A Unified Model of Exclusive ρ0\rho^0, ϕ\phi and \jpsi Electroproduction

A two-component model is developed for diffractive electroproduction of $\rho^0$, $\phi$ and \jpsi, based on non-perturbative and perturbative two-gluon exchange. This provides a common kinematical structure for non-perturbative and perturbative effects, and allows the role of the vector-meson vertex functions to be explored independently of the production dynamics. A good global description of the vector-meson data is obtained.Comment: 30 pages, 35 figure

Nanorings and rods interconnected by self-assembly mimicking an artificial network of neurons

[EN] Molecular electronics based on structures ordered as neural networks emerges as the next evolutionary milestone in the construction of nanodevices with unprecedented applications. However, the straightforward formation of geometrically defined and interconnected nanostructures is crucial for the production of electronic circuitry nanoequivalents. Here we report on the molecularly fine-tuned self-assembly of tetrakis-Schiff base compounds into nanosized rings interconnected by unusually large nanorods providing a set of connections that mimic a biological network of neurons. The networks are produced through self-assembly resulting from the molecular conformation and noncovalent intermolecular interactions. These features can be easily generated on flat surfaces and in a polymeric matrix by casting from solution under ambient conditions. The structures can be used to guide the position of electron-transporting agents such as carbon nanotubes on a surface or in a polymer matrix to create electrically conducting networks that can find direct use in constructing nanoelectronic circuits.The research leading to these results has received funding from ICIQ, ICREA, the Spanish Ministerio de Economia y Competitividad (MINECO) through project CTQ2011-27385 and the European Community Seventh Framework Program (FP7-PEOPLE-ITN-2008, CONTACT consortium) under grant agreement number 238363. We acknowledge E. C. Escudero-Adan, M. Martinez-Belmonte and E. Martin from the X-ray department of ICIQ for crystallographic analysis, and M. Moncusi, N. Argany, R. Marimon, M. Stefanova and L. Vojkuvka from the Servei de Recursos Cientifics i Tecnics from Universitat Rovira i Virgili (Tarragona, Spain).Escarcega-Bobadilla, MV.; Zelada-Guillen, GA.; Pyrlin, SV.; Wegrzyn, M.; Ramos, MMD.; Giménez Torres, E.; Stewart, A.... (2013). Nanorings and rods interconnected by self-assembly mimicking an artificial network of neurons. Nature Communications. 4:2648-2648. https://doi.org/10.1038/ncomms3648S264826484Champness, N. R. Making the right connections. Nat. Chem. 4, 149–150 (2012).Hopfield, J. J. & Tank, D. W. Computing with neural circuits: A model. Science 233, 625–633 (1986).Andres, P. R. et al. Self-assembly of a two-dimensional superlattice of molecularly linked metal clusters. Science 273, 1690–1693 (1996).Eichen, Y., Braun, E., Sivan, U. & Ben-Yoseph, G. Self-assembly of nanoelectronic components and circuits using biological templates. Acta Polym. 49, 663–670 (1998).Kawakami, T. et al. Possibilities of molecule-based spintronics of DNA wires, sheets, and related materials. Int. J. Quantum Chem. 105, 655–671 (2005).Kashtan, N., Itzkovitz, S., Milo, R. & Alon, U. Topological generalizations of network motifs. Phys. Rev. E 70, 031909 (2004).Grill, L. et al. Nano-architectures by covalent assembly of molecular building blocks. Nat. Nanotech. 2, 687–691 (2007).Lafferentz, L. et al. Controlling on-surface polymerization by hierarchical and substrate-directed growth. Nat. Chem. 4, 215–220 (2012).Alivisatos, A. P. et al. From molecules to materials: current trends and future directions. Adv. Mater. 10, 1297–1336 (1998).Pauling, L. The principles determining the structure of complex ionic crystals. J. Am. Chem. Soc. 51, 1010–1026 (1929).Damasceno, P. F., Engel, M. & Glotzer, S. C. Predictive self-assembly of polyhedra into complex structures. Science 337, 453–457 (2012).De Graaf, J. & Manna, L. A roadmap for the assembly of polyhedral particles. Science 337, 417–418 (2012).Percec, V. et al. Controlling polymer shape through the self-assembly of dendritic side-groups. Nature 391, 161–164 (1998).Stupp, S. I. et al. Supramolecular materials: self-organized nanostructures. Science 276, 384–389 (1997).Mann, S. The chemistry of form. Angew. Chem. Int. Ed. 39, 3392–3406 (2000).Sakakibara, K., Hill, J. P. & Ariga, K. Thin-film-based nanoarchitectures for soft matter: controlled assemblies into two-dimensional worlds. Small 7, 1288–1308 (2011).Huang, Z. et al. Pulsating tubules from noncovalent macrocycles. Science 337, 1521–1526 (2012).Ackermann, D., Jester, S.-S. & Famulok, M. Design strategy for DNA rotaxanes with a mechanically reinforced PX100 axle. Angew. Chem. Int. Ed. 27, 6771–6775 (2012).Marx, J. L. Microtubules: versatile organelles. Science 181, 1236–1237 (1973).Heus, H. A. & Pardi, A. Structural features that give rise to the unusual stability of RNA hairpins containing GNRA loops. Science 253, 191–194 (1991).Braun, E., Eichen, Y., Sivan, U. & Ben-Yoseph, G. DNA-templated assembly and electrode attachment of a conducting silver wire. Nature 391, 775–778 (1998).Zhang, S. Fabrication of novel biomaterials through molecular self-assembly. Nat. Biotechnol. 21, 1171–1178 (2003).Cai, X. et al. Integrated compact optical vortex beam emitters. Science 338, 363–365 (2012).Clark, A. W. & Cooper, J. M. Nanogap ring antennae as plasmonically coupled SERRS substrates. Small 7, 119–125 (2011).Armani, A. M., Kulkarni, R. P., Fraser, S. E., Flagan, R. C. & Vahala, K. J. Label-free, single-molecule detection with optical microcavities. Science 317, 783–787 (2007).Frischmann, P. D., Guieu, S., Tabeshi, R. & MacLachlan, M. J. Columnar organization of head-to-tail self-assembled Pt4 rings. J. Am. Chem. Soc. 132, 7668–7675 (2010).Frischmann, P. D. et al. Capsule formation, carboxylate exchange, and DFT exploration of cadmium cluster metallocavitands: highly dynamic supramolecules. J. Am. Chem. Soc. 132, 3893–3908 (2010).Akine, S., Hotate, S. & Nabeshima, T. A molecular leverage for helicity control and helix Inversion. J. Am. Chem. Soc. 133, 13868–13871 (2011).Salassa, G. et al. Extremely strong self-assembly of a bimetallic salen complex visualized at the single-molecule level. J. Am. Chem. Soc. 134, 7186–7192 (2012).Escárcega-Bobadilla, M. V., Salassa, G., Martínez Belmonte, M., Escudero-Adán, E. C. & Kleij, A. W. Versatile switching in substrate topicity: supramolecular chirality induction in di- and trinuclear host complexes. Chem. Eur. J. 18, 6805–6810 (2012).Frischmann, P. D., Jiang, J., Hui, J. K.-H., Grzybowski, J. J. & MacLachlan, M. J. Reversible—irreversible approach to Schiff base macrocycles. Access to isomeric macrocycles with multiple salphen pockets. Org. Lett. 10, 1255–1258 (2008).Glaser, T. Rational design of single-molecule magnets: a supramolecular approach. Chem. Commun. 47, 116–130 (2011).Lee, E. C. et al. Understanding of assembly phenomena by aromatic−aromatic interactions: benzene dimer and the substituted systems. J. Phys. Chem. A 111, 3446–3457 (2007).Grybowski, B. A., Wilmer, C. E., Kim, J., Browne, K. P. & Bishop, K. J. M. Self-assembly: from crystals to cells. Soft Matter. 5, 1110–1128 (2009).Martínez Belmonte, M. et al. Self-assembly of Zn(salphen) complexes: steric regulation, stability studies and crystallographic analysis revealing an unexpected dimeric 3,3′-t-Bu-substituted Zn(salphen) complex. Dalton Trans. 39, 4541–4550 (2010).Salassa, G., Castilla, A. M. & Kleij, A. W. Cooperative self-assembly of a macrocyclic Schiff base complex. Dalton Trans. 40, 5236–5243 (2011).Hormoz, S. & Brenner, M. P. Design principles for self-assembly with short-range interactions. Proc. Natl Acad. Sci. 108, 5193–5198 (2011).Biemans, H. A. M. et al. Hexakis porphyrinato benzenes. A new class of porphyrin arrays. J. Am. Chem. Soc. 120, 11054–11060 (1998).Lensen, M. C. et al. Aided self-assembly of porphyrin nanoaggregates into ring-shaped architectures. Chem. Eur. J. 10, 831–839 (2004).Martin, A., Buguin, A. & Brochard-Wyart, F. Dewetting nucleation centers at soft interfaces. Langmuir. 17, 6553–6559 (2001).Schenning, A. P. H. J., Benneker, F. B. G., Geurts, H. P. M., Liu, X. Y. & Nolte, R. J. M. Porphyrin wheels. J. Am. Chem. Soc. 118, 8549–8552 (1996).Deegan, R. D. et al. Capillary flow as the cause of ring strains from dried liquid drops. Nature 389, 827–829 (1997).Scriven, L. E. & Sternling, C. V. The Marangoni effects. Nature 187, 186–188 (1960).Cai, Y. & Newby, B. Z. Marangoni flow-induced self-assembly of hexagonal and stripe-like nanoparticle patterns. J. Am. Chem. Soc. 130, 6076–6077 (2008).Whitesides, G. M. & Grzybowski, B. Self-assembly at all scales. Science 295, 2418–2421 (2002).Mann, S. Self-assembly and transformation of hybrid nano-objects and nanostructures under equilibrium and non-equilibrium conditions. Nat. Mater. 8, 781–792 (2009).Gröschnel, A. H. et al. Precise hierarchical self-assembly of multicompartment micelles. Nat. Commun. 3, 710 (2012).Adam, M., Dogic, Z., Keller, S. L. & Fraden, S. Entropically driven microphase transitions in mixtures of colloidal rods and spheres. Nature 393, 349–352 (1998).Ohara, P. C., Heath, J. R. & Gelbart, W. M. Self-assembly of submicrometer rings of particles from solutions of nanoparticles. Angew. Chem. Int. Ed. 36, 1077–1080 (1997).Xu, J., Xia, J. & Lin, Z. Evaporation-induced self-assembly of nanoparticles from a sphere-on-flat geometry. Angew. Chem. Int. Ed. 46, 1860–1863 (2007).Yosef, G. & Rabani, E. Self-assembly of nanoparticles into rings: A lattice-gas model. J. Phys. Chem. B 110, 20965–20972 (2006).Khanal, B. P. & Zubarev, E. R. Rings of nanorods. Angew. Chem. Int. Ed. 46, 2195–2198 (2007).Wang, Z. et al. One-step, self-assembly, alignment, and patterning of organic semiconductor nanowires by controlled evaporation of confined microfluids. Angew. Chem. Int. Ed. 50, 2811–2815 (2011).Hong, S. W. et al. Directed self-assembly of gradient concentric carbon nanotube rings. Adv. Func. Mater. 18, 2114–2122 (2008).Palma, M. et al. Controlled formation of carbon nanotube junctions via linker-induced assembly in aqueous solution. J. Am. Chem. Soc. 135, 8440–8443 (2013).Horcas, I. et al. WSXM: A software for scanning probe microscopy and a tool for nanotechnology. Rev. Sci. Instrum. 78, 013705 (2007).Soler, J. M. et al. The SIESTA method for ab initio order-n materials simulation. J. Phys. Cond. Matter 14, 2745–2779 (2002).Haynes, P. D., Mostof, A. A., Skylaris, C. & Payne, M. C. ONETEP: Linear-scaling density-functional theory with plane-waves. J. Phys. Conf. Ser. 26, 143–148 (2006).Valiev, M. et al. NWCHEM: A comprehensive and scalable open-source solution for large scale molecular simulations. Comp. Phys. Commun. 181, 1477–1489 (2010).Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117, 1–19 (1995)

Eating disorders in weight-related therapy (EDIT): Protocol for a systematic review with individual participant data meta-analysis of eating disorder risk in behavioural weight management

The Eating Disorders In weight-related Therapy (EDIT) Collaboration brings together data from randomised controlled trials of behavioural weight management interventions to identify individual participant risk factors and intervention strategies that contribute to eating disorder risk. We present a protocol for a systematic review and individual participant data (IPD) meta-analysis which aims to identify participants at risk of developing eating disorders, or related symptoms, during or after weight management interventions conducted in adolescents or adults with overweight or obesity. We systematically searched four databases up to March 2022 and clinical trials registries to May 2022 to identify randomised controlled trials of weight management interventions conducted in adolescents or adults with overweight or obesity that measured eating disorder risk at pre- and post-intervention or follow-up. Authors from eligible trials have been invited to share their deidentified IPD. Two IPD meta-analyses will be conducted. The first IPD meta-analysis aims to examine participant level factors associated with a change in eating disorder scores during and following a weight management intervention. To do this we will examine baseline variables that predict change in eating disorder risk within intervention arms. The second IPD meta-analysis aims to assess whether there are participant level factors that predict whether participation in an intervention is more or less likely than no intervention to lead to a change in eating disorder risk. To do this, we will examine if there are differences in predictors of eating disorder risk between intervention and no-treatment control arms. The primary outcome will be a standardised mean difference in global eating disorder score from baseline to immediately post-intervention and at 6- and 12- months follow-up. Identifying participant level risk factors predicting eating disorder risk will inform screening and monitoring protocols to allow early identification and intervention for those at risk

Eating disorders in weight-related therapy (EDIT): protocol for a systematic review with individual participant data meta-analysis of eating disorder risk in behavioural weight management

The Eating Disorders In weight-related Therapy (EDIT) Collaboration brings together data from randomised controlled trials of behavioural weight management interventions to identify individual participant risk factors and intervention strategies that contribute to eating disorder risk. We present a protocol for a systematic review and individual participant data (IPD) meta-analysis which aims to identify participants at risk of developing eating disorders, or related symptoms, during or after weight management interventions conducted in adolescents or adults with overweight or obesity. We systematically searched four databases up to March 2022 and clinical trials registries to May 2022 to identify randomised controlled trials of weight management interventions conducted in adolescents or adults with overweight or obesity that measured eating disorder risk at pre- and post-intervention or follow-up. Authors from eligible trials have been invited to share their deidentified IPD. Two IPD meta-analyses will be conducted. The first IPD meta-analysis aims to examine participant level factors associated with a change in eating disorder scores during and following a weight management intervention. To do this we will examine baseline variables that predict change in eating disorder risk within intervention arms. The second IPD meta-analysis aims to assess whether there are participant level factors that predict whether participation in an intervention is more or less likely than no intervention to lead to a change in eating disorder risk. To do this, we will examine if there are differences in predictors of eating disorder risk between intervention and no-treatment control arms. The primary outcome will be a standardised mean difference in global eating disorder score from baseline to immediately post-intervention and at 6- and 12- months follow-up. Identifying participant level risk factors predicting eating disorder risk will inform screening and monitoring protocols to allow early identification and intervention for those at risk

Distributions of calcium in A and I bands of skinned vertebrate muscle fibers stretched to beyond filament overlap.

Measurements were made of the distributions of total calcium along the length of A and I bands in skinned frog semitendinosus muscles using electron probe x-ray microanalysis. Since calcium in the water space was kept below the detection limit of the technique, the signal was assumed to reflect the distribution of calcium bound to myofilament proteins. Data from sarcomeres with overlap between thick and thin filaments showed enhancement of calcium in this region, as previously demonstrated in rabbit psoas muscle fibers in rigor (Cantino, M. E., T. S. Allen, and A. M. Gordon. 1993. Subsarcomeric distribution of calcium in demembranated fibers of rabbit psoas muscle. Biophys. J. 64:211-222). Such enhancement could arise from intrinsic non-uniformities in calcium binding to either thick or thin filaments or from enhancement of calcium binding to either filament by rigor cross-bridge attachment. To test for intrinsic variations in calcium binding, calcium distributions were determined in fibers stretched to beyond filament overlap. Calcium binding was found to be relatively uniform along both thick and thin filaments, and therefore cannot account for the increased calcium observed in the overlap region. From these results it can be concluded that the observed enhancement of calcium is due to an increase in calcium binding to myofilaments as a result of rigor attachment of cross-bridges to actin. The source of the enhancement is most likely an increase in calcium binding to troponin, although enhancement of calcium binding to myosin light chains cannot be ruled out