dataesr/scientific-paragraphs-categorization huggingface dataset:
The X-rays were produced by a Molybdene anode. The wavelength of the incident beam was of 0. 722 Å. The scattered pattern was collected on a MAR detector. The sample-to-detector distance was 72 cm. The samples were inserted in 1. 5 mm diameter quartz capillaries sealed with paraffin wax or spread on a kapton cell (10% WMDF in PBS and 5% WMNF in NaOH). The spectra were manipulated and analyzed using the SaxsUtilities (www. sztucki. de/SAXSutilities/) and SasView (http://www. sasview. org/) softwares. Attempts to index the diffraction patterns were made using the DICVOL06 program. 37 A series of cells was proposed by the software from analysis of the 19 peaks common in the two diffractograms. We used the following criteria to select the most relevant cells: (i) Cells must be compatible with the dimensions of the peptide ~9 Å x ~20 Å, V = 1153 Å 3 ; (ii) All the peaks have to be indexed; (iii) Cells must not generate too many peaks that are not experimentally detected.
Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR). ATR-FTIR spectra were recorded at a 2-cm -1 resolution with a Bruker IFS 66 spectrophotometer equipped with a 45° N ZnSe ATR attachment. The spectra obtained resulted from the average of 30 scans and were corrected for the linear dependence on the wavelength of the absorption measured by ATR. The water signal was removed by subtraction of pure water spectrum multiplied by a coefficient chosen by superimposing the spectra in the 2500-4000 cm -1 region. Afterwards, the spectra were normalized by setting the highest peak of the amide I band to 1. The spectra were then deconvoluted with PeakFit (Systat Software Inc) as a sum of Gaussian-Lorentzian components using the secondary derivative method. A linear baseline was used. Fourier-Transform Raman Spectroscopy (FT-Raman).
We are also grateful to Sylvain Prevost for his assistance. The SAXS-WAXS experiments reported in Figure  3C  and preliminary data were collected on the in-house MOMAC set-up, funded by RTRA "Triangle de la Physique". This work benefited from the use of the SasView application, originally developed under NSF award DMR-0520547. SasView contains code developed with funding from the European Union's Horizon 2020 research and innovation programme under the SINE2020 project, grant agreement No 654000.
Data analysis From the overnight time-lapse imaging, a stack of images was obtained for analysis. For each slit, each image chosen was at least one mean cell division time later than the previous and following images; the duration between images of individual slits varied due to poor focus in some images, which were not included for analysis  [27]. Images were cropped to contain individual slits. These image portions were then fed into the open-source CELLPROFILER software to perform cell segmentation and then measurements of orientation. Orientation was found to be significantly affected by the presence of filamentous cells. As a result, images in the stack were filtered for the presence of a long filament. This was done in an automated way by determining the size of all cells in an image and then removing any image that contained a cell whose long axis was longer than four times the median of all cells in the population at each growth condition.
This removed roughly 20% of the images for the narrow slits and 70% of the images for the wide slits. Fortunately, microchemostats have high data throughput, so we still obtained large datasets for ensemble averaging. Ordering in a two-dimensional nematic is described by the tensor [41] Q = cos 2θ sin 2θ sin 2θ -cos 2θ , ( 2. 1) where θ is the angle between the long-axis of the cell and the slit wall. The resulting order parameter is given by φ = 1 2 Tr(Q 2 ) = [ cos 2θ 2 + sin 2θ 2 ] 1/2. (2. 2) For a perfectly ordered system, θ = 0 and φ = 1. Conversely, φ = 0 for a disordered system with θ uniformly distributed on [0, π ]. In what follows, we will take each image as a measurement of the order parameter φ and thus compute the average • • • over the cells within that image. Note that nematic ordering sometimes involves an aligning field.
Acknowledgements. Fabrication was performed in the  Minnesota Nanofabrication Center , which receives partial support through the  National Nanotechnology Infrastructure Network. We thank  Zhicheng Long  for helpful training on fabrication and running the growth experiments,  Luigi Feriani  for assistance with data analysis and  Mia Panlilio  and  Michal Wlodarski  for helpful conversations concerning growth experiments. We are grateful to the participants at the  Lake Como Summer School 'Quantitative Laws II'  for their constructive criticism.
Contig building can be performed with Finger-Printed Contigs (FPC)  (Soderlund et al. , 2000)  and Linear Topological Contig software (LTC)  (Frenkel et al. , 2010). Anchoring to a high-density map is crucial for using a physical map. This enables one to determine contig orientation and order along the chromosome(s) of interest  (Meksem et al. , 2005). Typically, genetic maps built from polymorphic markers, mainly single nucleotide polymorphisms (SNPs) are used for contig anchoring. Single nucleotide polymorphism markers are abundant and equally distributed throughout a genome. Unfortunately, genetic maps suffer from low resolution in centromeric and pericentriomeric regions as a consequence of reduced meiotic recombination. Such regions with poor genetic map resolution may comprise as much as 50% of the entire chromosome  (Saintenac et al.
, 2009; Karafiátová et al. , 2013; Aliyeva-Schnorr et al. , 2015). Alternative methods such as radiation hybrid mapping  (Wardrop et al. , 2002; Kalavacharla et al. , 2006)  or collinearity of conserved genes in related grass species  (Mayer et al. , 2009)  can be used to circumvent this limitation  (Feuillet et al. , 2012). In this work, we used the BAC library from chromosome arm 3DS comprising 36,864 clones to establish the physical map with 869 contigs containing 18,927 clones by using high information content fingerprinting technology and FPC software. Bacterial artificial chromosome contigs were anchored by following an in silico strategy as described by  Cviková et al. (2015).
Construction of the Physical Map The physical map of chromosome arm 3DS was constructed from the BAC library prepared in our previous study  (Šafář et al. , 2007)  using SNaPshot fingerprinting  (Luo et al. , 2003). This BAC library consists of 36,864 clones with an average insert size of 110 kbp and represents an 11-fold coverage of the chromosome arm 3DS (1C ~ 321 Mbp). Fingerprinting of all clones resulted in 27,880 High-information content fingerprints that were preprocessed (see Experimental procedures for details) and assembled via FPC software  (Soderlund et al. , 2000). The initial assembly was done at a low cutoff (1 × 10 -75 ) and resulted in 2628 contigs being formed from 16,333 clones. This first construction of the physical map resulted in a fragmented assembly with a cumulative length exceeding the estimated size of the 3DS.
Linear Topological Contig Verification of the FPC-Based Physical Map Linear Topological Contig software is an alternative to FPC that has been designed to build contigs or check existing contigs on the basis of the contig topology  (Frenkel et al. , 2010). Its algorithm is based on linear arrangement of DNA molecules, which should be preserved in the physical map. In this study, LTC has been used to check the topology of contigs built at a cutoff of 1 × 10 -15. Contigs with a nonlinear structure (i. e. , a width of 2 or more) as well as contigs with Q-clones and contigs with segments connected by a single BAC clone were investigated. Simultaneously, the positions of the markers in the contigs were evaluated. Seventy-two MTP clones defined by LTC as Q-clones were removed from the FPC assembly and 40 contigs were split on the basis of the topology provided by LTC and the presence of contradictory markers in the contigs (Fig.
Physical Contig Assembly Bacterial artificial chromosome clones were subsequently assembled via FingerPrinted Contigs software to contigs on the basis of the presence of shared fragments in their fingerprints  (Soderlund et al. , 2000). The parameters used for the initial assembly step were: cutoff score = 1 × 10 -75 ; tolerance = 12 (corresponding to the difference in fragment size = 0. 4 bp); gel length = 54,000 (the number of possible values for the fragment size after data preprocessing); formula used = Pure Sulston; fingerprinting method = HIFC; band size = 1100; best contig = 100 (number of iterations run to find the best solution for each contig). The contigs were then merged or split at successive increasing (less stringent) cutoffs until 1 × 10 -45 (in steps of 1 × 10 -5 ). First, singletons were added to the ends of existing contigs, then contigs were merged at their ends with the following settings: auto merge = ON; FromEnd = 50 (distance of the clone from the end of the contig to be counted as an end clone); Match = 1 (number of reciprocal matches required to perform a merge).
Evaluation of Contigs using via the LTC Algorithm Contigs at a cutoff of 1 × 10 -15 were verified with LTC software  (Frenkel et al. , 2010). The structure of contigs was evaluated after creation of a network with the following parameters: total gel length = 60,000; tolerance value = 12; number of bands for calculation of the Sulston score = 2250; cutoff = 10 -15. The contigs with a nonlinear topology (i. e. , a width of 2 or more) and Q-clones were manually checked and split to reach the linear structure (inappropriate BAC clones were removed to singletons) unless the markers proved correct order of BAC clones in the particular contig.
Analysis of Genic Sequences In total, 1906 high-confidence genes identified in the IWGSC survey sequence annotation (International Wheat Genome Sequencing Consortium, 2014; Institut National de la Recherche Agronomique, 2016) were anchored to the physical map of chromosome arm 3DS whenever a sequence containing a particular gene was unambiguously linked to BAC clone(s). Coding sequences were aligned with BLASTx  (Altschul et al. , 1997)  against databases of proteins of B. distachyon  (International Brachypodium Initiative, 2010) , O. sativa  (International Rice Genome Sequencing Project, 2005; Sakai et al. , 2013) , and S. bicolor  (Paterson et al. , 2009). All gene models with significant BLASTx hit (e-value £ 1 × 10 -5 , identity ³ 70%, and alignment length ³ 100 bp) with the closest homolog localized on chromosome 2 of B.
Acknowledgments We are grateful to  Marie Seifertová  and  Radomíra Tušková  for excellent technical assistance. This work was supported by the  Czech Science Foundation  (grant No. 13-08786S ) and by the  Ministry of Education, Youth and Sports of the Czech Republic  (award  LO1204  from the  National Program of Sustainability I). Computing was supported by the  National Grid Infrastructure MetaCentrum  (grant No. LM2010005  under the programme Projects of  Large Infrastructure for Research, Development, and Innovations ).
This elevated mutation rate was caused by defective mismatch Fig. 1  Workflow for improving phenotypes through model-guided multiplex genome editing. First, an initial set of target alleles (hundreds to thousands) is chosen for testing based on starting hypotheses. These targets may be designed based on differences from a reference strain, synthesis or design errors, or biophysical modeling. Multiplex genome editing creates a set of modified clones enriched with combinations of the targeted changes. Clones are screened for genotype and phenotype and predictive modeling is used to quantify allele effects. The workflow is repeated to validate and test new alleles. Beneficial alleles are combined to create an optimized genotype   The combinatorial diversity produced by sampling at regular intervals between consecutive rounds of multiplex genome engineering generates a dataset well suited for analysis by linear regression (Additional file 1: Supplementary Note 3).
Strains The construction and genotype of engineered E. coli strain C321. ΔA was previously described in detail  [6]. Here, before improving fitness, we constructed strain C321. ΔA. mutSfix. KO. tolCfix. Δbla:E by further modifying C321. ΔA to introduce the following changes: (1) the mutS gene was reinserted into the C321. ΔA strain in its original locus and MAGE was used to disable the gene by introduction of two internal stop codons and a frameshift; and (2) the carbenicillin-resistance marker bla was swapped for gentamicin resistance marker aacC1 in the lambda red insertion locus. Several control assays were performed in EcNR1. mutS. KO, a non-recoded by MAGE-enabled strain similar to EcNR2  [10]. All genomic positions reported in the manuscript are in the frame of MG1655 K12 (Genbank accession NC_000913.
Millstone, software for multiplex genome analysis and engineering Millstone  [15]  was used throughout the project to rapidly process WGS data and identify variants in each sample relative to the reference genome, to explore variant data, and to design oligonucleotides for MAGE. The Millstone analysis pipeline takes as input raw FASTQ reads for up to hundreds of clones and a reference genome as Genbank or FASTA format. The software then automates alignment of reads to the reference using the Burrows-Wheeler Aligner (BWA-MEM) followed by single nucleotide variant (SNV) calling using Freebayes. Millstone performs variant calling in diploid mode, even for bacterial genomes. This helps account for paralogy in the genome and results in mutation calls being reported as "homozygous alternate" (strong wild-type), "heterozygous" (marginal), or wild-type, along with an "alternate fraction" (AF) field that quantifies the fraction of aligned reads at the locus showing the alternate allele.
Multiplex automated genome engineering Single-stranded DNA oligonucleotides for MAGE were designed using Millstone's optMAGE integration (https:// github. com/churchlab/optmage). Oligos were designed to be 90 bp long with the mutation located at least 20 bp away from either end. We used the C321. ΔA reference genome (Genbank accession CP006698. 1) for oligo design to avoid inadvertently reverting intentional UAG-to-UAA changes. OptMAGE avoids strong secondary structure (< -12 kcal mol -1) and chooses the sense of the oligo to target the lagging strand of the replication fork  [10]. Phosphorothioate bonds were introduced between the first and second and second and third nucleotides at the 5-prime end of each oligo to inhibit exonuclease degradation  [10]. All DNA oligonucleotides were purchased with standard purification and desalting from Integrated DNA Technologies and dissolved in dH20.
Whole-genome sequencing Genomic DNA (gDNA) preparation for WGS of 96 clones (only 87 considered in manuscript because sequencing analysis revealed that nine cultures were polyclonal) was performed as in  [33]. Briefly, gDNA was prepared by shearing using a Covaris E210 AFA Ultrasonication machine. Illumina libraries were prepared for pooled sequencing as previously described  [34]. Barcoded Illumina adapters were used to barcode each strain in a 96-well plate. All 96 genomes were sequenced together on a single lane of a HiSeq 2500 PE150 (Additional file 9). Alternative inexpensive WGS library preparation methods have since become available  [23]. WGS data were processed to identify clonal genotypes in Millstone and then exported for further analysis (Additional file 10). Demultiplexed. fastq reads were aligned to the MG1655 reference genome.
Accordingly, the elastic net loss function used follows from Zou and Hastie  [18] : L λ 1 ; λ 2 ; β ð Þ¼ y-Xβ j j 2 þ λ 1 β j j 1 þ λ 2 β j j 2 where β j j 1 ¼ X p j¼1 β j β j j 2 ¼ X p j¼1 β 2 j And the coefficients were estimated according to: β ¼ argmin β L λ 1 ; λ 2 ; β ð Þ ð Þ Elastic net regression was performed using the Elastic-NetCV module from scikit-learn  [35]. This module introduces the hyperparameters alpha = λ 1 + λ 2 and 11 ratio ¼ λ 1 λ 1 ;þλ 2 and uses k-fold cross validation (k = 5) to identify the best choice of hyperparameters for a given training dataset. We specified the range of l1_ratio to search over as [0. 1, 0. 3, 0. 5, 0. 7, 0. 9, 0. 95, 0. 99, 1], which tests with higher resolution near L1-only penalty. This fits our hypothesis that a small number of mutations are responsible for a majority of the fitness effect.
Availability of data and materials Analysis and simulation code is available at https://github. com/churchlab/ optimizing-complex-phenotypes, released under the MIT license. WGS data for intermediate clones, the final strain C321. ΔA. opt, and the C321. ΔA. opt ancestor C321. ΔA. mutSfix. KO. tolCfix. Δbla:E have been deposited at the NIH Sequence Read Archive (SRA) under BioProject PRJNA382959. C321. ΔA. opt may be requested from AddGene (Bacterial strain #87359).
This indicates that the RNA-seq reads cover a substantial fraction of the transcriptome, and that the genome also has excellent coverage of most of the expressed N. brasiliensis genes. There was nonetheless an elevated proportion of fragmented USCOs (see below). We also noted a high proportion of duplicated USCOs. Inspection revealed that some of these were bone fide lineage-specific expansions. For example, the analysis uncovered 3 distinct loci encoding isoforms of fructose 1,6-bisphosphatase (PFAM: PF00316), as predicted also from the WTSI assembly. Pairs of USCOs were also found on homologous contigs. There were 4 such examples in the 12 "heterozygous branch" sub-graphs alone; this presumably reflects haplotypic variants. While generating a complete high-quality annotation was beyond the scope of this study, we made use of expert knowledge regarding Carbohydrate-Active Enzymes (CAZymes) to provide a complementary insight into the predicted genes compared to the WTSI set  [21].
Canu 1. 5 Changes to Canu relative to v1. 4 are documented in the v1. 5 release notes (available at https://github. com/marbl/canu/releases/tag/v1. 5). Briefly, one major change was a switch in the alignment algorithm used to generate consensus for the corrected reads and final contigs. This improved the corrected reads by slightly increasing their identity and splitting fewer reads. Canu also started using the raw read overlaps to estimate corrected read lengths and used that information to inform its selection of reads for correction. As with all such analysis packages, bugs continue to be identified and corrected. The first release of v1. 5, for example, included a bug that erroneously removed heterozygous edges such that in the type of sub-graphs shown in Figure  4A , the results presented would potentially correspond to lower bounds of complexity.
reads and called FASTQ files were obtained from ONT (called by workflow "1D RNN Basecalling 450bps FastQ v1. 121") following sequencing. Reads were filtered to exclude those from contaminating DNA using OneCodex [http://onecodex. com]. Reads that mapped to any DNA in the OneCodex database were excluded from the read set: (zcat OneCodex_RefSeq_132394. fastq. gz. results. tsv. gz | awk '{if($3 == 0){print $1}}'; zcat OneCodex_OCD_132394. fastq. gz. results. tsv. gz | awk '{if($3 == 0){print $1}}') | sort | uniq -d | gzip > OCunmapped_names_132394. txt. gz pv 132394. fastq. gz | zcat | ~/scripts/fastx-fetch. pl -i OCunmapped_names_132394. txt. gz | ~/scripts/fastx-fetch. pl -v -i ONTmapped_names_132394. txt. gz | gzip > OCunmapped_ONTunmapped_132394. fastq. gz Filtered reads from the WGA sample were assembled using Canu v1.
4, assuming a genome size of 150 Mb: ~/install/canu/canu-1. 4/Linux-amd64/bin/canu -nanopore-raw OCunmapped_ONTunmapped_132394. fastq. gz -p NbL5_ONTA -d NbL5_ONTA_test1 genomeSize=150M This is the WGA assembly presented in Figure 1B. The WGA reads were then re-called using the updated basecaller (Albacore 1. 1. 0) that implements a time-domain correction (transducer) for homopolymeric regions in the called sequence, also available in the open-source Scrappie (https://github. com/nanoporetech/scrappie) basecaller: read_fast5_basecaller. py -o fastq -i A_132394 -t 10 -s called_A_132394 -c r94_450bps_linear. cfg Reads with a length of greater than 10k were extracted for subsequent analysis: pv called_A_132394_albacore_1. 1. 0. fq. gz | zcat | ~/scripts/fastx-fetch. pl -min 10000 | gzip > 10k_called_A_132394_albacore_1.
Acknowledgements This study was supported by program grant funding from the  Health Research Council of New Zealand  ( 14/003 ) and the  Marjorie Barclay Trust. SK was supported by the  Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. We thank  Rick Maizel  for discussion,  Simon Mayes  and colleagues at  Oxford Nanopore Technologies  for their assistance. This study utilized the computational resources of the Biowulf system at the  National Institutes of Health, Bethesda, MD  (https://biowulf. nih. gov).
susceptible families  (Hama-Ali et al. 2015). Field experiments are needed to assess broader genetic diversity in the agronomic context, but QTL analyses on oil palm crosses in the field are often not sufficiently effective because of population size and allelic segregation limitations  (Jeennor and Volkaert 2014; Lee et al. 2014; Pootakham et al. 2015). Recent alternatives, based on data from breeding programs involving multiple connected families, have yielded promising results on agronomic traits  (Billotte et al. 2010; Tisné et al. 2015). In this study, an oil palm multi-parental population (Eg9PP) derived from nine founders of an ongoing breeding program was used to map Ganoderma disease resistance loci. Natural Ganoderma infection was monitored in the field, and the infection status of the 1200 Eg9PP individuals were recorded over 25 yr to generate a unique dataset.
Phenotype acquisition The G. boninense infection status was recorded biannually on the 1200 Eg9PP individuals over 25 yr, from the first year after planting (June 1987) to the uprooting of the genetic trial (June 2012). Symptoms were scored blindly on the basis of a six level scale, set up in the 1980s, and used since that time in the SOCFINDO plantations, with zero being a healthy palm tree and six a dead and fallen palm tree with the presence of Ganoderma fruiting bodies on the palm trunk (see Figure  S2  and Table  S2 ). A seventh level represented palm trees absent at the visit time, or dead for reasons other than Ganoderma disease. Raw data were then curated, and the occurrence of two events was recorded with the time associated: the first Ganoderma symptom appearance (T1S, first observation of score 2-6), and the death of the palm tree due to Ganoderma disease (TD, first observation of score 6).
A QTL region identified in the linkage analysis of the mapping population (n = 604) was investigated in further detail by genotyping all of the 757 sampled individuals from the 14 Eg9PP families with all of the SSR markers mapped in the region plus two markers (see Table  S4 ). DNA extraction, microsatellite amplification, and analyses were performed according to the protocol described in  Cochard et al. (2015) , and raw genotype data are available in Table  S5. The Eg9PP genetic map was constructed with CRI-MAP version 2. 507  (Green et al. 1990 ). This program allows simultaneous analysis of multiple families and performs multipoint linkage analysis. The nine families of the Eg9PP mapping population were treated as unrelated families, and all individuals were considered as being of undetermined sex.