Postnatal isoform switch and protein localization of LEF1 and TCF7L2 transcription factors in cortical, thalamic, and mesencephalic regions of the adult mouse brain.

ß-Catenin signaling, leading to the activation of lymphoid enhancer-binding factor 1/T cell factor (LEF1/TCF) transcription factors, plays a well-established role in transcription regulation during development and tissue homeostasis. In the adult organism, the activity of this pathway has been found in stem cell niches and postmitotic thalamic neurons. Recently, studies show that mutations in components of ß-catenin signaling networks have been associated with several psychiatric disorders, indicating the involvement of ß-catenin and LEF1/TCF proteins in the proper functioning of the brain. Here, we report a comprehensive analysis of LEF1/TCF protein localization and the expression profile of their isoforms in cortical, thalamic, and midbrain regions in mice. We detected LEF1 and TCF7L2 proteins in neurons of the thalamus and dorsal midbrain, i.e., subcortical regions specialized in the integration of diverse sources of sensory information. These neurons also exhibited nuclear localization of ß-catenin, suggesting the involvement of ß-catenin/LEF1/TCF7L2 in the regulation of gene expression in these regions. Analysis of alternative splicing and promoter usage identified brain-specific TCF7L2 isoforms and revealed a developmentally coordinated transition in the composition of LEF1 and TCF7L2 isoforms. In the case of TCF7L2, the typical brain isoforms lack the so-called C clamp; in addition, the dominant-negative isoforms are predominant in the embryonic thalamus but disappear postnatally. The present study provides a necessary framework to understand the role of LEF1/TCF factors in thalamic and midbrain development until adulthood and predicts that the regulatory role of these proteins in the adult brain is significantly different from their role in the embryonic brain or other non-neural tissues.

Fuzzy methods in tremor assessment, prediction, and rehabilitation.

Tremor is a disabling condition for a large segment of population, mainly elderly. To the present date, there are no adequate tools to diagnose and help rehabilitation of subjects with tremor, but recently there is a tremendous surge of interest in the research in the field. We report on the use of fuzzy methods in applications for rehabilitation, namely in tremor diagnosing and control. We synthesize our results regarding the characterization of the tremor by means of nonlinear dynamics techniques and fuzzy logic, and the prediction of tremor movements in view of rehabilitation purposes. Based on linear and nonlinear analysis of tremor, and using fuzzy aggregation, the fusing of tremor parameters in global functional disabling factors is proposed. Nonlinear dynamic parameters, namely correlation dimension and Lyapunov exponent is used in order to improve the assessment of tremor. The benefits of the fuzzy fused tremor parameters rely on more complete and accurate assessment of the functional impairment and on improved feedback for rehabilitation, based on the fused parameters of the tremor. Further steps in rehabilitation may require external muscular control. In turn, the control of tremor by electrical stimulation requires movement prediction. Several neural and neuro-fuzzy predictors are compared and a neuro-fuzzy predictor is presented, allowing us five-step ahead prediction, with an RMS error of the order of 10%. The benefits of the neuro-fuzzy predictor are good prediction capability, versatility, and apparently a high robustness to individual variations of the tremor. The reported research, which extended over several years and included development of sensors, equipment, and software, has been aimed to development of products. The results may also open new ways in tremor rehabilitation.