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A major update to the mass spectrometry imaging (MSI) software MSiReader is presented, offering a multitude of newly added features critical to MSI analyses. MSiReader is a free, open-source, and vendor-neutral software written in the MATLAB platform and is capable of analyzing most common MSI data formats. A standalone version of the software, which does not require a MATLAB license, is also distributed. The newly incorporated data analysis features expand the utility of MSiReader beyond simple visualization of molecular distributions. The MSiQuantification tool allows researchers to calculate absolute concentrations from quantification MSI experiments exclusively through MSiReader software, significantly reducing data analysis time. An image overlay feature allows the incorporation of complementary imaging modalities to be displayed with the MSI data. A polarity filter has also been incorporated into the data loading step, allowing the facile analysis of polarity switching experiments without the need for data parsing prior to loading the data file into MSiReader. A quality assurance feature to generate a mass measurement accuracy (MMA) heatmap for an analyte of interest has also been added to allow for the investigation of MMA across the imaging experiment. Most importantly, as new features have been added performance has not degraded, in fact it has been dramatically improved. These new tools and the improvements to the performance in MSiReader v1.0 enable the MSI community to evaluate their data in greater depth and in less time. Graphical Abstract ᅟ

Simulation studies, especially neutral comparison studies, are crucial for evaluating and comparing statistical methods as they investigate whether methods work as intended and can guide an appropriate method choice. Typically, the term simulation refers to parametric simulation, i.e. computer experiments using pseudo-random numbers. For these, the full data-generating process (DGP) and outcome-generating model (OGM) are known within the simulation. However, the specification of realistic DGPs might be difficult in practice leading to oversimplified assumptions. The problem is more severe for higher-dimensional data as the number of parameters to specify typically increases with the number of variables in the data. Plasmode simulation, which is a combination of resampling covariates from a real-life dataset from the DGP of interest together with a specified OGM is often claimed to solve this problem since no explicit specification of the DGP is necessary. However, this claim is not well supported by empirical results. Here, parametric and Plasmode simulations are compared in the context of a method comparison study for binary classification methods. We focus on studies conducted with some specific data type or application in mind whose true, unknown data-generating mechanism is mimicked. The performance of Plasmode and parametric comparison studies for estimating classifier performance is compared as well as their ability to reproduce the true method ranking. The influence of misspecifications of the DGP on the results of parametric simulation and of misspecifications of the OGM on the results of parametric and Plasmode simulation are investigated. Moreover, different resampling strategies are compared for Plasmode comparison studies. The study finds that misspecifications of the DGP and OGM negatively influence the ability of the comparison studies to estimate the classification performances and method rankings. The best choice of the resampling strategy in Plasmode simulation depends on the concrete scenario.

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.

The excellent mechanical properties of carbon nanotubes are being exploited in a growing number of applications from ballistic armour to nanoelectronics. However, measurements of these properties have not achieved the values predicted by theory due to a combination of artifacts introduced during sample preparation and inadequate measurements. Here we report multiwalled carbon nanotubes with a mean fracture strength >100 GPa, which exceeds earlier observations by a factor of approximately three. These results are in excellent agreement with quantum-mechanical estimates for nanotubes containing only an occasional vacancy defect, and are ∼80% of the values expected for defect-free tubes. This performance is made possible by omitting chemical treatments from the sample preparation process, thus avoiding the formation of defects. High-resolution imaging was used to directly determine the number of fractured shells and the chirality of the outer shell. Electron irradiation at 200 keV for 10, 100 and 1,800 s led to improvements in the maximum sustainable loads by factors of 2.4, 7.9 and 11.6 compared with non-irradiated samples of similar diameter. This effect is attributed to crosslinking between the shells. Computer simulations also illustrate the effects of various irradiation-induced crosslinking defects on load sharing between the shells. The mechanical properties of carbon nanotubes rarely match the values predicted by theory owing to a combination of artefacts introduced during sample preparation and inadequate measurements. However, by avoiding chemical treatments and using high-resolution imaging, it is possible to obtain values of the mean fracture strength that exceed previous values by approximately a factor of three.

The idea behind the marine cloud-brightening (MCB) geoengineering technique is that seeding marine stratocumulus clouds with copious quantities of roughly monodisperse sub-micrometre sea water particles might significantly enhance the cloud droplet number concentration, and thereby the cloud albedo and possibly longevity. This would produce a cooling, which general circulation model (GCM) computations suggest could-subject to satisfactory resolution of technical and scientific problems identified herein-have the capacity to balance global warming up to the carbon dioxide-doubling point. We describe herein an account of our recent research on a number of critical issues associated with MCB. This involves (i) GCM studies, which are our primary tools for evaluating globally the effectiveness of MCB, and assessing its climate impacts on rainfall amounts and distribution, and also polar sea-ice cover and thickness; (ii) high-resolution modelling of the effects of seeding on marine stratocumulus, which are required to understand the complex array of interacting processes involved in cloud brightening; (iii) microphysical modelling sensitivity studies, examining the influence of seeding amount, seed-particle salt-mass, air-mass characteristics, updraught speed and other parameters on cloud-albedo change; (iv) sea water spray-production techniques; (v) computational fluid dynamics studies of possible large-scale periodicities in Flettner rotors; and (vi) the planning of a three-stage limited-area field research experiment, with the primary objectives of technology testing and determining to what extent, if any, cloud albedo might be enhanced by seeding marine stratocumulus clouds on a spatial scale of around 100×100 km. We stress that there would be no justification for deployment of MCB unless it was clearly established that no significant adverse consequences would result. There would also need to be an international agreement firmly in favour of such action.

The mixing state of soot particles in the atmosphere is of crucial importance for assessing their climatic impact, since it governs their chemical reactivity, cloud condensation nuclei activity, and radiative properties. To improve the mixing state representation in models, we present a new approach, the stochastic particle‐resolved model PartMC‐MOSAIC, which explicitly resolves the composition of individual particles in a given population of different types of aerosol particles. This approach tracks the evolution of the mixing state of particles due to emission, dilution, condensation, and coagulation. To make this direct stochastic particle‐based method practical, we implemented a new multiscale stochastic coagulation method. With this method we achieved high computational efficiency for situations when the coagulation kernel is highly nonuniform, as is the case for many realistic applications. PartMC‐MOSAIC was applied to an idealized urban plume case representative of a large urban area to simulate the evolution of carbonaceous aerosols of different types due to coagulation and condensation. For this urban plume scenario we quantified the individual processes that contributed to the aging of the aerosol distribution, illustrating the capabilities of our modeling approach. The results showed for the first time the multidimensional structure of particle composition, which is usually lost in sectional or modal aerosol models.

A boundary control problem for the viscous Cahn–Hilliard equations with possibly singular potentials and dynamic boundary conditions is studied and first order necessary conditions for optimality are proved.

Quantifying groundwater/surface-water interactions is essential for managing water resources and revealing contaminant fate. There has been little concern on the exchange between streams and aquifers through an extensive aquitard thus far. In this study, hydrogeologic calculation and tritium modeling were jointly applied to characterize such interactions through an extensive aquitard in the interior of Jianghan Plain, an alluvial plain of Yangtze River, China. One groundwater simulation suggested that the lateral distance of influence from the river was about 1,000 m; vertical flow in the aquitard followed by lateral flow in the aquifer contributed significantly more (~90%) to the aquifer head change near the river than lateral bank storage in the aquitard followed by infiltration. The hydrogeologic calculation produced vertical fluxes of the order 0.01 m/day both near and farther from the river, suggesting that similar shorter-lived (half-monthly) vertical fluxes occur between the river and aquitard near the river, and between the surface end members and aquitard farther from the river. Tritium simulation based on the OTIS model produced an average groundwater residence time of about 15 years near the river and a resulting vertical flux of the order 0.001 m/day. Another tritium simulation based on a dispersion model produced a vertical flux of the order 0.0001 m/day away from the river, coupled with an average residence time of around 90 years. These results suggest an order of magnitude difference for the longer-lived (decadal) vertical fluxes between surface waters and the aquifer near and away from the river.

We describe recent algorithmic developments that have enabled large eddy simulations of reactor flows on up to P = 65, 000 processors on the IBM BG/P at the Argonne Leadership Computing Facility.

How living systems detect the presence of genotoxic damage embedded in a million-fold excess of undamaged DNA is an unresolved question in biology. Here we have captured and structurally elucidated a base-excision DNA repair enzyme, MutM, at the stage of initial encounter with a damaged nucleobase, 8-oxoguanine (oxoG), nested within a DNA duplex. Three structures of intrahelical oxoG-encounter complexes are compared with sequence-matched structures containing a normal G base in place of an oxoG lesion. Although the protein–DNA interfaces in the matched complexes differ by only two atoms—those that distinguish oxoG from G—their pronounced structural differences indicate that MutM can detect a lesion in DNA even at the earliest stages of encounter. All-atom computer simulations show the pathway by which encounter of the enzyme with the lesion causes extrusion from the DNA duplex, and they elucidate the critical free energy difference between oxoG and G along the extrusion pathway. First steps in DNA repair Repair of DNA damage requires first that the lesion be detected in an excess of undamaged DNA. The bacterial MutM/eukaryotic OGG1 DNA glycosylase is responsible for recognizing 8-oxoguanosine (which differs by only two atoms from guanosine) and excising the damaged base. In work combining structural biology and computational modelling, the Verdine and Karplus labs describe a series of snapshots of the initial encounter between the protein and either normal or damaged DNA, and define the pathway by which this interaction results in expulsion of the damaged base out of the DNA helix, and into the catalytic pocket of the enzyme. In living systems, the repair of genotoxic damage requires that the lesion first be detected in an excess of undamaged DNA. A base-excision DNA repair enzyme, MutM, is now captured and structurally elucidated at the stage of initial encounter with a damaged nucleobase within a DNA duplex. By combining structural biology and computational modelling, the pathway by which this encounter causes the damaged nucleobase to be extruded from the DNA duplex is defined.