EPR Evidence for Premonitory Charge-Ordering Fluctuations in Hydrothermally Grown Pr0.57Ca0.41Ba0.02MnO3 Nanowires

Electron paramagnetic resonance (EPR) and magnetic properties of nanowires of Pr0.57Ca0.41Ba0.02MnO3 (PCBMO) are studied and compared with those of the bulk material. PCBMO nanowires with diameter of 80-90 nm and length of similar to 3.5 mu m were synthesized by a low reaction temperature hydrothermal method and the bulk sample was prepared following a solid-state reaction route. The samples were characterized by X-ray diffraction, scanning electron microscopy and transmission electron microscopy. The bulk PCBMO manganite exhibits charge order at 230 K along with a ferromagnetic transition at 110 K. However, superconducting quantum interference device measurements on the PCBMO nanowires show a complete `melting' of charge ordering and a ferromagnetic transition at 115 K. This result is confirmed by the EPR intensity behavior as well. However, the EPR line width, which is reflective of the spin dynamics, shows a shallow minimum for nanowires at the temperature corresponding to the charge-ordering transition, i.e., 230 K. We interpret this result as an indication of the presence of charge-ordering fluctuations in the nanowires even though the static charge order is absent, thus heralding the occurrence of charge order in the bulk sample.

Evolutionary Significance of Self-acylation Property in Acyl Carrier Proteins

Acyl carrier protein is an integral component of many cellular metabolic processes. A number of studies have reported self-acylation behavior in acyl carrier proteins. Although AM exhibit high levels of similarity in their primary and tertiary structures, self-acylation behavior is restricted to only some ACPs that can be classified into two major families based on their function. The first family of ACPs is involved in polyketide biosynthesis, whereas the second family participates in fatty acid synthesis. Facilitated by the growing number of genome sequences available for analyses, large-scale phylogenetic studies were used in these studies to uncover as to how self-acylation behavior of acyl carrier proteins is linked with the evolution of metabolic pathways in organisms. These studies show that self-acylation behavior in acyl carrier proteins was lost during the course of evolution, with certain organisms and organelles viz. plastids, retaining it for specified functions. (C) 2009 IUBMB IUBMB Life, 61(8): 853-859, 2009

Multi-Tone Testing of Linear and Nonlinear Analog Circuits using Polynomial Coefficients

A method of testing for parametric faults of analog circuits based on a polynomial representation of fault-free function of the circuit is presented. The response of the circuit under test (CUT) is estimated as a polynomial in the applied input voltage at relevant frequencies in addition to DC. Classification or Cur is based on a comparison of the estimated polynomial coefficients with those of the fault free circuit. This testing method requires no design for test hardware as might be added to the circuit fly some other methods. The proposed method is illustrated for a benchmark elliptic filter. It is shown to uncover several parametric faults causing deviations as small as 5% from the nominal values.

The phylogeny and evolutionary timescale of muscoide (Diptera: Brachycera: Calyptratae) inferred from mitochondrial genomes

Muscoidea is a significant dipteran clade that includes house flies (Family Muscidae), latrine flies (F. Fannidae), dung flies (F. Scathophagidae) and root maggot flies (F. Anthomyiidae). It is comprised of approximately 7000 described species. The monophyly of the Muscoidea and the precise relationships of muscoids to the closest superfamily the Oestroidea (blow flies, flesh flies etc) are both unresolved. Until now mitochondrial (mt) genomes were available for only two of the four muscoid families precluding a thorough test of phylogenetic relationships using this data source. Here we present the first two mt genomes for the families Fanniidae (Euryomma sp.) (family Fanniidae) and Anthomyiidae (Delia platura (Meigen, 1826)). We also conducted phylogenetic analyses containing of these newly sequenced mt genomes plus 15 other species representative of dipteran diversity to address the internal relationship of Muscoidea and its systematic position. Both maximum-likelihood and Bayesian analyses suggested that Muscoidea was not a monophyletic group with the relationship: (Fanniidae + Muscidae) + ((Anthomyiidae + Scathophagidae) + (Calliphoridae + Sarcophagidae)), supported by the majority of analysed datasets. This also infers that Oestroidea was paraphyletic in the majority of analyses. Divergence time estimation suggested that the earliest split within the Calyptratae, separating (Tachinidae + Oestridae) from the remaining families, occurred in the Early Eocene. The main divergence within the paraphyletic muscoidea grade was between Fanniidae + Muscidae and the lineage ((Anthomyiidae + Scathophagidae) + (Calliphoridae + Sarcophagidae)) which occurred in the Late Eocene

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Generalised regression estimation for domain class frequencies

This study examines the properties of Generalised Regression (GREG) estimators for domain class frequencies and proportions. The family of GREG estimators forms the class of design-based model-assisted estimators. All GREG estimators utilise auxiliary information via modelling. The classic GREG estimator with a linear fixed effects assisting model (GREG-lin) is one example. But when estimating class frequencies, the study variable is binary or polytomous. Therefore logistic-type assisting models (e.g. logistic or probit model) should be preferred over the linear one. However, other GREG estimators than GREG-lin are rarely used, and knowledge about their properties is limited. This study examines the properties of L-GREG estimators, which are GREG estimators with fixed-effects logistic-type models. Three research questions are addressed. First, I study whether and when L-GREG estimators are more accurate than GREG-lin. Theoretical results and Monte Carlo experiments which cover both equal and unequal probability sampling designs and a wide variety of model formulations show that in standard situations, the difference between L-GREG and GREG-lin is small. But in the case of a strong assisting model, two interesting situations arise: if the domain sample size is reasonably large, L-GREG is more accurate than GREG-lin, and if the domain sample size is very small, estimation of assisting model parameters may be inaccurate, resulting in bias for L-GREG. Second, I study variance estimation for the L-GREG estimators. The standard variance estimator (S) for all GREG estimators resembles the Sen-Yates-Grundy variance estimator, but it is a double sum of prediction errors, not of the observed values of the study variable. Monte Carlo experiments show that S underestimates the variance of L-GREG especially if the domain sample size is minor, or if the assisting model is strong. Third, since the standard variance estimator S often fails for the L-GREG estimators, I propose a new augmented variance estimator (A). The difference between S and the new estimator A is that the latter takes into account the difference between the sample fit model and the census fit model. In Monte Carlo experiments, the new estimator A outperformed the standard estimator S in terms of bias, root mean square error and coverage rate. Thus the new estimator provides a good alternative to the standard estimator.

Family of Mixed-Valence Oxovanadium(IV/V) Dinuclear Entities Incorporating N4O3-Coordinating Heptadentate Ligands: Synthesis, Structure, and EPR Spectra

Dinuclear ((VVV)-V-IV) oxophenoxovanadates of general formula [V2O3L] have been synthesized in excellent yields by reacting bis(acetylacetonato)oxovanadium(IV) with H3L in a 2:1 ratio in acetone under an N-2 atmosphere. Here L3- is the deprotonated form of 2,6-bis[{{(2-hydroxybenzyl)(N',N'-(dimethylamino)ethyl)}amino}methyl]-4-methylphenol (H3L1), 2,6-bis[{{(5-methyl-2-hydroxybenzyl)(N',N'-(dimethylamino)ethyl)}amino}methyl]-4-methylphenol (H3L2) 2,6-bis[ {{(5-tert-butyl-2-hydroxybenzyl)(N',N'-(dimethylamino)ethyl)}amino}methyl]-4-methylphenoI (H3L3), 2,6-bis[{{(5-chloro-2-hydroxybenzyl)(N',N'-(dimethylamino)ethyl)}amino}methyl]-4-methylphenol (H3L4) , 2,6-bis[{{(5-bromo-2-hydroxybenzyl)(N',N'-(dimethylamino)ethyl)}amino}methyl]-4-methylphenol (H3L5), or 2,6-bis[{{(5-methoxy-2-hydroxybenzyl)(N',N'-(dimethylamino)ethyl)}amino}methyl]-4-methylphenol (H3L6). In [V2O3L1], both the metal atoms have distorted octahedral geometry. The relative disposition of two terminal V=O groups in the complex is essentially cis. The O=V...V=O torsion angle is 24.6(2)degrees. The V-O-oxo-V and V-O-phenoxo-V angles are 117.5(4) and 93.4(3)degrees, respectively. The V...V bond distance is 3.173(5) Angstrom. X-ray crystallography, IR, UV-vis, and H-1 and V-51 NMR measurements show that the mixed-valence complexes contain two indistinguishable vanadium atoms (type 111). The thermal ellipsoids of O2, O4, C10, C14, and C15 also suggests a type III complex in the solid state. EPR spectra of solid complexes at 77 K display a single line indicating the localization of the odd electron (3d(xy)(1)). Valence localization at 77 K is also consistent with the V-51 hyperfine structure of the axial EPR spectra (3d(xy)(1) ground state) of the complexes in frozen (77 K) dichloromethane solution: S = 1/2, g(parallel to) similar to 1.94, g(perpendicular to) similar to 1.98, A(parallel to) similar to 166 x 10(-4) cm(-1), and A(perpendicular to) similar to 68 x 10(-4) cm(-1). In contrast isotropic room-temperature solution spectra of the family have 15 hyperfine lines (g(iso) similar to 1.974 and A(iso) similar to 50 x 10(-4) cm(-1)) revealing that the unpaired electron is delocalized between the metal centers. Crystal data for the [V2O3L1].CH2Cl2 complex are as follows: chemical formula, C32H43O6N4C12V2; crystal system, monoclinic; space group, C2/c; a = 18.461(4), b = 17.230(3), c = 13.700(3) Angstrom; beta = 117.88(3)degrees; Z = 8.

Evolutionary and conservation biology of the Finnish house sparrow

Recently it has been recognized that evolutionary aspects play a major role in conservation issues of a species. In this thesis I have combined evolutionary research with conservation studies to provide new insight into these fields. The study object of this thesis is the house sparrow, a species that has features that makes it interesting for this type of study. The house sparrow has been ubiquitous almost all over the world. Even though being still abundant, several countries have reported major declines. These declines have taken place in a relatively short time covering both urban and rural habitats. In Finland this species has declined by more than two thirds in just over two decades. In addition, as the house sparrow lives only in human inhabited areas it can also raise public awareness to conservation issues. I used both an extensive museum collection of house sparrows collected in 1980s from all over Finland as well as samples collected in 2009 from 12 of the previously collected localities. I used molecular techniques to study neutral genetic variation within and genetic differentiation between the study populations. This knowledge I then combined with data gathered on morphometric measurements. In addition I analyzed eight heavy metals from the livers of house sparrows that lived in either rural or urban areas in the 1980s and evaluated the role of heavy metal pollution as a possible cause of the declines. Even though dispersal of house sparrows is limited I found that just as the declines started in 1980s the house sparrows formed a genetically panmictic population on the scale of the whole Finland. When compared to Norway, where neutral genetic divergence has been found even with small geographic distances, I concluded that this difference would be due to contrasting landscapes. In Finland the landscape is rather homogeneous facilitating the movements of these birds and maintaining gene flow even with the low dispersal. To see whether the declines have had an effect on the neutral genetic variation of the populations I did a comparison between the historical and contemporary genetic data. I showed that even though genetic diversity has not decreased due to the drastic declines the populations have indeed become more differentiated from each other. This shows that even in a still quite abundant species the declines can have an effect on the genetic variation. It is shown that genetic diversity and differentiation may approach their new equilibriums at different rates. This emphasizes the importance of studying both of them and if the latter has increased it should be taken as a warning sign of a possible loss of genetic diversity in the future. One of the factors suggested to be responsible for the house sparrow declines is heavy metal pollution. When studying the livers of house sparrows from 1980s I discovered higher levels of heavy metal concentrations in urban than rural habitats, but the levels of the metals were comparatively low and based on that heavy metal pollution does not seem to be a direct cause for the declines in Finland. However, heavy metals are known to decrease the amount of insects in urban areas and thus in the cities heavy metals may have an indirect effect on house sparrows. Although neutral genetic variation is an important tool for conservation genetics it does not tell the whole story. Since neutral genetic variation is not affected by selection, information can be one-sided. It is possible that even neutral genetic differentiation is low, there can be substantial variation in additive genetic traits indicating local adaptation. Therefore I performed a comparison between neutral genetic differentiation and phenotypic differentiation. I discovered that two traits out of seven are likely to be under directional selection, whereas the others could be affected by random genetic drift. Bergmann s rule may be behind the observed directional selection in wing length and body mass. These results highlight the importance of estimating both neutral and adaptive genetic variation.

Profligate Descent : The Cultural Transformations of Evolutionary Narrative

This dissertation traces a set of historical transformations the Darwinian evolutionary narrative has undergone toward the end of the twentieth century, especially as reflected in Anglo-American popular science books and novels. The study has three objectives. First, it seeks to understand the organizing logic of evolutionary narratives and the role that assumptions about gender and sexuality play in that logic. Second, it asks what kinds of cultural anxieties evolutionary theory raises and how evolutionary narratives negotiate them. Third, it examines the possibilities and limits of narrative transformation both as a historical phenomenon and as a theoretical question. This interdisciplinary dissertation is situated at the intersection of science studies, cultural studies, literary studies, and gender studies. Its understanding of science as a cultural practice that both emerges from and contributes to cultural expectations and institutional structures follows the tradition of science studies. Its focus on the question of popular appeal and the mechanisms of cultural change arises from cultural studies. Its view of narrative as a structural phenomenon is grounded in literary studies in general and feminist narrative theory in particular. Its understanding of gender and sexuality as implicated in discourses of epistemic authority builds on the view of gender and sexuality as contingent cultural categories central to gender studies. The primary material consists of over 25 British and American popular science books and novels, published roughly between 1990 and 2005. In order to highlight historical transformations, these texts are read in the context of Darwin s The Origin of Species and The Descent of Man, on the one hand, and such sociobiological classics as E. O. Wilson s On Human Nature and Richard Dawkins s The Selfish Gene, on the other. The research method combines feminist narrative analysis with cultural and historical contextualization, emphasizing discursive abruptions, recurrent narrative patterns, and underlying continuities. The dissertation demonstrates that the relationship between Darwin s evolutionary narrative and late twentieth-century evolutionary narratives is characterized by reemphasis, omissions, and continuous rewriting. In particular, contemporary evolutionary discourse extends the role assigned to reproduction both sexual and narrative in Darwin s writing, generating a narrative logic that imagines the desire to reproduce as the driving force of evolution and posits the reproductive sex act as the endlessly repeated narrative event that keeps the story going. The study argues that the popular appeal of evolutionary accounts of gender, sexuality, and human nature may arise, to an extent, from this reproductive narrative dynamic. This narrative dynamic, however, is not logically invulnerable. Since the continuation of the evolutionary narrative relies on successful reproduction, the possibility of reproductive failure poses a constant risk to narrative futurity, arousing cultural anxieties that evolutionary narratives need to address. The study argues that evolutionary narratives appease such anxieties by evoking a range of cultural narratives, especially romantic, religious, and national narratives. Furthermore, the study shows that the event-based logic of evolutionary narratives privileges observable acts over emotions, pleasures, identities, and desires, thus engendering a set of conceptual exclusions that limits the imaginative scope of evolution as a cultural narrative.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

Construction and use of linear regression models for processor performance analysis

Processor architects have a challenging task of evaluating a large design space consisting of several interacting parameters and optimizations. In order to assist architects in making crucial design decisions, we build linear regression models that relate Processor performance to micro-architecture parameters, using simulation based experiments. We obtain good approximate models using an iterative process in which Akaike's information criteria is used to extract a good linear model from a small set of simulations, and limited further simulation is guided by the model using D-optimal experimental designs. The iterative process is repeated until desired error bounds are achieved. We used this procedure to establish the relationship of the CPI performance response to 26 key micro-architectural parameters using a detailed cycle-by-cycle superscalar processor simulator The resulting models provide a significance ordering on all micro-architectural parameters and their interactions, and explain the performance variations of micro-architectural techniques.

Analysis of the beta-glucoside utilization (bgl) genes of Shigella sonnei: evolutionary implications for their maintenance in a cryptic state

The pattern of expression of the genes involved in the utilization of aryl beta-glucosides such as arbutin and salicin is different in the genus Shigella compared to Escherichia coli. The results presented here indicate that the homologue of the cryptic bgl operon of E. coli is conserved in Shigella sonnei and is the primary system involved in beta-glucoside utilization in the organism. The organization of the bgl genes in 5. sonnei is similar to that of E. coli; however there are three major differences in terms of their pattern of expression. (i) The bglB gene, encoding phospho-beta-glucosidase B, is insertionally inactivated in 5. sonnei. As a result, mutational activation of the silent bgl promoter confers an Arbutin-positive (Arb(+)) phenotype to the cells in a single step; however, acquiring a Salicin-positive (Sal(+)) phenotype requires the reversion or suppression of the bglB mutation in addition. (ii) Unlike in E. coli, a majority of the activating mutations (conferring the Arb(+) phenotype) map within the unlinked hns locus, whereas activation of the E. coli bgl operon under the same conditions is predominantly due to insertions within the bglR locus. (iii) Although the bgl promoter is silent in the wild-type strain of 5. sonnei (as in the case of E. coli), transcriptional and functional analyses indicated a higher basal level of transcription of the downstream genes. This was correlated with a 1 bp deletion within the putative Rho-independent terminator present in the leader sequence preceding the homologue of the bglG gene. The possible evolutionary implications of these differences for the maintenance of the genes in the cryptic state are discussed.

Semi-Supervised Classification Using Sparse Gaussian Process Regression

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.