dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the amplicon will be sequenced on both strands. The forward and reverse sequences are then compared to detect and eliminate sequencing errors. The forward sequence of the amplicon can be obtained from the reverse sequence by reverse complementing. Reverse complementing and alignment of the sequences of both strands can be performed using sequencing software (see below). Currently, Sanger sequencing platforms yield highly accurate sequence data of up to about 1,000 nucleotides in length  (Metzker 2005). To sequence larger amplicons, additional sequencing reactions are performed using primers spaced 500 to 800 nucleotides apart (the partial sequences must overlap to allow assembly into the full sequence of the amplicon). There are many programs (freeware and nonfreeware) available for analysis of sequence data, so it is really up to the user's personal preference as to which one to use.
A simple and free general-purpose program that can be routinely used to manipulate, edit, align, and assemble sequences is BioEdit (http:// www. mbio. ncsu. edu/bioedit/bioedit. html). An extensive compilation of software programs for a variety of evolutionary and molecular studies can be found at Joe Felsenstein's website (http://evolution. genetics. washington. edu/phylip/software. html). The sequences of the DNA molecules in the PCR product can differ due to biological reasons (i. e. , nucleotide polymorphism). For example, polyploid or heterozygous plants can contain different gene copies, and/or different alleles of a gene, which exhibit different nucleotide sequences. Sanger sequencing yields a single sequence resulting from the superimposition of the sequences of the different genes or alleles.
Primer Design for CAPS. Primers must flank the mutation (and thus the restriction site) and yield an amplicon that can be easily discriminated from the resulting digestion products by gel electrophoresis if the enzyme recognition site is present. For example, you can design primers to amplify a 300 bp fragment with the restriction site near the middle. Primer Design for dCAPS. Here, primer pairs consist of one ''dCAPS'' primer that creates the restriction site in the amplicon, and of one ''conventional'' PCR primer. dCAPS primer sequences targeting the codon of interest can be generated using the free software dCAPS Finder 2. 0 (http://helix. wustl. edu/ dcaps/dcaps. html) (Neff et al. 2002). This software provides a range of dCAPS primer and restriction enzyme combinations to be used in dCAPS assays.
Three technical replicates (i. e. , replicates of the same biological sample) should be run and the resultant quantification cycle (Cq) values averaged. Plate-to-plate variation can be assessed by including interplate control samples (i. e. , including one or more common samples on each plate). Individuals from multiple populations could also be included to evaluate background gene levels in the species. Absolute vs. Relative (Comparative Cq Method) Quantitation. Before beginning the qPCR experiment, one must decide how the data are to be analyzed. The results of your assay can be analyzed either by absolute or relative quantitation methods. Absolute quantitation involves determining quantities of unknown samples from a standard curve. This method can be useful if one wants to know the exact copy number of a particular gene of interest in a particular individual.
Reagent Amount for one reaction Amount for three triplicates each of 10 samples a ---------------------------ml --------------------------EvaGreen 5. 0 165. 0 forward primer (0. 2 to 0. 4 mM) 0. 3 9. 9 reverse primer (0. 2 to 0. Measuring Gene Expression Using Reverse-Transcription-qPCR (RT-qPCR). RT-qPCR is currently the technique of choice for gene expression quantification. However, a range of factors can influence the reliability of direct gene expression measurement (e. g. , experimental error due to variations in RNA or cDNA concentration among samples, operator error, and variation in the efficacy of the reverse-transcription reaction). Thus, expression data is generally analyzed as relative expression ratios using a normalization strategy: raw gene expression data is normalized using expression data of one (or preferably several) reference gene(s).
Obtaining Expression Data for Candidate Reference Genes. This process is as follows: 1. Choose a set of candidate reference genes. Tip: A crucial point here is to collect plant material rapidly and to neutralize plant metabolism without delay to avoid modifications of the gene expression patterns induced by response to wounding. The most widespread and effective method for this purpose is snap freezing of plant material in liquid nitrogen, followed by storage at 280 C. Frozen plant material must not be allowed to thaw, and must be processed as quickly as possible to avoid RNA degradation (see RNA extraction above). Tip: At least six candidate genes should be tested for stability of expression using RT-qPCR. It is thus wise to start the procedure with more than six potential candidate genes, because this means that at least six candidate genes must pass steps 2 to 13 below.
Ensembl annotated pre-miRNAs  (30)  were downloaded, and we excluded pre-miRNAs residing on coding exons and those annotated in miRBase-V20, leaving 227 pre-miRNAs. CpG islands (CGIs) were downloaded from UCSC genome browser database  (31)  and in-house custom annotated CGIs. Mapped small RNA and histone modification (aligned BAM files and peak calling) data from h1-ESC cell lines were downloaded from ENCODE  (32).
For pre-miRNAs residing on a coding host, upstream regions were extended up to the 5 -ends of host genes, and examined for evidence of independent transcription that are distinct from host promoters. For pre-miRNAs with undetected alternative promoters, it remains unclear whether they are co-transcribed by host genes or have independent promoters that remain undetected in our datasets. However, if pre-miRNAs are located close (<5 kB) to the 5 -ends of host genes, and had no evidence of independent transcription, we considered them to be transcribed by host gene, resulting in 13 such pre-miRNAs.
As an illustrative example, primary transcript of the intergenic miR-17-92 cluster is shown with full-length transcript, CAGE tags and H3K4me3 peaks (Figure  1B ). CAGE-seq defined TSS is located 1. 1 KB upstream from the first pre-miRNA, where the representative TSS is determined by the position of dominant CAGE tags within the tag cluster (Supplementary Figure  S1A ). Six pre-miRNAs of the miR-17-92 cluster are encoded by a single primary transcript, as reported in human  (6). Counting the 59 copies of miR-430 in a cluster as a single gene, we annotated pri-miRNA TSS for ∼58% of total pre-miRNAs (Figure  1C ). We were unable to detect pri-miRNA promoters for 55 intergenic pre-miRNAs (excluding the miR-430 cluster) and 42 intronic pre-miRNAs residing in protein coding host genes. Lack of evidence for independent transcription of intragenic pre-miRNAs suggests that they are either co-transcribed with their host genes or, alternatively that intragenic pre-miRNAs might have tissue specific pri-miRNAs (13) that remain undetected in the analyzed datasets.
However, this cannot be confirmed without additional data. The lack of pri-miRNA TSSs for 56 intergenic pre-miRNAs might be due to one of several reasons: (i) tissue-specific or low expression that remains undetectable in our dataset or (ii) transcribed by PolIII (4) that is undetected by CAGE-seq. Even so, the depth of the annotated pri-miRNAs is comparable to previous studies in human and mouse  (7, 8). We also analyzed the region between the annotated pri-miRNA promoters and pre-miRNAs for potential alternative promoters, and identified additional CAGE TCs associated with 47 pre-miRNAs (Supplementary Table  S3 ), but those TCs had low expression levels compared with the main identified promoter. The TSSs of predicted pri-miRNAs are located from a few hundred bases up to tens of kB upstream of pre-miRNAs (Figure  1D ), similar to previous observations in human and mouse  (7, 8).
ACKNOWLEDGEMENTS C. N. was supported by B. L. , V. M. S. , P. M. , J. B. A. , during different stages of the project. V. M. S. acknowledges grant to  Genomics Core Facility at the University of Bergen , which is part of the  Norwegian Genomics Consortium. Author Contributions: C. N. , F. M. , B. L. conceived the project. C. N. analyzed data. C. N. , F. M. , B. L. interpreted results. M. C. , D. C. , L. B. C. analyzed and interpreted in situ and 5 RACE PCR results. C. N. , F. M. , B. L. wrote the first draft of manuscript with input and revision from M. C. , L. B. C. , D. C. , P. M. , P. C. , V. M. S, J. B. A.
FUNDING  Norwegian Research Council  (YFF to B. L. );  Bergen Research Foundation  awarded (to B. L. );  European Commission  [ FP1313-HEALTH-306029  'TRIGGER' to P. M. ]; National Science Center [ 2014/14/E/NZ6/00162 , Poland];  EU   FP6  large integrated projects EuTRACC,  ZF Health  and  BBSRC [BB/L010488/1  to F. M. and B. L. ];  EU  project  ZF-Health [FP7/2010-2015  grant agreement no. 242048  to M. C. and L. B-C. ];  ANR  [ ANR-2012-BSV4-0004-01 ];  European Research Council  [AdG 322936]; INSERM (to 3080 Nucleic Acids Research, 2016, Vol. 44, No. 7 M. C. );  Danish Cancer Society  [ R98-A6446  to J. B. A. ];  Research Grant  from the  Japanese Ministry of Education, Culture, Sports and Technology (MEXT)  to the  RIKEN Center for Life Science Technologies  (to P. C. ). Funding for open access charge:  EU  project  ZF-Health [FP7/2010-2015  grant agreement no.
III. GNSS SIGNAL MODEL & NEW STATE-SPACE FORMULATION Taking into account the problem at hand (i. e. , carrier phase tracking under scintillation conditions), a simplified signal model with a perfect timing synchronization can be considered, at the input of the carrier tracking stage is given by y k = α k e jθ k + n k , (2) 1 The CSM has been embedded in the so-called Cornell Scintillation Simulation Matlab toolkit, which is available at http://gps. ece. cornell. edu/tools. php. where k stands for the discrete time t k = kT s , A k is the signal amplitude at the output of the correlators after accumulation over T s , the amplitude α k may include the scintillation amplitude effects, α k = A k ρ s,k ; the carrier phase includes both the phase variations due to the receiver's dynamics, θ d,k , and the scintillation phase variation, θ s,k , θ k = θ d,k + θ s,k ; and the Gaussian measurement noise is n k ∼ N (0, σ 2 n,k ).
We used two large, publicly available data sets of HIV-1 envelope sequences, both obtained from plasma samples by high-fidelity limiting-dilution PCR  40, 41. These two data sets yielded very similar stop codon mutation frequencies after dividing by the number of possible stop mutations, or non-sense mutation targets (NSMTs), 4. 7 Â 10 -5 and 4. 8 Â 10 -5 (Table  3 ). However, as in the shuttle vector, these mutations were not evenly distributed throughout the env sequence. The 1 kb region encoding the gp120 OA domains harboured only 21% (17/82) of the total observed stops despite encompassing 40% of the env sequence (Fisher's test: P ¼ 0. 016). To further explore this, we carried out a similar analysis using viral DNA instead of plasma-derived sequences. We purified total DNA from PBMC pellets obtained from 10 untreated, chronically infected patients, amplified 50 individual env clones per patient by high-fidelity limiting-dilution PCR, and subjected them to massive parallel sequencing using the Illumina technology.
This search was performed using the HIV sequence database (www. hiv. lanl. gov). The deduced protein sequences were obtained for each reading frame and Shannon entropy at each site of the alignment (Fig. 2a ) was calculated as H ¼ À S p i log(p i ), where p i is the frequency of each amino acid present in the alignment, using the Entropy-One tool of the HIV sequence database. These alignments were also used to calculate the abundance of mutationprone motifs (GG, GA, TGG and so on) in each subtype (Table  2 ). To infer the mutation rate from patient data using the lethal mutation method, premade curated alignments were downloaded from the HIV sequence database (see links on Table  3 ). Following previous work  38, 39  , at each site of the alignment we counted the number of UGA, UAG and UAA stop codons and the number of codons that could be mutated to one of these stops by one or two different single substitutions to obtain the total number of NSMTs.
Software. Calculations and statistics were performed with MS Excel, IBM SPSS v19, and R (www. r-project. org). Protein structure was visualized with PyMOL (www. pymol. org), and RNA structure with Qiagen CLC Main Workbench. Sanger chromatograms were edited with Staden v2. 0 (staden. sourceforge. net). Sequences from the HIV database were aligned with the Muscle algorithm implemented in MEGA v5 (www. megasoftware. net). Fastq files from Illumina sequencing were cleaned and trimmed using FASTX toolkit v0. 0. 14 (hannonlab. cshl. edu/fas-tx_toolkit) and dereplicated using Prinseq-lite v0. 20. 3 (prinseq. sourceforge. net). Sampling, pooling and mapping of reads to obtain patient consensus sequences was done with Bowtie 2 v2. 2. 4 (bowtie-bio. sourceforge. net) and VICUNA. Reads were mapped to references using V-Phaser2 and MOSAIK-2.
Acknowledgements We thank our laboratory members  Pablo Hernandez ,  Silvia Torres ,  Joan B. Peris  and  Raquel Garijo  for technical support,  Professor Ricardo Flores  ( IBMCP-CSIC ) for providing the PSTVd clone and helpful comments on the manuscript,  Professor Menendez Arias  ( CBMSO-CSIC ) for providing us the HIV-1 RT and helpful comments, and  Dr Lopez-Galı ´ndez  ( Instituto de Salud Carlos III ) for help with the limiting-dilution PCRs. We want to particularly acknowledge the patients in this study for their participation, and  Dr Lopez-Aldeguer  ( Hospital Universitario La Fe ) and the  HIV BioBank  integrated in the  Spanish AIDS Research Network  and collaborating centres for the generous gifts of clinical samples used in this work. The  HIV BioBank , integrated in the  Spanish AIDS Research Network , is supported by the  Spanish Instituto de Salud Carlos III  (grant  RD06/0006/0035  and  RD12/0017/0037 ).
CoRIS is funded by the  Instituto de Salud Carlos III  ( RIS C03/173  and  RD12/0017/0018 ). This work was funded by the  RETIC of Instituto de Salud Carlos III  ( RD12/0017 -RIS ), a  Starting Grant  from the  European Research Council  ( ERC-2011-StG-281191-VIRMUT ), and a grant from the  Spanish MINECO  ( BFU2013-41329-P ) to R. S, and a PhD fellowship from the  Generalitat Valenciana  to P. D-C.
a b s t r a c t Tree crown size determines among others tree's growth, carbon sequestration, shading, filtering of fine air particulates, and risk of wind-breaking. The dependence of crown size on species, resource supply, and tree age complicates an accurate evaluation of a tree's space requirement, and its size-dependent functions and services in urban as well as in forested areas. Based on a world-wide dataset of tree crown measurements of 22 common urban tree species we first derived species-specific crown radius-stem diameter relationships for open grown conditions. By cluster analysis we then assigned the 22 species to 5 crown extension types and developed mean relationships of tree height, crown radius, crown projection area, and crown volume depending on tree diameter for each type.
Tree growth experiments covering different density and competition levels show, that tree height growth is rather stable within a broad range of stand density levels from solitary to dense stand conditions. Tree crown and tree stem diameter growth in contrast are very sensitive to competition. So, especially in the case of the allometry between tree diameter and crown dimensions a transfer of size relationships and dynamics from forest trees to open-grown trees in urban areas (e. g. , along boulevards, in parks, places) must take into account those forest trees which exhibit maximum crown size at a given diameter i. e. which grew up under very low competition. We use a rather unique dataset from urban trees worldwide and suitable trees from long term experimental plots comprising 22 species in order to scrutinize their stem diameter-crown width allometry.
Material Our data cover in total 39,057 single tree observations with information about crown size. We pooled data from urban trees of 9 metropolises worldwide (Table  1 ), comprising solitary street trees, as well as trees from city parks and urban forests. When collecting data from city trees we paid attention to have a balanced sample size of trees from city centres, from suburban, and from rural areas. By this, sampled trees followed an urban climatic gradient. The investigated metropolises represent different climates ranging from boreal to subtropical conditions. We further pooled data from long term forest research plots mainly located in Southern Germany (Table  1 ), where trees grew under different competition conditions ranging from solitary situations to strong canopy closure. This combination results in a coverage of 22 different tree species which are internationally important in urban greening.
In case of sampling solitary grown trees we tried to exclude pruned trees in order to get reliable values of the species-specific maximum reach of the crown. As all measurements followed internationally concerted standards, the quality of the data is comparable independent of the data source. For all trees, -among other variables -diameter at breast height, d, and crown radii, r in the eight subcardinal directions (N, NW,. , NE) were measured. Diameters at breast height were recorded using diameter measurement tapes. For determining crown radii and crown projection area, there are two frequently applied methods both determining crown radius as the distance from the centre of the trunk to the perimeter of the crown (Fig. 1 ,  Röhle, 1986). The vertical sighting method  (Preuhsler, 1979)  is quick, though, rather inaccurate compared to the projection method, which uses a plummet and is very accurate but time consuming  (Röhle and Huber, 1985; Röhle, 1986).
The measurements behind our data were mostly done using the vertical sighting method whereas in about 10% of each days' measurements the plummet was used to continuously train the measuring person and minimize his/her personal bias. The mean crown radius cr is to be understood as the quadratic mean, cr = (r 2 N + r 2 NW +. + r 2 NE )/8 ensuring a bias-free transition between crown radius to crown projection area, cpa = cr 2 × , which expresses the area occupied by a tree. For most species, our data cover a diameter at breast height range from 5 up to more than 60 cm, and a crown radius range from 1 to more than 5 m (Table  1 ). The tree ages start at about 20 years and end with more than 100, in some cases even with more than 200 years. For urban trees, information about tree age was derived from tree cores, for trees from long-term plots tree age is known from plot documentation.
Additional relationships While the typical cr-d-curves give an intuitive impression of intra-tree size relationships and should be used to identify allometric types with empirical data for tree species not covered in this study, we transformed them also into crown projection area (cpa)-stem diameter curves, simply based on the equation cpa = cr 2 ×. This results in the allometric model ln(cpa) = a + ˛ × ln(d) (3) with a = ln + 2 × a and ˛ = 2 × ˛ when a and ˛ are taken from Eq. (  2 ). The cpa-d-curves show the diameter dependent growing space requirement for each allometric type. In addition, we took all data where tree heights h were measured and fitted the allometric model ln (h) = a + ˛ × ln (d) (4) for describing the relationship between diameter and tree height. In order to cover open-grown trees, which have lowest heights at a given diameter, we fitted the model at the 5% conditional quantile.
To our knowledge, the authors' dataset is rather unique comprising standardised crown measurements since the 1950s. Compared with other more specific or fragmentary crown studies  (Alves and Santos, 2002; Monserud and Marshall, 1999; Osunkoya et al. , 2007)  the reported species-specific allometric relationships and underlying dataset have the advantage of (i) a wide range of boreal, temperate, mediterranean, and subtropical tree species with different ecological and morphological traits, (ii) a wide range of neighbourhood conditions including trees growing in maximum density and solitarily, (iii) availability of combined measurements of several crown measures at the same tree, so that the convex hull can be derived, (iv) combination of trees in urban and rural areas, (v) standardized crown measurements according to  Röhle (1986) ,  Röhle and Huber (1985)  and  Pretzsch (2009, pp.
Acknowledgements Thanks to the AUDI environmental foundation for funding the project " Response of urban trees on climate change " and to the  Bavarian State Ministry for Environment and Health  for funding the project "Urban trees under climate change: their growth, functions & services, and perspectives" ( TUF01UF-64971 ). We further wish to thank the  Bavarian State Ministry for Nutrition, Agriculture and Forestry  for permanent support of the project  W 07  " Long-term experimental plots for forest growth and yield research " ( 7831-23953-2014 ). While the first two projects provided records for urban trees, the latter contributed information on trees in forests. Thanks are also due to  Ulrich Kern  for graphical artwork, and anonymous reviewers for their helpful criticism.
Patients and Tumors Eighty-seven specimens of PHEOs and PGLs of sympathetic origin obtained from 79 unrelated patients were retrospectively selected in this study according to the following criteria: The tumors were obtained from three different institutions in France (Strasbourg, Marseille, and Nancy University Hospitals) and were distributed as follows: 48 sporadic (42 PHEOs, 6 sympathetic PGLs), 23 SDHx (5 PHEOs, 18 sympathetic PGLs), 7 VHL (5 PHEOs, 2 sympathetic PGLs), 5 RET (PHEOs), 3 NF1 (PHEOs), and 1 hypoxiainducible factor 2α (EPAS1/HIF2A) (sympathetic PGL). The 23 samples of SDHx tumors were obtained from 23 distinct tumors belonging to 15 patients and were distributed as follows: 9 SDHB and 14 SDHD. Data were acquired under regular clinical care conditions, with Ethics Committee approval obtained for the use of these data for scientific purposes.
HRMAS NMR Spectroscopy HRMAS NMR spectra were recorded on a Bruker Avance III 500 spectrometer operating at a proton frequency of 500. 13 MHz, installed at the Pathological Department of Strasbourg University Hospital. The amount of frozen tissue used for NMR analysis ranged from 15 to 20 mg. A one-dimensional (1D) proton spectrum using a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence with presaturation of the water signal was acquired for each sample. 1D HRMAS NMR spectra were bucketed into integral regions 0. 01 ppm wide (ppm range, 1-8. 65) using AMIX 3. 8 software (Bruker GmbH, Rheinstetten, Germany) and exported into SIMCA P (version 11. 0; Umetrics AB, Umeå, Sweden). To confirm resonance assignments, two-dimensional (2D) heteronuclear ( 1 H-13 C) experiments were also recorded immediately after the end of 1D spectra acquisition.
Because the duration of these experiments is long and tissue degradation occurs during NMR acquisition, only a few representative samples were analyzed by 2D experiments. Metabolites were assigned using standard metabolite chemical shift tables available in the literature  [11, 12]. Metabolite quantification has been previously described  [6]. Briefly, quantification was performed using an external reference standard of sodium lactate, scanned under the same analytical conditions. Spectra were normalized according to sample weight. Peaks of interest were automatically defined by an in-house program using MATLAB 7. 0 (Mathworks, Natick, MA). Peak integration was then compared to the one obtained with the lactate reference and was corrected according to the number of protons. For our experiments, only peaks that were well resolved in 1D CPMG spectra were quantified.