks of injury and again at 3 months after injury, rating the frequency of PCS on a 4-point scale. Common factor analysis with target rotation was used to rotate the ratings to four hypothesized dimensions, representing cognitive, somatic, emotional, and behavioral symptoms. The rotated factor matrix for baseline parent ratings was consistent with the target matrix. The rotated matrix for baseline child ratings was consistent with the target matrix for cognitive and somatic symptoms but not for emotional and behavioral symptoms. The rotated matrices for ratings obtained 3 months after injury were largely consistent with the target matrix derived from analyses of baseline ratings, except that parent ratings of behavioral symptoms did not cluster as before. Parent and child ratings of PCS following mild TBI yield consistent factors reflecting cognitive and somatic symptom dimensions, but dimensions of emotional and behavioral symptoms are less robust across time and raters. (JINS, 2009, 15, 19-30.).

Matrix modulation and heart failure: new concepts question old beliefs.

PubMed

Deschamps, Anne M; Spinale, Francis G

2005-05-01

Myocardial remodeling is a complex process involving several molecular and cellular factors. Extracellular matrix has been implicated in the remodeling process. Historically, the myocardial extracellular matrix was thought to serve solely as a means to align cells and provide structure to the tissue. Although this is one of its important functions, evidence suggests that the extracellular matrix plays a complex and divergent role in influencing cell behavior. This paper characterizes some of the notable studies on this dynamic entity and on adverse myocardial remodeling that have been published over the past year, which further question the belief that the extracellular matrix is a static structure. Progress has been made in understanding how the extracellular matrix is operative in the three major conditions (myocardial infarction, left ventricular hypertrophy due to overload, and dilated cardiomyopathy) that involve myocardial remodeling. Several studies have examined plasma profiles of matrix metalloproteinases and tissue inhibitors of matrix metalloproteinases following myocardial infarction and during left ventricular hypertrophy as surrogate markers of remodeling/remodeled myocardium. It has been demonstrated that bioactive signaling molecules and growth factors, proteases, and structural proteins influence cell-matrix interactions in the context of left ventricular hypertrophy. Finally, studies that either removed or added tissue inhibitor of metalloproteinases species in the myocardium demonstrated the importance of this regulatory protein in the remodeling process. Understanding the cellular and molecular triggers that in turn give rise to changes in the extracellular matrix could provide opportunities to modify the remodeling process.

Movement behaviour within and beyond perceptual ranges in three small mammals: effects of matrix type and body mass.

PubMed

Prevedello, Jayme Augusto; Forero-Medina, Germán; Vieira, Marcus Vinícius

2010-11-01

1. For animal species inhabiting heterogeneous landscapes, the tortuosity of the dispersal path is a key determinant of the success in locating habitat patches. Path tortuosity within and beyond perceptual range must differ, and may be differently affected by intrinsic attributes of individuals and extrinsic environmental factors. Understanding how these factors interact to determine path tortuosity allows more accurate inference of successful movements between habitat patches. 2. We experimentally determined the effects of intrinsic (body mass and species identity) and extrinsic factors (distance to nearest forest fragment and matrix type) on the tortuosity of movements of three forest-dwelling didelphid marsupials, in a fragmented landscape of the Atlantic Forest, Brazil. 3. A total of 202 individuals were captured in forest fragments and released in three unsuitable matrix types (mowed pasture, abandoned pasture and manioc plantation), carrying spool-and-line devices. 4. Twenty-four models were formulated representing a priori hypotheses of major determinants of path tortuosity, grouped in three scenarios (only intrinsic factors, only extrinsic factors and models with combinations of both), and compared using a model selection approach. Models were tested separately for individuals released within the perceptual range of the species, and for individuals released beyond the perceptual range. 5. Matrix type strongly affected path tortuosity, with more obstructed matrix types hampering displacement of animals. Body mass was more important than species identity to determine path tortuosity, with larger animals moving more linearly. Increased distance to the fragment resulted in more tortuous paths, but actually reflects a threshold in perceptual range: linear paths within perceptual range, tortuous paths beyond. 6. The variables tested explained successfully path tortuosity, but only for animals released within the perceptual range. Other factors, such as wind intensity and direction of plantation rows, may be more important for individuals beyond their perceptual range. 7. Simplistic scenarios considering only intrinsic or extrinsic factors are inadequate to predict path tortuosity, and to infer dispersal success in heterogeneous landscapes. Perceptual range represents a fundamental threshold where the effects of matrix type, body mass and individual behaviour change drastically. © 2010 The Authors. Journal compilation © 2010 British Ecological Society.

The AdS{sub 5}xS{sup 5} superstring worldsheet S matrix and crossing symmetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Janik, Romuald A.

2006-04-15

An S matrix satisfying the Yang-Baxter equation with symmetries relevant to the AdS{sub 5}xS{sup 5} superstring recently has been determined up to an unknown scalar factor. Such scalar factors are typically fixed using crossing relations; however, due to the lack of conventional relativistic invariance, in this case its determination remained an open problem. In this paper we propose an algebraic way to implement crossing relations for the AdS{sub 5}xS{sup 5} superstring worldsheet S matrix. We base our construction on a Hopf-algebraic formulation of crossing in terms of the antipode and introduce generalized rapidities living on the universal cover of themore » parameter space which is constructed through an auxillary, coupling-constant dependent, elliptic curve. We determine the crossing transformation and write functional equations for the scalar factor of the S matrix in the generalized rapidity plane.« less

Joint refinement model for the spin resolved one-electron reduced density matrix of YTiO3 using magnetic structure factors and magnetic Compton profiles data.

PubMed

Gueddida, Saber; Yan, Zeyin; Kibalin, Iurii; Voufack, Ariste Bolivard; Claiser, Nicolas; Souhassou, Mohamed; Lecomte, Claude; Gillon, Béatrice; Gillet, Jean-Michel

2018-04-28

In this paper, we propose a simple cluster model with limited basis sets to reproduce the unpaired electron distributions in a YTiO 3 ferromagnetic crystal. The spin-resolved one-electron-reduced density matrix is reconstructed simultaneously from theoretical magnetic structure factors and directional magnetic Compton profiles using our joint refinement algorithm. This algorithm is guided by the rescaling of basis functions and the adjustment of the spin population matrix. The resulting spin electron density in both position and momentum spaces from the joint refinement model is in agreement with theoretical and experimental results. Benefits brought from magnetic Compton profiles to the entire spin density matrix are illustrated. We studied the magnetic properties of the YTiO 3 crystal along the Ti-O 1 -Ti bonding. We found that the basis functions are mostly rescaled by means of magnetic Compton profiles, while the molecular occupation numbers are mainly modified by the magnetic structure factors.

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

Monocyte activation by smooth muscle cell-derived matrices.

PubMed

Kaufmann, J; Jorgensen, R W; Martin, B M; Franzblau, C

1990-12-01

Mononuclear phagocytes adhere to and penetrate the vessel wall endothelium and contact the subendothelial space prior to the development of the atherosclerotic plaque. In an attempt to model the early events of plaque development we used an elastin-rich, multicomponent, cell-derived matrix from neonatal rat aortic smooth muscle cells as a substratum for monocytes. Using this model, we show that human monocyte morphology and metabolism are markedly altered by the matrix substratum. When a mixed mononuclear cell population is seeded on matrix or plastic, only monocytes adhere to the matrix surface. In contrast, lymphocytes as well as monocytes adhere to the plastic surface. The matrix-adherent monocytes develop large intracellular granules and form extensive clusters of individual cells. Metabolically, these cells develop sodium fluoride resistant non-specific esterase activity and their media contain more growth factor activity and PGE2. Although total protein synthesis is equivalent in both cultures, the matrix contact induces an increase in specific proteins in the media. We also show that a purified alpha-elastin substratum induces some, but not all, of the monocyte changes seen when using the matrix substratum. Using the alpha-elastin substratum, there is selective adhesion of monocytes and increased growth factor activity, however, the cells are morphologically different from the matrix-adherent cells. Thus, the use of the smooth muscle cell-derived matrix, in conjunction with purified matrix components, serves as a model that can provide insight into the mechanisms of monocyte adhesion and stimulation by the matrix environment that exists in vivo. Such mechanisms may be particularly important in atherogenesis.

Access of Hydrogen-Radicals to the Peptide-Backbone as a Measure for Estimating the Flexibility of Proteins Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

PubMed Central

Takayama, Mitsuo; Nagoshi, Keishiro; Iimuro, Ryunosuke; Inatomi, Kazuma

2014-01-01

A factor for estimating the flexibility of proteins is described that uses a cleavage method of “in-source decay (ISD)” coupled with matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). The MALDI-ISD spectra of bovine serum albumin (BSA), myoglobin and thioredoxin show discontinuous intense ion peaks originating from one-side preferential cleavage at the N-Cα bond of Xxx-Asp, Xxx-Asn, Xxx-Cys and Gly-Xxx residues. Consistent with these observations, Asp, Asn and Gly residues are also identified by other flexibility measures such as B-factor, turn preference, protection and fluorescence decay factors, while Asp, Asn, Cys and Gly residues are identified by turn preference factor based on X-ray crystallography. The results suggest that protein molecules embedded in/on MALDI matrix crystals partly maintain α-helix and that the reason some of the residues are more susceptible to ISD (Asp, Asn, Cys and Gly) and others less so (Ile and Val) is because of accessibility of the peptide backbone to hydrogen-radicals from matrix molecules. The hydrogen-radical accessibility in MALDI-ISD could therefore be adopted as a factor for measuring protein flexibility. PMID:24828203

The Effect of Multi Wall Carbon Nanotubes on Some Physical Properties of Epoxy Matrix

NASA Astrophysics Data System (ADS)

Al-Saadi, Tagreed M.; hammed Aleabi, Suad; Al-Obodi, Entisar E.; Abdul-Jabbar Abbas, Hadeel

2018-05-01

This research involves using epoxy resin as a matrix for making a composite material, while the multi wall carbon nanotubes (MWNCTs) is used as a reinforcing material with different fractions (0.0,0.02, 0.04, 0.06) of the matrix weight. The mechanical ( hardness ), electrical ( dielectric constant, dielectric loss factor, dielectric strength, electrical conductivity ), and thermal properties (thermal conductivity ) were studied. The results showed the increase of hardness, thermal conductivity, electrical conductivity and break down strength with the increase of MWCNT concentration, but the behavior of dielectric loss factor and dielectric constant is opposite that.

Chaos and random matrices in supersymmetric SYK

NASA Astrophysics Data System (ADS)

Hunter-Jones, Nicholas; Liu, Junyu

2018-05-01

We use random matrix theory to explore late-time chaos in supersymmetric quantum mechanical systems. Motivated by the recent study of supersymmetric SYK models and their random matrix classification, we consider the Wishart-Laguerre unitary ensemble and compute the spectral form factors and frame potentials to quantify chaos and randomness. Compared to the Gaussian ensembles, we observe the absence of a dip regime in the form factor and a slower approach to Haar-random dynamics. We find agreement between our random matrix analysis and predictions from the supersymmetric SYK model, and discuss the implications for supersymmetric chaotic systems.

Physiological ranges of matrix rigidity modulate primary mouse hepatocyte function in part through hepatocyte nuclear factor 4 alpha.

PubMed

Desai, Seema S; Tung, Jason C; Zhou, Vivian X; Grenert, James P; Malato, Yann; Rezvani, Milad; Español-Suñer, Regina; Willenbring, Holger; Weaver, Valerie M; Chang, Tammy T

2016-07-01

Matrix rigidity has important effects on cell behavior and is increased during liver fibrosis; however, its effect on primary hepatocyte function is unknown. We hypothesized that increased matrix rigidity in fibrotic livers would activate mechanotransduction in hepatocytes and lead to inhibition of liver-specific functions. To determine the physiologically relevant ranges of matrix stiffness at the cellular level, we performed detailed atomic force microscopy analysis across liver lobules from normal and fibrotic livers. We determined that normal liver matrix stiffness was around 150 Pa and increased to 1-6 kPa in areas near fibrillar collagen deposition in fibrotic livers. In vitro culture of primary hepatocytes on collagen matrix of tunable rigidity demonstrated that fibrotic levels of matrix stiffness had profound effects on cytoskeletal tension and significantly inhibited hepatocyte-specific functions. Normal liver stiffness maintained functional gene regulation by hepatocyte nuclear factor 4 alpha (HNF4α), whereas fibrotic matrix stiffness inhibited the HNF4α transcriptional network. Fibrotic levels of matrix stiffness activated mechanotransduction in primary hepatocytes through focal adhesion kinase. In addition, blockade of the Rho/Rho-associated protein kinase pathway rescued HNF4α expression from hepatocytes cultured on stiff matrix. Fibrotic levels of matrix stiffness significantly inhibit hepatocyte-specific functions in part by inhibiting the HNF4α transcriptional network mediated through the Rho/Rho-associated protein kinase pathway. Increased appreciation of the role of matrix rigidity in modulating hepatocyte function will advance our understanding of the mechanisms of hepatocyte dysfunction in liver cirrhosis and spur development of novel treatments for chronic liver disease. (Hepatology 2016;64:261-275). © 2016 by the American Association for the Study of Liver Diseases.

Application of the matrix exponential kernel

NASA Technical Reports Server (NTRS)

Rohach, A. F.

1972-01-01

A point matrix kernel for radiation transport, developed by the transmission matrix method, has been used to develop buildup factors and energy spectra through slab layers of different materials for a point isotropic source. Combinations of lead-water slabs were chosen for examples because of the extreme differences in shielding properties of these two materials.

Evidence-Based Practice: A Matrix for Predicting Phonological Generalization

ERIC Educational Resources Information Center

Gierut, Judith A.; Hulse, Lauren E.

2010-01-01

This paper describes a matrix for clinical use in the selection of phonological treatment targets to induce generalization, and in the identification of probe sounds to monitor during the course of intervention. The matrix appeals to a set of factors that have been shown to promote phonological generalization in the research literature, including…

Active subspace: toward scalable low-rank learning.

PubMed

Liu, Guangcan; Yan, Shuicheng

2012-12-01

We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candès, Li, Ma, & Wright, 2009 ) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.

Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, Clarence C., Jr.

1989-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 deg and +/- 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was develolped to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Far-field strains at failure were calculated from the strain intensity factor, and then strengths were calculated from the far-field strains using uniaxial stress-strain curves. The predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only +/- 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.

Expression Levels of Myostatin and Matrix Metalloproteinase 14 mRNAs in Uterine Leiomyoma are Correlated With Dysmenorrhea.

PubMed

Tsigkou, Anastasia; Reis, Fernando M; Ciarmela, Pasquapina; Lee, Meng H; Jiang, Bingjie; Tosti, Claudia; Shen, Fang-Rong; Shi, Zhendan; Chen, You-Guo; Petraglia, Felice

2015-12-01

Uterine leiomyoma is the most common benign neoplasm of female reproductive system, found in about 50% of women in reproductive age. The mechanisms of leiomyoma growth include cell proliferation, which is modulated by growth factors, and deposition of extracellular matrix (ECM). Activin A and myostatin are growth factors that play a role in proliferation of leiomyoma cells. Matrix metalloproteinases (MMPs) are known for their ability to remodel the ECM in different biological systems. The aim of this study was to evaluate the expression levels of activin βA-subunit, myostatin, and MMP14 messenger RNAs (mRNAs) in uterine leiomyomas and the possible correlation of these factors with clinical features of the disease. Matrix metalloproteinase 14 was highly expressed in uterine leiomyoma and correlated with myostatin and activin A mRNA expression. Moreover, MMP14 and myostatin mRNA expression correlated significantly and directly with the intensity of dysmenorrhea. Overall, the present findings showed that MMP14 mRNA is highly expressed in uterine leiomyoma, where it correlates with the molecular expression of growth factors and is further increased in cases of intense dysmenorrhea. © The Author(s) 2015.

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

PubMed

Saccenti, Edoardo; Timmerman, Marieke E

2017-03-01

Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

Augmenting matrix factorization technique with the combination of tags and genres

NASA Astrophysics Data System (ADS)

Ma, Tinghuai; Suo, Xiafei; Zhou, Jinjuan; Tang, Meili; Guan, Donghai; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah

2016-11-01

Recommender systems play an important role in our daily life and are becoming popular tools for users to find what they are really interested in. Matrix factorization methods, which are popular recommendation methods, have gained high attention these years. With the rapid growth of the Internet, lots of information has been created, like social network information, tags and so on. Along with these, a few matrix factorization approaches have been proposed which incorporate the personalized information of users or items. However, except for ratings, most of the matrix factorization models have utilized only one kind of information to understand users' interests. Considering the sparsity of information, in this paper, we try to investigate the combination of different information, like tags and genres, to reveal users' interests accurately. With regard to the generalization of genres, a constraint is added when genres are utilized to find users' similar ;soulmates;. In addition, item regularizer is also considered based on latent semantic indexing (LSI) method with the item tags. Our experiments are conducted on two real datasets: Movielens dataset and Douban dataset. The experimental results demonstrate that the combination of tags and genres is really helpful to reveal users' interests.

Differential modulation of degradative and repair responses of interleukin-1-treated chondrocytes by platelet-derived growth factor.

PubMed Central

Harvey, A K; Stack, S T; Chandrasekhar, S

1993-01-01

Interleukin 1 (IL-1) plays a dual role in cartilage matrix degeneration by promoting extracellular proteinase action such as the matrix metalloproteinases (increased degradation) and by suppressing the synthesis of extracellular matrix molecules (inhibition of repair). Platelet-derived growth factor (PDGF) is a wound-healing hormone which is released along with IL-1 during the inflammatory response. Since previous studies have shown that PDGF enhances IL-1 alpha effects on metalloproteinase activity, in this report, we have examined whether PDGF modifies IL-1 beta effects on cartilage proteoglycan synthesis. Initially, we confirmed that rabbit articular chondrocytes treated with IL-1 beta + PDGF induced higher proteinase activity, in comparison with IL-1-treated cells. We further observed that the increased proteinase activity correlated with an increase in the synthesis of collagenase/stromelysin proteins and a corresponding increase in the steady-state mRNA levels for both the enzymes. Studies on IL-1 receptor expression suggested that PDGF caused an increase in IL-1 receptor expression which, by augmenting the IL-1 response, may have led to the increase in proteinase induction. Analysis of proteoglycan synthesis confirmed that IL-1 reduced the incorporation of sulphated proteoglycan, aggrecan, into the extracellular matrix of chondrocytes, whereas PDGF stimulated it. However, cells treated with IL-1 + PDGF synthesized normal levels of aggrecan. This is in contrast with cells treated with IL-1 + fibroblast growth factor, in which case only proteinase activity was potentiated. The results allow us to conclude that (a) the two effector functions that play a role in matrix remodelling, namely matrix lysis (proteinase induction) and matrix repair (proteoglycan synthesis), occur via distinct pathways and (b) PDGF may play a crucial role in cartilage repair by initially causing matrix degradation followed by promoting new matrix synthesis. Images Figure 1 Figure 2 Figure 5 Figure 6 PMID:8503839

Factorization in large-scale many-body calculations

DOE PAGES

Johnson, Calvin W.; Ormand, W. Erich; Krastev, Plamen G.

2013-08-07

One approach for solving interacting many-fermion systems is the configuration-interaction method, also sometimes called the interacting shell model, where one finds eigenvalues of the Hamiltonian in a many-body basis of Slater determinants (antisymmetrized products of single-particle wavefunctions). The resulting Hamiltonian matrix is typically very sparse, but for large systems the nonzero matrix elements can nonetheless require terabytes or more of storage. An alternate algorithm, applicable to a broad class of systems with symmetry, in our case rotational invariance, is to exactly factorize both the basis and the interaction using additive/multiplicative quantum numbers; such an algorithm recreates the many-body matrix elementsmore » on the fly and can reduce the storage requirements by an order of magnitude or more. Here, we discuss factorization in general and introduce a novel, generalized factorization method, essentially a ‘double-factorization’ which speeds up basis generation and set-up of required arrays. Although we emphasize techniques, we also place factorization in the context of a specific (unpublished) configuration-interaction code, BIGSTICK, which runs both on serial and parallel machines, and discuss the savings in memory due to factorization.« less

Tissue Engineering Using Transfected Growth-Factor Genes

NASA Technical Reports Server (NTRS)

Madry, Henning; Langer, Robert S.; Freed, Lisa E.; Trippel, Stephen; Vunjak-Novakovic, Gordana

2005-01-01

A method of growing bioengineered tissues includes, as a major component, the use of mammalian cells that have been transfected with genes for secretion of regulator and growth-factor substances. In a typical application, one either seeds the cells onto an artificial matrix made of a synthetic or natural biocompatible material, or else one cultures the cells until they secrete a desired amount of an extracellular matrix. If such a bioengineered tissue construct is to be used for surgical replacement of injured tissue, then the cells should preferably be the patient s own cells or, if not, at least cells matched to the patient s cells according to a human-leucocyteantigen (HLA) test. The bioengineered tissue construct is typically implanted in the patient's injured natural tissue, wherein the growth-factor genes enhance metabolic functions that promote the in vitro development of functional tissue constructs and their integration with native tissues. If the matrix is biodegradable, then one of the results of metabolism could be absorption of the matrix and replacement of the matrix with tissue formed at least partly by the transfected cells. The method was developed for articular chondrocytes but can (at least in principle) be extended to a variety of cell types and biocompatible matrix materials, including ones that have been exploited in prior tissue-engineering methods. Examples of cell types include chondrocytes, hepatocytes, islet cells, nerve cells, muscle cells, other organ cells, bone- and cartilage-forming cells, epithelial and endothelial cells, connective- tissue stem cells, mesodermal stem cells, and cells of the liver and the pancreas. Cells can be obtained from cell-line cultures, biopsies, and tissue banks. Genes, molecules, or nucleic acids that secrete factors that influence the growth of cells, the production of extracellular matrix material, and other cell functions can be inserted in cells by any of a variety of standard transfection techniques.

Interactions of cytokines, growth factors, and the extracellular matrix in the cellular biology of uterine leiomyomata.

PubMed

Sozen, Ibrahim; Arici, Aydin

2002-07-01

To review the available information regarding the role of cytokines, growth factors, and the extracellular matrix in the pathophysiology of uterine leiomyomata and to integrate this information in a suggested model of disease at the cellular level. A thorough literature and MEDLINE search was conducted to identify the relevant studies in the English literature published between January, 1966 and October, 2001. A model of disease at the cellular level was developed using the most likely cytokines to be involved in the pathogenesis of leiomyomata as determined by our assessment of the available literature. A number of cytokines and growth factors, including transforming growth factor-beta (TGF-beta), epidermal growth factor, monocyte chemotactic protein-1, insulin-like growth factors 1 and 2, prolactin, parathyroid-hormone-related peptide, basic fibroblast growth factor, platelet-derived growth factor, interleukin-8, and endothelin, have been investigated in myometrium and leiomyoma. Among these cytokines, TGF-beta appears to be the only growth factor that has been shown to be overexpressed in leiomyoma vs. myometrium, be hormonally-regulated both in vivo and in vitro, and be both mitogenic and fibrogenic in these tissues. In addition to the cytokines, extracellular matrix components such as collagen, fibronectin, proteoglycans, matrix metalloproteinases, and tissue inhibitors of metalloproteinases seem to play pivotal roles in the pathogenesis of leiomyomata. We believe that, given the extent and depth of the current research on the cellular biology of leiomyomata, the cellular mechanisms responsible in the pathogenesis of leiomyomata will be identified clearly within the foreseeable future. This will enable researchers to develop therapy directed against the molecules and mechanisms at the cellular level.

Mimicking the extracellular matrix with functionalized, metal-assembled collagen peptide scaffolds.

PubMed

Hernandez-Gordillo, Victor; Chmielewski, Jean

2014-08-01

Natural and synthetic three-dimensional (3-D) scaffolds that mimic the microenvironment of the extracellular matrix (ECM), with growth factor storage/release and the display of cell adhesion signals, offer numerous advantages for regenerative medicine and in vitro morphogenesis and oncogenesis modeling. Here we report the design of collagen mimetic peptides (CMPs) that assemble into a highly crosslinked 3-D matrix in response to metal ion stimuli, that may be functionalized with His-tagged cargoes, such as green fluorescent protein (GFP-His8) and human epidermal growth factor (hEGF-His6). The bound hEGF-His6 was found to gradually release from the matrix in vitro and induce cell proliferation in the EGF-dependent cell line MCF10A. The additional incorporation of a cell adhesion sequence (RGDS) at the N-terminus of the CMP creates an environment that facilitated the organization of matrix-encapsulated MCF10A cells into spheroid structures, thus mimicking the ECM environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multifunctional and biologically active matrices from multicomponent polymeric solutions

NASA Technical Reports Server (NTRS)

Kiick, Kristi L. (Inventor); Yamaguchi, Nori (Inventor); Rabolt, John (Inventor); Casper, Cheryl (Inventor)

2012-01-01

A functionalized electrospun matrix for the controlled-release of biologically active agents, such as growth factors, is presented. The functionalized matrix comprises a matrix polymer, a compatibilizing polymer and a biomolecule or other small functioning molecule. In certain aspects the electrospun polymer fibers comprise at least one biologically active molecule functionalized with low molecular weight heparin.

Calabi-Yau structures on categories of matrix factorizations

NASA Astrophysics Data System (ADS)

Shklyarov, Dmytro

2017-09-01

Using tools of complex geometry, we construct explicit proper Calabi-Yau structures, that is, non-degenerate cyclic cocycles on differential graded categories of matrix factorizations of regular functions with isolated critical points. The formulas involve the Kapustin-Li trace and its higher corrections. From the physics perspective, our result yields explicit 'off-shell' models for categories of topological D-branes in B-twisted Landau-Ginzburg models.

Tensor-GMRES method for large sparse systems of nonlinear equations

NASA Technical Reports Server (NTRS)

Feng, Dan; Pulliam, Thomas H.

1994-01-01

This paper introduces a tensor-Krylov method, the tensor-GMRES method, for large sparse systems of nonlinear equations. This method is a coupling of tensor model formation and solution techniques for nonlinear equations with Krylov subspace projection techniques for unsymmetric systems of linear equations. Traditional tensor methods for nonlinear equations are based on a quadratic model of the nonlinear function, a standard linear model augmented by a simple second order term. These methods are shown to be significantly more efficient than standard methods both on nonsingular problems and on problems where the Jacobian matrix at the solution is singular. A major disadvantage of the traditional tensor methods is that the solution of the tensor model requires the factorization of the Jacobian matrix, which may not be suitable for problems where the Jacobian matrix is large and has a 'bad' sparsity structure for an efficient factorization. We overcome this difficulty by forming and solving the tensor model using an extension of a Newton-GMRES scheme. Like traditional tensor methods, we show that the new tensor method has significant computational advantages over the analogous Newton counterpart. Consistent with Krylov subspace based methods, the new tensor method does not depend on the factorization of the Jacobian matrix. As a matter of fact, the Jacobian matrix is never needed explicitly.

Transforming growth factor-beta stimulates wound healing and modulates extracellular matrix gene expression in pig skin. I. Excisional wound model.

PubMed

Quaglino, D; Nanney, L B; Kennedy, R; Davidson, J M

1990-09-01

The effect of transforming growth factor-beta 1 (TGF-beta 1) on matrix gene expression has been investigated during the process of wound repair, where the formation of new connective tissue represents a critical step in restoring tissue integrity. Split-thickness excisional wounds in the pig were studied by in situ hybridization in order to obtain subjective findings on the activity and location of cells involved in matrix gene expression after the administration of recombinant TGF-beta 1. Data focus on the stimulatory role of this growth factor in granulation tissue formation, on the enhanced mRNA content of collagen types I and III, fibronectin, TGF-beta 1 itself, and on the reduction in stromelysin mRNA, suggesting that increased matrix formation measured after treatment with TGF-beta 1 is due to fibroplasia regulated by the abundance of mRNAs for several different structural, matrix proteins as well as inhibition of proteolytic phenomena elicited by metalloproteinases. These studies reveal elastin mRNA early in the repair process, and elastin mRNA expression is enhanced by administration of TGF-beta 1. Moreover, we show that TGF-beta 1 was auto-stimulating in wounds, accounting, at least in part, for the persistent effects of single doses of this multipotential cytokine.

Recursive Factorization of the Inverse Overlap Matrix in Linear-Scaling Quantum Molecular Dynamics Simulations.

PubMed

Negre, Christian F A; Mniszewski, Susan M; Cawkwell, Marc J; Bock, Nicolas; Wall, Michael E; Niklasson, Anders M N

2016-07-12

We present a reduced complexity algorithm to compute the inverse overlap factors required to solve the generalized eigenvalue problem in a quantum-based molecular dynamics (MD) simulation. Our method is based on the recursive, iterative refinement of an initial guess of Z (inverse square root of the overlap matrix S). The initial guess of Z is obtained beforehand by using either an approximate divide-and-conquer technique or dynamical methods, propagated within an extended Lagrangian dynamics from previous MD time steps. With this formulation, we achieve long-term stability and energy conservation even under the incomplete, approximate, iterative refinement of Z. Linear-scaling performance is obtained using numerically thresholded sparse matrix algebra based on the ELLPACK-R sparse matrix data format, which also enables efficient shared-memory parallelization. As we show in this article using self-consistent density-functional-based tight-binding MD, our approach is faster than conventional methods based on the diagonalization of overlap matrix S for systems as small as a few hundred atoms, substantially accelerating quantum-based simulations even for molecular structures of intermediate size. For a 4158-atom water-solvated polyalanine system, we find an average speedup factor of 122 for the computation of Z in each MD step.

Recursive Factorization of the Inverse Overlap Matrix in Linear Scaling Quantum Molecular Dynamics Simulations

DOE PAGES

Negre, Christian F. A; Mniszewski, Susan M.; Cawkwell, Marc Jon; ...

2016-06-06

We present a reduced complexity algorithm to compute the inverse overlap factors required to solve the generalized eigenvalue problem in a quantum-based molecular dynamics (MD) simulation. Our method is based on the recursive iterative re nement of an initial guess Z of the inverse overlap matrix S. The initial guess of Z is obtained beforehand either by using an approximate divide and conquer technique or dynamically, propagated within an extended Lagrangian dynamics from previous MD time steps. With this formulation, we achieve long-term stability and energy conservation even under incomplete approximate iterative re nement of Z. Linear scaling performance ismore » obtained using numerically thresholded sparse matrix algebra based on the ELLPACK-R sparse matrix data format, which also enables e cient shared memory parallelization. As we show in this article using selfconsistent density functional based tight-binding MD, our approach is faster than conventional methods based on the direct diagonalization of the overlap matrix S for systems as small as a few hundred atoms, substantially accelerating quantum-based simulations even for molecular structures of intermediate size. For a 4,158 atom water-solvated polyalanine system we nd an average speedup factor of 122 for the computation of Z in each MD step.« less

Time-oriented experimental design method to optimize hydrophilic matrix formulations with gelation kinetics and drug release profiles.

PubMed

Shin, Sangmun; Choi, Du Hyung; Truong, Nguyen Khoa Viet; Kim, Nam Ah; Chu, Kyung Rok; Jeong, Seong Hoon

2011-04-04

A new experimental design methodology was developed by integrating the response surface methodology and the time series modeling. The major purposes were to identify significant factors in determining swelling and release rate from matrix tablets and their relative factor levels for optimizing the experimental responses. Properties of tablet swelling and drug release were assessed with ten factors and two default factors, a hydrophilic model drug (terazosin) and magnesium stearate, and compared with target values. The selected input control factors were arranged in a mixture simplex lattice design with 21 experimental runs. The obtained optimal settings for gelation were PEO, LH-11, Syloid, and Pharmacoat with weight ratios of 215.33 (88.50%), 5.68 (2.33%), 19.27 (7.92%), and 3.04 (1.25%), respectively. The optimal settings for drug release were PEO and citric acid with weight ratios of 191.99 (78.91%) and 51.32 (21.09%), respectively. Based on the results of matrix swelling and drug release, the optimal solutions, target values, and validation experiment results over time were similar and showed consistent patterns with very small biases. The experimental design methodology could be a very promising experimental design method to obtain maximum information with limited time and resources. It could also be very useful in formulation studies by providing a systematic and reliable screening method to characterize significant factors in the sustained release matrix tablet. Copyright © 2011 Elsevier B.V. All rights reserved.

Hyaluronan Induces the Selective Accumulation of Matrix- and Cell-Associated Proteoglycans by Mesangial Cells

PubMed Central

Kastner, Sabine; Thomas, Gareth J.; Jenkins, Robert H.; Davies, Malcolm; Steadman, Robert

2007-01-01

Mesangial cells (MCs) are essential for normal renal function through the synthesis of their own extracellular matrix, which forms the structural support of the renal glomerulus. In many renal diseases this matrix is reorganized in response to a variety of cytokines and growth factors. This study examines proteoglycan and hyaluronan (HA) synthesis by MCs triggered by proinflammatory agents and investigates the effect of an exogenous HA matrix on matrix synthesis by MCs. Metabolic labeling, ion exchange and size exclusion chromatography, Western blotting, and immunocytochemistry were used to identify changes in matrix accumulation. When incubated with interleukin-1, platelet-derived growth factor, or fetal calf serum, MCs initiated rapid HA synthesis associated with the up-regulation of HA synthase-2 and increased the synthesis of versican, perlecan, and decorin/biglycan. HA was both released into the medium and incorporated into extensive pericellular coats. Adding exogenous HA to unstimulated cells that had undetectable pericellular coats of HA selectively reduced perlecan and versican turnover, whereas other proteoglycans were unaffected. These results suggest that high levels of HA in the mesangium in disease is a mechanism controlling the accumulation of specific mesangial matrix components. HA may thus be an attractive target for therapeutic intervention. PMID:17974600

Superfund Chemical Data Matrix (SCDM) Query - Popup

EPA Pesticide Factsheets

This site allows you to to easily query the Superfund Chemical Data Matrix (SCDM) and generate a list of the corresponding Hazardous Ranking System (HRS) factor values, benchmarks, and data elements that you need.

Superfund Chemical Data Matrix (SCDM) Query

EPA Pesticide Factsheets

This site allows you to to easily query the Superfund Chemical Data Matrix (SCDM) and generate a list of the corresponding Hazard Ranking System (HRS) factor values, benchmarks, and data elements that you need.

Tissue architecture and breast cancer: the role of extracellular matrix and steroid hormones

PubMed Central

Hansen, R K; Bissell, M J

2010-01-01

The changes in tissue architecture that accompany the development of breast cancer have been the focus of investigations aimed at developing new cancer therapeutics. As we learn more about the normal mammary gland, we have begun to understand the complex signaling pathways underlying the dramatic shifts in the structure and function of breast tissue. Integrin-, growth factor-, and steroid hormone-signaling pathways all play an important part in maintaining tissue architecture; disruption of the delicate balance of signaling results in dramatic changes in the way cells interact with each other and with the extracellular matrix, leading to breast cancer. The extracellular matrix itself plays a central role in coordinating these signaling processes. In this review, we consider the interrelationships between the extracellular matrix, integrins, growth factors, and steroid hormones in mammary gland development and function. PMID:10903527

Perceived barriers to medical-error reporting: an exploratory investigation.

PubMed

Uribe, Claudia L; Schweikhart, Sharon B; Pathak, Dev S; Dow, Merrell; Marsh, Gail B

2002-01-01

Medical-error reporting is an essential component for patient safety enhancement. Unfortunately, medical errors are largely underreported across healthcare institutions. This problem can be attributed to different factors and barriers present at organizational and individual levels that ultimately prevent individuals from generating the report. This study explored the factors that affect medical-error reporting among physicians and nurses at a large academic medical center located in the midwest United States. A nominal group session was conducted to identify the most relevant factors that act as barriers for error reporting. These factors were then used to design a questionnaire that explored the likelihood of the factors to act as barriers and their likelihood to be modified. Using these two parameters, the results were analyzed and combined into a Factor Relevance Matrix. The matrix identifies the factors for which immediate actions should be undertaken to improve medical-error reporting (immediate action factors). It also identifies factors that require long-term strategies (long-term strategy factors) as well as factors that the organization should be aware of but that are of lower priority (awareness factors). The strategies outlined in this study may assist healthcare organizations in improving medical-error reporting, as part of the efforts toward patient-safety enhancement. Although factors affecting medical-error reporting may vary between different organizations, the process used in identifying the factors and the Factor Relevance Matrix developed in this study are easily adaptable to any organizational setting.

Minimally invasive esthetic ridge preservation with growth-factor enhanced bone matrix.

PubMed

Nevins, Marc L; Said, Sherif

2017-12-28

Extraction socket preservation procedures are critical to successful esthetic implant therapy. Conventional surgical approaches are technique sensitive and often result in alteration of the soft tissue architecture, which then requires additional corrective surgical procedures. This case series report presents the ability of flapless surgical techniques combined with a growth factor-enhanced bone matrix to provide esthetic ridge preservation at the time of extraction for compromised sockets. When considering esthetic dental implant therapy, preservation, or further enhancement of the available tissue support at the time of tooth extraction may provide an improved esthetic outcome with reduced postoperative sequelae and decreased treatment duration. Advances in minimally invasive surgical techniques combined with recombinant growth factor technology offer an alternative for bone reconstruction while maintaining the gingival architecture for enhanced esthetic outcome. The combination of freeze-dried bone allograft (FDBA) and rhPDGF-BB (platelet-derived growth factor-BB) provides a growth-factor enhanced matrix to induce bone and soft tissue healing. The use of a growth-factor enhanced matrix is an option for minimally invasive ridge preservation procedures for sites with advanced bone loss. Further studies including randomized clinical trials are needed to better understand the extent and limits of these procedures. The use of minimally invasive techniques with growth factors for esthetic ridge preservation reduces patient morbidity associated with more invasive approaches and increases the predictability for enhanced patient outcomes. By reducing the need for autogenous bone grafts the use of this technology is favorable for patient acceptance and ease of treatment process for esthetic dental implant therapy. © 2017 Wiley Periodicals, Inc.

Identification of Extracellular Matrix Components and Biological Factors in Micronized Dehydrated Human Amnion/Chorion Membrane

PubMed Central

Lei, Jennifer; Priddy, Lauren B.; Lim, Jeremy J.; Massee, Michelle; Koob, Thomas J.

2017-01-01

Objective: The use of bioactive extracellular matrix (ECM) grafts such as amniotic membranes is an attractive treatment option for enhancing wound repair. In this study, the concentrations, activity, and distribution of matrix components, growth factors, proteases, and inhibitors were evaluated in PURION® Processed, micronized, dehydrated human amnion/chorion membrane (dHACM; MiMedx Group, Inc.). Approach: ECM components in dHACM tissue were assessed by using immunohistochemical staining, and growth factors, cytokines, proteases, and inhibitors were quantified by using single and multiplex ELISAs. The activities of proteases that were native to the tissue were determined via gelatin zymography and EnzChek® activity assay. Results: dHACM tissue contained the ECM components collagens I and IV, hyaluronic acid, heparin sulfate proteoglycans, fibronectin, and laminin. In addition, numerous growth factors, cytokines, chemokines, proteases, and protease inhibitors that are known to play a role in the wound-healing process were quantified in dHACM. Though matrix metalloproteinases (MMPs) were present in dHACM tissues, inhibitors of MMPs overwhelmingly outnumbered the MMP enzymes by an overall molar ratio of 28:1. Protease activity assays revealed that the MMPs in the tissue existed primarily either in their latent form or complexed with inhibitors. Innovation: This is the first study to characterize components that function in wound healing, including inhibitor and protease content and activity, in micronized dHACM. Conclusion: A variety of matrix components and growth factors, as well as proteases and their inhibitors, were identified in micronized dHACM, providing a better understanding of how micronized dHACM tissue can be used to effectively promote wound repair. PMID:28224047

Identification of Extracellular Matrix Components and Biological Factors in Micronized Dehydrated Human Amnion/Chorion Membrane.

PubMed

Lei, Jennifer; Priddy, Lauren B; Lim, Jeremy J; Massee, Michelle; Koob, Thomas J

2017-02-01

Objective: The use of bioactive extracellular matrix (ECM) grafts such as amniotic membranes is an attractive treatment option for enhancing wound repair. In this study, the concentrations, activity, and distribution of matrix components, growth factors, proteases, and inhibitors were evaluated in PURION ® Processed, micronized, dehydrated human amnion/chorion membrane (dHACM; MiMedx Group, Inc.). Approach: ECM components in dHACM tissue were assessed by using immunohistochemical staining, and growth factors, cytokines, proteases, and inhibitors were quantified by using single and multiplex ELISAs. The activities of proteases that were native to the tissue were determined via gelatin zymography and EnzChek ® activity assay. Results: dHACM tissue contained the ECM components collagens I and IV, hyaluronic acid, heparin sulfate proteoglycans, fibronectin, and laminin. In addition, numerous growth factors, cytokines, chemokines, proteases, and protease inhibitors that are known to play a role in the wound-healing process were quantified in dHACM. Though matrix metalloproteinases (MMPs) were present in dHACM tissues, inhibitors of MMPs overwhelmingly outnumbered the MMP enzymes by an overall molar ratio of 28:1. Protease activity assays revealed that the MMPs in the tissue existed primarily either in their latent form or complexed with inhibitors. Innovation: This is the first study to characterize components that function in wound healing, including inhibitor and protease content and activity, in micronized dHACM. Conclusion: A variety of matrix components and growth factors, as well as proteases and their inhibitors, were identified in micronized dHACM, providing a better understanding of how micronized dHACM tissue can be used to effectively promote wound repair.

Coordinate regulation of estrogen-mediated fibronectin matrix assembly and epidermal growth factor receptor transactivation by the G protein-coupled receptor, GPR30.

PubMed

Quinn, Jeffrey A; Graeber, C Thomas; Frackelton, A Raymond; Kim, Minsoo; Schwarzbauer, Jean E; Filardo, Edward J

2009-07-01

Estrogen promotes changes in cytoskeletal architecture not easily attributed to the biological action of estrogen receptors, ERalpha and ERbeta. The Gs protein-coupled transmembrane receptor, GPR30, is linked to specific estrogen binding and rapid estrogen-mediated release of heparin-bound epidermal growth factor. Using marker rescue and dominant interfering mutant strategies, we show that estrogen action via GPR30 promotes fibronectin (FN) matrix assembly by human breast cancer cells. Stimulation with 17beta-estradiol or the ER antagonist, ICI 182, 780, results in the recruitment of FN-engaged integrin alpha5beta1 conformers to fibrillar adhesions and the synthesis of FN fibrils. Concurrent with this cellular response, GPR30 promotes the formation of Src-dependent, Shc-integrin alpha5beta1 complexes. Function-blocking antibodies directed against integrin alpha5beta1 or soluble Arg-Gly-Asp peptide fragments derived from FN specifically inhibited GPR30-mediated epidermal growth factor receptor transactivation. Estrogen-mediated FN matrix assembly and epidermal growth factor receptor transactivation were similarly disrupted in integrin beta1-deficient GE11 cells, whereas reintroduction of integrin beta1 into GE11 cells restored these responses. Mutant Shc (317Y/F) blocked GPR30-induced FN matrix assembly and tyrosyl phosphorylation of erbB1. Interestingly, relative to recombinant wild-type Shc, 317Y/F Shc was more readily retained in GPR30-induced integrin alpha5beta1 complexes, yet this mutant did not prevent endogenous Shc-integrin alpha5beta1 complex formation. Our results suggest that GPR30 coordinates estrogen-mediated FN matrix assembly and growth factor release in human breast cancer cells via a Shc-dependent signaling mechanism that activates integrin alpha5beta1.

Factors affecting miniature Izod impact strength of tungsten-fiber-metal-matrix

NASA Technical Reports Server (NTRS)

Winsa, E. A.; Petrasek, D. W.

1973-01-01

The miniature Izod and Charpy impact strengths of copper, copper-nickel, and nickel-base superalloy uniaxially reinforced with continuous tungsten fibers were studied. In most cases, impact strength was increased by increasing fiber or matrix toughness, decreasing fibermatrix reaction, increasing test temperature, hot working, or heat treating. Notch sensitivity was reduced by increasing fiber content or matrix toughness. An equation relating impact strength to fiber and matrix properties and fiber content was developed. Program results imply that tungsten alloy-fiber/superalloy matrix composites can be made with adequate impact resistance for turbine blade or vane applications.

Modeling fatigue crack growth in cross ply titanium matrix composites

NASA Technical Reports Server (NTRS)

Bakuckas, J. G., Jr.; Johnson, W. S.

1993-01-01

In this study, the fatigue crack growth behavior of fiber bridging matrix cracks in cross-ply SCS-6/Ti-15-3 and SCS-6/Timetal-21S laminates containing center holes was investigated. Experimental observations revealed that matrix cracking was far more extensive and wide spread in the SCS-6/Ti-15-3 laminates compared to that in the SCS-6/Timetal-21S laminates. In addition, the fatigue life of the SCS-6/Ti-15-3 laminates was significantly longer than that of the SCS-6/Timetal-21S laminates. The matrix cracking observed in both material systems was analyzed using a fiber bridging (FB) model which was formulated using the boundary correction factors and weight functions for center hole specimen configurations. A frictional shear stress is assumed in the FB model and was used as a curve fitting parameter to model matrix crack growth data. The higher frictional shear stresses calculated in the SCS-6/Timetal-21S laminates resulted in lower stress intensity factors in the matrix and higher axial stresses in the fibers compared to those in the SCS-6/Ti-15-3 laminates at the same applied stress levels.

Grapevine tissues and phenology differentially affect soluble carbohydrates determination by capillary electrophoresis.

PubMed

Moreno, Daniela; Berli, Federico; Bottini, Rubén; Piccoli, Patricia N; Silva, María F

2017-09-01

Soluble carbohydrates distribution depends on plant physiology and, among other important factors, determines fruit yield and quality. In plant biology, the analysis of sugars is useful for many purposes, including metabolic studies. Capillary electrophoresis (CE) proved to be a powerful green separation technique with minimal sample preparation, even in complex plant tissues, that can provide high-resolution efficiency. Matrix effect refers to alterations in the analytical response caused by components of a sample other than the analyte of interest. Thus, the assessment and reduction of the matrix factor is fundamental for metabolic studies in different matrices. The present study evaluated the source and levels of matrix effects in the determination of most abundant sugars in grapevine tissues (mature and young leaves, berries and roots) at two phenological growth stages. Sucrose was the sugar that showed the least matrix effects, while fructose was the most affected analyte. Based on plant tissues, young leaves presented the smaller matrix effects, irrespectively of the phenology. These changes may be attributed to considerable differences at chemical composition of grapevine tissues with plant development. Therefore, matrix effect should be an important concern for plant metabolomics. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Superfund Chemical Data Matrix (SCDM) Query - April 2016

EPA Pesticide Factsheets

This site allows you to to easily query the Superfund Chemical Data Matrix (SCDM) and generate a list of the corresponding Hazardous Ranking System (HRS) factor values, benchmarks, and data elements that you need.

Efficient Matrix Models for Relational Learning

DTIC Science & Technology

2009-10-01

74 4.5.3 Comparison to pLSI- pHITS . . . . . . . . . . . . . . . . . . . . 76 5 Hierarchical Bayesian Collective...Behaviour of Newton vs. Stochastic Newton on a three-factor model. 4.5.3 Comparison to pLSI- pHITS Caveat: Collective Matrix Factorization makes no guarantees...leads to better results; and another where a co-clustering model, pLSI- pHITS , has the advantage. pLSI- pHITS [24] is a relational clustering technique

WALLY 1 ...A large, principal components regression program with varimax rotation of the factor weight matrix

Treesearch

James R. Wallis

1965-01-01

Written in Fortran IV and MAP, this computer program can handle up to 120 variables, and retain 40 principal components. It can perform simultaneous regression of up to 40 criterion variables upon the varimax rotated factor weight matrix. The columns and rows of all output matrices are labeled by six-character alphanumeric names. Data input can be from punch cards or...

More efficient parameter estimates for factor analysis of ordinal variables by ridge generalized least squares.

PubMed

Yuan, Ke-Hai; Jiang, Ge; Cheng, Ying

2017-11-01

Data in psychology are often collected using Likert-type scales, and it has been shown that factor analysis of Likert-type data is better performed on the polychoric correlation matrix than on the product-moment covariance matrix, especially when the distributions of the observed variables are skewed. In theory, factor analysis of the polychoric correlation matrix is best conducted using generalized least squares with an asymptotically correct weight matrix (AGLS). However, simulation studies showed that both least squares (LS) and diagonally weighted least squares (DWLS) perform better than AGLS, and thus LS or DWLS is routinely used in practice. In either LS or DWLS, the associations among the polychoric correlation coefficients are completely ignored. To mend such a gap between statistical theory and empirical work, this paper proposes new methods, called ridge GLS, for factor analysis of ordinal data. Monte Carlo results show that, for a wide range of sample sizes, ridge GLS methods yield uniformly more accurate parameter estimates than existing methods (LS, DWLS, AGLS). A real-data example indicates that estimates by ridge GLS are 9-20% more efficient than those by existing methods. Rescaled and adjusted test statistics as well as sandwich-type standard errors following the ridge GLS methods also perform reasonably well. © 2017 The British Psychological Society.

Extracellular matrix and growth factor engineering for controlled angiogenesis in regenerative medicine

DOE PAGES

Martino, Mikael M.; Brkic, Sime; Bovo, Emmanuela; ...

2015-04-01

In this study, blood vessel growth plays a key role in regenerative medicine, both to restore blood supply to ischemic tissues and to ensure rapid vascularization of clinical-size tissue-engineered grafts. For example, vascular endothelial growth factor (VEGF) is the master regulator of physiological blood vessel growth and is one of the main molecular targets of therapeutic angiogenesis approaches. However, angiogenesis is a complex process and there is a need to develop rational therapeutic strategies based on a firm understanding of basic vascular biology principles, as evidenced by the disappointing results of initial clinical trials of angiogenic factor delivery. In particular,more » the spatial localization of angiogenic signals in the extracellular matrix (ECM) is crucial to ensure the proper assembly and maturation of new vascular structures. Here, we discuss the therapeutic implications of matrix interactions of angiogenic factors, with a special emphasis on VEGF, as well as provide an overview of current approaches, based on protein and biomaterial engineering that mimic the regulatory functions of ECM to optimize the signaling microenvironment of vascular growth factors.« less

Extracellular Matrix-Inspired Growth Factor Delivery Systems for Skin Wound Healing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Briquez, Priscilla S.; Hubbell, Jeffrey A.; Martino, Mikaël M.

2015-08-01

Blood vessel growth plays a key role in regenerative medicine, both to restore blood supply to ischemic tissues and to ensure rapid vascularization of clinical-size tissue-engineered grafts. For example, vascular endothelial growth factor (VEGF) is the master regulator of physiological blood vessel growth and is one of the main molecular targets of therapeutic angiogenesis approaches. However, angiogenesis is a complex process and there is a need to develop rational therapeutic strategies based on a firm understanding of basic vascular biology principles, as evidenced by the disappointing results of initial clinical trials of angiogenic factor delivery. In particular, the spatial localizationmore » of angiogenic signals in the extracellular matrix (ECM) is crucial to ensure the proper assembly and maturation of new vascular structures. Here, we discuss the therapeutic implications of matrix interactions of angiogenic factors, with a special emphasis on VEGF, as well as provide an overview of current approaches, based on protein and biomaterial engineering that mimic the regulatory functions of ECM to optimize the signaling microenvironment of vascular growth factors.« less

Extracellular matrix and growth factor engineering for controlled angiogenesis in regenerative medicine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, Mikael M.; Brkic, Sime; Bovo, Emmanuela

In this study, blood vessel growth plays a key role in regenerative medicine, both to restore blood supply to ischemic tissues and to ensure rapid vascularization of clinical-size tissue-engineered grafts. For example, vascular endothelial growth factor (VEGF) is the master regulator of physiological blood vessel growth and is one of the main molecular targets of therapeutic angiogenesis approaches. However, angiogenesis is a complex process and there is a need to develop rational therapeutic strategies based on a firm understanding of basic vascular biology principles, as evidenced by the disappointing results of initial clinical trials of angiogenic factor delivery. In particular,more » the spatial localization of angiogenic signals in the extracellular matrix (ECM) is crucial to ensure the proper assembly and maturation of new vascular structures. Here, we discuss the therapeutic implications of matrix interactions of angiogenic factors, with a special emphasis on VEGF, as well as provide an overview of current approaches, based on protein and biomaterial engineering that mimic the regulatory functions of ECM to optimize the signaling microenvironment of vascular growth factors.« less

Extracellular matrix and growth factor engineering for controlled angiogenesis in regenerative medicine.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martino, Mikael M.; Brkic, Sime; Bovo, Emmanuela

Blood vessel growth plays a key role in regenerative medicine, both to restore blood supply to ischemic tissues and to ensure rapid vascularization of clinical-size tissue-engineered grafts. For example, vascular endothelial growth factor (VEGF) is the master regulator of physiological blood vessel growth and is one of the main molecular targets of therapeutic angiogenesis approaches. However, angiogenesis is a complex process and there is a need to develop rational therapeutic strategies based on a firm understanding of basic vascular biology principles, as evidenced by the disappointing results of initial clinical trials of angiogenic factor delivery. In particular, the spatial localizationmore » of angiogenic signals in the extracellular matrix (ECM) is crucial to ensure the proper assembly and maturation of new vascular structures. Here, we discuss the therapeutic implications of matrix interactions of angiogenic factors, with a special emphasis on VEGF, as well as provide an overview of current approaches, based on protein and biomaterial engineering that mimic the regulatory functions of ECM to optimize the signaling microenvironment of vascular growth factors.« less

Two-faced property of a market factor in asset pricing and diversification effect

NASA Astrophysics Data System (ADS)

Eom, Cheoljun

2017-04-01

This study empirically investigates the test hypothesis that a market factor acting as a representative common factor in the pricing models has a negative influence on constructing a well-diversified portfolio from the Markowitz mean-variance optimization function (MVOF). We use the comparative correlation matrix (C-CM) method to control a single eigenvalue among all eigenvalues included in the sample correlation matrix (S-CM), through the random matrix theory (RMT). In particular, this study observes the effect of the largest eigenvalue that has the property of the market factor. According to the results, the largest eigenvalue has the highest explanatory power on the stock return changes. The C-CM without the largest eigenvalue in the S-CM constructs a more diversified portfolio capable of improving the practical applicability of the MVOF. Moreover, the more diversified portfolio constructed from this C-CM has better out-of-sample performance in the future period. These results support the test hypothesis for the two-faced property of the market factor, defined by the largest eigenvalue.

Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, C. C., Jr.

1988-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 to + or - 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was developed to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Strengths of specimens containing crack-like slits were calculated from predicted failing strains using uniaxial stress-strain curves. Predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only + or - 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.

GRAY: a program to calculate gray-body radiation heat-transfer view factors from black-body view factors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, R. L.

1976-06-14

Program GRAY is written to perform the matrix manipulations necessary to convert black-body radiation heat-transfer view factors to gray-body view factors as required by thermal analyzer codes. The black-body view factors contain only geometric relationships. Program GRAY allows the effects of multiple gray-body reflections to be included. The resulting effective gray-body factors can then be used with the corresponding fourth-power temperature differences to obtain the net radiative heat flux. The program is written to accept a matrix input or the card image output generated by the black-body view factor program CNVUFAC. The resulting card image output generated by GRAY ismore » in a form usable by the TRUMP thermal analyzer.« less

Numerical examination of the factors controlling DNAPL migration through a single fracture.

PubMed

Reynolds, D A; Kueper, B H

2002-01-01

The migration of five dense nonaqueous phase liquids (DNAPLs) through a single fracture in a clay aquitard was numerically simulated with the use of a compositional simulator. The effects of fracture aperture, fracture dip, matrix porosity, and matrix organic carbon content on the migration of chlorobenzene, 1,2-dichloroethylene, trichloroethylene, tetra-chloroethylene, and 1,2-dibromoethane were examined. Boundary conditions were chosen such that DNAPL entry into the system was allowed to vary according to the stresses applied. The aperture is the most important factor of those studied controlling the migration rate of DNAPL through a single fracture embedded in a clay matrix. Loss of mass to the matrix through diffusion does not significantly retard the migration rate of the DNAPL, particularly in larger aperture fractures (e.g., 50 microm). With time, the ratio of diffusive loss to the matrix to DNAPL flux into the fracture approaches an asymptotic value lower than unity. The implication is that matrix diffusion cannot arrest the migration of DNAPL in a single fracture. The complex relationships between density, viscosity, and solubility that, to some extent, govern the migration of DNAPL through these systems prevent accurate predictions without the use of numerical models. The contamination potential of the migrating DNAPL is significantly increased through the transfer of mass to the matrix. The occurrence of opposite concentration gradients within the matrix can cause dissolved phase contamination to exist in the system for more than 1000 years after the DNAPL has been completely removed from the fracture.

Tissue architecture and breast cancer: the role of extracellular matrix and steroid hormones

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hansen, R K; Bissell, M J

The changes in tissue architecture that accompany the development of breast cancer have been the focus of investigations aimed at developing new cancer therapeutics. As we learn more about the normal mammary gland, we have begun to understand the complex signaling pathways underlying the dramatic shifts in the structure and function of breast tissue. Integrin-, growth factor-, and steroid hormone-signaling pathways all play an important part in maintaining tissue architecture; disruption of the delicate balance of signaling results in dramatic changes in the way cells interact with each other and with the extracellular matrix, leading to breast cancer. The extracellularmore » matrix itself plays a central role in coordinating these signaling processes. In this review, we consider the interrelationships between the extracellular matrix, integrins, growth factors, and steroid hormones in mammary gland development and function.« less

Analysis of IFE, EFE and QSPM matrix on business development strategy

NASA Astrophysics Data System (ADS)

Zulkarnain, A.; Wahyuningtias, D.; Putranto, T. S.

2018-03-01

IFE matrix, EFE matrix, and QSPM matrix are business strategy tools that can be used to identify the threat, opportunity, weakness, strength as internal, external business factors. The goal of Danti’s Deli Bakery is to provide pastry product and distribute to other food and beverage outlet all around Jakarta. Thus, Danti’s Deli Bakery requires development strategy in order to win the tight competition. Applied descriptive research and data collected from focus group discussion, questionnaire, interview, observation and literature review. The objectives of this paper are (1) to identify and evaluate internal and external factors, (2) to formulate alternative strategy toward business development program, and (3) to give effective recommendation. The result shows that Danti’s Deli Bakery should apply product differentiation strategy. Implementation of this study is providing the recommendation for pastry and bakery industry to establish a successful business.

Necessary and sufficient conditions for the complete controllability and observability of systems in series using the coprime factorization of a rational matrix

NASA Technical Reports Server (NTRS)

Callier, F. M.; Nahum, C. D.

1975-01-01

The series connection of two linear time-invariant systems that have minimal state space system descriptions is considered. From these descriptions, strict-system-equivalent polynomial matrix system descriptions in the manner of Rosenbrock are derived. They are based on the factorization of the transfer matrix of the subsystems as a ratio of two right or left coprime polynomial matrices. They give rise to a simple polynomial matrix system description of the tandem connection. Theorem 1 states that for the complete controllability and observability of the state space system description of the series connection, it is necessary and sufficient that certain 'denominator' and 'numerator' groups are coprime. Consequences for feedback systems are drawn in Corollary 1. The role of pole-zero cancellations is explained by Lemma 3 and Corollaires 2 and 3.

Multilineage differentiation of rhesus monkey embryonic stem cells in three-dimensional culture systems

NASA Technical Reports Server (NTRS)

Chen, Silvia S.; Revoltella, Roberto P.; Papini, Sandra; Michelini, Monica; Fitzgerald, Wendy; Zimmerberg, Joshua; Margolis, Leonid

2003-01-01

In the course of normal embryogenesis, embryonic stem (ES) cells differentiate along different lineages in the context of complex three-dimensional (3D) tissue structures. In order to study this phenomenon in vitro under controlled conditions, 3D culture systems are necessary. Here, we studied in vitro differentiation of rhesus monkey ES cells in 3D collagen matrixes (collagen gels and porous collagen sponges). Differentiation of ES cells in these 3D systems was different from that in monolayers. ES cells differentiated in collagen matrixes into neural, epithelial, and endothelial lineages. The abilities of ES cells to form various structures in two chemically similar but topologically different matrixes were different. In particular, in collagen gels ES cells formed gland-like circular structures, whereas in collagen sponges ES cells were scattered through the matrix or formed aggregates. Soluble factors produced by feeder cells or added to the culture medium facilitated ES cell differentiation into particular lineages. Coculture with fibroblasts in collagen gel facilitated ES cell differentiation into cells of a neural lineage expressing nestin, neural cell adhesion molecule, and class III beta-tubulin. In collagen sponges, keratinocytes facilitated ES cell differentiation into cells of an endothelial lineage expressing factor VIII. Exogenous granulocyte-macrophage colony-stimulating factor further enhanced endothelial differentiation. Thus, both soluble factors and the type of extracellular matrix seem to be critical in directing differentiation of ES cells and the formation of tissue-like structures. Three-dimensional culture systems are a valuable tool for studying the mechanisms of these phenomena.

Human mesenchymal stem cells cultured on silk hydrogels with variable stiffness and growth factor differentiate into mature smooth muscle cell phenotype.

PubMed

Floren, Michael; Bonani, Walter; Dharmarajan, Anirudh; Motta, Antonella; Migliaresi, Claudio; Tan, Wei

2016-02-01

Cell-matrix and cell-biomolecule interactions play critical roles in a diversity of biological events including cell adhesion, growth, differentiation, and apoptosis. Evidence suggests that a concise crosstalk of these environmental factors may be required to direct stem cell differentiation toward matured cell type and function. However, the culmination of these complex interactions to direct stem cells into highly specific phenotypes in vitro is still widely unknown, particularly in the context of implantable biomaterials. In this study, we utilized tunable hydrogels based on a simple high pressure CO2 method and silk fibroin (SF) the structural protein of Bombyx mori silk fibers. Modification of SF protein starting water solution concentration results in hydrogels of variable stiffness while retaining key structural parameters such as matrix pore size and β-sheet crystallinity. To further resolve the complex crosstalk of chemical signals with matrix properties, we chose to investigate the role of 3D hydrogel stiffness and transforming growth factor (TGF-β1), with the aim of correlating the effects on the vascular commitment of human mesenchymal stem cells. Our data revealed the potential to upregulate matured vascular smooth muscle cell phenotype (myosin heavy chain expression) of hMSCs by employing appropriate matrix stiffness and growth factor (within 72h). Overall, our observations suggest that chemical and physical stimuli within the cellular microenvironment are tightly coupled systems involved in the fate decisions of hMSCs. The production of tunable scaffold materials that are biocompatible and further specialized to mimic tissue-specific niche environments will be of considerable value to future tissue engineering platforms. This article investigates the role of silk fibroin hydrogel stiffness and transforming growth factor (TGF-β1), with the aim of correlating the effects on the vascular commitment of human mesenchymal stem cells. Specifically, we demonstrate the upregulation of mature vascular smooth muscle cell phenotype (myosin heavy chain expression) of hMSCs by employing appropriate matrix stiffness and growth factor (within 72h). Moreover, we demonstrate the potential to direct specialized hMSC differentiation by modulating stiffness and growth factor using silk fibroin, a well-tolerated and -defined biomaterial with an impressive portfolio of tissue engineering applications. Altogether, our study reinforce the fact that complex differentiation protocols may be simplified by engineering the cellular microenvironment on multiple scales, i.e. matrix stiffness with growth factor. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

State-Space System Realization with Input- and Output-Data Correlation

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

1997-01-01

This paper introduces a general version of the information matrix consisting of the autocorrelation and cross-correlation matrices of the shifted input and output data. Based on the concept of data correlation, a new system realization algorithm is developed to create a model directly from input and output data. The algorithm starts by computing a special type of correlation matrix derived from the information matrix. The special correlation matrix provides information on the system-observability matrix and the state-vector correlation. A system model is then developed from the observability matrix in conjunction with other algebraic manipulations. This approach leads to several different algorithms for computing system matrices for use in representing the system model. The relationship of the new algorithms with other realization algorithms in the time and frequency domains is established with matrix factorization of the information matrix. Several examples are given to illustrate the validity and usefulness of these new algorithms.

Matrix Remodeling in Pulmonary Fibrosis and Emphysema

PubMed Central

O’Reilly, Philip; Antony, Veena B.; Gaggar, Amit

2016-01-01

Pulmonary fibrosis and emphysema are chronic lung diseases characterized by a progressive decline in lung function, resulting in significant morbidity and mortality. A hallmark of these diseases is recurrent or persistent alveolar epithelial injury, typically caused by common environmental exposures such as cigarette smoke. We propose that critical determinants of the outcome of the injury-repair processes that result in fibrosis versus emphysema are mesenchymal cell fate and associated extracellular matrix dynamics. In this review, we explore the concept that regulation of mesenchymal cells under the influence of soluble factors, in particular transforming growth factor-β1, and the extracellular matrix determine the divergent tissue remodeling responses seen in pulmonary fibrosis and emphysema. PMID:26741177

Metal- and intermetallic-matrix composites for aerospace propulsion and power systems

NASA Astrophysics Data System (ADS)

Doychak, J.

1992-06-01

Successful development and deployment of metal-matrix composites and intermetallic- matrix composites are critical to reaching the goals of many advanced aerospace propulsion and power development programs. The material requirements are based on the aerospace propulsion and power system requirements, economics, and other factors. Advanced military and civilian aircraft engines will require higher specific strength materials that operate at higher temperatures, and the civilian engines will also require long lifetimes. The specific space propulsion and power applications require hightemperature, high-thermal-conductivity, and high-strength materials. Metal-matrix composites and intermetallic-matrix composites either fulfill or have the potential of fulfilling these requirements.

A Chess-Like Game for Teaching Engineering Students to Solve Large System of Simultaneous Linear Equations

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.; Mohammed, Ahmed Ali; Kadiam, Subhash

2010-01-01

Solving large (and sparse) system of simultaneous linear equations has been (and continues to be) a major challenging problem for many real-world engineering/science applications [1-2]. For many practical/large-scale problems, the sparse, Symmetrical and Positive Definite (SPD) system of linear equations can be conveniently represented in matrix notation as [A] {x} = {b} , where the square coefficient matrix [A] and the Right-Hand-Side (RHS) vector {b} are known. The unknown solution vector {x} can be efficiently solved by the following step-by-step procedures [1-2]: Reordering phase, Matrix Factorization phase, Forward solution phase, and Backward solution phase. In this research work, a Game-Based Learning (GBL) approach has been developed to help engineering students to understand crucial details about matrix reordering and factorization phases. A "chess-like" game has been developed and can be played by either a single player, or two players. Through this "chess-like" open-ended game, the players/learners will not only understand the key concepts involved in reordering algorithms (based on existing algorithms), but also have the opportunities to "discover new algorithms" which are better than existing algorithms. Implementing the proposed "chess-like" game for matrix reordering and factorization phases can be enhanced by FLASH [3] computer environments, where computer simulation with animated human voice, sound effects, visual/graphical/colorful displays of matrix tables, score (or monetary) awards for the best game players, etc. can all be exploited. Preliminary demonstrations of the developed GBL approach can be viewed by anyone who has access to the internet web-site [4]!

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

NASA Technical Reports Server (NTRS)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

Computationally efficient modeling and simulation of large scale systems

NASA Technical Reports Server (NTRS)

Jain, Jitesh (Inventor); Cauley, Stephen F. (Inventor); Li, Hong (Inventor); Koh, Cheng-Kok (Inventor); Balakrishnan, Venkataramanan (Inventor)

2010-01-01

A method of simulating operation of a VLSI interconnect structure having capacitive and inductive coupling between nodes thereof. A matrix X and a matrix Y containing different combinations of passive circuit element values for the interconnect structure are obtained where the element values for each matrix include inductance L and inverse capacitance P. An adjacency matrix A associated with the interconnect structure is obtained. Numerical integration is used to solve first and second equations, each including as a factor the product of the inverse matrix X.sup.1 and at least one other matrix, with first equation including X.sup.1Y, X.sup.1A, and X.sup.1P, and the second equation including X.sup.1A and X.sup.1P.

Factorized three-body S-matrix restrained by the Yang–Baxter equation and quantum entanglements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Li-Wei, E-mail: NKyulw@gmail.com; Zhao, Qing, E-mail: qzhaoyuping@bit.edu.cn; Ge, Mo-Lin, E-mail: geml@nankai.edu.cn

2014-09-15

This paper investigates the physical effects of the Yang–Baxter equation (YBE) to quantum entanglements through the 3-body S-matrix in entangling parameter space. The explicit form of 3-body S-matrix Ř{sub 123}(θ,φ) based on the 2-body S-matrices is given due to the factorization condition of YBE. The corresponding chain Hamiltonian has been obtained and diagonalized, also the Berry phase for 3-body system is given. It turns out that by choosing different spectral parameters the Ř(θ,φ)-matrix gives GHZ and W states respectively. The extended 1-D Kitaev toy model has been derived. Examples of the role of the model in entanglement transfer are discussed.more » - Highlights: • We give the relation between 3-body S-matrix and 3-qubit entanglement. • The relation between 3-qubit and 2-qubit entanglements is investigated via YBE. • 1D Kitaev toy model is derived by the Type-II solution of YBE. • The condition of YBE kills the “Zero boundary mode” in our chain model.« less

BCYCLIC: A parallel block tridiagonal matrix cyclic solver

NASA Astrophysics Data System (ADS)

Hirshman, S. P.; Perumalla, K. S.; Lynch, V. E.; Sanchez, R.

2010-09-01

A block tridiagonal matrix is factored with minimal fill-in using a cyclic reduction algorithm that is easily parallelized. Storage of the factored blocks allows the application of the inverse to multiple right-hand sides which may not be known at factorization time. Scalability with the number of block rows is achieved with cyclic reduction, while scalability with the block size is achieved using multithreaded routines (OpenMP, GotoBLAS) for block matrix manipulation. This dual scalability is a noteworthy feature of this new solver, as well as its ability to efficiently handle arbitrary (non-powers-of-2) block row and processor numbers. Comparison with a state-of-the art parallel sparse solver is presented. It is expected that this new solver will allow many physical applications to optimally use the parallel resources on current supercomputers. Example usage of the solver in magneto-hydrodynamic (MHD), three-dimensional equilibrium solvers for high-temperature fusion plasmas is cited.

Dynamic SPECT reconstruction from few projections: a sparsity enforced matrix factorization approach

NASA Astrophysics Data System (ADS)

Ding, Qiaoqiao; Zan, Yunlong; Huang, Qiu; Zhang, Xiaoqun

2015-02-01

The reconstruction of dynamic images from few projection data is a challenging problem, especially when noise is present and when the dynamic images are vary fast. In this paper, we propose a variational model, sparsity enforced matrix factorization (SEMF), based on low rank matrix factorization of unknown images and enforced sparsity constraints for representing both coefficients and bases. The proposed model is solved via an alternating iterative scheme for which each subproblem is convex and involves the efficient alternating direction method of multipliers (ADMM). The convergence of the overall alternating scheme for the nonconvex problem relies upon the Kurdyka-Łojasiewicz property, recently studied by Attouch et al (2010 Math. Oper. Res. 35 438) and Attouch et al (2013 Math. Program. 137 91). Finally our proof-of-concept simulation on 2D dynamic images shows the advantage of the proposed method compared to conventional methods.

High-Throughput Screening of Vascular Endothelium-Destructive or Protective Microenvironments: Cooperative Actions of Extracellular Matrix Composition, Stiffness, and Structure.

PubMed

Ding, Yonghui; Floren, Michael; Tan, Wei

2017-06-01

Pathological modification of the subendothelial extracellular matrix (ECM) has closely been associated with endothelial activation and subsequent cardiovascular disease progression. To understand regulatory mechanisms of these matrix modifications, the majority of previous efforts have focused on the modulation of either chemical composition or matrix stiffness on 2D smooth surfaces without simultaneously probing their cooperative effects on endothelium function on in vivo like 3D fibrous matrices. To this end, a high-throughput, combinatorial microarray platform on 2D and 3D hydrogel settings to resemble the compositions, stiffness, and structure of healthy and diseased subendothelial ECM has been established, and further their respective and combined effects on endothelial attachment, proliferation, inflammation, and junctional integrity have been investigated. For the first time, the results demonstrate that 3D fibrous structure resembling native ECM is a critical endothelium-protective microenvironmental factor by maintaining the stable, quiescent endothelium with strong resistance to proinflammatory stimuli. It is also revealed that matrix stiffening, in concert with chemical compositions resembling diseased ECM, particularly collagen III, could aggravate activation of nuclear factor kappa B, disruption of endothelium integrity, and susceptibility to proinflammatory stimuli. This study elucidates cooperative effects of various microenvironmental factors on endothelial activation and sheds light on new in vitro model for cardiovascular diseases. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Parallel O(log n) algorithms for open- and closed-chain rigid multibody systems based on a new mass matrix factorization technique

NASA Technical Reports Server (NTRS)

Fijany, Amir

1993-01-01

In this paper, parallel O(log n) algorithms for computation of rigid multibody dynamics are developed. These parallel algorithms are derived by parallelization of new O(n) algorithms for the problem. The underlying feature of these O(n) algorithms is a drastically different strategy for decomposition of interbody force which leads to a new factorization of the mass matrix (M). Specifically, it is shown that a factorization of the inverse of the mass matrix in the form of the Schur Complement is derived as M(exp -1) = C - B(exp *)A(exp -1)B, wherein matrices C, A, and B are block tridiagonal matrices. The new O(n) algorithm is then derived as a recursive implementation of this factorization of M(exp -1). For the closed-chain systems, similar factorizations and O(n) algorithms for computation of Operational Space Mass Matrix lambda and its inverse lambda(exp -1) are also derived. It is shown that these O(n) algorithms are strictly parallel, that is, they are less efficient than other algorithms for serial computation of the problem. But, to our knowledge, they are the only known algorithms that can be parallelized and that lead to both time- and processor-optimal parallel algorithms for the problem, i.e., parallel O(log n) algorithms with O(n) processors. The developed parallel algorithms, in addition to their theoretical significance, are also practical from an implementation point of view due to their simple architectural requirements.

Factor Analysis and Counseling Research

ERIC Educational Resources Information Center

Weiss, David J.

1970-01-01

Topics discussed include factor analysis versus cluster analysis, analysis of Q correlation matrices, ipsativity and factor analysis, and tests for the significance of a correlation matrix prior to application of factor analytic techniques. Techniques for factor extraction discussed include principal components, canonical factor analysis, alpha…

Matrix Interdiction Problem

NASA Astrophysics Data System (ADS)

Kasiviswanathan, Shiva Prasad; Pan, Feng

In the matrix interdiction problem, a real-valued matrix and an integer k is given. The objective is to remove a set of k matrix columns that minimizes in the residual matrix the sum of the row values, where the value of a row is defined to be the largest entry in that row. This combinatorial problem is closely related to bipartite network interdiction problem that can be applied to minimize the probability that an adversary can successfully smuggle weapons. After introducing the matrix interdiction problem, we study the computational complexity of this problem. We show that the matrix interdiction problem is NP-hard and that there exists a constant γ such that it is even NP-hard to approximate this problem within an n γ additive factor. We also present an algorithm for this problem that achieves an (n - k) multiplicative approximation ratio.

Modeling cometary photopolarimetric characteristics with Sh-matrix method

NASA Astrophysics Data System (ADS)

Kolokolova, L.; Petrov, D.

2017-12-01

Cometary dust is dominated by particles of complex shape and structure, which are often considered as fractal aggregates. Rigorous modeling of light scattering by such particles, even using parallelized codes and NASA supercomputer resources, is very computer time and memory consuming. We are presenting a new approach to modeling cometary dust that is based on the Sh-matrix technique (e.g., Petrov et al., JQSRT, 112, 2012). This method is based on the T-matrix technique (e.g., Mishchenko et al., JQSRT, 55, 1996) and was developed after it had been found that the shape-dependent factors could be separated from the size- and refractive-index-dependent factors and presented as a shape matrix, or Sh-matrix. Size and refractive index dependences are incorporated through analytical operations on the Sh-matrix to produce the elements of T-matrix. Sh-matrix method keeps all advantages of the T-matrix method, including analytical averaging over particle orientation. Moreover, the surface integrals describing the Sh-matrix elements themselves can be solvable analytically for particles of any shape. This makes Sh-matrix approach an effective technique to simulate light scattering by particles of complex shape and surface structure. In this paper, we present cometary dust as an ensemble of Gaussian random particles. The shape of these particles is described by a log-normal distribution of their radius length and direction (Muinonen, EMP, 72, 1996). Changing one of the parameters of this distribution, the correlation angle, from 0 to 90 deg., we can model a variety of particles from spheres to particles of a random complex shape. We survey the angular and spectral dependencies of intensity and polarization resulted from light scattering by such particles, studying how they depend on the particle shape, size, and composition (including porous particles to simulate aggregates) to find the best fit to the cometary observations.

Expression of receptors for putative anabolic growth factors in human intervertebral disc: implications for repair and regeneration of the disc.

PubMed

Le Maitre, Christine L; Richardson, Stephen M A; Baird, Pauline; Freemont, Anthony J; Hoyland, Judith A

2005-12-01

Low back pain (LBP) is a common, debilitating and economically important disorder. Current evidence implicates loss of intervertebral disc (IVD) matrix consequent upon 'degeneration' as a major cause of LBP. Degeneration of the IVD involves increases in degradative enzymes and decreases in the extracellular matrix (ECM) component in a process that is controlled by a range of cytokines and growth factors. Studies have suggested using anabolic growth factors to regenerate the normal matrix of the IVD, hence restoring disc height and reversing degenerative disc disease. However, for such therapies to be successful it is vital that the target cells (i.e. the disc cells) express the appropriate receptors. This immunohistochemical study has for the first time investigated the expression and localization of four potentially beneficial growth factor receptors (i.e. TGFbetaRII, BMPRII, FGFR3 and IGFRI) in non-degenerate and degenerate human IVDs. Receptor expression was quantified across regions of the normal and degenerate disc and showed that cells of the nucleus pulposus (NP) and inner annulus fibrosus (IAF) expressed significantly higher levels of the four growth factor receptors investigated. There were no significant differences between the four growth factor expression in non-degenerate and degenerate biopsies. However, expression of TGFbetaRII, FGFR3 and IGFRI, but not BMP RII, were observed in the ingrowing blood vessels that characterize part of the disease aetiology. In conclusion, this study has demonstrated the expression of the four growth factor receptors at similar levels in the chondrocyte-like cells of the NP and IAF in both non-degenerate and degenerate discs, implicating a role in normal disc homeostasis and suggesting that the application of these growth factors to the degenerate human IVD would stimulate matrix production. However, the expression of some of the growth factor receptors on ingrowing blood vessels might be problematic in a therapeutic approach. Copyright 2005 Pathological Society of Great Britain and Ireland.

An analysis of thermal response factors and how to reduce their computational time requirement

NASA Technical Reports Server (NTRS)

Wiese, M. R.

1982-01-01

Te RESFAC2 version of the Thermal Response Factor Program (RESFAC) is the result of numerous modifications and additions to the original RESFAC. These modifications and additions have significantly reduced the program's computational time requirement. As a result of this work, the program is more efficient and its code is both readable and understandable. This report describes what a thermal response factor is; analyzes the original matrix algebra calculations and root finding techniques; presents a new root finding technique and streamlined matrix algebra; supplies ten validation cases and their results.

Arrowheaded enhanced multivariance products representation for matrices (AEMPRM): Specifically focusing on infinite matrices and converting arrowheadedness to tridiagonality

NASA Astrophysics Data System (ADS)

Özdemir, Gizem; Demiralp, Metin

2015-12-01

In this work, Enhanced Multivariance Products Representation (EMPR) approach which is a Demiralp-and-his- group extension to the Sobol's High Dimensional Model Representation (HDMR) has been used as the basic tool. Their discrete form have also been developed and used in practice by Demiralp and his group in addition to some other authors for the decomposition of the arrays like vectors, matrices, or multiway arrays. This work specifically focuses on the decomposition of infinite matrices involving denumerable infinitely many rows and columns. To this end the target matrix is first decomposed to the sum of certain outer products and then each outer product is treated by Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) which has been developed by Demiralp and his group. The result is a three-matrix- factor-product whose kernel (the middle factor) is an arrowheaded matrix while the pre and post factors are invertable matrices decomposed of the support vectors of TMEMPR. This new method is called as Arrowheaded Enhanced Multivariance Products Representation for Matrices. The general purpose is approximation of denumerably infinite matrices with the new method.

Matrix Gla Protein polymorphism, but not concentrations, is associated with radiographic hand osteoarthritis

USDA-ARS?s Scientific Manuscript database

Objective. Factors associated with mineralization and osteophyte formation in osteoarthritis (OA) are incompletely understood. Genetic polymorphisms of matrix Gla protein (MGP), a mineralization inhibitor, have been associated clinically with conditions of abnormal calcification. We therefore evalua...

Headspace versus direct immersion solid phase microextraction in complex matrixes: investigation of analyte behavior in multicomponent mixtures.

PubMed

Gionfriddo, Emanuela; Souza-Silva, Érica A; Pawliszyn, Janusz

2015-08-18

This work aims to investigate the behavior of analytes in complex mixtures and matrixes with the use of solid-phase microextraction (SPME). Various factors that influence analyte uptake such as coating chemistry, extraction mode, the physicochemical properties of analytes, and matrix complexity were considered. At first, an aqueous system containing analytes bearing different hydrophobicities, molecular weights, and chemical functionalities was investigated by using commercially available liquid and solid porous coatings. The differences in the mass transfer mechanisms resulted in a more pronounced occurrence of coating saturation in headspace mode. Contrariwise, direct immersion extraction minimizes the occurrence of artifacts related to coating saturation and provides enhanced extraction of polar compounds. In addition, matrix-compatible PDMS-modified solid coatings, characterized by a new morphology that avoids coating fouling, were compared to their nonmodified analogues. The obtained results indicate that PDMS-modified coatings reduce artifacts associated with coating saturation, even in headspace mode. This factor, coupled to their matrix compatibility, make the use of direct SPME very practical as a quantification approach and the best choice for metabolomics studies where wide coverage is intended. To further understand the influence on analyte uptake on a system where additional interactions occur due to matrix components, ex vivo and in vivo sampling conditions were simulated using a starch matrix model, with the aim of mimicking plant-derived materials. Our results corroborate the fact that matrix handling can affect analyte/matrix equilibria, with consequent release of high concentrations of previously bound hydrophobic compounds, potentially leading to coating saturation. Direct immersion SPME limited the occurrence of the artifacts, which confirms the suitability of SPME for in vivo applications. These findings shed light into the implementation of in vivo SPME strategies in quantitative metabolomics studies of complex plant-based systems.

Improving the analyte ion signal in matrix-assisted laser desorption/ionization imaging mass spectrometry via electrospray deposition by enhancing incorporation of the analyte in the matrix.

PubMed

Malys, Brian J; Owens, Kevin G

2017-05-15

Matrix-assisted laser desorption/ionization (MALDI) is widely used as the ionization method in high-resolution chemical imaging studies that seek to visualize the distribution of analytes within sectioned biological tissues. This work extends the use of electrospray deposition (ESD) to apply matrix with an additional solvent spray to incorporate and homogenize analyte within the matrix overlayer. Analytes and matrix are sequentially and independently applied by ESD to create a sample from which spectra are collected, mimicking a MALDI imaging mass spectrometry (IMS) experiment. Subsequently, an incorporation spray consisting of methanol is applied by ESD to the sample and another set of spectra are collected. The spectra prior to and after the incorporation spray are compared to evaluate the improvement in the analyte signal. Prior to the incorporation spray, samples prepared using α-cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB) as the matrix showed low signal while the sample using sinapinic acid (SA) initially exhibited good signal. Following the incorporation spray, the sample using SA did not show an increase in signal; the sample using DHB showed moderate gain factors of 2-5 (full ablation spectra) and 12-336 (raster spectra), while CHCA samples saw large increases in signal, with gain factors of 14-172 (full ablation spectra) and 148-1139 (raster spectra). The use of an incorporation spray to apply solvent by ESD to a matrix layer already deposited by ESD provides an increase in signal by both promoting incorporation of the analyte within and homogenizing the distribution of the incorporated analyte throughout the matrix layer. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Directing the osteoblastic and chondrocytic differentiations of mesenchymal stem cells: matrix vs. induction media

PubMed Central

He, Jing; Guo, Jianglong; Jiang, Bo; Yao, Ruijuan; Wu, Yao

2017-01-01

Abstract While both induction culture media and matrix have been reported to regulate the stem cell fate, little is known about which factor plays a more decisive role in directing the MSC differentiation lineage as well as the underlying mechanisms. To this aim, we seeded MSCs on HA-collagen and HA-synthetic hydrogel matrixes, which had demonstrated highly different potentials toward osteoblastic and chondrocytic differentiation lineages, respectively, and cultured them with osteogenic, chondrogenic and normal culture media, respectively. A systematic comparison has been carried out on the effects of induction media and matrix on MSC adhesion, cytoskeleton organization, proliferation, and in particular differentiation into the osteoblastic and chondrocytic lineages. The results demonstrated that the matrix selection had a much more profound effect on directing the differentiation lineage than the induction media did. The strong modulation effect on the transcription activities might be the critical factor contributing to the above observations in our study, where canonical Wnt-β-Catenin signal pathway was directly involved in the matrix-driven osteoblastic differentiation. Such findings not only provide a critical insight on natural cellular events leading to the osteoblastic and chondrocytic differentiations, but also have important implications in biomaterial design for tissue engineering applications. PMID:29026640

Massive data compression for parameter-dependent covariance matrices

NASA Astrophysics Data System (ADS)

Heavens, Alan F.; Sellentin, Elena; de Mijolla, Damien; Vianello, Alvise

2017-12-01

We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated data sets which are required to estimate the covariance matrix required for the analysis of Gaussian-distributed data. This is relevant when the covariance matrix cannot be calculated directly. The compression is especially valuable when the covariance matrix varies with the model parameters. In this case, it may be prohibitively expensive to run enough simulations to estimate the full covariance matrix throughout the parameter space. This compression may be particularly valuable for the next generation of weak lensing surveys, such as proposed for Euclid and Large Synoptic Survey Telescope, for which the number of summary data (such as band power or shear correlation estimates) is very large, ∼104, due to the large number of tomographic redshift bins which the data will be divided into. In the pessimistic case where the covariance matrix is estimated separately for all points in an Monte Carlo Markov Chain analysis, this may require an unfeasible 109 simulations. We show here that MOPED can reduce this number by a factor of 1000, or a factor of ∼106 if some regularity in the covariance matrix is assumed, reducing the number of simulations required to a manageable 103, making an otherwise intractable analysis feasible.

A Study of Influencing Factors on the Tensile Response of a Titanium Matrix Composite With Weak Interfacial Bonding

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Arnold, Steven M.

2000-01-01

The generalized method of cells micromechanics model is utilized to analyze the tensile stress-strain response of a representative titanium matrix composite with weak interfacial bonding. The fiber/matrix interface is modeled through application of a displacement discontinuity between the fiber and matrix once a critical debonding stress has been exceeded. Unidirectional composites with loading parallel and perpendicular to the fibers are examined, as well as a cross-ply laminate. For each of the laminates studied, analytically obtained results are compared to experimental data. The application of residual stresses through a cool-down process was found to have a significant effect on the tensile response. For the unidirectional laminate with loading applied perpendicular to the fibers, fiber packing and fiber shape were shown to have a significant effect on the predicted tensile response. Furthermore, the interface was characterized through the use of semi-emperical parameters including an interfacial compliance and a "debond stress;" defined as the stress level across the interface which activates fiber/matrix debonding. The results in this paper demonstrate that if architectural factors are correctly accounted for and the interface is appropriately characterized, the macro-level composite behavior can be correctly predicted without modifying any of the fiber or matrix constituent properties.

Matrix representations of SOn + 2 in an SOn × SO2 basis and some isoscalar factors for SOn + 2 ⊃ SOn × SO2

NASA Astrophysics Data System (ADS)

Pan, Feng; Cao, Yu-Fang

1992-02-01

Vector coherent state (VCS) theory is applied to the group chain SOn+2⊇SOn×SO2. Matrix elements of SOn+2 generators in the SOn+2⊇SOn×SO2 basis are derived. A new formula for the evaluation of some isoscalar factors for SOn+2⊇SOn×SO2 with branching multiplicity is derived in the VCS framework. As a simple example, a new expression of some isoscalar factors for SO5⊇SO3×SO2, which involves only 6j coefficients and K-normalization factors, are obtained by using this formula.

A quasi-likelihood approach to non-negative matrix factorization

PubMed Central

Devarajan, Karthik; Cheung, Vincent C.K.

2017-01-01

A unified approach to non-negative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proven using the Expectation-Maximization algorithm. In addition, a measure to evaluate the goodness-of-fit of the resulting factorization is described. The proposed methods allow modeling of non-linear effects via appropriate link functions and are illustrated using an application in biomedical signal processing. PMID:27348511

Recursive inverse factorization.

PubMed

Rubensson, Emanuel H; Bock, Nicolas; Holmström, Erik; Niklasson, Anders M N

2008-03-14

A recursive algorithm for the inverse factorization S(-1)=ZZ(*) of Hermitian positive definite matrices S is proposed. The inverse factorization is based on iterative refinement [A.M.N. Niklasson, Phys. Rev. B 70, 193102 (2004)] combined with a recursive decomposition of S. As the computational kernel is matrix-matrix multiplication, the algorithm can be parallelized and the computational effort increases linearly with system size for systems with sufficiently sparse matrices. Recent advances in network theory are used to find appropriate recursive decompositions. We show that optimization of the so-called network modularity results in an improved partitioning compared to other approaches. In particular, when the recursive inverse factorization is applied to overlap matrices of irregularly structured three-dimensional molecules.

A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence

PubMed Central

Devarajan, Karthik; Ebrahimi, Nader; Soofi, Ehsan

2017-01-01

The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems. PMID:28868206

Bone matrix to growth factors: location, location, location

PubMed Central

Todorovic, Vesna

2010-01-01

The demonstration that fibrillin-1 mutations perturb transforming growth factor (TGF)–β bioavailability/signaling in Marfan syndrome (MFS) changed the view of the extracellular matrix as a passive structural support to a dynamic modulator of cell behavior. In this issue, Nistala et al. (2010. J. Cell Biol. doi: 10.1083/jcb.201003089) advance this concept by demonstrating how fibrillin-1 and -2 regulate TGF-β and bone morphogenetic protein (BMP) action during osteoblast maturation. PMID:20855500

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, Edmond

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Extracellular matrix protein 1, a direct targeting molecule of parathyroid hormone-related peptide, negatively regulates chondrogenesis and endochondral ossification via associating with progranulin growth factor.

PubMed

Kong, Li; Zhao, Yun-Peng; Tian, Qing-Yun; Feng, Jian-Quan; Kobayashi, Tatsuya; Merregaert, Joseph; Liu, Chuan-Ju

2016-08-01

Chondrogenesis and endochondral ossification are precisely controlled by cellular interactions with surrounding matrix proteins and growth factors that mediate cellular signaling pathways. Here, we report that extracellular matrix protein 1 (ECM1) is a previously unrecognized regulator of chondrogenesis. ECM1 is induced in the course of chondrogenesis and its expression in chondrocytes strictly depends on parathyroid hormone-related peptide (PTHrP) signaling pathway. Overexpression of ECM1 suppresses, whereas suppression of ECM1 enhances, chondrocyte differentiation and hypertrophy in vitro and ex vivo In addition, target transgene of ECM1 in chondrocytes or osteoblasts in mice leads to striking defects in cartilage development and endochondral bone formation. Of importance, ECM1 seems to be critical for PTHrP action in chondrogenesis, as blockage of ECM1 nearly abolishes PTHrP regulation of chondrocyte hypertrophy, and overexpression of ECM1 rescues disorganized growth plates of PTHrP-null mice. Furthermore, ECM1 and progranulin chondrogenic growth factor constitute an interaction network and act in concert in the regulation of chondrogenesis.-Kong, L., Zhao, Y.-P., Tian, Q.-Y., Feng, J.-Q., Kobayashi, T., Merregaert, J., Liu, C.-J. Extracellular matrix protein 1, a direct targeting molecule of parathyroid hormone-related peptide, negatively regulates chondrogenesis and endochondral ossification via associating with progranulin growth factor. © FASEB.

Platelet-rich fibrin matrix improves wound angiogenesis via inducing endothelial cell proliferation.

PubMed

Roy, Sashwati; Driggs, Jason; Elgharably, Haytham; Biswas, Sabyasachi; Findley, Muna; Khanna, Savita; Gnyawali, Urmila; Bergdall, Valerie K; Sen, Chandan K

2011-11-01

The economic, social, and public health burden of chronic ulcers and other compromised wounds is enormous and rapidly increasing with the aging population. The growth factors derived from platelets play an important role in tissue remodeling including neovascularization. Platelet-rich plasma (PRP) has been utilized and studied for the last four decades. Platelet gel and fibrin sealant, derived from PRP mixed with thrombin and calcium chloride, have been exogenously applied to tissues to promote wound healing, bone growth, hemostasis, and tissue sealing. In this study, we first characterized recovery and viability of as well as growth factor release from platelets in a novel preparation of platelet gel and fibrin matrix, namely platelet-rich fibrin matrix (PRFM). Next, the effect of PRFM application in a delayed model of ischemic wound angiogenesis was investigated. The study, for the first time, shows the kinetics of the viability of platelet-embedded fibrin matrix. A slow and steady release of growth factors from PRFM was observed. The vascular endothelial growth factor released from PRFM was primarily responsible for endothelial mitogenic response via extracellular signal-regulated protein kinase activation pathway. Finally, this preparation of PRFM effectively induced endothelial cell proliferation and improved wound angiogenesis in chronic wounds, providing evidence of probable mechanisms of action of PRFM in healing of chronic ulcers. 2011 by the Wound Healing Society.

A synoptic approach for analyzing erosion as a guide to land-use planning

USGS Publications Warehouse

Brown, William M.; Hines, Walter G.; Rickert, David A.; Beach, Gary L.

1979-01-01

A synoptic approach has been devised to delineate the relationships that exist' between physiographic factors, land-use activities, and resultant erosional problems. The approach involves the development of an erosional-depositional province map and a numerical impact matrix for rating the potential for erosional problems. The province map is prepared by collating data on the natural terrain factors that exert the dominant controls on erosion and deposition in each basin. In addition, existing erosional and depositional features are identified and mapped from color-infrared, high-altitude aerial imagery. The axes of the impact matrix are composed of weighting values for the terrain factors used in developing the map and by a second set of values for the prevalent land-use activities. The body of the matrix is composed of composite erosional-impact ratings resulting from the product of the factor sets. Together the province map and problem matrix serve as practical tools for estimating the erosional impact of human activities on different types of terrain. The approach has been applied to the Molalla River basin, Oregon, and has proven useful for the recognition of problem areas. The same approach is currently being used by the State of Oregon (in the 208 assessment of nonpoint-source pollution under Public Law 92-500) to evaluate the impact of land-management practices on stream quality.

Extracellular matrix protein 1, a direct targeting molecule of parathyroid hormone–related peptide, negatively regulates chondrogenesis and endochondral ossification via associating with progranulin growth factor

PubMed Central

Kong, Li; Zhao, Yun-Peng; Tian, Qing-Yun; Feng, Jian-Quan; Kobayashi, Tatsuya; Merregaert, Joseph; Liu, Chuan-Ju

2016-01-01

Chondrogenesis and endochondral ossification are precisely controlled by cellular interactions with surrounding matrix proteins and growth factors that mediate cellular signaling pathways. Here, we report that extracellular matrix protein 1 (ECM1) is a previously unrecognized regulator of chondrogenesis. ECM1 is induced in the course of chondrogenesis and its expression in chondrocytes strictly depends on parathyroid hormone–related peptide (PTHrP) signaling pathway. Overexpression of ECM1 suppresses, whereas suppression of ECM1 enhances, chondrocyte differentiation and hypertrophy in vitro and ex vivo. In addition, target transgene of ECM1 in chondrocytes or osteoblasts in mice leads to striking defects in cartilage development and endochondral bone formation. Of importance, ECM1 seems to be critical for PTHrP action in chondrogenesis, as blockage of ECM1 nearly abolishes PTHrP regulation of chondrocyte hypertrophy, and overexpression of ECM1 rescues disorganized growth plates of PTHrP-null mice. Furthermore, ECM1 and progranulin chondrogenic growth factor constitute an interaction network and act in concert in the regulation of chondrogenesis.—Kong, L., Zhao, Y.-P., Tian, Q.-Y., Feng, J.-Q., Kobayashi, T., Merregaert, J., Liu, C.-J. Extracellular matrix protein 1, a direct targeting molecule of parathyroid hormone–related peptide, negatively regulates chondrogenesis and endochondral ossification via associating with progranulin growth factor. PMID:27075243

The Empirical Canadian High Arctic Ionospheric Model (E-CHAIM): NmF2 and hmF2 specification

NASA Astrophysics Data System (ADS)

Themens, David; Thayyil Jayachandran, P.

2017-04-01

It is well known that the International Reference Ionosphere (IRI) suffers reduced accuracy in its representation of monthly median ionospheric electron density at high latitudes (Themens et al. 2014, Themens et al. 2016). These inaccuracies are believed to stem from a historical lack of data from these regions. Now, roughly thirty and forty years after the development of the original URSI and CCIR foF2 maps, respectively, there exists a much larger dataset of high latitude observations of ionospheric electron density. These new measurements come in the form of new ionosonde deployments, such as those of the Canadian High Arctic Ionospheric Network, the CHAMP, GRACE, and COSMIC radio occultation missions, and the construction of the Poker Flat, Resolute, and EISCAT Incoherent Scatter Radar systems. These new datasets afford an opportunity to revise the IRI's representation of the high latitude ionosphere. For this purpose, we here introduce the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM), which incorporates all of the above datasets, as well as the older observation records, into a new climatological representation of the high latitude ionosphere. In this presentation, we introduce the NmF2 and hmF2 portions of the model, focusing on both climatological and storm-time representations, and present a validation of the new model with respect to ionosonde observations from four high latitude stations. A comparison with respect to IRI performance is also presented, where we see improvements by up to 70% in the representation of peak electron density through using the new E-CHAIM model. In terms of RMS errors, the E-CHAIM model is shown to represent a near-universal improvement over the IRI, sometimes by more than 1 MHz in foF2. For peak height, the E-CHAIM model demonstrates overall RMS errors of 13km at each test site compared to values of 18-35km for the IRI, depending on location. Themens, D.R., P. T. Jayachandran, et al. (2014). J. Geophys. Res. Space Physics, 119, 6689-6703, doi:10.1002/2014JA020052. Themens, D.R., and P.T. Jayachandran (2016). J. Geophys. Res. Space Physics, 121, doi:10.1002/2016JA022664.

Recognition of Risk Information - Adaptation of J. Bertin's Orderable Matrix for social communication

NASA Astrophysics Data System (ADS)

Ishida, Keiichi

2018-05-01

This paper aims to show capability of the Orderable Matrix of Jacques Bertin which is a visualization method of data analyze and/or a method to recognize data. That matrix can show the data by replacing numbers to visual element. As an example, using a set of data regarding natural hazard rankings for certain metropolitan cities in the world, this paper describes how the Orderable Matrix handles the data set and show characteristic factors of this data to understand it. Not only to see a kind of risk ranking of cities, the Orderable Matrix shows how differently danger concerned cities ones and others are. Furthermore, we will see that the visualized data by Orderable Matrix allows us to see the characteristics of the data set comprehensively and instantaneously.

Understanding the Evolution and Stability of the G-Matrix

PubMed Central

Arnold, Stevan J.; Bürger, Reinhard; Hohenlohe, Paul A.; Ajie, Beverley C.; Jones, Adam G.

2011-01-01

The G-matrix summarizes the inheritance of multiple, phenotypic traits. The stability and evolution of this matrix are important issues because they affect our ability to predict how the phenotypic traits evolve by selection and drift. Despite the centrality of these issues, comparative, experimental, and analytical approaches to understanding the stability and evolution of the G-matrix have met with limited success. Nevertheless, empirical studies often find that certain structural features of the matrix are remarkably constant, suggesting that persistent selection regimes or other factors promote stability. On the theoretical side, no one has been able to derive equations that would relate stability of the G-matrix to selection regimes, population size, migration, or to the details of genetic architecture. Recent simulation studies of evolving G-matrices offer solutions to some of these problems, as well as a deeper, synthetic understanding of both the G-matrix and adaptive radiations. PMID:18973631

Methods for Estimating Uncertainty in Factor Analytic Solutions

EPA Science Inventory

The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DI...

Research on the recycling industry development model for typical exterior plastic components of end-of-life passenger vehicle based on the SWOT method.

PubMed

Zhang, Hongshen; Chen, Ming

2013-11-01

In-depth studies on the recycling of typical automotive exterior plastic parts are significant and beneficial for environmental protection, energy conservation, and sustainable development of China. In the current study, several methods were used to analyze the recycling industry model for typical exterior parts of passenger vehicles in China. The strengths, weaknesses, opportunities, and challenges of the current recycling industry for typical exterior parts of passenger vehicles were analyzed comprehensively based on the SWOT method. The internal factor evaluation matrix and external factor evaluation matrix were used to evaluate the internal and external factors of the recycling industry. The recycling industry was found to respond well to all the factors and it was found to face good developing opportunities. Then, the cross-link strategies analysis for the typical exterior parts of the passenger car industry of China was conducted based on the SWOT analysis strategies and established SWOT matrix. Finally, based on the aforementioned research, the recycling industry model led by automobile manufacturers was promoted. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Hair growth effect of minoxidil].

PubMed

Otomo, Susumu

2002-03-01

The length and size of hair are depend on the anagen term in its hair cycle. It has been reported that the some cell growth factors, such as VEGF, FGF-5S, IGF-1 and KGF, induce the proliferation of cells in the matrix, dermal papilla and dermal papillary vascular system and increase the amount of extra cellular matrix in dermal papilla and then maintain follicles in the anagen phase. On the other hand, negative factors, like FGF-5, thrombospondin, or still unknown ones, terminate the anagen phase. If the negative factors become dominant against cell proliferation factors according to fulfilling some time set by the biological clock for hair follicles, TGF beta induced in the matrix tissues evokes apoptosis of matrix cells and shifts the follicles from anagen to catagen. Androgenetic alopecia is caused by miniaturizing of hair follicles located in the frontal or crown part of scalp and are hereditarily more sensitive to androgen. In their hair cycles, the androgen shortens the anagen phase of follicles and shifts them to the catagen phase earlier than usual. The mode of action of hair growth effect of minoxidil is not completely elucidated, but the most plausible explanation proposed here is that minoxidil works as a sulfonylurea receptor (SUR) activator and prolongs the anagen phase of hair follicles in the following manner: minoxidil (1) induces cell growth factors such as VEGF, HGF, IGF-1 and potentiates HGF and IGF-1 actions by the activation of uncoupled SUR on the plasma membrane of dermal papilla cells, (2) inhibits of TGF beta induced apoptosis of hair matrix cells by opening the Kir 6.0 channel pore coupled with SUR on the mitochondrial inner membrane, and (3) dilates hair follicle arteries and increases blood flow in dermal papilla by opening the Kir 6.0 channel pore coupled with SUR on the plasma membrane of vascular smooth muscle cells.

Two-way learning with one-way supervision for gene expression data.

PubMed

Wong, Monica H T; Mutch, David M; McNicholas, Paul D

2017-03-04

A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material. The simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively. Initial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

Different wound healing properties of dermis, adipose, and gingiva mesenchymal stromal cells.

PubMed

Boink, Mireille A; van den Broek, Lenie J; Roffel, Sanne; Nazmi, Kamran; Bolscher, Jan G M; Gefen, Amit; Veerman, Enno C I; Gibbs, Susan

2016-01-01

Oral wounds heal faster and with better scar quality than skin wounds. Deep skin wounds where adipose tissue is exposed, have a greater risk of forming hypertrophic scars. Differences in wound healing and final scar quality might be related to differences in mesenchymal stromal cells (MSC) and their ability to respond to intrinsic (autocrine) and extrinsic signals, such as human salivary histatin, epidermal growth factor, and transforming growth factor beta1. Dermis-, adipose-, and gingiva-derived MSC were compared for their regenerative potential with regards to proliferation, migration, and matrix contraction. Proliferation was assessed by cell counting and migration using a scratch wound assay. Matrix contraction and alpha smooth muscle actin was assessed in MSC populated collagen gels, and also in skin and gingival full thickness tissue engineered equivalents (reconstructed epithelium on MSC populated matrix). Compared to skin-derived MSC, gingiva MSC showed greater proliferation and migration capacity, and less matrix contraction in full thickness tissue equivalents, which may partly explain the superior oral wound healing. Epidermal keratinocytes were required for enhanced adipose MSC matrix contraction and alpha smooth muscle actin expression, and may therefore contribute to adverse scarring in deep cutaneous wounds. Histatin enhanced migration without influencing proliferation or matrix contraction in all three MSC, indicating that salivary peptides may have a beneficial effect on wound closure in general. Transforming growth factor beta1 enhanced contraction and alpha smooth muscle actin expression in all three MSC types when incorporated into collagen gels. Understanding the mechanisms responsible for the superior oral wound healing will aid us to develop advanced strategies for optimal skin regeneration, wound healing and scar formation. © 2015 by the Wound Healing Society.

Multi-site Field Verification of Laboratory Derived FDOM Sensor Corrections: The Good, the Bad and the Ugly

NASA Astrophysics Data System (ADS)

Saraceno, J.; Shanley, J. B.; Aulenbach, B. T.

2014-12-01

Fluorescent dissolved organic matter (FDOM) is an excellent proxy for dissolved organic carbon (DOC) in natural waters. Through this relationship, in situ FDOM can be utilized to capture both high frequency time series and long term fluxes of DOC in small streams. However, in order to calculate accurate DOC fluxes for comparison across sites, in situ FDOM data must be compensated for matrix effects. Key matrix effects, include temperature, turbidity and the inner filter effect due to color. These interferences must be compensated for to develop a reasonable relationship between FDOM and DOC. In this study, we applied laboratory-derived correction factors to real time data from the five USGS WEBB headwater streams in order to gauge their effectiveness across a range of matrix effects. The good news is that laboratory derived correction factors improved the predicative relationship (higher r2) between DOC and FDOM when compared to uncorrected data. The relative importance of each matrix effect (i.e. temperature) varied by site and by time, implying that each and every matrix effect should be compensated for when available. In general, temperature effects were more important on longer time scales, while corrections for turbidity and DOC inner filter effects were most prevalent during hydrologic events, when the highest instantaneous flux of DOC occurred. Unfortunately, even when corrected for matrix effects, in situ FDOM is a weaker predictor of DOC than A254, a common surrogate for DOC, implying that either DOC fluoresces at varying degrees (but should average out over time), that some matrix effects (e.g. pH) are either unaccounted for or laboratory-derived correction factors do not encompass the site variability of particles and organics. The least impressive finding is that the inherent dependence on three variables in the FDOM correction algorithm increases the likelihood of record data gaps which increases the uncertainty in calculated DOC flux values.

Cell and matrix modulation in prenatal and postnatal equine growth cartilage, zones of Ranvier and articular cartilage

PubMed Central

Löfgren, Maria; Ekman, Stina; Svala, Emilia; Lindahl, Anders; Ley, Cecilia; Skiöldebrand, Eva

2014-01-01

Formation of synovial joints includes phenotypic changes of the chondrocytes and the organisation of their extracellular matrix is regulated by different factors and signalling pathways. Increased knowledge of the normal processes involved in joint development may be used to identify similar regulatory mechanisms during pathological conditions in the joint. Samples of the distal radius were collected from prenatal and postnatal equine growth plates, zones of Ranvier and articular cartilage with the aim of identifying Notch signalling components and cells with stem cell-like characteristics and to follow changes in matrix protein localisation during joint development. The localisation of the Notch signalling components Notch1, Delta4, Hes1, Notch dysregulating protein epidermal growth factor-like domain 7 (EGFL7), the stem cell-indicating factor Stro-1 and the matrix molecules cartilage oligomeric matrix protein (COMP), fibromodulin, matrilin-1 and chondroadherin were studied using immunohistochemistry. Spatial changes in protein localisations during cartilage maturation were observed for Notch signalling components and matrix molecules, with increased pericellular localisation indicating new synthesis and involvement of these proteins in the formation of the joint. However, it was not possible to characterise the phenotype of the chondrocytes based on their surrounding matrix during normal chondrogenesis. The zone of Ranvier was identified in all horses and characterised as an area expressing Stro-1, EGFL7 and chondroadherin with an absence of COMP and Notch signalling. Stro-1 was also present in cells close to the perichondrium, in the articular cartilage and in the fetal resting zone, indicating stem cell-like characteristics of these cells. The presence of stem cells in the articular cartilage will be of importance for the repair of damaged cartilage. Perivascular chondrocytes and hypertrophic cells of the cartilage bone interface displayed positive staining for EGFL7, which is a novel finding and suggests a role of EGFL7 in the vascular infiltration of growth cartilage. PMID:25175365

Source apportionment of stack emissions from research and development facilities using positive matrix factorization

NASA Astrophysics Data System (ADS)

Ballinger, Marcel Y.; Larson, Timothy V.

2014-12-01

Research and development (R&D) facility emissions are difficult to characterize due to their variable processes, changing nature of research, and large number of chemicals. Positive matrix factorization (PMF) was applied to volatile organic compound (VOC) concentrations measured in the main exhaust stacks of four different R&D buildings to identify the number and composition of major contributing sources. PMF identified between 9 and 11 source-related factors contributing to stack emissions, depending on the building. Similar factors between buildings were major contributors to trichloroethylene (TCE), acetone, and ethanol emissions; other factors had similar profiles for two or more buildings but not all four. At least one factor for each building was identified that contained a broad mix of many species and constraints were used in PMF to modify the factors to resemble more closely the off-shift concentration profiles. PMF accepted the constraints with little decrease in model fit.

Using Strassen's algorithm to accelerate the solution of linear systems

NASA Technical Reports Server (NTRS)

Bailey, David H.; Lee, King; Simon, Horst D.

1990-01-01

Strassen's algorithm for fast matrix-matrix multiplication has been implemented for matrices of arbitrary shapes on the CRAY-2 and CRAY Y-MP supercomputers. Several techniques have been used to reduce the scratch space requirement for this algorithm while simultaneously preserving a high level of performance. When the resulting Strassen-based matrix multiply routine is combined with some routines from the new LAPACK library, LU decomposition can be performed with rates significantly higher than those achieved by conventional means. We succeeded in factoring a 2048 x 2048 matrix on the CRAY Y-MP at a rate equivalent to 325 MFLOPS.

Quantum Double of Yangian of strange Lie superalgebra Qn and multiplicative formula for universal R-matrix

NASA Astrophysics Data System (ADS)

Stukopin, Vladimir

2018-02-01

Main result is the multiplicative formula for universal R-matrix for Quantum Double of Yangian of strange Lie superalgebra Qn type. We introduce the Quantum Double of the Yangian of the strange Lie superalgebra Qn and define its PBW basis. We compute the Hopf pairing for the generators of the Yangian Double. From the Hopf pairing formulas we derive a factorized multiplicative formula for the universal R-matrix of the Yangian Double of the Lie superalgebra Qn . After them we obtain coefficients in this multiplicative formula for universal R-matrix.

Relational Learning via Collective Matrix Factorization

DTIC Science & Technology

2008-06-01

well-known example of such a schema is pLSI- pHITS [13], which models document-word counts and document-document citations: E1 = words and E2 = E3...relational co- clustering include pLSI, pLSI- pHITS , the symmetric block models of Long et. al. [23, 24, 25], and Bregman tensor clustering [5] (which can...to pLSI- pHITS In this section we provide an example where the additional flexibility of collective matrix factorization leads to better results; and

Identification of candidate angiogenic inhibitors processed by matrix metalloproteinase 2 (MMP-2) in cell-based proteomic screens: disruption of vascular endothelial growth factor (VEGF)/heparin affin regulatory peptide (pleiotrophin) and VEGF/Connective tissue growth factor angiogenic inhibitory complexes by MMP-2 proteolysis.

PubMed

Dean, Richard A; Butler, Georgina S; Hamma-Kourbali, Yamina; Delbé, Jean; Brigstock, David R; Courty, José; Overall, Christopher M

2007-12-01

Matrix metalloproteinases (MMPs) exert both pro- and antiangiogenic functions by the release of cytokines or proteolytically generated angiogenic inhibitors from extracellular matrix and basement membrane remodeling. In the Mmp2-/- mouse neovascularization is greatly reduced, but the mechanistic aspects of this remain unclear. Using isotope-coded affinity tag labeling of proteins analyzed by multidimensional liquid chromatography and tandem mass spectrometry we explored proteome differences between Mmp2-/- cells and those rescued by MMP-2 transfection. Proteome signatures that are hallmarks of proteolysis revealed cleavage of many known MMP-2 substrates in the cellular context. Proteomic evidence of MMP-2 processing of novel substrates was found. Insulin-like growth factor binding protein 6, follistatin-like 1, and cystatin C protein cleavage by MMP-2 was biochemically confirmed, and the cleavage sites in heparin affin regulatory peptide (HARP; pleiotrophin) and connective tissue growth factor (CTGF) were sequenced by matrix-assisted laser desorption ionization-time of flight mass spectrometry. MMP-2 processing of HARP and CTGF released vascular endothelial growth factor (VEGF) from angiogenic inhibitory complexes. The cleaved HARP N-terminal domain increased HARP-induced cell proliferation, whereas the HARP C-terminal domain was antagonistic and decreased cell proliferation and migration. Hence the unmasking of cytokines, such as VEGF, by metalloproteinase processing of their binding proteins is a new mechanism in the control of cytokine activation and angiogenesis.

Identification of Candidate Angiogenic Inhibitors Processed by Matrix Metalloproteinase 2 (MMP-2) in Cell-Based Proteomic Screens: Disruption of Vascular Endothelial Growth Factor (VEGF)/Heparin Affin Regulatory Peptide (Pleiotrophin) and VEGF/Connective Tissue Growth Factor Angiogenic Inhibitory Complexes by MMP-2 Proteolysis▿ †

PubMed Central

Dean, Richard A.; Butler, Georgina S.; Hamma-Kourbali, Yamina; Delbé, Jean; Brigstock, David R.; Courty, José; Overall, Christopher M.

2007-01-01

Matrix metalloproteinases (MMPs) exert both pro- and antiangiogenic functions by the release of cytokines or proteolytically generated angiogenic inhibitors from extracellular matrix and basement membrane remodeling. In the Mmp2−/− mouse neovascularization is greatly reduced, but the mechanistic aspects of this remain unclear. Using isotope-coded affinity tag labeling of proteins analyzed by multidimensional liquid chromatography and tandem mass spectrometry we explored proteome differences between Mmp2−/− cells and those rescued by MMP-2 transfection. Proteome signatures that are hallmarks of proteolysis revealed cleavage of many known MMP-2 substrates in the cellular context. Proteomic evidence of MMP-2 processing of novel substrates was found. Insulin-like growth factor binding protein 6, follistatin-like 1, and cystatin C protein cleavage by MMP-2 was biochemically confirmed, and the cleavage sites in heparin affin regulatory peptide (HARP; pleiotrophin) and connective tissue growth factor (CTGF) were sequenced by matrix-assisted laser desorption ionization-time of flight mass spectrometry. MMP-2 processing of HARP and CTGF released vascular endothelial growth factor (VEGF) from angiogenic inhibitory complexes. The cleaved HARP N-terminal domain increased HARP-induced cell proliferation, whereas the HARP C-terminal domain was antagonistic and decreased cell proliferation and migration. Hence the unmasking of cytokines, such as VEGF, by metalloproteinase processing of their binding proteins is a new mechanism in the control of cytokine activation and angiogenesis. PMID:17908800

University Organization. A Matrix Analysis of the Academic Professions.

ERIC Educational Resources Information Center

Bess, James L.

Using the latest research instruments, including questionnaires, interviews, factor analysis, and matrix construction, the present restraints on professorial effectiveness and the contributions of departmental and university structures to professorial malaise is examined for the purpose of improving ways that administrators can increase faculty…

Development and Validation of a Job Exposure Matrix for Physical Risk Factors in Low Back Pain

PubMed Central

Solovieva, Svetlana; Pehkonen, Irmeli; Kausto, Johanna; Miranda, Helena; Shiri, Rahman; Kauppinen, Timo; Heliövaara, Markku; Burdorf, Alex; Husgafvel-Pursiainen, Kirsti; Viikari-Juntura, Eira

2012-01-01

Objectives The aim was to construct and validate a gender-specific job exposure matrix (JEM) for physical exposures to be used in epidemiological studies of low back pain (LBP). Materials and Methods We utilized two large Finnish population surveys, one to construct the JEM and another to test matrix validity. The exposure axis of the matrix included exposures relevant to LBP (heavy physical work, heavy lifting, awkward trunk posture and whole body vibration) and exposures that increase the biomechanical load on the low back (arm elevation) or those that in combination with other known risk factors could be related to LBP (kneeling or squatting). Job titles with similar work tasks and exposures were grouped. Exposure information was based on face-to-face interviews. Validity of the matrix was explored by comparing the JEM (group-based) binary measures with individual-based measures. The predictive validity of the matrix against LBP was evaluated by comparing the associations of the group-based (JEM) exposures with those of individual-based exposures. Results The matrix includes 348 job titles, representing 81% of all Finnish job titles in the early 2000s. The specificity of the constructed matrix was good, especially in women. The validity measured with kappa-statistic ranged from good to poor, being fair for most exposures. In men, all group-based (JEM) exposures were statistically significantly associated with one-month prevalence of LBP. In women, four out of six group-based exposures showed an association with LBP. Conclusions The gender-specific JEM for physical exposures showed relatively high specificity without compromising sensitivity. The matrix can therefore be considered as a valid instrument for exposure assessment in large-scale epidemiological studies, when more precise but more labour-intensive methods are not feasible. Although the matrix was based on Finnish data we foresee that it could be applicable, with some modifications, in other countries with a similar level of technology. PMID:23152793

Exploratory Bi-factor Analysis: The Oblique Case.

PubMed

Jennrich, Robert I; Bentler, Peter M

2012-07-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (Psychometrika 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (Psychometrika 76:537-549, 2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bifactor rotation criterion designed to produce a rotated loading matrix that has an approximate bi-factor structure. Among other things this can be used as an aid in finding an explicit bi-factor structure for use in a confirmatory bi-factor analysis. They considered only orthogonal rotation. The purpose of this paper is to consider oblique rotation and to compare it to orthogonal rotation. Because there are many more oblique rotations of an initial loading matrix than orthogonal rotations, one expects the oblique results to approximate a bi-factor structure better than orthogonal rotations and this is indeed the case. A surprising result arises when oblique bi-factor rotation methods are applied to ideal data.

Cyclic stretching of soft substrates induces spreading and growth

PubMed Central

Cui, Yidan; Hameed, Feroz M.; Yang, Bo; Lee, Kyunghee; Pan, Catherine Qiurong; Park, Sungsu; Sheetz, Michael

2015-01-01

In the body, soft tissues often undergo cycles of stretching and relaxation that may affect cell behaviour without changing matrix rigidity. To determine whether transient forces can substitute for a rigid matrix, we stretched soft pillar arrays. Surprisingly, 1–5% cyclic stretching over a frequency range of 0.01–10 Hz caused spreading and stress fibre formation (optimum 0.1 Hz) that persisted after 4 h of stretching. Similarly, stretching increased cell growth rates on soft pillars comparative to rigid substrates. Of possible factors linked to fibroblast growth, MRTF-A (myocardin-related transcription factor-A) moved to the nucleus in 2 h of cyclic stretching and reversed on cessation; but YAP (Yes-associated protein) moved much later. Knockdown of either MRTF-A or YAP blocked stretch-dependent growth. Thus, we suggest that the repeated pulling from a soft matrix can substitute for a stiff matrix in stimulating spreading, stress fibre formation and growth. PMID:25704457

Hardware Implementation of a MIMO Decoder Using Matrix Factorization Based Channel Estimation

NASA Astrophysics Data System (ADS)

Islam, Mohammad Tariqul; Numan, Mostafa Wasiuddin; Misran, Norbahiah; Ali, Mohd Alauddin Mohd; Singh, Mandeep

2011-05-01

This paper presents an efficient hardware realization of multiple-input multiple-output (MIMO) wireless communication decoder that utilizes the available resources by adopting the technique of parallelism. The hardware is designed and implemented on Xilinx Virtex™-4 XC4VLX60 field programmable gate arrays (FPGA) device in a modular approach which simplifies and eases hardware update, and facilitates testing of the various modules independently. The decoder involves a proficient channel estimation module that employs matrix factorization on least squares (LS) estimation to reduce a full rank matrix into a simpler form in order to eliminate matrix inversion. This results in performance improvement and complexity reduction of the MIMO system. Performance evaluation of the proposed method is validated through MATLAB simulations which indicate 2 dB improvement in terms of SNR compared to LS estimation. Moreover complexity comparison is performed in terms of mathematical operations, which shows that the proposed approach appreciably outperforms LS estimation at a lower complexity and represents a good solution for channel estimation technique.

Matrix Metalloproteinases in Non-Neoplastic Disorders

PubMed Central

Tokito, Akinori; Jougasaki, Michihisa

2016-01-01

The matrix metalloproteinases (MMPs) are zinc-dependent endopeptidases belonging to the metzincin superfamily. There are at least 23 members of MMPs ever reported in human, and they and their substrates are widely expressed in many tissues. Recent growing evidence has established that MMP not only can degrade a variety of components of extracellular matrix, but also can cleave and activate various non-matrix proteins, including cytokines, chemokines and growth factors, contributing to both physiological and pathological processes. In normal conditions, MMP expression and activity are tightly regulated via interactions between their activators and inhibitors. Imbalance among these factors, however, results in dysregulated MMP activity, which causes tissue destruction and functional alteration or local inflammation, leading to the development of diverse diseases, such as cardiovascular disease, arthritis, neurodegenerative disease, as well as cancer. This article focuses on the accumulated evidence supporting a wide range of roles of MMPs in various non-neoplastic diseases and provides an outlook on the therapeutic potential of inhibiting MMP action. PMID:27455234

Constrained Least Squares Estimators of Oblique Common Factors.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1981-01-01

An expression is given for weighted least squares estimators of oblique common factors of factor analyses, constrained to have the same covariance matrix as the factors they estimate. A proof of the uniqueness of the solution is given. (Author/JKS)

High aspect ratio template and method for producing same for central and peripheral nerve repair

NASA Technical Reports Server (NTRS)

Sakamoto, Jeff S. (Inventor); Chan, Christina (Inventor); Tuszynski, Mark Henry (Inventor); Mehrotra, Sumit (Inventor); Gros, Thomas (Inventor)

2011-01-01

Millimeter to nano-scale structures manufactured using a multi-component polymer fiber matrix are disclosed. The use of dissimilar polymers allows the selective dissolution of the polymers at various stages of the manufacturing process. In one application, biocompatible matrixes may be formed with long pore length and small pore size. The manufacturing process begins with a first polymer fiber arranged in a matrix formed by a second polymer fiber. End caps may be attached to provide structural support and the polymer fiber matrix selectively dissolved away leaving only the long polymer fibers. These may be exposed to another product, such as a biocompatible gel to form a biocompatible matrix. The polymer fibers may then be selectively dissolved leaving only a biocompatible gel scaffold with the pores formed by the dissolved polymer fibers. The scaffolds may be used in, among other applications, the repair of central and peripheral nerves. Scaffolds for the repair of peripheral nerves may include a reservoir for the sustained release of nerve growth factor. The scaffolds may also include a multifunctional polyelectrolyte layer for the sustained release of nerve growth factor and enhance biocompatibility.

Thermal expansion of composites: Methods and results. [large space structures

NASA Technical Reports Server (NTRS)

Bowles, D. E.; Tenney, D. R.

1981-01-01

The factors controlling the dimensional stability of various components of large space structures were investigated. Cyclic, thermal and mechanical loading were identified as the primary controlling factors of the dimensional stability of cables. For organic matrix composites, such as graphite-epoxy, it was found that these factors include moisture desorption in the space environment, thermal expansion as the structure moves from the sunlight to shadow in its orbit, mechanical loading, and microyielding of the material caused by microcracking of the matrix material. The major focus was placed on the thermal expansion of composites and in particular the development and testing of a method for its measurement.

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states.

PubMed

Kohn, Lucas; Tschirsich, Ferdinand; Keck, Maximilian; Plenio, Martin B; Tamascelli, Dario; Montangero, Simone

2018-01-01

We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond or local dimension of up to 24 times in quasi-two-dimensional cylindrical systems.

Factor Analytic Approach to Transitive Text Mining using Medline Descriptors

NASA Astrophysics Data System (ADS)

Stegmann, J.; Grohmann, G.

Matrix decomposition methods were applied to examples of noninteractive literature sets sharing implicit relations. Document-by-term matrices were created from downloaded PubMed literature sets, the terms being the Medical Subject Headings (MeSH descriptors) assigned to the documents. The loadings of the factors derived from singular value or eigenvalue matrix decomposition were sorted according to absolute values and subsequently inspected for positions of terms relevant to the discovery of hidden connections. It was found that only a small number of factors had to be screened to find key terms in close neighbourhood, being separated by a small number of terms only.

Probabilistic low-rank factorization accelerates tensor network simulations of critical quantum many-body ground states

NASA Astrophysics Data System (ADS)

Kohn, Lucas; Tschirsich, Ferdinand; Keck, Maximilian; Plenio, Martin B.; Tamascelli, Dario; Montangero, Simone

2018-01-01

We provide evidence that randomized low-rank factorization is a powerful tool for the determination of the ground-state properties of low-dimensional lattice Hamiltonians through tensor network techniques. In particular, we show that randomized matrix factorization outperforms truncated singular value decomposition based on state-of-the-art deterministic routines in time-evolving block decimation (TEBD)- and density matrix renormalization group (DMRG)-style simulations, even when the system under study gets close to a phase transition: We report linear speedups in the bond or local dimension of up to 24 times in quasi-two-dimensional cylindrical systems.

Markov model of the loan portfolio dynamics considering influence of management and external economic factors

NASA Astrophysics Data System (ADS)

Bozhalkina, Yana; Timofeeva, Galina

2016-12-01

Mathematical model of loan portfolio in the form of a controlled Markov chain with discrete time is considered. It is assumed that coefficients of migration matrix depend on corrective actions and external factors. Corrective actions include process of receiving applications, interaction with existing solvent and insolvent clients. External factors are macroeconomic indicators, such as inflation and unemployment rates, exchange rates, consumer price indices, etc. Changes in corrective actions adjust the intensity of transitions in the migration matrix. The mathematical model for forecasting the credit portfolio structure taking into account a cumulative impact of internal and external changes is obtained.

Quantum kinetic expansion in the spin-boson model: Matrix formulation and system-bath factorized initial state.

PubMed

Gong, Zhihao; Tang, Zhoufei; Wang, Haobin; Wu, Jianlan

2017-12-28

Within the framework of the hierarchy equation of motion (HEOM), the quantum kinetic expansion (QKE) method of the spin-boson model is reformulated in the matrix representation. The equivalence between the two formulations (HEOM matrices and quantum operators) is numerically verified from the calculation of the time-integrated QKE rates. The matrix formulation of the QKE is extended to the system-bath factorized initial state. Following a one-to-one mapping between HEOM matrices and quantum operators, a quantum kinetic equation is rederived. The rate kernel is modified by an extra term following a systematic expansion over the site-site coupling. This modified QKE is numerically tested for its reliability by calculating the time-integrated rate and non-Markovian population kinetics. For an intermediate-to-strong dissipation strength and a large site-site coupling, the population transfer is found to be significantly different when the initial condition is changed from the local equilibrium to system-bath factorized state.

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

PubMed Central

Jaeger, Sébastien; Thieffry, Denis

2017-01-01

Abstract Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines. PMID:28591841