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.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Identification of assembly precursors to photosystems emitting fluorescence at 683 nm and 687 nm by cryogenic fluorescence microspectroscopy

Photosystem I (PSI) and photosystem II (PSII) play key roles in photoinduced electron-transfer reaction in oxygenic photosynthesis. Assemblies of these PSs can be initiated by illumination of the etiolated seedlings (greening). The study aimed to identify specific fluorescence spectral components relevant to PSI and PSII assembly intermediates emerging in greening seedlings of Zea mays, a typical C4 plant. The different PSII contents between the bundle sheath (BS) and mesophyll (M) cells were utilized to spectrally isolate the precursors to PSI and PSII. The greening Zea mays leaf thin sections were observed with the cryogenic microscope combined with a spectrometer. With the aid of the singular-value decomposition analysis, we could identify four independent fluorescent species, SAS677, SAS685, SAS683, and SAS687, named after their fluorescence peak wavelengths. SAS677 and SAS685 are the dominant components after the 30-minute greening, and the distributions of these components showed no clear differences between M and BS cells, indicating immature cell differentiation in this developing stage. On the other hand, the 1-hour greening resulted in reduced distributions of SAS683 in BS cells leading us to assign this species to PSII precursors. The 2-hour greening induced the enrichment of SAS687 in BS cells suggesting its PSI relevance. Similarity in the peak wavelengths of SAS683 and the reported reaction center of PSII implied their connection. SAS687 showed an intense sub-band at around 740 nm, which can be assigned to the emission from the red chlorophylls specific to the mature PSI.     

Solution NMR Studies of Chlorella Virus DNA Ligase-adenylate

DNA ligases are essential guardians of genome integrity by virtue of their ability to recognize and seal 3′-OH/5′-phosphate nicks in duplex DNA. The substrate binding and three chemical steps of the ligation pathway are coupled to global and local changes in ligase structure, involving both massive protein domain movements and subtle remodeling of atomic contacts in the active site. Here we applied solution NMR spectroscopy to study the conformational dynamics of the Chlorella virus DNA ligase (ChVLig), a minimized eukaryal ATP-dependent ligase consisting of nucleotidyltransferase, OB, and latch domains. Our analysis of backbone ¹⁵N spin relaxation and ¹⁵N,¹H residual dipolar couplings of the covalent ChVLig-AMP intermediate revealed conformational sampling on fast (picosecond to nanosecond) and slow timescales (microsecond to millisecond), indicative of interdomain and intradomain flexibility. We identified local and global changes in ChVLig-AMP structure and dynamics induced by phosphate. In particular, the chemical shift perturbations elicited by phosphate were clustered in the peptide motifs that comprise the active site. We hypothesize that phosphate anion mimics some of the conformational transitions that occur when ligase-adenylate interacts with the nick 5′-phosphate.     

风场对太湖叶绿素a空间分布的影响

叶绿素a的浓度是水环境评价的重要参数。根据2005—2009年太湖全湖32个采样点的20次太湖采样数据,结合气象要素资料模拟的太湖风场,探求了风场对太湖叶绿素a空间分布的影响。结果表明:叶绿素a浓度的高值中心位于太湖西北侧、竺山湾以及梅梁湾流域,而太湖东南部的叶绿素a浓度较低;全太湖全年以东南风为主,南部风速较大,北部风速较弱;风场对叶绿素a的输移作用明显,在风场作用下,叶绿素向太湖西北部和北部输移,造成了该地区太湖流域叶绿素浓度普遍偏高。     

Singular value decomposition analysis of protein sequence alignment score data.

One of the standard tools for the analysis of data arranged in matrix form is singular value decomposition (SVD). Few applications to genomic data have been reported to date mainly for the analysis of gene expression microarray data. We review SVD properties, examine mathematical terms and assumptions implicit in the SVD formalism, and show that SVD can be applied to the analysis of matrices representing pairwise alignment scores between large sets of protein sequences. In particular, we illustrate SVD capabilities for data dimension reduction and for clustering protein sequences. A comparison is performed between SVD-generated clusters of proteins and annotation reported in the SWISS-PROT Database for a set of protein sequences forming the calycin superfamily, entailing all entries corresponding to the lipocalin, cytosolic fatty acid-binding protein, and avidin-streptavidin Prosite patterns.     

Rejoinder to Use of Principal Component Analysis and the GE -Biplot for the Graphical Exploration of Gene Expression Data

Summary This note is in response to Wouters et al. (2003, Biometrics 59, 1131–1139) who compared three methods for exploring gene expression data. Contrary to their summary that principal component analysis is not very informative, we show that it is possible to determine principal component analyses that are useful for exploratory analysis of microarray data. We also present another biplot representation, the GE‐biplot (Gene Expression biplot), that is a useful method for exploring gene expression data with the major advantage of being able to aid interpretation of both the samples and the genes relative to each other.     

Synchronous protein cycling in batch cultures of the yeast Saccharomyces cerevisiae at log growth phase

The assumption that cells are temporally organized systems, i.e. showing relevant dynamics of their state variables such as gene expression or protein and metabolite concentration, while tacitly given for granted at the molecular level, is not explicitly taken into account when interpreting biological experimental data. This conundrum stems from the (undemonstrated) assumption that a cell culture, the actual object of biological experimentation, is a population of billions of independent oscillators (cells) randomly experiencing different phases of their cycles and thus not producing relevant coordinated dynamics at the population level. Moreover the fact of considering reproductive cycle as by far the most important cyclic process in a cell resulted in lower attention given to other rhythmic processes. Here we demonstrate that growing yeast cells show a very repeatable and robust cyclic variation of the concentration of proteins with different cellular functions. We also report experimental evidence that the mechanism governing this basic oscillator and the cellular entrainment is resistant to external chemical constraints. Finally, cell growth is accompanied by cyclic dynamics of medium pH. These cycles are observed in batch cultures, different from the usual continuous cultures in which yeast metabolic cycles are known to occur, and suggest the existence of basic, spontaneous, collective and synchronous behaviors of the cell population as a whole.     

Structural signatures of enzyme binding pockets from order-independent surface alignment: a study of metalloendopeptidase and NAD binding proteins

Detecting similarities between local binding surfaces can facilitate identification of enzyme binding sites and prediction of enzyme functions, and aid in our understanding of enzyme mechanisms. Constructing a template of local surface characteristics for a specific enzyme function or binding activity is a challenging task, as the size and shape of the binding surfaces of a biochemical function often vary. Here we introduce the concept of signature binding pockets, which captures information on preserved and varied atomic positions at multiresolution levels. For proteins with complex enzyme binding and activity, multiple signatures arise naturally in our model, forming a signature basis set that characterizes this class of proteins. Both signatures and signature basis sets can be automatically constructed by a method called SOLAR (Signature Of Local Active Regions). This method is based on a sequence-order-independent alignment of computed binding surface pockets. SOLAR also provides a structure-based multiple sequence fragment alignment to facilitate the interpretation of computed signatures. By studying a family of evolutionarily related proteins, we show that for metzincin metalloendopeptidase, which has a broad spectrum of substrate binding, signature and basis set pockets can be used to discriminate metzincins from other enzymes, to predict the subclass of metzincins functions, and to identify specific binding surfaces. Studying unrelated proteins that have evolved to bind to the same NAD cofactor, we constructed signatures of NAD binding pockets and used them to predict NAD binding proteins and to locate NAD binding pockets. By measuring preservation ratio and location variation, our method can identify residues and atoms that are important for binding affinity and specificity. In both cases, we show that signatures and signature basis set reveal significant biological insight.