Polymer-bound haloate(I) anions by iodine(III)-mediated oxidation of polymer-bound iodide: Synthetic utility in natural product transformations

A set of polymer-attached hypervalent iodate(I) complexes were prepared from polymer-bound iodide anion by ligand transfer of acetate and trifluoro acetate present in the corresponding iodine(III) reagents onto the iodide anion. The synthetic versatility of these polymer-bound reagents in terms of efficacy and ease of workup is demonstrated for selected examples in natural product synthesis and natural product derivatization. Thus, iodoacetoxylation of glycals is the initial step for the preparation of two deoxygenated disaccharides which are part of the carbohydrate units of the landomycins. In a second example, a one-pot multistep rearrangement of the decanolide decarestrictine D backbone is shown which is initiated by iodotrifluoroacylation of the olefinic double bond.Fonds der Chemischen Industri

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Improving Image Quality of Sparse-view Lung Cancer CT Images with a Convolutional Neural Network

Purpose: To improve the image quality of sparse-view computed tomography (CT) images with a U-Net for lung cancer detection and to determine the best trade-off between number of views, image quality, and diagnostic confidence. Methods: CT images from 41 subjects (34 with lung cancer, seven healthy) were retrospectively selected (01.2016-12.2018) and forward projected onto 2048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views, respectively. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, seven healthy) for a single-blinded reader study. The selected slices, for all levels of subsampling, with and without post-processing by the U-Net model, were presented to three readers. Image quality and diagnostic confidence were ranked using pre-defined scales. Subjective nodule segmentation was evaluated utilizing sensitivity (Se) and Dice Similarity Coefficient (DSC) with 95% confidence intervals (CI). Results: The 64-projection sparse-view images resulted in Se = 0.89 and DSC = 0.81 [0.75,0.86] while their counterparts, post-processed with the U-Net, had improved metrics (Se = 0.94, DSC = 0.85 [0.82,0.87]). Fewer views lead to insufficient quality for diagnostic purposes. For increased views, no substantial discrepancies were noted between the sparse-view and post-processed images. Conclusion: Projection views can be reduced from 2048 to 64 while maintaining image quality and the confidence of the radiologists on a satisfactory level

How FAIR is your data?: Self Assessment of Biodiversity Exploratories Data + Repository

In our poster we present the ‘FAIR Guiding Principles for scientific data management and stewardship’ as published in Scientific Data [1]. The authors intended to provide guidelines to improve reusability of data by defining principles regarding the findability, accessibility, interoperability, and reuse of digital data. We have checked our data (namely the public data of the Biodiversity Exploratories project) against these principles. On our poster, you can find the results of our self-assessment – and start thinking about how FAIR your data is. [1] https://doi.org/10.1038/sdata.2016.1

A Test Collection for Dataset Retrieval in Biodiversity Research

Searching for scientific datasets is a prominent task in scholars' daily research practice. A variety of data publishers, archives and data portals offer search applications that allow the discovery of datasets. The evaluation of such dataset retrieval systems requires proper test collections, including questions that reflect real world information needs of scholars, a set of datasets and human judgements assessing the relevance of the datasets to the questions in the benchmark corpus. Unfortunately, only very few test collections exist for a dataset search. In this paper, we introduce the BEF-China test collection, the very first test collection for dataset retrieval in biodiversity research, a research field with an increasing demand in data discovery services. The test collection consists of 14 questions, a corpus of 372 datasets from the BEF-China project and binary relevance judgements provided by a biodiversity expert

Towards FAIR data and repository within the Biodiversity Exploratories

The Biodiversity Exploratories Information System (BExIS) acts as centralized data management platform for the Biodiversity Exploratories project. It stores all project-related datasets and provides features to support scientist throughout the whole data lifecycle. The FAIR data principles [1] are a set of guiding principles in order to make data Findable, Accessible, Interoperable, and Reusable. Currently these principles are highly recognized in the data driven community and gained at a lot of support from funding agencies, publishers, data repositories, and researchers alike. It has been widely agreed that the realization of these principles changes the way of thinking about sharing data and should boost the use and reuse of data. We have worked lately on our public data and the repository itself to meet the principles or at least to step up in inherent requirements. On our poster, you can find the results of a self-assessment we have done to check the FAIRness and to indicate open activities which needs to be undertaken. [1] https://doi.org/10.1038/sdata.2016.1

New Limit on Axion-Dark-Matter using Cold Neutrons

We report on a search for axion-like dark matter using a Ramsey-type apparatus for cold neutrons. A hypothetical axion-gluon-coupling would manifest in a neutron electric dipole moment signal oscillating in time. Twenty-four hours of data have been analyzed in a frequency range from 23 $\mu$Hz to 1 kHz, and no significant oscillating signal has been found. The usage of present axion and dark-matter models allowed excluding the coupling of axions to gluons in the mass range from $1.5 \times 10^{-20}$ to $6.6 \times 10^{-13}$ eV with a best sensitivity of $C_G / f_a m_a = (3.1 \pm 0.2) \times 10^{12}$ GeV$^{-2}$ (95% C.L.)