On the construction of general solution of the generalized sylvester equation

The problem of construction the general solution of the generalized matrix Sylvester equation is considered. Conditions of existence of solution of this equation are obtained and the algorithm for construction of this solution is given. For construction of the algorithm of this solution and the formulation of the condition of existence of this solution, the standard procedures of MATLAB package are used.Publisher's Versio

Identifying Character Non-Independence in Phylogenetic Data using Data Mining Techniques

Undiscovered relationships in a data set may confound analyses, particularly those that assume data independence. Such problems occur when characters used for phylogenetic analyses are not independent of one another. A main assumption of phylogenetic inference methods such as maximum likelihood and parsimony is that each character serves as an independent hypothesis of evolution. When this assumption is violated, the resulting phylogeny may not reflect true evolutionary history. Therefore, it is imperative that character non-independence be identified prior to phylogenetic analyses. To identify dependencies between phylogenetic characters, we applied three data mining techniques: 1) Bayesian networks, 2) decision tree induction, and 3) rule induction from coverings. We briefly discuss the main ideas behind each strategy, show how each technique performs on a small sample data set, and apply each method to an existing phylogenetic data set. We discuss the interestingness of the results of each method, and show that, although each method has its own strengths and weaknesses, rule induction from coverings presents the most useful solution for determining dependencies among phylogenetic data at this time

Climate Change and Sea Ice: Local Observations from the Canadian Western Arctic

Can local observations and indigenous knowledge be used to provide information that complements research on climate change? Using participatory research methodology and semi-directed interviews, we explored local and traditional knowledge about changes in sea ice in the area of Sachs Harbour, Northwest Territories. In this small Inuvialuit community, we interviewed all of the 16 community members and elders considered to be local experts on sea ice to ask about their observations. We organized their comments under the headings multiyear ice, first-year ice, fractures and pressure ridges, breakup and freeze-up seasons, and other climate-related variables that influence sea ice (such as changes in winter, spring and summer temperatures, wind, rain, and thunderstorms). Observations were remarkably consistent in providing evidence of local change in such variables as multiyear ice distribution, first-year ice thickness, and ice breakup dates. The changes observed in the 1990s were said to be without precedent and outside the normal range of variation. In assessing the relevance of Inuvialuit knowledge to scientific research on climate change, we note some of the areas in which sharing of information between the two systems of knowledge may be mutually beneficial. These include the analysis of options for adapting to climate change and the generation of research questions and hypotheses for future studies.Est-ce que les observations locales et le savoir des Autochtones peuvent aider à fournir de l'information complétant la recherche sur le changement climatique? En faisant appel à une méthodologie de recherche participative et des entrevues semi-dirigées, on a examiné le savoir local et traditionnel concernant les changements de la banquise dans la région de Sachs Harbour (Territoires du Nord-Ouest). Dans cette petite communauté inuvialuite, on a interviewé les 16 membres et aînés de la communauté considérés comme des experts locaux de la banquise pour les interroger sur leurs observations. On a organisé leurs commentaires sous les rubriques suivantes: glace pluriannuelle, glace de l'année, crêtes de fractures et de pression, saisons de débâcle et d'engel, ainsi que d'autres variables reliées au climat qui influencent la banquise (comme les changements dans les températures hivernale, printanière et estivale, le vent, la pluie et les orages). Il y avait une concordance frappante dans les observations quant aux preuves de changements à l'échelle locale dans des variables comme la distribution de la glace pluriannuelle, l'épaisseur de la glace de l'année et les dates de la débâcle. Les changements observés au cours des années 1990 étaient, selon les Autochtones, sans précédent et ils dépassaient la gamme normale des variations. En évaluant la pertinence du savoir des Inuvialuits pour la recherche scientifique sur le changement climatique, on souligne certains des domaines dans lesquels le partage de l'information entre les deux systèmes de savoir pourrait être mutuellement profitable. Ces domaines comprennent l'analyse des options visant l'adaptation au changement climatique et la formulation de questions et hypothèses de recherche pour des études ultérieures

An Automated Method for Rapid Identification of Putative Gene Family Members in Plants

BACKGROUND: Gene duplication events have played a significant role in genome evolution, particularly in plants. Exhaustive searches for all members of a known gene family as well as the identification of new gene families has become increasingly important. Subfunctionalization via changes in regulatory sequences following duplication (adaptive selection) appears to be a common mechanism of evolution in plants and can be accompanied by purifying selection on the coding region. Such negative selection can be detected by a bias toward synonymous over nonsynonymous substitutions. However, the process of identifying this bias requires many steps usually employing several different software programs. We have simplified the process and significantly shortened the time required by condensing many steps into a few scripts or programs to rapidly identify putative gene family members beginning with a single query sequence. RESULTS: In this report we 1) describe the software tools (SimESTs, PCAT, and SCAT) developed to automate the gene family identification, 2) demonstrate the validity of the method by correctly identifying 3 of 4 PAL gene family members from Arabidopsis using EST data alone, 3) identify 2 to 6 CAD gene family members from Glycine max (previously unidentified), and 4) identify 2 members of a putative Glycine max gene family previously unidentified in any plant species. CONCLUSION: Gene families in plants, particularly that subset where purifying selection has occurred in the coding region, can be identified quickly and easily by integrating our software tools and commonly available contig assembly and ORF identification programs

Cutaneous Papilloma and Squamous Cell Carcinoma Therapy Utilizing Nanosecond Pulsed Electric Fields (nsPEF)

Nanosecond pulsed electric fields (nsPEF) induce apoptotic pathways in human cancer cells. The potential therapeutic effective of nsPEF has been reported in cell lines and in xenograft animal tumor model. The present study investigated the ability of nsPEF to cause cancer cell death in vivo using carcinogen-induced animal tumor model, and the pulse duration of nsPEF was only 7 and 14 nano second (ns). An nsPEF generator as a prototype medical device was used in our studies, which is capable of delivering 7-30 nanosecond pulses at various programmable amplitudes and frequencies. Seven cutaneous squamous cell carcinoma cell lines and five other types of cancer cell lines were used to detect the effect of nsPEF in vitro. Rate of cell death in these 12 different cancer cell lines was dependent on nsPEF voltage and pulse number. To examine the effect of nsPEF in vivo, carcinogen-induced cutaneous papillomas and squamous cell carcinomas in mice were exposed to nsPEF with three pulse numbers (50, 200, and 400 pulses), two nominal electric fields (40 KV/cm and 31 KV/cm), and two pulse durations (7 ns and 14 ns). Carcinogen-induced cutaneous papillomas and squamous carcinomas were eliminated efficiently using one treatment of nsPEF with 14 ns duration pulses (33/39 = 85%), and all remaining lesions were eliminated after a 2nd treatment (6/39 = 15%). 13.5% of carcinogen-induced tumors (5 of 37) were eliminated using 7 ns duration pulses after one treatment of nsPEF. Associated with tumor lysis, expression of the anti-apoptotic proteins Bcl-xl and Bcl-2 were markedly reduced and apoptosis increased (TUNEL assay) after nsPEF treatment. nsPEF efficiently causes cell death in vitro and removes papillomas and squamous cell carcinoma in vivo from skin of mice. nsPEF has the therapeutic potential to remove human squamous carcinoma

Intelligent data analysis to interpret major risk factors for diabetic patients with and without ischemic stroke in a small population

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention

A framework for automated enrichment of functionally significant inverted repeats in whole genomes

<p>Abstract</p> <p>Background</p> <p>RNA transcripts from genomic sequences showing dyad symmetry typically adopt hairpin-like, cloverleaf, or similar structures that act as recognition sites for proteins. Such structures often are the precursors of non-coding RNA (ncRNA) sequences like microRNA (miRNA) and small-interfering RNA (siRNA) that have recently garnered more functional significance than in the past. Genomic DNA contains hundreds of thousands of such inverted repeats (IRs) with varying degrees of symmetry. But by collecting statistically significant information from a known set of ncRNA, we can sort these IRs into those that are likely to be functional.</p> <p>Results</p> <p>A novel method was developed to scan genomic DNA for partially symmetric inverted repeats and the resulting set was further refined to match miRNA precursors (pre-miRNA) with respect to their density of symmetry, statistical probability of the symmetry, length of stems in the predicted hairpin secondary structure, and the GC content of the stems. This method was applied on the <it>Arabidopsis thaliana</it> genome and validated against the set of 190 known Arabidopsis pre-miRNA in the miRBase database. A preliminary scan for IRs identified 186 of the known pre-miRNA but with 714700 pre-miRNA candidates. This large number of IRs was further refined to 483908 candidates with 183 pre-miRNA identified and further still to 165371 candidates with 171 pre-miRNA identified (i.e. with 90% of the known pre-miRNA retained).</p> <p>Conclusions</p> <p>165371 candidates for potentially functional miRNA is still too large a set to warrant wet lab analyses, such as northern blotting, on all of them. Hence additional filters are needed to further refine the number of candidates while still retaining most of the known miRNA. These include detection of promoters and terminators, homology analyses, location of candidate relative to coding regions, and better secondary structure prediction algorithms. The software developed is designed to easily accommodate such additional filters with a minimal experience in Perl.</p

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions