Integrated Analysis of Seaweed Components during Seasonal Fluctuation by Data Mining Across Heterogeneous Chemical Measurements with Network Visualization

Biological information is intricately intertwined with several factors. Therefore, comprehensive analytical methods such as integrated data analysis, combining several data measurements, are required. In this study, we describe a method of data preprocessing that can perform comprehensively integrated analysis based on a variety of multimeasurement of organic and inorganic chemical data from <i>Sargassum fusiforme</i> and explore the concealed biological information by statistical analyses with integrated data. Chemical components including polar and semipolar metabolites, minerals, major elemental and isotopic ratio, and thermal decompositional data were measured as environmentally responsive biological data in the seasonal variation. The obtained spectral data of complex chemical components were preprocessed to isolate pure peaks by removing noise and separating overlapping signals using the multivariate curve resolution alternating least-squares method before integrated analyses. By the input of these preprocessed multimeasurement chemical data, principal component analysis and self-organizing maps of integrated data showed changes in the chemical compositions during the mature stage and identified trends in seasonal variation. Correlation network analysis revealed multiple relationships between organic and inorganic components. Moreover, in terms of the relationship between metal group and metabolites, the results of structural equation modeling suggest that the structure of alginic acid changes during the growth of <i>S. fusiforme</i>, which affects its metal binding ability. This integrated analytical approach using a variety of chemical data can be developed for practical applications to obtain new biochemical knowledge including genetic and environmental information

Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures

The abundant observation of chemical fragment information for molecular complexities is a major advantage of biological NMR analysis. Thus, the development of a novel technique for NMR signal assignment and metabolite identification may offer new possibilities for exploring molecular complexities. We propose a new signal assignment approach for metabolite mixtures by assembling H–H, H–C, C–C, and Q–C fragmental information obtained by multidimensional NMR, followed by the application of graph and network theory. High-speed experiments and complete automatic signal assignments were achieved for 12 combined mixtures of ¹³C-labeled standards. Application to a ¹³C-labeled seaweed extract showed 66 H–C, 60 H–H, 326 C–C, and 28 Q–C correlations, which were successfully assembled to 18 metabolites by the automatic assignment. The validity of automatic assignment was supported by quantum chemical calculations. This new approach can predict entire metabolite structures from peak networks of biological extracts

Clinical characteristics of the patients with Parkinson’s disease.

<p>Clinical characteristics of the patients with Parkinson’s disease.</p

Pretreatment and Integrated Analysis of Spectral Data Reveal Seaweed Similarities Based on Chemical Diversity

Extracting useful information from high dimensionality and large data sets is a major challenge for data-driven approaches. The present study was aimed at developing novel integrated analytical strategies for comprehensively characterizing seaweed similarities based on chemical diversity. The chemical compositions of 107 seaweed and 2 seagrass samples were analyzed using multiple techniques, including Fourier transform infrared (FT-IR) and solid- and solution-state nuclear magnetic resonance (NMR) spectroscopy, thermogravimetry-differential thermal analysis (TG-DTA), inductively coupled plasma-optical emission spectrometry (ICP-OES), CHNS/O total elemental analysis, and isotope ratio mass spectrometry (IR-MS). The spectral data were preprocessed using non-negative matrix factorization (NMF) and NMF combined with multivariate curve resolution-alternating least-squares (MCR-ALS) methods in order to separate individual component information from the overlapping and/or broad spectral peaks. Integrated analysis of the preprocessed chemical data demonstrated distinct discrimination of differential seaweed species. Further network analysis revealed a close correlation between the heavy metal elements and characteristic components of brown algae, such as cellulose, alginic acid, and sulfated mucopolysaccharides, providing a componential basis for its metal-sorbing potential. These results suggest that this integrated analytical strategy is useful for extracting and identifying the chemical characteristics of diverse seaweeds based on large chemical data sets, particularly complicated overlapping spectral data

Baseline characteristics between healthy subjects and patients with Parkinson’s disease.

<p>Baseline characteristics between healthy subjects and patients with Parkinson’s disease.</p

¹¹C-raclopride binding potential (RAC-BP) of the three ROI within the right striatum in healthy subjects (HS) and patients with Parkinson’s disease (PD).

<p>¹¹C-raclopride binding potential (RAC-BP) of the three ROI within the right striatum in healthy subjects (HS) and patients with Parkinson’s disease (PD).</p

Additional file 1 of Blood levels of glial fibrillary acidic protein for predicting clinical progression to Alzheimer’s disease in adults without dementia: a systematic review and meta-analysis protocol

Additional file 1: Appendix 1. Search strategies. Appendix 2. Data extraction form

Mean pinching force changes in initial skill-training on Day 1 and mean acceleration changes in prepractice on Day 2.

<p>(A) Changes of the mean pinching force in the initial skill training between baseline (before session 1) and post session 3. Y axis indicates mean pinching force (kg). Error bars show the SD. *<i>P</i><0.05. (B) Mean accelerations in the motor prepractice between block 1 and block 4. Y axis indicates mean accelerations (cm/s²). Error bars show the SEM. N.S. not significant.</p

Experimental procedures.

<p>(A) Time schedule of experiments. Subjects were scanned for evaluating striatal dopamine levels using ¹¹C-raclopride PET on Day 1 (initial skill-training condition) and Day 2 (acquired condition). (B) ¹¹C-raclopride PET scanning. ¹¹C-raclopride (555 MBq) was injected into the right vein just before the emission data was acquired. They practiced rapid contraction of their left thumb via visual feedback during PET scanning. (C) Display seen by subjects during motor practice. Three coloured horizontal lines were graphically presented to help subjects understand the quality of their performance: peak acceleration of the present movement (blue), mean peak acceleration of all previous trials within a block (green) and maximum peak acceleration of all previous trials within a block (red).</p

¹¹C-raclopride BP changes in sub-regions of the striatum between the two conditions.

<p>Left panel shows the grass brain map of the voxel-based analysis of ¹¹C-raclopride BP change (upper figure: initial skill-training condition < acquired condition and lower figure: acquired condition < initial skill-training condition). Right panel shows coronal and axial sections of the statistical parametric map of ¹¹C-raclopride BP change in the initial skill-training condition versus acquired condition overlaying the MRI T1 image in stereotaxic space. Right side image corresponds to right side brain. The displayed cluster shows the significant area of decreased ¹¹C-raclopride BP in the right antero-dorsal and lateral part of the putamen. The peak coordinate in the right putamen was located at X = 30, Y = 4, Z = 12. No BP change was observed in the left putamen.</p