In their Commentary paper, Villaverde and Massonis (On testing structural identifiability by a simple scaling method: relying on scaling symmetries can be misleading) have commented on our paper in ...which we proposed a simple scaling method to test structural identifiability. Our scaling invariance method (SIM) tests for scaling symmetries only, and Villaverde and Massonis correctly show the SIM may fail to detect identifiability problems when a model has other types of symmetries. We agree with the limitations raised by these authors but, also, we emphasize that the method is still valuable for its applicability to a wide variety of models, its simplicity, and even as a tool to introduce the problem of identifiability to investigators with little training in mathematics.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The matching problem is known since the beginning of the eighteenth century and Bayesian solutions have been proposed. In this paper, we present a Bayesian analysis of an experiment that also leads ...to the matching problem. Since in this paper we consider the order in which assignments are made and not only the number of matches, our approach is different from the literature on this problem. Our approach also considers that it is possible to have different abilities for the different objects that will be guessed. Hence, we have a parameter that measures the matching ability for each specific object or assignment. As a consequence, we need to have some replicates of the experiment and a prior distribution for each of the parameters. Conjugate prior distributions for the parameters are discussed. The frequentist solution has a particular Bayesian interpretation under a non-informative prior distribution. A real data set is analyzed using the proposed methodology.
Scaling Up DNA Origami Lattice Assembly Xin, Yang; Shen, Boxuan; Kostiainen, Mauri A. ...
Chemistry : a European journal,
June 10, 2021, Letnik:
27, Številka:
33
Journal Article
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The surface‐assisted hierarchical assembly of DNA origami nanostructures is a promising route to fabricate regular nanoscale lattices. In this work, the scalability of this approach is explored and ...the formation of a homogeneous polycrystalline DNA origami lattice at the mica‐electrolyte interface over a total surface area of 18.75 cm2 is demonstrated. The topological analysis of more than 50 individual AFM images recorded at random locations over the sample surface showed only minuscule and random variations in the quality and order of the assembled lattice. The analysis of more than 450 fluorescence microscopy images of a quantum dot‐decorated DNA origami lattice further revealed a very homogeneous surface coverage over cm2 areas with only minor boundary effects at the substrate edges. At total DNA costs of € 0.12 per cm2, this large‐scale nanopatterning technique holds great promise for the fabrication of functional surfaces.
The surface‐assisted assembly of DNA origami lattices is a promising technique for fabricating functional surfaces. Here, the formation of a homogeneous DNA origami lattice on mica over a total surface area of 18.75 cm2 is demonstrated. Atomic force and fluorescence microscopy mapping reveal the high uniformity and homogeneity of the lattice over almost the entire surface area. The total DNA costs amount to €0.12 per cm2.
Although a small proportion of patients with asthma have severe disease, it accounts for the majority of morbidity related to the illness. Severe asthma comprises a heterogeneous group of phenotypes. ...Targeted treatments for these phenotypes represent a major advancement in the management of severe asthma. Omalizumab, a monoclonal antibody to IgE, improves asthma control in patients with a predominant allergic phenotype. Monoclonal antibodies targeted to interleukin 4α and interleukin 5 have shown substantial benefit in patients with the eosinophilic asthma phenotype; so too have monoclonal antibodies targeted to interleukin 13 in patients with a type 2 allergic phenotype. Bronchial thermoplasty, a new technique to reduce airway smooth muscle mass, improves symptoms and reduces exacerbations in patients with severe uncontrolled asthma and the chronic airflow obstruction phenotype. While awaiting comparative trials, we can now use a targeted approach with these phenotypes, guiding our treatment selection with the best evidence. This Review will focus on the latest developments in these new treatments and inform the clinician on how to select the appropriate patient for these treatments.
This paper outlines experimental procedures and numerical analyses to investigate hydrogen embrittlement (HE) of S13Cr supermartensitic stainless steel (SMSS) subjected to various cathodic ...potentials. The hydrogen diffusion behavior was investigated using two electrochemical permeation techniques, namely, the double potentiostatic method (DPM) and the step method (SM), along with thermal desorption spectroscopy (TDS) tests. The apparent hydrogen diffusion coefficients varied from 1.4 × 10−13 to 4.7 × 10−12 m2/s, and TDS revealed the existence of deep traps, such as interfaces around precipitates and between retained austenite and ferritic matrix. The effects of HE in terms of a reduction in ductility were analyzed through tensile tests and fractographic analysis. A maximum reduction in elongation of approximately 14% was measured, and a majority of brittle fracture along the entire net section was observed in test samples pre-charged under -1.5 V/SCE and -1.7 V/SCE. A computational simulation was performed using the finite element method to predict the loss of ductility using the obtained experimental data. The computational model used a fracture-controlled method under static structural conditions, which links the reduction in elongation of the tensile specimens to the decrease in critical fracture energies, which, in turn, were calculated through a new mean field approach that used thermodynamic excess variables. The observations presented here suggest that S13Cr has good resistance to HE and that the computational model is reliable because only slight deviations in the magnitude (5%) were observed between the experimental and numerical results.
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•A quick and simple synthesis methodology is used to obtain Bi2MoO6/ZnO heterostructures.•The morphology of ZnO evolves from rods to flowers with the addition of Bi2MoO6.•The ...heterostructure ZN8B1 presents excellent photocatalytic properties for degradation of methylene blue and diclofenac under sunlight.•The powder immobilized on a glass plate with epoxy resin shows good reusability in 10 cycles.
In this study, the Bi2MoO6/ZnO heterostructures were quickly and easily obtained using the sonochemical method and impregnated on a glass plate using epoxy resin. The influence of increasing the percentage of ZnO (1:1, 2:1, 4:1 and 8:1% by weight) in relation to Bi2MoO6 on the formation of the heterostructure was investigated. The XRD results of the heterostructures indicated the presence of ZnO phases with a hexagonal structure and Bi2MoO6 with an orthorhombic structure. According to FESEM and HRTEM analyses, the morphology of ZnO is controlled with the addition of Bi2MoO6. The photocatalytic activity of the heterostructures was evaluated using a mixture (MIX) of methylene blue dye and diclofenac potassium, obtaining photocatalytic efficiency of 99.7 % and 90 % in 100 min under solar irradiation, with the ZN8B1 heterostructure, respectively. Aiming to increase the reuse of the material, the ZN8B1 heterostructure was impregnated into a glass plate, obtaining an efficiency of on average 65.2 % for 10 reuse cycles, showing that the impregnate can be applied to effluent treatment. The evaluation of the effect of photocatalyst dosage, contaminant concentration and pH effect was carried out with the MIX solution in order to simulate real conditions. Furthermore, a possible photocatalytic mechanism was proposed based on inhibition tests, verifying that the •O2– and •OH radicals were the dominant active species, which followed the Z-scheme mechanism.
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Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and ...meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ±2σ deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina’s extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters’ mitigation efforts.
This article deals with Bayesian inference in the comparison of measurement systems. Agreement between two systems can be evaluated using data from several measurement systems and using only data ...from the two systems being compared. With a measurement error model for replicated observations and the probability of agreement to compare measurement systems, we develop methods to compare measurement systems with either homoscedastic or heteroscedastic measurement errors under the Bayesian paradigm via Markov chain Monte Carlo methods. A graphical tool is described to check model adequacy. The methodology developed in the article is illustrated using a real dataset and through simulations.
In regression analysis, when the covariates are not exactly observed, measurement error models extend the usual regression models toward a more realistic representation of the covariates. It is ...common in the literature to directly propose prior distributions for the parameters in normal measurement error models. Posterior inference requires Markov chain Monte Carlo (MCMC) computations. However, the regression model can be seen as a reparameterization of the bivariate normal distribution. In this paper, general results for objective Bayesian inference under the bivariate normal distribution were adapted to the regression framework. So, posterior inferences for the structural parameters of a measurement error model under a great variety of priors were obtained in a simple way. The methodology is illustrated by using five common prior distributions showing good performance for all prior distributions considered. MCMC methods are not necessary at all. Model assessment is also discussed. Results from a simulation study and applications to real data sets are reported.
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