Es posible un nuevo modelo de hospital universitario? : sin temor a los cambios

Fil: Gherardi, Carlos R. Universidad de Buenos Aires. Hospital de Clínicas José de San Martín; Argentina.El propósito de este trabajo no es presentar un nuevo modelo teórico de hospital universitario sino de\nreflexionar sobre la factibilidad de desarrollar sobre el actual Hospital de Clínicas los cambios\nestructurales que puedan promover la incorporación de modalidades de atención y de gestión hacia el\npaciente que lo aproximen a los requerimientos de la sociedad y considerar también aspectos que\nmodifiquen su sustentabilidad económica

Las tragedias de Amia y Cromañón : el hospital en la emergencia asistencial

Fil: Gherardi, Carlos R. Universidad de Buenos Aires. Hospital de Clínicas José de San Martín; Argentina.Fil: Jorge, Miguel A. Universidad de Buenos Aires. Hospital de Clínicas José de San Martín; Argentina.La Universidad de Buenos Aires y la propia Ciudad de Buenos Aires tienen el privilegio de tener hoy\ntodavía un Hospital Universitario enclavado en pleno centro geográfico de la ciudad, y donde hace\nmucho más que cien años se concentra el esfuerzo en la formación del recurso humano necesario\npara atender la salud de la comunidad. En esta última década, precisamente en sus extremos, 1994 y\n2004, el Hospital de Clínicas tuvo que actuar en las dos catástrofes no naturales más terribles que\nasolaron a nuestro país, y que quedarán también registradas en el mundo por su magnitud, en un caso\nproducto del terrorismo (AMIA) y en otro como resultado de la negligencia y el infortunio (incedio en\nRepública Cromañón)

Criticality and conformality in the random dimer model

In critical systems, the effect of a localized perturbation affects points that are arbitrarily far from the perturbation location. In this paper, we study the effect of localized perturbations on the solution of the random dimer problem in two dimensions. By means of an accurate numerical analysis, we show that a local perturbation of the optimal covering induces an excitation whose size is extensive with finite probability. We compute the fractal dimension of the excitations and scaling exponents. In particular, excitations in random dimer problems on nonbipartite lattices have the same statistical properties of domain walls in spin glass. Excitations produced in bipartite lattices, instead, are compatible with a loop-erased self-avoiding random walk process. In both cases, we find evidence of conformal invariance of the excitations that is compatible with SLEκ with parameter κ depending on the bipartiteness of the underlying lattice onl

Universal mean-field upper bound for the generalization gap of deep neural networks

Modern deep neural networks (DNNs) represent a formidable challenge for theorists: according to the commonly accepted probabilistic framework that describes their performance, these architectures should overfit due to the huge number of parameters to train, but in practice they do not. Here we employ results from replica mean field theory to compute the generalization gap of machine learning models with quenched features, in the teacher-student scenario and for regression problems with quadratic loss function. Notably, this framework includes the case of DNNs where the last layer is optimized given a specific realization of the remaining weights. We show how these results-combined with ideas from statistical learning theory-provide a stringent asymptotic upper bound on the generalization gap of fully trained DNN as a function of the size of the dataset P. In particular, in the limit of large P and N-out (where N-out is the size of the last layer) and N-out << P, the generalization gap approaches zero faster than 2N(out)/P, for any choice of both architecture and teacher function. Notably, this result greatly improves existing bounds from statistical learning theory. We test our predictions on a broad range of architectures, from toy fully connected neural networks with few hidden layers to state-of-the-art deep convolutional neural networks

Induction of resistance and enhancing agronomic performance in grapevines under greenhouse and in open fields by applications of plasma activated water

The exposure of water to a cold atmospheric pressure plasma (CAP) enables the production of plasma activated water (PAW), having high content of reactive species, whose applications were tested on grapevine plants, both in greenhouse and in vineyard conditions. Two different CAPs were used for PAW production, evaluating their effectiveness as a possible mean to control plant diseases. Grapevines infected with yellows associated with the presence of phytoplasmas were treated evaluating qualitative and quantitative yield parameters, phytoplasma presence, and gene expression. The results show the capability of PAW to enhance plant defence mechanisms and, as demonstrated in the field trials, confirmed its ability to improve the health status of the treated plants. Quantitative (q)RT-PCR analyses allowed to determine the transcription level of genes involved in the plant defence response (phenylalanine ammonia-lyase, pal) and in the plant phytoalexin metabolism of PAW-treated materials. The number of symptomatic grapevine plants in vineyards was significantly reduced by the treatments. Transcriptional and post-transcriptional molecular analyses highlighted the PAW ability to enhance the expression of genes encoding the main enzymes involved in the phytoalexin biosynthetic pathway (flavonoids and stilbenes). The PAW ability to enhance some of the plant defence mechanisms also improving the health status of the treated plants was therefore experimentally demonstrated. After three years of trials the overall results demonstrated the possible use of PAW to reduce the disease severity, induce plant resistance both in open field and greenhouse, improving plant healthy status and grapevine yield production

Organizational learning and emotion: constructing collective meaning in support of strategic themes

Missing in the organizational learning literature is an integrative framework that reflects the emotional as well as the cognitive dynamics involved. Here, we take a step in this direction by focusing in depth over time (five years) on a selected organization which manufactures electronic equipment for the office industry. Drawing on personal construct theory, we define organizational learning as the collective re-construal of meaning in the direction of strategically significant themes. We suggest that emotions arise as members reflect on progress or lack of progress in achieving organizational learning. Our evidence suggests that invalidation – where organizational learning fails to correspond with expectations – gives rise to anxiety and frustration, while validation – where organizational learning is aligned with or exceeds expectations – evokes comfort or excitement. Our work aims to capture the key emotions involved as organizational learning proceeds