Charging changes contact composition in binary sphere packings

Equal volume mixtures of small and large polytetrafluorethylene (PTFE) spheres are shaken in an atmosphere of controlled humidity which allows to also control their tribo-charging. We find that the contact numbers are charge-dependent: as the charge density of the beads increases, the number of same-type contacts decreases and the number of opposite-type contacts increases. This change is not caused by a global segregation of the sample. Hence, tribo-charging can be a way to tune the local composition of a granular material.Comment: 7 Pages, 5 Figure

Structural similarity between dry and wet sphere packings

The mechanical properties of granular materials change significantly in the presence of a wetting liquid which creates capillary bridges between the particles. Here we demonstrate, using X-ray tomographies of dry and wet sphere packings, that this change in mechanical properties is not accompanied by structural differences between the packings. We characterize the latter by the average numbers of contacts of each sphere $\langle Z\rangle$ and the shape isotropy $\langle \beta_0^{2,0} \rangle$ of the Voronoi cells of the particles. Additionally, we show that the number of liquid bridges per sphere $\langle B\rangle$ is approximately equal to $\langle Z\rangle + 2$, independent of the volume fraction of the packing. These findings will be helpful in guiding the development of both particle-based models and continuum mechanical descriptions of wet granular matter.Comment: slightly revised versio

Pomelo, a tool for computing Generic Set Voronoi Diagrams of Aspherical Particles of Arbitrary Shape

We describe the development of a new software tool, called "Pomelo", for the calculation of Set Voronoi diagrams. Voronoi diagrams are a spatial partition of the space around the particles into separate Voronoi cells, e.g. applicable to granular materials. A generalization of the conventional Voronoi diagram for points or monodisperse spheres is the Set Voronoi diagram, also known as navigational map or tessellation by zone of influence. In this construction, a Set Voronoi cell contains the volume that is closer to the surface of one particle than to the surface of any other particle. This is required for aspherical or polydisperse systems. Pomelo is designed to be easy to use and as generic as possible. It directly supports common particle shapes and offers a generic mode, which allows to deal with any type of particles that can be described mathematically. Pomelo can create output in different standard formats, which allows direct visualization and further processing. Finally, we describe three applications of the Set Voronoi code in granular and soft matter physics, namely the problem of packings of ellipsoidal particles with varying degrees of particle-particle friction, mechanical stable packings of tetrahedra and a model for liquid crystal systems of particles with shapes reminiscent of pearsComment: 4 pages, 9 figures, Submitted to Powders and Grains 201

Highly Cooperative Photoswitching in Dihydropyrene Dimers

We present a strategy to achieve highly cooperative photoswitching, where the initial switching event greatly facilitates subsequent switching of the neighboring unit. By linking donor/acceptor substituted dihydropyrenes via suitable π-conjugated bridges, the quantum yield of the second photochemical ring-opening process could be enhanced by more than two orders of magnitude as compared to the first ring-opening. As a result, the intermediate mixed switching state is not detected during photoisomerization although it is formed during the thermal back reaction. Comparing the switching behavior of various dimers, both experimentally and computationally, helped to unravel the crucial role of the bridging moiety connecting both photochromic units. The presented dihydropyrene dimer serves as model system for longer cooperative switching chains, which, in principle, should enable efficient and directional transfer of information along a molecularly defined path. Moreover, our concept allows to enhance the photosensitivity in oligomeric and polymeric systems and materials thereof. © 2020 The Authors. Published by Wiley-VCH Gmb

From algebra to logic: there and back again -- the story of a hierarchy

This is an extended survey of the results concerning a hierarchy of languages that is tightly connected with the quantifier alternation hierarchy within the two-variable fragment of first order logic of the linear order.Comment: Developments in Language Theory 2014, Ekaterinburg : Russian Federation (2014

Identification of suitable controls for miRNA quantification in T-cells and whole blood cells in sepsis

Complex immune dysregulation is a hallmark of sepsis. The occurring phases of immunosuppression and hyperinflammation require rapid detection and close monitoring. Reliable tools to monitor patient's immune status are yet missing. Currently, microRNAs are being discussed as promising new biomarkers in sepsis. However, no suitable internal control for normalization of miRNA expression by qPCR has been validated so far, thus hampering their potential benefit. We here present the first evaluation of endogenous controls for miRNA analysis in human sepsis. Novel candidate reference miRNAs were identified via miRNA microArray. TaqMan qPCR assays were performed to evaluate these microRNAs in T-cells and whole blood cells of sepsis patients and healthy controls in two independent cohorts. In T-cells, U48 and miR-320 proved suitable as endogenous controls, while in whole blood cells, U44 and miR-942 provided best stability values for normalization of miRNA quantification. Commonly used snRNA U6 exhibited worst stability in all sample groups. The identified internal controls have been prospectively validated in independent cohorts. The critical importance of housekeeping gene selection is emphasized by exemplary quantification of imuno-miR-150 in sepsis patients. Use of appropriate internal controls could facilitate research on miRNA-based biomarker-use and might even improve treatment strategies in the future

Towards increasing the clinical applicability of machine learning biomarkers in psychiatry.

Due to a lack of objective biomarkers, psychiatric diagnoses still rely strongly on patient reporting and clinician judgement. The ensuing subjectivity negatively affects the definition and reliability of psychiatric diagnoses1,2. Recent research has suggested that a combination of advanced neuroimaging and machine learning may provide a solution to this predicament by establishing such objective biomarkers for psychiatric conditions, improving the diagnostic accuracy, prognosis and development of novel treatments3.These promises led to widespread interest in machine learning applications for mental health4, including a recent paper that reports a biological marker for one of the most difficult yet momentous questions in psychiatry—the assessment of suicidal behaviour5. Just et al. compared a group of 17 participants with suicidal ideation with 17 healthy controls, reporting high discrimination accuracy using task-based functional magnetic resonance imaging signatures of life- and death-related concepts3. The authors further reported high discrimination between nine ideators who had attempted suicide versus eight ideators who had not. While being a laudable effort into a difficult topic, this study unfortunately illustrates some common conceptual and technical issues in the field that limit translation into clinical practice and raise unrealistic hopes when the results are communicated to the general public.From a conceptual point of view, machine learning studies aimed at clinical applications need to carefully consider any decisions that might hamper the interpretation or generalizability of their results. Restrictiveness to an arbitrary setting may become detrimental for machine learning applications by providing overly optimistic results that are unlikely to generalize. As an example, Just et al. excluded more than half of the patients and healthy controls initially enrolled in the study from the main analysis due to missing desired functional magnetic resonance imaging effects (a rank accuracy of at least 0.6 based on all 30 concepts). This exclusion introduces a non-assessable bias to the interpretation of the results, in particular when considering that only six of the 30 concepts were selected for the final classification procedure. While Just et al. attempt to address this question by applying the trained classifier to the initially excluded 21 suicidal ideators, they explicitly omit the excluded 24 controls from this analysis, preventing any interpretation of the extent to which the classifier decision is dependent on this initial choice.From a technical point of view, machine learning-based predictions based on neuroimaging data in small samples are intrinsically highly variable, as stable accuracy estimates and high generalizability are only achieved with several hundreds of participants6,7. The study by Just et al. falls into this category of studies with a small sample size. To estimate the impact of uncertainty on the results by Just et al., we adapted a simulation approach with the code and data kindly provided by the authors, randomly permuting (800 times) the labels across the groups using their default settings and computing the accuracies. These results showed that the 95% confidence interval for classification accuracy obtained using this dataset is about 20%, leaving large uncertainty with respect to any potential findings.Special care is also required with respect to any subjective choices in feature and classifier settings or group selection. While ad-hoc selection of a specific setting is subjective, testing of different ones and outcome-based post-hoc justification of such leads to overfitting, thus limiting the generalizability of any classification. Such overfitting may occur when multiple models or parameter choices are tested with respect to their ability to predict the testing data and only those that perform best are reported. To illustrate this issue, we performed an additional analysis with the code and data kindly provided by Just et al. More specifically, in the code and the manuscript, we identified the following non-exhaustive number of prespecified settings: (1) removal of occipital cortex data; (2) subdivision of clusters larger than 11 mm; (3) selection of voxels with at least four contributing participants in each group; (4) selection of stable clusters containing at least five voxels; (5) selection of the 1,200 most stable features; and (6) manual copying and replacing of a cluster for one control participant. Importantly, according to the publication or code documentation, all of these parameters were chosen ad hoc and for none of these settings was a parameter search performed. We systematically evaluated the effect of each of these choices on the accuracy for differentiation between suicide ideators and controls in the original dataset provided by Just et al. As shown in Fig. 1, each of the six parameters represents an optimum choice for differentiation accuracy in this dataset, with any (even minor) change often resulting in substantially lower accuracy estimates. Similarly, data leakage may also contribute to optimistic results when information outside the training set is used to build a prediction model. More generally, whenever human interventions guide the development of machine learning models for the prediction of clinical conditions, a careful evaluation and reporting of any researcher’s degrees of freedom is essential to avoid data leakage and overfitting. Subsequent sharing of data processing and analysis pipelines, as well as collected data, is a further key step to increase reproducibility and facilitate replication of potential findings

Cell Biology of Cnidarian-Dinoflagellate Symbiosis

The symbiosis between cnidarians (e.g., corals or sea anemones) and intracellular dinoflagellate algae of the genus Symbiodinium is of immense ecological importance. In particular, this symbiosis promotes the growth and survival of reef corals in nutrient-poor tropical waters; indeed, coral reefs could not exist without this symbiosis. However, our fundamental understanding of the cnidarian-dinoflagellate symbiosis and of its links to coral calcification remains poor. Here we review what we currently know about the cell biology of cnidarian-dinoflagellate symbiosis. In doing so, we aim to refocus attention on fundamental cellular aspects that have been somewhat neglected since the early to mid-1980s, when a more ecological approach began to dominate. We review the four major processes that we believe underlie the various phases of establishment and persistence in the cnidarian/coral-dinoflagellate symbiosis: (i) recognition and phagocytosis, (ii) regulation of host-symbiont biomass, (iii) metabolic exchange and nutrient trafficking, and (iv) calcification. Where appropriate, we draw upon examples from a range of cnidarian-alga symbioses, including the symbiosis between green Hydra and its intracellular chlorophyte symbiont, which has considerable potential to inform our understanding of the cnidarian-dinoflagellate symbiosis. Ultimately, we provide a comprehensive overview of the history of the field, its current status, and where it should be going in the future.Keywords: inorganic carbon uptake, Anemone aiptasia pallida, Coral stylophora pistillata, Pocillopora damicornis linnaeus, Host release factor, Free amino acids, Reef building corals, Alga invertebrate symbiosis, Symbiodinium microadriaticum freudenthal, Green hydra symbiosi