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Repeated (but not acute) exposure to brief, noninjurious seizures evoked by minimal electroconvulsive shock (ECS) decreases neuronal death in limbic system and increases mRNA levels for nerve growth factor (NGF). Thus, the induction of NGF is a potential mechanism for the neuroprotection evoked by repeated ECS. The neuroprotective action of NGF is mediated by the TrkA receptor. This study determined whether repeated ECS exposure increased TrkA and NGF protein levels. To determine the functional significance of changes in these proteins, we compared the effects of ECS given daily either for 7 days (chronic ECS) or for 1 day (acute ECS). After chronic ECS, upregulation of both NGF and TrkA was found in perirhinal cortex, thalamus, and amygdala. In hippocampus, TrkA was upregulated in CA2, CA3 and CA4. NGF increase in hippocampus was found in CA1 and dentate gyrus. In frontal cortex and substantia innominata, an increase in NGF (but not in TrkA) was found. In most brain regions, TrkA and NGF remained unchanged after acute ECS. Our results demonstrate that repeated exposure to ECS causes an upregulation of TrkA and NGF proteins in several limbic areas in which neuroprotective effects are observed suggesting that NGF contributes to ECS-evoked neuroprotection.

Abstract-Indoor Localization and Tracking have become an attractive research topic because of the wide range of potential applications. These applications are highly demanding in terms of estimation accuracy and rise a challenge due to the complexity of the scenarios modeled. Approaches for these topics are mainly based on either deterministic or probabilistic methods such as Kalman or Particles Filter. These techniques are improved by fusing information from different sources such as wireless or optical sensors. In this paper, a novel MUlti-sensor Fusion using Adaptive Fingerprint (MUFAF) Algorithm is presented and compared with several multi-sensor indoor localization and tracking methods. MUFAF is mainly divided in four phases: first, a Target Position Estimation (TPE) process is performed by every sensor; second, a Target Tracking Process (TTP) stage; third, a Multi-Sensor fusion (MMF) combines the sensor information and finally, an Adaptive Fingerprint Update (AFU) is applied. For TPE, a complete environment characterization in combination with a Kernel Density Estimation (KDE) technique are employed to obtain object position. A Modified Kalman Filter (MKF) is applied to TPE output in order to smooth target routes and avoid outliers effect. Moreover, two fusion methods are described in this work: Track-To-Track Fusion (TTTF) and Kalman Sensor Group Fusion (KSGF). Finally, AFU will endow the algorithm with responsiveness to environment changes by using Kriging interpolation to update the scenario fingerprint. MUFAF is implemented and compared in a testbed showing that it provides a significant improvement in estimation accuracy and long-term adaptivity to condition changes.

Macromolecular ligands have been widely used in the past two decades with the objective of preparing structurally defined heterogeneous catalysts from soluble organometallic complexes. This activity has been largely reconsidered and focused on few specific systems. In this connection the present paper reviews recent data concerning the preparation of macromolecular metal complexes derived from transition metals which can produce active catalytic complexes for olefin polymerization and oligomerization and comparison is made about the suitability of both organic resins (crosslinked polystyrene) and inorganic materials (silica, alumina and zeolites).

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