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Proceedings of the National Academy of... Sep 2023Metabolic scaling theory (MST) provides an understanding of scaling in organismal morphology. Empirical data on the apparently universal pattern of tip-to-base conduit...
Metabolic scaling theory (MST) provides an understanding of scaling in organismal morphology. Empirical data on the apparently universal pattern of tip-to-base conduit widening across vascular plants motivate a set of generalized MST (gMST) relationships allowing for variable rates of conduit coalescence and taper and a transition between transport and diffusive domains. Our model, with coalescence limited to the distalmost part of the conductive system, reconciles previous MST-based models and extends their applicability to the entire plant. We derive an inverse relationship between stem volume taper and conduit widening, which implies that plant morphology is dictated by vascular optimality and not the assumption of constant sapwood area across all branching levels, contradicting Leonardo's rule. Thus, energy efficiency controls conduit coalescence rate, lowering the carbon cost needed to sustain the vascular network. Our model shows that as a plant grows taller, it must increase conduit widening and coalescence, which may make it more vulnerable to drought. We calculated how our gMST model implies a lower carbon cost to sustain a similar network compared to previous MST-based models. We also show that gMST predicts the cross-sectional area of vessels and their frequency along the relative length better than previous MST models for a range of plant types. We encourage further research obtaining data that would allow testing other gMST predictions that remain unconfirmed empirically, such as conduit coalescence rate in stems. The premise of energy efficiency can potentially become instrumental to our understanding of plant carbon allocation.
Topics: Tracheophyta; Carbon; Cardiac Conduction System Disease; Diffusion; Droughts
PubMed: 37722036
DOI: 10.1073/pnas.2215047120 -
Analytical Chemistry Mar 2022Synchrotron-based X-ray fluorescence microscopy (XFM) analysis is a powerful technique that can be used to visualize elemental distributions across a broad range of...
Synchrotron-based X-ray fluorescence microscopy (XFM) analysis is a powerful technique that can be used to visualize elemental distributions across a broad range of sample types. Compared to conventional mapping techniques such as laser ablation inductively coupled plasma mass spectrometry or benchtop XFM, synchrotron-based XFM provides faster and more sensitive analyses. However, access to synchrotron XFM beamlines is highly competitive, and as a result, these beamlines are often oversubscribed. Therefore, XFM experiments that require many large samples to be scanned can penalize beamline throughput. Our study was largely driven by the need to scan large gels (170 cm) using XFM without decreasing beamline throughput. We describe a novel approach for acquiring two sets of XFM data using two fluorescence detectors in tandem; essentially performing two separate experiments simultaneously. We measured the effects of tandem scanning on beam quality by analyzing a range of contrasting samples downstream while simultaneously scanning different gel materials upstream. The upstream gels were thin (<200 μm) diffusive gradients in thin-film (DGT) binding gels. DGTs are passive samplers that are deployed in water, soil, and sediment to measure the concentration and distribution of potentially bioavailable nutrients and contaminants. When deployed on soil, DGTs are typically small (2.5 cm), so we developed large DGTs (170 cm), which can be used to provide extensive maps to visualize the diffusion of fertilizers in soil. Of the DGT gel materials tested (-acrylamide, polyacrylamide, and polyurethane), polyurethane gels were most suitable for XFM analysis, having favorable handling, drying, and analytical properties. This gel type enabled quantitative (>99%) transmittance with minimal (<3%) flux variation during raster scanning, whereas the other gels had a substantial effect on the beam focus. For the first time, we have (1) used XFM for mapping analytes in large DGTs and (2) developed a tandem probe analysis mode for synchrotron-based XFM, effectively doubling throughput. The novel tandem probe analysis mode described here is of broad applicability across many XFM beamlines as it could be used for future experiments where any uniform, highly transmissive sample could be analyzed upstream in the "background" of downstream samples.
Topics: Diffusion; Gels; Polyurethanes; Soil; Synchrotrons
PubMed: 35276040
DOI: 10.1021/acs.analchem.1c04255 -
PloS One 2022Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex...
Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex networks. It is essential to identify an attribute that can compute and rank the diffusion power of nodes with high accuracy, despite the plurality of nodes and many relationships between them. Most methods presented only use one structural attribute to capture the influence of individuals, which is not entirely accurate in most networks. The reason is that network structures are disparate, and these methods will be inefficient by altering the network. A possible solution is to use more than one attribute to examine the characteristics aspect and address the issue mentioned. Therefore, this study presents a method for identifying and ranking node's ability to spread information. The purpose of this study is to present a multi-attribute decision making approach for determining diffusion power and classification of nodes, which uses several local and semi-local attributes. Local and semi-local attributes with linear time complexity are used, considering different aspects of the network nodes. Evaluations performed on datasets of real networks demonstrate that the proposed method performs satisfactorily in allocating distinct ranks to nodes; moreover, as the infection rate of nodes increases, the accuracy of the proposed method increases.
Topics: Humans; Pregnancy; Female; Diffusion; Pregnancy, Multiple
PubMed: 36441805
DOI: 10.1371/journal.pone.0278129 -
Journal of Biomedical Optics Jul 2021Diffuse light is ubiquitous in biomedical optics and imaging. Understanding the process of migration of an initial photon population entering tissue to a completely...
SIGNIFICANCE
Diffuse light is ubiquitous in biomedical optics and imaging. Understanding the process of migration of an initial photon population entering tissue to a completely randomized, diffusely scattered population provides valuable insight to the interpretation and design of optical measurements.
AIM
The goal of this perspective is to present a brief, unifying analytical framework to describe how properties of light transition from an initial state to a distributed state as light diffusion occurs.
APPROACH
First, measurement parameters of light are introduced, and Monte Carlo simulations along with a simple analytical expression are used to explore how these individual parameters might exhibit diffusive behavior. Second, techniques to perform optical measurements are considered, highlighting how various measurement parameters can be leveraged to subsample photon populations.
RESULTS
Simulation results reinforce the fact that light undergoes a transition from a non-diffuse population to one that is first subdiffuse and then fully diffuse. Myriad experimental methods exist to isolate subpopulations of photons, which can be broadly categorized as source- and/or detector-encoded techniques, as well as methods of tagging the tissue of interest.
CONCLUSIONS
Characteristic properties of light progressing to diffusion can be described by some form of Gaussian distribution that grows in space, time, angle, wavelength, polarization, and coherence. In some cases, these features can be approximated by simpler exponential behavior. Experimental methods to subsample features of the photon distribution can be achieved or theoretical methods can be used to better interpret the data with this framework.
Topics: Computer Simulation; Diffusion; Monte Carlo Method; Optics and Photonics; Photons
PubMed: 34216136
DOI: 10.1117/1.JBO.26.7.070601 -
PloS One 2023Preventing unauthorized access to sensitive data has always been one of the main concerns in the field of information security. Accordingly, various solutions have been...
Preventing unauthorized access to sensitive data has always been one of the main concerns in the field of information security. Accordingly, various solutions have been proposed to meet this requirement, among which encryption can be considered as one of the first and most effective solutions. The continuous increase in the computational power of computers and the rapid development of artificial intelligence techniques have made many previous encryption solutions not secure enough to protect data. Therefore, there is always a need to provide new and more efficient strategies for encrypting information. In this article, a two-way approach for information encryption based on chaos theory is presented. To this end, a new chaos model is first proposed. This model, in addition to having a larger key space and high sensitivity to slight key changes, can demonstrate a higher level of chaotic behavior compared to previous models. In the proposed method, first, the input is converted to a vector of bytes and first diffusion is applied on it. Then, the permutation order of chaotic sequence is used for diffusing bytes of data. In the next step, the chaotic sequence is used for applying second diffusion on confused data. Finally, to further reduce the data correlation, an iterative reversible rule-based model is used to apply final diffusion on data. The performance of the proposed method in encrypting image, text, and audio data was evaluated. The analysis of the test results showed that the proposed encryption strategy can demonstrate a pattern close to a random state by reducing data correlation at least 28.57% compared to previous works. Also, the data encrypted by proposed method, show at least 14.15% and 1.79% increment in terms of MSE and BER, respectively. In addition, key sensitivity of 10-28 and average entropy of 7.9993 in the proposed model, indicate its high resistance to brute-force, statistical, plaintext and differential attacks.
Topics: Humans; Artificial Intelligence; Confusion; Correlation of Data; Diffusion; Entropy
PubMed: 37768960
DOI: 10.1371/journal.pone.0291759 -
Scientific Reports Jun 2022Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble...
Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available.
Topics: Diffusion; Motion; Single Molecule Imaging; Software
PubMed: 35689015
DOI: 10.1038/s41598-022-13446-0 -
Angewandte Chemie (International Ed. in... Jan 2023Inspired by ideas from NMR, we have developed Infrared Diffusion-Ordered Spectroscopy (IR-DOSY), which simultaneously characterizes molecular structure and size. We rely...
Inspired by ideas from NMR, we have developed Infrared Diffusion-Ordered Spectroscopy (IR-DOSY), which simultaneously characterizes molecular structure and size. We rely on the fact that the diffusion coefficient of a molecule is determined by its size through the Stokes-Einstein relation, and achieve sensitivity to the diffusion coefficient by creating a concentration gradient and tracking its equilibration in an IR-frequency resolved manner. Analogous to NMR-DOSY, a two-dimensional IR-DOSY spectrum has IR frequency along one axis and diffusion coefficient (or equivalently, size) along the other, so the chemical structure and the size of a compound are characterized simultaneously. In an IR-DOSY spectrum of a mixture, molecules with different sizes are nicely separated into distinct sets of IR peaks. Extending this idea to higher dimensions, we also perform 3D-IR-DOSY, in which we combine the conformation sensitivity of femtosecond multi-dimensional IR spectroscopy with size sensitivity.
Topics: Magnetic Resonance Spectroscopy; Diffusion; Spectrophotometry, Infrared; Molecular Structure
PubMed: 36259515
DOI: 10.1002/anie.202213424 -
Biophysical Journal Jun 2021From nutrient uptake to chemoreception to synaptic transmission, many systems in cell biology depend on molecules diffusing and binding to membrane receptors....
From nutrient uptake to chemoreception to synaptic transmission, many systems in cell biology depend on molecules diffusing and binding to membrane receptors. Mathematical analysis of such systems often neglects the fact that receptors process molecules at finite kinetic rates. A key example is the celebrated formula of Berg and Purcell for the rate that cell surface receptors capture extracellular molecules. Indeed, this influential result is only valid if receptors transport molecules through the cell wall at a rate much faster than molecules arrive at receptors. From a mathematical perspective, ignoring receptor kinetics is convenient because it makes the diffusing molecules independent. In contrast, including receptor kinetics introduces correlations between the diffusing molecules because, for example, bound receptors may be temporarily blocked from binding additional molecules. In this work, we present a modeling framework for coupling bulk diffusion to surface receptors with finite kinetic rates. The framework uses boundary homogenization to couple the diffusion equation to nonlinear ordinary differential equations on the boundary. We use this framework to derive an explicit formula for the cellular uptake rate and show that the analysis of Berg and Purcell significantly overestimates uptake in some typical biophysical scenarios. We confirm our analysis by numerical simulations of a many-particle stochastic system.
Topics: Diffusion; Kinetics; Ligands; Models, Biological; Receptors, Cell Surface
PubMed: 33794148
DOI: 10.1016/j.bpj.2021.03.021 -
Analytical Chemistry Aug 2022The paramagnetic spin relaxation (PSR) filter allows the selective NMR signal suppression of components in mixtures according to their complexation ability to a...
The paramagnetic spin relaxation (PSR) filter allows the selective NMR signal suppression of components in mixtures according to their complexation ability to a paramagnetic ion. It relies on the faster relaxation of nuclei in paramagnetic environments and thus is complementary to classical diffusion and relaxation filters. So far, the PSR filter has established Gd as the sole PSR agent, restricting the paramagnetic filtering repertoire. Herein, we present Cu as a robust PSR agent with characteristic filtering properties. While Gd depends on unspecific ion-pair interactions with anionic components, Cu stands out for filtering species via ordered coordination complexes. An evaluation of the paramagnetic effect of Cu over more than 50 small molecules and polymers has unveiled different sensitivities to Cu (especially high for pyridines, diamines, polyamines, and amino alcohols) and precise filtering conditions for mixtures (H, COSY, and HMQC) that were challenged with a test bed of commercial drugs. The advantage of integrating Cu and Gd for the stepwise PSR filtering of complex mixtures is also shown.
Topics: Complex Mixtures; Coordination Complexes; Diffusion; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy
PubMed: 35895678
DOI: 10.1021/acs.analchem.2c01983 -
Nature Communications Nov 2022Methane is supersaturated in surface seawater and shallow coastal waters dominate global ocean methane emissions to the atmosphere. Aerobic methane oxidation (MOx) can...
Methane is supersaturated in surface seawater and shallow coastal waters dominate global ocean methane emissions to the atmosphere. Aerobic methane oxidation (MOx) can reduce atmospheric evasion, but the magnitude and control of MOx remain poorly understood. Here we investigate methane sources and fates in the East China Sea and map global MOx rates in shallow waters by training machine-learning models. We show methane is produced during methylphosphonate decomposition under phosphate-limiting conditions and sedimentary release is also source of methane. High MOx rates observed in these productive coastal waters are correlated with methanotrophic activity and biomass. By merging the measured MOx rates with methane concentrations and other variables from a global database, we predict MOx rates and estimate that half of methane, amounting to 1.8 ± 2.7 Tg, is consumed annually in near-shore waters (<50 m), suggesting that aerobic methanotrophy is an important sink that significantly constrains global methane emissions.
Topics: Methane; Oxidation-Reduction; Seawater; Atmosphere; Diffusion
PubMed: 36437260
DOI: 10.1038/s41467-022-35082-y