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Symmetrical components for power systems engineering
Emphasizing a practical conception of system unbalances, basic circuits, and calculations, this essential reference text presents the foundations of symmetrical components with a review of per unit (percent), phasors, and polarity--keeping the mathematics as simple as possible throughout.
Book
DNA unwinding mechanism of a eukaryotic replicative CMG helicase
High-resolution structures have not been reported for replicative helicases at a replication fork at atomic resolution, a prerequisite to understanding the unwinding mechanism. The eukaryotic replicative CMG (Cdc45, Mcm2-7, GINS) helicase contains a Mcm2-7 motor ring, with the N-tier ring in front and the C-tier motor ring behind. The N-tier ring is structurally divided into a zinc finger (ZF) sub-ring followed by the oligosaccharide/oligonucleotide-binding (OB) fold ring. Here we report the cryo-EM structure of CMG on forked DNA at 3.9 Å, revealing that parental DNA enters the ZF sub-ring and strand separation occurs at the bottom of the ZF sub-ring, where the lagging strand is blocked and diverted sideways by OB hairpin-loops of Mcm3, Mcm4, Mcm6, and Mcm7. Thus, instead of employing a specific steric exclusion process, or even a separation pin, unwinding is achieved via a “dam-and-diversion tunnel” mechanism that does not require specific protein-DNA interaction. The C-tier motor ring contains spirally configured PS1 and H2I loops of Mcms 2, 3, 5, 6 that translocate on the spirally-configured leading strand, and thereby pull the preceding DNA segment through the diversion tunnel for strand separation.
The DNA duplex is known to be split apart in a steric exclusion manner during replication, but the specific mechanism has remained unclear. Here the authors present a cryo-EM structure of a eukaryotic replicative CMG helicase on forked DNA, revealing the mechanism of DNA unwinding.
Journal Article
Beam delivery and automating other beamline tasks using State Notation Language
The EPICS State Notation Language (SNL) and associated sequencer can be used to design a sequence of events which are defined by states. This state function ability has prompted us to investigate application of SNL to automate beamline tasks starting with a simpler process of automating monochromator warming. Further extending the idea to implement beam delivery. Our aim here is to drive the optical components of the beamline in a learned manner, to deliver beam from source to sample position.
Journal Article
Principal Component Analysis of High-Frequency Data
We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components, and provide the asymptotic distribution of these estimators. Empirically, we study the high-frequency covariance structure of the constituents of the S&P 100 Index using as little as one week of high-frequency data at a time, and examines whether it is compatible with the evidence accumulated over decades of lower frequency returns. We find a surprising consistency between the low- and high-frequency structures. During the recent financial crisis, the first principal component becomes increasingly dominant, explaining up to 60% of the variation on its own, while the second principal component drives the common variation of financial sector stocks. Supplementary materials for this article are available online.
Journal Article
Independent Component Analysis via Distance Covariance
This article introduces a novel statistical framework for independent component analysis (ICA) of multivariate data. We propose methodology for estimating mutually independent components, and a versatile resampling-based procedure for inference, including misspecification testing. Independent components are estimated by combining a nonparametric probability integral transformation with a generalized nonparametric whitening method based on distance covariance that simultaneously minimizes all forms of dependence among the components. We prove the consistency of our estimator under minimal regularity conditions and detail conditions for consistency under model misspecification, all while placing assumptions on the observations directly, not on the latent components. U statistics of certain Euclidean distances between sample elements are combined to construct a test statistic for mutually independent components. The proposed measures and tests are based on both necessary and sufficient conditions for mutual independence. We demonstrate the improvements of the proposed method over several competing methods in simulation studies, and we apply the proposed ICA approach to two real examples and contrast it with principal component analysis.
Journal Article
Fresh frozen plasma versus prothrombin complex concentrate in patients with intracranial haemorrhage related to vitamin K antagonists (INCH): a randomised trial
Haematoma expansion is a major cause of mortality in intracranial haemorrhage related to vitamin K antagonists (VKA-ICH). Normalisation of the international normalised ratio (INR) is recommended, but optimum haemostatic management is controversial. We assessed the safety and efficacy of fresh frozen plasma (FFP) versus prothrombin complex concentrate (PCC) in patients with VKA-ICH.
We did an investigator-initiated, multicentre, prospective, randomised, open-label, blinded-endpoint trial. Patients aged at least 18 years with VKA-ICH who presented within 12 h after symptom onset with an INR of at least 2·0 were randomly assigned (1:1) by numbered sealed envelopes to 20 mL/kg of intravenous FFP or 30 IU/kg of intravenous four-factor PCC within 1 h after initial cerebral CT scan. The primary endpoint was the proportion of patients with INR 1·2 or lower within 3 h of treatment initiation. Masking of treatment was not possible, but the primary analysis was observer masked. Analyses were done using a treated-as-randomised approach. This trial is registered with EudraCT, number 2008-005653-37, and ClinicalTrials.gov, number NCT00928915.
Between Aug 7, 2009, and Jan 9, 2015, 54 patients were randomly assigned (26 to FFP and 28 to PCC) and 50 received study drug (23 FFP and 27 PCC). The trial was terminated on Feb 6, 2015, after inclusion of 50 patients after a safety analysis because of safety concerns. Two (9%) of 23 patients in the FFP group versus 18 (67%) of 27 in the PCC group reached the primary endpoint (adjusted odds ratio 30·6, 95% CI 4·7–197·9; p=0·0003). 13 patients died: eight (35%) of 23 in the FFP group (five from haematoma expansion, all occurring within 48 h after symptom onset) and five (19%) of 27 in the PCC group (none from haematoma expansion), the first of which occurred on day 5 after start of treatment. Three thromboembolic events occurred within 3 days (one in the FFP group and two in the PCC group), and six after day 12 (one and five). 43 serious adverse events (20 in the FFP group and 23 in the PCC group) occurred in 26 patients. Six serious adverse events were judged to be FFP related (four cases of haematoma expansion, one anaphylactic reaction, and one ischaemic stroke) and two PCC related (ischaemic stroke and pulmonary embolism).
In patients with VKA-related intracranial hemorrhage, four-factor PCC might be superior to FFP with respect to normalising the INR, and faster INR normalisation seemed to be associated with smaller haematoma expansion. Although an effect of PCC on clinical outcomes remains to be shown, our data favour the use of PCC over FFP in intracranial haemorrhage related to VKA.
Octapharma.
Journal Article
Software Component Models
Component-based development (CBD) is an important emerging topic in software engineering, promising long-sought-after benefits like increased reuse, reduced time to market, and, hence, reduced software production cost. The cornerstone of a CBD technology is its underlying software component model, which defines components and their composition mechanisms. Current models use objects or architectural units as components. These are not ideal for component reuse or systematic composition. In this paper, we survey and analyze current component models and classify them into a taxonomy based on commonly accepted desiderata for CBD. For each category in the taxonomy, we describe its key characteristics and evaluate them with respect to these desiderata.
Journal Article
Diode-End-Pumped Continuous-Wave Tunable Ndsup.3+:LiYFsub.4 Laser Operating on the sup.4Fsub.3/2→sup.4Isub.13/2 Transition
A laser diode (LD) end-pumped continuous-wave (CW) tunable Nd[sup.3+]:LiYF[sub.4] (Nd:YLF) laser operating on the [sup.4]F[sub.3/2]→[sup.4]I[sub.13/2] transition was performed. Four single-wavelength (SW) lasing at 1321, 1314, 1371, and 1364 nm in the π-polarized direction and three SW lasing at 1314, 1326, and 1371 nm in the σ-polarized direction were achieved using a tuning prism. At 20 W pump power, the σ-polarized 1314 nm emission generated 7.3 W power output with 39.4% slope efficiency. Further, the three-pair of switchable π-polarized dual-wavelengths (DWs) at 1321/1314 nm, 1371/1364 nm, and 1321/1364 nm and the two-pair of switchable σ-polarized DWs at 1314/1326 nm and 1314/1371 nm were also realized by rotating an intracavity birefringence filter (BF). In addition, by employing dual intracavity BFs, the balanced DW output power was attained, achieving 6.4 W total maximum output at 1314/1321 nm in the π-polarized direction.
Journal Article
Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition
Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.
•It is currently a common practice to apply dimension reduction to EEG data using PCA before performing ICA decomposition.•We tested the quality of Independent Components (ICs) after different levels of rank reduction to a principal subspace.•PCA rank reduction adversely affected dipolarity and stability of ICs accounting for brain and known non-brain processes.•PCA rank reduction also increased inter-subject variance in IC source locations (by equivalent dipole fitting) and spectra.•For EEG data at least, PCA rank reduction should be avoided or carefully tested before applying it as a preprocessing step.
Journal Article
Detecting and dating structural breaks in functional data without dimension reduction
Methodology is proposed to uncover structural breaks in functional data that is ‘fully functional’ in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight that the fully functional procedures perform best under conditions when analogous estimators based on functional principal component analysis are at their worst, namely when the feature of interest is orthogonal to the leading principal components of the data. The theoretical findings are confirmed by means of a Monte Carlo simulation study in finite samples. An application to annual temperature curves illustrates the practical relevance of the procedures proposed.
Journal Article