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In this study, we investigated the impact on surface topography and micro-mechanical properties of polyvinyl alcohol (PVA) thin films when loaded with hardwood-derived biocarbon particles (BCP). The PVA/BCP composites were prepared with the conventional film casting method, after varying BCP concentrations of 6, 8, 10, 12, and 20 wt% were added to the PVA solution. Atomic force microscopy (AFM) investigations and nanoindentation tests were performed. The average roughness of the thin films increased with the increase in BCP content. The alternation between dark and light patterns observed in the AFM images showed an irregular surface topography with alternating high peaks and deep valleys. The skewness and kurtosis parameters showed that the different dispersion degrees of the BCP within the PVA matrix influenced the composites' surface roughness. The micro-mechanical properties of the thin film composites depended on the BCP type and concentration. Films reinforced with red oak-derived BCP had higher hardness and Young's modulus compared to films reinforced with willow SV1 and yellow-poplar BCP, which was attributed to the high carbon content and low ash content of red oak BCP. We argue that these results may be extrapolated to other types of BCP-reinforced thin films and can significantly contribute to enabling more efficient methods and protocols for reinforcing polymers with hardwood biocarbon.

A wide palette of nanoscale imaging techniques operating in the near-field regime has been reported to date, enabling an important number of scientific breakthroughs. While the tuning and benchmarking of near-field microscopes represent a very important step for optimizing the outputs of the imaging sessions, no generally acknowledged standards exist yet in terms of calibration of near-field microscopes, which would play an important role in fully exploiting the potential of these instruments. With this work, we aim to contribute to filling in this gap, by introducing a prototypical sample, that holds potential for becoming a benchmark with respect to comparing the performances of diverse near-field measurement techniques, including traditional, aperture based, scanning near field microscopy (SNOM), or apertureless variants, such as scattering-type scanning nearfield optical microscopy (s-SNOM). The proposed samples have been thoroughly simulated, and an easy fabrication procedure is presented and demonstrated. In this latter context, Au-SiO2 samples sharing different configurations, in terms of geometry, number and depth of contrast yielding layers, enabling both surface and sub-surface nanoscopy measurements, were designed and fabricated. We argue that the proposed prototypical samples can be highly useful for benchmarking the outputs of various near-field microscopy techniques, as they facilitate a broad range of tests, relevant for comparing the performances and accuracy of many diverse investigation methods. We also introduce a methodology for numerically simulating the samples and their near-field after illuminating them with light of different wavelengths, as well as their simple process flow. This methodology can considerably augment their future use as a prototypical sample for the evaluation and calibration of current and next generation near-field nanoscopy techniques. Experimental evidence on the usefulness of these samples as s-SNOM testing and benchmarking tools is provided in the context of differentiation of surface and sub-surface structures, and influence of tip-sample distance on attainable amplitude and phase signals. We consider these efforts to represent an important, required step, in advancing the near-field imaging field, with important potential to augment the outputs of current near-field imaging systems, and to facilitate the development and benchmarking of next generation of near-field instrumentation.

Abstract Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter’s type using random forest.

Background: Acknowledged by the World Health Organisation (WHO), over 200 diseases ranging from mild to fatal are linked to the consumption of food products subjected to physical, chemical, or biological contamination. Nevertheless, conventional methods commonly used for the identification of health hazards in foodstuffs have problems coping with the sensitivity requirements imposed by latest-hour regulations in the field. Additionally, their use and availability is wildly limited by aspects such as instrument dimension, prohibitive costs, detection complexity and required operational knowledge. Aim of review: This review provides an overview of recent efforts that have focused on the assesment of food contamination based on near infrared (NIR) photoluminescent sensors. Important endeavors that have targeted the precise detection of various inorganic and organic contaminants, including hydrogen sulfide, cyanide anions, mycotoxins, antibiotic residues, etc., are presented and relevant challenges that lie en route as stumbling blocks for such sensors to reach the next level of maturity and to become more available, are systematically discussed and enunciated. Key scientific concepts of review: Ingenious food contamination sensors that rely on conventional or up-conversion photoluminescence in the NIR region represent an emerging topic. To date, such sensors have been demonstrated as promising detection candidates, possessing important advantages such as: high efficiency, facile implementation, and convenient flexibility, thereby promising significant contributions to expand the current state of the art in food security.

Measurement(s) Type I Collagen Technology Type(s) multi-photon laser scanning microscopy Factor Type(s) second order susceptibility tensor elements Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment laboratory environment Sample Characteristic - Location Romania

Two-photon microscopy techniques are non-linear optical imaging methods which are gaining momentum in the investigation of fixed tissue sections, fresh tissue or even for in vivo experiments. Two-photon excited fluorescence and second harmonic generation are two non-linear optical contrast mechanisms which can be simultaneously used for offering complementary information on the tissue architecture. While the former can originate from endogenous autofluorescence sources (e.g., NADH, FAD, elastin, keratin, lipofuscins, or melanin), or exogenous eosin, the latter is generated in fibrillar structures within living organisms (e.g., collagen and myosin). Here we test the ability of both these contrast mechanisms to highlight features of the extramammary Paget disease on fixed tissue sections prepared for standard histological examination using immunohistochemical markers and hematoxylin and eosin staining. We also demonstrate the label-free abilities of both imaging techniques to highlight histological features on unstained fixed tissue sections. The study demonstrated that two-photon microscopy can detect specific cellular features of the extramammary Paget disease in good correlation with histopathological results.

Quantitative second harmonic generation microscopy methods that provide an estimate for the molecular structure of collagen in tissues by fitting theoretical models with experimental data at pixel-level are gaining increasing momentum. Such methods offer reliable diagnostic-relevant information that complement traditional histology procedures. We provide guidelines on selecting the optimal experimental data acquisition parameters (frame or pixel averaging, pixel dwell time, pixel size, zoom factor) and the most suited pre-processing steps (thresholding, averaging of pixels) for ensuring experimental data fitting with very high performance. Under controlled image acquisition conditions, we also propose pixel fitting efficiency calculation which varies across distinct tissue types and has potential to be used to differentiate tissues exhibiting distinct collagen architectures.

Abstract Acclimatable colors in response to environmental stimuli, which are naturally endowed with some living things, can provide an opportunity for humans to recognize hazardous substances without taking empirical risks. Despite efforts to create artificial responsive colors, realistic applications in everyday life require an immediate/distinct colorimetric realization with wide chromatic selectivity. A dynamically responsive virus (M‐13 phage)‐based changeable coloring strategy is presented with a highly lossy resonant promoter (HLRP). An ultrathin M‐13 phage layer for rapid response to external stimuli displays colorimetric behavior, even in its subtle swelling with strong resonances on HLRP, which is modeled using the complex effective refractive index. Optimal designs of HLRP for several material combinations allow selective chromatic responsivity from the corresponding wide color palette without modification of the dynamic responsive layer. As a practical demonstration, the spatially designed colorimetric indicator, which is insensitive/sensitive to external stimuli, provides an intuitive perception of environmental changes with hidden/revealed patterns. Furthermore, the proposed colorimetric sensor is tested by exposure to various volatile organic chemicals and endocrine disrupting chemicals for versatile detectability, and is fabricated in a wafer‐scale sample for large‐area scalability.

Summary: Image analysis is key to extracting quantitative information from scientific microscopy images, but the methods involved are now often so refined that they can no longer be unambiguously described by written protocols. We introduce BIAFLOWS, an open-source web tool enabling to reproducibly deploy and benchmark bioimage analysis workflows coming from any software ecosystem. A curated instance of BIAFLOWS populated with 34 image analysis workflows and 15 microscopy image datasets recapitulating common bioimage analysis problems is available online. The workflows can be launched and assessed remotely by comparing their performance visually and according to standard benchmark metrics. We illustrated these features by comparing seven nuclei segmentation workflows, including deep-learning methods. BIAFLOWS enables to benchmark and share bioimage analysis workflows, hence safeguarding research results and promoting high-quality standards in image analysis. The platform is thoroughly documented and ready to gather annotated microscopy datasets and workflows contributed by the bioimaging community. The Bigger Picture: Image analysis is currently one of the major hurdles in the bioimaging chain, especially for large datasets. BIAFLOWS seeds the ground for virtual access to image analysis workflows running in high-performance computing environments. Providing a broader access to state-of-the-art image analysis is expected to have a strong impact on research in biology, and in other fields where image analysis is a critical step in extracting scientific results from images. BIAFLOWS could also be adopted as a federated platform to publish microscopy images together with the workflows that were used to extract scientific data from these images. This is a milestone of open science that will help to accelerate scientific progress by fostering collaborative practices.

Multiphoton microscopy (MPM) is currently acknowledged as a very powerful method for the visualization and analysis of tissues in biomedicine. It allows high resolution, deep optical sectioning and reduced photodamage. MPM does not require labeling and is deployable both in-vivo and ex-vivo, which simplifies the diagnostic procedure compared to traditional histology approaches based on excisional biopsy, tissue fixation and staining. Among the important applications of MPM in medicine, differentiation of healthy from pathological tissues has gained massive interest over the past years, but MPM is also very useful for acquiring new insights on how various pathologies originate and progress. In this work we review the use of MPM in imaging assays focused on investigating unlabeled oral tissues (teeth and oral mucosa) and discuss a series of important results which hold potential for enabling a next generation of oral tissue characterization/diagnostic frameworks. The surveyed literature shows that non-linear optical imaging tools can significantly contribute to achieve a better understanding of oral cavity tissues, by allowing the accurate analysis of morphological structures and relevant biochemical processes.

Laser scanning microscopy (LSM) techniques are of paramount importance at this time for key domains such as biology, medicine, or materials science. Computer vision methods are instrumental for boosting the potential of LSM, providing reliable results for important tasks, such as image segmentation, registration, classification, or retrieval in a fraction of the time that a human expert would require (at similar or even higher accuracy levels). Image keypoint extraction and description represent essential building blocks of modern computer vision approaches, and the development of such techniques has gained massive interest over the past couple of decades. In this paper, we compare side-by-side five popular keypoint description techniques, scale invariant feature transform (SIFT), speeded-up robust features (SURF), binary robust invariant scalable keypoints (BRISK), fast retina keypoint (FREAK) and BLOCK, with respect to their capacity to represent in a reproducible manner image regions contained in LSM data sets acquired under different acquisition conditions. We evaluate this capacity in terms of descriptor matching performance, using data sets acquired in a principled manner and a thorough Precision-Recall analysis. We identify which of the five evaluated techniques is most robust to specific LSM image modifications associated to the laser beam power, photomultiplier gain, or pixel dwell, and show that certain pre-processing steps have the potential to enhance keypoint matching.

The conversion of histology slides into electronic format represents a key element in modern histopathology workflows. The most common way of converting physical histology slides into digital versions consists of tile-based scanning. In such approaches, the entire image of the slide is generated by consecutively scanning adjacent sample regions with a degree of overlap and then stitching these together to constitute an image mosaic. To achieve a high-quality result, the image acquisition protocol for collecting the mosaic tiles requires a recalibration of the microscope when moving from one sample region to another. This recalibration procedure typically involves focus and illumination adjustments, aimed at rendering a homogeneous image mosaic in terms of brightness, contrast, and other important image properties. The accurate evaluation of the digital slide's quality factor is, therefore, an important matter, as it can lead to designing efficient (and automated) mosaic generation protocols. We introduce here a new methodology for the evaluation of image mosaics collected with brightfield microscopy on histology slides, coined Objective Quantifiable Scoring System (OQSS). It relies on objective scoring criteria that take into consideration fundamental characteristics of image mosaics, and on histology specific aspects. We present the theoretical principles of this methodology and discuss the potential utility of this framework as a quality ground-truth tagging mechanism of histology slide image mosaics applicable for the reliable benchmarking of image quality assessment algorithms.

Abstract Although silicon carbide is a highly promising crystalline material for a wide range of electronic devices, extended and point defects which perturb the lattice periodicity hold deep implications with respect to device reliability. There is thus a great need for developing new methods that can detect silicon carbide defects which are detrimental to device functionality. Our experiment demonstrates that polarization-resolved second harmonic generation microscopy can extend the efficiency of the “optical signature” concept as an all-optical rapid and non-destructive set of investigation methods for the differentiation between hexagonal and cubic stacking faults in silicon carbide. This technique can be used for fast and in situ characterization and optimization of growth conditions for epilayers of silicon carbide and similar materials.

Despite intense research on high entropy films, the mechanism of film growth and the influence of key factors remain incompletely understood. In this study, high entropy films consisting of five elements (FeCoNiCrAl) with columnar and nanometer-scale grains were prepared by magnetron sputtering. The high entropy film growth mechanism, including the formation of the amorphous domain, equiaxial nanocrystalline structure and columnar crystal was clarified by analyzing the microstructure in detail. Besides, the impacts of the important deposition parameters including the substrate temperature, the powder loaded in the target, and the crystal orientation of the substrate on the grain size and morphology, phase structure, crystallinity and elemental uniformity were revealed. The mechanical properties of high entropy films with various microstructure features were investigated by nanoindentation. With the optimized grain size and microstructure, the film deposited at 350 °C using a power of 100 W exhibits the highest hardness of 11.09 GPa. Our findings not only help understanding the mechanisms during the high entropy film deposition, but also provide guidance in manufacturing other novel high entropy films.