Christian Doppler Laboratory for Thermoelectricity

The latest developments in the efficiency and long-term stability of perovskite solar cells are summarized.

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

An algorithm for the assignment of phosphorylation sites in peptides is described. The program uses tandem mass spectrometry data in conjunction with the respective peptide sequences to calculate site probabilities for all potential phosphorylation sites. Tandem mass spectra from synthetic phosphopeptides were used for optimization of the scoring parameters employing all commonly used fragmentation techniques. Calculation of probabilities was adapted to the different fragmentation methods and to the maximum mass deviation of the analysis. The software includes a novel approach to peak extraction, required for matching experimental data to the theoretical values of all isoforms, by defining individual peak depths for the different regions of the tandem mass spectrum. Mixtures of synthetic phosphopeptides were used to validate the program by calculation of its false localization rate versus site probability cutoff characteristic. Notably, the empirical obtained precision was higher than indicated by the applied probability cutoff. In addition, the performance of the algorithm was compared to existing approaches to site localization such as Ascore. In order to assess the practical applicability of the algorithm to large data sets, phosphopeptides from a biological sample were analyzed, localizing more than 3000 nonredundant phosphorylation sites. Finally, the results obtained for the different fragmentation methods and localization tools were compared and discussed.

Adipocytokines are mainly adipocyte-derived cytokines regulating metabolism and as such are key regulators of insulin resistance. Some adipocytokines such as adiponectin and leptin affect immune and inflammatory functions. Visfatin (pre-B cell colony-enhancing factor) has recently been identified as a new adipocytokine affecting insulin resistance by binding to the insulin receptor. In this study, we show that recombinant visfatin activates human leukocytes and induces cytokine production. In CD14(+) monocytes, visfatin induces the production of IL-1beta, TNF-alpha, and especially IL-6. Moreover, it increases the surface expression of costimulatory molecules CD54, CD40, and CD80. Visfatin-stimulated monocytes show augmented FITC-dextran uptake and an enhanced capacity to induce alloproliferative responses in human lymphocytes. Visfatin-induced effects involve p38 as well as MEK1 pathways as determined by inhibition with MAPK inhibitors and we observed activation of NF-kappaB. In vivo, visfatin induces circulating IL-6 in BALB/c mice. In patients with inflammatory bowel disease, plasma levels of visfatin are elevated and its mRNA expression is significantly increased in colonic tissue of Crohn's and ulcerative colitis patients compared with healthy controls. Macrophages, dendritic cells, and colonic epithelial cells might be additional sources of visfatin as determined by confocal microscopy. Visfatin can be considered a new proinflammatory adipocytokine.

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.

Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high quality diagnosis/therapy and successfully deal with the complexity of 21st century ophthalmology.

The aim of this review is to give a comprehensive overview of the current knowledge on plant metabolites of mycotoxins, also called masked mycotoxins. Mycotoxins are secondary fungal metabolites, toxic to human and animals. Toxigenic fungi often grow on edible plants, thus contaminating food and feed. Plants, as living organisms, can alter the chemical structure of mycotoxins as part of their defence against xenobiotics. The extractable conjugated or non-extractable bound mycotoxins formed remain present in the plant tissue but are currently neither routinely screened for in food nor regulated by legislation, thus they may be considered masked. Fusarium mycotoxins (deoxynivalenol, zearalenone, fumonisins, nivalenol, fusarenon-X, T-2 toxin, HT-2 toxin, fusaric acid) are prone to metabolisation or binding by plants, but transformation of other mycotoxins by plants (ochratoxin A, patulin, destruxins) has also been described. Toxicological data are scarce, but several studies highlight the potential threat to consumer safety from these substances. In particular, the possible hydrolysis of masked mycotoxins back to their toxic parents during mammalian digestion raises concerns. Dedicated chapters of this article address plant metabolism as well as the occurrence of masked mycotoxins in food, analytical aspects for their determination, toxicology and their impact on stakeholders.

and organics from a selection of nonrecyclable waste-including microplastics (polyester microfibers) and food-contaminated plastic-and upscaling the system from 2 to 120 mL while maintaining its efficiency for plastic conversion.

Carbon quantum dots (CQDs) are established as excellent photosensitizers in combination with a molecular catalyst for solar light driven hydrogen production in aqueous solution. The inexpensive CQDs can be prepared by straightforward thermolysis of citric acid in a simple one-pot, multigram synthesis and are therefore scalable. The CQDs produced reducing equivalents under solar irradiation in a homogeneous photocatalytic system with a Ni-bis(diphosphine) catalyst, giving an activity of 398 μmolH2 (gCQD)(-1) h(-1) and a "per Ni catalyst" turnover frequency of 41 h(-1). The CQDs displayed activity in the visible region beyond λ > 455 nm and maintained their full photocatalytic activity for at least 1 day under full solar spectrum irradiation. A high quantum efficiency of 1.4% was recorded for the noble- and toxic-metal free photocatalytic system. Thus, CQDs are shown to be a highly sustainable light-absorbing material for photocatalytic schemes, which are not limited by cost, toxicity, or lack of scalability. The photocatalytic hybrid system was limited by the lifetime of the molecular catalyst, and intriguingly, no photocatalytic activity was observed using the CQDs and 3d transition metal salts or platinum precursors. This observation highlights the advantage of using a molecular catalyst over commonly used heterogeneous catalysts in this photocatalytic system.

Abstract In this Review, the extreme care that must be taken when predicting the optical properties of conjugated polymers via the oligomer approach, and when comparing theoretical and experimental data, is illustrated. In the first part, conceptual strategies for the correct determination of optical transitions from experimental spectra and relevant extrapolation procedures at the polymer limit are introduced. The impact of conformational, substitution, solvent, and solid‐state effects on the optical properties is discussed in light of experimental data reported for molecular backbones based on phenylene, phenylenevinylene, and thiophene repeat units. A comparison is then made between experimental results and those provided by standard quantum‐chemical methods, to assess their reliability.

PURPOSE: Development and validation of a fully automated method to detect and quantify macular fluid in conventional OCT images. DESIGN: Development of a diagnostic modality. PARTICIPANTS: The clinical dataset for fluid detection consisted of 1200 OCT volumes of patients with neovascular age-related macular degeneration (AMD, n = 400), diabetic macular edema (DME, n = 400), or retinal vein occlusion (RVO, n = 400) acquired with Zeiss Cirrus (Carl Zeiss Meditec, Dublin, CA) (n = 600) or Heidelberg Spectralis (Heidelberg Engineering, Heidelberg, Germany) (n = 600) OCT devices. METHODS: A method based on deep learning to automatically detect and quantify intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) was developed. The performance of the algorithm in accurately identifying fluid localization and extent was evaluated against a manual consensus reading of 2 masked reading center graders. MAIN OUTCOME MEASURES: Performance of a fully automated method to accurately detect, differentiate, and quantify intraretinal and SRF using area under the receiver operating characteristics curves, precision, and recall. RESULTS: The newly designed, fully automated diagnostic method based on deep learning achieved optimal accuracy for the detection and quantification of IRC for all 3 macular pathologies with a mean accuracy (AUC) of 0.94 (range, 0.91-0.97), a mean precision of 0.91, and a mean recall of 0.84. The detection and measurement of SRF were also highly accurate with an AUC of 0.92 (range, 0.86-0.98), a mean precision of 0.61, and a mean recall of 0.81, with superior performance in neovascular AMD and RVO compared with DME, which was represented rarely in the population studied. High linear correlation was confirmed between automated and manual fluid localization and quantification, yielding an average Pearson's correlation coefficient of 0.90 for IRC and of 0.96 for SRF. CONCLUSIONS: Deep learning in retinal image analysis achieves excellent accuracy for the differential detection of retinal fluid types across the most prevalent exudative macular diseases and OCT devices. Furthermore, quantification of fluid achieves a high level of concordance with manual expert assessment. Fully automated analysis of retinal OCT images from clinical routine provides a promising horizon in improving accuracy and reliability of retinal diagnosis for research and clinical practice in ophthalmology.

In this review, we focus on the saturation microstructure that evolves during severe plastic deformation (SPD). These nanocrystalline or ultrafine-grained microstructures consist predominantly of high-angle boundaries, although low-angle boundaries are also present. Deformation temperature, alloying, and strain path are the dominant factors controlling the saturation grain size in single-phase materials. The saturation grain size decreases significantly with decreasing deformation temperature, although the dependency is stronger at medium homologous temperatures and less in the low-temperature regime. The saturation microstructure is sensitive to strain rate at medium temperatures and less so at low temperatures. The addition of alloying elements to pure metals also reduces the saturation grain size. The results indicate that grain boundary migration is the dominant process responsible for the limitation in refinement by SPD. Therefore, second-phase particles of the nanometer scale can stabilize even finer microstructures. This mechanism of stabilization of the microstructure is an effective tool for overcoming the limit in refinement of single-phase materials by SPD. The improved thermal stability of the obtained nanostructures is another benefit of the introduction of second-phase particles.

Plastic waste is recycled to hydrogen using sunlight and a CdS photocatalyst.

In the past two decades, optical coherence tomography (OCT) has been established as an adjunct diagnostic technique for noninvasive, high-resolution, cross-sectional imaging in a variety of medical fields. The rapid development of ultrabroad bandwidth light sources has recently enabled a significant improvement in OCT imaging resolution, demonstrating the potential of OCT to accomplish its original goal of performing noninvasive optical biopsies, i.e., the in vivo visualization of microstructural morphology in situ, which had previously only been possible with histopathology. In addition, these novel light sources might also enable the use of spectroscopic OCT, an extension of ultrahigh-resolution OCT, for enhancing image contrast as well as detecting spatially resolved functional, biochemical tissue information. State-of-the-art-light sources that now permit ultrahigh-resolution OCT covering the whole wavelength region from 500 to 1600 nm are reviewed and fundamental limitations of OCT image resolution are discussed. Ex vivo ultrahigh-resolution OCT tomograms are compared with histological results; first clinical in vivo ultrahigh-resolution OCT and preliminary spectroscopic OCT results are presented and their impact for future clinical and research applications is discussed.

It's not magic! The effects observed in microwave-irradiated chemical transformations can in most cases be rationalized by purely bulk thermal phenomena associated with rapid heating to elevated temperatures. As discussed in this Essay, the existence of so-called nonthermal or specific microwave effects is highly doubtful.

Artificial photosynthesis is the mimicry of the natural process of solar energy conversion into chemical energy carriers. Photocatalytic systems that combine light-harvesting materials and catalysts in solution or suspension provide a promising route towards this goal. A key requirement for a sustainable solar fuel production system is a low-cost, stable and non-toxic light harvester. Photoluminescent carbon nanoparticles, carbon dots (CDs), are promising emerging light-harvesters for photocatalytic fuel production systems. CDs possess many desirable properties for this purpose, such as inexpensive, scalable synthetic routes, low-toxicity and tuneable surface chemistry. In this tutorial review, the integration of CDs in photocatalytic fuel generation systems with metallic, molecular and enzymatic catalysts is discussed. An overview of CD types, synthesis and properties is given along with a discussion of tuneable CD properties that can be optimised for applications in photocatalysis. Current understanding of the photophysical electron transfer processes present in CD photocatalytic systems is outlined and various avenues for their further development are highlighted.

Photocatalytic conversion of CO 2 into carbonaceous feedstock chemicals is a promising strategy to mitigate greenhouse gas emissions and simultaneously store solar energy in chemical form. Photocatalysts for this transformation are typically based on precious metals and operate in nonaqueous solvents to suppress competing H 2 generation. In this work, we demonstrate selective visible-light-driven CO 2 reduction in water using a synthetic photocatalyst system that is entirely free of precious metals. We present a series of self-assembled nickel terpyridine complexes as electrocatalysts for the reduction of CO 2 to CO in organic media. Immobilization on CdS quantum dots allows these catalysts to be active in purely aqueous solution and photocatalytically reduce CO 2 with >90% selectivity under UV-filtered simulated solar light irradiation (AM 1.5G, 100 mW cm –2, λ > 400 nm, pH 6.7, 25 °C). Correlation between catalyst immobilization efficiency and product selectivity shows that anchoring the molecular catalyst on the semiconductor surface is key in controlling the selectivity for CO 2 reduction over H 2 evolution in aqueous solution.

Object tracking is a key enabling technology in the context of computer-assisted medical interventions. Allowing the continuous localization of medical instruments and patient anatomy, it is a prerequisite for providing instrument guidance to subsurface anatomical structures. The only widely used technique that enables real-time tracking of small objects without line-of-sight restrictions is electromagnetic (EM) tracking. While EM tracking has been the subject of many research efforts, clinical applications have been slow to emerge. The aim of this review paper is therefore to provide insight into the future potential and limitations of EM tracking for medical use. We describe the basic working principles of EM tracking systems, list the main sources of error, and summarize the published studies on tracking accuracy, precision and robustness along with the corresponding validation protocols proposed. State-of-the-art approaches to error compensation are also reviewed in depth. Finally, an overview of the clinical applications addressed with EM tracking is given. Throughout the paper, we report not only on scientific progress, but also provide a review on commercial systems. Given the continuous debate on the applicability of EM tracking in medicine, this paper provides a timely overview of the state-of-the-art in the field.

Optical coherence tomography (OCT) provides non-contact, rapid in vivo imaging of ocular structures, and has become a key part of evaluating the anterior segment of the eye. Over the years, improvements to technology have increased the speed of capture and resolution of images, leading to the increasing impact of anterior segment OCT imaging on clinical practice. In this review, we summarize the historical development of anterior segment OCT, and provide an update on the research and clinical applications of imaging the ocular surface, cornea, anterior chamber structures, aqueous outflow system, and most recently anterior segment vessels. We also describe advancements in anterior segment OCT technology that have improved understanding with greater detail, such as tear film in dry eye disease evaluation, intra-operative real-time imaging for anterior segment surgery, and aqueous outflow with angle assessment for glaucoma. Improvements to image processing and software have also improved the ease and utility of interpreting anterior segment OCT images in everyday clinical practice. Future developments include refinement of assessing vascular networks for the anterior segment, in vivo ultra-high resolution anterior segment optical coherence tomography with histology-like detail, en-face image with 3-dimensional reconstruction as well as functional extensions of the technique.

Optical coherence tomography (OCT) with unprecedented submicrometer axial resolution achieved by use of a photonic crystal fiber in combination with a compact sub-10-fs Ti:sapphire laser (Femtolasers Produktions) is demonstrated for what the authors believe is the first time. The emission spectrum ranges from 550 to 950 nm (lambda(c)=725 nm , P(out)=27 mW) , resulting in a free-space axial OCT resolution of ~0.75 mum , corresponding to ~0.5 mum in biological tissue. Submicrometer-resolution OCT is demonstrated in vitro on human colorectal adenocarcinoma cells HT-29. This novel light source has great potential for development of spectroscopic OCT because its spectrum covers the absorption bands of several biological chromophores.