Test Model Predictions Research Articles

Novel Experimental Simulations of the Atmospheric Injection of Meteoric Metals

A newly developed laboratory, Meteoric Ablation Simulator (MASI), is used to test model predictions of the atmospheric ablation of interplanetary dust particles (IDPs) with experimental Na, Fe, and Ca vaporization profiles. MASI is the first laboratory setup capable of performing time-resolved atmospheric ablation simulations, by means of precision resistive heating and atomic laser-induced fluorescence detection. Experiments using meteoritic IDP analogues show that at least three mineral phases (Na-rich plagioclase, metal sulfide, and Mg-rich silicate) are required to explain the observed appearance temperatures of the vaporized elements. Low melting temperatures of Na-rich plagioclase and metal sulfide, compared to silicate grains, preclude equilibration of all the elemental constituents in a single melt. The phase-change process of distinct mineral components determines the way in which Na and Fe evaporate. Ca evaporation is dependent on particle size and on the initial composition of the molten silicate. Measured vaporized fractions of Na, Fe, and Ca as a function of particle size and speed confirm differential ablation (i.e., the most volatile elements such as Na ablate first, followed by the main constituents Fe, Mg, and Si, and finally the most refractory elements such as Ca). The Chemical Ablation Model (CABMOD) provides a reasonable approximation to this effect based on chemical fractionation of a molten silicate in thermodynamic equilibrium, even though the compositional and geometric description of IDPs is simplistic. Improvements in the model are required in order to better reproduce the specific shape of the elemental ablation profiles.

Prediction and visualization of redox conditions in the groundwater of Central Valley, California

Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.

Vocational and Career Tech Education in American High Schools: The Value of Depth over Breadth

Vocational education is a large part of the high school curriculum, yet we have little understanding of what drives vocational enrollment or whether these courses help or harm early careers. To address this we develop a framework for curriculum choice, taking into account ability and preferences for academic and vocational work. We test model predictions using detailed transcript and earnings information from the NLSY97. Our results are two-fold. First, students positively sort into vocational courses, suggesting the belief that low ability students are funneled into vocational coursework is unlikely true. Second, we find higher earnings among students taking more upper-level vocational courses – a nearly 2% wage premium for each additional year, yet we find no gain from introductory vocational courses. These results suggest (a) policies limiting students’ ability to take vocational courses may not be welfare enhancing, and (b) the benefits of vocational coursework accrue to those who focus on depth over breadth.

Molecular simulation of the surface tension of 33 multi-site models for real fluids

Molecular models of real fluids are validated by comparing the vapour-liquid surface tension from molecular dynamics (MD) simulation to correlations of experimental data. The considered molecular models consist of up to 28 interaction sites, including Lennard-Jones sites, point charges, dipoles, and quadrupoles. They represent 33 real fluids, such as ethylene oxide, sulfur dioxide, phosgene, benzene, ammonia, formaldehyde, methanol, and water, and were adjusted to reproduce the saturated liquid density, the vapour pressure, and the enthalpy of vaporization. The models were not adjusted to interfacial properties, however, so that the present MD simulations are a test of model predictions. It is found that all of the considered models overestimate the surface tension. In most cases, however, the relative deviation between the simulation results and correlations to experimental data is smaller than 20%. This observation corroborates the outcome of previous studies on the surface tension of two-centre Lennard-Jones plus point quadrupole (2CLJQ) and two-centre Lennard-Jones plus point dipole (2CLJD) fluid models, where an overestimation of the order of 10 to 20% was found.

Model-guided combinatorial optimization of complex synthetic gene networks.

Constructing gene circuits that satisfy quantitative performance criteria has been a long‐standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three‐gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high‐throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model‐guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.

Inferential and forward projection modeling to evaluate options for controlling invasive mammals on islands.

Successful pest-mammal eradications from remote islands have resulted in important biodiversity benefits. Near-shore islands can also serve as refuges for native biota but require ongoing effort to maintain low-pest or pest-free status. Three management options are available in the presence of reinvasion risk: (1) control-to-zero density, in which immigration may occur but reinvaders are removed; (2) sustained population suppression (to relatively low numbers); or (3) no action. Biodiversity benefits can result from options one and two. The management challenge is to make evidence-based decisions on the selection of an appropriate objective and to identify a financially feasible control strategy that has a high probability of success. This requires understanding the pest species population dynamics and how it will respond to a range of potential management strategies, each with an associated financial cost. We developed a two-stage modeling approach that consisted of (1) Bayesian inferential modeling to estimate parameters for a model of pest population dynamics and control, and (2)a forward projection model to simulate a range of plausible management scenarios and quantify the probability of obtaining zero density within four years. We applied the model to an ongoing, six-year trapping program to control stoats (Mustela erminea) on Resolution Island, New Zealand. Zero density has not yet been achieved. Results demonstrate that management objectives were impeded by a combination of a highly fecund population, insufficient trap attractiveness, and a substantial proportion of the population that did not enter traps. Immigration is known to occur because the founding population arrived on the island by swimming from the mainland. However, immigration rate during this study was indistinguishable from zero. The forward projection modeling showed that control-to-zero density was feasible but required greater than a two-fold budget increase to intensify the trapping rate relative to population growth. The two-stage modeling provides the foundation for a management program in which broad-scale trials of additional trapping effort or improved trap lures would test model predictions and increase our understanding of system dynamics.

Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada

Abstract. The values of the snow and soil thermal conductivity, ksnow and ksoil, strongly impact the thermal regime of the ground in the Arctic, but very few data are available to test model predictions for these variables. We have monitored ksnow and ksoil using heated needle probes at Bylot Island in the Canadian High Arctic (73° N, 80° W) between July 2013 and July 2015. Few ksnow data were obtained during the 2013–2014 winter, because little snow was present. During the 2014–2015 winter ksnow monitoring at 2, 12 and 22 cm heights and field observations show that a depth hoar layer with ksnow around 0.02 W m−1 K−1 rapidly formed. At 12 and 22 cm, wind slabs with ksnow around 0.2 to 0.3 W m−1 K−1 formed. The monitoring of ksoil at 10 cm depth shows that in thawed soil ksoil was around 0.7 W m−1 K−1, while in frozen soil it was around 1.9 W m−1 K−1. The transition between both values took place within a few days, with faster thawing than freezing and a hysteresis effect evidenced in the thermal conductivity–liquid water content relationship. The fast transitions suggest that the use of a bimodal distribution of ksoil for modelling may be an interesting option that deserves further testing. Simulations of ksnow using the snow physics model Crocus were performed. Contrary to observations, Crocus predicts high ksnow values at the base of the snowpack (0.12–0.27 W m−1 K−1) and low ones in its upper parts (0.02–0.12 W m−1 K−1). We diagnose that this is because Crocus does not describe the large upward water vapour fluxes caused by the temperature gradient in the snow and soil. These fluxes produce mass transfer between the soil and lower snow layers to the upper snow layers and the atmosphere. Finally, we discuss the importance of the structure and properties of the Arctic snowpack on subnivean life, as species such as lemmings live under the snow most of the year and must travel in the lower snow layer in search of food.

Schizophrenia is associated with a pattern of spatial working memory deficits consistent with cortical disinhibition

Schizophrenia is associated with severe cognitive deficits, including impaired working memory (WM). A neural mechanism that may contribute to WM impairment is the disruption in excitation-inhibition (E/I) balance in cortical microcircuits. It remains unknown, however, how these alterations map onto quantifiable behavioral deficits in patients. Based on predictions from a validated microcircuit model of spatial WM, we hypothesized two key behavioral consequences: i) increased variability of WM traces over time, reducing performance precision; and ii) decreased ability to filter out distractors that overlap with WM representations. To test model predictions, we studied N=27 schizophrenia patients and N=28 matched healthy comparison subjects (HCS) who performed a spatial WM task designed to test the computational model. Specifically, we manipulated delay duration and distractor distance presented during the delay. Subjects used a high-sensitivity joystick to indicate the remembered location, yielding a continuous response measure. Results largely followed model predictions, whereby patients exhibited increased variance and less WM precision as the delay period increased relative to HCS. Schizophrenia patients also exhibited increased WM distractibility, with reports biased toward distractors at specific spatial locations, as predicted by the model. Finally, the magnitude of the WM drift and distractibility were significantly correlated, indicating a possibly shared underlying mechanism. Effects are consistent with elevated E/I ratio in schizophrenia, establishing a framework for translating neural circuit computational model of cognition to human experiments, explicitly testing mechanistic behavioral hypotheses of cellular-level neural deficits in patients.

Fractional order model for granular soils under drained cyclic loading

Summary The theory of fractional calculus has been successfully applied to model the triaxial behaviour of soils under static loading conditions. However, limited work has been carried out in using the fractional calculus to describe the cyclic behaviour of granular soils. In this paper, a fractional order constitutive model for granular soils under drained cyclic loading is proposed by incorporating the concept of fractional rate for strain accumulation. The fractional rate for strain accumulation is obtained from the analysis of the experimental data by utilizing the fractional calculus. Comparison between the test results and model predictions is presented. The key feature of the proposed model is that it can reasonably characterise the cyclic deformation of granular soils under both low and high loading cycles. Copyright © 2016 John Wiley & Sons, Ltd.

A self-enhanced transport mechanism through long noncoding RNAs for X chromosome inactivation.

X-chromosome inactivation (XCI) is the mammalian dosage compensation strategy for balancing sex chromosome content between females and males. While works exist on initiation of symmetric breaking, the underlying allelic choice mechanisms and dynamic regulation responsible for the asymmetric fate determination of XCI remain elusive. Here we combine mathematical modeling and experimental data to examine the mechanism of XCI fate decision by analyzing the signaling regulatory circuit associated with long noncoding RNAs (lncRNAs) involved in XCI. We describe three plausible gene network models that incorporate features of lncRNAs in their localized actions and rapid transcriptional turnovers. In particular, we show experimentally that Jpx (a lncRNA) is transcribed biallelically, escapes XCI, and is asymmetrically dispersed between two X’s. Subjecting Jpx to our test of model predictions against previous experimental observations, we identify that a self-enhanced transport feedback mechanism is critical to XCI fate decision. In addition, the analysis indicates that an ultrasensitive response of Jpx signal on CTCF is important in this mechanism. Overall, our combined modeling and experimental data suggest that the self-enhanced transport regulation based on allele-specific nature of lncRNAs and their temporal dynamics provides a robust and novel mechanism for bi-directional fate decisions in critical developmental processes.

Ready to reform: How popular initiatives can be successful

We study whether the number of signatures collected to qualify a popular initiative affects the probability of reforming the status quo. The initiative process is modeled as a sequential game under uncertainty: petitioners make an entry decision and collect signatures to qualify the initiative. Politicians decide about a political compromise – a counter proposal – after which petitioners have the option to withdraw the initiative before the vote. In equilibrium, politicians infer the initiative's popularity from the number of signatures and collection time. The more the initiative is perceived as a threat to the status quo, the more likely politicians come up with a counter proposal. Under certain conditions, petitioners have the incentive to collect more signatures than required for qualification to demonstrate high success probability.We test model predictions using the data set of all Swiss constitutional initiatives at the federal level between 1891 and 2010. Overall, we find supporting evidence for the model mechanisms. Fast signature collection is associated with a higher probability of reform. The effect is mediated through a higher probability of provoking a counter proposal. Ultimately, counter proposals are key to amending the status quo. Restricting the signature collection time reduces the informative mechanism of the signature collection process considerably.

A new iterative model updating technique based on least squares minimal residual method using measured modal data

Finite element (FE) models are useful for many applications in engineering community such as structural analysis, dynamic behavior prediction, structural condition assessment, and damage detection. In reality, the FE models often differ with the real structures due to significant discrepancies between test measurements and model predictions. This study is intended to propose an iterative model updating method to adjust the mass and stiffness matrices of the FE model by improving model updating formulations. Under dynamic discrepancy theory, the mass and stiffness orthogonality conditions are independently expanded to establish two model updating formulations. In the proposed iterative method, each of the improved equations is solved by a robust iterative technique named as least squares minimal residual (LSMR) to compute structural discrepancy matrices after transforming linear matrix systems of the model updating equations into linear vector systems. In the following, the mass and stiffness matrices are updated in the algorithm of the proposed iterative model updating method using the structural discrepancy matrices obtained from the LSMR technique. The major contributions of this article are to propose a new iterative finite element model updating method and introduce the novel and robust LSMR approach for the vibration-based applications. Another novelty is to enhance the two well-known model updating formulations for achieving more appropriate updating results. The efficiency and accuracy of the proposed methods are numerically verified by a simple planner truss and two shear building models. Results demonstrate that the proposed methods provide reliable estimates of finite element model updating using the measured modal data.

Integrating force and position: testing model predictions

In this study, we investigated the integration of force and position information in a task in which participants were asked to estimate the center of a weak force field. Two hypotheses, describing how participants solved this task, were tested: (1) by only using the position(s) where the force reaches the detection threshold, and (2) by extrapolating the force field based on perceived stiffness. Both hypotheses were also described formally, assuming a psychophysical function obeying a power law with an exponent smaller than one. The hypotheses were tested in two psychophysical experiments, in which 12 participants took part. In Experiment 1, an asymmetric force field was used and the presence of visual feedback about hand position was varied. In Experiment 2, a unilateral force field was used. For both experiments, hypothesis 1 predicts biases between (Experiment 1) or at (Experiment 2) the position(s) of the force detection threshold, while hypothesis 2 predicts smaller biases. The measured data show significant biases in both experiments that coincide with the biases predicted by using force detection thresholds from the literature. The average measured responses and their variabilities also fitted very well with the mathematical model of hypothesis 1. These results underline the validity of hypothesis 1. So, participants did not use a percept of the stiffness of the force field, but based their estimation of the center of the force field on the position(s) where the force reached the detection threshold. This shows that force and position information were not integrated in this task.

Resource levels and prey state influence antipredator behavior and the strength of nonconsumptive predator effects

The risk of predation can drive trophic cascades by causing prey to engage in antipredator behavior (e.g. reduced feeding), but these behaviors can be energetically costly for prey. The effects of predation risk on prey (nonconsumptive effects, NCEs) and emergent indirect effects on basal resources should therefore depend on the ecological context (e.g. resource abundance, prey state) in which prey manage growth/predation risk tradeoffs. Despite an abundance of behavioral research and theory examining state‐dependent responses to risk, there is a lack of empirical data on state‐dependent NCEs and their impact on community‐level processes. We used a rocky intertidal food chain to test model predictions for how resources levels and prey state (age/size) shape the magnitude of NCEs. Risk cues from predatory crabsCarcinus maenascaused juvenile and sub‐adult snailsNucella lapillusto increase their use of refuge habitats and decrease their growth and per capita foraging rates on barnaclesSemibalanus balanoides. Increasing resource levels (high barnacle density) and prey state (sub‐adults) enhanced the strength of NCEs.Our results support predictions that NCEs will be stronger in resource‐rich systems that enhance prey state and suggest that the demographic composition of prey populations will influence the role of NCEs in trophic cascades. Contrary to theory, however, we found that resources and prey state had little to no effect on snails in the presence of predation risk. Rather, increases in NCE strength arose because of the strong positive effects of resources and prey state on prey foraging rates in the absence of risk. Hence, a common approach to estimating NCE strength – integrating measurements of prey traits with and without predation risk into a single metric – may mask the underlying mechanisms driving variation in the strength and relative importance of NCEs in ecological communities.

Stratified NH and ND emission in the prestellar core 16293E in L1689N

High degrees of deuterium fractionation are commonly found in cold prestellar cores and in the envelopes around young protostars. As it brings strong constraints to chemical models, deuterium chemistry is often used to infer core history or molecule formation pathways. Whereas a large number of observations is available regarding interstellar deuterated stable molecules, relatively little is known about the deuteration of hydride radicals, as their fundamental rotational transitions are at high frequencies where the atmosphere is mostly opaque. Nitrogen hydride radicals are important species in nitrogen chemistry, as they are thought to be related to ammonia formation. Observations have shown that ammonia is strongly deuterated, with [NH2D]/[NH3] ~ 10%. Models predict similarly high [ND]/[NH] ratios, but so far only one observational determination of this ratio is available, towards the envelope of the protostar IRAS16293-2422. In order to test model predictions, we aim here at determining [ND]/[NH] in a dense, starless core. We observed NH and ND in 16293E with the HIFI spectrometer on board the Herschel Space Observatory as part of the CHESS guaranteed time key programme, and derived the abundances of these two species using a non-LTE non-local radiative transfer model. Both NH and ND are detected in the source, with ND in emission and NH in absorption against the continuum arising from the cold dust emission. Our model shows however that the ND emission and the NH absorption originate from different layers in the cloud, as further evidenced by their different velocities. In the central region of the core, we can set a lower limit to the [ND]/[NH] ratio of ~2%. This estimate is consistent with recent pure gas-phase models of nitrogen chemistry

Evaluation of the potential of three non-woven flax fiber reinforcements: Spunlaced, needlepunched and paper process mats

This paper presents results from an experimental study of three types of non-woven preforms (needlepunched, spunlaced and mat manufactured using a paper-making process) intended as composite reinforcement. These are potentially very attractive for transport applications. First, the influence of processing on elementary fiber tensile properties is shown to be limited. Then the preforms are evaluated in polypropylene matrix composites and mechanical properties are determined. The structure of non-woven reinforcements is strongly dependent on the manufacturing route. By varying the fiber content it is shown that the most efficient reinforcement for flax fibers is the mat produced by paper processing. The new spunlaced reinforced composites are shown to have slightly lower tensile properties (15% lower strength, and 25% lower stiffness) compared to mat composites at equivalent volume fraction, but further optimization is possible for these materials. Based on the measured constituent properties micromechanics models have been used to estimate composite stiffness. A good correlation is obtained between test results and model predictions.

Apaired neodymium and oxygen isotopic perspective on paleoceanographic changes across the Dubuque/Maquoketa contact in the Late Ordovician Laurentian seaway

The sedimentological, paleontological, and geochemical record of the Late Ordovician Laurentian epicontinental seaway shows a number of shifts that have been variously interpreted as indicating progressive cooling, episodes of upwelling, intervals of enhanced weathering, and times of organic carbon burial with seafloor anoxia, among other hypotheses. Determining the nature and cause of changes during this interval has general significance as these events are critical in understanding the evolution of the Late Ordovician ocean-climate system during the transition from a greenhouse to an icehouse world culminating in the brief end-Ordovician glacial event and mass extinction. Discriminating among alternative explanations, though, has proved difficult. The increasingly well-resolved dataset (taxonomically, spatially, and stratigraphically) of conodont oxygen isotopic measurements as well as a growing neodymium isotopic data set are providing independent constraints on temperature and circulation patterns. Both variables are invoked in many models, and these data can, thus, be used to test model predictions. In this study we show that there is no oxygen isotopic evidence for systematic cooling across the contact between the Dubuque Formation and the Maquoketa Formation in the Upper Mississippi Valley, there is neodymium isotopic evidence for expansion of waters with the signature expected of the Iapetus Ocean onto the Galena Platform, and there is a subtle, opposite neodymium isotopic shift in correlative deposits from the Cincinnati Region. From these results, we argue that sediment starvation resulting in condensed deposition is a better explanation for the formation of the phosphatic lower portions of the Maquoketa Formation than enhanced productivity driven by upwelling of cool waters sourced from the open ocean. Further, neodymium isotopes support circulation changes associated with the initiation of the Richmondian Invasive in the Cincinnati Region.

Anxiety-Like Behavioural Inhibition Is Normative under Environmental Threat-Reward Correlations.

Behavioural inhibition is a key anxiety-like behaviour in rodents and humans, distinct from avoidance of danger, and reduced by anxiolytic drugs. In some situations, it is not clear how behavioural inhibition minimises harm or maximises benefit for the agent, and can even appear counterproductive. Extant explanations of this phenomenon make use of descriptive models but do not provide a formal assessment of its adaptive value. This hampers a better understanding of the neural computations underlying anxiety behaviour. Here, we analyse a standard rodent anxiety model, the operant conflict test. We harvest Bayesian Decision Theory to show that behavioural inhibition normatively arises as cost-minimising strategy in temporally correlated environments. Importantly, only if behavioural inhibition is aimed at minimising cost, it depends on probability and magnitude of threat. Harnessing a virtual computer game, we test model predictions in four experiments with human participants. Humans exhibit behavioural inhibition with a strong linear dependence on threat probability and magnitude. Strikingly, inhibition occurs before motor execution and depends on the virtual environment, thus likely resulting from a neural optimisation process rather than a pre-programmed mechanism. Individual trait anxiety scores predict behavioural inhibition, underlining the validity of this anxiety model. These findings put anxiety behaviour into the context of cost-minimisation and optimal inference, and may ultimately pave the way towards a mechanistic understanding of the neural computations gone awry in human anxiety disorder.

Modulation and pre-amplification of PAR1 signaling by ADP acting via the P2Y12 receptor during platelet subpopulation formation

BackgroundTwo major soluble blood platelet activators are thrombin and ADP. Of these two, only thrombin can induce mitochondrial collapse and programmed cell death leading to phosphatidylserine (PS) exposure required for blood clotting reactions acceleration. Thrombin can also greatly potentiate collagen-induced PS exposure. However, ADP acting through the P2Y12 receptor was shown to increase the PS-exposing (PS+) platelets fraction produced by thrombin or thrombin-plus-collagen via an unknown mechanism. MethodsWe developed a comprehensive multicompartmental computational model of platelet PAR1-and-P2Y12 calcium signal transduction that included cytoplasmic signaling, dense tubular system and mitochondria. To test model predictions, flow cytometry experiments with washed, annexin V-labeled platelets were performed. ResultsStimulation of thrombin receptor PAR1 in the model induced cytoplasmic calcium oscillations, calcium uptake by mitochondria, opening of the permeability transition pore and collapse of the mitochondrial membrane potential. ADP stimulation of P2Y12 led to cAMP decrease that, in turn, caused changes in phospholipase C phosphorylation by protein kinase A, increase in cytoplasmic calcium level and, consequently, PS+ platelet formation. ADP addition before stimulation of PAR1 produced much greater increase of the PS+ fraction because cAMP concentration had time to go down prior to calcium oscillations; this prediction was also tested and confirmed experimentally. ConclusionThese results suggest a mechanism of ADP-dependent PS exposure regulation and show a likely mode of action that could be important for the PS exposure regulation in thrombi, where ADP is released before thrombin formation.

A metallicity recipe for rocky planets

Planets with sizes between those of Earth and Neptune divide into two populations: purely rocky bodies whose atmospheres contribute negligibly to their sizes, and larger gas-enveloped planets possessing voluminous and optically thick atmospheres. We show that whether a planet forms rocky or gas-enveloped depends on the solid surface density of its parent disk. Assembly times for rocky cores are sensitive to disk solid surface density. Lower surface densities spawn smaller planetary embryos; to assemble a core of given mass, smaller embryos require more mergers between bodies farther apart and therefore exponentially longer formation times. Gas accretion simulations yield a rule of thumb that a rocky core must be at least 2$M_\oplus$ before it can acquire a volumetrically significant atmosphere from its parent nebula. In disks of low solid surface density, cores of such mass appear only after the gas disk has dissipated, and so remain purely rocky. Higher surface density disks breed massive cores more quickly, within the gas disk lifetime, and so produce gas-enveloped planets. We test model predictions against observations, using planet radius as an observational proxy for gas-to-rock content and host star metallicity as a proxy for disk solid surface density. Theory can explain the observation that metal-rich stars host predominantly gas-enveloped planets.