Speed sensorless vector control of induction machines

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method will evaluate rotor velocity, ωr, on-line, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRAS or Kalman filters. Furthermore, the new scheme will operate at all velocities including zero with very little error. The procedure employs motor model equations, however all differential and integral terms have been eliminated giving a very fast, low-cost, effective and practical alternative to the current available methods. Simulation results verify the operation of the scheme under ideal and PWM conditions.

Making DNA without iron: induction of a manganese-dependent ribonucleotide reductase in response to iron starvation

Ribonucleotide reductases supply cells with their deoxyribonucleotides. Three enzyme types are known, classes I, II and III. Class II enzymes are anaerobic whereas class I enzymes are aerobic, and so class I and II enzymes are often produced by the same organism under opposing oxygen regimes. Escherichia coli contains two types of class I enzyme (Ia and Ib) with the Fe-dependent Ia enzyme (NrdAB) performing the major role aerobically, leaving the purpose of the Ib enzyme (NrdEF) unclear. Several papers have recently focused on the class Ib enzymes showing that they are Mn (rather than Fe) dependent and suggesting that the E. coli NrdEF may function under redox-stress conditions. A paper published in this issue of Molecular Microbiology from James Imlay's group confirms that this unexplained NrdEF Ib enzyme is Mn-dependent, but shows that it does not substitute for NrdAB during redox stress. Instead, a role during iron restriction is demonstrated. Thus, the purpose of NrdEF (and possibly other class Ib enzymes) is to enhance growth under aerobic, low-iron conditions, and to functionally replace the Fe-dependent NrdAB when iron is unavailable. This finding reveals a new mechanism by which bacteria adjust to life under iron deprivation.

Induction of the ferritin gene (ftnA) of Escherichia coli by Fe2+–Fur is mediated by reversal of H-NS silencing and is RyhB independent

FtnA is the major iron-storage protein of Escherichia coli accounting for < or = 50% of total cellular iron. The FtnA gene (ftnA) is induced by iron in an Fe(2+)-Fur-dependent fashion. This effect is reportedly mediated by RyhB, the Fe(2+)-Fur-repressed, small, regulatory RNA. However, results presented here show that ftnA iron induction is independent of RyhB and instead involves direct interaction of Fe(2+)-Fur with an 'extended' Fur binding site (containing five tandem Fur boxes) located upstream (-83) of the ftnA promoter. In addition, H-NS acts as a direct repressor of ftnA transcription by binding at multiple sites (I-VI) within, and upstream of, the ftnA promoter. Fur directly competes with H-NS binding at upstream sites (II-IV) and consequently displaces H-NS from the ftnA promoter (sites V-VI) which in turn leads to derepression of ftnA transcription. It is proposed that H-NS binding within the ftnA promoter is facilitated by H-NS occupation of the upstream sites through H-NS oligomerization-induced DNA looping. Consequently, Fur displacement of H-NS from the upstream sites prevents cooperative H-NS binding at the downstream sites within the promoter, thus allowing access to RNA polymerase. This direct activation of ftnA transcription by Fe(2+)-Fur through H-NS antisilencing represents a new mechanism for iron-induced gene expression.

The role of myeloid receptors on murine plasmacytoid dendritic cells in induction of type I interferon

This study tested the hypothesis that a set of predominantly myeloid restricted receptors (F4/80, CD36, Dectin-1, CD200 receptor and mannan binding lectins) and the broadly expressed CD200 played a role in a key function of plasmacytoid DC (pDC), virally induced type I interferon (IFN) production. The Dectin-1 ligands zymosan, glucan phosphate and the anti-Dectin-1 monoclonal antibody (mAb) 2A11 had no effect on influenza virus induced IFNα/β production by murine splenic pDC. However, mannan, a broad blocking reagent against mannose specific receptors, inhibited IFNα/β production by pDC in response to inactivated influenza virus. Moreover, viral glycoproteins (influenza virus haemagglutinin and HIV-1 gp120) stimulated IFNα/β production by splenocytes in a mannan-inhibitable manner, implicating the function of a lectin in glycoprotein induced IFN production. Lastly, the effect of CD200 on IFN induction was investigated. CD200 knock-out macrophages produced more IFNα than wild-type macrophages in response to polyI:C, a MyD88-independent stimulus, consistent with CD200's known inhibitory effect on myeloid cells. In contrast, blocking CD200 with an anti-CD200 mAb resulted in reduced IFNα production by pDC-containing splenocytes in response to CpG and influenza virus (MyD88-dependent stimuli). This suggests there could be a differential effect of CD200 on MyD88 dependent and independent IFN induction pathways in pDC and macrophages. This study supports the hypothesis that a mannan-inhibitable lectin and CD200 are involved in virally induced type I IFN induction.

Rensch's rule in large herbivorous mammals derived from metabolic scaling

Rensch’s rule, which states that the magnitude of sexual size dimorphism tends to increase with increasing body size, has evolved independently in three lineages of large herbivorous mammals: bovids (antelopes), cervids (deer), and macropodids (kangaroos). This pattern can be explained by a model that combines allometry,life-history theory, and energetics. The key features are thatfemale group size increases with increasing body size and that males have evolved under sexual selection to grow large enough to control these groups of females. The model predicts relationships among body size and female group size, male and female age at first breeding,death and growth rates, and energy allocation of males to produce body mass and weapons. Model predictions are well supported by data for these megaherbivores. The model suggests hypotheses for why some other sexually dimorphic taxa, such as primates and pinnipeds(seals and sea lions), do or do not conform to Rensh’s rule.

Jmax-pruning: a facility for the information theoretic pruning of modular classification rules

The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.