Relativistic Chasles' theorem and the conjugacy classes of the inhomogeneous Lorentz group

This work is devoted to the relativistic generalization of Chasles' theorem, namely to the proof that every proper orthochronous isometry of Minkowski spacetime, which sends some point to its chronological future, is generated through the frame displacement of an observer which moves with constant acceleration and constant angular velocity. The acceleration and angular velocity can be chosen either aligned or perpendicular, and in the latter case the angular velocity can be chosen equal or smaller than than the acceleration. We start reviewing the classical Euler's and Chasles' theorems both in the Lie algebra and group versions. We recall the relativistic generalization of Euler's theorem and observe that every (infinitesimal) transformation can be recovered from information of algebraic and geometric type, the former being identified with the conjugacy class and the latter with some additional geometric ingredients (the screw axis in the usual non-relativistic version). Then the proper orthochronous inhomogeneous Lorentz Lie group is studied in detail. We prove its exponentiality and identify a causal semigroup and the corresponding Lie cone. Through the identification of new Ad-invariants we classify the conjugacy classes, and show that those which admit a causal representative have special physical significance. These results imply a classification of the inequivalent Killing vector fields of Minkowski spacetime which we express through simple representatives. Finally, we arrive at the mentioned generalization of Chasles' theorem.Comment: Latex2e, 49 pages. v2: few typos correcte

TRAIL and Taurolidine induce apoptosis and decrease proliferation in human fibrosarcoma

<p>Abstract</p> <p>Background</p> <p>Disseminated soft tissue sarcoma still represents a therapeutic dilemma because effective cytostatics are missing. Therefore we tested TRAIL and Tarolidine (TRD), two substances with apoptogenic properties on human fibrosarcoma (HT1080).</p> <p>Methods</p> <p>Viability, apoptosis and necrosis were visualized by TUNEL-Assay and quantitated by FACS analysis (Propidiumiodide/AnnexinV staining). Gene expression was analysed by RNA-Microarray and the results validated for selected genes by rtPCR. Protein level changes were documented by Western Blot analysis. NFKB activity was analysed by ELISA and proliferation assays (BrdU) were performed.</p> <p>Results and discussion</p> <p>The single substances TRAIL and TRD induced apoptotic cell death and decreased proliferation in HT1080 cells significantly. Gene expression of several genes related to apoptotic pathways (TRAIL: <it>ARHGDIA</it>, <it>NFKBIA</it>, <it>TNFAIP3</it>; TRD: <it>HSPA1A/B</it>, <it>NFKBIA</it>, <it>GADD45A</it>, <it>SGK</it>, <it>JUN</it>, <it>MAP3K14</it>) was changed. The combination of TRD and TRAIL significantly increased apoptotic cell death compared to the single substances and lead to expression changes in a variety of genes (<it>HSPA1A/B</it>, <it>NFKBIA</it>, <it>PPP1R15A</it>, <it>GADD45A</it>, <it>AXL</it>, <it>SGK</it>, <it>DUSP1</it>, <it>JUN</it>, <it>IRF1</it>, <it>MYC</it>, <it>BAG5</it>, <it>BIRC3</it>). NFKB activity assay revealed an antipodal regulation of the several subunits of NFKB by TRD and TRD+TRAIL compared to TRAIL alone.</p> <p>Conclusion</p> <p>TRD and TRAIL are effective to induce apoptosis and decrease proliferation in human fibrosarcoma. A variety of genes seems to be involved, pointing to the NFKB pathway as key regulator in TRD/TRAIL-mediated apoptosis.</p

High-throughput synthesis of CeO₂ nanoparticles for transparent nanocomposites repelling Pseudomonas aeruginosa biofilms

Preventing bacteria from adhering to material surfaces is an important technical problem and a major cause of infection. One of nature’s defense strategies against bacterial colonization is based on the biohalogenation of signal substances that interfere with bacterial communication. Biohalogenation is catalyzed by haloperoxidases, a class of metal-dependent enzymes whose activity can be mimicked by ceria nanoparticles. Transparent CeO(2)/polycarbonate surfaces that prevent adhesion, proliferation, and spread of Pseudomonas aeruginosa PA14 were manufactured. Large amounts of monodisperse CeO(2) nanoparticles were synthesized in segmented flow using a high-throughput microfluidic benchtop system using water/benzyl alcohol mixtures and oleylamine as capping agent. This reduced the reaction time for nanoceria by more than one order of magnitude compared to conventional batch methods. Ceria nanoparticles prepared by segmented flow showed high catalytic activity in halogenation reactions, which makes them highly efficient functional mimics of haloperoxidase enzymes. Haloperoxidases are used in nature by macroalgae to prevent formation of biofilms via halogenation of signaling compounds that interfere with bacterial cell–cell communication (“quorum sensing”). CeO(2)/polycarbonate nanocomposites were prepared by dip-coating plasma-treated polycarbonate panels in CeO(2) dispersions. These showed a reduction in bacterial biofilm formation of up to 85% using P. aeruginosa PA14 as model organism. Besides biofilm formation, also the production of the virulence factor pyocyanin in is under control of the entire quorum sensing systems P. aeruginosa. CeO(2)/PC showed a decrease of up to 55% in pyocyanin production, whereas no effect on bacterial growth in liquid culture was observed. This indicates that CeO(2) nanoparticles affect quorum sensing and inhibit biofilm formation in a non-biocidal manner

Multi-dimensionality and variability in folk classification of stingless bees (Apidae: Meliponini)

Background: Not long ago Eugene Hunn suggested using a combination of cognitive, linguistic, ecological and evolutionary theories in order to account for the dynamic character of ethnoecology in the study of folk classification systems. In this way he intended to question certain homogeneity in folk classifications models and deepen in the analysis and interpretation of variability in folk classifications. This paper studies how a rural culturally mixed population of the Atlantic Forest of Misiones (Argentina) classified honey-producing stingless bees according to the linguistic, cognitive and ecological dimensions of folk classification. We also analyze the socio-ecological meaning of binomialization in naming and the meaning of general local variability in the appointment of stingless bees. Methods: We used three different approaches: the classical approach developed by Brent Berlin which relies heavily on linguistic criteria, the approach developed by Eleonor Rosch which relies on psychological (cognitive) principles of categorization and finally we have captured the ecological dimension of folk classification in local narratives. For the second approximation, we developed ways of measuring the degree of prototypicality based on a total of 107 comparisons of the type "X is similar to Y" identified in personal narratives. Results: Various logical and grouping strategies coexist and were identified as: graded of lateral linkage, hierarchical and functional. Similarity judgments among folk taxa resulted in an implicit logic of classification graded according to taxa's prototypicality. While there is a high agreement on naming stingless bees with monomial names, a considerable number of underrepresented binomial names and lack of names were observed. Two possible explanations about reported local naming variability are presented. Conclusions: We support the multidimensionality of folk classification systems. This confirms the specificity of local classification systems but also reflects the use of grouping strategies and mechanisms commonly observed in other cultural groups, such as the use of similarity judgments between more or less prototypical organisms. Also we support the idea that alternative naming results from a process of fragmentation of knowledge or incomplete transmission of knowledge. These processes lean on the facts that culturally based knowledge, on the one hand, and biologic knowledge of nature on the other, can be acquired through different learning pathways.Fil: Zamudio, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); ArgentinaFil: Hilgert, Norma Ines. Consejo Nacional de Investigaciones Cientiâ­ficas y Tecnicas. Centro Cientifico Tecnologico Nordeste. Instituto de Biologia Subtropical. Instituto de Biologia Subtropical - Nodo Puerto Iguazu; Argentin

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services