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Purpose Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID‐MRSI) data. Methods The measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel‐Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over‐parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID‐MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets. Results The MM model using nine individual AMARES‐parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice. Conclusion The proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231–1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines started in 2006 with the goals to make observations possible that previously have not been possible. Specifically the aims are to obtain measurements data in unprecedented quantity and quality based on technological advances. This paper describes a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment 5 on the Matterhorn Hörnligrat, Zermatt Switzerland at 3500 m a.s.l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make this data available in near real-time and tightly monitor and control the running experiments. This data set (DOI: doi.pangaea.de/10.1594/PANGAEA.897640, Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period 10 of ten+ years. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models., Earth System Science Data Discussions, ISSN:1866-3591

Figure S1. Illustration of the influence of logistic model parameters on curve, and the model fitted to a CKC. From left to right: Îą defines the asymmetry of the logistic model, Ď the steepness of the curve and k influences the terminal slope. The regression curve fitted to a given CKC for a malignant (blue) and a benign lesion (green). Figure S2. Boxplot of automatic segmentation performance in terms of Dice similarity coefficient (DSC). DWI, diffusion-weighted imaging; GI, Gini Importance; mRMR, minimum-Redundancy-Maximum-Relevance; PET, positron emission tomography; w/o, without. (DOCX 219 kb)

Rock slope destabilization due to warming or thawing permafrost poses a risk to the safety of local communities and infrastructure in populated mountain regions. The analysis of fracture kinematics in the context of local temperature evolution in the longer-term is a common approach aiming to identify its forcing (e.g. Wegmann and Gudmundsson, 1999, Matsuoka and Murton, 2008, Blikra and Christiansen, 2014). Hasler et al. (2012) and Weber et al. (2017) analyzed fracture dilatation data measured at Matterhorn Hörnligrat at 3500 m a.s.l. and suggest thawing related processes, such as meltwater percolation into fractures to cause irreversible displacement. However, this finding so far has not been backed up by data from different instruments or analysis methods. Hence, misinterpretation of the existing data can not reliably be excluded. Based on further data consisting of surface displacements measured with D-GPS, inclinometers, ambient seismic vibrations and ground resistivity captured and compiled over a period of ten years, we apply a multi-data cross validation technique to detect and quantify temperature-induced rock slope dynamics and identify the components of derived process knowledge that predict behavior across differing observation methods. The combined analysis of this multi-modal dataset allows to further develop and analyse our limited understanding of the dominant processes governing rock slope stability, in our case a steep bedrock mountain permafrost buttress. Based on this evidence we conclude that the kinematics observed at the surface in the winter/refreezing period is negligible compared to those observed during spring initiated by the thawing and mobilization of fluid water w.r.t. destabilization and precursory signs of rockfall at a larger scale. Therefore, future research should focus on the quantification of water supply, distribution and mobility both in the frozen and fluid state.

This work presents a new approach for high‐resolution MRSI of the brain at 7 T in clinically feasible measurement times. Two major problems of MRSI are the long scan times for large matrix sizes and the possible spectral contamination by the transcranial lipid signal. We propose a combination of free induction decay (FID)‐MRSI with a short acquisition delay and acceleration via in‐plane two‐dimensional generalised autocalibrating partially parallel acquisition (2D‐GRAPPA) with adiabatic double inversion recovery (IR)‐based lipid suppression to allow robust high‐resolution MRSI. We performed Bloch simulations to evaluate the magnetisation pathways of lipids and metabolites, and compared the results with phantom measurements. Acceleration factors in the range 2–25 were tested in a phantom. Five volunteers were scanned to verify the value of our MRSI method in vivo. GRAPPA artefacts that cause fold‐in of transcranial lipids were suppressed via double IR, with a non‐selective symmetric frequency sweep. The use of long, low‐power inversion pulses (100 ms) reduced specific absorption rate requirements. The symmetric frequency sweep over both pulses provided good lipid suppression (>90%), in addition to a reduced loss in metabolite signal‐to‐noise ratio (SNR), compared with conventional IR suppression (52–70%). The metabolic mapping over the whole brain slice was not limited to a rectangular region of interest. 2D‐GRAPPA provided acceleration up to a factor of nine for in vivo FID‐MRSI without a substantial increase in g‐factors (

Abweichender Titel nach ��bersetzung von der Verfasserin/des Verfassers

Figure S1. Sequencing and assembly strategy of the A. salmonicida subsp. pectinolytica strain 34mel genome. Figure S2. Gene similarity and Mummer alignments to other Aeromonas genomes. Figure S3. Transposon conglomerates from A. salmonicida subsp. pectinolytica strain 34mel. Figure S4. Transposon family content in analyzed Aeromonas strains. Figure S5. Comparison of the A. salmonicida subsp. pectinolytica strain 34mel genome, plasmid pFBAOT6 from Aeromonas caviae, and transposon Tn1721. Table S1. Pathogenic as well as temperature-dependent growth features of analyzed Aeromonas strains. Table S2. General genome features of A. salmonicida subsp. pectinolytica strain 34mel. Table S3. Polymorphic sites in rRNAs from A. salmonicida subsp. pectinolytica strain 34mel. Table S4. Genes disrupted by transposons in A. salmonicida subsp. pectinolytica strain 34mel. Table S5. Transposon details from analyzed Aeromonas strains. Table S6. Genes retained in transposon TnAs1 from the environmental IncP-9 TOL plasmid pWW0 from Pseudomonas putida. Text S1. Annotation details for RNA genes in the A. salmonicida subsp. pectinolytica strain 34mel genome. Text S2. Short unassigned contigs from the A. salmonicida subsp. pectinolytica strain 34mel draft genome. Text S3. Transposons: nomenclature and completeness. Text S4. On transposon counting and the meaning of â ISAs11â . Text S5. Insertions on pFBAOT6 in transposon Tn1721 and details of the regions shared with the A. salmonicida subsp. pectinolytica strain 34mel genome. Text S6. Details of PacBio library preparation, sequencing, and assembly of the genome from A. salmonicida subsp. pectinolytica strain 34mel. (PDF 1171 kb)