Quantics Tensor Cross Interpolation for High-Resolution Parsimonious Representations of Multivariate Functions.

Multivariate functions of continuous variables arise in countless branches of science. Numerical computations with such functions typically involve a compromise between two contrary desiderata: accurate resolution of the functional dependence, versus parsimonious memory usage. Recently, two promising strategies have emerged for satisfying both requirements: (i) The quantics representation, which expresses functions as multi-index tensors, with each index representing one bit of a binary encoding of one of the variables; and (ii) tensor cross interpolation (TCI), which, if applicable, yields parsimonious interpolations for multi-index tensors. Here, we present a strategy, quantics TCI, which combines the advantages of both schemes. We illustrate its potential with an application from condensed matter physics: the computation of Brillouin zone integrals.

Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders.

Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine influential factors in patient progression and treatment, for which the retinal layers of the human eye are investigated to assess a patient's health status and eyesight. In this contribution, we propose a machine learning (ML)-based multistage system of stacked multiscale encoders and decoders for the image segmentation of OCT imagery of the retinal layers to enable the following evaluation regarding the physiological and pathological states. Our proposed system's results highlight its benefits compared to currently investigated approaches by combining commonly deployed methods from deep learning (DL) while utilizing deep neural networks (DNN). We conclude that by stacking multiple multiscale encoders and decoders, improved scores for the image segmentation task can be achieved. Our retinal-layer-based segmentation results in a final segmentation performance of up to 82.25±0.74% for the Sørensen-Dice coefficient, outperforming the current best single-stage model by 1.55% with a score of 80.70±0.20%, given the evaluated peripapillary OCT data set. Additionally, we provide results on the data sets Duke SD-OCT, Heidelberg, and UMN to illustrate our model's performance on especially noisy data sets.

[Extraction of features from clinical routine data using text mining]. / Merkmalsextraktion aus klinischen Routinedaten mittels Text-Mining.

BACKGROUND: Anti-VEGF drugs are currently used to treat macular diseases. This has led to a wealth of additional data, which could help understand and predict treatment courses; however, this information is usually only available in free text form. OBJECTIVE: A retrospective study was designed to analyze how far interpretable information can be obtained from clinical texts by automated extraction. The aim was to assess the suitability of a text mining method that was customized for this purpose. MATERIAL AND METHODS: Data on 3683 patients were available, including 40,485 discharge letters. Some of the data of interest, e.g. visual acuity (VA), intraocular pressure (IOP) and accompanying diagnoses, were not only recorded textually but also entered in a database and could thus serve as a gold standard for text analysis. The text was analyzed using the Averbis Health Discovery text mining platform. To optimize the extraction task, rule knowledge and a German language technical vocabulary linked to the international medical terminology standard systematized nomenclature of medicine (SNOMED CT) was manually added. RESULTS: The correspondence between extracted data and the structured database entries is described by the F1 value. There was agreement of 94.7% for VA, 98.3% for IOP and 94.7% for the accompanying diagnoses. Manual analysis of noncorresponding cases showed that in 50% text content did not match the database content for various reasons. After an adjustment, F1 values 1-3% above the previously determined values were obtained. CONCLUSION: Text mining procedures are very well suited for the considered discharge letter corpus and the problem described in order to extract contents from clinical texts in a structured manner for further evaluation.

The right response at the right time: Exploring helminth immune modulation in sticklebacks by experimental coinfection.

Parasites are one of the strongest selective agents in nature. They select for hosts that evolve counter-adaptive strategies to cope with infection. Helminth parasites are special because they can modulate their hosts' immune responses. This phenomenon is important in epidemiological contexts, where coinfections may be affected. How different types of hosts and helminths interact with each other is insufficiently investigated. We used the three-spined stickleback (Gasterosteus aculeatus) - Schistocephalus solidus model to study mechanisms and temporal components of helminth immune modulation. Sticklebacks from two contrasting populations with either high resistance (HR) or low resistance (LR) against S. solidus, were individually exposed to S. solidus strains with characteristically high growth (HG) or low growth (LG) in G. aculeatus. We determined the susceptibility to another parasite, the eye fluke Diplostomum pseudospathaceum, and the expression of 23 key immune genes at three time points after S. solidus infection. D. pseudospathaceum infection rates and the gene expression responses depended on host and S. solidus type and changed over time. Whereas the effect of S. solidus type was not significant after three weeks, T regulatory responses and complement components were upregulated at later time points if hosts were infected with HG S. solidus. HR hosts showed a well orchestrated immune response, which was absent in LR hosts. Our results emphasize the role of regulatory T cells and the timing of specific immune responses during helminth infections. This study elucidates the importance to consider different coevolutionary trajectories and ecologies when studying host-parasite interactions.

Simulations of camera-based single-molecule fluorescence experiments.

Single-molecule microscopy has become a widely used technique in (bio)physics and (bio)chemistry. A popular implementation is single-molecule Förster Resonance Energy Transfer (smFRET), for which total internal reflection fluorescence microscopy is frequently combined with camera-based detection of surface-immobilized molecules. Camera-based smFRET experiments generate large and complex datasets and several methods for video processing and analysis have been reported. As these algorithms often address similar aspects in video analysis, there is a growing need for standardized comparison. Here, we present a Matlab-based software (MASH-FRET) that allows for the simulation of camera-based smFRET videos, yielding standardized data sets suitable for benchmarking video processing algorithms. The software permits to vary parameters that are relevant in cameras-based smFRET, such as video quality, and the properties of the system under study. Experimental noise is modeled taking into account photon statistics and camera noise. Finally, we survey how video test sets should be designed to evaluate currently available data analysis strategies in camera-based sm fluorescence experiments. We complement our study by pre-optimizing and evaluating spot detection algorithms using our simulated video test sets.

Virulence in the three-spined stickleback specific parasite Schistocephalus solidus is inherited additively.

Parasite virulence is a key trait in host-parasite interactions and plays a crucial role in infection dynamics. Our study system offers the rare opportunity to study the virulence of an individual macroparasite (Schistocephalus solidus) in its vertebrate fish host (Gasterosteus aculeatus). The size of the tapeworm in the fish can be regarded as a good proxy for individual parasite virulence, as parasite size correlates negatively with fitness traits of the stickleback host (i.e. the bigger the parasite, the lower the host's reproductive success) as well as directly with the number of parasite offspring to be expected. To investigate how virulence is inherited, laboratory bred, parasite-naïve stickleback were infected with a cross of two S. solidus populations of either high or low virulence, as well as one hybrid cross between the two. The relative weight of the parasite as expressed in the parasite index served as a measure of virulence. Furthermore, we measured several condition and immune related traits in the fish host to assess parasite impact on the stickleback. We hypothesized that parasite virulence is to a large extent genetically determined and correlated with several fitness traits in the stickleback host. We found that virulence is inherited additively in S. solidus, with hybrids of high and low virulence parasites displaying intermediate levels. However, contrary to expectation, infection rate of S. solidus in three-spined stickleback is not related to virulence. Even though the presence of the parasite caused differences in host condition, these were indistinguishable between the different levels of virulence in this experiment. Fish immune traits also showed a response to infection but had no correlation with level of parasite virulence. With this experiment we have taken the first step towards understanding how virulence is inherited and how it is driven in the Schistocephalus-stickleback system, even though virulence, as measured here, does not directly translate into cost for the host. A better understanding of the costs inflicted on the host by S. solidus infection is needed to understand this interaction in greater detail.