The GBM cancer-specific miRNA thermodynamic signature distinguishes GBM and healthy patients.

<p>A. The heat map shows the miRNAs with the greatest contribution to the GBM cancer phenotypic state, up regulated and down regulated with respect to the balanced state (on a ln scale, see colour code in the inset). Similar thermodynamic behavior is observed across patients, however patient specific variability is observed. B. The plots shows the patient potential in the disease signature ( =  first Lagrange multiplier), on the same scale of patient index, <i>n</i>, coinciding with the heat map in A. Distinct difference in sign of the lagrange multiplier is observed between healthy and GBM diseased patients, delineating the two phenotypic states. C. A histogram of the patient potentials in the disease signature computed for the 10 healthy patients and 20 groups of 10 diseased patients each, showing altogether 200 diseased patients. D Different groups of diseased patients have consistent signatures in both balance state and disease. Disease signatures of 19 different patient groups, 2 to 20, are shown as a scatter plot vs. the signature of patients group 1. Each group has 10 healthy patients and a different set of 10 diseased ones. The groups are identified in the legend. Despite patient variability the signatures of the different groups are very consistent across the entire range of possible values of G_i.</p

The balance state is common to GBM diseased and healthy patients.

<p>The heat map A is a representation that highlights the invariance across patients where each column is an individual patient. Each row is a different miRNA. The miRNAs with the greatest contribution to the balance state are listed in order of descending contribution (and decreasing energetic stability, on a ln scale, see inset on the right) where dark is more stable and yellow is less stable. In the balance state, all patients are exhibiting similar expression pattern. B. The plot shows the patient potential in the balance state ( =  Lagrange multiplier for the balanced state), as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108171#s4" target="_blank">methods</a>, vs. the patient index, <i>n</i>, coinciding with the heat map in A. No significant variation of is observed between healthy and GBM diseased patients, as expected for a balance state that is common to both GBM and normal patients. C. An alternative graphical representation of the stability of the balanced state. Shown is a histogram of the patient potentials in the balanced state, computed for the 10 healthy patients and 20 groups of 10 diseased patients each, showing altogether 200 diseased patients. The histograms is a rather narrow peak, indicating a common value to both healthy and diseased patients. The range of the ordinate in B is the same as the range of the abscissa used in the histogram. D. Signatures of the balance state of 19 different patient groups, 2 to 20, are shown in the legend as a scatter plot vs. the signature of patients group 1. Each group has 10 healthy patients and different sets of 10 diseased ones. Despite patient variability the signatures of the different groups are very consistent across the entire range of (only negative) possible values of G_i.</p

Electronic Coherences Excited by an Ultra Short Pulse Are Robust with Respect to Averaging over Randomly Oriented Molecules as Shown by Singular Value Decomposition

We report a methodology for averaging quantum photoexcitation vibronic dynamics over the initial orientations of the molecules with respect to an ultrashort light pulse. We use singular value decomposition of the ensemble density matrix of the excited molecules, which allows the identification of the few dominant principal molecular orientations with respect to the polarization direction of the electric field. The principal orientations provide insights into the specific stereodynamics of the corresponding principal molecular vibronic states. The massive compaction of the vibronic density matrix of the ensemble of randomly oriented pumped molecules enables a most efficient fully quantum mechanical time propagation scheme. Two examples are discussed for the quantum dynamics of the LiH molecule in the manifolds of its electronically excited Σ and Π states. Our results show that electronic and vibrational coherences between excited states of the same symmetry are resilient to averaging over an ensemble of molecular orientations and can be selectively excited at the ensemble level by tuning the pulse parameters

pH-Programmable DNA Logic Arrays Powered by Modular DNAzyme Libraries

Nature performs complex information processing circuits, such the programmed transformations of versatile stem cells into targeted functional cells. Man-made molecular circuits are, however, unable to mimic such sophisticated biomachineries. To reach these goals, it is essential to construct programmable modular components that can be triggered by environmental stimuli to perform different logic circuits. We report on the unprecedented design of artificial pH-programmable DNA logic arrays, constructed by modular libraries of Mg²⁺- and UO₂²⁺-dependent DNAzyme subunits and their substrates. By the appropriate modular design of the DNA computation units, pH-programmable logic arrays of various complexities are realized, and the arrays can be erased, reused, and/or reprogrammed. Such systems may be implemented in the near future for nanomedical applications by pH-controlled regulation of cellular functions or may be used to control biotransformations stimulated by bacteria

Surprisal analysis of genome-wide transcript profiling identifies differentially expressed genes and pathways associated with four growth conditions in the microalga <i>Chlamydomonas</i>

<div><p>The usual cultivation mode of the green microalga <i>Chlamydomonas</i> is liquid medium and light. However, the microalga can also be grown on agar plates and in darkness. Our aim is to analyze and compare gene expression of cells cultivated in these different conditions. For that purpose, RNA-seq data are obtained from <i>Chlamydomonas</i> samples of two different labs grown in four environmental conditions (agar@light, agar@dark, liquid@light, liquid@dark). The RNA seq data are analyzed by surprisal analysis, which allows the simultaneous meta-analysis of all the samples. First we identify a balance state, which defines a state where the expression levels are similar in all the samples irrespectively of their growth conditions, or lab origin. In addition our analysis identifies additional constraints needed to quantify the deviation with respect to the balance state. The first constraint differentiates the agar samples versus the liquid ones; the second constraint the dark samples versus the light ones. The two constraints are almost of equal importance. Pathways involved in stress responses are found in the agar phenotype while the liquid phenotype comprises ATP and NADH production pathways. Remodeling of membrane is suggested in the dark phenotype while photosynthetic pathways characterize the light phenotype. The same trends are also present when performing purely statistical analysis such as K-means clustering and differentially expressed genes.</p></div

Comparison of top-contributing genes according to surprisal analysis and K-means clustering of transcripts.

<p>(A) Centroid plots with mean and standard deviation of the expression values [Ln(meancenteredFPKM)] of the different genes belonging to the different clusters for each sample. Agar-upregulated (cluster 1, black), dark-upregulated (cluster 2, blue), light-upregulated (cluster 3, grey) and liquid-upregulated (cluster 4, green). (B) Cumulative barplot describing how many of the 250 most contributing genes to the different phenotypes described according to the first 2 constraints (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195142#pone.0195142.g001" target="_blank">Fig 1</a>) belong to the 4 different clusters according to K-means clustering. The same color code as in (A) is used for representing the samples corresponding to the different phenotypes.</p

Genes with identified function among the first 100 genes in dark- and light- grown samples.

<p>(A) dark-grown samples. (B) light-grown samples.</p

KEGG pathways contributing most to the balance state.

<p>KEGG pathways contributing most to the balance state.</p

Genes with identified function among the first 100 genes in agar- and liquid- grown samples.

<p>(A) agar-grown samples. (B) liquid-grown samples.</p

Top 10 KEGG pathways most enriched in light grown samples.

<p>Top 10 KEGG pathways most enriched in light grown samples.</p