132 research outputs found

    Empirical Comparison of Distributed Source Localization Methods for Single-Trial Detection of Movement Preparation

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    The development of technologies for the treatment of movement disorders, like stroke, is still of particular interest in brain-computer interface (BCI) research. In this context, source localization methods (SLMs), that reconstruct the cerebral origin of brain activity measured outside the head, e.g., via electroencephalography (EEG), can add a valuable insight into the current state and progress of the treatment. However, in BCIs SLMs were often solely considered as advanced signal processing methods that are compared against other methods based on the classification performance alone. Though, this approach does not guarantee physiological meaningful results. We present an empirical comparison of three established distributed SLMs with the aim to use one for single-trial movement prediction. The SLMs wMNE, sLORETA, and dSPM were applied on data acquired from eight subjects performing voluntary arm movements. Besides the classification performance as quality measure, a distance metric was used to asses the physiological plausibility of the methods. For the distance metric, which is usually measured to the source position of maximum activity, we further propose a variant based on clusters that is better suited for the single-trial case in which several sources are likely and the actual maximum is unknown. The two metrics showed different results. The classification performance revealed no significant differences across subjects, indicating that all three methods are equally well-suited for single-trial movement prediction. On the other hand, we obtained significant differences in the distance measure, favoring wMNE even after correcting the distance with the number of reconstructed clusters. Further, distance results were inconsistent with the traditional method using the maximum, indicating that for wMNE the point of maximum source activity often did not coincide with the nearest activation cluster. In summary, the presented comparison might help users to select an appropriate SLM and to understand the implications of the selection. The proposed methodology pays attention to the particular properties of distributed SLMs and can serve as a framework for further comparisons

    CheS-Mapper - Chemical Space Mapping and Visualization in 3D

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    Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis

    Pharmacogenetic Associations of MMP9 and MMP12 Variants with Cardiovascular Disease in Patients with Hypertension

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    MMP-9 and -12 function in tissue remodeling and may play roles in cardiovascular disease (CVD). We assessed associations of four MMP polymorphisms and three antihypertensive drugs with cardiovascular outcomes.Hypertensives (n = 42,418) from a double-blind, randomized, clinical trial were randomized to chlorthalidone, amlodipine, lisinopril, or doxazosin treatment (mean follow up, 4.9 years). The primary outcome was coronary heart disease (CHD). Secondary outcomes included combined CHD, all CVD outcomes combined, stroke, heart failure (HF), and mortality. Genotype-treatment interactions were tested. = 0.015). for CHD and composite CVD. The data suggest that these genes may provide useful clinical information with respect to treatment decisions

    Generation of an in vitro 3D PDAC stroma rich spheroid model

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    Pancreatic ductal adenocarcinoma (PDAC) is characterized by a prominent desmoplastic/stromal reaction, which contributes to the poor clinical outcome of this disease. Therefore, greater understanding of the stroma development and tumor-stroma interactions is highly required. Pancreatic stellate cells (PSC) are myofibroblast-like cells that located in exocrine areas of the pancreas, which as a result of inflammation produced by PDAC migrate and accumulate in the tumor mass, secreting extracellular matrix components and producing the dense PDAC stroma. Currently, only a few orthotopic or ectopic animal tumor models, where PDAC cells are injected into the pancreas or subcutaneous tissue layer, or genetically engineered animals offer tumors that encompass some stromal component. Herein, we report generation of a simple 3D PDAC in vitro micro-tumor model without an addition of external extracellular matrix, which encompasses a rich, dense and active stromal compartment. We have achieved this in vitro model by incorporating PSCs into 3D PDAC cell culture using a modified hanging drop method. It is now known that PSCs are the principal source of fibrosis in the stroma and interact closely with cancer cells to create a tumor facilitatory environment that stimulates local and distant tumor growth. The 3D micro-stroma models are highly reproducible with excellent uniformity, which can be used for PDAC-stroma interaction analysis and high throughput automated drug-screening assays. Additionally, the increased expression of collagenous regions means that molecular based perfusion and cytostaticity of gemcitabine is decreased in our Pancreatic adenocarcinoma stroma spheroids (PDAC-SS) model when compared to spheroids grown without PSCs. We believe this model will allow an improved knowledge of PDAC biology and has the potential to provide an insight into pathways that may be therapeutically targeted to inhibit PSC activation, thereby inhibiting the development of fibrosis in PDAC and interrupting PSC-PDAC cell interactions so as to inhibit cancer progression

    Biodiversity and Health: Implications for Conservation

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    The human health and well-being benefits of contact with nature are becoming increasingly recognised and well understood, yet the implications of nature experiences for biodiversity conservation are far less clear. Theoretically, there are two plausible pathways that could lead to positive conservation outcomes. The first is a direct win-win scenario where biodiverse areas of high conservation value are also disproportionately beneficial to human health and well-being, meaning that the two sets of objectives can be simultaneously and directly achieved, as long as such green spaces are safeguarded appropriately. The second is that experiencing nature can stimulate people’s interest in biodiversity, concern for its fate, and willingness to take action to protect it, therefore generating conservation gains indirectly. To date, the two pathways have rarely been distinguished and scarcely studied. Here we consider how they may potentially operate in practice, while acknowledging that the mechanisms by which biodiversity might underpin human health and well-being benefits are still being determined
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