dataesr/scientific-paragraphs-categorization huggingface dataset:
On the upper floor, the low resistive feature aligns with a clay-filled fracture observed next to the survey area in the adjacent rock wall (see Figure  2b ). (b) Resistivity-depth profile at the location of the collapsed cave roof, where cave depth and water table were measured (as indicated in red and blue bars). Shown is also the resistivity gradient for the depths >4. 5 m. At the measured depth of the cave, the gradient approaches zero and eventually becomes positive, indicating increasing resistivities at 8. 25 m depth. The results of the additional ground truthing using GPR are shown in Figure  10 , together with the 2. 5 kΩ•m iso-surface from the 3D ERT model and field observations. Generally, the data show some weak reflections and diffraction hyperbolas that can be related to fractures or voids in the otherwise homogeneous marble.
At y = -20--24 m the data show high amplitude hyperbolas that correspond in their location with the mapped fracture and voids. The hyperbolas of profiles P1 and P2 seem to originate from very shallow features, while on P3 the most pronounced hyperbola has its origin at ≈1. 2 m depth (using a wave velocity of 0. 117 m/ns for time-to-depth conversion, measured on an adjacent marble block). This hyperbola has a negative amplitude. Hence, it is likely that it corresponds to a contrast between marble and a water-filled void  [54]. Note that this interface would be at a higher level than imaged in the ERT results. However, between the two surveys, extraction advanced significantly and the water table was lowered to allow for continuing extraction. Although the data quality in the study presented here was excellent, performing ERT measurements on rock faces remains challenging.
Due to the high resistivities, potentials propagate far away from the current injection dipoles. Hence, resistivity measurements are sensitive to subsurface resistivity anomalies outside the actual survey area. While this has to be considered in the processing and interpretation of 3D data sets, it is even more important for 2D ERT data sets, where the intrinsic assumption of a 2. 5D subsurface is likely being violated. As shown in the case study here, by modeling the entire quarry domain, these outer-space sensitivities  [55]  can be accounted for and artifacts in the resulting resistivity model avoided. However, this requires a fine discretization of the quarry domain, resulting in potentially large computational meshes, which require sufficient computational power and storage. Recent work aims to reduce the computational cost of ERT forward and inverse modelling  [36, 52, 56] , and will allow a wider application of this geophysical technique, allowing the requirement of HPC technology to be overcome.
Summary and Conclusions Here, a case study is presented where electrical resistivity tomography (ERT) has been applied to characterise the volumetric electrical properties of an operational marble quarry. This technique was chosen as electrical resistivity is sensitive to changes in clay and water content, which are parameters linked to weathering, karstification and fracturing. These processes may alter rock properties that will impact the economic value of the quarried stone and its safe extraction. Application of ERT within hard-rock environments is challenging. Contact resistances are very high and potentials propagate far from the injection dipoles, requiring a careful decision of the employed electrodes, measurement system, and data processing algorithms. We show how these challenges can be overcome, and that despite the unfavourable conditions for conducting electrical resistivity measurements, good quality data can be obtained.
Comparing the derived resistivity model with field observations and complementary ground penetrating radar (GPR) data highlights the reliability of the resistivity data in imaging faults, and air-and water-filled voids. These results show that ERT can be used as a non-invasive exploration technique to image stone properties before excavation, and thus as a tool to aid the design of mining operations. Future developments of the approach considered here could include the integrated interpretation of geophysical models with laboratory data to define stone quality classes. By using geo-statistical interpolation techniques, such as kriging or inverse distance interpolation, these integrated data analyses could be used to further refine resource estimates, and optimize and guide extraction progress.
For instance, if p = 100 yearly load/generation profiles with a time step of ΔT = 10 minutes are considered over a period of A = 10 years (≈ 5. 3 × 10 6 minutes), then n = pA / ΔT ≈ 5. 3 × 10 7 loadflow calculations are required to assess the performance of alternative solutions for a given network. This means around seven hours of computation 1 when using a Forward-Backward load-flow solver  [20]  under MATLAB for one of the radial 400-bus networks studied in Section 3. 2. Such a computation time is not acceptable for Distribution System Operators (DSO), who are often responsible for the medium/long-term development of several hundred or even thousand primary substations. The possible options to reduce the computation time needed for multi-period load-flow calculations can be classified into three categories: 1) time subsampling of the load/generation profiles, 2) intrusive approximation of the load-flow solver, and 3) non-intrusive approximation of the load-flow solver.
General information about the case studies presented in Sections 3. 2 and 3. 3 The purpose is to compute the 10-minute profiles of load-flow outputs quickly and accurately for different radial distribution networks. This means performing n = 52 560 exact load-flow calculations per year. A Forward-Backward algorithm is used as a benchmark for solving load-flow equations  [20]. This load-flow solver is especially efficient for radial networks. It takes 25 seconds on average to compute n = 52 560 load-flow calculations under MATLAB for the radial 400-bus networks studied in Section 3. 2. For techno-economic issues, four sets of load-flow outputs have to be estimated: U, I, Ploss, and S0. For the radial 400-bus networks studied in Section 3. between all the generators and loads proportionally to their rated real power (for producers) or peak apparent power (for consumers).
The 100% SG-positive cells were achieved by permeabilization of the cells using 0. 02% Triton X-100. For some experiments, death of mTRAIL-R-positive cells was analyzed by flow cytometry. In this case, cells were resuspended in Annexin V-binding buffer containing Annexin V-APC (BD Biosciences, San Jose, CA, USA) and SYTOX Blue (Life Technologies) shortly before analysis by flow cytometry (FACSVerse, Becton Dickinson, Franklin Lakes, NJ, USA). The data were then analyzed using FlowJo software. Expression of the different inducible forms of mTRAIL-R was induced 24 h prior to cell death analysis by addition of doxycycline at the concentrations indicated in the figures.
Cell surface expression of mTRAIL-R Plasma membrane expression of mTRAIL-R was achieved by flow cytometry. Cells were harvested and resuspended in cold PBS containing 0. 5% FCS. The cells were then incubated in PBS-0. 5% FCS containing anti-mTRAIL-R-PE (eBioscience, San Diego, CA, USA; # 12-5883) or isotype control at 4 °C for 30 min. Cells were then washed three times in cold PBS-0. 5% FCS before analysis by the cytometer (FACSVerse). The data were then analyzed using FlowJo software.
Acknowledgements We thank  Prof. Savvides  ( VIB/UGent, Belgium ) and  Prof. Callewaert  ( VIB/UGent, Belgium ) for insightful discussions, and  Béatrice Vanbervliet  for her assistance in cell culture. We are grateful to  Prof. Mak  ( University of Toronto, Canada ) for the mTRAIL-R-deficient MEFs. Research in the groups of  Prof. M. Bertrand  and  Prof. P. Vandenabeele  is supported by grants from the  Vlaams Instituut voor Biotechnologie (VIB) , grants from  Ghent University  (MRP, GROUP-ID consortium), grants from the  Fonds voor Wetenschappelijk Onderzoek Vlaanderen (FWO)  ( G017212N ,  G013715N ,  G078713N ), from the  Flemish Government  (Methusalem  BOF09/01M00709  and  BOF16/MET_V/007 ), and from the  Belgian science policy office (BELSPO)  ( IAP 7/32 ). Dr. Dondelinger  is holder of a FWO postdoctoral fellowship.
Nitrogen was used as the nebulizing and drying gas. Data were acquired by using MassLynx software (waters) for following ions (m/z): 279. 1457, 286. 1749, 307. 1717, 314. 2076, 280. 1297, 150. 0919, and 156. 1113 for l-arginine, D7-arginine, ADMA, SDMA, D7-ADMA, l-citrulline, DMA, and D6-DMA, respectively (for mass spectrum see Fig. 1 ).
Accuracy, Precision, and Stability Low, medium, and high concentrations of quality control samples (respectively, QC1, QC2, and QC3) were used for determination of accuracy and precision. The results are summarized in Table  1. Stability of the derivatives was tested for QC directly after derivatization procedure (0 h) and after 1 day (24 h). Samples were stored in an autosampler cooled to 6 °C. The results are depicted in Table  2  (presented data are average of five replicates for within-run analysis or of ten replicates for between-run analysis). All calculated CVs were below 15% and as such consistent with FDA guidelines for validation of bioanalytical methods  [29].
Statistical Analysis Correlations between continuous variables were evaluated using the Spearman's rank correlation test. Effects of sex on metabolic parameters were tested using the Mann-Whitney U test. Results were considered as statistically significant if the p value was < 0. 05. Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 20. There were no significant correlations between general characteristics of healthy volunteers and metabolic parameters except for a significant negative correlation between serum levels of dimethylamine and age (Table  5 ). Correlations between metabolic parameters are presented in Table  6. There was a significant positive correlation between serum levels of l-arginine, ADMA, and l-arginine:ADMA ratio. Serum l-citrulline levels positively correlated with serum levels of ADMA, SDMA, and dimethylamine.
The objective was to describe and validate a new and alternative software procedure for 24locus mycobacterial interspersed repetitive unit-variable number-tandem repeat (MIRU-VNTR) typing of Mycobacterium tuberculosis (Mtb) based on the multipurpose BioNumerics software. DNA from randomly selected isolates of Mtb from two European laboratories, including external control samples for MIRU-VNTR typing, were analysed. Samples were genotyped using the commercial 24-locus VNTR typing kit from GenoScreen. The PCR amplified fragments were separated by capillary electrophoresis. For the subsequent analyses, the currently used software GeneMapper was compared with BioNumerics. The endpoint was the level of concordance when comparing genotyping results obtained from BioNumerics with results obtained from GeneMapper and the ECDC proficiency study reference results.
Also, the number of necessary manual standard size corrections and allele assignments in the two different software methods were compared. In total, 272 DNA samples, including the ECDC proficiency panel, were analysed. For all samples, there were 100% concordance of results. For a randomly selected set of 96 samples the numbers of manual corrections needed for size standards were 199 with GeneMapper versus zero for BioNumerics. The numbers of manual corrections for allele assignments were 122 with GeneMapper versus 16 with BioNumerics. In conclusion, we have validated the multipurpose software BioNumerics for standard 24-locus MIRU-VNTR typing and the software shows promising benefits in terms of simplification and minimization of hand-on time.
In multiple TB reference laboratories globally, MIRU-VNTR typing is performed using high throughput versions with multiplex PCR and fragment analysis on capillary sequencers  [5, 6]. Results from the fragment separation are analysed in the GeneMapper software, where electropherograms are visualized and processed for semi-automated allele calling  [7]. However, analysing in Gen-eMapper is limited by the amount of manual corrections frequently needed for relabeling the size standard fragments and the subsequent allelic assignments of the MIRU-VNTR amplicons. In this study, we describe and validate a new, alternative procedure based on the multipurpose BioNumerics software  [8, 9]  which addresses these limitations. This procedure allows integrating downstream operations completely and analysing the obtained genotypes using one single system, including databasing, attribution of genotype names synchronized with the MIRU-VNTRplus nomenclature [10], cluster analysis, and phylogenetic identification.
6, Applied Maths, were used for the analysis in accordance with the user manuals  [7, 8]. The analyses of samples in GeneMapper and in BioNumerics were done independently and blindly in Copenhagen and Lille, respectively, i. e. during the analysis, there was no access to results or data from the other software. The primary endpoint was the level of concordance between results obtained from BioNumerics and results obtained from GeneMapper and the ECDC proficiency study results. The secondary endpoints were the number of manual standard size corrections and modifications of allele assignments needed in BioNumerics and GeneMapper, respectively. The study was based on randomly selected Mtb isolates and data were accessed and analysed anonymously. As genotyping are performed as a part of national surveillance of Mtb transmission, the authors had access to identifying information in Denmark only.
Results Both the setup and the analysis using BioNumerics are described in detail in the supplemental material. In principle, BioNumerics works similarly to GeneMapper, that is i) electrophoresis data is imported as. fsa-files, ii) sizes of the size standard are specified automatically and any errors corrected manually, iii) alleles are assigned according to calculated sizes of the amplified fragments, and iv) 24-locus genotypes are obtained from aggregated alleles. The allele assignment is based on predefined sized bins, which are specified during initial setup of the database based on the experimental calibration of the sizes for different alleles of each of the 24 MIR-U-VNTR loci upon electrophoresis, using the calibration kit with the allelic ladders. The attribution of existing and new genotype names is automatically done using the MIRU-VNTR plug-in, which synchronizes genotype names with the standard nomenclature and already registered genotypes maintained on the MIRU-VNTRplus website  [12].
This is another significant difference compared to the use of GeneMapper, where the obtained genotypes have to be uploaded manually to the website for naming after genotypic analysis. Importantly, when electrophoresis data are imported, BioNumerics appears to perform very well in automatically assigning sizes of the size standard fragments, and also perform well in the following automatic assignments of the alleles for the MIRU-VNTR amplicons and the naming of resulting genotypes, as seen from our evaluation as follows: In total 272 samplescorresponding to 1632 quadruplexes-were analysed using both GeneMapper and BioNumerics-including a set of 20 external samples from an ECDC proficiency testing round. For all samples, results obtained with BioNumerics and those obtained with GeneMapper were 100% concordant.
For a randomly selected set of 96 samples-corresponding to 576 quadruplexes-we registered the number of size standard corrections and allele assignment modifications that had to be performed manually with either software. For these samples, the number of manual corrections needed for size standards were 199 with GeneMapper versus zero for BioNumerics. The number of manual corrections for allele assignments were 122 with GeneMapper versus 16 with BioNumerics. The total sample set (i. e. from Institut Pasteur de Lille, the International Reference Laboratory, Statens Serum Institut, and the ECDC panel) included diverse isolates from major lineages and sublineages such as EAI, CAS, Ghana, Harleem, S, LAM, Uganda, Cameroon, Beijing, H37rv, with a broad overall distribution of alleles/repeat unit numbers across the 24 loci, ranging from 0 to 16 in total.
Discussion In this study performed in two European laboratories, we report the evaluation and validation of the use of BioNumerics for standard 24-locus MIRU-VNTR genotyping of Mtb. We found 100% concordance with the classical use of GeneMapper, and a substantial simplification and minimization of hands-on time in the overall workflow, due to a reduction of the amount of manual modifications of both size standards and allele assignments needed. We provide specific and detailed instructions on how to setup the software and how to perform the analysis using BioNumerics as supplemental material. Our study supplements the manufacturer's manuals, enabling implementation of a simpler and faster workflow in many laboratories currently employing high throughput MIRU-VNTR typing using GeneMapper. It is appealing to use BioNumerics not only for the more streamlined primary analysis, but also for the fact that the subsequent data handling can be done in the same software.
Thus, data can be processed by creating phylogenetic trees, maps etc. , but also by connecting genotyping data to metadata such as clinical, epidemiological and whole genome sequencing data, if available. This final level of analysis is key for the epidemiological understanding of TB transmission, e. g. for delineating an outbreak or the extent of ongoing transmission in a population  [13]. The analysis in BioNumerics could potentially be improved further. The main reasons for the remaining automatic misassignments of alleles were, when artefact peaks were taller than the true peak and thus falsely assigned by the software. Most frequent artefacts are "pull-up peaks" (or "bleed-through bands"), reflecting interference from other, strongly amplified fragment labeled in another colour or "stutter peaks", caused by slippage of the DNA polymerase during PCR (S1 File.
Setup of MIRU-VNTR analysis in BioNumerics). A setting in the software may be enabled to automatically detect "pull-up peaks"-however, this function did not appear to work satisfactorily in our study. Also, for larger fragments, the peaks were sometimes positioned just outside the predefined bin causing the peak to miss an assignment. However, this may be handled by manually increasing the size of the defined bins, when initially setting up the software. The study has some limitations. We did not measure the actual time spent on the analysis in each software. However, the number of necessary manual corrections was much smaller when using BioNumerics than in GeneMapper. Nevertheless, the benefits of simplification and minimization of hands-on time were clear. Also, a cost-benefit analysis was beyond the scope of this study.
2. 1). To reduce the dominance of high fluorescence intensity signals, the EEMs were first scaled to a unit of variance within the sample mode to construct the calibration model  (Bro, 1997). PARAFAC models from 2 to 7 components with constraints of non-negativity in all modes were successively conducted with MATLAB (version 2008b; MathWorks 2008) using DOM Fluorescence Toolbox (DOM Fluor version 1. 6) and validated as described by  Stedmon and Bro (2008). The parameters obtained from the PARAFAC model were used to calculate an approximate abundance of each component, expressed as F max in Raman's unit (R. U. ), which corresponds to the maximum fluorescence intensity for a particular sample. Based on analysis of triplicate samples from Sta. M01, M08, and M10, the uncertainty of F max for each modeled component was <2%.
Relationships between quantitative DOM variables and salinity Surface and bottom data for each variable in each season form a consistent property-salinity pattern (data not shown) and are thus treated as a single dataset. All variables displayed large variations and/or sharp decreases at salinity <5 but remained rather constant ([DOC] in May and November) or declined linearly (all other cases) at salinity >5 (Figs. 3  and 4 ). Results of linear regressions for the saltier zone are summarized in Table  4. At a 95% confidence level, both the slopes (SLP) and intercepts are statistically no different between August and January for  [DOC]  and a 330 and between all three seasons for C3 and C2+C4, indicating that the multi-season data on each of these occasions can be combined into a single dataset.
SLP a330/Nov is, however, ≥32% lower than those in August and January. SLP C1+C5 presents significant seasonal variations, with the value in January 23% and 89% higher than those in November and August, respectively. The percent decrease of each variable per unit increase of salinity across the saltier zone was calculated using the known regression equations shown in Table  4. a 330 decreased 2. 1 and 2. 7 times faster than  [DOC]  in August and January, respectively (Table  S3 ). [FDOM] declined faster than [CDOM] but their difference was much smaller than that between [CDOM] and  [DOC]. The percent decreases in the FDOM components were 5-35% higher than those in a 330 , with November showing the largest difference (25-35%) followed by August (5-21%) and January (<10%) (Table  S3 ). As for the quantitative variables, the surface and bottom data of the qualitative metrics can also be treated as a single dataset in relation to salinity (data not shown).
Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-403 Manuscript under review for journal Biogeosciences Discussion started: 8 October 2018 c Author(s) 2018. CC BY 4. 0 License. modeled the different FDOM profiles in the dataset. Based on published spectral characteristics of PARAFAC-modeled components (e. g. Stedmon et al. , 2003; Cory and McKnight, 2005; Yamashita and Jaffé, 2008; Murphy et al. , 2008; Santín et al. , 2009; Massicotte and Frenette, 2011) , components 1 (C1) and 5 (C5) as tyrosine-like and tryptophan-like fluorophores, components 2 (C2) and 4 (C4) were assigned as terrestrial or ubiquitous humic-like DOM fractions, and component 3 (C3) as oxidized quinone-like moieties, respectively. As C1 and C2 are highly correlated with C5 and C4, respectively (Table.
Acknowledgments. We are grateful to the captain and crews of the cruises for their corporation and to  Z. Shi ,  M. Chen ,  Q. Sun , and  L. Han  for their help during sampling. Editor's comments improved the manuscript. This study was supported by grants from  National Natural Science Foundation of China  ( 41606098  and  41376081 ) and  Tianjin Natural Science Foundation  ( 16JCQNJC08000 ). HX was holding an adjunct professorship at  Tianjin University of Science  &Technology during this work.
Disulfide Cross-Linking Causes an Increase in Bending Stiffness of Tpm Models used for MD simulations are shown in Figure  1. A crystal structure of the middle part of Tpm  [26]  (PDB code 2B9C) with a disulfide bond between two Cys190 residues was used as the starting model of cross-linked Tpm. Models of uncross-linked Tpm and the C190A Tpm mutant were prepared from the 2B9C structure with UCSF CHIMERA package  [27]. Details of MD simulation and methods used for analysis of the MD trajectories are described in Section 4. Models used for MD simulations are shown in Figure  1. A crystal structure of the middle part of Tpm  [26]  (PDB code 2B9C) with a disulfide bond between two Cys190 residues was used as the starting model of cross-linked Tpm. Models of uncross-linked Tpm and the C190A Tpm mutant were prepared from the 2B9C structure with UCSF CHIMERA package  [27].