Functional ecology of the biological soil crust in semiarid SE Spain: sun and shade populations of Diploschistes diacapsis (Ach.) Lumbsch

The Tabernas badlands in semiarid south-east Spain is one of the driest regions in Europe with a mean annual precipitation of c. 240 mm. The landscape is deeply dissected, with canyons, ramblas and sparsely vegetated eroded badland slopes. The vegetation is predominantly a biological soil crust consisting of different types of lichen-rich communities, one of the more conspicuous being dominated by Diploschistes diacapsis (Ach.) Lumbsch. This lichen is mainly restricted to the north- facing slopes, where it forms extensive whitish carpets and probably plays an important role in preventing erosion of the slopes and allowing plant colonization. South-facing slopes are much more eroded and generally lack vegetation. %The photosynthetic performance of north (shade) and south-facing (sun) populations of D. diacapsis was studied to determine if these different populations showed any adaptations to the microclimatic conditions of their individual habitats. The response of CO2 exchange to light intensity, temperature and water content was measured under controlled conditions in the laboratory. Dry weight-based net photosynthetic rates were higher in the southern-exposed population but quantum efficiency, and light compensation points were similar. Thallus weight per unit area (LMA) was considerably higher for shade specimens but maximum water content and optimal water content were very similar and chlorophyll content on a dry weight basis was also similar. Chlorophyll content on an area basis was higher in the northern-exposed population and always much larger than those reported in other studies on the same species (up to 8 times larger) with the result that NP values on a chlorophyll basis were relatively low. The larger LMA meant that shade thalli stored more water per unit area which should ensure longer active periods than sun thalli. The results support a strategy pair of high NP and short active time versus low NP and long active time, both having been reported for other soil crust species. However, the visibly larger biomass of the shade D. diacapsis suggests that the lichen is at the limit of its adaptability in these habitats

Surface Curvature Differentially Regulates Stem Cell Migration and Differentiation via Altered Attachment Morphology and Nuclear Deformation

Signals from the microenvironment around a cell are known to influence cell behavior. Material properties, such as biochemical composition and substrate stiffness, are today accepted as significant regulators of stem cell fate. The knowledge of how cell behavior is influenced by 3D geometric cues is, however, strongly limited despite its potential relevance for the understanding of tissue regenerative processes and the design of biomaterials. Here, the role of surface curvature on the migratory and differentiation behavior of human mesenchymal stem cells (hMSCs) has been investigated on 3D surfaces with well-defined geometric features produced by stereolithography. Time lapse microscopy reveals a significant increase of cell migration speed on concave spherical compared to convex spherical structures and flat surfaces resulting from an upward-lift of the cell body due to cytoskeletal forces. On convex surfaces, cytoskeletal forces lead to substantial nuclear deformation, increase lamin-A levels and promote osteogenic differentiation. The findings of this study demonstrate a so far missing link between 3D surface curvature and hMSC behavior. This will not only help to better understand the role of extracellular matrix architecture in health and disease but also give new insights in how 3D geometries can be used as a cell-instructive material parameter in the field of biomaterial-guided tissue regeneration.Peer reviewe

High-resolution tSZ cartography of clusters of galaxies with NIKA at the IRAM 30-m telescope

The thermal Sunyaev-Zeldovich effect (tSZ) is a powerful probe to study clusters of galaxies and is complementary with respect to X-ray, lensing or optical observations. Previous arcmin resolution tSZ observations ({\it e.g.} SPT, ACT and Planck) only enabled detailed studies of the intra-cluster medium morphology for low redshift clusters ($z < 0.2$). Thus, the development of precision cosmology with clusters requires high angular resolution observations to extend the understanding of galaxy cluster towards high redshift. NIKA2 is a wide-field (6.5 arcmin field of view) dual-band camera, operated at $100 \ {\rm mK}$ and containing $\sim 3300$ KID (Kinetic Inductance Detectors), designed to observe the millimeter sky at 150 and 260 GHz, with an angular resolution of 18 and 12 arcsec respectively. The NIKA2 camera has been installed on the IRAM 30-m telescope (Pico Veleta, Spain) in September 2015. The NIKA2 tSZ observation program will allow us to observe a large sample of clusters (50) at redshift ranging between 0.5 and 1. As a pathfinder for NIKA2, several clusters of galaxies have been observed at the IRAM 30-m telescope with the NIKA prototype to cover the various configurations and observation conditions expected for NIKA2.Comment: Proceedings of the 28th Texas Symposium on Relativistic Astrophysics, Geneva, Switzerland, December 13-18, 201

Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures

[EN] Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the ability to dynamically power on the nodes of physical clusters as required. Therefore, this paper introduces an open-source framework to deploy elastic virtual clusters running on elastic physical clusters where the computing capabilities of the virtual clusters are dynamically changed to satisfy both the user application's computing requirements and to minimise the amount of energy consumed by the underlying physical cluster that supports an on-premises Cloud. For that, we integrate: i) an elasticity manager both at the infrastructure level (power management) and at the virtual infrastructure level (horizontal elasticity); ii) an automatic Virtual Machine (VM) consolidation agent that reduces the amount of powered on physical nodes using live migration and iii) a vertical elasticity manager to dynamically and transparently change the memory allocated to VMs, thus fostering enhanced consolidation. A case study based on real datasets executed on a production infrastructure is used to validate the proposed solution. The results show that a multi-elastic virtualized datacenter provides users with the ability to deploy customized scalable computing clusters while reducing its energy footprint.The results of this work have been partially supported by ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing), funded by the European Commission under the Cooperation Programme, Horizon 2020 grant agreement No 777154.Alfonso Laguna, CD.; Caballer Fernández, M.; Calatrava Arroyo, A.; Moltó, G.; Blanquer Espert, I. (2018). Multi-elastic Datacenters: Auto-scaled Virtual Clusters on Energy-Aware Physical Infrastructures. Journal of Grid Computing. 17(1):191-204. https://doi.org/10.1007/s10723-018-9449-zS191204171Buyya, R.: High Performance Cluster Computing: Architectures and Systems. 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In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’13, pp 56:1–56:12. ACM, New York (2013). https://doi.org/10.1145/2503210.2503236 . http://doi.acm.org/10.1145/2503210.2503236Bialecki, A., Cafarella, M., Cutting, D., Omalley, O.: Hadoop: a framework for running applications on large clusters built of commodity hardware. Tech. rep. Apache Hadoop. http://hadoop.apache.org (2005)MIT: StarCluster Elastic Load Balancer. http://web.mit.edu/stardev/cluster/docs/0.92rc2/manual/load_balancer.htmlAppliance, C.C.S.: Creating elastic virtual clusters. http://cernvm.cern.ch/portal/elasticclusters (2015)Research project, T.G.: The games research project. http://www.green-datacenters.eu (2013)Cioara, T., Anghel, I., Salomie, I., Copil, G., Moldovan, D., Kipp, A.: Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning. In: 2011 10th International Symposium on Parallel and Distributed Computing (ISPDC), pp 163–169 (2011). https://doi.org/10.1109/ISPDC.2011.32Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp 500–507 (2014). https://doi.org/10.1109/PDP.2014.109Masoumzadeh, S., Hlavacs, H.: Integrating vm selection criteria in distributed dynamic vm consolidation using fuzzy q-learning. In: 2013 9th International Conference on Network and Service Management (CNSM), pp 332–338 (2013). https://doi.org/10.1109/CNSM.2013.6727854Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: 2011 12th IEEE/ACM International Conference on Grid Computing (GRID), pp 26–33 (2011). https://doi.org/10.1109/Grid.2011.13Pop, C.B., Anghel, I., Cioara, T., Salomie, I., Vartic, I.: A swarm-inspired data center consolidation methodology. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, WIMS ’12, pp 41:1–41:7. ACM, New York (2012). https://doi.org/10.1145/2254129.2254180Marzolla, M., Babaoglu, O., Panzieri, F.: Server consolidation in clouds through gossiping. In: Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WOWMOM ’11, pp 1–6. IEEE Computer Society, Washington, DC (2011). https://doi.org/10.1109/WoWMoM.2011.5986483Ghafari, S., Fazeli, M., Patooghy, A., Rikhtechi, L.: Bee-mmt: A load balancing method for power consumption management in cloud computing. 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Metagenomics reveals our incomplete knowledge of global diversity

Financial support was provided by projects BFU2006-06003 from the Spanish Ministry of Education and Science (MEC) and GV/2007/050 from the Generalitat Valenciana, Spain. J.T. is a recipient of a contract in the FIS Program from ISCIII, Spanish Ministry of Health.Pignatelli, M.; Aparicio Pla, G.; Blanquer Espert, I.; Hernández García, V.; Moya, A.; Tamames, J. (2008). Metagenomics reveals our incomplete knowledge of global diversity. Bioinformatics. 24(18):2124-2125. https://doi.org/10.1093/bioinformatics/btn355S212421252418Koski, L. B., & Golding, G. B. (2001). The Closest BLAST Hit Is Often Not the Nearest Neighbor. Journal of Molecular Evolution, 52(6), 540-542. doi:10.1007/s002390010184Krause, L., Diaz, N. N., Goesmann, A., Kelley, S., Nattkemper, T. W., Rohwer, F., … Stoye, J. (2008). Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Research, 36(7), 2230-2239. doi:10.1093/nar/gkn038Mavromatis, K., Ivanova, N., Barry, K., Shapiro, H., Goltsman, E., McHardy, A. C., … Kyrpides, N. C. (2007). Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nature Methods, 4(6), 495-500. doi:10.1038/nmeth1043Tamames, J., & Moya, A. (2008). Estimating the extent of horizontal gene transfer in metagenomic sequences. BMC Genomics, 9(1), 136. doi:10.1186/1471-2164-9-136Tringe, S. G. (2005). Comparative Metagenomics of Microbial Communities. Science, 308(5721), 554-557. doi:10.1126/science.1107851Tringe, S. G., Zhang, T., Liu, X., Yu, Y., Lee, W. H., Yap, J., … Ruan, Y. (2008). The Airborne Metagenome in an Indoor Urban Environment. PLoS ONE, 3(4), e1862. doi:10.1371/journal.pone.0001862COLE, F. N. (1900). AMERICAN MATHEMATICAL SOCIETY. Science, 11(263), 66-67. doi:10.1126/science.11.263.6

SOLUS: Multimodal System Combining Ultrasounds and Diffuse Optics for Tomographic Imaging of Breast Cancer

An innovative multimodal system for breast imaging was developed combining in a single probe B-mode ultrasound, shear-wave elastography and multi-wavelength time-domain diffuse optical tomography. The clinical validation is ongoing aiming at improving the diagnostic specificity

CO I Barcoding Reveals New Clades and Radiation Patterns of Indo-Pacific Sponges of the Family Irciniidae (Demospongiae: Dictyoceratida)

DNA barcoding is a promising tool to facilitate a rapid and unambiguous identification of sponge species. Demosponges of the order Dictyoceratida are particularly challenging to identify, but are of ecological as well as biochemical importance.Here we apply DNA barcoding with the standard CO1-barcoding marker on selected Indo-Pacific specimens of two genera, Ircinia and Psammocinia of the family Irciniidae. We show that the CO1 marker identifies several species new to science, reveals separate radiation patterns of deep-sea Ircinia sponges and indicates dispersal patterns of Psammocinia species. However, some species cannot be unambiguously barcoded by solely this marker due to low evolutionary rates.We support previous suggestions for a combination of the standard CO1 fragment with an additional fragment for sponge DNA barcoding

PRIMAGE project : predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours