In this work, we develop a machine learning-based method to characterize intracluster concentration (ρc), background concentration (ρb), clustering radius (r̄), and radius dispersity (δr) in simulated atom probe tomography data using multiple spatial statistics summary functions to train a Bayesian regularized neural network. We build upon previous work that utilized Ripley's K-function by incorporating additional features from nearest-neighbor spatial statistics summary functions to better characterize concentration-based metrics. The addition of nearest-neighbor based features allows for highly accurate estimates of ρc and ρb, both with 90% of the predictions within 4.0% of the real value; the root-mean-square errors are reduced by 81.5% and 92.8% from predictions using only K-function based features, respectively. Additionally, including these nearest-neighbor based features improves the ability to differentiate between r̄ and δr.
Modulation of photoassimilate export from the chloroplast is essential for controlling the distribution of fixed carbon in the cell and maintaining optimum photosynthetic rates. In this study we identified chloroplast TRIOSE PHOSPHATE/PHOSPHATE TRANSLOCATOR2 (CreTPT2) and CreTPT3 in the green alga Chlamydomonas (Chlamydomonas reinhardtii), which exhibit similar substrate specificities but whose encoding genes are differentially expressed over the diurnal cycle. We focused mostly on CreTPT3 because of its high level of expression and the severe phenotype exhibited by tpt3 relative to tpt2 mutants. Null mutants for CreTPT3 had a pleiotropic phenotype that affected growth, photosynthetic activities, metabolite profiles, carbon partitioning, and organelle-specific accumulation of H2O2. These analyses demonstrated that CreTPT3 is a dominant conduit on the chloroplast envelope for the transport of photoassimilates. In addition, CreTPT3 can serve as a safety valve that moves excess reductant out of the chloroplast and appears to be essential for preventing cells from experiencing oxidative stress and accumulating reactive oxygen species, even under low/moderate light intensities. Finally, our studies indicate subfunctionalization of the CreTPT transporters and suggest that there are differences in managing the export of photoassimilates from the chloroplasts of Chlamydomonas and vascular plants.
Bodily engagement with the material and sociocultural world is ubiquitous in doing and learning science. However, science education researchers have often tended to emphasize the disembodied and nonmaterial aspects of science learning, thereby overlooking the crucial role of the body in meaning-making processes. While in recent years we have seen a turn towards embracing embodied perspectives, there persist considerable theoretical and methodological differences within research on embodiment in science education that hamper productive discourse. What is needed is a careful examination of how different traditions and disciplines, among them philosophy, social semiotics, and cognitive science, bear on embodiment in science education research. This paper aims to explore and articulate the differences and convergences of embodied perspectives in science education research in the form of a dialogue between three fictitious personas that stand for the cognitive, social-interactionist, and phenomenological research traditions. By bringing these traditions into dialogue, we aim to better position the role of the body in the science education research landscape. In doing so, we take essential steps towards unifying terminology across different research traditions and further exploring the implications of embodiment for science education research.
Photosynthetic organisms frequently experience abiotic stress that restricts their growth and development. Under such circumstances, most absorbed solar energy cannot be used for CO2 fixation and can cause the photoproduction of reactive oxygen species (ROS) that can damage the photosynthetic reaction centers of photosystem I and II (PSI and PSII), resulting in a decline in primary productivity. This work describes a biological 'switch' in the green alga Chlamydomonas reinhardtii that reversibly restricts photosynthetic electron transport (PET) at the cytochrome b6f (Cyt b6f) complex when the capacity for accepting electrons downstream of PSI is severely limited. We specifically show this restriction in STARCHLESS6 (sta6) mutant cells, which cannot synthesize starch when they are limited for nitrogen (growth inhibition) and subjected to a dark-to-light transition. This restriction represents a form of photosynthetic control that causes diminished electron flow to PSI and thereby prevents PSI photodamage but does not appear to rely on a ΔpH. Furthermore, when electron flow is restricted, the plastid alternative oxidase (PTOX) becomes active, functioning as an electron valve that dissipates some excitation energy absorbed by PSII and allows the formation of a proton motive force (PMF) that would drive some ATP production [potentially sustaining PSII repair and non-photochemical quenching (NPQ)]. The restriction at the Cyt b6f complex can be gradually relieved with continued illumination. This study provides insights into how photosynthetic electron transport responds to a marked reduction in availability of downstream electron acceptors and the protective mechanisms involved.
Air-water interfacial retention of poly- and perfluoroalkyl substances (PFASs) is increasingly recognized as an important environmental process. Herein, column transport experiments were used to measure air-water interfacial partitioning values for several perfluoroalkyl ethers and for PFASs derived from aqueous film-forming foam, while batch experiments were used to determine equilibrium Kia data for compounds exhibiting evidence of rate-limited partitioning. Experimental results suggest a Freundlich isotherm best describes PFAS air-water partitioning at environmentally relevant concentrations (101-106 ng/L). A multiparameter regression analysis for Kia prediction was performed for the 15 PFASs for which equilibrium Kia values were determined, assessing 246 possible combinations of 8 physicochemical and system properties. Quantitative structure-property relationships (QSPRs) based on three to four parameters provided predictions of high accuracy without model overparameterization. Two QSPRs (R2 values of 0.92 and 0.83) were developed using an assumed average Freundlich n value of 0.65 and validated across a range of relevant concentrations for perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and hexafluoropropylene oxide-dimer acid (i.e., GenX). A mass action model was further modified to account for the changing ionic strength on PFAS air-water interfacial sorption. The final result was two distinct QSPRs for estimating PFAS air-water interfacial partitioning across a range of aqueous concentrations and ionic strengths.
To expand the breadth of knowledge of actinide chemistry in molten chloride salts, chloride room-temperature ionic liquids (RTILs) were used to probe the influence of RTIL cation on second-sphere coordination for anionic complexes of uranium and neptunium. Six chloride RTILs were studied to represent a range of cation polarizing strength, size, and charge density to correlate changes in the complex geometry and redox behaviors. Optical spectroscopy indicated that actinides were dissolved at equilibrium as octahedral AnCl62- (An = U, Np) as is observed in comparable high-temperature molten chloride salts. These anionic metal complexes were sensitive to the RTIL cation polarizing strength and hydrogen bond donating strength and displayed varying levels of fine structure and hypersensitive transition splitting depending on the degree of perturbation to the complex's coordination symmetry. Furthermore, voltammetry experiments on the redox-active complexes indicated a stabilizing effect on lower valence actinide oxidation states by more polarizing RTIL cations whereby the measured E1/2 potentials for both U(IV/III) and Np(IV/III) couples shifted positively by about 600 mV across the different systems. These results indicate that more polarizing RTIL cations inductively remove electron density from the actinide metal center over An-Cl-Cation bond networks to stabilize electron-deficient oxidation states. Electron-transfer kinetics were generally much slower than in molten chloride systems, partially due to lower temperatures and higher viscosities in the working systems and showed diffusion coefficients of 1.8 × 10-8 to 6.4 × 10-8 cm2 s-1 for UIV and 4.4 × 10-8 to 8.3 × 10-8 cm2 s-1 for NpIV. We also detect a one-electron oxidation of NpIV that we have attributed to the formation of NpV as NpCl6-. Overall, we observe a coordination environment for the anionic actinide complexes that is susceptible to even small changes in RTIL cation properties.
The formation of nuclei in slightly proton-rich regions of the neutrino-driven wind of core-collapse supernovae could be attributed to the neutrino-p process (νp-process). As it proceeds via a sequence of (p,γ) and (n,p) reactions, it may produce elements in the range of Ni and Sn, considering adequate conditions. Recent studies identify a number of decisive (n,p) reactions that control the efficiency of the νp-process. The study of one such (n,p) reaction via the measurement of the reverse (p,n) in inverse kinematics was performed with SECAR at NSCL/FRIB. Proton-induced reaction measurements, especially at the mass region of interest, are notably difficult since the recoils have nearly identical masses as the unreacted projectiles. Such measurements are feasible with the adequate separation level achieved with SECAR, and the in-coincidence neutron detection. Adjustments of the SECAR system for the first (p,n) reaction measurement included the development of new ion beam optics, and the installation of the neutron detection system. The aforementioned developments along with a discussion on the preliminary results of the p(⁵⁸Fe,n)⁵⁸Co reaction measurement are presented.
The r-process has been shown to be robust in reproducing the abundance distributions of heavy elements, such as europium, seen in ultra-metal poor stars. In contrast, observations of elements 26 < Z < 47 display overabundances relative to r-process model predictions. A proposed additional source of early nucleosynthesis is the weak r-process in neutrino-driven winds of core-collapse supernovae. It has been shown that in this site (α,n) reactions are both crucial to nucleosynthesis and the main source of uncertainty in model-based abundance predictions. Aiming to improve the certainty of nucleosynthesis predictions, the cross section of the important reaction ⁸⁶Kr(α,n)⁸⁹Sr has been measured at an energy relevant to the weak r-process. This experiment was conducted in inverse kinematics at TRIUMF with the EMMA recoil mass spectrometer and the TIGRESS gamma-ray spectrometer. A novel type of solid helium target was used.
A comprehensive, generalized approach to predict the retention of per- and polyfluoroalkyl substances (PFAS) from aqueous film-forming foam (AFFF) by a soil matrix as a function of PFAS molecular and soil physiochemical properties was developed. An AFFF with 34 major PFAS (12 anions and 22 zwitterions) was added to uncontaminated soil in one-dimensional saturated column experiments and PFAS mass retained was measured. PFAS mass retention was described using an exhaustive statistical approach to generate a poly-parameter quantitative structure-property relationship (ppQSPR). The relevant predictive properties were PFAS molar mass, mass fluorine, number of nitrogens in the PFAS molecule, poorly crystalline Fe oxides, organic carbon, and specific (BET-N2) surface area. The retention of anionic PFAS was nearly independent of soil properties and largely a function of molecular hydrophobicity, with the size of the fluorinated side chain as the main predictor. Retention of nitrogen-containing zwitterionic PFAS was related to poorly crystalline metal oxides and organic carbon content. Knowledge of the extent to which a suite of PFAS may respond to variations in soil matrix properties, as developed here, paves the way for the development of reactive transport algorithms with the ability to capture PFAS dynamics in source zones over extended time frames.
Knowledge of snow cover distribution and disappearance dates over a wide range of scales is imperative for understanding hydrological dynamics and for habitat management of wildlife species that rely on snow cover. Identification of snow refugia, or places with relatively late snow disappearance dates compared to surrounding areas, is especially important as climate change alters snow cover timing and duration. The purpose of this study was to increase understanding of snow refugia in complex terrain spanning the rain-snow transition zone at fine spatial and temporal scales. To accomplish this objective, we used remote cameras to provide relatively high temporal and spatial resolution measurements on snowpack conditions. We built linear models to relate snow disappearance dates (SDDs) at the monitoring sites to topoclimatic and canopy cover metrics. One model to quantify SDDs included elevation, aspect, and an interaction between canopy cover and cold-air pooling potential. High-elevation, north-facing sites in cold-air pools had the latest SDDs, but isolated lower-elevation points also exhibited relatively late potential SDDs. Importantly, canopy cover had a much stronger effect on SDDs in cold-air pools than in non-cold-air pools, indicating that best practices in forest management for snow refugia could vary across microtopography. A second model that included in situ hydroclimate observations (DJF temperature and March 1 snow depth) indicated that March 1 snow depth had little impact on SDD at the coldest winter temperatures, and that DJF temperatures had a stronger effect on SDD at lower snow depths, implying that the relative importance of snowfall and temperature could vary across hydroclimatic contexts in their impact on snow refugia. This new understanding of factors influencing snow refugia can guide forest management actions to increase snow retention and inform management of snow-dependent wildlife species in complex terrain.
This chapter synthesizes the physics education research work related to the interplay of visualization and mathematization in physics teaching and learning, specifically as mediated by dynamic, interactive digital visualization tools. In structuring our synthesis, we build on existing theories of visualization and mathematization to propose two “functions” that visualizations tools exhibit in facilitating mathematization: (1) bridging between physical phenomena and formalisms, and (2) bridging between idealized models of physical phenomena and formalisms. We populate these two broad categories with illustrative examples of visualization tools and conclude with a summary of the developmental history of those tools in physics education research.
Environment stress is a major threat to the existence of coral reefs and has generated a lot of interest in the coral research community. Under the environmental stress, corals can experience tissue loss and/or the breakdown of symbiosis between the cnidarian host and its symbiotic algae causing the coral tissue to appear white as the skeleton can be seen by transparency. Image analysis is a common method used to assess tissue response under the environmental stress. However, the traditional approach is limited by the dynamic nature of the coral-algae symbiosis. Here, we observed coral tissue response in the scleractinian coral, Montipora capricornis, using high frequency image analysis throughout the experiment, as opposed to the typical start/end point assessment method. Color analysis reveals that the process can be divided into five stages with two critical stages according to coral tissue morphology and color ratio. We further explore changes to the morphology of individual polyps by means of the Pearson correlation coefficient and recurrence plots, where the quasi-periodic and nonstationary dynamics can be identified. The recurrence quantification analysis also allows the comparison between the different polyps. Our research provides a detailed visual and mathematical analysis of coral tissue response to environmental stress, which potentially shows universal applicability. Moreover, our approach provides a robust quantitative advancement for improving our insight into a suite of biotic responses in the perspective of coral health evaluation and fate prediction.
Copper is expected to play a big role in the global move, as solar, wind, and electric vehicles increase. Understanding the metal market and forecasting price changes can help players plan for future changes in supply and demand. Developing dynamic models of demand and supply requires considering price elasticity. In static prediction models, price elasticity is ignored, and the future quantity demanded is predicted without taking into consideration the relationship between price and quantity. A framework is proposed to determine the price elasticity of supply and demand of copper from 1990 to 2020 using production, consumption, and price data. The presented results show that both supply and demand price elasticities in the copper market in the long run are small but statistically significant. In this situation, rather than no change in price, there would be a small change in price, and thus, a small change in quantity demanded and supplied.
A normalizing flow (NF) is a mapping that transforms a chosen probability distribution to a normal distribution. Such flows are a common technique used for data generation and density estimation in machine learning and data science. The density estimate obtained with a NF requires a change of variables formula that involves the computation of the Jacobian determinant of the NF transformation. In order to tractably compute this determinant, continuous normalizing flows (CNF) estimate the mapping and its Jacobian determinant using a neural ODE. Optimal transport (OT) theory has been successfully used to assist in finding CNFs by formulating them as OT problems with a soft penalty for enforcing the standard normal distribution as a target measure. A drawback of OT-based CNFs is the addition of a hyperparameter, [Formula: see text], that controls the strength of the soft penalty and requires significant tuning. We present JKO-Flow, an algorithm to solve OT-based CNF without the need of tuning [Formula: see text]. This is achieved by integrating the OT CNF framework into a Wasserstein gradient flow framework, also known as the JKO scheme. Instead of tuning [Formula: see text], we repeatedly solve the optimization problem for a fixed [Formula: see text] effectively performing a JKO update with a time-step [Formula: see text]. Hence we obtain a "divide and conquer" algorithm by repeatedly solving simpler problems instead of solving a potentially harder problem with large [Formula: see text].
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