Labels for non-individuals

Quasi-set theory is a first order theory without identity, which allows us to cope with non-individuals in a sense. A weaker equivalence relation called ``indistinguishability'' is an extension of identity in the sense that if $x$ is identical to $y$ then $x$ and $y$ are indistinguishable, although the reciprocal is not always valid. The interesting point is that quasi-set theory provides us a useful mathematical background for dealing with collections of indistinguishable elementary quantum particles. In the present paper, however, we show that even in quasi-set theory it is possible to label objects that are considered as non-individuals. We intend to prove that individuality has nothing to do with any labelling process at all, as suggested by some authors. We discuss the physical interpretation of our results.Comment: 11 pages, no figure

Performance of winter pasture species in different integrated crop-livestock systems in lowlands of Southern Brazil.

The introduction of winter forage species in succession to rice cropping in lowlands of Southern Brazil is an option for the productive system diversification..

Animal production in different integrated crop-livestock systems in a lowland of Southern Brazil.

To achieve higher lowland use efficiency in Brazilian Southern, a region commonly used for rice production, the livestock activity during the winter period (in succession to summer crops) is a sustainable alternative..

Photoionization of Metastable O^+ Ions: Experiment and Theory

Relevant data is available at: http://www.astronomy.ohio-state.edu/~nahar/nahar_radiativeatomicdata/index.htmlHigh-resolution absolute experimental measurements and two independent theoretical calculations were performed for photoionization of O^+ ions from the ^2 P° and ^2 D° metastable levels and from the ^4 S° ground state in the photon energy range 30–35.5 eV. This is believed to be the first comparison of experiment and theory to be reported for photoionization from metastable states of ions. While there is correspondence between the predicted and measured positions and relative strengths of the resonances, the cross-section magnitudes and fine structure are sensitive to the choice of basis states.The experimental work was supported in part by the DOE Divisions of Chemical Sciences, Geosciences and Biosciences, and Materials Sciences, by the DOE Facilities Initiative, by Nevada DOE/EPSCoR, by CONACyT and DGAPA (Mexico), and by CNPq (Brazil). The theoretical work was supported in part by NSF, by the Ohio Supercomputer Center, by ITAMP/Harvard-Smithsonian, and by EPSRC (UK)

Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee.

The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica). This study used 245 individuals of a F2 population derived from the self-fertilization of the F1 H511-1 hybrid, resulting from a crossing between the susceptible cultivar Catuaí Amarelo IAC 64 (UFV 2148-57) and the resistant parent Híbrido de Timor (UFV 443-03). The 245 individuals were genotyped with 137 markers. Artificial neural networks and Bayesian generalized linear regression analyses were performed. The artificial neural networks were able to identify four important markers belonging to linkage groups that have been recently mapped, while the Bayesian generalized model identified only two markers belonging to these groups. Lower prediction error rates (1.60%) were observed for predicting leaf rust resistance in Arabica coffee when artificial neural networks were used instead of Bayesian generalized linear regression (2.4%). The results showed that artificial neural networks are a promising approach for predicting leaf rust resistance in Arabica coffee.Título em português: Redes neurais artificiais comparadas com modelos lineares generalizados sob o enfoque bayesiano para predição de resistência à ferrugem em café arábica

Depression and sickness behavior are Janus-faced responses to shared inflammatory pathways

It is of considerable translational importance whether depression is a form or a consequence of sickness behavior. Sickness behavior is a behavioral complex induced by infections and immune trauma and mediated by pro-inflammatory cytokines. It is an adaptive response that enhances recovery by conserving energy to combat acute inflammation. There are considerable phenomenological similarities between sickness behavior and depression, for example, behavioral inhibition, anorexia and weight loss, and melancholic (anhedonia), physio-somatic (fatigue, hyperalgesia, malaise), anxiety and neurocognitive symptoms. In clinical depression, however, a transition occurs to sensitization of immuno-inflammatory pathways, progressive damage by oxidative and nitrosative stress to lipids, proteins, and DNA, and autoimmune responses directed against self-epitopes. The latter mechanisms are the substrate of a neuroprogressive process, whereby multiple depressive episodes cause neural tissue damage and consequent functional and cognitive sequelae. Thus, shared immuno-inflammatory pathways underpin the physiology of sickness behavior and the pathophysiology of clinical depression explaining their partially overlapping phenomenology. Inflammation may provoke a Janus-faced response with a good, acute side, generating protective inflammation through sickness behavior and a bad, chronic side, for example, clinical depression, a lifelong disorder with positive feedback loops between (neuro)inflammation and (neuro)degenerative processes following less well defined triggers