Magnetic field stabilization for high-accuracy mass measurements on exotic nuclides

The magnetic-field stability of a mass spectrometer plays a crucial role in precision mass measurements. In the case of mass determination of short-lived nuclides with a Penning trap, major causes of instabilities are temperature fluctuations in the vicinity of the trap and pressure fluctuations in the liquid helium cryostat of the superconducting magnet. Thus systems for the temperature and pressure stabilization of the Penning trap mass spectrometer ISOLTRAP at the ISOLDE facility at CERN have been installed. A reduction of the fluctuations by at least one order of magnitude downto dT=+/-5mK and dp=+/-50mtorr has been achieved, which corresponds to a relative frequency change of 2.7x10^{-9} and 1.5x10^{-10}, respectively. With this stabilization the frequency determination with the Penning trap only shows a linear temporal drift over several hours on the 10 ppb level due to the finite resistance of the superconducting magnet coils.Comment: 23 pages, 13 figure

Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we use a real-time, remote-sensing, non-invasive, text-based approach---a kind of hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage and we show how a highly robust metric can be constructed and defended.Comment: 27 pages, 17 figures, 3 tables. Supplementary Information: 1 table, 52 figure

The Ramsey method in high-precision mass spectrometry with Penning traps: Experimental results

The highest precision in direct mass measurements is obtained with Penning trap mass spectrometry. Most experiments use the interconversion of the magnetron and cyclotron motional modes of the stored ion due to excitation by external radiofrequency-quadrupole fields. In this work a new excitation scheme, Ramsey's method of time-separated oscillatory fields, has been successfully tested. It has been shown to reduce significantly the uncertainty in the determination of the cyclotron frequency and thus of the ion mass of interest. The theoretical description of the ion motion excited with Ramsey's method in a Penning trap and subsequently the calculation of the resonance line shapes for different excitation times, pulse structures, and detunings of the quadrupole field has been carried out in a quantum mechanical framework and is discussed in detail in the preceding article in this journal by M. Kretzschmar. Here, the new excitation technique has been applied with the ISOLTRAP mass spectrometer at ISOLDE/CERN for mass measurements on stable as well as short-lived nuclides. The experimental resonances are in agreement with the theoretical predictions and a precision gain close to a factor of four was achieved compared to the use of the conventional excitation technique.Comment: 12 pages, 14 figures, 2 table

OSN Mood Tracking: Exploring the Use of Online Social Network Activity as an Indicator of Mood Changes

Online social networks (OSNs) have become an integral part of our everyday lives, where we share our thoughts and feelings. This study analyses the extent to which the changes of an individual’s real-world psychological mood can be inferred by tracking their online activity on Facebook and Twitter. By capturing activities from the OSNs and ground truth data via experience sampling, it was found that mood changes can be detected within a window of 7 days for 61% of the participants by using specific, combined online activity signals. The participants fall into three distinct groups: those whose mood correlates positively with their online activity, those who correlate negatively and those who display a weak correlation. We trained two classifiers to identify these groups using features from their online activity, which achieved precision of 95.2% and 84.4% respectively. Our results suggest that real-world mood changes can be passively tracked through online activity on OSNs

Mutant and chimeric recobinant plasminogen activatorsproduction in eukaryotic cellsand preliminary characterization

Mutant urokinase-type plasminogen activator (u-PA) genes and hybrid genes between tissue-type plasminogen activator (t-PA) and u-PA have been designed to direct the synthesis of new plasminogen activators and to investigate the structure-function relationship in these molecules. The following classes of constructs were made starting from cDNA encoding human t-PA or u-PA: 1) u-PA mutants in which the Arg156 and Lys158 were substituted with threonine, thus preventing cleavage by thrombin and plasmin; 2) hybrid molecules in which the NH2-terminal regions of t-PA (amino acid residues 1-67, 1-262, or 1-313) were fused with the COOH-terminal region of u-PA (amino acids 136-411, 139-411, or 195-411, respectively); and 3) a hybrid molecule in which the second kringle of t-PA (amino acids 173-262) was inserted between amino acids 130 and 139 of u-PA. In all cases but one, the recombinant proteins, produced by transfected eukaryotic cells, were efficiently secreted in the culture medium. The translation products have been tested for their ability to activate plasminogen after in situ binding to an insolubilized monoclonal antibody directed against urokinase. All recombinant enzymes were shown to be active, except those in which Lys158 of u-PA was substituted with threonine. Recombination of structural regions derived from t-PA, such as the finger, the kringle 2, or most of the A-chain sequences, with the protease part or the complete u-PA molecule did not impair the catalytic activity of the hybrid polypeptides. This observation supports the hypothesis that structural domains in t-PA and u-PA fold independently from one to another

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered

Latent Variable GIMME Using Model Implied Instrumental Variables (MIIVs)

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct and use methods, such as factor analysis, to arrive at scores from these indices. In addition to accurately measuring individuals, researchers also need to find the model that best describes the relations among the latent constructs. Most currently available data-driven searches do not include latent variables. We present an approach that builds from the strong foundations of group iterative multiple model estimation (GIMME), the idiographic filter, and model implied instrumental variables with two-stage least squares estimation (MIIV-2SLS) to provide researchers with the option to include latent variables in their data-driven model searches. The resulting approach is called latent variable GIMME (LV-GIMME). GIMME is utilized for the data-driven search for relations that exist among latent variables. Unlike other approaches such as the idiographic filter, LV-GIMME does not require that the latent variable model to be constant across individuals. This requirement is loosened by utilizing MIIV-2SLS for estimation. Simulated data studies demonstrate that the method can reliably detect relations among latent constructs, and that latent constructs provide more power to detect effects than using observed variables directly. We use empirical data examples drawn from functional MRI and daily self-report data

Position-sensitive ion detection in precision Penning trap mass spectrometry

A commercial, position-sensitive ion detector was used for the first time for the time-of-flight ion-cyclotron resonance detection technique in Penning trap mass spectrometry. In this work, the characteristics of the detector and its implementation in a Penning trap mass spectrometer will be presented. In addition, simulations and experimental studies concerning the observation of ions ejected from a Penning trap are described. This will allow for a precise monitoring of the state of ion motion in the trap.Comment: 20 pages, 13 figure

Automatic Metadata Generation using Associative Networks

In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content analysis which can be costly and inefficient. The automatic metadata generation system proposed in this article leverages resource relationships generated from existing metadata as a medium for propagation from metadata-rich to metadata-poor resources. Because of its independence from content analysis, it can be applied to a wide variety of resource media types and is shown to be computationally inexpensive. The proposed method operates through two distinct phases. Occurrence and co-occurrence algorithms first generate an associative network of repository resources leveraging existing repository metadata. Second, using the associative network as a substrate, metadata associated with metadata-rich resources is propagated to metadata-poor resources by means of a discrete-form spreading activation algorithm. This article discusses the general framework for building associative networks, an algorithm for disseminating metadata through such networks, and the results of an experiment and validation of the proposed method using a standard bibliographic dataset