Real-time large-area imaging of the corneal subbasal nerve plexus.

The morphometric assessment of the corneal subbasal nerve plexus (SNP) by confocal microscopy holds great potential as a sensitive biomarker for various ocular and systemic conditions and diseases. Automated wide-field montages (or large-area mosaic images) of the SNP provide an opportunity to overcome the limited field of view of the available imaging systems without the need for manual, subjective image selection for morphometric characterization. However, current wide-field montaging solutions usually calculate the mosaic image after the examination session, without a reliable means for the clinician to predict or estimate the resulting mosaic image quality during the examination. This contribution describes a novel approach for a real-time creation and visualization of a mosaic image of the SNP that facilitates an informed evaluation of the quality of the acquired image data immediately at the time of recording. In cases of insufficient data quality, the examination can be aborted and repeated immediately, while the patient is still at the microscope. Online mosaicking also offers the chance to identify an overlap of the imaged tissue region with previous SNP mosaic images, which can be particularly advantageous for follow-up examinations.

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.

When approaching thyroid gland tumor classification, the differentiation between samples with and without "papillary thyroid carcinoma-like" nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen's Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen's Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen's Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen's Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.

Open database analysis of scaling and spatio-temporal properties of power grid frequencies.

The electrical energy system has attracted much attention from an increasingly diverse research community. Many theoretical predictions have been made, from scaling laws of fluctuations to propagation velocities of disturbances. However, to validate any theory, empirical data from large-scale power systems are necessary but are rarely shared openly. Here, we analyse an open database of measurements of electric power grid frequencies across 17 locations in 12 synchronous areas on three continents. The power grid frequency is of particular interest, as it indicates the balance of supply and demand and carries information on deterministic, stochastic, and control influences. We perform a broad analysis of the recorded data, compare different synchronous areas and validate a previously conjectured scaling law. Furthermore, we show how fluctuations change from local independent oscillations to a homogeneous bulk behaviour. Overall, the presented open database and analyses constitute a step towards more shared, collaborative energy research.

Recent Developments in Ozone Sensor Technology for Medical Applications.

There is increasing interest in the utilisation of medical gases, such as ozone, for the treatment of herniated disks, peripheral artery diseases, and chronic wounds, and for dentistry. Currently, the in situ measurement of the dissolved ozone concentration during the medical procedures in human bodily liquids and tissues is not possible. Further research is necessary to enable the integration of ozone sensors in medical and bioanalytical devices. In the present review, we report selected recent developments in ozone sensor technology (2016-2020). The sensors are subdivided into ozone gas sensors and dissolved ozone sensors. The focus thereby lies upon amperometric and impedimetric as well as optical measurement methods. The progress made in various areas-such as measurement temperature, measurement range, response time, and recovery time-is presented. As inkjet-printing is a new promising technology for embedding sensors in medical and bioanalytical devices, the present review includes a brief overview of the current approaches of inkjet-printed ozone sensors.

Siloxane-functionalised surface patterns as templates for the ordered deposition of thin lamellar objects.

A novel method is demonstrated for ordered deposition of thin lamellar objects from a liquid environment onto solid substrates by solid/fluid/solid-driven organisation. Surface functionalisation forms a template pattern that accumulates the lamellar objects by site-selective wetting of the target area without the need for a physical fluid containment. Contrary to conventional handling methods, no mechanical contact occurs, which facilitates the ordered deposition without wrinkles or ruptures. An additive and a subtractive process for the creation of such templates are presented. The subtractive process starts with the complete silanisation of the substrate in the vapour phase followed by site-selective oxygen plasma treatment of the siloxane film. The additive process uses microcontact printing to transfer the target pattern. Both processes are characterised by optical inspection of the wetting contours and it is found that site-selective plasma treatment shows a better pattern fidelity. The patterns obtained by site-selective plasma treatment are also subject to ToF-SIMS analysis and show good chemical contrast between hydrophilic and hydrophobic areas. The ordered deposition of lamellar objects by this new method is demonstrated for 60 nm thick ultramicrotome sections of epoxide resin on pre-patterned glass substrates.

3D confocal laser-scanning microscopy for large-area imaging of the corneal subbasal nerve plexus.

The capability of corneal confocal microscopy (CCM) to acquire high-resolution in vivo images of the densely innervated human cornea has gained considerable interest in using this non-invasive technique as an objective diagnostic tool for staging peripheral neuropathies. Morphological alterations of the corneal subbasal nerve plexus (SNP) assessed by CCM have been shown to correlate well with the progression of neuropathic diseases and even predict future-incident neuropathy. Since the field of view of single CCM images is insufficient for reliable characterisation of nerve morphology, several image mosaicking techniques have been developed to facilitate the assessment of the SNP in large-area visualisations. Due to the limited depth of field of confocal microscopy, these approaches are highly sensitive to small deviations of the focus plane from the SNP layer. Our contribution proposes a new automated solution, combining guided eye movements for rapid expansion of the acquired SNP area and axial focus plane oscillations to guarantee complete imaging of the SNP. We present results of a feasibility study using the proposed setup to evaluate different oscillation settings. By comparing different image selection approaches, we show that automatic tissue classification algorithms are essential to create high-quality mosaic images from the acquired 3D datasets.

Replication of Overmolded Orthopedic Implants with a Functionalized Thin Layer of Biodegradable Polymer.

The present paper reports on the development of a biodegradable overmolded orthopedic implant: a metal bone fixing screw, which has been overmolded with a functionalized thin layer of biodegradable polymer to enhance cell adhesion during the healing process. The main challenges were to integrate precise, high-throughput and repeatable solutions to achieve a thin, defect-free structured polymer layer and to ensure a high and consistent implant quality. The work carried out entailed determining proper materials (Purasorb PDLG 5010) for the biodegradable overmolding layer and its economical substitute (NaKu PLA 100HF) to be used during initial tool and process development, designing the surface structure of the overmolded polymer layer, development of injection molding tools, as well as feeding and handling procedures. The injection overmolding process of Purasorb PDLG 5010 polymer was controlled, and the process parameters were optimized. In particular, the dominant process parameters for the overmolding, namely injection pressure, barrel temperature and mold temperature, were experimentally examined using a circumscribed three-factor central composite design and two quality marks; overmolding roughness and mass of polymer. The analysis of the experimental results shows that the mass of the overmolding is not feasible for use as the quality mark. However, the optimal parameters for the overmolding of a metallic implant screw with a thin, micro-structured polymer layer with a predefined roughness of the surface texture have been identified successfully.