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In the SARS-CoV-2 lineage, RNA elements essential for its viral life cycle, including genome replication and gene expression, have been identified. Still, the precise structures and functions of these RNA regions in coronaviruses remain poorly understood. This lack of knowledge points out the need for further research to better understand these crucial aspects of viral biology and, in time, prepare for future outbreaks. In this research, the in silico analysis of the cis RNA structures that act in the alpha-, beta-, gamma-, and deltacoronavirus genera has provided a detailed view of the presence and adaptation of the structures of these elements in coronaviruses. The results emphasize the importance of these cis elements in viral biology and their variability between different viral variants. Some coronavirus variants in some groups, depending on the cis element (stem-loop1 and -2; pseudoknot stem-loop1 and -2, and s2m), exhibited functional adaptation. Additionally, the conformation flexibility of the s2m element in the SARS variants was determined, suggesting a coevolution of this element in this viral group. The variability in secondary structures suggests genomic adaptations that may be related to replication processes, genetic regulation, as well as the specific pathogenicity of each variant. The results suggest that RNA structures in coronaviruses can adapt and evolve toward different viral variants, which has important implications for viral adaptation, pathogenicity, and future therapeutic strategies.

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A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them. The mean of permutation Lempel-Ziv complexity and permutation Shanon entropy used simultaneously for each of the four states are distinguishable from each other. However, used separately, the differences between permutation Lempel-Ziv complexity or permutation Shanon entropy of some states were not statistically significant. This shows that the joint use of both metrics provides more information than the separate use of each of them. Despite their wide use in medicine, LMEMs have not been commonly applied to simultaneously model metrics that quantify EEG signals. Modeling EEGs using a model that characterizes more than one response variable and their possible correlations represents a new way of analyzing EEG data in neuroscience.

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Betalains are bioactive compounds with attractive antioxidant properties for the food industry, endowing them with potential application in food coatings to maintain quality and extend shelf life. However, they have low stability to factors such as light, temperature, and humidity. An alternative to protect bioactive compounds is nanoencapsulation; one of the most used techniques to produce an encapsulation is coaxial electrospraying. In this research, the preparation and characterization of gelatin-betalain nanoparticles were carried out using the coaxial electrospray technique. Betalains were extracted from pitaya (Stenocereus thurberi) and encapsulated in gelatin. The obtained material was evaluated by SEM, FTIR, TGA, and DSC techniques and for its antioxidant capacity. By SEM, nanoparticles with spherical and monodisperse morphologies were observed, with betalain concentrations of 1 and 3% w/v and average diameters of 864 and 832 µm, respectively. By FTIR, the interaction between betalain and gelatin was observed through amino groups and hydrogen bonds. Likewise, the antioxidant activity of the betalains was maintained at the time of encapsulation, increasing the antioxidant activity as the concentration increased. The results of the DPPH, ABTS, and total phenols methods were 645.4592 µM T/g, 832.8863 ± 0.0110 µM T/g, and 59.8642 ± 0.0279 mg GAE/g for coaxial nanoparticles with 3% betalains, respectively. Therefore, the coaxial electrospray technique was useful for obtaining nanoparticles with good antioxidant properties, and due to the origin of its components and since the use of toxic solvents is not necessary in the technique, the material obtained can be considered food grade with potential application as a coating on functional foods.

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We study the roughening properties of the anharmonic elastic interface in the presence of temporally correlated noise. The model can be seen as a generalization of the anharmonic Larkin model, recently introduced by Purrello, Iguain, and Kolton [Phys. Rev. E 99, 032105 (2019)2470-004510.1103/PhysRevE.99.032105], to investigate the effect of higher-order corrections to linear elasticity in the fate of interfaces. We find analytical expressions for the critical exponents as a function of the anharmonicity index n, the noise correlator range θ∈[0,1/2], and dimension d. In d=1 we find that the interface becomes faceted and exhibits anomalous scaling for θ>1/4 for any degree of anharmonicity n>1. Analytical expressions for the anomalous exponents α_{loc} and κ are obtained and compared with a numerical integration of the model. Our theoretical results show that anomalous roughening cannot exist for this model in dimensions d>1.

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The fruit of Vanilla planifolia is broadly preferred by the agroindustry and gourmet markets due to its refined flavor and aroma. Peruvian Vanilla has been proposed as a possible source for genetic improvement of existing Vanilla cultivars, but, little has been done to facilitate comprehensive studies of these and other Vanilla. Here, a nuclear magnetic resonance (NMR) metabolomic platform was developed to profile for the first time the leaves - organ known to accumulate vanillin putative precursors - of V. planifolia and those of Peruvian V. pompona, V. palmarum, and V. ribeiroi, with the aim to determine metabolic differences among them. Analysis of the NMR spectra allowed the identification of thirty-six metabolites, twenty-five of which were quantified. One-way ANOVA and post-hoc Tukey test revealed that these metabolites changed significantly among species, whilst multivariate-analyses allowed the identification of malic and homocitric acids, together with two vanillin precursors, as relevant metabolic markers for species differentiation.

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We study the performance of an electromechanical harmonic oscillator chain as an energy harvester to extract power from finite-bandwidth ambient random vibrations, which are modelled by colored noise. The proposed device is numerically simulated and its performance assessed by means of the net electrical power generated and its efficiency in converting the external noise-supplied power into electrical power. Our main result is a much enhanced performance, both in the net electrical power delivered and in efficiency, of the harmonic chain with respect to the popular single oscillator resonator. Our numerical findings are explained by means of an analytical approximation, in excellent agreement with numerics.

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Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water-deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation-based network analysis was employed, constructing one network for normal irrigation and one network for water-recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome-wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change.

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We investigate Kardar-Parisi-Zhang (KPZ) surface growth in the presence of power-law temporally correlated noise. By means of extensive numerical simulations of models in the KPZ universality class we find that, as the noise correlator index increases above some threshold value, the surface exhibits anomalous kinetic roughening of the type described by the generic scaling theory of Ramasco et al. [Phys. Rev. Lett. 84, 2199 (2000)PRLTAO0031-900710.1103/PhysRevLett.84.2199]. Remarkably, as the driving noise temporal correlations increase, the surface develops a characteristic pattern of macroscopic facets that completely dominates the dynamics in the long time limit. We argue that standard scaling fails to capture the behavior of KPZ subject to long-range temporally correlated noise. These phenomena are not not described by the existing theoretical approaches, including renormalization group and self-consistent approaches.

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Even though Pure Shift NMR methods have conveniently been used in the assessment of crowded spectra, they are not commonly applied to the analysis of metabolomics data. This paper exploits the recently published SAPPHIRE-PSYCHE methodology in the context of plant metabolome. We compare single pulse, PSYCHE, and SAPPHIRE-PSYCHE spectra obtained from aqueous extracts of Physalis peruviana fruits. STOCSY analysis with simplified SAPPHIRE-PSYCHE spectra of six types of Cape gooseberry was carried out and the results attained compared with classical STOCSY data. PLS coefficients analysis combined with 1D-STOCSY was performed in an effort to simplify biomarker identification. Several of the most compromised proton NMR signals associated with critical constituents of the plant mixture, such as amino acids, organic acids, and sugars, were more cleanly depicted and their inter and intra correlation better reveled by the Pure Shift methods. The simplified data allowed the identification of glutamic acid, a metabolite not observed in previous studies of Cape gooseberry due to heavy overlap of its NMR signals. Overall, the results attained indicated that Ultra-Clean Pure Shift spectra increase the performance of metabolomics data analysis such as STOCSY and multivariate coefficients analysis, and therefore represent a feasible and convenient additional tool available to metabolomics.

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Chagas disease is a trypanosomiasis disease inflicted by Trypanosoma cruzi parasite. In Latin America, at least 10 million people are infected and annually, 10,000 casualties are deplored. Macrophage infectivity potentiator protein is one of the major virulence factors secreted by T. cruzi (TcMIP) in order to infect its host but little is known about its mechanism of action. Studies confer TcMIP an important role in the extracellular matrix transmigration and basal lamina penetration. Here, we report the backbone 1H, 13C, and 15N resonance assignment of TcMIP and the comparison of the secondary structure obtained against reported X-ray crystallography data.

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