Laboratory rotational ground state transitions of NH3_3D+^+ and CF+^+

Aims. This paper reports accurate laboratory frequencies of the rotational ground state transitions of two astronomically relevant molecular ions, NH3D+ and CF+. Methods. Spectra in the millimeter-wave band were recorded by the method of rotational state-selective attachment of He-atoms to the molecular ions stored and cooled in a cryogenic ion trap held at 4 K. The lowest rotational transition in the A state (ortho state) of NH$_3$D$^+$ ($J_K = 1_0 - 0_0$), and the two hyperfine components of the ground state transition of CF$^+$($J = 1 - 0$) were measured with a relative precision better than $10^{-7}$. Results. For both target ions the experimental transition frequencies agree with recent observations of the same lines in different astronomical environments. In the case of NH$_3$D$^+$ the high-accuracy laboratory measurements lend support to its tentative identification in the interstellar medium. For CF$^+$ the experimentally determined hyperfine splitting confirms previous quantum-chemical calculations and the intrinsic spectroscopic nature of a double-peaked line profile observed in the $J = 1 - 0$ transition towards the Horsehead PDR.Comment: 7 pages, 2 figure

CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows

Here we present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: Research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practices and thus support changes in workflow and data structure. Nevertheless it should encourage and support the development and observation of standards and furthermore facilitate the automation of data acquisition and processing with specialized software. The storage data model of an RDMS must reflect these complexities with appropriate semantics and ontologies while offering simple methods for finding, retrieving, and understanding relevant data. We show how CaosDB responds to these challenges and give an overview of the CaosDB Server, its data model and its easy-to-learn CaosDB Query Language. We briefly discuss the status of the implementation, how we currently use CaosDB, and how we plan to use and extend it

Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.Comment: Accepted conference paper at MICCAI 201

N-[(1S,2S)-2-Amino-1,2-diphenyl­eth­yl]-4-methyl­benzene­sulfonamide [(S,S)-TsDPEN]

The crystal structure of the title compound, C21H22N2O2S, shows a network of N—H⋯N and N—H⋯O hydrogen bonds. The tolyl and 1-phenyl rings are almost mutually coplanar [7.89 (9)°], while the 2-phenyl ring makes a dihedral angle of 50.8 (1) ° with the 1-phenyl ring. An intra­molecular N—H⋯N hydrogen bond stabilizes the mol­ecular conformation

Agile Research Data Management with FDOs using LinkAhead

One essential question with regard to the implementation of FAIR (Wilkinson et al. 2016) Digital Objects (FDOs) in everyday research is the following: How is data that is acquired in some way transformed into FDOs? Creating FDOs from data is a two-fold problem: "FAIR principles are policies, whereas the digital objects are technical abstractions" (Schwardmann 2020). Regarding the technical side, in order to become FDOs, raw data stored in files and databases have to be bundled with their metadata and PIDs have to be assigned. With good tools at hand, sharing data as an FDO with others might only be a matter of a few mouse clicks -- if the metadata is readily available.However, the process of collecting metadata comes with significant challenges of its own.While sometimes necessary, the manual annotation with metadata is error-prone and time-consuming. Due to resource constraints and time pressure, researchers might skip this task whenever it does not have any direct benefits for their work in the time frame of their current project. The experience with existing data repositories tells us that adding metadata at a late stage in the research data life cycle (for example just before publication) delays the problem in the best case. In the worst case, important information has already been lost at that stage. Furthermore, there are the FAIR principles which, for researchers, mean more rules to follow and thus more time spent on data management.Research data should be enriched with FAIR metadata as early as possible to ensure that the research data is FDO-ready when needed. In order to do this, researchers need tools that assist them with the task of making their data FDO-ready and those tools must not hinder the research process but in the best case even promote it. This means that the drawbacks of making data FDO-ready need to be mitigated and compensated by direct benefits to researchers.In this contribution, we present how early-on FDO-readyness can be achieved with the open source research data management toolkit LinkAhead and how researchers profit from the FDO-readyness directly in their work. LinkAhead, a CaosDB (Fitschen et al. 2019) distribution, assists its users from the very first steps of data acquisition to the completion of FDOs and data publication by means of a semantic data modal, metadata annotation to raw data and a powerful search capabilities.Why would researchers do what is necessary to make their data FOD-ready, early-on?With LinkAhead, the FDO-readyness is a welcome side-effect for users. Even though LinkAhead cannot magically generate all relevant metadata and make data FAIR, LinkAheads allows the automation of the process where possible and assists users elsewhere. The inevitable additional work for researchers is reduced as well as compensated with new possiblities for users to work with their data. Thus users are nudged into storing their data in clear and understandable structures and into annotating their data with high-quality metadata. We will highlight in the following, how users benefit from early FDO-readiness in their daily work due to those characteristics of LinkAhead. LinkAhead adapts to the changing needs of the researchers. It thus allows research data management to be an agile process and ensures that researchers can efficiently conduct their daily work. At the same time, it supports the development, documentation and observance of standards which is vital for the commensurability, reusability, and reproducibility of research findings. LinkAhead is designed to be the first tool after data acquisition and the last tool before the publication of data. It can be fed with data from LIMS, ELNs, simulation and analysis software, helps with automation of workflows, and manages raw data in files.Which direct benefit can LinkAhead offer to its users if they do what is necessary to make FDOs from data?When searching for data in general or FDOs in particular researchers can employ metadata and the connections among data in order to find what they are looking for. Thereby, browsing the data for example in the LinkAhead web interface can be very targeted. Additionally, these search capabilities can be used within analysis workflows in order to create the correct basis of data using FDOs directly for the question at hand. Client libraries, like the Python client, allow to include this into automated analyses. Since manual data insertion is inefficient in many research environments, LinkAhead does not only offer the insertion of data via web forms, but encourages the usage of automatic processes like the LinkAhead Crawler. While metadata should be added already during this insertion step if possible, LinkAhead assists in completing metadata after the initial insertion in order to strike a balance between interrupting the research workflow and running into the above mentioned challenges when adding metadata too late. This automatic data insertion process is highly customizable and allows to complement data as soon as possible such that FDOs are constituted.The semantic data model of LinkAhead allows researchers to use ontologies of their domain but also to extend those where necessary for the work at hand. This allows an agile adaption to changed requirements or new challenges and LinkAhead assures compatibility with old data if possible. The data model can capture both relations within an FDO, among metadata, data, and possibly files and references to other FDOs directly within LinkAhead or FDOs stored elsewhere using PIDs. The semantic data model and additional constraints facilitate the creation and validation of FDOs.LinkAhead allows to seamlessly integrate the FDO concept into the workflows of LinkAhead users. Thereby collecting information necessary for FDOs is not a burden for the researcher but the information can be directly used. The search capabilities of LinkAhead can employ the metadata of FDOs and the connections to other FDOs and their metadata. Components of the LinkAhead toolkit like the web interface allow users to access FDOs in an intuitive way while the LinkAhead API allows the direct use of data (and FDOs) in automatic processing and analysis. Thus, LinkAhead is a tool which does not only assist in the technical process of creating digital objects, it also creates incentives for its target users to adhere to FAIR guiding principles. It brings the benefits of FDOs to the people who have to do the extra work. Strategically, this is of utmost importance if the FDO initiative as a whole is to succeed

Metabolic alterations: A biomarker for radiation-induced normal brain injury—an MR spectroscopy study

Purpose To assess if interval changes in metabolic status in normal cerebral tissue after radiation therapy (RT) can be detected by 2D CSI (chemical shift imaging) proton spectroscopy. Materials and Methods Eleven patients with primary brain tumors undergoing cranial radiation therapy (RT) were included. 2D-CSI MRS was performed before, during, and after the course of RT with the following parameters: TE/TR 144/1500 ms, field of view (FOV) 24, thickness 10 mm, matrix 16 × 16. The metabolic ratios choline/creatine (Cho/Cr), N-acetylaspartate (NAA)/Cr, and NAA/Cho in normal brain tissue were calculated. Results NAA/Cr and Cho/Cr were significantly decreased at week 3 during RT and at 1 month and 6 months after RT compared to values prior to RT ( P < 0.01). The NAA/Cr ratio decreased by −0.19 ± 0.05 (mean ± standard error [SE]) at week 3 of RT, −0.14 ± 0.06 at the last week of RT, −0.14 ± 0.05 at 1 month after RT, and −0.30 ± 0.08 at 6 months after RT compared to the pre-RT value of 1.43 ± 0.04. The Cho/Cr ratio decreased by −0.27 ± 0.05 at week 3 of RT, −0.11 ± 0.05 at the last week of RT, −0.26 ± 0.05 at 1 month after RT and −0.25 ± 0.07 at 6 months after RT from the pre-RT value of 1.29 ± 0.03. Changes in Cho/Cr were correlated with the interaction of the radiation dose and dose-volume at week 3 of RT, during the last week of RT ( P < 0.005), and at 1 month after RT ( P = 0.017). Conclusion The results of this study suggest that MRS can detect early metabolic changes in normal irradiated brain tissue. J. Magn. Reson. Imaging 2009;29:291–297. © 2009 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61530/1/21657_ftp.pd

Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: A correlation with histopathology

To correlate histopathologic findings from biopsy specimens with their corresponding location within enhancing areas, non-enhancing areas and necrotic areas on contrast enhanced T1-weighted MRI scans (cT1).In 37 patients with newly diagnosed glioblastoma who underwent stereotactic biopsy, we obtained a correlation of 561 1mm3 biopsy specimens with their corresponding position on the intraoperative cT1 image at 1.5 Tesla. Biopsy points were categorized as enhancing (CE), non-enhancing (NE) or necrotic (NEC) on cT1 and tissue samples were categorized as "viable tumor cells", "blood" or "necrotic tissue (with or without cellular component)". Cell counting was done semi-automatically.NE had the highest content of tissue categorized as viable tumor cells (89% vs. 60% in CE and 30% NEC, respectively). Besides, the average cell density for NE (3764 ± 2893 cells/mm2) was comparable to CE (3506 ± 3116 cells/mm2), while NEC had a lower cell density with 2713 ± 3239 cells/mm2. If necrotic parts and bleeds were excluded, cell density in biopsies categorized as "viable tumor tissue" decreased from the center of the tumor (NEC, 5804 ± 3480 cells/mm2) to CE (4495 ± 3209 cells/mm2) and NE (4130 ± 2817 cells/mm2).The appearance of a glioblastoma on a cT1 image (circular enhancement, central necrosis, peritumoral edema) does not correspond to its diffuse histopathological composition. Cell density is elevated in both CE and NE parts. Hence, our study suggests that NE contains considerable amounts of infiltrative tumor with a high cellularity which might be considered in resection planning