dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, a significant divergence from the structure of PDB entry 3pla is observed in the substrate-unbound half of the complexes. Importantly, neither the [on,off]-nor the [on,on]-ensemble are able to reproduce the combination of PRE and SAS data satisfactorily for each of the substrate D-or the substrate D'-loaded RNPs. The PRE intensity-ratios measured for the Nop5-NTD-E65C mutant on the methyl-groups of L7Ae indicate the presence of conformers in the [on,off]-state. In agreement with this, the [on,off]-structures of Figure  3  reproduce the PRE data reasonably well both for the substrate D-and substrate D'loaded complexes (Figure  3 -figure supplements 3 and 4). However, these structures are unable to fit the 2 H-Fib SANS, 2 H-Fib/ 2 H-RNA SANS and SAXS curves in a satisfactory manner (Figure  3 figure supplement 5).
Accordingly, the combination of PRE and SAS data could not be fit with an ensemble consisting of [on,off]-conformers only, as the c 2 value of the SAXS curve remained as poor as that obtained with a single [on,off]-conformer (>250) (Figure  4 -figure supplement 1). Conversely, ensembles containing only [off,off]-conformers failed to reproduce the PRE dataset of the complex containing spin-labelled Nop5-E65C (Figure  5 -figure  supplement 1b ). When we fitted the PRE and SAS data with ensembles consisting of both [on,off]-and [off,off]conformers, we could reproduce all experimental data satisfactorily with a population of [on,off]-conformers of 34 ± 10% (Figures  4  and 5 ).
To calculate the structural models of the substrate D-and substrate D'-loaded RNPs we developed a novel hybrid structure-calculation protocol that fits a combination of NMR and SAS data to an ensemble of conformations. The application of integrative structural biology approaches is particularly relevant to the detection of inter-domain dynamics of RNP complexes, as the different types of structural data are sensitive to conformational changes in different ways. In this case, the combination of NMR PRE data and SAS data was essential for revealing the equilibrium between RNA-bound and RNA-unbound fibrillarin states. The computational workflow developed here allows interpretation of hybrid structural data in terms of structural ensembles, rather than as individual conformations. The protocol proceeds in a step-wise fashion, where the structural ensemble becomes progressively well-defined while increasing the demand on the quality of the fit between predicted and experimental data.
Small-angle X-ray scattering (SAXS) Box C/D sRNPs reconstituted in 50 mM NaPi pH 6. 6, 500 mM NaCl were recorded at 40˚C and concentrations varying from 0. 4 to 5 mg/ml, unless otherwise specified. In most experiments a temperature of 40˚C instead of 55˚C was used for SAXS measurements due to the difficulty in collecting data with high salt concentrations at the higher temperature. For all measurements, 2 mM dithiothreitol (DTT) was added to mitigate radiation damage. Data collection was performed at the ESRF bioSAXS beamline BM29 with exposure of 10 frames each of 1 s duration. The curves were compared, merged, and the buffer contribution subtracted by the beamline software BsxCube  (Pernot et al. , 2013). Forward scattering intensity I(0) values were normalized relative to an ideal protein in an ideal solution, and were reported as  288, 194, 215 and 197  for the apo st-sR26 RNP, the substrate D'bound st-sR26 RNP, the substrate D-bound st-sR26 RNP and the holo st-sR26 RNP, respectively, all at 5 mg/ml.
Small-angle neutron scattering (SANS) 2 H-L7Ae, 2 H-Nop5, 2 H-fibrillarin, 2 H-RNA, 2 H-fibrillarin/ 2 H-RNA and 2 H(70%)-Nop5/ 2 H-RNA samples were measured in 50 mM NaPi pH 6. 6, 500 mM NaCl, 42%:58% D 2 O:H 2 O solutions, in order to mask the contribution of the 1 H-proteins. The curves corresponding to 2 H-L7Ae, 2 H-Nop5, 2 H-RNA and 2 H(70%)-Nop5/ 2 H-RNA were acquired at D22 at the Institute Laue Langevin (ILL, Grenoble, France), with a neutron wavelength of 6 A ˚. The 2 H-fibrillarin and 2 H-fibrillarin/ 2 H-RNA curves were acquired at KWS-1 at JCNS (Munich, Germany)  (Feoktystov et al. , 2015)  with a neutron wavelength of 5 A ˚. Both instruments were configured with sample-detector distances of 4 m and collimation lengths of 4 m. Data reduction and radial integration were done with standard procedures using beamline-specific software.
Structure calculation and selection Structures were calculated using an adapted version of the protocol described in  Lapinaite et al. (2013) ; Nilges, 1995 according to the workflow described in Figure  3 -figure supplement 1. The starting st-sR26 RNA structures, bound to either substrate D or substrate D', were generated in separate calculation runs using restraints to impose an A-form helical geometry on the substrate-guide duplex, and to yield the appropriate K-turn structures. Starting protein conformations were generated from the PDB entry 3nmu and assembled into two L7Ae-Nop5-Fib protomers, in which the L7Ae-Nop5-CTD and Nop5-NTD-Fib interaction interfaces of 3nmu were preserved, but not the relative orientation of the Nop5-NTD and CTD, which were randomised. The two copies of the protomers within the sRNP were separated and randomly rotated with respect to each other.
The SAS curves including the contribution from fibrillarin were left out, because we expected the position of fibrillarin to be variable when it is not in contact with the RNA. SAS fitness was calculated with the programs CRYSOL and CRYSON, from the ATSAS suite, version 2. 7. 5  (Svergun et al. , 1998). Based on the distribution of fitness for all structures in each of the runs, we set loose cut-offs, which excluded only structures beyond the smooth, linearly increasing portion of the distribution curve. For the substrate D'-loaded complex, we selected structures within the top 90% The average pair-wise RMSD of the structures of each ensemble, calculated over the Ca and P atoms of the protein and RNA structured domains, including the fibrillarin units not bound to the RNA, was below 5 and 7 A ˚for the [on,off] and [off,off] conformers, respectively, with a maximum RMSD value of less than 10 A ˚in all cases.
Among the selected structures of each of the four runs ([on,off]-and [off,off]-states of both substrate D-and substrate D'-loaded sRNPs), the one with the lowest restraint-violation energy that maintained the correct RNA topology was chosen as the starting point for refinement in Cartesian space. The four refinement runs comprised 1500 structures each spanning up to 10 A ˚RMSD of Ca and P atoms relative to the starting structure (number calculated for the substrate D'-loaded [on, off]-state). At the end of the refinement, we applied stringent selection criteria with respect to the SAS curves and loose criteria with respect to the energy. The cut-offs for the SAS data were set upon visual inspection of the c 2 distributions for each run and curve, whereby we allowed more structures to be selected when the c 2 distribution was flat.
The process of parent selection, sub-sampling and scoring was repeated 250 times. The theoretical scattering curve of the ensemble was computed as the linear combination of the scattering curves of each individual conformer (scaling the populations to represent molar fractions rather than volume fractions, which is the standard ATSAS output). The c 2 value with respect to the experimental data was calculated by OLIGOMER  (Konarev et al. , 2003). The normalization of c 2 of all sub-sampled ensembles and across iterations was done according to Equation 6  (Karaca et al. , 2017) : 2 norm ¼ 2 ensemble À 2 min 2 max À 2 min (6) where c 2 ensemble is the fitness of an individual ensemble, and c 2 min and c 2 max are the respective minimum and maximum values across the iterations or sub-ensembles being considered.
The PRE fitness was quantified using c 2 to all experimental PRE values using Equation (4). Distances were computed from the PDB files using the Biopython Bio. PDB module  (Cock et al. , 2009). The fitness of PRE data was normalized using Equation (  6 ) and summed with the SAS-fitness score, to yield a consensus PRE-SAS score for each ensemble within the 20 sub-sampling events, and across the 250 iterations. Three independent runs of the scoring algorithm were performed for substrate D'-and substrate D-loaded sRNPs, with the top scoring ensemble, judged by the consensus PRE-SAS score, displayed in Figure  4. After this selection, the conformations of each individual tag were refined by generating additional 3000 conformers per tag and by using the same pseudo-genetic algorithm to select the ensembles of tag conformations that best fitted each individual PRE dataset.
Acknowledgements The authors thank  Dr. Artem Feoktystov  ( MLZ Munich ) for assistance with recording and processing SANS data at KWS-1;  Dr. Roman Lichtenecker  ( University of Vienna ), for kindly providing the leucine methyl labelling precursor sodium [ 3-2 H 2  , 4-2 H, 5-13 C, 5'-2 H3  ]-a-ketoiso-caproate;  Dr. Pawel Masiewicz  ( EMBL Heidelberg ) and  Susanne Zur Lage  ( HZI Braunschweig ) for RNA production and  Dr. Bernd Simon  ( EMBL Heidelberg ) for assistance with structure calculations.
Data availability All data underlying the figures, including relevant structures, is available in Dryad with DOI https:// doi. org/10. 5061/dryad. q573n5tdv. All SAS curves have been deposited to the SASBDB under accession codes SASDGV2, SASDGW2, SASDGX2, SASDGY2, SASDGZ2, SASDG23, SASDG33, SASDG43, SASDG53, SASDG63, SASDG73, SASDG83, SASDG93, SASDGA3 (project accession code 860). The following datasets were generated:.
Laboratory Exercise Examples The laboratory exercises aim to introduce university students to the world of real-time simulation and its various applications in the power system domain. Several modules have been created which start from an introduction to real-time simulation, leading to more advanced modelling and interfacing techniques, using a hands-on approach. At first, the students become familiar with real-time simulation software (RSCAD of RTDS) by executing several examples. Interfacing with other software tools is introduced (e. g. Matlab and Python), highlighting the co-simulation possibilities. The possibility of developing custom component models and the small time-step modelling of power electronic devices are explained. Next, the use of communication protocols (e. g. IEC 61850) is highlighted, along with interfacing techniques with hardware equipment.
Microgrid Balancing In order to facilitate the understanding of concepts such as multi-domain experiments, interoperability of control devices and real-time simulation of physical components, the Microgrid Balancing remote lab application was designed and implemented. The basic idea of this experiment is that a user can remotely connect to the experimental microgrid and interact with the SCADA system in order to achieve a power balancing operation based on specific market policy. In the setup the battery, load, and grid connection are all physical components, whereas the PV unit is simulated (in MATLAB/Simulink) in order to introduce the analytical mathematical models of the PV system (e. g. calculation of I-V characteristic, Maximum Power Point, injected AC power). The input signals to the PV model are global horizontal irradiance and ambient temperature from real-time measurements.
Interactive (Jupyter) Notebooks A main issue with smart grid validation, especially for large-scale systems, is the complexity of the resulting simulation. When instructing in these complex simulations, a large portion of time is spent trying to couple the overall learning of the activity into the code that students must write or engage with. As the complexity of the code base inevitably increases, students who are not used to dealing with this aspect may feel lost in the aspects of the code itself. In the worst case, this may cause the student to miss the high-level learning involved. One way to address this issue is to apply interactive notebooks in instruction. Jupyter notebooks are a merge between a standard text book and what real programming in python looks like  [2]. The notebooks can be built from explanatory text and figures, while a full Python kernel allows the student to execute python code.
As an example of the application of this tool, a pair of notebooks were given to the students for hands-on experience with a co-simulation environment and Design of Experiments  [17]. The example used consisted of a typical Home Energy Management System in which a scenario is built up step by step, to include a house with solar panels, battery and a controller that can connect the house to the grid or discharge the battery. All the components and interconnections were modelled in behind-the-scenes python scripts and the simulation ran using the python module mosaik. An example of the final notebook can be seen in Fig. 5.
Results Overall approach. In order to dissect the molecular relationships between non-invasive dysplastic lesions and invasive pancreatic cancers, we performed whole exome sequencing of 39 neoplastic tissue samples from 18 patients with small invasive carcinomas (<2. 5 cm) associated with neoplastic pancreatic cysts, including 16 patients with IPMNs and 2 patients with MCNs (Supplementary Data 1). Whole exome sequencing was performed on one sample from the noninvasive component with high-grade dysplasia and one sample from the invasive cancer in each case, and for three cases an additional noninvasive sample with lowgrade dysplasia was also analyzed by whole exome sequencing. Matched normal samples were analyzed by whole exome sequencing in each case to exclude germline variants and to identify somatic mutations.
Whole exome sequencing was performed with an average total coverage of 177× (distinct coverage of 145×), generating 1. 3 TB of sequencing data (Supplementary Data 2). In addition to whole exome analyses, we performed targeted next generation sequencing of 109 microdissected tissue samples from seven of the above cases (six IPMNs and one MCN). For these targeted analyses, we performed laser capture microdissection to isolate neoplastic cells from every available tissue block of the noninvasive cyst and cancer specimens. Separate samples were microdissected based on grade of dysplasia, cell morphology, architecture, and spatial location. This resulted in 8-22 additional samples per case. The targeted panel analyses included all mutated loci identified in the whole exome sequencing of these seven cases, as well as the entire coding regions of 15 wellcharacterized pancreatic driver genes (Supplementary Data 3).
4 , Supplementary Fig. S5 ). As our study represents the largest cohort of comprehensively sequenced IPMNs/MCNs, we also analyzed mutational signatures in our dataset. Intriguingly, our data contrast somewhat with the mutational signatures previously reported in pancreatic ductal adenocarcinoma (PDAC)  16, 17. Like PDAC, the most prominent mutational signature was associated with age (Signature 1A), which was identified in almost every case (Supplementary Fig. S19 ). However, we also identified signatures associated with APOBEC enzymes (four cases), smoking (three cases), and mismatch repair deficiency (11 cases). Although smoking is considered a risk factor for pancreatic cancer, until now the mutational signature associated with smoking has not been reported in pancreatic neoplasia  18. Evolutionary timeline of high-grade IPMN to PDAC.
Discussion This study represents the largest dataset of whole exome sequencing of IPMNs and MCNs to date. Importantly, our data established that both IPMNs and MCNs are direct precursors of invasive pancreatic cancer (Fig. 4 ). This conclusion has been previously suggested by the morphological relationship between the noninvasive neoplasms and invasive cancer on traditional histologic sections, as well as shared driver gene mutations in targeted sequencing studies  6, 7, [9] [10] [11]. However, the presence of many shared driver and passenger mutations clearly demonstrated the common origin of IPMNs/MCNs and invasive pancreatic cancers in our study, and evolutionary analyses revealed that dysplastic lesions precede invasive cancers. Evolutionary analyses suggested that high-grade noninvasive lesions occur over 3 years before invasive carcinoma, providing a window of opportunity for early detection and intervention.
Briefly, genomic DNA was fragmented, followed by end-repair, A-tailing, adapter ligation, and polymerase chain reaction. PCR products were purified, and exonic regions were captured in solution using the Agilent Sure Select kit according to the manufacturer's instructions (Agilent). Pair-end sequencing was performed on a Hi-Seq2500 next-generation sequencing instrument (Illumina). Primary processing of sequence data was performed using Illumina CASAVA software (v1. 8). Two cases (MTP15 and MTP20) failed quality control during sequencing and were excluded from further analysis. Fastq-files were further processed according to the GATK best practices workflow: BWA (v0. 7. 17) for alignment to hg19, picard (v2. 18. 1) for duplicate reads flagging and GATK (v3. 7) for base quality score recalibration.
Acknowledgements The authors acknowledge the following sources of support:  NIH/NCI   P50 CA62924 ;  NIH/NIDDK K08 DK107781 ;  NIH/NCI   R01 CA121113 ;  NIH/NCI   R00 CA190889 ;  NIH/ NCI   P30 CA006973 ;  Sol Goldman Pancreatic Cancer Research Center; Buffone Family Gastrointestinal Cancer Research Fund ; Carol S. and  Robert M. Long Pancreatic Cancer Research Fund ;  Kaya Tuncer Career Development Award in Gastrointestinal Cancer Prevention; AGA-Bernard Lee Schwartz Foundation Research Scholar Award in Pancreatic Cancer ;  Sidney Kimmel Foundation for Cancer Research   Kimmel Scholar Award; AACR-Incyte Corporation Career Development Award for Pancreatic Cancer Research ;  American Cancer Society   Research Scholar Grant ;  Emerson Collective Cancer Research Fund; Rolfe Pancreatic Cancer Foundation ;  Joseph C Monastra Foundation ; The  Gerald O Mann Charitable Foundation (Harriet and Allan Wulfstat, Trustees) ;  Susan Wojcicki  and  Denis Troper ;  Dr.
II. DATA ANALYSIS All results presented in this paper were obtained with the PHENIX spectrometer from data collected in the Year-2012 data taking period at RHIC. A detailed description of the PHENIX experimental set-up can be found elsewhere  [27]. Event selection is performed with two beam-beam counters (BBCs)  [28]  located towards the north and south beam directions in the 3. 0 < |η| < 3. 9 pseudorapidity interval. The collision vertex coordinate along the beam direction (z BBC ) is determined by the time difference between two hits in the north and south BBCs with an accuracy of 0. 6-2 cm (depending on the particle multiplicity). The analyzed data set was taken with the minimum-bias (MB) trigger, which required a north-south coincidence and an online vertex position within ±30 cm. After offline reconstruction an additional cut of |z BBC | < 20 cm was applied; the remaining data set comprises 9.
Data Extraction We extracted data related to study and iCBT service-related characteristics, acceptability, effects on symptom change, negative effects, and data related to the risk of bias of reported results. Study characteristics included the year of publication, the country in which the study was conducted, the year of data collection, sample size, eligibility criteria (establishment of depression and/or anxiety diagnosis at baseline [standardized clinical interview, cutoff on standardized questionnaire, and clinical judgment], inclusion of severe cases [yes/no], and exclusion of cases with suicidal ideation [yes/no], and approach to data analysis [ITT/completer]). iCBT service-related characteristics included intervention name, the symptoms targeted (depression and/or anxiety), if it was a blended treatment (yes/no), evidence base for the used intervention (positive results based on at least one randomized clinical trial [yes/no]), and whether it was a symptom-specific or transdiagnostic treatment.
Statistical Analysis Our primary outcome was the reduction of depressive or anxiety symptoms from pre-to posttest assessment. We calculated the difference in depression and anxiety symptoms between pre-post assessment divided by the weighted, pooled standard deviation (Hedges' g). We have chosen Hedges' g because it allows for small sample size bias correction  [37]. As we expected considerable heterogeneity among the studies, we used the random effects model. As a rule of thumb, effect sizes of 0. 8 can be viewed as large, 0. 5 as moderate, and 0. 2 as small  [38]. In our main analysis, we included mixed depression and/or anxiety studies into the separate depression and anxiety data sets. Statistical analysis was conducted using the Comprehensive Meta-analysis program (version 2. 2. 2), and pooled proportions were calculated with R  [39]  package meta  [40].
The examined subgroups were related to the method of analysis, time to post assessment, recruitment pathways, disorders, guidance moment (specific timing or as a reaction), guidance modality (email, message, and synchronous), guide profession (with or without specific CBT training), supervision provided (yes/no), guide training provided (yes/no), intervention manual provided (yes/no), approach to data analysis (ITT/completer), and diagnostic method (interview/questionnaire). Subgroup analyses were only carried out with regard to the effects on symptom change. We used the mixed effects model, testing pooled studies within subgroups with random effects models while testing for significant differences between those subgroups with fixed effects models. We only conducted subgroup analysis if the number of studies per category was not less than three.
Risk of Bias of the Included Studies The quality of the included studies varied. Of the studies, 67. 0% (k=20/30) were rated with a high risk of bias on Researcher Allegiance. Of the studies, 63. 0% (k=19/30) did not exclude patients who were participating in other psychotherapeutic treatments (Treatment Inclusion Confounding), and none of the studies reported on the adjustment for confounders in the data analysis. Intention-to-treat data could be extracted from 73. 3% of the studies (k=22/30), and none of the studies were preceded by a published study protocol. The risk of bias assessment is depicted in Figure  2.
Participant Satisfaction Of the 17 studies, 10 (58. 8%) reported participants' satisfaction. Participant satisfaction outcomes were reported inconsistently, using varying measures and different reporting forms. Therefore, these data could not be pooled, but the detailed results and the data extracted on patient satisfaction are depicted in Multimedia Appendix 7  [81, [121] [122] [123]. Within the studies reporting participants' satisfaction, five studies reported a high and four a very high participants' satisfaction.
, 2007, SOP 6a). Salinity and temperature were measured in situ using a handheld conductivity meter (Hanna Instruments HI98192). 105 Furthermore, the salinity was also measured in the laboratory using a high-precision Portasal salinometer, accurate to ±0. 001. The pH under "in situ" conditions, the partial pressure of carbon dioxide (pCO2) and the saturation states of calcium carbonate forms (Ω aragonite and Ω calcite) were determined from AT and CT data using with the CO2sys program  (Pierrot et al. , 2006). We used the linear interpolation method to represents the AT, CT, pHT,is, Ω aragonite and calcite parameters measurement when anomalies were found.