3,134 publications from this institution
Bioinspired photonics is rapidly advancing, leveraging nature's light-managing mechanisms to enhance sustainability, resilience, and processability in nanophotonic applications. A recent breakthrough in the field is the discovery of iridoplast, a chloroplast type with an efficient Bragg reflector structure that boosts light absorption through slow light effects. In this paper, we showcase how all-organic, metal-free photonic structures inspired by iridoplast exhibit optical properties that are thought to be unique to metals or complex oxide compositions. Firstly, we demonstrate that by replicating iridoplast using organic materials, a photonic crystal with Near-Zero-Index (NZI) properties is produced. We show experiments where the organic dye introduces strong absorptions, resulting in an NZI response and photonic stopbands that enhance light absorption in the VIS. Secondly, we show that, with the same materials but a different structure, it's possible to enable Optical Tamm States. Our results demonstrate that photosynthesis is a promising and yet poorly explored source of inspiration for sustainable photonics.
Abstract The desire to extend the wisdom of traditional health systems has motivated the trade of many phytomedicine on a global scale for centuries, especially some dietary herbs, making a great overlap exits between western and eastern phytomedicine. Despite the communication since ancient times, a key disconnect still exists in the dialog among western and eastern herbal researchers. There is very little systematic effort to tap into the friction and fusion of eastern and western wisdom in utilizing phytomedicine. In this review, we analyzed the similarities and differences of three representative phytomedicine, namely Rhodiola , seabuckthorn, and fenugreek, aiming to open up new horizons in developing novel health products by integrating the wisdom of the east and the west.
Using low-loss energy-filtering transmission electron microscopy (EFTEM) imaging, we map surface plasmon resonances (SPRs) at optical wavelengths on single triangular silver nanoprisms. We show that EFTEM imaging combining high spatial sampling and high energy resolution enables the detection and for the first time, to the best of our knowledge, mapping at the nanoscale of an extra multipolar SPR on these nanoparticles. As illustrated on a 276.5 nm long nanoprism, this eigenmode is found to be enhanced on the three edges where it exhibits a two-lobe distribution.
Living cells have several mechanisms to avoid acidic conditions created by excess protons. The protons are extruded to the extracellular space via the plasma membrane by various types of transporters such as the Na + /H + exchanger and monocarboxylate transporters, which hereby contribute to the exclusion of protons in the cells of metabolic tissues including the liver, skeletal muscle, and adipose tissue. Disturbances in the functioning of the transporters cause abnormal intracellular fluid pH of the cells and dysfunction of metabolic homeostasis, leading to the development of metabolic diseases and a decrease in physical fitness level. In addition to the intracellular fluid pH regulation, growing evidence shows that the fluid pH in the interstitial space around metabolic tissues is easily reduced due to weaker pH buffering capacity than that in the cytosol and blood circulation. Therefore, pH reduction in the interstitial fluid may cause the onset of metabolic dysfunction. In contrast, several dietary foods have direct and indirect benefits in maintaining the interstitial fluid pH to the normal range by improving buffering capacities, suppressing proton production, and activating proton transporters, which strengthen the effect of appropriate diet on metabolic health.
Despite the relevant role of models of nucleotide substitution in phylogenetics, choosing among different models remains a problem. Several statistical methods for selecting the model that best fits the data at hand have been proposed, but their absolute and relative performance has not yet been characterized. In this study, we compare under various conditions the performance of different hierarchical and dynamic likelihood ratio tests, and of Akaike and Bayesian information methods, for selecting best-fit models of nucleotide substitution. We specifically examine the role of the topology used to estimate the likelihood of the different models and the importance of the order in which hypotheses are tested. We do this by simulating DNA sequences under a known model of nucleotide substitution and recording how often this true model is recovered by the different methods. Our results suggest that model selection is reasonably accurate and indicate that some likelihood ratio test methods perform overall better than the Akaike or Bayesian information criteria. The tree used to estimate the likelihood scores does not influence model selection unless it is a randomly chosen tree. The order in which hypotheses are tested, and the complexity of the initial model in the sequence of tests, influence model selection in some cases. Model fitting in phylogenetics has been suggested for many years, yet many authors still arbitrarily choose their models, often using the default models implemented in standard computer programs for phylogenetic estimation. We show here that a best-fit model can be readily identified. Consequently, given the relevance of models, model fitting should be routine in any phylogenetic analysis that uses models of evolution.
The Air Cushion Vehicle (ACV, also called hovercraft) is greatly influenced by the environment when it is sailing at sea, and it has serious nonlinearity and large inertia. So it is difficult to establish accurate mathematical model. Because of the nonlinear mapping ability of neural networks, the neural network is used to identify the motion model of ACV. The comparison tests between the network model and the simulation model are carried out, and the results verify the feasibility of using the neural network to establish the aerodynamic model of ACV.
Abstract Light-harvesting complexes in natural photosynthetic systems, such as those in purple bacteria, consist of photo-reactive chromophores embedded in densely packed “antenna” systems organized in well-defined nanostructures. In the case of purple bacteria, the chromophore antennas are composed of natural J-aggregates such as bacteriochlorophylls and carotenoids. Inspired by the molecular composition of such biological systems, we create a library of organic materials composed of densely packed J-aggregates in a polymeric matrix, in which the matrix mimics the optical role of a protein scaffold. This library of organic materials shows polaritonic properties which can be tuned from the visible to the infrared by choice of the model molecule. Inspired by the molecular architecture of the light-harvesting complexes of Rhodospirillum molischianum bacteria, we study the light–matter interactions of J-aggregate-based nanorings with similar dimensions to the analogous natural nanoscale architectures. Electromagnetic simulations show that these nanorings of J-aggregates can act as resonators, with subwavelength confinement of light while concentrating the electric field in specific regions. These results open the door to bio-inspired building blocks for metamaterials from visible to infrared in an all-organic platform, while offering a new perspective on light–matter interactions at the nanoscale in densely packed organic matter in biological organisms including photosynthetic organelles.
Abstract The effective population size ( N e ) is a key parameter to quantify the magnitude of genetic drift and inbreeding, with important implications in human evolution. The increasing availability of high-density genetic markers allows the estimation of historical changes in N e across time using measures of genome diversity or linkage disequilibrium between markers. Selection is expected to reduce diversity and N e , and this reduction is modulated by the heterogeneity of the genome in terms of recombination rate. Here we investigate by computer simulations the consequences of selection (both positive and negative) and of recombination rate heterogeneity in the estimation of historical N e . We also investigate the relationship between diversity parameters and N e across the different regions of the genome using human marker data. We show that the estimates of historical N e obtained from linkage disequilibrium between markers ( N e LD ) are virtually unaffected by selection. In contrast, those estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, what could have important consequences for the estimation of human demography. The simulation results are supported by the analysis of human data. The estimates of N e LD obtained for particular genomic regions do not correlate with recombination rate, nucleotide diversity, polymorphism, background selection statistic, minor allele frequency of SNPs, loss of function and missense variants and gene density. This suggests that N e LD measures are merely indicative of demographic changes in population size across generations. Author summary The inference of the demographic history of populations is of great relevance in evolutionary biology. This inference can be made from genomic data using coalescence methods or linkage disequilibrium methods. However, the assessment of these methods is usually made assuming neutrality (absence of selection). Here we show by computer simulations and analyses of human data that the estimates of historical effective population size obtained from linkage disequilibrium between markers are unaffected by natural selection, either positive or negative. In contrast, estimates obtained by coalescence mutation-recombination-based methods can be strongly affected by it, which could have important consequences for recent estimations of human demography. Thus, only linkage disequilibrium methods appear to provide unbiased estimates of the population census size.
As a result of the greater consciousness within the analytical community on the impact of chemicals on human health and environment, green issues are increasingly taken into account when choosing an established analytical method or developing a new one. Apart from typical analytical characteristics (e.g., sensitivity, limit of detection, repeatability, etc.), other features such as the amount of sample/reagents, operation time, use of energy-effective apparatus, waste production, etc. should be highlighted in order to meet the principles of Green Chemistry. Although conventional approaches for trace element analysis by atomic spectrometry usually involve well-established sample pre-treatments based on 'wet chemistry', and high consumption of gases, reagents, etc. is inherent to many techniques in this group, there are still many avenues where green issues can be implemented. For greening atomic spectrometry, green chemistry principles should be applied to every step of the analytical process, i.e., from sampling and sample pre-treatment to data processing. In this review, main pathways for greening atomic spectrometry such as downsizing of instrumentation, use of portable instruments, solid sampling, application of clean energies (ultrasound, microwaves, etc.) for sample pre-treatment, development of on-site, on-line and at-line approaches vs. typical off-line methods, application of modern extraction techniques (e.g., solid- and liquid-phase microextraction), green solvents and derivatization agents and use of chemometric tools for method optimization, signal processing, etc. are discussed in a critical way.
