Wave separation versus bandpass filtering: a comparative non-linear analysis of brain α-EEG signals with and without psychotropic drug treatment

Attempts to demonstrate low-dimensional attractor behaviour in the analysis of electroencephalographic (EEG) signals meet with difficulties that in part stem from a departure from single-system dynamics. In order to address this problem, the -waves can be extracted by digital filtering or by wave separation; these two techniques are compared in order to specify the conditions in which finite impulse response (FIR) bandpass filters can be used. The comparison was made using 18 EEG records of 3 min duration under resting conditions (6 subjects, 3 records per subject: prior to apomorphine administration, then 90 min and 150 min post-treatment). No presence of low-dimensional dynamic episodes in -signals was observed without digital processing. Sixty 5 s sections showing attractor behaviour were found after filtering and twenty five 5 s sections after wave separation. The mean correlation dimension was calculated for each experimental condition and for 4 subjects, in order to observe the temporal profile of the drug. When attractors were found after wave separation, bandpass filtering then also showed attractor behaviour, with the same temporal profile. However, the reverse is not true: attractors were found after bandpass filtering that were not present after wave separation; in this case the results deserve confirmation, although the temporal profiles for all cases in which attractors were found after filtering remained comparable.     

植物资源与化学数据库系统（DPRC）的功能与应用

由中国科学院昆植物研究所和云南省机械研究设计院的植物资源化学数据库系统是一个将植物资源,植物化学成分及化学结构的多种信息、数据和图象融为一体的、开和式多功能软件系统、,包括植资源检索系统、植物化学成分检索系统及天然产物化学结构检索系统等三个子系统。     

Some improvements in the quality of NAA procedures and the reliability of the results

After considering the need for quality control in NAA, the concept of quality in NAA procedures themselves is discussed, and some important factors identified. Two approaches to improve quality are then described in more detail. The first concerns the unique ability of NAA using different isotopic reactions and different modes (INAA/RNAA) to provide independent data sets in the same laboratory, thus allowing internal validation or crosschecking. The second discusses the need for chemical yield measurements in RNAA and the advantages of the radioisotopic tracer technique. Some recent advances and further possibilities for this use of tracers are listed.     

PHYLOGENETIC RELATIONSHIPS WITHIN THE COLONIAL VOLVOCALES (CHLOROPHYTA) INFERRED FROM CLADISTIC ANALYSIS BASED ON MORPHOLOGICAL DATA1

A cladistic analysis was used to deduce the phylogenetic relationships within the colonial Volvocales. Forty-one pairs of characters related to gross morphology and ultrastructure of vegetative colonies as well as asexual and sexual reproduction were analyzed based on parsimony, using the PAUP 3.0 computer program, for 25 species belonging to nine volvocacean and goniacean genera of the colonial Volvocales. Chlamydomonas reinhardtii Dangeard was the outgroup. The strict consensus tree indicated the presence of two monophyletic groups, one composed of all the volvocacean species analyzed in this study and the other containing the goniacean species except for the four-celled species Gonium sociale (Dujardin) Warming. In addition, these two groups constitute a large monophyletic group, to which G. sociale is a sister group. A new combination Tetrabaena socialis (Dujardin) Nozaki et Itoh and a new family Tetrabaenaceae Nozaki et Itoh are thus proposed for G. sociale. In addition, the analysis suggests that the volvocacean genera Eudorina and Pleodorina are paraphyletic groups, respectively, and that the monotypic genus Yamagishiella has no autapomorphic characters and represents primitive features of the anisogamous and oogamous genera of the Volvocaceae. Phylogenetic relationships within the Volvocaceae and the Goniaceae, as well as the various modes of sexual reproduction exhibited by these organisms, are discussed on the basis of the analysis.     

Bayesian signal extraction from noisy FT NMR spectra

Summary The statistical interpretation of the histogram representation of NMR spectra is described, leading to an estimation of the probability density function of the noise. The white-noise and Gaussian hypotheses are discussed, and a new estimator of the noise standard deviation is derived from the histogram strategy. The Bayesian approach to NMR signal detection is presented. This approach homogeneously combines prior knowledge, obtained from the histogram strategy, together with the posterior information resulting from the test of presence of a set of reference shapes in the neighbourhood of each data point. This scheme leads to a new strategy in the local detection of NMR signals in 2D and 3D spectra, which is illustrated by a complete peak-picking algorithm.     

长白山站的研究数据库管理系统

长白山站的研究数据库管理系统翟永华,赵士洞（中国科学院沈阳应用生态研究所１１００１５）Ｄａｔｅ－ＢａｓｅＭａｎａｇｅｍｅｎｔＳｙｓｔｅｍｉｎＣｈａｎｇｂａｉｓｈａｎＦｏｒｅｓｔＥｃｏｓｙｓｔｅｍＲｅｓｅａｒｃｈＳｔａｔｉｏｎ．￥ＺｈａｉＹｏｎｇｈｕａ...     

Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge

Summary Prior information on gene effects at individual quantitative trait loci (QTL) and on recombination rates between marker loci and QTL is derived. The prior distribution of QTL gene effects is assumed to be exponential with major effects less likely than minor ones. The prior probability of linkage between a marker and another single locus is a function of the number and length of chromosomes, and of the map function relating recombination rate to genetic distance among loci. The prior probability of linkage between a marker locus and a quantitative trait depends additionally on the number of detectable QTL, which may be determined from total additive genetic variance and minimum detectable QTL effect. The use of this prior information should improve linkage tests and estimates of QTL effects.     

Wave-separation in complex systems. Application to brain-signals

A departure from single-system dynamics, that may arise in characterizing self-organized dynamics of complex systems, is dealt with by using the Karhunen-Loève expansion of the trajectory matrix to decompose an experimental signal in a sum of spectral features. For an electroencephalographic -signal, a separation of waves and extraction of additive sub-signals are achieved, each sub-signal covering a well-bounded and physiologically meaningful frequency range. From the subsignals, an attractor that vanishes on phase-randomizing the data is characterized, under conditions where none was found for the recorded signal.