Isolation and screening of euryhaline <Emphasis Type="Italic">Tetraselmis</Emphasis> spp. suitable for large-scale outdoor culture in hypersaline media for biofuels

Natural saline lakes in Western Australia were sampled for microalgae species and strains with potential for large-scale outdoor cultivation over a wide range of salinities for biofuels production. Using a rational isolation and screening process, several Tetraselmis strains (Chlorophyta, Chlorodendrales) with a broad range of salinity tolerance were identified and were characterised further for their potential for biofuels production. Specific growth rates increased from 0.8 to 1.2 days^?1 when the medium salinity was decreased from 11 to 3 % (w/v) NaCl (1.88 to 0.51 M NaCl) in batch cultivation mode, thereby indicating quick adaptation to large salinity changes. In general, ash-free dry weight (AFDW), total lipid, protein and carbohydrate contents per cell were highest in the early stages of growth. Salinity increases led to an increase in cell AFDW, with the highest mean maximum of 2555?±?659 pg AFDW.cell^?1 at 11 % (w/v) NaCl in the strains Tetraselmis MUR 167 and MUR 219 which had been in culture for many years, as compared to the mean maximum of 981?±?141 pg AFDW.cell^?1 the in newly isolated strains MUR 230, 231, 232 and 233. Similar observations on total lipid, protein and carbohydrate content per cell were made between the two groups of strains. Overall, all strains yielded high biomass and total lipid productivities over a very wide range of salinities without large variation in their gross biochemical composition and growth pattern. Based on AFDW and total lipid productivity data, the order of preference for selecting strains for further investigation for large-scale culture was MUR 231?>?MUR 233?>?MUR 219?>?MUR 230?>?MUR 232?>?MUR 167. The Tetraselmis spp. were also very competitive as shown by the outdoor cultivation of diatom, Halamphora coffeaeformis MUR 158, in parallel with Tetraselmis sp. MUR 167 which resulted in the diatom being outcompeted by the green alga. Our results demonstrate the high commercial potential of euryhaline Tetraselmis spp. for cultivation over a broad range of salinity in outdoor cultures.     

Analyzing complicated protein folding kinetics rapidly by analytical Laplace inversion using a Tikhonov regularization variant

Kinetic experiments provide much information about protein folding mechanisms. Time-resolved signals are often best described by expressions with many exponential terms, but this hinders the extraction of rate constants by nonlinear least squares (NLS) fitting. Numerical inverse Laplace transformation, which converts a time-resolved dataset into a spectrum of amplitudes as a function of rate constant, allows easy estimation of the rate constants, amplitudes, and number of processes underlying the data. Here, we present a Tikhonov regularization-based method that converts a dataset into a rate spectrum, subject to regularization constraints, without requiring an iterative search of parameter space. This allows more rapid generation of rate spectra as well as analysis of datasets too noisy to process by existing iterative search algorithms. This method's simplicity also permits highly objective, largely automatic analysis with minimal human guidance. We show that this regularization method reproduces results previously obtained by NLS fitting and that it is effective for analyzing datasets too complex for traditional fitting methods. This method's reliability and speed, as well as its potential for objective, model-free analysis, make it extremely useful as a first step in analysis of complicated noisy datasets and an excellent guide for subsequent NLS analysis.     

Efficient computation of Hessian-based enhancement filters for tubular structures in 3D images

This work presents guidelines for a computationally efficient implementation of multiscale image filters based on eigenanalysis of the Hessian matrix, for the enhancement of tubular structures. Our focus is the application to 3D medical images of blood vessels. The method uses matrix trace, determinant and sign to discard voxels unlikely to belong to vessels, prior to the calculation of the Hessian eigenvalues. As example of time savings, we provide results obtained in four computed tomography datasets (300 × 300 × 300 voxels) containing coronary and pulmonary arteries. The test based on the Hessian trace avoided the computation of the eigenvalues in half of the voxels on average, while the test combining the Hessian determinant and sign eliminated up to 10% additional voxels. The actual time savings depend on the algorithm used to compute the eigenvalues for the remaining voxels. With a very fast algorithm using a closed-form solution, the computational time was reduced from 20.5 to 12.5 seconds per scale, but the time gained thanks to the more complex of the two tests was negligible. However, this fast algorithm is prone to numerical instabilities. Accurate computation of the eigenvalues requires the use of iterative or hybrid algorithms. In this case, both tests produce time savings and the computational time can be reduced by several minutes per scale.     

Optimization of Single-Base-Pair Mismatch Discrimination in Oligonucleotide Microarrays

The discrimination between perfect-match and single-base-pair-mismatched nucleic acid duplexes was investigated by using oligonucleotide DNA microarrays and nonequilibrium dissociation rates (melting profiles). DNA and RNA versions of two synthetic targets corresponding to the 16S rRNA sequences of Staphylococcus epidermidis (38 nucleotides) and Nitrosomonas eutropha (39 nucleotides) were hybridized to perfect-match probes (18-mer and 19-mer) and to a set of probes having all possible single-base-pair mismatches. The melting profiles of all probe-target duplexes were determined in parallel by using an imposed temperature step gradient. We derived an optimum wash temperature for each probe and target by using a simple formula to calculate a discrimination index for each temperature of the step gradient. This optimum corresponded to the output of an independent analysis using a customized neural network program. These results together provide an experimental and analytical framework for optimizing mismatch discrimination among all probes on a DNA microarray.     

DNA melting profiles from a matrix method

In this article we give a new method for the calculation of DNA melting profiles. Based on the matrix formulation of the DNA partition function, the method relies for its efficiency on the fact that the required matrices are very sparse, essentially reducing matrix multiplication to vector multiplication and thus making the computer time required to treat a DNA molecule containing N base pairs proportional to N(2). A key ingredient in the method is the result that multiplication by the inverse matrix can also be reduced to vector multiplication. The task of calculating the melting profile for the entire genome is further reduced by treating regions of the molecule between helix-plateaus, thus breaking the molecule up into independent parts that can each be treated individually. The method is easily modified to incorporate changes in the assignment of statistical weights to the different structural features of DNA. We illustrate the method using the genome of Haemophilus influenzae.     

A Limited-Memory BFGS Algorithm Based on a Trust-Region Quadratic Model for Large-Scale Nonlinear Equations

In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the limited-memory BFGS (L-M-BFGS) update matrix is used in the trust-region subproblem to improve the effectiveness of the algorithm for large-scale problems. The global convergence of the presented method is established under suitable conditions. The numerical results of the test problems show that the method is competitive with the norm method.     

Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.     

Fast calculation of the infrared spectra of large biomolecules

Vibrational spectra of proteins potentially give insight into biologically significant molecular motion and the proportions of different types of secondary structure. Vibrational spectra can be calculated either from normal modes obtained by diagonalizing the mass-weighted Hessian or from the time autocorrelation function derived from molecular dynamics trajectories. The Hessian matrix is calculated from force fields because it is not practical to calculate the Hessian from quantum mechanics for large molecules. As an alternative to molecular dynamics the spectral response can be calculated from a time autocorrelation derived from numerical solution of the harmonic equations of motion, resulting in calculations at least 4 times faster. Because the calculation also scales linearly with number of atoms, N, it is faster than normal-mode calculations that scale as N ³ for proteins with more then 4,700 atoms. Using this method it is practical to perform all-atom calculations for large biological systems, for example viral capsids, with the order of 10⁵ atoms.     

Lake Macrophyte Nutrient Index of standing waters in Serbia (LIMNIS)

The aim of this study was to derive a Macrophyte Nutrient Index for standing waters in Serbia, based on the LEAFPACS method and to correlate it against existing Lake Typology.A single data matrix of 46 species and 1463 sample quadrats was compiled from two datasets: a revised phytocenological database given according to Braun-Blanquet Cover Abundance Scale and a database of sample quadrats collected during the summer months of 2009, 2010 and 2011 at 31 lakes in Serbia. The nutrient indices (MSI) for 46 lake hydrophytes were calculated using the Reprediction Algorithm on N-Ellenberg's values. A Macrophyte Nutrient Index (LIMNIS) was calculated as the weighted average of MSI values for Serbian lakes. Correlation between rescaled MSI and original N-values was strong (0.82), and the same applies to the root-mean-square error value (0.75). Moderate LIMNIS values were derived for meso-oligotrophic lakes, as well as for eutrophic swamps and fens of the Danube floodplain. In the same manner, lakes classified as eutrophic showed the relatively higher LIMNIS values, and those recognised as potential conservation resources were characterised by moderate or low LIMNIS scores.Median LIMNIS values of six Lake Groups differed by less than one unit, which confirmed the similarity of the previously given Lake Types.     

Nonnegative decomposition of functional count data

We present a novel decomposition of nonnegative functional count data that draws on concepts from nonnegative matrix factorization. Our decomposition, which we refer to as NARFD (nonnegative and regularized function decomposition), enables the study of patterns in variation across subjects in a highly interpretable manner. Prototypic modes of variation are estimated directly on the observed scale of the data, are local, and are transparently added together to reconstruct observed functions. This contrasts with generalized functional principal component analysis, an alternative approach that estimates functional principal components on a transformed scale, produces components that typically vary across the entire functional domain, and reconstructs observations using complex patterns of cancellation and multiplication of functional principal components. NARFD is implemented using an alternating minimization algorithm, and we evaluate our approach in simulations. We apply NARFD to an accelerometer dataset comprising observations of physical activity for healthy older Americans.     

A Modified BFGS Formula Using a Trust Region Model for Nonsmooth Convex Minimizations

This paper proposes a modified BFGS formula using a trust region model for solving nonsmooth convex minimizations by using the Moreau-Yosida regularization (smoothing) approach and a new secant equation with a BFGS update formula. Our algorithm uses the function value information and gradient value information to compute the Hessian. The Hessian matrix is updated by the BFGS formula rather than using second-order information of the function, thus decreasing the workload and time involved in the computation. Under suitable conditions, the algorithm converges globally to an optimal solution. Numerical results show that this algorithm can successfully solve nonsmooth unconstrained convex problems.     

The effect of rat strain, diet composition and feeding period on the development of a nutritional model of non-alcoholic fatty liver disease in rats

Non-alcoholic fatty liver disease (NAFLD) is an important cause of liver-related morbidity and mortality. The aim of this work was to establish and characterize a nutritional model of NAFLD in rats. Wistar or Sprague-Dawley male rats were fed ad libitum a standard diet (ST-1, 10 % kcal fat), a medium-fat gelled diet (MFGD, 35 % kcal fat) and a high-fat gelled diet (HFGD, 71 % kcal fat) for 3 or 6 weeks. We examined the serum biochemistry, the hepatic malondialdehyde, reduced glutathione (GSH) and cytokine concentration, the respiration of liver mitochondria, the expression of uncoupling protein-2 (UCP-2) mRNA in the liver and histopathological samples. Feeding with MFGD and HFGD in Wistar rats or HFGD in Sprague-Dawley rats induced small-droplet or mixed steatosis without focal inflammation or necrosis. Compared to the standard diet, there were no significant differences in serum biochemical parameters, except lower concentrations of triacylglycerols in HFGD and MFGD groups. Liver GSH was decreased in rats fed HFGD for 3 weeks in comparison with ST-1. Higher hepatic malondialdehyde was found in both strains of rats fed HFGD for 6 weeks and in Sprague-Dawley groups using MFGD or HFGD for 3 weeks vs. the standard diet. Expression of UCP-2 mRNA was increased in Wistar rats fed MFGD and HFGD for 6 weeks and in Sprague-Dawley rats using HFGD for 6 weeks compared to ST-1. The present study showed that male Wistar and Sprague-Dawley rats fed by HFGD developed comparable simple steatosis without signs of progression to non-alcoholic steatohepatitis under our experimental conditions.     

How orientational order governs collectivity of folded proteins

The past decade has witnessed the development and success of coarse‐grained network models of proteins for predicting many equilibrium properties related to collective modes of motion. Curiously, the results are usually robust toward the different cutoff distances used for constructing the residue networks from the knowledge of the experimental coordinates. In this study, we present a systematical study of network construction and their effect on the predicted properties. Probing bond orientational order around each residue, we propose a natural partitioning of the interactions into an essential and a residual set. In this picture, the robustness originates from the way with which new contacts are added, so that an unusual local orientational order builds up. These residual interactions have a vanishingly small effect on the force vectors on each residue. The stability of the overall force balance then translates into the Hessian as small shifts in the slow modes of motion and an invariance of the corresponding eigenvectors. We introduce a rescaled version of the Hessian matrix and point out a link between the matrix Frobenius norm based on spectral stability arguments and orientational local order. A recipe for the optimal choice of partitioning the interactions into essential and residual components is prescribed. Implications for the study of biologically relevant properties of proteins are discussed with specific examples. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

An Algorithm for Cladistic Taxonomy—Method of Maximal Same Step Length

This paper deals with the numerical cladistic taxonomy. A method for constructing evolutionary tree (method of maximal same step length) is proposed in the applications and practice of cladistic taxonomy. Its algorithm runs as follows: 1) According to the order of evolution, characters are coded with nonnegative integers, producing the original data matrix. 2) Calculate the same step coefficients Sij (i≠j) by the formula (3) and form the coefficient matrix. 3. Find the maximal value S_pq of the same step coefficients in the coefficient matrix. 4) According to the maximal same step length S_pq, the most recent common ancestor CTU, of CTUp and CTUq can be determinated by (2). 5) draw the cladistic edges of cladogram representing the evolutionary relationship from OTUT to OTUp and OTUq. If the number of CTUs in the data matrix≤2, go to (2), otherwise stop. An example of 6 species from the family Campanulaceae is given for illustration (See Table 1). In general case, the evolutionary length of the cladogram obtained by this method is shorter than that by monothetic and other methods. Its algorithm is easily performed and is especially suitable for computerizing.相似文献   

Finding distinct biclusters from background in gene expression matrices

Biclustering, or the discovery of subsets of samples and genes that are homogeneous and distinct from the background, has become an important technique in analyzing current microarray datasets. Most existing biclustering methods define a bicluster type as a fixed (predefined) pattern and then trying to get results in some searching process. In this work, we propose a novel method for finding biclusters or 2-dimensional patterns that are significantly distinct from the background without the need for pre-defining a pattern within the bicluster. The method named Distinct 2-Dimensional Pattern Finder (D2D) is composed of an iterative reordering step of the rows and columns in the matrix using a new similarity measure, and a flexible scanning-and-growing step to identify the biclusters. Experiments on a large variety of simulation data show that the method works consistently well under different conditions, whereas the existing methods compared may work well under some certain conditions but fail under some other conditions. The impact of noise levels, overlapping degrees between clusters and different setting of parameters were also investigated, which indicated that the D2D method is robust against these factors. The proposed D2D method can efficiently discover many different types of biclusters given that they have distinctive features from the background. The computer program is available upon request.     

Susceptibility of Spodoptera exigua to 9 toxins from Bacillus thuringiensis

Nine of the most common lepidopteran active Cry proteins from Bacillus thuringiensis have been tested for activity against Spodoptera exigua. Because of possible intraspecific variability, three laboratory strains (FRA, HOL, and MUR) have been used. Mortality assays were performed with the three strains. LC₅₀ values for the active toxins were determined to the FRA and the HOL strains, whereas susceptibility of the MUR strain was assessed using only two concentrations. The results showed that Cry1Ca, Cry1Da, and Cry1Fa were the most effective toxins with all strains. Cry1Ab was found effective for the HOL strain, but very little effective against FRA (6.5-fold) and MUR strains. Cry1Aa and Cry1Ac were marginally toxic to all strains, whereas the rest of the toxins tested (Cry1Ba, Cry2Aa, and Cry2Ab) were non toxic. Significant differences in susceptibility among strains were also found for Cry1Da, being the FRA strain 25-fold more susceptible than the HOL strain. Growth inhibition, as an additional susceptibility parameter, was determined in the FRA strain with the 9 toxins. The toxicity profile obtained differed from that observed in mortality assays. Cry1Aa, Cry1Ab, Cry1Ac, Cry1Ca, Cry1Da, and Cry1Fa toxins produced a similar larval growth inhibition. Cry2Aa had a lower but clear effect on larval growth inhibition, whereas Cry1Ba and Cry2Ab did not have any effect.     

Structure of the MUR1 GDP-mannose 4,6-dehydratase from Arabidopsis thaliana: implications for ligand binding and specificity

GDP-D-mannose 4,6-dehydratase catalyzes the first step in the de novo synthesis of GDP-L-fucose, the activated form of L-fucose, which is a component of glycoconjugates in plants known to be important to the development and strength of stem tissues. We have determined the three-dimensional structure of the MUR1 dehydratase isoform from Arabidopsis thaliana complexed with its NADPH cofactor as well as with the ligands GDP and GDP-D-rhamnose. MUR1 is a member of the nucleoside-diphosphosugar modifying subclass of the short-chain dehydrogenase/reductase enzyme family, having homologous structures and a conserved catalytic triad of Lys, Tyr, and Ser/Thr residues. MUR1 is the first member of this subfamily to be observed as a tetramer, the interface of which reveals a close and intimate overlap of neighboring NADP(+)-binding sites. The GDP moiety of the substrate also binds in an unusual syn conformation. The protein-ligand interactions around the hexose moiety of the substrate support the importance of the conserved triad residues and an additional Glu side chain serving as a general base for catalysis. Phe and Arg side chains close to the hexose ring may serve to confer substrate specificity at the O2 position. In the MUR1/GDP-D-rhamnose complex, a single unique monomer within the protein tetramer that has an unoccupied substrate site highlights the conformational changes that accompany substrate binding and may suggest the existence of negative cooperativity in MUR1 function.     

Uncovering Community Structures with Initialized Bayesian Nonnegative Matrix Factorization

Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks. However, these algorithms exhibit some drawbacks, such as unstable results and inefficient running times. In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed. First, based on singular value decomposition, we obtain simple initialized matrix factorizations from approximate decompositions of the complex network’s adjacency matrix. Then, within a few iterations, the final matrix factorizations are achieved by the Bayesian nonnegative matrix factorization method with the initialized matrix factorizations. Thus, the network’s community structure can be determined by judging the classification of nodes with a final matrix factor. Experimental results show that the proposed method is highly accurate and offers competitive performance to that of the state-of-the-art methods even though it is not designed for the purpose of modularity maximization.     

Muscular utilization of the plantarflexors, hip flexors and extensors in persons with hemiparesis walking at self-selected and maximal speeds.

Gait performance secondary to a stroke is partially dependent on residual muscle strength. However, to pinpoint more precisely the mechanism of this relationship, biomechanical models, such as the muscular utilization ratio (MUR) that integrates both muscle strength and gait parameters into the concept of level of effort, are warranted. The aim of the present study was to evaluate the MUR of plantarflexors, hip flexors and extensor muscles during their concentric action in 17 chronic hemiparetic participants walking at self-selected and maximal speeds. Results revealed that peak MUR increased with gait speed. At self-selected speed (0.73+/-0.27 m/s), peak MUR values on the paretic side were 64% (+/-18.7), 46% (+/-27.6) and 33% (+/-25.6) for the plantarflexors, hip flexors and extensor muscles, respectively. At maximal speed (1.26+/-0.39 m/s), corresponding values were 77% (+/-23.6), 72% (+/-33.0) and 58% (+/-32.1). Peak MUR showed negative associations (-0.33-0.68), although not all significant, with voluntary muscle strength. The results of this study indicated that the peak MUR increased with gait speed. The plantarflexors were the most used muscle group at self-selected speed, whereas at maximal speed the three muscle groups showed similar peak MUR values. This last finding suggested an important role of the hip muscles in reaching a faster speed. Lastly, because moderate associations were found between peak MUR values and the voluntary muscle strength of hip flexors and extensors, it can be concluded that the weakest paretic muscle groups show, in general, the highest level of effort during gait.