Wood-based superblack.

Light is a powerful and sustainable resource, but it can be detrimental to the performance and longevity of optical devices. Materials with near-zero light reflectance, i.e. superblack materials, are sought to improve the performance of several light-centered technologies. Here we report a simple top-down strategy, guided by computational methods, to develop robust superblack materials following metal-free wood delignification and carbonization (1500 °C). Subwavelength severed cells evolve under shrinkage stresses, yielding vertically aligned carbon microfiber arrays with a thickness of ~100 µm and light reflectance as low as 0.36% and independent of the incidence angle. The formation of such structures is rationalized based on delignification method, lignin content, carbonization temperature and wood density. Moreover, our measurements indicate a laser beam reflectivity lower than commercial light stoppers in current use. Overall, the wood-based superblack material is introduced as a mechanically robust surrogate for microfabricated carbon nanotube arrays.

Enlarged PLIN5-uncoated lipid droplets in inner regions of skeletal muscle type II fibers associate with type 2 diabetes.

Skeletal muscle physiology remains of paramount importance in understanding insulin resistance. Due to its high lipid turnover rates, regulation of intramyocellular lipid droplets (LDs) is a key factor. Perilipin 5 (PLIN5) is one of the most critical agents in such regulation, being often referred as a protector against lipotoxicity and consequent skeletal muscle insulin resistance. We examined area fraction, size, subcellular localization and PLIN5 association of LDs in two fiber types of type 2 diabetic (T2D), obese (OB) and healthy (HC) individuals by means of fluorescence microscopy and image analysis. We found that T2D type II fibers have a significant sub-population of large and internalized LDs, uncoated by PLIN5. Based on this novel result, additional hypotheses for the pathophysiology of skeletal muscle insulin resistance are formulated, together with future research directions.

Author Correction: Shape fidelity and structure of 3D printed high consistency nanocellulose.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Shape fidelity and structure of 3D printed high consistency nanocellulose.

The aim of the present study was to investigate the additive manufacturing process for high consistency nanocellulose. Unlike thermoformable plastics, wood derived nanocelluloses are typically processed as aqueous dispersions because they are not melt-processable on their own. The ability to use nanocellulose directly in additive manufacturing broadens the possibilities regarding usable raw materials and achievable properties thereof. Modern additive manufacturing systems are capable of depositing nanocellulose with micrometer precision, which enables the printing of accurate three-dimensional wet structures. Typically, these wet structures are produced from dilute aqueous fibrillar dispersions. As a consequence of the high water content, the structures deform and shrink during drying unless the constructs are freeze-dried. While freeze-drying preserves the geometry, it results in high porosity which manifests as poor mechanical and barrier properties. Herein, we study an additive manufacturing process for high consistency enzymatically fibrillated cellulose nanofibers in terms of printability, shape retention, structure, and mechanical properties. Particular emphasis is placed on quantitative shape analysis based on 3D scanning, point cloud analysis, and x-ray microtomography. Despite substantial volumetric as well as anisotropic deformation, we demonstrate repeatability of the printed construct and its properties.

Cellulose nanofibrils prepared by gentle drying methods reveal the limits of helium ion microscopy imaging.

TEMPO-oxidized cellulose nanofibrils (TCNFs) have unique properties, which can be utilised in many application fields from printed electronics to packaging. Visual characterisation of TCNFs has been commonly performed using Scanning Electron Microscopy (SEM). However, a novel imaging technique, Helium Ion Microscopy (HIM), offers benefits over SEM, including higher resolution and the possibility of imaging non-conductive samples uncoated. HIM has not been widely utilized so far, and in this study the capability of HIM for imaging of TCNFs was evaluated. Freeze drying and critical point drying (CPD) techniques were applied to preserve the open fibril structure of the gel-like TCNFs. Both drying methods worked well, but CPD performed better resulting in the specific surface area of 386 m2 g-1 when compared to 172 m2 g-1 and 42 m2 g-1 of freeze dried samples frozen in propane and nitrogen, respectively. HIM imaging of TCNFs was successful but high magnification imaging was challenging because the ion beam tended to degrade the TCNFs. The effect of the imaging parameters on the degradation was studied and an ion dose as low as 0.9 ion per nm2 was required to prevent the damage. This study points out the differences between the gentle drying methods of TCNFs and demonstrates beam damage during imaging like none previously reported with HIM. The results can be utilized in future studies of cellulose or other biological materials as there is a growing interest for both the HIM technique and bio-based materials.

Interface Detection Using a Quenched-Noise Version of the Edwards-Wilkinson Equation.

We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, and its efficient discretization.

Classification and retrieval on macroinvertebrate image databases.

Aquatic ecosystems are continuously threatened by a growing number of human induced changes. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensive human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing classification and data retrieval that are instrumental when processing large macroinvertebrate image datasets. To accomplish this for routine biomonitoring, in this paper we shall investigate the feasibility of automated river macroinvertebrate classification and retrieval with high precision. Besides the state-of-the-art classifiers such as Support Vector Machines (SVMs) and Bayesian Classifiers (BCs), the focus is particularly drawn on feed-forward artificial neural networks (ANNs), namely multilayer perceptrons (MLPs) and radial basis function networks (RBFNs). Since both ANN types have been proclaimed superior by different investigations even for the same benchmark problems, we shall first show that the main reason for this ambiguity lies in the static and rather poor comparison methodologies applied in most earlier works. Especially the most common drawback occurs due to the limited evaluation of the ANN performances over just one or few network architecture(s). Therefore, in this study, an extensive evaluation of each classifier performance over an ANN architecture space is performed. The best classifier among all, which is trained over a dataset of river macroinvertebrate specimens, is then used in the MUVIS framework for the efficient search and retrieval of particular macroinvertebrate peculiars. Classification and retrieval results present high accuracy and can match an experts' ability for taxonomic identification.

Effects of diet-induced obesity and voluntary wheel running on the microstructure of the murine distal femur.

BACKGROUND: Obesity and osteoporosis, two possibly related conditions, are rapidly expanding health concerns in modern society. Both of them are associated with sedentary life style and nutrition. To investigate the effects of diet-induced obesity and voluntary physical activity we used high resolution micro-computed tomography (µCT) together with peripheral quantitative computed tomography (pQCT) to examine the microstructure of the distal femoral metaphysis in mice. METHODS: Forty 7-week-old male C57BL/6J mice were assigned to 4 groups: control (C), control + running (CR), high-fat diet (HF), and high-fat diet + running (HFR). After a 21-week intervention, all the mice were sacrificed and the left femur dissected for pQCT and µCT measurements. RESULTS: The mice fed the high-fat diet showed a significant weight gain (over 70% for HF and 60% for HFR), with increased epididymal fat pad mass and impaired insulin sensitivity. These obese mice had significantly higher trabecular connectivity density, volume, number, thickness, area and mass, and smaller trabecular separation. At the whole bone level, they had larger bone circumference and cross-sectional area and higher density-weighted maximal, minimal, and polar moments of inertia. Voluntary wheel running decreased all the cortical bone parameters, but increased the trabecular mineral density, and decreased the pattern factor and structure model index towards a more plate-like structure. CONCLUSIONS: The results suggest that in mice the femur adapts to obesity by improving bone strength both at the whole bone and micro-structural level. Adaptation to running exercise manifests itself in increased trabecular density and improved 3D structure, but in a limited overall bone growth.

Dynamics and interactions of parvoviral NS1 protein in the nucleus.

Nuclear positioning and dynamic interactions of viral proteins with nuclear substructures play essential roles during infection with DNA viruses. Visualization of the intranuclear interactions and motility of the parvovirus replication protein (NS1) in living cells gives insight into specific parvovirus protein-cellular structure interactions. Confocal analysis of highly synchronized infected Norden Laboratory Feline Kidney cells showed accumulation of nuclear NS1 in discrete interchromosomal foci. NS1 fused with enhanced yellow fluorescence protein (NS1-EYFP) provided a marker in live cells for dynamics of NS1 traced by photobleaching techniques. Fluorescence Recovery after Photobleaching suggested that the NS1 protein is not freely diffusing but undergoes transient interactions with nuclear compartments. Fluorescence Loss in Photobleaching demonstrated for the first time the shuttling of a parvoviral protein between the nucleus and the cytoplasm as assayed with NS1-EYFP. Finally, time-lapse imaging of infected cells revealed that the intranuclear distribution of NS1-EYFP evolves dramatically starting from the formation of NS1 foci and proceeding to a homogenous distribution extending throughout the nucleus.