dataesr/scientific-paragraphs-categorization huggingface dataset:
This should be tested with more long-term stations in the Southern Hemisphere. Model simulations of interannual variability is sensitive to both meteorology as well as model construction, and thus drawing firm conclusions about how best to capture observed variability based on only one model is likely to be difficult. Our results that model construction, as well as meteorology, is important is consistent with general circulation model studies which suggest that modeling groups tend to have models which behave similarly  (Knutti et al. , 2013). These studies also complement reanalysis-based studies of the energy and water cycle, showing that issues remain with the reanalysis datasets  (Trenberth et al. , 2011; Trenberth and Fasullo, 2013). Here we considered the hypothesis from  Ridley et al.
g. ,  Prospero and Lamb, 2003; Mulitza et al. , 2010; Mahowald et al. , 2010). Because of the strong variability in dust, model simulations suggest that observations need to be made for around 1 year in many regions, but in remote regions, especially in the Southern Hemisphere, observations need to be made for more than 2 years in order to sample the modeled variability and correctly capture the annual mean concentrations (Fig. 11 ). Of course, this assumes there is no long-term variability. Long-term records of dust concentrations represent some of our most important data records to characterize variability in desert dust (e. g. ,  Prospero and Lamb, 2003) , and more long-term datasets should be collected.
Acknowledgements. We acknowledge the support of  NSF   0932946  and  1003509  and  DOE DE-SC0006735  and  DE-SC0016362 , as well as the assistance of  Computational and Information Systems Laboratory  of the  National Center for Atmospheric Research , whose resources were used for these simulations. David A. Ridley  and  Colette L. Heald  acknowledge support from  NASA  ( NN14AP38G ). We thank the PIs of the AERONET stations for access to the AERONET data (Table 2). CMAP Precipitation data provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their Web site at http://www. esrl. noaa. gov/psd/. Edited by: B. Vogel Reviewed by: two anonymous referees.
Simulated benchmarks We created five simulated data sets for benchmarking, consisting of 2 × 250-bp Illumina MiSeq reads and representing quasispecies infections from different viruses: human immunodeficiency virus (HIV), hepatitis C virus (HCV), and Zika virus (ZIKV). We varied the number of strains per sample as well as the relative abundances of those strains and the pairwise divergence between strains. To get data sets as realistic as possible, we used true viral genomes from the NCBI database and Illumina MiSeq error profiles during simulations. Characteristics of each benchmark are given in Table  1 , and additional information can be found in Supplemental Methods.
Lab mix In addition to the simulated benchmarks, we also considered a real Illumina MiSeq (2 × 250-bp) data set with an average coverage of ∼20,000×, obtained from a lab mixture of five HIV strains (see also Table  1 ). This data set was recently presented as a gold standard benchmark (Di  Giallonardo et al. 2014)  and is available at https ://github. com/cbg-ethz/5-virus-mix; we will refer to it as the lab mix.
Zika virus sample We applied SAVAGE to a sample of Asian-lineage ZIKV consisting of Illumina MiSeq 2 × 300-bp sequencing reads (∼30,000× coverage) obtained from a rhesus macaque after 4 d of infection (animal 393422)  (Dudley et al. 2016). This data set is available in the NCBI Sequence Read Archive under experiment SRX1678783, run SRR3332513.
For the 600× HIV mix, the strains were homogeneously distributed with a relative abundance of 20% each. the 10-strain HCV mix, and the 15-strain ZIKV mix. The only method capable of assembling at least half of a viral quasispecies on a 20,000× simulated data set is SPAdes, the only close alternative being metaSPAdes, with 45. 9% on the 10-strain HCV mix. For the 5-strain HIV mix and the 10-strain HCV mix, SPAdes assembles 91. 3%-91. 7% (SAVAGE-de-novo: ≥99. 6%) of the true viral genomes at an error rate of 0. 015%-0. 084% (SAVAGE-denovo: 0. 004%), showing that SPAdes misses assembling a considerable fraction of the quasispecies. This becomes more evident on the 15-strain ZIKV mix, which contains several low-frequency strains: SPAdes only recovers 65. 6% of the target genomes (SAVAGE-de-novo: 99.
Finally, SAVAGE-b-ref also depends on the quality of the reference sequence: the target genome coverage is 13. 6 points lower compared to SAVAGE-h-ref on the 10-strain HCV mix. This, of course, had to be expected: if reference coordinates are too mistaken, overlaps cannot be detected. This last point underscores that a full de novo approach can come with decisive extra advanatages. Of course, there is still room for improvements. While substantially faster and more space-efficient than previous overlap graph-based viral quasispecies assembly algorithms, SAVAGE has been particularly tailored toward dividing deep-coverage data sets into chunks of 500-1000× and merging the contigs of the chunks in subsequent steps, because this reflects its statistical calibration. While this works well, it sets certain limits on the frequency of strains it can recover-haplotypes of frequencies below 1% remain difficult to reconstruct.
Other methods used for evaluation For benchmarking, we compared SAVAGE against the state-of-theart approaches ShoRAH  (Zagordi et al. 2011)  and PredictHaplo  (Prabhakaran et al. 2014). Both methods were run with default parameter settings, after aligning the reads to the reference genome using BWA-MEM  (Li 2013). The de novo assembler MLEHaplo  (Malhotra et al. 2016b ) required the reads to be errorcorrected first, for which we used MultiRes  (Malhotra et al. 2016a)  with default settings (recommended by the authors). Unfortunately, we could not compare against VGA  (Mangul et al. 2014)  and HaploClique  (Töpfer et al. 2014 ) because these software packages were no longer maintained.
Data simulations To evaluate performance of SAVAGE, we designed several simulated data sets. We used the software SimSeq (https://github. com/jstjohn/SimSeq) to simulate Illumina MiSeq reads from the genome of interest. In order to obtain reads similar to real MiSeq data, we simulated 2 × 250-bp paired-end reads, with a fragment size of 450 bp and the MiSeq error profile provided with the software. In addition, we also simulated a 5-strain HIV mix with shorter reads (2 × 150 bp). The genomes used for each data set are listed in the Supplemental Methods.
Acknowledgments A. S. acknowledges funding through  Vidi  grant  639. 072. 309 ,provided by the  Netherlands Organisation for Scientific Research (NWO). E. R. , and A. Z. E. A. are supported by the  Institut de Biologie Computationnelle  ( ANR-11-BINF-0002 ), the  ATGC bioinformatics platform, ANR Colib'read  ( ANR-12-BS02-0008 ), and  France Génomique.
3. The data shows firstly that the cac is substantially lower than for peptide 1, due to the presence of the additional hydrophobic Boc group. In addition, significantly different values are obtained for the cac from the two fluorescence assays. This suggests that hydrophobic collapse occurs before formation of amyloid fibrils for sample 2 and that in between the two cac values (5. 9 ± 0. 1) × 10 -3 wt% and (1. 9 ± 0. 1) × 10 -2 wt% oligomers may be present. Electrospray Ionization Mass Spectrometry. ESI experiments were performed at 0. 029 wt% to investigate the peptide's aggregation stability. According to the results above, 0. 029 wt% is below the cac for 1 but at the onset of peptide aggregation for 2. ESI studies were first performed for peptides dissolved in water. Subsequently, the same study was repeated for peptides dissolved in HFIP: water (1:9; v/v).
, Holliston, USA). The desolvation and source temperature were 150 °C and 80 °C, respectively, and the cone voltage was set to 40 V. ESI mass spectra were acquired in triplicate in sensitivity mode with the exception of higher resolution mass spectra, which were acquired once in resolution mode with ion mobility separation enabled. For the latter, wave velocity and wave height were set to 650 m/s and 40 V, respectively, and the helium and nitrogen flow rates were 180 and 90 mL/min, respectively. The acquired data was converted to mzML format using the msconvert tool from the ProteoWizard software package  61. In-house developed software based on the pymzML software library  62  was used to extract the integrated isotopomer signal intensities of interest from the converted data files. The integrated ion signal intensities of the monomer (S M ) and all oligomers (S O ) were summed for each acquired spectrum to determine the total ion signal intensity of the analysed peptide species (S T ): = + S S S (1) T M O The relative extent of oligomerisation (E O ) was determined by dividing the integrated ion signal intensities of the oligomers (S O ) by the total ion signal intensity of the analysed peptide (S T ): = E S S (2) O O T Circular Dichroism (CD) Spectroscopy.
CD spectra were recorded using a Chirascan spectropolarimeter (Applied Photophysics, UK). Peptide solutions were placed in a cover slip cuvette (1 × 10 -2 or 0. 1 mm thick), spectra presented being with absorbance A < 2 at any measured point with a 0. 5 nm step, 1 nm bandwidth, and 1 s collection time per step. The CD signal from the background was subtracted from the CD data of the sample solutions. The circular dichroism of the samples was measured as a function of the time for the sample containing pure 26 μ M Aβ (1-42) or mixtures of Aβ (1-42) with sample 1 or sample 2. CD experiments were performed using a Quartz cuvette with 1mm path length. Fourier Transform Infra-Red (FTIR) Spectroscopy. Spectra were recorded using a Nexus-FTIR spectrometer equipped with a DTGS detector. Samples were measured using an ATR configuration, with a PEARL liquid cell, or a transmission configuration, with the sample placed between two CaF 2 plate windows (spacer 1.
Small-Angle and X-Ray Scattering (SAXS). Synchrotron SAXS experiments were performed on beamline BM29 at the ESRF (France), using a BioSAXS robot, and on beamline BM26B (DUBBLE, ESRF). On BM29 solutions were loaded into the 96 well plate of an EMBL BioSAXS robot, and then injected via an automated sample exchanger into a quartz capillary (1. 8 mm internal diameter) in the X-ray beam. The quartz capillary was enclosed in a vacuum chamber, in order to avoid parasitic scattering. After the sample was injected into the capillary and reached the X-ray beam, the flow was stopped during the SAXS data acquisition. BM29 operated with an X-ray wavelength λ = 1. 03 Å (12 keV). The images were captured using a PILATUS 1 M detector, while data processing was performed using dedicated beamline software ISPYB (BM29).
Acknowledgements This work was supported by  EPSRC  grant  EP/L020599/1  to  IWH  and  EP/L006227/1  to RC. We thank the  ESRF  for the award of bioSAXS beamtime on BM29 (ref. MX-1769) and  Gabriele Giachin  for assistance with the measurements. We are grateful to  Nick Spencer  ( University of Reading ) for the XRD measurements in the Chemical Analysis Facility.
The scheme of the model development is illustrated in Fig. 1A. Light signalling pathways are interlinked with other external stimuli such as variations in temperature. To investigate the heat shock response (HSR) in C. reinhartii, which is observed upon exposure to large temperature changes  (Schroda et al. , 2015) , a kinetic model based on the mechanisms that sense temperature variations by the accumulation of unfolded proteins was developed  (Magni et al. , 2016). The HSR activates genes coding for heat shock proteins (HSP), which act as chaperones repairing the heat-induced damage. The system of ODEs describing the signalling network was reconstructed and calibrated from multiple experimental time-resolved datasets available in the literature (e. g. Schmollinger et al. , 2013). We showed that the system can adapt to higher temperatures by shifting to a new steady state.
reinhardtii and P. tricornutum were constructed from their respective BioCyc databases  (Caspi et al. , 2015) , which contain the biochemical reactions of organisms based on their genome sequences, and previously published models  (Chang et al. , 2011; Cheung et al. , 2013; Hunt et al. , 2014). They were then manually curated to fill the gaps and to ensure conservation of mass and energy  (Gevorgyan et al. , 2008; Poolman et al. , 2009) , resulting in networks containing approximately 500 (P. tricornutum) and 2500 (C. reinhardtii and A. thaliana) reactions. Gap-filling (Satish-  Kumar et al. , 2007; Christian et al. , 2009)  is a necessary process, as gene annotation is far from perfect. In each of the resulting networks, around 50 reactions had to be added during the gap filling process. All three models were used to identify possible metabolic cycles acting as energy dissipation modes under supra-optimal light conditions.
An E-712 controller (PI) drives the actuators and reads out the limit switches. The load cells are read out by TEDS readout panels (DPM-3, Transducer Techniques), that are connected by a serial device server (Moxa, Nport 5410). The encoders of the linear positioners are read out by nano position sensors (MCS-3S-ES-SDS15-TAB, SmarAct) connected to a sensor module (MCS-6CC-USB, SmarAct). A custom-written LabVIEW program controls the complete setup.
9 ), which can be related to the PLC effect. Due to the complex geometry and the related ring effect the relationship between applied force and the local stress state at the centre of the sample cannot be expressed by analytical equations. Instead, FEM simulations are needed. ABAQUS/ Standard software  [42]  is used to perform the FE simulations for the cruciform and dog-bone samples. In order to improve the computational efficiency, only 1/8th of the entire geometry is simulated with symmetric boundary conditions on appropriate surfaces. A structured hexahedron mesh is employed with linear 8-node mesh elements (C3D8 in ABAQUS) for  both the cruciform and dog-bone geometries. Isotropic material properties of AlMg5 were used. The plastic response is modelled using the ABAQUS material model that is based on the von Mises yield criterion and the associated flow rule.
Acknowledgements The authors gratefully acknowledge  Antonio Cervellino  and  Michael Lange  for their support with the setup at the  Materials Science beamline ,  Stefan Ritter  ( Nuclear Energy and Safety, PSI)  for his support with the setup at the SEM and  Rolf Brönnimann  ( Reliability Science and Technology Laboratory, EMPA ) for helping with sample preparation. HVS, AG, MD and MVU thank the  European Research Council  for the financial support within the advanced grant MULTIAX (339245).
Although a few popular long-read-based genome assemblers  [10] [11] [12]  and one yeast-specific gene annotation tools  13  have been developed, a single integrated solution that handles the entire process in a seemless way is still much needed. Toward this goal, we revamped our original workflow for deriving the yeast population-level reference genomes  5  into a highly streamlined and selfcontained package with modular design and automated implementation. Under the hood, LRSDAY contains a series of task-specific modules handling long-read-based de novo genome assembly, short-read-based assembly polishing, reference-guided assembly scaffolding, as well as comprehensive genomic feature annotations. These tasks can be run individually, selectively or coordinately depending on users' needs. LRSDAY supports both leading long-read sequencing technologies: PacBio and Oxford Nanopore.
Running the full LRSDAY workflow, the final output is a chromosome-level genome assembly with high-quality annotations of centromeres, protein-coding genes, tRNAs, transposable elements (TEs; Ty1-Ty5 for yeasts), and telomere associated core X and Y' elements. LRSDAY is shipped with various auxiliary scripts, configuration files and supporting data that enable its semi-automatic installation, configuration, and execution with minimal manual intervention. This design concept greatly alleviates the technical barrier for bench biologists with limited bioinformatics experiences (Box 1). In addition, a real case example and its final outputs are also provided for users' test and comparison. All these task-specific modules, auxiliary files, installed tools, sample outputs, together with the user-created project directories for the testing example and users' own data are hosted under the same home directory ($LRSDAY_HOME) in a self-contained fashion (Fig.
g. to specify the input and output file paths) and execute the task-specific pipeline scripts pre-placed in these subdirectories. These pipeline scripts will automatically set environment variables, process the data, and formulate the results. All computationally intensive tasks can be processed using multiple threads to substantially save computation time. Below we briefly describe the computational processes underneath each task-specific module: 01. Long-read-based_Genome_Assembly: Long reads generated from PacBio or Oxford Nanopore technologies are used to perform de novo genome assembly using an overlap-layout-consensus (OLC) algorithm.
Y_Prime_Element_Annotation: The pre-shipped representative S. cerevisiae Y' element sequence is searched against the final genome assembly to annotate Y' elements. Note that Y' element can have long, short or degenerated forms  14  , and we used a representative long-form Y' element as the query to maximize the power of our Y' element annotation. 12. Gene_Orthology_Identification: The annotated protein-coding genes are compared with the reference protein-coding genes based on both sequence homology and gene order conservation to identify gene orthology relationship between these two sets. Based on such gene orthology relationship, the Saccharomyces Genome Database (SGD; http://www. yeastgenome. org/) systematic names are assigned to the annotated proteincoding genes.
Limitations and potential adaptation In its distributed form, LRSDAY is tailored for the model budding yeast S. cerevisiae and its closely related sister species S. paradoxus with a number of pre-shipped auxiliary data files configured accordingly. However, given its modular design, the backbone of LRSDAY can be adapted for virtually any eukaryotic organisms to perform genome assembly, assembly polishing, reference-guided scaffolding, protein-coding genes and tRNA annotations, gene orthology identification, and annotation integration. Moreover, those assembly polishing, scaffolding and various annotation modules (Task 02-13) can also be used to analyze existing genome assemblies derived from any or any combination of sequencing technologies. Such flexibility makes LRSDAY very useful for expanded use cases and therefore suits the needs of a broader audience.
Expected improvements As thousands of yeast strains have been or are currently under sequencing  [15] [16] [17]  , our knowledge in the overall genome content diversity  18  of this important model organism is expanding rapidly, revealing a whole new picture of the pan-genome diversity of S. cerevisiae. For example, our lab is currently working on characterizing the pan-genome of >1,000 S. cerevisiae isolates across the globe (http://1002genomes. u-strasbg. fr/). Future developments of LRSDAY will incorporate such pan-genome dataset to provide additional annotation information for those non-reference genes, especially with regard to their evolutionary origin, population prevalence, and putative functions. Such information will greatly help users to dissect and interpret complex genotype-phenotype interactions in diverse ecological and biotechnological settings.
Software or library requirements • Bash (https://www. gnu. org/software/bash/) • Bzip2 (http://www. bzip. org/) • Cmake (https://cmake. org/) • GCC and G++ v4. 7 or newer (https://gcc. gnu. org/) • Git (https://git-scm. com/) • GNU make (https://www. gnu. org/software/make/) • Gzip (https://www. gnu. org/software/gzip/) • Java runtime environment (JRE) v1. 8. 0 or newer (https://www. java. com) • Perl v5. 12 or newer (https://www. perl. org/) • Python v2. 7. 9 or newer (https://www. python. org/) • Python v3. 6 or newer (https://www. python. org/) • Tar (https://www. gnu. org/software/tar/) • Virtualenv v15. 1. 0 or newer (https://virtualenv. pypa. io) • Wget (https://www. gnu. org/software/wget/).
Example data • The test run sequencing data comes from our recent study 5 , which consists of both PacBio and Illumina reads produced from the S. cerevisiae strain SK1. The PacBio reads can be retrieved with the ENA analysis accession number ERZ448251. The Illumina reads can be retrieved with the SRA sequencing run accession number SRR4074258. The S. cerevisiae reference genome files used by LRSDAY is taken from the same study with the Genbank accession number GCA_002057635. 1. For the testing example, we have provided bash scripts to automatically download and setup these data in LRSDAY.
Download, install and configure LRSDAY • TIMING <1 h 1) Enter the following commands in a terminal window: $ git clone https://github. com/yjx1217/LRSDAY. git $ cd LRSDAY $ bash install_dependencies. sh ▲CRITICAL STEP Make sure you have a fast and stable internet connection when running this step since many tools will be downloaded here. Check that all the prerequisites (see this protocol or the "prerequisite. txt" file in the downloaded LRSDAY directory) have been installed on your system. ▲CRITICAL STEP Upon the successful completion of executing the bash script, you should see a confirmation message prompted out: "LRSDAY message: This bash script has been successfully processed! :)". Otherwise, it means an error has occurred during the execution of the bash script, which interrupted the automatic installation process.
? TROUBLESHOOTING 3) Pay attention to the final message produced by the install_dependencies. sh script (Step 1) on manual setup instructions. Although most required tools have been automatically installed and configured, manual downloading and/or configuration are needed for GATK  19  , RepeatMasker  20  and MAKER  21  due to license restriction of these tools or of their dependent databases. Below, we will go over this process in Step 4-22. 4) Go to the official website of GATK (https://software. broadinstitute. org/gatk/download) and download GATK-3. 8 after logging in. Registration is needed for unregistered GATK users. $ perl. /configure 22) The configure script above will prompt several questions: • Enter "env" for the question about the installation path of Perl. • Just press enter for the question about the installation path of RepeatMasker.
canu. stats. txt" for this test example) will be generated to report some basic summary statistics (e. g. total assembly size, N50, L50, GC%, etc) to help you to gauge the genome assembly quality (Table  1 ). $ cd. /. /01. Long-read-based_Genome_Assembly $ bash LRSDAY. 01. Long-read-based_Genome_Assembly. sh ▲CRITICAL STEP Note this step will take long to finish (see TIMING), so we recommend to run this step and all the other time-consuming steps using "nohup" (https://en. wikipedia. org/wiki/Nohup), which allows the process to continue running after you exit the terminal or logout from the server. As an example, you can run the bash script using nohup as follows: $ nohup bash LRSDAY. 01. Long-read-based_Genome_Assembly. sh >run_log. txt 2>&1 & ▲CRITICAL STEP When running LRSDAY with your own data, modify the bash script to specify the file paths and the prefix for your input and output data (same for all following steps).