The effects of temperature (4–20°C), relative humidity (RH, 0–100%), pH (3–7), availability of nutrients (0–5 g/l sucrose) and artificial light (0–494 μmol/m2/s) on macroconidial germination of ...Fusarium graminearum were studied. Germ tubes emerged between 2 and 6 h after inoculation at 100% RH and 20°C. Incubation in light (205 ± 14 μmol/m/s) retarded the germination for approximately 0.5 h in comparison with incubation in darkness. The times required for 50% of the macroconidia to germinate were 3.5 h at 20°C, 5.4 h at 14°C and 26.3 h at 4°C. No germination was observed after an incubation period of 18 h at 20°C in darkness at RH less than 80%. At RH greater than 80%, germination increased with humidity. Germination was observed when macroconidia were incubated in glucose (5 g/l) or sucrose (concentration range from 2.5 × 10−4 to 5 g/l) whereas no germination was observed when macroconidia were incubated in sterile deionized water up to 22 h. Macroconidia germinated quantitatively within 18 h at pH 3–7. Repeated freezing (−15°C) and thawing (20°C) water agar plates with either germinated or non‐germinated macroconidia for up to five times did not prevent fungal growth after thawing. However, the fungal growth rate of mycelium was negatively related to the number of freezing events the non‐germinated macroconidia experienced. The fungal growth rate of mycelium was not significantly affected by the number of freezing events the germinated spores experienced. Incubation of macroconidia at low humidity (0–53% RH) suppressed germination and decreased the viability of the spores.
Summary
Combined focused ion beam and scanning electron microscope (FIB‐SEM) tomography is a well‐established technique for high resolution imaging and reconstruction of the microstructure of a wide ...range of materials. Segmentation of FIB‐SEM data is complicated due to a number of factors; the most prominent is that for porous materials, the scanning electron microscope image slices contain information not only from the planar cross‐section of the material but also from underlying, exposed subsurface pores. In this work, we develop a segmentation method for FIB‐SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These materials are used for coating pharmaceutical oral dosage forms (tablets or pellets) to control drug release. We study three samples of ethyl cellulose and hydroxypropyl cellulose with different volume fractions where the hydroxypropyl cellulose phase has been leached out, resulting in a porous material. The data are segmented using scale‐space features and a random forest classifier. We demonstrate good agreement with manual segmentations. The method enables quantitative characterization and subsequent optimization of material structure for controlled release applications. Although the methodology is demonstrated on porous polymer films, it is applicable to other soft porous materials imaged by FIB‐SEM. We make the data and software used publicly available to facilitate further development of FIB‐SEM segmentation methods.
Lay Description
For imaging of very fine structures in materials, the resolution limits of, e.g. X‐ray computed tomography quickly become a bottleneck. Scanning electron microscopy (SEM) provides a way out, but it is essentially a two‐dimensional imaging technique. One manner in which to extend it to three dimensions is to use a focused ion beam (FIB) combined with a scanning electron microscopy and acquire tomography data. In FIB‐SEM tomography, ions are used to perform serial sectioning and the electron beam is used to image the cross section surface. This is a well‐established method for a wide range of materials. However, image analysis of FIB‐SEM data is complicated for a variety of reasons, in particular for porous media. In this work, we analyse FIB‐SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These films are used as coatings for controlled drug release. The aim is to perform image segmentation, i.e. to identify which parts of the image data constitute the pores and the solid, respectively. Manual segmentation, i.e. when a trained operator manually identifies areas constituting pores and solid, is too time‐consuming to do in full for our very large data sets. However, by performing manual segmentation on a set of small, random regions of the data, we can train a machine learning algorithm to perform automatic segmentation on the entire data sets. The method yields good agreement with the manual segmentations and yields porosities of the entire data sets in very good agreement with expected values. The method facilitates understanding and quantitative characterization of the geometrical structure of the materials, and ultimately understanding of how to tailor the drug release.
Summary
We implement a massively parallel population Monte Carlo approximate Bayesian computation (PMC‐ABC) method for estimating diffusion coefficients, sizes and concentrations of diffusing ...nanoparticles in liquid suspension using confocal laser scanning microscopy and particle tracking. The method is based on the joint probability distribution of diffusion coefficients and the time spent by a particle inside a detection region where particles are tracked. We present freely available central processing unit (CPU) and graphics processing unit (GPU) versions of the analysis software, and we apply the method to characterize mono‐ and bidisperse samples of fluorescent polystyrene beads.
Lay description
Acquiring microscopy videos of particles performing random diffusive motion in a liquid can be used to determine sizes and concentrations of the particles. We use a technique called confocal laser scanning microscopy to follow particle trajectories through time as they move, and develop advanced statistical techniques for determining sizes and concentrations, and also supply software for this analysis. We demonstrate the usefulness of the method on particles of a single size and also on a mixture of two sizes.
Integrated strategies of community health promotion (ISCHP) are based on intersectoral collaborations using the Health in All Policies approach to address determinants of health. While effects on ...health determinants have been shown, evidence on the effectiveness of ISCHP on health outcomes is scarce. The aim of this study is to assess the long-term effects of ISCHP on diabetes mellitus mortality (DMM) in German communities. A nonrandomized evaluation based on secondary county-level official data (1998–2016) was performed. In April 2019, 149 communities in Germany with ISCHP out of 401 were identified. Communities with < 5 measurements of DMM, starting before 1999 or after 2015, were excluded. Analyses included 65 communities with ISCHP (IG) and 124 without ISCHP (CG). ISCHP ran for a mean of 5.6 years. Fixed effects (FE) models were used to estimate effects of ISCHP and duration on DMM taking into account the time-varying average age. The FE estimator for DMM is
b
= − 2.48 (95% CI − 3.45 to − 1.51) for IG vs. CG and b = − 0.30 (95% CI − 0.46 to − 0.14) for ISCHP duration (0–16 years). In the first year of an ISCHP, a reduction of the annual DMM of 0.3 per 100,000 population (1%), and in the 16th year of 4.8 (14%) was achieved. This study provides preliminary evidence of the effectiveness of ISCHP in Germany. Limitations include inaccuracies to classify IG and CG and possible selection bias. Longitudinal county-level data may be an efficient data source to evaluate complex interventions, thereby contributing to further strengthen evidence-based integrated health promotion.
Diatoms possess fucoxanthin chlorophyll proteins (FCP) as light-harvesting systems. These membrane intrinsic proteins bind fucoxanthin as major carotenoid and Chl c as accessory chlorophyll. The ...relatively high sequence homology to higher plant light-harvesting complex II gave rise to the assumption of a similar overall structure. From centric diatoms like
Cyclotella meneghiniana
, however, two major FCP complexes can be isolated. FCPa, composed of Fcp2 and Fcp6 subunits, was demonstrated to be trimeric, whereas FCPb, known to contain Fcp5 polypeptides, is of higher oligomeric state. No molecular structure of either complex is available so far. Here we used electron microscopy and single particle analysis to elucidate the overall architecture of FCPb. The complexes are built from trimers as basic unit, assembling into nonameric moieties. The trimer itself is smaller, i.e. more compact than LHCII, but the main structural features are conserved.
We performed computational screening of effective diffusivity in different configurations of cubes and cuboids compared with spheres and ellipsoids. In total, more than 1500 structures are generated ...and screened for effective diffusivity. We studied simple cubic and face-centered cubic lattices of spheres and cubes, random configurations of cubes and spheres as a function of volume fraction and polydispersity, and finally random configurations of ellipsoids and cuboids as a function of shape. We found some interesting shape-dependent differences in behavior, elucidating the impact of shape on the targeted design of granular materials.
We performed computational screening of effective diffusivity in cube and cuboid systems, elucidating the impact of shape on the granular material design.
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A porous network acts as transport paths for drugs through films for controlled drug release. The interconnectivity of the network strongly influences the transport properties. It is ...therefore important to quantify the interconnectivity and correlate it to transport properties for control and design of new films. This work presents a novel method for 3D visualisation and analysis of interconnectivity. High spatial resolution 3D data on porous polymer films for controlled drug release has been acquired using a focused ion beam (FIB) combined with a scanning electron microscope (SEM). The data analysis method enables visualisation of pore paths starting at a chosen inlet pore, dividing them into groups by length, enabling a more detailed quantification and visualisation. The method also enables identification of central features of the porous network by quantification of channels where pore paths coincide. The method was applied to FIB-SEM data of three leached ethyl cellulose (EC)/hydroxypropyl cellulose (HPC) films with different weight percentages. The results from the analysis were consistent with the experimentally measured release properties of the films. The interconnectivity and porosity increase with increasing amount of HPC. The bottleneck effect was strong in the leached film with lowest porosity.
The three-dimensional microstructure of functional materials determines its effective properties, like the mass transport properties of a porous material. Hence, it is desirable to be able to tune ...the properties by tuning the microstructure accordingly. In this work, we study a class of spinodoid i.e. spinodal decomposition-like structures with tunable anisotropy, based on Gaussian random fields. These are realistic yet computationally efficient models for bicontinuous porous materials. We use a convolutional neural network for predicting effective diffusivity in all three directions. We demonstrate that by incorporating the predictions of the neural network in an approximate Bayesian computation framework for inverse problems, we can in a computationally efficient manner design microstructures with prescribed diffusivity in all three directions.
Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the ...geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.
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