Spatial component analysis is often used to explore multidimensional time series data whose sources cannot be measured directly. Several methods may be used to decompose the data into a set of spatial components with temporal loadings. Component selection is of crucial importance, and should be supported by objective criteria. In some applications, the use of a well defined component selection criterion may provide for automation of the analysis. In this paper we describe a novel approach for ranking of spatial components calculated from the EEG or MEG data recorded within evoked response paradigm. Our method is called Mutual Information (MI) Spectrum and is based on gauging the amount of MI of spatial component temporal loadings with a synthetically created reference signal. We also describe the appropriate randomization based statistical assessment scheme that can be used for selection of components with statistically significant amount of MI. Using simulated data with realistic trial to trial variations and SNR corresponding to the real recordings we demonstrate the superior performance characteristics of the described MI based measure as compared to a more conventionally used power driven gauge. We also demonstrate the application of the MI Spectrum for the selection of task-related independent components from real MEG data. We show that the MI spectrum allows to identify task-related components reliably in a consistent fashion, yielding stable results even from a small number of trials. We conclude that the proposed method fits naturally the information driven nature of ICA and can be used for routine and automatic ranking of independent components calculated from the functional neuroimaging data collected within event-related paradigms.
Critical conditions for natural selection in multidimensional evolutionary spaces and general requirements following from these conditions and corresponding to the prebiotic evolutionary stage are discussed.
Humans are unique in developing large lexicons as their communication tool; to achieve this, they are able to learn new words rapidly. However, neural bases of this rapid learning, which may be an expression of a more general cognitive mechanism likely rooted in plasticity at cellular and synaptic levels, are not yet understood. In this update, the author highlights a selection of recent studies that attempted to trace word learning in the human brain noninvasively. A number of brain areas, most notably in hippocampus and neocortex, appear to take part in word acquisition. Critically, the currently available data not only demonstrate the hippocampal role in rapid encoding followed by slow-rate consolidation of cortical word memory traces but also suggest immediate neocortical involvement in the word memory trace formation. Echoing early behavioral studies in ultra-rapid word learning, the reviewed neuroimaging experiments can be taken to suggest that our brain may effectively form new cortical circuits online, as it gets exposed to novel linguistic patterns in the sensory input.
We study the fraction f of nucleotides involved in the formation of a cactuslike secondary structure of random heteropolymer RNA-like molecules. In the low-temperature limit, we study this fraction as a function of the number c of different nucleotide species. We show, that with changing c, the secondary structures of random RNAs undergo a morphological transition:f(c)→1 for c≤ccr as the chain length n goes to infinity, signaling the formation of a virtually perfect gapless secondary structure; while f(c)<1 for c>ccr, which means that a nonperfect structure with gaps is formed. The strict upper and lower bounds 2≤ccr≤4 are proven, and the numerical evidence for ccr is presented. The relevance of the transition from the evolutional point of view is discussed.
Neuraminidase 1 (NEU1) is a lysosomal sialidase catalyzing the removal of terminal sialic acids from sialyloconjugates. A plasma membrane-bound NEU1 modulating a plethora of receptors by desialylation, has been consistently documented from the last ten years. Despite a growing interest of the scientific community to NEU1, its membrane organization is not understood and current structural and biochemical data cannot account for such membrane localization. By combining molecular biology and biochemical analyses with structural biophysics and computational approaches, we identified here two regions in human NEU1 - segments 139–159 (TM1) and 316–333 (TM2) - as potential transmembrane (TM) domains. In membrane mimicking environments, the corresponding peptides form stable α-helices and TM2 is suited for self-association. This was confirmed with full-size NEU1 by co-immunoprecipitations from membrane preparations and split-ubiquitin yeast two hybrids. The TM2 region was shown to be critical for dimerization since introduction of point mutations within TM2 leads to disruption of NEU1 dimerization and decrease of sialidase activity in membrane. In conclusion, these results bring new insights in the molecular organization of membrane-bound NEU1 and demonstrate, for the first time, the presence of two potential TM domains that may anchor NEU1 in the membrane, control its dimerization and sialidase activity.