dataesr/scientific-paragraphs-categorization huggingface dataset:
bohemicum. The M. bohemicum genome was locally aligned 2-by-2 using the BLAT algorithm  (25, 26)  against each of the nine selected genomes, and DDH values were estimated from a generalized linear model  (27)  These data confirm that M. bohemicum is a unique species more closely related to the M. avium complex. Nucleotide sequence accession numbers. The M. bohemicum strain DSM 44277 T genome sequence has been deposited at EMBL under the accession numbers CSTD01000001 to CSTD01000018.
The set of species sequenced by MGI consortium includes fungi engaged in the various types of mycorrhizal symbiotic interactions (e. g. , arbuscular, ericoid, orchid mycorrhizae and ectomycorrhiza). The species selected by MGI consortium belong to the phyla Glomeromycota, Ascomycota and Basidiomycota. They represent three different classes of Ascomycota (Leotiomycotina, Dothideomycotina and Pezizomycotina), and five orders within the class of Agaricomycotina (Basidiomycota) (Table  2 ). The generated data provide an excellent opportunity for the comparative genomics analysis of evolutionary adaptations of fungi to the symbiosis with plants. We were interested in how the fungal ABC genes, and, in particular, ABC transporters were influenced by the adaptations of fungi to their symbiotic lifestyle.
ABC genes identification To identify gene loci encoding ABC proteins in the fungal genomes, multiple tblastn and blastp searches against selected genomes were performed at the website of the Fungal Genomics Program of the Department of Energy Joint Genome Institute (JGI)  [25]. Sequences of S. cerevisiae and Coprinopsis cinerea ABC proteins representing all known subfamilies were used as queries. All hits producing E-values below 10 -4 were further analysed. Gene models were manually curated and, when necessary, the positions of N and C termini and exon-intron boundaries were adjusted.
Phylogenetic analysis Phylogenetic analysis was performed with the program package MEGA6  [26]. Initial assignment of identified sequences to the subfamilies was done separately for every species in a following way. All proteins identified in a particular genome were aligned with known ABC proteins from S. cerevisiae, Aspergillus nidulans, C. cinerea and Ustilago maydis using MUSCLE algorithm integrated in MEGA6 package. Neighbor-joining phylogenetic trees were reconstituted based on the obtained alignments. All recognized subfamilies of ABC proteins could be separated on trees produced in such way, allowing the fast assignment of the analysed sequences to the particular subfamily. The detailed analysis was performed separately for each subfamily and, in the case of ABC-B transporters, also for full-length and half-size proteins.
Multiple sequence alignments were constructed with the MUSCLE algorithm integrated in MEGA6 package using default settings. Alignments were quality trimmed with Gblocks 0. 91 b  [27]. Both full-length and trimmed alignments were used to produce phylogenetic trees. Neighbor-joining trees were constructed using Jones-Taylor-Thornton substitution model with 500 bootstrap replications. Maximum-likelihood trees were obtained using Jones-Taylor-Thornton model  [28]  with 100 bootstrap replications. Although the topologies of the phylogenetic trees produced with the different algorithms showed some minor differences, the same major groups of ABC proteins were recognized in all reconstructions.
D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA C5-H5Á Á ÁO2 i 0. (i) Àx þ 1; y À 1 2 ; Àz þ 1 2 ; (ii) Àx þ 1; Ày þ 1; Àz; (iii) Àx þ 2; y þ 1 2 ; Àz þ 1 2. Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT; program(s) used to solve structure: SIR2002  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: WinGX  (Farrugia, 2012). Phthalazine derivatives have been reported to act as anticonvulsants  (Carling et al. 2004) , potential inhibitors of serotonin reuptake  (Cashman & Ghirmai, 2009) , anti-proliferative agents against different human and murine tumor cells  (Hall et al. , 1992 (Hall et al.
For this work, and the work described above, the UM is run at 60 km horizontal resolution on 85 vertical levels, NEMO is at 25 km horizontal resolution on 75 vertical model levels, and CICE is run with five thickness categories. The coupled model is corrected using two separate 6-h-window DA systems: a 4DVAR system for the atmosphere assimilating the standard set of atmosphere data  (Rawlins et al. 2007 ) (with associated soil moisture content nudging and snow analysis schemes); and a 3DVAR First Guess at Analysis Time system NEMOVAR  (Waters et al. 2014)  for the ocean and sea-ice (using in situ SST, temperature and salinity profile, satellite SST, satellite altimeter, and sea ice concentration data). The background information in the DA systems comes from a previous 6-h forecast of the coupled model.
ESPC is a national partnership among federal agencies and the research community in the US to develop the future capability to meet the grand challenge of environmental predictions in the rapidly changing environment. The system will be based on NUOPC, use analysis fields of each component as initial conditions and make daily forecasts out to 10 days. Throughout each weekly cycle, predictions out to 30 days will be constructed. DA in coupled COAMPS currently consists of independent 3DVar analyses in the ocean and atmosphere. The first-guess fields (6-or 12-h forecasts) for each fluid are obtained from the coupled model state. This assimilation configuration is referred to as weakly coupled. For the global ESPC coupled model, a hybrid version of the Navy Coupled Ocean Data Assimilation (NCODA) 3DVAR  (Cummings & Smedstad 2013)  has been developed.
Quantification of the site occupancy by densitometry Evaluation of the N-glycosylation site occupancy percentage was assessed by densitometry using the ImageJ software. Briefly, for each interesting lane of the NuPAGE Bis-Tris gel electrophoresis, squares were used to select the zone of interest. Then, the selection was treated using the submenu of ImageJ software with the option called gel analysis thus generating lane profile plots. Lines were drawn manually on the plots to define precisely each area. Finally, the peak areas under the curve were measure using the wand tool.
Interpretations of the MS spectra Raw data were analyzed using MassHunter (B. 06. 00; Agilent Technologies). First, compound list was extracted using the "Find Compounds by Molecular Features" submenu of Masshunter Bioconfirm workflow (Agilent Technologies) with the following parameters: peaks filter with height, 1000 counts; peak spacing tolerance, 0,0025 m/z, plus 7,0 ppm; charge state, 3 maximum; isotope model, peptides. Then, using the "Define and Match sequences" submenu, heavy and light chains were imported and were edited with cysteine carbamidomethylation and methionine oxidation as global modifications and with trypsin digestion including 2 miss cleavages maximum. The sequences were matched with the extracted compound lists. The corresponding proposed spectral annotations were checked manually and the sequence coverage was calculated manually.
Moreover, the extracted ion chromatogram (EIC) was selected to interpret manually glycans, glycopeptides and tryptic peptides MS and MS/MS spectra. The LC-MS and MS-MS data have been uploaded to the Figshare public repository and are accessible through the link: http://figshare. com/articles/Production_of_glycosylated_human_ anti_Hepatitis_B_monoclonal_antibodies_in_the_microalgae_Phaeodactylum_tricornutum/ 1489703.
This completes and reinforces previous datasets which conclude that the diatom P. tricornutum possesses the ER machinery required for polypeptide translocation and protein quality control  [8, 11]. Such mechanisms are similar if not identical to the ones previously described for other eukaryotes  [30]. It is worth noting that the N-terminal peptide identified by MS and MS/MS for the heavy chain carries a N-terminal pyroglutamine residue (noted pQ on the Fig  4A ). This modification is formed by cyclisation of the glutamine residue (Q) and is very often reported for recombinant mAbs produced in mammalian cells  [3, 20, 21]. Such modification is described to be a chemical non-enzymatic modification occurring for the majority (up to 90%) during the cellular process  [31, 32]. It is also present on human IgGs as most of the N-terminal glutamine of heavy chains of human IgGs is cyclized to pyroglutamate  [33].
Alignments of the 101. 14 and M4 DNA sequences onto PN40024 were performed with PASS v. 2. 0 software  (Campagna et al. , 2009)  using auto-optimized parameters for trimming and 80% identity. The same platform was used for SNP calling. De novo assembly of unaligned reads was obtained with an optimized script of the Velvet 1. 2 package (VelvetOptimize;  Zerbino and Birney, 2008)  fixing a k-mer size of 27. Results are available at the http://genomes. cribi. unipd. it/grape/serres/ website.
mRNA-Seq and metabolite analyses Leaves and roots of both 101. 14 and M4 genotypes grown upon WW and WS conditions were collected from three plants at T1, T2, and T3 and from six plants at T4 and pooled as previously described in  Meggio et al. (2014). For both genotypes, the zero time samples (T0) were collected from six plants of each genotype grown under WW conditions. The whole experiment was performed on two separate biological replicates for a total of 36 samples from leaves and 36 samples from roots [2 genotypes×2 treatments×4 time points (T1-T4)+2 genotypes in WW conditions (T0)=18 samples×2 replicates] (Supplementary Fig. S1  at JXB online). Methods for wholetranscriptome analysis are reported in Supplementary Methods S1. The mRNA sequencing (mRNA-Seq) data obtained in this study have been deposited at the NCBI Short Read Archive (http:// www.
ncbi. nlm. nih. gov/Traces/sra/sra. cgi) under accession number SRA110531. ABA was extracted from leaf tissues using a modified protocol based on  Zhang et al. (2008)  (Supplementary Methods S2 at JXB online). For stilbene quantification, root samples were powdered in liquid N 2 and extracted in 3 vols of 90% (v/v) methanol, 0. 1% (v/v) formic acid (FA). After shaking at 4 °C for 10 min, samples were centrifuged at 10 000 g for 10 min at 4 °C, filtered by Millipore Millex HV cartridges (0. 45 μm), and dried in a Speed-Vac at room temperature for 90 min. The pellets were then solubilized in 4% (v/v) methanol, 0. 1% (v/v) formic acid. Liquid chromatographyelecrospray ionization-mass spectrometry (LC-ESI-MS) analyses were conducted using an Agilent Technologies 1200 Series capillary pump coupled with a dual ESI source on a 6520 Q-TOF mass spectrometer.
Multifactorial and pairwise statistical analyses Statistical analyses for discovering differentially expressed genes (DEGs) were performed with the DEseq R package (http://www. rproject. org/) (Maza et al. , 2013). In order to evaluate the individual effects of the genotype (101. 14 and M4), treatment (WW and WS), and time point (T1-T4) on gene expression, a multifactorial analysis was conducted using the multifactor designs method of the DEseq R package  (Anders and Huber, 2010 ; http://bioconductor. org/packages/release/bioc/html/DESeq. html). This method evaluates the weight of each factor considered in the analysis and its impact on DEGs, according to a false discovery rate (FDR)-adjusted P-value <0. 05. The genotype effect (101. 14 and M4) is indicated as 'G', the type of treatment imposed indicated as 'T' (WW and WS plants), and the time point considered within the stress treatment indicated as 'P' (T1, T2, T3, T4).
Ontology and differential clustering analysis (DCA) In order to classify those genes affected by WS treatment functionally, those DEGs that, based on the multifactorial analysis, were affected by all components (common DEGs between G, T, and P) and by the genotype and treatment (common DEGs between G and T), were associated with their Gene Ontology (GO) terms, imported in Blast2GO software v 2. 5. 0 and grouped into enriched GO categories  (Götz et al. , 2008). Within the most represented GO categories, those DEGs associated with GO terms related to plant hormones, secondary metabolism, sugars, stresses, cell wall, and transcription factors were selected for a DCA. This approach, previously applied by  Ihmels et al. (2005) , was further improved to better capture differential expression patterns and systematically characterize both similarities and differences in the fine structure of co-regulation patterns.
Acknowledgements This article is dedicated to the memory of our friend and colleague  Angelo Ramina. We miss his constructive criticisms and suggestions. The authors would like to thank  Dr Ian B. Dry  for his indispensable help in the process of manuscript revision. This work was supported by the  AGER  ' SERRES ' Project , [grant no. 2010' Project, [grant no. -2105]]. MC, AV, and FM performed the research. CB, AR, and ML designed the research. MC, AV, EM, FM, AP, AT, MDA, ASN, PB, and RS helped with specific physiological, metabolic, molecular, and bioinformatics analysies. MC, AV, NV, GV, and CB analysed the data. The manuscript was written by MC, AV, NV, GV, MB, ML, and  CB , and approved by all other authors.
Acknowledgements We are grateful for the financial support from the  UK's ESRC  for our research project ' Anti-Politics: Characterising and Accounting for Political Disaffection '  RES-000-22-4441. The research was conducted with the  UK's Hansard Society  and in particular with  Ruth Fox  and  Matt Korris  with the support of  Joel Blackwell. Some  of its other findings were reported in two Hansard Society publications: Audit of Political Engagement 9 Parts One and Two (available at: http://www. hansardsociety. org. uk/research/public-attitudes/audit-of-politicalengagement/). Anjelica Finnegan ,  Emily Rainsford ,  Emma Thompson  and  Kate Dommett  helped with the conduct of the focus groups. We thank all for their help in undertaking the research but absolve them from any responsibility for the arguments presented here.
Microarray hybridization and analysis Exponential phase cells grown in the presence of 1mM IPTG were pelleted by centrifugation washed twice with 0. 85% saline solution, and split into two cultures that contained either 1mM IPTG or no IPTG (CtrA depletion). RNA was isolated from triplicate samples of t = 0 S. meliloti cells as well as from +IPTG (control) and-IPTG (ctrA depleted) 1, 2, 4 and 6 hours after depletion were converted to cDNA and hybridized to custom Agilent microarrays containing 6046 S. melioti ORF (GPL18182). To determine which S. meliloti genes may be transcriptionally regulated by CtrA, the log 2 fold change (logFC) of expression between average triplicate-IPTG and +IPTG samples at 1, 2, and 4 hours were calculated using the limma package in R. The 6-hours time point, although highly correlative with the earlier time points, was excluded from the analysis due to a lack of replicate samples.
RNA isolation, cDNA synthesis and labeling, and microarray hybridization are as described  [40]. Only one channel was used for hybridization. Data were normalized as previously described  [35]. Normalized microarray data of IPTGtreated and non-treated (CtrA-depletion) samples were directly compared by using the Limma package in R  [66, 67]. A linear model was fitted to the normalized log2 values for each gene at the 1-, 2,-and 4-hour time points and used to generate estimated coefficients and standard errors for the compared samples. Moderated t-statistics, moderated F-statistics and log-odds of differential expression using an empirical Bayes approach were applied to the parameter estimates and standard errors from the linear models for each probe. P-value adjustment for multiple testing was performed using the Benjamini-Hochberg false discovery rate procedure.
Genes identified as differentially expressed had logFC values 1. 0 or -1. 0 and an adjusted p value of 0. 05. For heat map generation, the replicate-average log2 expression values for the 126 differentially expressed gene identified above were row normalized across the time points as described  [35]. Normalized values were then clustered by using Gene Cluster 3. 0 and the city block similarity metric with complete linkage clustering. For the heat map of direct and indirect targets of CtrA, the average log2 expression for each gene in +IPTG samples (1-, 2-and 4-hour) and the replicate-average log2 expression values for a gene at each time point was row normalized and clustered as described above. The microarray data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  [68, 69]  and are accessible through GEO Series accession number GSE68218 (http://www.
Acknowledgments We are grateful to  Xavier De Bolle  and  Peter Mergaert  for comments on the manuscript. We thank  Veronique Dhennin  of the UMR8199 sequencing service LIGAN-PM  Equipex  ( Lille Integrated Genomics Advanced Network  for personalized medicine). We thank the  BioMicro Center at MIT  for their contributions to the microarray studies. We are grateful to  Didier Monte ,  Silvia Ardissone  for technical assistance. We also thank  Turlough M. Finan  for the Min system mutant.
This work was supported by the  French Agence National Recherche  ( ANR-JCJC-2011-Castacc ), the  Region Pas-De-Calais CPER  (to FP, SSM, AF and EGB), the  National Institutes of Health (NIH)  Grant  GM31010  (to GCW) and  P30 ES002109  (to the  MIT Center for Environmental Health  insight to fit the diverse lifestyles of different bacteria. Sciences). NJDN was supported in part by the  NIH  pre-doctoral training grant  T32GM007287. The microarray studies were also partially funded by the  National Cancer Institute of the NIH  under Award  P30 CA14051. GCW is an  American Cancer Society  professor. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods subjects and motor task Data were obtained from ten healthy young subjects (young group [YG], two females and eight males) aged 21-30 years (mean age 26. 6±2. 95 years) and ten frail aged adults (frail group [FG], six females and four males) aged 83-95 years (mean age 87. 9±4. 2 years). FG was composed of patients who were following a functional rehabilitation program managed by only one physiotherapist. These patients were suffering from different pathologies, involving loss of functional independence such as chronic obstructive pulmonary disease (COPD) (×2), shoulder, hip (×2), and thoracolumbar (×3) deficiency (see Table  1  for a description of these patients). They were included in this group according to the Fried criteria of the frailty syndrome (unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity).
recording system and data analysis Kinematic trajectories during TUG were recorded by means of a wearable system for real-time balance assessment in the aged subjects. The instrument used a Kinect sensor that allowed us to detect the subjects' TUG movements. This sensor produces accurate results, especially for when tracking shoulder movements (segment lengths and angle estimation). 23 The sensor can reach a frame rate of 30 images per second at a resolution of 640×480 with 8 bits per channel. The infrared camera operates at 30 Hz. The field of view for the system is 58° horizontal, 45° vertical, 70° diagonal, and the operational range is between 0. 8 m and 3. 5 m. The Kinect does not require any calibration. It was placed at a distance of 2. 5 m from the chair with a tilt angle of 20°. No markers or wearable sensors were attached to the body.
This choice is restricted by space limitations and does not imply ignoring that a general assessment of poverty and inequality should also include other nonmonetary dimensions (e. g. health, education) and other monetary variables (e. g. wealth). Other chapters in the Handbook contribute to fill those gaps. The analysis in this chapter is mostly focused on inequality and poverty within countries and not within supra-national regions or in the world. 1  Although issues of global inequality are increasingly relevant, inequality is still primarily a national concern. People are generally worried about inequality mainly in their countries, and public policies are typically aimed at reducing disparities among individuals within national boundaries. The empirical evidence shown in this chapter is drawn from the academic literature, regional and country papers, and open-access databases, in particular the PovcalNet project developed in the World Bank.
Although most of the evidence is based on statistics obtained from national household surveys, we also report results from tax records (the World Top Incomes Database, WTID) and international surveys (the Gallup World Poll) to illustrate some issues. Even though the main purpose of the chapter is presenting basic evidence on levels and trends, we also briefly review the main discussions on determinants of recent changes in inequality and poverty. The rest of the chapter is organized as follows. In section 2 we briefly characterize the economies in the developing world, and discuss the data sources and some measurement issues. The following two sections are assigned to the main topic in this volume -inequality. In section 3 we document the levels of income inequality in the developing world, while in section 4 we summarize the evidence on trends since the early 1980s.
Distributive statistics for the developing world are rare before the 1970s, and reasonably robust only from the 1990s on. There has been a remarkable increase in the availability of national household surveys over the last decades. A chapter like this one, that includes a broad assessment of income inequality and poverty in developing countries, could hardly have been written two decades ago, and is a sign of the huge progress made on data collection. However, as we discuss below, data limitations are still stringent, and allow only a still blurred picture of inequality and poverty. The databases for international distributive analysis can be classified into two groups: those that produce statistics with microdata from surveys or administrative records, and those that collect, organize and report statistics from other sources.
The former group includes the World Bank´s PovcalNet, the Luxembourg Income Study, the World Income Distribution database, the World Top Incomes Database and some regional initiatives. The second one includes the seminal work by  Deininger and Squire (1996)  and its follow-up -the WIDER´s World Income Inequality Database, the All the Ginis database, and some other projects. The main source of information for poverty and inequality analysis at a large international scale in the developing world is the World Bank´s PovcalNet, a compilation of distributive data built up from national household surveys, generally fielded by national statistical offices. PovcalNet, used for the World Bank's World Development Indicators, includes statistics constructed mostly from household survey microdata, and in some few countries from grouped tabulations.
At the moment of writing this database includes more than 850 surveys from almost 130 countries, representing more than 90% of the population of the developing world, spanning the period 1979-2011. The website of PovcalNet provides public access to data to generate estimates for selected countries and alternative poverty lines from grouped data. 4  Martin Ravallion and Shaohua Chen, the developers of PovcalNet, have produced several papers exploiting the dataset  (Ravallion and Chen, 1997; Chen and Ravallion, 2001 , 2010 , 2012). This project has been increasingly influential in shaping the assessment of inequality, and in particular poverty, in the developing world by researchers and policy practitioners. It is, for instance, the source used to monitor the poverty-reduction goal of the MDGs.