International Glossina Genome Initiative 2004-2014: a driver for post-genomic era research on the African continent

Human African trypanosomiasis (HAT), also known as sleeping sickness, is a neglected disease that impacts 70 million people distributed over 1.55 million km2 in sub- Saharan Africa and includes at least 50% of the population of theDemocratic Republic of the Congo [1]. Trypanosoma brucei gambiense accounts for more than 98% of the infections in central and West Africa, the remaining infections being from Trypanosoma brucei rhodesiense in East Africa [2]. The parasites are transmitted to the hosts through the bite of an infected tsetse fly. Disease control is challenging as there are no vaccines, and effective, easily delivered drugs are still lacking. Treatment invariably involves lengthy hospitalization, with both medical and socioeconomic consequences.Web of Scienc

Cellular responses of Candida albicans to phagocytosis and the extracellular activities of neutrophils are critical to counteract carbohydrate starvation, oxidative and nitrosative stress

Acknowledgments We thank Alexander Johnson (yhb1D/D), Karl Kuchler (sodD/D mutants), Janet Quinn (hog1D/D, hog1/cap1D/D, trx1D/D) and Peter Staib (ssu1D/D) for providing mutant strains. We acknowledge helpful discussions with our colleagues from the Microbial Pathogenicity Mechanisms Department, Fungal Septomics and the Microbial Biochemistry and Physiology Research Group at the Hans Kno¨ll Institute (HKI), specially Ilse D. Jacobsen, Duncan Wilson, Sascha Brunke, Lydia Kasper, Franziska Gerwien, Sea´na Duggan, Katrin Haupt, Kerstin Hu¨nniger, and Matthias Brock, as well as from our partners in the FINSysB Network. Author Contributions Conceived and designed the experiments: PM HW IMB AJPB OK BH. Performed the experiments: PM CD HW. Analyzed the data: PM HW IMB AJPB OK BH. Wrote the paper: PM HW OK AJPB BH.Peer reviewedPublisher PD

Microspatial variability in community structure and photophysiology of calcified macroalgal microbiomes revealed by coupling of hyperspectral and high-resolution fluorescence imaging

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The attached file is the published version of the article

Spatial Re-Establishment Dynamics of Local Populations of Vectors of Chagas Disease

Chagas disease is transmitted by blood-sucking bugs (vectors) and presents a severe public health threat in the Americas. Worldwide there are approximately 10 million people infected with Chagas disease, a disease for which there is currently no effective cure. Vector suppression is the main strategy to control the spread of this disease. Unfortunately, the vectors have been resurgent in some areas. It is important to understand the dynamics of reinfestation where it occurs. Here we show how different models fitted to patch-level bug infestation data can elucidate different aspects of re-establishment dynamics. Our results demonstrated a 6-month time lag between detection of a new infestation and dispersal events, seasonality in dispersal rates and effects of previous vector infestation on subsequent vector establishment rates. In addition we provide estimates of dispersal distances and the effect of insecticide spraying on rates of vector re-establishment. While some of our results confirm previous findings, the effects of season and previous infestation on bug establishment challenge our current understanding of T. infestans ecology and highlight important gaps in our knowledge of T. infestans dispersal

Azimuthal anisotropy of charged particles at high transverse momenta in PbPb collisions at sqrt(s[NN]) = 2.76 TeV

The azimuthal anisotropy of charged particles in PbPb collisions at nucleon-nucleon center-of-mass energy of 2.76 TeV is measured with the CMS detector at the LHC over an extended transverse momentum (pt) range up to approximately 60 GeV. The data cover both the low-pt region associated with hydrodynamic flow phenomena and the high-pt region where the anisotropies may reflect the path-length dependence of parton energy loss in the created medium. The anisotropy parameter (v2) of the particles is extracted by correlating charged tracks with respect to the event-plane reconstructed by using the energy deposited in forward-angle calorimeters. For the six bins of collision centrality studied, spanning the range of 0-60% most-central events, the observed v2 values are found to first increase with pt, reaching a maximum around pt = 3 GeV, and then to gradually decrease to almost zero, with the decline persisting up to at least pt = 40 GeV over the full centrality range measured.Comment: Replaced with published version. Added journal reference and DO

Compressed representation of a partially defined integer function over multiple arguments

In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM models, which are constructed with different negative samples and reflect different aspects of classifications, are utilized as inputs to the second-layer SVM. In order to design negative data which produce fewer false positive predictions, we iteratively construct SVM models or classification boundaries from positive and tentative negative samples and select additional negative sample candidates according to pre-determined rules. Moreover, in order to fully utilize the advantages of statistical learning methods, we propose a strategy to effectively feedback experimental results to computational predictions with consideration of biological effects of interest. We show the usefulness of our approach in predicting potential ligands binding to human androgen receptors from more than 19 million chemical compounds and verifying these predictions by in vitro binding. Moreover, we utilize this experimental validation as feedback to enhance subsequent computational predictions, and experimentally validate these predictions again. This efficient procedure of the iteration of the in silico prediction and in vitro or in vivo experimental verifications with the sufficient feedback enabled us to identify novel ligand candidates which were distant from known ligands in the chemical space

Transmission of malaria and genotypic variability of Plasmodium falciparum on the Island of Annobon (Equatorial Guinea)

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