DataSheet_1_The metabolism of nonstructural carbohydrates, lipids, and energy in two Cycas species with differential tolerance to unexpected freezing stress.doc

IntroductionWith the climate warming, the occurrence of freezing events is projected to increase in late spring and early autumn in the Northern Hemisphere. Observation of morphological traits showed that Cycas panzhihuaensis was more tolerant to unexpected freezing stress than C. bifida. Energy balance is crucial for plant tolerance to stress. Here, we aimed to determine whether the different responses of the two species to the unpredicted freezing stress were associated with the metabolism of energy and related substances.MethodsThe effects of unexpected freezing temperatures on C. panzhihuaensis and C. bifida were studied by measuring chlorophyll fluorescence parameters, energy charge and the profile of nonstructural carbohydrates (NSC) and lipids.ResultsC. panzhihuaensis exhibited higher stability of photosynthetic machinery than C. bifida under unpredicted freezing events. Significant interaction between species and treatments were observed in the energy charge, the level of NSC and its most components and the amount of most lipid categories and lipid classes. The decrease of soluble sugar and the increase of neutral glycerolipids at the early freezing stage, the accumulation of membrane glycerolipids at the late freezing stage and the continuous decrease of energy charge during the freezing period were the characteristics of C. panzhihuaensis responding to unexpected freezing stress. The degradation of membrane glycerolipids and the continuous decrease of soluble sugar during the freezing period and the accumulation of neutral glycerolipids and energy charge at the late freezing stage represented the characteristics of C. bifida responses.DiscussionThe different freezing sensitivity between C. panzhihuaensis and C. bifida might be associated with the differential patterns of the metabolism of energy, NSC and lipids. C. panzhihuaensis possesses the potential to be introduced to the areas of higher latitudes and altitudes.</p

List of brain regions extracted from the Automated Anatomical Labeling (AAL) template (Tzourio-Mazoyer et al., 2002) and their abbreviations as used in this study.

<p>The same 45 brain regions were extracted from the right and left hemispheres to provide 90 regional time series in total for each subject.</p

Determination of Vascular Dementia Brain in Distinct Frequency Bands with Whole Brain Functional Connectivity Patterns

<div><p>Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01∼0.027 Hz), slow-4 (0.027∼0.073 Hz), and whole-band (0.01∼0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01∼0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD.</p> </div

The selected features, that is, the inter-regional functional connections in the resting state functional connectivity pattern classification for the VaD group and the control group in the three frequency bands.

<p>Upper panel: The features selected in the VaD patients. The node size is proportional to the frequency occurrence of the brain region in the selected features and the line thickness to the mean value of the feature. The red (green) lines represent features that were increased (decreased) in the VaD group compared with the controls. Lower panel: Same as the upper panel but for the control group. For both panels: slow-5∶0.01∼0.27 Hz (low frequency); slow-4∶0.027∼0.073 Hz (high frequency); whole-band: 0.01∼0.073 Hz.</p

Demographic characteristics and primary neuropsychological information of the subjects in the present study.

<p>Data are presented as the range from min–max (mean ± SD). The <i>p-</i>value was calculated by using a two samples two-tail <i>t-</i>test. VaD, vascular dementia; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment (Beijing version).</p

The flow chart of the rsFC pattern classification obtained by applying MVPA to the sample dataset in the slow-5 frequency band.

<p>The sample dataset was a 39-by-4005 matrix in this study and was comprised of 4005 features, that is, inter-regional functional connections. Each row and each column of the sample dataset represented a subject and a feature, respectively. Recursive feature elimination (RFE) was applied to the sample dataset for feature selection and to calculate the rank of the feature weight (a row vector). The subsets of the sample dataset were produced according to the feature weighting rank. The classifier was used to separate the VaD brains from those of the controls in each subset of the sample dataset by using a leave one-out-subject-cross-validation (LOOCV). The accuracy rate was used to compare the discriminative information in each subset of the sample dataset.</p

The preprocessing procedure used in the construction of the sample dataset based on resting state fMRI (rsfMRI) data in three frequency bands.

<p>The rsfMRI data were normalized to the MNI standard space using the EPI template and filtered using 0.01∼0.027 Hz (slow-5), 0.027∼0.073 Hz (slow-4), and 0.01∼0.073 Hz (whole-band) frequencies. Brain regions were defined according to the AAL atlas, and the time series was extracted for each region. The whole brain functional network was constructed for each frequency band by taking each cortical region as a node and the inter-regional Pearson’s correlation coefficient as the edge for each subject. All the independent elements of the individual functional connectivity matrix (90×90) were arranged into a 1-by-4005 row vector. We assembled all the row vectors for all 39 subjects into a 39-by-4005 matrix and normalized the data using their z-scores. The normalized matrix and the subject labels (patients were labeled by 1s and healthy subjects as -1s) constituted the sample dataset for further SVM analysis. In total, we obtained three sample datasets corresponding to the three different frequency bands.</p

Statistical comparisons between the selected features of the VaD patients and the controls in the three frequency bands.

<p>Group mean value for each selected feature was calculated by averaging the feature values, the inter-regional functional connections, across all subjects in the VaD group or in the control group. Positive or negative feature values represent inter-regional functional correlations or anticorrelations, respectively. Statistical comparisons of the feature values between the VaD patients and the controls were estimated based on a two samples <i>t</i>-test. A positive (negative) <i>t</i>-value stands for a feature that was significantly increased (decreased) in the VaD group compared with the control group. The features are listed in order of decreasing weighted rank in the pattern discrimination. The symbol “**” represents significant difference determined by <i>p</i><0.01 and “*” determined by <i>p</i><0.05.</p

Brain regions involved in the features selected from the functional connectivity pattern for differentiating the brain states of the VD patients from those of the controls in the three frequency bands.

<p>The brain regions in bold type appeared in all three frequency band subsets of the sample dataset. The abbreviations of the brain regions and their coordinates in the MNI standard space can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054512#pone.0054512.s001" target="_blank">Table S1</a>.</p

Hub regions of the human brain anatomical networks derived from both the conventional DTI (C-DTI) and FLAIR-DTI (F-DTI) datasets.

<p>1. Achard et al 2006.</p><p>2. He et al 2007.</p><p>3. Hagmann et al 2008.</p><p>4. Iturria-Medina et al 2008.</p><p>5. Gong et al 2009.</p><p>6. He et al 2009.</p><p>7. Li et al 2009.</p><p>8. Shu et al 2009.</p><p>9. Yan et al 2010.</p><p>10. Tian et al 2011.</p><p>11. Chen et al 2008.</p><p>Note: <b>^a</b>The hub regions shared by the networks derived from both types of DTI datasets. <b>^b</b>The hub regions detected only from the conventional DTI datasets. The remaining six regions are the hubs detected only from the FLAIR-DTI datasets. The symbol “–” stands for “not reported” in these previous studies. “Y” indicates that the region has been identified as a “hub”, and “N” indicates that it has not been identified as a hub.</p