dataesr/scientific-paragraphs-categorization huggingface dataset:
The primers were designed in the 3 0 untranslated region if possible and span an exon-exon junction if possible (sequences of the primers and fragments in Supplementary Data 15). Specificity was tested by virtual PCR (FastPCR; primerdigital. com/fastpcr. html) against the genome and by sequencing the amplified fragment. PCR was performed with the power SYBR green PCR master mix (Applied Biosystems) in a reaction volume of 15 ml containing 4-7 ng of cDNA. Cycling was carried out with the following parameters: 10 min activation of AmpliTaq Gold Polymerase at 95 °C, 40 cycles of 15 s denaturation at 95 °C, 60 s annealing and extension at 60 °C, followed by a dissociation step of 15 s at 95 °C, 60 sec at 60 °C, and 30 s at 95 °C to detect primer dimers and nonspecific amplification products. For each primer pair, we determined the PCR efficiency and the dynamic range of PCR by plotting the threshold cycle (C t ) values generated over a range of dilutions against the log input of cDNA amount from diverse samples.
Primer efficiencies were also calculated in each qPCR reaction for standard curves. To obtain accurate results, only primer pairs yielding PCR efficiencies of 90-110% (slope of regression between À 3. 2 and À 3. 5) were considered. For several samples, reproducibility was checked by diluting cDNA and performing PCR in triplicate within the same plate and in different qPCR reactions and was confirmed to be high. To quantify the transcripts, cDNAs of at least three independent replicates (for example, mycorrhizas of plants grown in different pots, mycelia from different Petri dishes) were diluted and used in qPCR reactions. Copy numbers of the transcripts were calculated from standard curves that were obtained using synthesized single-stranded sense olignonucleotides (Sigma-Aldrich, Switzerland; Supplementary Data 15) specifying amplicons of the selected genes and using the information from the manufacturer to calculate the copy number of the oligonucleotides present in respective volumes.
The reads were assembled and cleaned from contaminations (bacteria, rRNA) resulting in 34,304 and 9,622 contigs and singletons, respectively, which were used for the array design. They were assigned to fungal or plant sequences on the basis of BLASTN and BLASTX scores obtained from searches against NCBI databases. The arrays contained three independant, nonidentical 60mer probes per sequence. For 700 randomly selected probes, technical replicates were present on the array. Single-dye labelling of samples, the hybridization procedures and data acquisition were all performed at the NimbleGen facilities in Reykjavik, Iceland, following their standard protocol. Three and two biological replicates were performed for each condition of ECM and NM, respectively. Microarray probe intensities with fungal or plant assignment were separately quantile normalized across all chips.
Average expression levels were calculated for each gene from the independent probes on the array and were used for further analysis. A transcript was deemed expressed when its signal intensity was threefold higher than the mean signal-to-noise threshold (cut-off value) of randomly designed oligonucleotide probes present on the array (50 to 100 arbitrary units). The maximum signal intensity values were B65,000 arbitrary units. Normalized data were subjected to the Cyber-T statistical framework  28. We used a Bayesian confidence estimate value of 6 and a sliding window of 101 genes for the standard deviation estimation. Transcripts with a significant P value o0. 05 and a PPDE40. 97 were considered as significantly differentially expressed. Differentially expressed transcripts were annotated in more detail by blasting them against diverse databases such as Swissprot, KEGG, KOG and PFAM as well as by using the MERCATOR tool  66  for the pine transcripts differentially regulated between ECM fungi.
Acknowledgements We thank  Dr David Hibbett  and our colleagues from the  Mycorrhizal Genomics Initiative consortium  for helpful discussions. We thank WSL-staff  Raphael Appenzeller ,  Rosmarie Eppenberger  and  Sylvia Hutter  for technical assistance and  Terence Menard  for microscopic cuttings. We are indebted to  Dr Annette Peter  and  Annette Hintelmann  ( University of Bremen ) for providing Xenopus oocytes and technical support,  Nicolas Cichocki  and  Maı ´ra de Freitas Pereira  ( INRA-Nancy ) for support in RNA extractions and microscopic pictures. This material is based on work conducted by the  US Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , under contract no. DE-AC02-05CH11231. It was supported by the  Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ), the  ARBRE-WSL  joint project  BLACK-SECRET , the  Genomic Science Program  ( Plant-Microbe Interactions  project) funded by the  US Department of Energy, Office of Science, Biological and Environmental Research  (contract  DE-AC05-00OR22725 ), and the  Lorraine Region Council  (to F.
Data availability. Genome assemblies and annotations for the organisms used in this study are available via the JGI fungal genome portal MycoCosm 14 (http:// jgi. doe. gov/fungi). In addition, the newly sequenced genome assemblies and annotations have been deposited to GenBank under the following accessions codes/BioProjects: Cenococcum geophilum: LKKR00000000/PRJNA196023; Glonium stellatum: LKAO00000000/PRJNA295956; Lepidopterella palustris: LKAR00000000/PRJNA196014. Amino acid alignment and phylogenetic tree files have been deposited in the TreeBase repository under accession number S19566. The complete transcriptome data sets are available at the Gene Expression Omnibus at the National Center for Biotechnology Information (http:// www. ncbi. nlm. nih. gov/geo/). The microarray derived data sets as series GSE80549, the RNA-Seq derived data as GSE83909.
The interface, both linear and nonlinear, couples software modules from academia to the device and electromagnetic field simulator from MAGWEL, offering flexibility in adapting modules and allowing for different time integration procedures. In this way, coupling of electronic circuits with electromagnetics and with semiconductor material is achieved. It also allows for state-space formulations of subparts to which Model Order Reduction can be applied. The interface treats the space discretization in the field simulator and generates a system of Differential-Algebraic Equations (DAEs). Especially, when including semiconductor material, large differences of magnitude made careful scaling during the assembly essential to guarantee that the overall system was stable [, ].
The evaluation of this formula is computationally inexpensive such that time-consuming D simulation can be avoided. However, a coupling of the bondwire model to a D simulator is necessary if the integration in the overall system behaviour should be simulated. Thus, TUD also implemented a nonlinear in-house simulation code based on the Finite Integration Technique (FIT) to analyze the coupling before transferring the concept to partner MAG  []. ONN aims to use the algorithm within a GUI (Graphical User Interface). T w (y, t) ∼ = T o + α κ θ w (y, t) +  α κ -  α κ. (  ) ONN and BUT made measurements of DC and dynamical fusing of bondwires  []. ONN fabricated test chips (SOIC package so far), where the individual bondwires with different lengths, diameters and materials have been encapsulated.
BUT prepared a complete methodology and experimental setup to do such investigations. The setup (hardware tester and MATLAB GUI) allows measurements of all six bondwires in one IC package. The tester consists of the  independent channels, the  driving stages, the  Kelvin probe sensing stages, the demultiplexing core for the driving stages (specification of address of the bondwire) and the multiplexing core for the Kelvin probe sensing stages. The block structure, the single channel operation and photographs of the final hardware solution are shown in Figure  , Figure   and Figure . The frequency limitation of the measurement setup is about  kHz (limits of the used MOS power-switches and PCI card). The software part of the project consists of two executable MATLAB scripts with a GUI.
Structure preserving models with sufficient accuracy are obtained for nonlinear parametric coupled problems as well. We refer to Figure  for the results of the ROM. The relative error of the output produced by the ROM is of the order  -. Furthermore, we have constructed a ROM for a Powercell electro-thermal model with size n = ,, and m =  inputs, l =  outputs. Simulation of such a large-scale system with numerous terminals is a hard task for commercial simulation software, e. g. , Spectre. The simulation immediately breaks down due to out of memory. We proposed  an efficient method: BDSM-ET [], which uses the superposition principle to construct a ROM with block-wise sparsity. The size of the ROM is r = , and thus almost two orders of magnitude smaller than the Full Order Model (FOM).
Uncertainty quantification UGW, BUW, MAG, TUD worked on methods for Uncertainty Quantification and applied the approaches to address variations of material properties as well as in the geometry  []. Apart from in-house software of UGW and BUW, interfacing with libraries from Sandia National Laboratories [] was achieved. In order to demonstrate robustness of an   optimization algorithm under uncertainties, a Power Transistor Model was considered as a test example. We reduced the thermal instability by optimizing the geometry within the device layout, while taking both the conductive power losses and shape variations of source/drain into account. In [] we focused on a shape/topology optimization problem of a power MOS device with three metal layers under geometrical and material uncertainties to reduce the current density overshoot.
A clever software solution was developed and is reported in  []. By this, also the faulty problems could benefit from an enhanced form of hierarchical bypassing. Because each candidate fault is a low-rank modification of the designed circuit an hierarchical variant of the Sherman-Morrison formula was exploited. Fast fault simulation is achieved in which the golden solution and all faulty solutions are calculated over the same time step. The results are stored in a database. This database is of help to first externally diagnose a faulty IC and to identify the candidate circuit submodels where the fault may have happened. After that the IC can be studied further internally. This can help to improve next productions. Moreover, the collection of simulations can also be helpful as a priori check before layouting.
ACC has prepared several designs, made simulations and realized test boards. They served for step by step study, for comparison to measurements and for validation of the enhanced MAGWEL software, in close cooperation with BUT, NXP and ONN. As simple example, we mention here test chips that include passive structures (inductances, capacitances, baluns, resonators). These structures, that are easily measurable, are used to validate further EM extraction and model reduction. Simulated results are compared to measurements, EM solver extraction and then with extraction plus netlist reduction in terms of accuracy, memory usage and time simulation.
Conclusion The unique combination of the nanoCOPS consortium allows to already report the following intermediate, innovative highlights halfway the project, to which all partners have contributed. • The coupling interface with the MAGWEL software has been improved, tested and is operational. • Successful large-scale EM-heat simulation was achieved. • Grid-adaptive multirate circuit simulation was established. • Model Order Reduction was successfully applied to coupled EM-heat problems. • Accurate bond wire modelling for fast usage at industry was demonstrated and was validated by measurements. • Uncertainty Quantification was applied to variations of material parameters and geometry and was used in robust topology optimization. Apart from the topics, this at best demonstrates the robustness of the integrated software -to achieve optimization one addresses all parts of the codes.
Acknowledgements This FP7 Collaborative Project nanoCOPS is supported by the  European Union  in the  FP7-ICT-2013-11 Programme  under Grant Agreement Number  619166  (Project nanoCOPS -nanoelectronic COupled Problems Solutions). For further details see http://www. fp7-nanocops. eu/. Received: 10 February 2016 Accepted: 20 June 2016.
The determination of enantiomeric excesses was carried out on a UPLC Acquity Waters at 40 °C with a UV PDA detector (Waters, Milford, Massachusetts, United States) and a cyclobond I2000HP RSP15cm/4. 6 5 um column with a mixture of H2O/CH3CN = 60/40. TEM images of particles in the solid state and dispersed in glycerol were obtained from transmission electron microscopes JEOL JEM (JEOLn Akishima, Tokyo, Japan) 1400 running at 120 kV and HR-TEM from JEOL JEM 2100F running at 200 kV equipped with X PGT (detection of light elements, resolution 135 eV). The nanoparticles size, distribution and average diameter were determined from TEM images with Image-J software (Image processing, Public domain, https://imagej. nih. gov/ij/) associated to a Microsoft Excel macro developed by Christian Pradel. IR spectra were recorded in the range of 4000-400 cm -1 on a Varian 640-IR FTIR Spectrometer (Varian, Salt Lake City, United States).
The determination of enantiomeric excesses was carried out on a UPLC Acquity Waters at 40 • C with a UV PDA detector (Waters, Milford, Massachusetts, United States) and a cyclobond I2000HP RSP15cm/4. 6 5 um column with a mixture of H 2 O/CH 3 CN = 60/40. TEM images of particles in the solid state and dispersed in glycerol were obtained from transmission electron microscopes JEOL JEM (JEOLn Akishima, Tokyo, Japan) 1400 running at 120 kV and HR-TEM from JEOL JEM 2100F running at 200 kV equipped with X PGT (detection of light elements, resolution 135 eV). The nanoparticles size, distribution and average diameter were determined from TEM images with Image-J software (Image processing, Public domain, https://imagej. nih. gov/ij/) associated to a Microsoft Excel macro developed by Christian Pradel.
Introduction The extensive use of magnetic resonance imaging (MRI) to investigate pathology in the brain entails identification of specific regions of interest (ROI) for quantitative analysis. Accurate manual tracing of deep brain structures, such as the thalamus and hippocampus, demands a high level of tracer expertise and preferably standardized segmentation protocols. Introducing automatic or semi-automatic techniques into postprocessing pipelines accelerate data analysis and offers reproducible and consistent decisions across datasets in large studies, which is crucial for obtaining reliable results  [1]. Several software solutions for automatic segmentation are publicly available. Frequently used software in clinical research include "Oxford Centre for Functional MRI of the Brain" (FMRIB) Software Library (FSL) (http://fsl.
fmrib. ox. ac. uk/fsl/fslwiki/), FreeSurfer (http://surfer. nmr. mgh. harvard. edu), and Statistical Parametric Mapping (SPM) (http://www. fil. ion. ucl. ac. uk). The segmentation techniques applied in FSL, FreeSurfer and SPM are model-based methods. In highly variable data, such as MRI of the brain, it may be difficult for the segmentation tools to model the ROIs with sufficient accuracy, even when the techniques are trained on representative datasets. To address this, multiatlas label fusion has been suggested and has demonstrated excellent segmentation abilities  [2]  [3]  [4]. Label fusion relies on a representative image library with corresponding validated structure segmentations (atlases). Recently, multi-atlas segmentation techniques, such as patch-based segmentation, have become popular  [5] [6].
SPM: SPM is a MATLAB-based (MathWorks Inc. ) freely available software, which can be downloaded online (http://www. fil. ion. ucl. ac. uk). Segmentation was performed with SPM12 and MATLAB R2015b by combining the unified segmentation tool with the neuroinformatics SPM template  [23] , which consists of multiple structures of the brain segmented in Montreal Neurological Institute (MNI) space.
Acknowledgments This work was funded in part by  MINDLab UNIK initiative at Aarhus University , funded by the  Danish Ministry of Science, Technology and Innovation , grant agreement number  09-065250 , partly by the  Spanish  grant  TIN2013-43457-R  from the  Ministerio de Economia competitividad  and with financial support from the  French State , managed by the  French National Research Agency (ANR)  in the frame of the  Investments for the future Programme IdEx Bordeaux  ( ANR-10-IDEX-03-02 ) by funding  HL-DTI  grant, Cluster of excellence  CPU ,  LaBEX TRAIL  ( HR-DTI ANR-10-LABX-57 ) and the  CNRS  multidisciplinary project " Défi ImagIn.
Observation methodology and coding of data The execution of the tests was systematically recorded in order to save all data. -A camera recorded the procedures performed in the PRE-TEST and POST-TEST. At the same time, the researcher compiled an observation sheet to secure data in case there might have been an error in the starting of the recording, if the battery of the camera got discharged, etc. -A screen and audio recording system had been installed in order to record the LabQuest training protocol. In traditional training, only the audio was recorded, to have a record of comments made by the subjects. -A screen and audio recording system had been implemented so as to record the detection of mistakes made by people while watching the video.
Data coding All the collected data were inserted in the database software used for data analysis: SPSS. Every finished procedure was decomposed into a series of simple tasks that had been accomplished. Each task was transformed into a nominal variable: while observing the pre-tests and post-tests, the researcher classified the tasks as "well executed," "poorly executed," "well executed, but at the wrong time," etc. Errors reported during the video broadcast were classified as "relevant error reported," "relevant not reported error," "non-relevant reported error" and "non-relevant not reported error. " It was made a difference between a relevant and non-relevant detected error. In fact, a "relevant error" is defined as a noncompliant gesture or behaviour according to GMP rules. For instance, touching the outward appearance of the uniform is recognised, by most of the people, as something not conform to the GMP.
The key element of our framework is QuantumClone, a method to cluster variants into clones, which we have thoroughly tested on simulated data. QuantumClone takes into account VAFs and genotypes of corresponding regions together with information about normal cell contamination. We applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We discovered specic pathways recurrently altered by deleterious mutations in dierent clonal populations. Some such pathways were previously reported (e. g. , MAPK and neuritogenesis) while some were novel (e. g. , epithelialmesenchymal transition, cell survival and DNA repair). Most pathways and their modules had more mutations at relapse compared to diagnosis.
Results The QuantumClone method presented here applies an expectation-maximization (EM) algorithm and allows for accurate inference of clonal structure using VAFs from one or several tumor samples sequenced using WGS. It can analyze variants coming from highly rearranged and hyper-diploid cancer genomes. We extensively validated QuantumClone on simulated data, where we compared it with recently published methods  (Miller et al. , 2014; Roth et al. , 2014). We complement QuantumClone with a robust framework for the functional assessment of mutations based on signaling pathway analysis combined with the clonal assignment (Fig. 1 ). The overall framework was applied to WGS neuroblastoma datasets: 19 patients' primary and relapse samples including 7 new triplets. Novel and previously published samples  (Eleveld et al.
e. , support variants serve as input to QuantumClone or an alternative method. Functional mutations are dened as variants with deleterious properties that aect either genes reported in the Cancer Census List  (Futreal et al. , 2004)  or genes from gene modules/signaling pathways that are recurrently aected by mutations in a given cancer type (Methods). At the last step of our framework, functional mutations are mapped to the clonal structure inferred from support variants based on the likelihood values. The QuantumClone R package includes functions for both clonal reconstruction using support variants and assignment of functional mutations to the dened clones. We propose the following options to be used in the analysis framework. Deleterious mutations can be determined using SIFT  (Ng and Heniko, 2003)  , PolyPhen-2  (Adzhubei et al.
, 2013)  and FunSeq2  (Khurana et al. , 2013). Gene module enrichment analysis may be performed using the R package ACSNMineR (Deveau P. , Barillot E. , Boeva V. , Zinovyev A. , Bonnet E. , In Press) using maps and modules of the Atlas of Cancer Signalling Networks (ACSN)  (Kuperstein et al. , 2015)  completed with the user-dened/cancer-specic modules. For the specic case of neuroblastoma, we created aNeuritogenesis' map extracted from  Molenaar et al. (2012). Characterization of neuroblastoma clonal evolution from diagnosis to relapse: application of the QuantumClone-based framework We applied our framework to investigate the clonal composition of neuroblastoma primary and relapse tumors and study its clonal evolution. We characterized clonal structure of tumors of 22 neuroblastoma patients (clinical data available in Suppl.
Annotation of functional mutations in each sample based on the global pathway enrichment analysis In our framework, we assumed that functional mutations in a given cancer type, i. e. , putative drivers, should target specic signaling pathways or pathway modules (Fig. 1 , Step 4). These modules can be identied as those enriched in coding deleterious mutations across the cohort. Thus, we mapped the total of 541 deleterious mutations obtained with tolerant lters (Fig. 1 , Methods) to the ACSN maps and detected recurrently altered gene modules using the ACSNmineR package. Overall, ve general gene maps (apoptosis, DNA repair, EMT/cell motility, cell survival and neuritogenesis) and their 15 gene modules were found to be enriched in mutations (threshold 0. 05 on the p-value corrected to account for multiple testing with the Benjamini-Hochberg False Discovery Rate correction, corresponding to the q-value)) (Figure  6B , Supp.
2 × 10 -6 ). As a control, we ran ACSNMineR on synonymous variants passing the same lters as the non-synonymous ones (1060 synonymous mutations in 771 genes). Enrichment analysis provided no modules enriched in synonymous variants (minimal p-value > 0. 28), thus conrming the biological signicance of the discovered enrichment in functional mutations for 20 ACSN modules and maps. Strikingly, similar enrichment analysis of intronic variants showed recurrently aected pathways similar to those aected by coding deleterious mutations, such as EMT-motility (Odds ratio = 5. 3, p -value < 10 -20 ) or cell survival (Odds ratio = 4. 0, p -value < 10 -20 ), highlighting the possible role of intronic SNVs in neuroblastoma tumorigenesis.