dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunofluorescence analysis To stain mitochondria of the wild-type cell lines, 200 nM MitoTracker Red CMXRos (Invitrogen) were added to the culture, followed by a 20 min incubation and washes in PBS. Then wild-type cells were fixed with 4% formaldehyde in PBS, permeabilized with 1% Triton X-100, and spread on poly-L-lysine-coated slides. The slides were then incubated for 45 min in PBS containing 5% BSA, followed by incubation in PBS with 2% BSA and the primary antiserum, 1:50 diluted rabbit anti-TDH, mouse anti-AceCS or mouse anti-PDH-E1a. After washing with PBS, the slides were incubated with 2 mg/mL Alexa 594 anti-rabbit IgG conjugate or Alexa Fluor 594 anti-mouse IgG conjugate (Molecular Probes). Slides were then washed and mounted in the SlowFade antifade reagent (Invitrogen). Cells were visualized with a Leica DM5500B microscope, and images were captured by an ORCA-R2 camera (Hamamatsu) and Leica MM AF Imaging System software (MetaMorph).
Measurements were recorded at 25uC with an ERETIC method. This method provides an electronically synthesized reference signal  [33]. Acquisition conditions were as follows: 90u flip angle, 5,000 Hz spectral width, 32 K memory size, and 9. 3 sec total recycle time. Measurements were performed with 256 scans for a total time close to 40 min. Before each experiment, the phase of the ERETIC peak was precisely adjusted. Resonances of the obtained spectra were integrated and results were expressed relative to ERETIC peak integration. The linear production of pyruvate and acetate throughout the experiment was confirmed by 1 H-NMR quantification of the endproducts excreted by the wild type trypanosomes incubated for 6 h in PBS containing 4 mM [U- 13 C]-glucose (data not shown).
6C ). Deletion of both TDH alleles was confirmed by PCR analyses (Fig. 6B ), western blot analyses and enzymatic assays (insets of Fig. 6C ). Second, RNAimediated down-regulation of PDH-E2 was performed in the Dtdh background. Growth of the Dtdh/ RNAi PDH. i cell lines was abolished three days post-induction before cell death seven days later (Fig. 6E ). Addition of 4 mM acetate in the medium does not rescue growth of the Dtdh/ RNAi PDH. i mutant (data not shown) suggesting that acetate and/or acetyl-coA need to be produced inside the mitochondrion to feed the essential mitochondrial fatty acid pathway, may be through the production of the precursor butyryl-CoA  [36]. This result confirms that abolition of molecular weight markers are indicated in kDa on the right margin of each panel. The PDH and TDH activities (milliunits/mg of protein) were determined in both forms of the parasite.
Threonine contributes to acetate production in BSF in vivo BALB/c mice immunocompromised by Endoxan treatment were injected with wild-type and RNAi PDH cells and kept with or without doxycycline, a stable tetracycline analog, in the drinking water to down-regulate expression of PDH-E2. Animal survival and the blood parasite levels were monitored. No differences were observed between the four groups of animals, in which parasite density started to rise at day three post-infection. All mice were dead at days 6-7 post-infection (data not shown). This shows that acetate production from glucose is not necessary for the viability of T. brucei in vivo, suggesting that a possible acetate source (threonine) that is present in the blood is absent in the threonine-depleted in vitro culture medium. As mentioned above, mammalian blood contains approximately 150 mM threonine  [35, 37] , which is 10times higher than in the threonine-depleted IMDM medium.
This work was supported by the  Agence Nationale de la Recherche (ANR)  through grant  ACETOTRYP  of the ANR-BLANC-2010 call to FB and PMoreau (http://www. agence-nationale-recherche. fr/). FB is also supported by the  Centre National de la Recherche Scientifique  (CNRS, http://www. cnrs. fr/), the  Universite Bordeaux Segalen  (http://www. univ-bordeauxsegalen. fr/fr/index. html),the  ParaMet PhD programme of Marie Curie Initial Training Network  ( FP7)  (http://www. paramet. eu/), the  Conseil Regional d'Aquitaine  (http://aquitaine. fr/). FB and PV are also supported by the  Laboratoire d'Excellence (LabEx) ParaFrap   ANR-11-LABX-0024  (http://www. agence-nationale-recherche. fr/investissementsdavenir/AAP-LABEX-2011. html). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
sanger. ac. uk/genetics/CGP/Census/). Oligonucleotide positions were defined according to the NCBI36/hg18 assembly  (March 2006). Hybridisation, scanning and feature extraction were carried out as previously described  (Buffart et al, 2008). The aCGH data have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al, 2002)  and are accessible through GEO accession number GSE41253 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE41253). Preprocessing and processing. Data normalisation, segmentation and calling were performed in R (v. 2. 8. 2 and 2. 13; http://www. r-project. org), using median normalisation and Wave Smoothing and CGH-call packages (van de  Wiel and Zhang, 2007). For visualisation and downstream processing, data were analysed in Nexus Copy Number v5. 1 (BioDiscovery, Inc.
, El Segundo, CA, USA). The WECCA (Weighted Clustering of Called aCGH Data) R package  (Van Wieringen et al, 2008)  was used for unsupervised hierarchical clustering (total linkage and overall similarity algorithms). Degree of genomic instability: number and length of alterations. To determine the degree of genomic instability in each subgroup of tumours (BRCA1/2/X and sporadic), total number of alterations and of a particular type (homozygous deletions (HDs)/losses/gains/ amplifications) were calculated per sample. Total size of altered genome and size accounted for by gains and losses was calculated by adding up the lengths of individual segments. Next, average number of changes and average size of altered genome were calculated for sporadic tumours and for each group of familial tumours.
02). The list of potentially group-specific regions was further refined using data from the TCGA ovarian study (TCGA, 2011) as described in Supplementary Materials. Immunohistochemical analysis. To validate our hybridisation and analytical approaches, we selected three high-amplitude events (HDs at the CDKN2A and RB1 loci and amplification at the CCNE1 locus) to determine the consistency between the assigned DNA copy number status and the expression levels of the target proteins. Details are explained in Supplementary Materials and shown in Supplementary Table  S2. Statistical analyses. Statistical analyses were performed using SPSS 17. 0 (SPSS Inc. , Chicago, IL, USA). Comparison of continuous variables (number and size of alterations between different tumour groups and clusters) was done using Student's two-tailed t-test (for variables of normal distribution) or the Mann-Whitney test (non-parametric distributions).
ACKNOWLEDGEMENTS We are very grateful to  Marı ´a del Carmen Gonzalez-Neira  and  Victoria Ferna  ´ndez (from the  CNIO Human Cancer Genetics group)  and to the  CNIO Tumor Bank Network , in particular to  Marı ´a Jesus Artiga , for exceptional coordination of tumour collection and clinical data retrieval. We also thank the  CNIO Histology and Immunohistochemistry Core Unit , especially  Lydia Sa  ´nchez for excellent assistance. Also, we would like to acknowledge  Paul P Eijk ,  Danie ¨lle Israeli ,  Franc¸ois Rustenburg ,  Dirk Vanessen  and  Jolanda Reek  ( VU University Medical Center, Department of Pathology, Amsterdam, The Netherlands ) for their technical and laboratory support. MJG is the recipient of a research contract from the  Instituto de Salud Carlos III, Ministerio Espan ˜ol de Sanidad y Consumo (Miguel Servet Program ,  CP07/ 00113 ).
MMK receives financial support from the  Fundacion La Caixa. This study was funded by the  Fondo de Investigacion Sanitaria (FIS) ,  Instituto de Salud Carlos III  (grants  CP07/00113  and  PS09/01094 ). This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
15 quasars deg -2 (see  Ross et al. 2012). Exposure times for each plate are determined during observations to obtain a uniform depth across the survey; on average, a plate is observed for five individual exposures of 15 minutes each. The data are processed and calibrated by a data reduction pipeline referred to as "idlspec2d"  (Schlegel et al. in prep. ). The functions of idlspec2d that are of consequence to Lyα studies occur primarily in the first stage of the pipeline, where data are extracted from the CCD images. In this stage, the variance for each pixel is estimated using read noise and the observed photon counts, sky background is subtracted using a model derived from the sky fibers, and flux calibration is performed using the spectra from the standard stars. Each exposure produces a sample of independent, flux-calibrated spectra for each object on the plate.
The full version of these files includes both the coadded spectrum and the individual exposure spectra; the "lite" version does not include the individual exposures. The format of these files is described in detail within the SDSS-III website 30. Header Data Unit (HDU) 1 of these files contains vectors with the vacuum wavelength solution (in logarithmic units), co-added observed flux density (in units of 10 -17 erg s -1 cm -2 Å-1 ), estimated inverse variance of the noise, and bit mask vectors -these are listed in the top half of Table  1. The spectra released (labeled with the file prefix "speclya") with this paper expand this format to include additional masks, noise corrections, Damped Lyα (DLA) system corrections, and a continuum fit as described in § 4. Only HDU 1 is changed; other HDUs are the same as the original DR9 files.
A BOSS ancillary program is now in place to observe a number of spectrophotometric standard stars through the quasar fibers in order to improve the spectrophotometric calibration, but the results of this program will not be incorporated until future data releases. Furthermore, the blue end of the spectrum is more susceptible to differential atmospheric refraction, causing the spectrophotometry of the spectra to vary as a func-   8 ), from the multiple transmission fields in the top panel. Bottom panel: The histogram of the pull distribution shown in the middle panel. It is consistent with a Gaussian distribution with unit standard deviation, indicating our continuum-fitting method has removed the spectrophotometric variations from the transmission field. tion of observed zenith angle. This effect is illustrated in Figure  9 , where we show three spectra of a BOSS quasar that had been observed on multiple nights.
DATA ACCESS AND USAGE GUIDELINES The files associated with the BOSS DR9 Lyα Forest Sample described in this paper can be downloaded from the SDSS-III website 38. We have generated BOSSLyaDR9_cat, a catalog listing the objects in this sample along with the additional information useful for Lyα forest analysis (described in Table  3 ). It available in both FITS and ASCII formats. The main components of the sample are individual 'speclya' spectral files corresponding to each object in our sample. These files are a value-added version of the 'lite' per-object BOSS format (see § 2), but with additional masks and corrections as listed in Table  1. Note that these masks and corrections have not been applied to the pipeline flux, f p , nor inverse-variances, w p ≡ σ -2 p by default, but are included as separate vectors in each file.
sdss3. org/dr9/algorithms/lyaf_sample. php 39 Interpolated to the individual wavelength grids from residcorr v5 4 45. dat described in § 5. 2 MF-PCA continua, C MF (CONT), to obtain the transmitted Lyα forest flux. The same operations are applied to the pipeline noise, σ p (although this is stored as the inverse-variance, w p ≡ σ -2 p , IVAR in the data files), but with the additional step of dividing by the noise corrections cor tot (NOISE_CORR). In other words, the Lyα forest transmission field, F i , is extracted from each spectrum i like so: F i (z α ) = f p,i (λ) ǫ dla,i (λ) ǫ flux (λ) C MF,i (λ) , (9) where (1 + z α ) = λ/1215. 67 Å. The corresponding inverse variance weights are derived from the pipeline inverse variances, w p,i , as follows: w F,i (z α ) = w p,i (λ) cor 2 tot (λ) ǫ flux (λ) C MF,i (λ) ǫ dla,i (λ) 2 (10) All pixels with MASK_COMB set or w p,i = 0 should be masked or discarded.
Thus, FEA considers whether there is a functional category that is exceptionally common for genes overlapped by dispersed CNVs identified in the genomes of patients that present the same disorder. It thus associates function with the disorder and nominates those copy number variable genes that participate in that function as candidate disease genes  [9]. The functional category types used in FEA approaches are key to the biological insights that they can provide. Different approaches have been applied to investigate the genetics underlying autism, including literature annotations  [6, 13] , protein-protein interactions  [14, 15] , mouse model phenotypes  [13] , gene co-expression  [16]  and functional linkage networks  [6, 17]. As the application of these different approaches in autism studies often accompanies the publication of a novel genetic dataset, each method has highlighted many, usually novel, candidate genes that add to a rapidly growing list  [18]  and replication of significant functional enrichments has only rarely been attempted, let alone achieved  [13, 19].
Despite chronologically limiting our mouse phenotypic dataset to avoid bias (see Materials and Methods), the similarities between the behavioural mouse phenotypes associated with the AGP dn CNVs and human ASD presentations appear clear, with abnormal social/conspecific interaction, stereotypic behaviour and abnormal memory/ learning/conditioning phenotypes all over-represented (Figure  1 ). Many of our study's ASD-associated phenotypes bear a striking resemblance to other frequently co-occurring symptoms, such as impaired coordination  [25, 26, 27] , anxiety-related phenotypes  [28] , and absence and tonic-clonic seizures  [29, 30, 31]  (Figure  1 ). Finally, we observe a strong enrichment of genes whose disruption yields hearing phenotypes in mice. This observation accords well with estimates in the literature that hearing abnormalities (including sensorineural hearing disorders) affect between 33-46% of ASD cases (Figure  1G )  [32, 33].
Assigning genes to CNVs Human genes were assigned to ASD dn CNVs according to Ensembl Ensmart54  [56]. To be confident that the expressed coding sequence of a gene is affected by the copy number change, we conservatively required at least one coding exon of every known transcript of a gene to be overlapped by a CNV for that gene to be deemed overlapped (Table  S2 ). Particular consideration was given to showing that our gene assignment procedure and statistical over-representation analyses did not yield any functional bias under the null hypothesis (see Figure  S5  and Methods S1). Genes observed to be copy number variable in the same direction (gain/loss) within a set of CNVs employed by the AGP as a control, i. e. identified from individuals with no obvious psychiatric history in a previous study were removed from the ASD dn CNV gene lists because these are less likely to be associated with ASD  [6].
Genomic extent and ENSEMBL gene content for ASD-associated de novo CNVs and AGP control CNVs. In CNV datasets, the genes considered are those remaining after excluding genes also overlapped by benign CNVs in the same direction of copy change (see Materials and Methods). Sets termed All are formed by combining both Gain and Loss CNVs. (XLSX) Table S2 (5 sheets): De novo CNVs identified in individuals with ASD employed in this study: Sheet A lists the de novo CNVs reported by the Autism Genome Project (AGP), Sheet B lists the de novo CNVs reported in the Marshall et al. study, Sheet C lists the de novo CNVs reported in the Levy et al study and Sheet D lists the de novo CNVs reported in the Sanders.
The motivation for deployment of driftsondes in T-PARC was to provide measurements over datasparse regions of the tropical central Pacific. The data from the driftsonde complemented satellite observations and provided calibration and validation data for new satellite-based observations  (Hawkins and Velden 2011)  and global reanalysis products  (Wang et al. 2010 ). During T-PARC, 16 driftsondes were launched from the southern end of the Big Island of Hawaii between 15 August and 30 September 2008. Thirteen of the driftsondes traveled at an altitude of about 30 km for up to five days to reach the western North Pacific and the primary T-PARC observation region (Fig. 8 ). Throughout T-PARC, 254 dropsondes were deployed from the driftsondes. The location and timing of the dropsonde deployments were coordinated from the T-PARC operations center at the Naval Postgraduate School in Monterey, California, taking advantage of the now web-based driftsonde control and display software.
Overall, the 13 driftsonde gondolas returned 644 high-quality profiles, with only 14 failed drops. This is a much higher success rate than in either AMMA or T-PARC, and resulted in an excellent spatial distribution of observations both over the Antarctic continent and the surrounding ocean. Figure  10  shows the comprehensive coverage and distribution of drop locations over the full experiment, as well as an example of the coverage of the constellation on a single day. Many dropsondes were released to coincide with driftsonde overpasses of Concordia station, allowing for comparison of dropsonde and radiosonde profiles, and also to coincide with overpasses of the Meteorological Operation (MetOp) satellite, allowing comparison with data from the Infrared Atmospheric Sounding Interferometer (IASI).
IASI is an advanced infrared sounder that has a large impact on NWP systems in general. However, there are some difficulties in its use over polar areas because the extremely cold polar environment makes it more difficult to extract temperature information from infrared spectra and makes it difficult to detect cloud properties. As a consequence, IASI is currently underutilized over Antarctica. A number of important results have already come from the 2010 Concordiasi dataset, as described in the Concordiasi workshop report  (Rabier et al. 2013). Wang et al. (2013)  compare sonde profiles with satellite retrievals, using the National Oceanic and Atmospheric Administration (NOAA) Products Validation System (NPROVS) to match Concordiasi dropsonde and radiosonde profiles with profiles from several satellite products.
A comparison of temperature profiles shows a cold bias present in all satellite data. The cold bias has a larger magnitude relative to the dropsonde data than the radiosonde for all satellite products except the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC; Fig. 11 ). The difference between the radiosonde and dropsonde bias can be traced to a larger cold bias over the Antarctic continent than over the coast and ocean, since all radiosonde stations but two are located along the coast (Fig. 10a ). The source of this bias remains a topic of investigation. Aside from the bias, and an inability to resolve detailed thermodynamic structures near the surface and tropopause, the satellite retrievals reproduce the temperature profiles reasonably well. In addition to temperature and humidity profiles, cloud properties such as cloud-top pressure and cloud effective emissivity (emissivity of an equivalent single-layer cloud) can be retrieved from IASI measurements.
These retrievals are highly dependent on the quality of temperature and humidity profiles. As reported in  Rabier et al. (2013) , detection of cloud properties can be improved by using an accurate atmospheric profile provided by the Concordiasi dropsondes rather than the atmospheric model. Another result from this dataset comes from the use of Concordiasi driftsonde observations in real time at NWP centers. As noted in  Rabier et al. (2013) , large systematic differences exist between various NWP analyses and forecasts for temperature over Antarctica, and for winds on the surrounding oceans. Comparison between short-range forecasts and the Concordiasi dropsonde data show that models poorly represent near-surface temperature over the Antarctic high terrain. The strong thermal inversions are challenging because numerical models need very good representations of both turbulent exchange processes and snow processes to simulate this extreme atmospheric behavior.
ACKNOWLEDGMENTS. Development and field use of the driftsonde system is a joint effort of many people, institutions, and nations. We thank all who made the development and deployments to AMMA,  T-PARC , and Concordiasi successful. We are grateful to  Rolf Langland ,  Mark Bradford ,  Joe VanAndel ,  Dean Lauritsen ,  Chip Owens ,  Clayton Arendt , and  Mary Hanson  for contributions to the development. Melvyn Shapiro  is especially acknowledged for his resurrection of the early ideas of the late  Vin Lally , which were developed decades before their time. In many regards, this paper is a testament to Vin's creativity. We are grateful for funding support from the  National Science Foundation's Division of Atmospheric and Geospace Sciences and Office of Polar Programs  (Grants  ATM-0301213 ,  ATM-9732665 ,  ANT-0733007 ,  ANT-1002057 , and  AGS-0736003 ), the  National Oceanic and Atmospheric Administration  (Grant  NA17GP1376 ), the  National Science Council of Taiwan  (Grants  NSC 96-2745-M-002-004 ,  NSC 97-2111-M-002-005 , and  NSC 97-2111-M-002-016-MY3 ), the  Taiwan Central Weather Bureau  (Grant  MOTC-CWB-97-6M-01 ), the  Office of Naval Research  (Grants  N00173-08-1-G007  and  N00014-09-WR20008 ), and the support of the national and international  THORPEX  project offices.
vivax samples sequenced by us  [14]  and others  [17]  to the human (UCSC build hg18,  [18] ) and the P. vivax Sal I strain  [15]  reference genome sequences using bowtie 2. We did not include in our analyses an isolate from Peru previously sequenced  [19]  due to its lower coverage. For each of the monkey-adapted strains sequenced at the Broad Institute, we used one lane of sequence data retrieved from NCBI Short Read Archive: SRR332569 (Brazil), SRR332913 (India VII), SRR332413 (Mauritania I) and SRR332562 (North Korea). We.
The specificity and prevalence of this polymorphism suggest that the parasite genome has responded to the barrier of Duffy negativity through the duplication of the PvDBP gene. Our results indicate that the PvDBP duplication is a recent event and provide novel research avenues to understand alternative pathways for P. vivax RBC invasion. mapped separately the ends of each read pair (i. e. , without treating them as paired-end reads) allowing for zero mismatch in the 15 nucleotide long seed. We allowed for up to 20 consecutive seed extensions and up to 3 re-seedings. We only considered in our analyses read pairs for which each end mapped uniquely in the P. vivax genome (i. e. , a unique best location on the final assembly of the 14 chromosomes). We identified read pairs that mapped to the exact same positions (and could represent molecules amplified during the library preparation) and randomly discarded all but one pair using custom Perl scripts to parse the alignment output from bowtie 2 (all scripts are available upon request).