Effect of inter-stimulus interval (ISI) on the average amplitude of the auditory and somatosensory N1 and P2 waves.

<p><i>y</i> axis: amplitude (µV); <i>x</i> axis: ISI category. Both in the auditory and in the somatosensory modality the ISI had an opposite effect on the auditory and somatosensory N1 and P2: at shorter ISIs, the N1 displayed significantly larger amplitudes while the P2 displayed significantly smaller amplitudes. Error bars represent the variance across subjects (standard error of the mean). Asterisks highlight ISI categories in which the average peak amplitude was significantly different from the peak amplitude at the category ‘100–200 ms’ (* p<0.05; ** p<0.01).</p

A novel hypothesis to explain the effect of inter-stimulus interval (ISI) on the amplitude of the N1 and P2 waves.

<p>The effect of ISI on the amplitude of the auditory and somatosensory N1 and P2 could be explained by the modulation of a single neural component whose time course overlaps the peak latency of the N1 and P2 waves (middle column). This component could appear in the EEG either (A) as a negative deflection that is <i>enhanced</i> at very short ISIs, or (B) as a positive deflection that is <i>reduced</i> at very short ISIs. In both cases, at very short ISIs the magnitude of the N1 would be increased and the magnitude of the P2 would be decreased (right column). Note that in this model, the neural components underlying the N1 and P2 waves <i>per se</i> are not modulated by ISI (left column).</p

Effect of inter-stimulus interval (ISI) on the somatosensory N1 and P2 waves.

<p>Trials were classified according to ISI, yielding nine categories ranging from 100 to 1000 ms in steps of 100 ms. Response overlap was corrected using the Adjacent Response procedure <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003929#pone.0003929-Woldorff1" target="_blank">[35]</a> (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003929#s4" target="_blank">Methods</a>). The <i>middle panel</i> displays the somatosensory ERP obtained at each ISI category (group-level average; P3 vs. average reference). Each ISI category is colour coded. <i>x</i> axis, time (ms); <i>y</i> axis, amplitude (µV). <i>Upper</i> and <i>lower panels</i> display the N1 and P2 waves and their scalp distributions, separately for each ISI. Note the opposite effect of ISI on the amplitude of the somatosensory N1 and P2 waves: at very short ISIs, the N1 displays significantly larger amplitudes, while the P2 displays significantly smaller amplitudes.</p

Elucidating Xylose Metabolism of <i>Scheffersomyces stipitis</i> for Lignocellulosic Ethanol Production

The conversion of pentose to ethanol is one of the major barriers of industrializing the lignocellulosic ethanol processes. As one of the most promising native strains for pentose fermentation, Scheffersomyces stipitis (formerly known as Pichia stipitis) has been widely studied for its xylose fermentation. In spite of the abundant experimental evidence regarding ethanol and byproducts production under various aeration conditions, the mathematical descriptions of the processes are rare. In this work, a constraint-based metabolic network model for the central carbon metabolism of S. stipitis was reconstructed by integrating genomic (S. stipitis v2.0, KEGG), biochemical (ChEBI, PubChem), and physiological information available for this microorganism and other related yeast. The model consists of the stoichiometry of metabolic reactions, biosynthetic requirements for growth, and other constraints. Flux balance analysis is applied to characterize the phenotypic behavior of S. stipitis grown on xylose. The model predictions are in good agreement with published experimental results. To understand the effect of redox balance on xylose fermentation, we propose a system identification-based metabolic analysis framework to extract biological knowledge embedded in a series of designed in silico experiments. In the proposed framework, we first design in silico experiments to perturb the metabolic network in order to investigate the interested properties and then perform system identification, whereby applying principal component analysis (PCA) to the data generated by the designed in silico experiments. By combining the in silico perturbation experiments with system identification tools, biologically meaningful information contained in the complex network structure can be decomposed and translated into easily interpretable information that is useful for biologist. The PCA analysis identifies the phenotypic changes caused by oxygen supply and reveals key metabolic reactions related to redox homeostasis in different phenotypes. In addition, the influence of the cofactor preference of key enzyme (xylose reductase) in xylose metabolism is investigated using the proposed approach, and the results provide important insights on cofactor engineering of xylose metabolism

Experimental paradigm.

<p>EEG data was collected in a single session. Within this session, four blocks of auditory (grey) and four blocks of somatosensory (black) stimulation were presented in alternation (top panel). Each block lasted approximately 11 minutes, and consecutive blocks were separated by a 3-minute break. Auditory stimuli consisted in brief 800 Hz tones delivered binaurally through headphones, and somatosensory stimuli consisted in electrical pulses delivered to the right median nerve through surface electrodes. In each block (middle panel), 1200 identical stimuli were delivered, and the inter-stimulus interval was randomly varied from trial to trial between 100 and 1000 ms (bottom panel).</p

Effect of inter-stimulus interval (ISI) on the auditory N1 and P2 waves.

<p>Trials were classified according to ISI, yielding nine categories ranging from 100 to 1000 ms in steps of 100 ms. At short ISIs, the brain activity elicited by two consecutive stimuli was likely to overlap and therefore distort the obtained ERP waveform. This distortion was corrected using the Adjacent Response procedure <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003929#pone.0003929-Woldorff1" target="_blank">[35]</a> (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003929#s4" target="_blank">Methods</a>). The <i>middle panel</i> displays the auditory ERP obtained at each ISI category (group-level average; Cz vs. average reference). Each ISI category is colour coded. <i>x</i> axis, time (ms); <i>y</i> axis, amplitude (µV). <i>Upper</i> and <i>lower panels</i> display the N1 and P2 waves and their scalp distributions, separately for each ISI. Note the opposite effect of ISI on the amplitude of the auditory N1 and P2 waves: at very short ISIs, the N1 displays significantly larger amplitudes, while the P2 displays significantly smaller amplitudes.</p

Absorption Performance and Mechanism of CO₂ in Aqueous Solutions of Amine-Based Ionic Liquids

Several amine-based ionic liquids (ILs) were synthesized via a one-step method using low-priced organic amines and inorganic acids, and they were mixed with water to form new CO₂ absorbents. The effects of the ionic structure, IL concentration, temperature, and pressure on the CO₂ absorption performance were investigated. The absorption performance of ILs was closely related to the ionic structure, and the CO₂ molar absorption capacity in ILs with the same cation followed the order of [NO₃] > [BF₄] > [SO₄] or [HSO₄], whereas that with the same anion ranked in the following order: multiple amine > diamine > monoamine. The IL [TETA]­[NO₃] with 40 wt % concentration showed the best capacity for CO₂ absorption. Moreover, low temperature and high pressure favored CO₂ absorption. The reaction mechanism of the amine group with CO₂ in aqueous solutions of [TETA]­[NO₃], primary amine, and secondary amine was studied via <i>in situ</i> infrared (IR) spectrophotometry. The results showed that the primary and secondary amines first reacted with CO₂ to form carbamate, which decomposed further into bicarbonate with the continuous addition of CO₂. However, carbamate generated from the reaction of [TETA]­[NO₃] with CO₂ did not decompose further

Protein identities and their relative changes under salt stress in <i>Kandelia candel</i> leaves from 2D-Gel analysis.

<p>^a Numbering corresponds to the 2-DE gel in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083141#pone-0083141-g003" target="_blank">Figure 3</a>.</p><p>^b The number of the predicted protein in NCBInr.</p><p>^c The matched peptide sequences.</p><p>^d Molecular mass and p<i>I</i> theoretical and experimental.</p><p>^e Protein score and ion score.</p><p>^f Number of matched peptides and total searched peptides.</p><p>^g Percentage of predicated protein sequence with matched sequence.</p

Hierarchical clustering analysis of the 48 differentially expressed proteins from <i>K. candel</i> leaves under different salinity levels.

<p>The rows represent individual proteins. The proteins that increased and decreased in abundance are indicated in red and green, respectively. Proteins not detected on control gels are indicated in gray. The intensity of the colors increases as the expression differences increase, as shown in the bar at the bottom.</p

Schematic presentation of a mechanism for salt tolerance in <i>K. candel</i>.

<p>Most differentially expressed proteins were integrated, and were indicated in red (up-regulated at least under 450 mM NaCl treatment) or blue (down-regulated), respectively. Abbreviations: ADP, adenosine diphosphate; AKG, oxoglutarate; BPG, 1,3-bisphosphoglycerate; cytb6f, cytochrome b6f; DHA, dehydroascrobate; DHAP, dihydroxyacetone phosphate; EA, enolase; eIF, eukaryotic translation initiation factor; F6P, fructose-6-phosphate; FADH₂, reduced flavin adenine dinucleotide; FtsH, Cell division protein ftsH; G3P, glyceraldehydes-3-phosphate; G6P, glucose-6-phosphate; GS, glutamine synthetase; GSH, reduced glutathione; GSSH, oxidized glutathione; IMD, isopropylmalate dehydratase; MDHA, monodehydroascorbate; MDHAR, MDHA reductase; NADP⁺/NADPH, nicotinamide adenine dinucleotide phosphate; OAA, oxaloacetic acid; PEP, phosphoenolpyruvate; PG, phosphoglycolate; PGD, phosphoglycerate dehydrogenase; PPIase, peptidyl-prolyl cis-trans isomerase; PRS, proteasome; Q, quinone; R5P, ribose-5-phosphate; Ru5P, ribulose-5-phosphate; RuBisCO, ribulose-1,5-bisphosphate carboxylase/oxygenase; RuBP, ribulose-1,5-bisphosphate; RIM, reductoisomerase; TPI, triosephosphate isomerase.</p