Pattern formation in the dipolar Ising model on a two-dimensional honeycomb lattice

We present Monte Carlo simulation results for a two-dimensional Ising model with ferromagnetic nearest-neighbor couplings and a competing long-range dipolar interaction on a honeycomb lattice. Both structural and thermodynamic properties are very similar to the case of a square lattice, with the exception that structures reflect the sixfold rotational symmetry of the underlying honeycomb lattice. To deal with the long-range nature of the dipolar interaction we also present a simple method of evaluating effective interaction coefficients, which can be regarded as a more straightforward alternative to the prevalent Ewald summation techniques.Comment: 5 pages, 5 figure

New perspectives for viability studies with high-content analysis Raman spectroscopy (HCA-RS)

Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications

INTEGRAL observations of the blazar Mrk 421 in outburst (Results of a multi-wavelength campaign)

We report the results of a multi-wavelength campaign on the blazar Mrk 421 during outburst. We observed four strong flares at X-ray energies that were not seen at other wavelengths (partially because of missing data). From the fastest rise in the X-rays, an upper limit could be derived on the extension of the emission region. A time lag between high-energy and low-energy X-rays was observed, which allowed an estimation of the magnetic-field strength. The spectral analysis of the X-rays revealed a slight spectral hardening of the low-energy (3 - 43 keV) spectral index. The hardness-ratio analysis of the Swift-XRT (0.2 - 10 keV) data indicated a small correlation with the intensity; i. e., a hard-to-soft evolution was observed. At the energies of IBIS/ISGRI (20 - 150 keV), such correlations are less obvious. A multiwavelength spectrum was composed and the X-ray and bolometric luminosities are calculated.Comment: 15 pages, 18 figures; accepted by Astronomy & Astrophysic

Robust texture features for still-image retrieval

A detailed evaluation of the use of texture features in a query-by-example approach to image retrieval is presented. Three radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view are used. The features were evaluated and tuned on retrieval tasks from the Corel collection and then evaluated and tested on the TRECVID 2003 and ImageCLEF 2004 collections. For the latter two the effects of combining texture features with a colour feature were studied. Texture features that perform particularly well are identified, demonstrating that they provide robust performance across a range of datasets

Structure-Based Screening of Tetrazolylhydrazide Inhibitors versus KDM4 Histone Demethylases

Human histone demethylases are known to play an important role in the development of several tumor types. Consequently, they have emerged as important medical targets for the treatment of human cancer. Herein, structural studies on tetrazolylhydrazide inhibitors as a new scaffold for a certain class of histone demethylases, the JmjC proteins, are reported. A series of compounds are structurally described and their respective binding modes to the KDM4D protein, which serves as a high-resolution model to represent the KDM4 subfamily in crystallographic studies, are examined. Similar to previously reported inhibitors, the compounds described herein are competitors for the natural KDM4 cofactor, 2-oxoglutarate. The tetrazolylhydrazide scaffold fills an important gap in KDM4 inhibition and newly described, detailed interactions of inhibitor moieties pave the way to the development of compounds with high target-binding affinity and increased membrane permeability, at the same time

Creative professional users musical relevance criteria

Although known item searching for music can be dealt with by searching metadata using existing text search techniques, human subjectivity and variability within the music itself make it very difficult to search for unknown items. This paper examines these problems within the context of text retrieval and music information retrieval. The focus is on ascertaining a relationship between music relevance criteria and those relating to relevance judgements in text retrieval. A data-rich collection of relevance judgements by creative professionals searching for unknown musical items to accompany moving images using real world queries is analysed. The participants in our observations are found to take a socio-cognitive approach and use a range of content and context based criteria. These criteria correlate strongly with those arising from previous text retrieval studies despite the many differences between music and text in their actual content

Circular job-related spatial mobility in Germany:Comparative analyses of two representative surveys on the forms, prevalence and relevance in the context of partnership and family development

Die Mobilitätsanforderungen in der Arbeitswelt nehmen zu und gleichzeitig sind vielfältigere und komplexere Formen berufsbezogener Mobilität zu beobachten. Der Zusammenhang zwischen berufsbezogenem Mobilitätsverhalten und familienbezogenen Prozessen erfährt in der Folge zunehmende Aufmerksamkeit im Bereich der Mobilitäts- und Familienforschung. Erfassung und Analyse berufsbezogener Mobilität erfolgten jedoch bisher selten einheitlich und systematisch. Mit dem europäisch-vergleichenden Survey „Job Mobilities and Family Lives in Europe“ (JobMob) und dem „Beziehungs- und Familienpanel“ (pairfam) liegen für Deutschland zwei repräsentative Datensätze vor, die weitgehend vergleichbare Operationalisierungen bereithalten. Dies erlaubt es, in systematischer Weise vergleichende Analysen durchzuführen. Damit bietet sich in diesem Forschungsfeld erstmalig die Möglichkeit, inhaltliche Befunde einer unmittelbaren wechselseitigen Validierung zu unterziehen und diese auf ihre Generalisierbarkeit zu überprüfen.Der vorliegende Beitrag verfolgt diesbezüglich drei zentrale Ziele. Zunächst wird für die beiden Surveys ein gemeinsamer Indikator für zirkuläres berufsbezogenes Mobilitätsverhalten vorgestellt. Auf der Grundlage dieses gemeinsamen Indikators wird die Verbreitung verschiedener Mobilitätsformen und deren Zusammensetzung nach zentralen soziodemografischen Merkmalen für beide Stichproben im Vergleich untersucht. Darüber hinaus wird anhand multivariater Analysen die Relevanz berufsbezogener Mobilität im Kontext der Partnerschafts- und  Familienentwicklung illustriert. Die Befunde verweisen dabei auf das Mobilitätsverhalten als wichtigen individuellen Kontextfaktor bei der Erklärung partnerschaftlicher und familialer Prozesse. Insbesondere beruflich mobile Frauen leben demnach seltener in hoch institutionalisierten Partnerschaften und sind seltener Mütter.Over the past few decades, employees have had to come to terms with increased demands of the labour market requiring greater flexibility and mobility. At the same time, increasingly versatile and complex forms of job-related spatial mobility are emerging. Consequently, the correlation between job mobility patterns and family-related processes is attracting more and more attention in the field of mobility and family research. However, to date there has rarely been a standard by which to systematically record and analyse job mobility. “Job Mobilities and Family Lives in Europe” (JobMob), a comparative European survey, and the “Panel Analysis of Intimate Relationships and Family Dynamics” (pairfam) constitute two sets of representative data for Germany, which provide largely comparable operationalisations for several forms of circular job mobility, thus allowing us to systematically perform comparative analyses. For the first time ever in this field of research, it is now possible to subject findings to a direct reciprocal validation process and to check whether general rules and correlations can be derived from them.In this regard, the present article aims at achieving three essential objectives. First, we will introduce a common indicator for circular job mobility patterns found in the two surveys. On the basis of this common indicator, we will comparatively analyse the prevalence of different mobility forms and their composition according to key socio-demographic characteristics. In addition, we will use multivariate analyses to illustrate the relevance of job mobility for partnership and family development. Results suggest mobility patterns to be an important individual context factor when explaining processes relevant to partnerships and family. In particular, women who exhibit some degree of job mobility are less often married and rarely have children

Application of High-Throughput Screening Raman Spectroscopy (HTS-RS) for Label-Free Identification and Molecular Characterization of Pollen

Pollen studies play a critical role in various fields of science. In the last couple of decades, replacement of manual identification of pollen by image-based methods using pollen morphological features was a great leap forward, but challenges for pollen with similar morphology remain, and additional approaches are required. Spectroscopy approaches for identification of pollen, such as Raman spectroscopy has potential benefits over traditional methods, due to the investigation of the intrinsic molecular composition of a sample. However, current Raman-based characterization of pollen is complex and time-consuming, resulting in low throughput and limiting the statistical significance of the acquired data. Previously demonstrated high-throughput screening Raman spectroscopy (HTS-RS) eliminates the complexity as well as human interaction by incorporation full automation of the data acquisition process. Here, we present a customization of HTS-RS for pollen identification, enabling sampling of a large number of pollen in comparison to other state-of-the-art Raman pollen investigations. We show that using Raman spectra we are able to provide a preliminary estimation of pollen types based on growth habits using hierarchical cluster analysis (HCA) as well as good taxonomy of 37 different Pollen using principal component analysis-support vector machine (PCA-SVM) with good accuracy even for the pollen specimens sharing similar morphological features. Our results suggest that HTS-RS platform meets the demands for automated pollen detection making it an alternative method for research concerning pollen

Imputing missing data in plant traits: A guide to improve gap‐filling

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap-filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi-and multivariate) and (3) taxonomic and functional clustering (valuewise, uni-and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation