We derive the equations of motion of relativistic, non-resistive, second-order dissipative magnetohydrodynamics from the Boltzmann equation using the method of moments. We assume the fluid to be composed of a single type of point-like particles with vanishing dipole moment or spin, so that the fluid has vanishing magnetization and polarization. In a first approximation, we assume the fluid to be non-resistive, which allows to express the electric field in terms of the magnetic field. We derive equations of motion for the irreducible moments of the deviation of the single-particle distribution function from local thermodynamical equilibrium. We analyze the Navier-Stokes limit of these equations, reproducing previous results for the structure of the first-order transport coefficients. Finally, we truncate the system of equations for the irreducible moments using the 14-moment approximation, deriving the equations of motion of relativistic, non-resistive, second-order dissipative magnetohydrodynamics. We also give expressions for the new transport coefficients appearing due to the coupling of the magnetic field to the dissipative quantities.

The reduction in the red to far-red light ratio (R/FR) and photosynthetically active radiation caused by dense planting initiates shade avoidance responses (SARs) to help plants compete against their neighbors. However, deep shade attenuates shade-induced stem elongation to suppress excessive reversion toward skotomorphogenic development, in which photoreceptor phytochrome A (PHYA) has been known to play the major role. However, the molecular mechanism underlying PHYA function in deep shade is poorly understood. Here, we report that shade-accumulated PHYA can release auxin/indole-3-acetic acid (AUX/IAA), suppressors in the auxin signaling pathway, from SCF, an auxin receptor, to weaken auxin signaling and negatively regulate shade response. Corroborating this, phyA mutants display an enhanced auxin response to deep shade and auxin treatment. Specifically, PHYA competes with TIR1 by directly binding and stabilizing AUX/IAA. Our findings illustrate a mechanistic model of how plants sense different shade levels to fine-tune auxin signaling and generate appropriate SAR.

Shade avoidance syndrome enables shaded plants to grow and compete effectively against their neighbors. In Arabidopsis, the shade-induced de-phosphorylation of the transcription factor PIF7 (PHYTOCHROME-INTERACTING FACTOR 7) is the key event linking light perception to stem elongation. However, the mechanism through which phosphorylation regulates the activity of PIF7 is unclear. Here, we show that shade light induces the de-phosphorylation and nuclear accumulation of PIF7. Phosphorylation-resistant site mutations in PIF7 result in increased nuclear localization and shade-induced gene expression, and consequently augment hypocotyl elongation. PIF7 interacts with 14-3-3 proteins. Blocking the interaction between PIF7 and 14-3-3 proteins or reducing the expression of 14-3-3 proteins accelerates shade-induced nuclear localization and de-phosphorylation of PIF7, and enhances the shade phenotype. By contrast, the 14-3-3 overexpressing line displays an attenuated shade phenotype. These studies demonstrate a phosphorylation-dependent translocation of PIF7 when plants are in shade and a novel mechanism involving 14-3-3 proteins, mediated by the retention of PIF7 in the cytoplasm that suppresses the shade response.

One of the key mediators of the antiviral and antiproliferative actions of interferon is double-stranded-RNA-dependent protein kinase (PKR). PKR activity is also involved in the regulation of cell proliferation, apoptosis and signal transduction. We have recently identified PACT, a novel protein activator of PKR, as an important modulator of PKR activity in cells in the absence of viral infection. PACT heterodimerizes with PKR and activates it by direct protein-protein interactions. Endogenous PACT acts as an activator of PKR in response to diverse stress signals, such as serum starvation and peroxide or arsenite treatment, and is therefore a novel, stress-modulated physiological activator of PKR. In this study, we have characterized the functional domains of PACT that are required for PKR activation. Our results have shown that, unlike the N-terminal conserved domains 1 and 2, the third conserved domain of PACT is dispensable for its binding of double-stranded RNA and inter action with PKR. However, a deletion of domain 3 results in a loss of PKR activation ability, in spite of a normal interaction with PKR, thereby indicating that domain 3 plays an essential role in PKR activation. Purified recombinant domain 3 could also activate PKR efficiently in vitro. Our results indicate that, although dispensable for PACT's high-affinity interaction with PKR, the third motif is essential for PKR activation. In addition, domain 3 and eukaryotic initiation factor 2alpha both interact with PKR through the same region within PKR, which we have mapped to lie between amino acid residues 318 and 551.

Pain is a highly unpleasant sensory and emotional experience, and no objective diagnosis test exists to assess it. In clinical practice there are two main methods for the estimation of pain, a patient’s self-report and clinical judgement. However, these methods are highly subjective and the need of biomarkers to measure pain is important to improve pain management, reduce risk factors, and contribute to a more objective, valid, and reliable diagnosis. Therefore, in this study we propose the use of functional near-infrared spectroscopy (fNIRS) and machine learning for the identification of a possible biomarker of pain. We collected pain information from 18 volunteers using the thermal test of the quantitative sensory testing (QST) protocol, according to temperature level (cold and hot) and pain intensity (low and high). Feature extraction was completed in three different domains (time, frequency, and wavelet), and a total of 69 features were obtained. Feature selection was carried out according to three criteria, information gain (IG), joint mutual information (JMI), and Chi-squared ( χ 2 ). The significance of each feature ranking was evaluated using three learning models separately, linear discriminant analysis (LDA), the K-nearest neighbour (K-NN) and support vector machines (SVM) using the linear and Gaussian and polynomial kernels. The results showed that the Gaussian SVM presented the highest accuracy (94.17%) using only 25 features to identify the four types of pain in our database. In addition, we propose the use of the top 13 features according to the JMI criteria, which exhibited an accuracy of 89.44%, as promising biomarker of pain. This study contributes to the idea of developing an objective assessment of pain and proposes a potential biomarker of human pain using fNIRS.