dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements -This study was only possible thanks to the support of  Daniel Früh ,  Dominik Kleger ,  Herbert Stark ,  Thomas Steuri , and  Gregory Wills. We thank  Cecilia Nilsson  and two anonymous reviewers for their insightful comments that helped to further improve the quality of this article. Funding -We acknowledge the financial support of  COST -European Cooperation in Science and Technology -through the Action ES1305 'European Network  for the  Radar Surveillance of Animal Movement' (ENRAM)  for facilitating international collaboration. Author contributions -BS and FL conceived the study. BS, MB, SCV, JWC collected the data. BS, FL, SZ conducted the analyses. BS wrote the manuscript with substantial contributions from all authors.
Intensity of bands were measured using ImageJ software of Fuji. Average and scatter from three independent biological replicates was plotted. Chromatin Immunoprecipitation (ChIP) and Real Time PCR. ChIP and Real Time PCR estimations were performed as described earlier 75. Cells were grown in YPD at 30 °C and fixed with 1% formaldehyde. Cells were washed thoroughly and lysed mechanically by glass beads in lysis buffer (50 mM K-HEPES pH 7. 6, 1% Triton-X 100, 0. 1% sodium deoxycholate, 2 mM EDTA, 150 mM NaCl) at 4 °C. Chromatin was sheared to average size of 300 bp by sonication and used for immunoprecipitation with corresponding antibodies (anti-Myc, anti-HA from Millipore) along with Protein A or Protein G agarose beads (GE Healthcare). Beads were washed in wash buffers and eluted at 65 °C in elution buffer (10 mM Tris-Cl pH 8, 2 mM EDTA, 200 mM NaCl, 1% SDS) for 30 min.
The membrane was washed 3 times for 15 min with wash buffer (2X SSC and 0. 2% SDS) at 42 °C and exposed to phosphorimaging screen (Fujifilm). Bands were visualized in phosphoimager (BIORAD) and intensities were quantified using Image Gauge software from Fuji. RNA levels were also estimated by the tRNA-HySeq method, essentially as described previously  52  , with the modifications as described under supplementary methods (Supplementary File S1). Genome-wide data analysis. The processed tab files containing forward and reverse tag counts, with their respective chromosomal location were downloaded from GSE83348 and used for the analysis of the ChIP-exo data  33  on the occupancy of the PAF1C subunits on the tRNA genes as detailed under the Supplementary Information File S1. All Raw data files for nucleosomal DNA  41  from the wild type and paf1∆ cells as treatment group and whole genome fragmented DNA as a control group were downloaded from NCBI GEO under accession no.
GSE44879. The two sample analysis was performed on the nucleosomal occupancy data by employing Affymetrix Tiling Analysis Software (TAS) v1. 1. Data were quantile normalized in pairs using only perfect match probes with a bandwidth of 30. Resulting bar files were used for Interval analysis with p-value threshold of 0. 05, minimum run of 50 and maximum gap of 20 probes. Processed MNase-seq data files on histone methylations were downloaded under accession no GSE61888 from NCBI GEO. The plotHeatmap" function from deepTools was further used for mapping of respective histone methylations  49  to yeast tRNA genes. For calculating the tRNA and Paf1 enrichment around replication origins and TAD boundaries, the chromosomal coordinates of start sites for tRNAs and ORFs were obtained from SGD database (https://www.
yeastgenome. org/). NGS reads for Paf1 ChIP-exo were obtained from GEO entry GSE83348  33  and the counts for forward and reversed tags were summed. The replication origins and the TAD boundaries were taken from McCune et al. 58  and Hsieh et al. 59  respectively. We considered the ORF start sites as control and tested the enrichment of tRNAs and Paf1 by aggregating the counts in genomic bins around replication origins and TAD boundaries.
, 2014b) , and the misrepresentation of convective momentum transport in the lower troposphere (Zermeño-  Diaz and Zhang, 2013; Richter et al. , 2014b). Tackling these problems will require detailed observations of the lower troposphere to gather observations that can guide error diagnosis and efforts toward improving convective parameterizations. Another factor that may contribute to the cold tongue bias is insufficient representation of the oceanic thermocline  (Hazeleger and Haarsma, 2005; Xu et al. , 2014b) , which may be related to deficiencies in vertical mixing parameterizations. Another   (Adler et al. , 2003)  for precipitation, the European Centre for Medium-Range Weather Forecasts Interim Reanalysis  (Dee et al. , 2011)  for surface winds, and Reynolds optimally interpolated dataset  (Reynolds et al.
, 2008; Wahl et al. , 2011) , and efforts to improve cloud representation in models are ongoing  (Bodas-Salcedo et al. , 2011; Qu et al. , 2014; Dal Gesso et al. , 2015; Neggers, 2015; Webb et al. , 2017). The cause of parameterization biases is often model specific  (Medeiros et al. , 2012) , and ongoing observations of the boundary layer thermodynamic structure and its cloud, precipitation, and aerosol vertical structure, including the diurnal cycle, are needed for continued model improvement. Such observational datasets remain sparse for the Southeast Atlantic. An observational network aimed at understanding and reducing model biases should include comprehensive measurements of the lower troposphere as well as surface heat and freshwater fluxes, wind stress, and ocean currents, and mixing in the equatorial and southeastern tropical Atlantic.
Fish stock assessment models have developed quantitative methods to optimize the population dynamics parameters of exploited species based mainly on catch and fishing effort data, and in some cases tagging data  (Maunder and Punt, 2013). Conversely, ecosystem models have rarely included such quantitative approaches. The Spatial Ecosystem and Population Dynamics Model (SEAPODYM) is an example of a modeling framework for the dynamics of phytoplankton, zooplankton, and micronekton (small but actively swimming organisms) and the detailed dynamics of key exploited fish populations (e. g. , tunas, swordfish, mackerel) and their fisheries. Parameters of each model component (zooplankton, micronekton, exploited species and fisheries) are estimated using observations and data assimilation methods. A recent major development is the use of acoustic observations at multiple frequencies rather than a single one (usually 38 kHz) to reconstruct a proxy of micronekton biomass, since various species or groups of species can be distinguished by their specific responses in frequency space  (Verma et al.
The 10 • W and 23 • W equatorial moorings have been equipped with turbulence sensors (χpods) as part of an Oregon State University (OSU) Ocean Mixing Group program that will run for 5 years. In 2017, 10 additional point acoustic current meters were implemented at 4 • N, 23   (Bourlès et al. , 2019). It Other moorings in the tropical Atlantic include the Northwest Tropical Atlantic Station for air-sea flux measurements (NTAS) at 15 • N, 51 • W, the Melax air-sea buoy in the Senegalese part of the Canary Current Upwelling System at 14 • N, 17 • W, the Cape Verde Ocean Observatory (CVOO) at 17. 6 • N 24. 3 • W, and several meteorological and wave buoys maintained by the National Data Buoy Center (NDBC). NTAS has been operational since 2001 and measures the same parameters as the PIRATA buoys (surface meteorology including longwave radiation and atmospheric pressure), plus ocean currents from a point meter at 10 m and an upward-looking ADCP at 100 m, and enhanced vertical resolutions of temperature and salinity.
Surface Drifting Buoys Global Drifter Program (GDP) observations from buoys drogued at 15 m were first collected in the tropical Atlantic in 1990, and the array was sustained beginning in 1997 (Figure  2 ). Since In addition to the standard drifter measurements of SST, surface currents, and barometric pressure, drifters have also been developed to measure subsurface temperature, surface and subsurface salinity, wind velocity, and directional wave spectra. However, few of these observations are being collected by drifters in the tropical Atlantic. Surface velocity estimates can also be calculated from Argo float trajectories while they are transmitting their data at the surface  (Lebedev et al. , 2007). As Argo transitions to Iridium data transmission, the floats will spend less time at the surface and thus be more strongly affected by high-frequency motion than was the case with floats using Service Argos.
Satellite Observations Most satellites provide measurements in real-time or near real-time, and the records for many parameters extend back at least 20 years. SST is obtained from passive infrared and microwave radiometers. By combining data from several satellites with in situ measurements, daily maps of SST at high spatial resolution (0. 25 • or better) are possible going back to 1997, when microwave measurements started. Estimates of tropical rainfall and precipitable water are also available from microwave sensors. Satellite-based measurements of SSS began in 2009 with the launch of the SMOS sensor. SSS was measured by Aquarius during 2011-2015 and is currently measured by the SMAP radiometer. From these satellites, complete coverage of the tropical Atlantic is achieved in approximately one week at a spatial resolution of 1/2 • -1 •.
Direct measurements of the AMOC from in situ monitoring arrays are also needed for validation of ocean state estimates. More comprehensive design, analysis, and intercomparison of OSSEs should be explored in order to quantify the number, type, and spatial locations of additional measurements that would most likely improve ocean data assimilation and state estimates. The tropical Atlantic observing system generates large quantities of data covering a wide range of temporal and spatial scales and across many platforms. A key challenge is to ensure consistent data processing, archiving, availability, and visibility within and across all datasets. This is important not only to increase the usability of the data for advancement of scientific knowledge, but also to improve data assimilation and predictability of weather, climate, and ecosystems.
A consistent data record is also needed to evaluate the performance of the observing system over time and recommend changes to it. The archiving and availability of data has progressed over the past decade because of several developments: (1) the use of digital object identifiers (DOIs) for datasets has expanded, (2) scientific journals have started requiring statements of data accessibility, (3) funding agencies require data management and archiving plans, and (4) common data formats such as NetCDF have gained wider acceptance. However, many important datasets remain difficult to find or are stored in formats that are difficult to read or do not contain adequate metadata. A push for open data availability and visibility and consistent archiving is needed and may be achieved through a combination of approaches, including continuation of those listed above.
FUNDING MM-R received funding from the  MORDICUS grant  under contract  ANR-13-SENV-0002-01  and the  MSCA-IF-EF-ST FESTIVAL  ( H2020-EU  project  797236 ). GF, MG, RLu, RP, RW, and CS were supported by  NOAA/OAR  through base funds to AOML and the  Ocean Observing and Monitoring Division  (OOMD; fund reference 100007298). This is NOAA/PMEL contribution # 4918. PB, MDe, JH, RH, and JL are grateful for continuing support from the  GEOMAR Helmholtz Centre for Ocean Research Kiel. German participation is further supported by different programs funded by the  Deutsche Forschungsgemeinschaft , the  Deutsche Bundesministerium für Bildung und Forschung (BMBF) , and the  European Union. The  EU- PREFACE  project funded by the  EU   FP7/2007-2013 programme  (Grant No. 603521 ) contributed to results synthesized here.
Data recording The data being measured included: -Natural potential -Polarization time -Current per zone -Potential ON -Potential OFF at 0. 5 to 1 second -Depolarization -Temperature In addition to the above-mentioned data being collected, potential mapping of the concrete surface adjacent to the zinc strips was performed using a Ag/AgCl, KCl 0. 5M reference electrode. 3 Results obtained on the beam area.
Image acquisition Images were acquired with a 3T Philips Achieva scanner (Philips Medical Systems, Best, The Netherlands). Functional data were acquired using a T2*-weighted echo-planar imaging (EPI) sequence with the following acquisition parameters: TR = 2000 ms, TE = 30 ms, flip angle = 78°, in-plane resolution = 3 × 3 mm, FOV = 240 mm, slice thickness = 3 mm, inter-slice gap = 1 mm. The autobiographical memory task consisted of 360 volumes. Slices (32 per volume) were acquired with an interleaved order parallel to the AC-PC plane. Before the functional sequences, a high-resolution anatomical 3D volume was acquired using a Turbo Field Echo sequence for anatomical reference and inspection (TR = 8. 15 ms; TE = 3. 73 ms; flip angle = 8°; voxel size = 0. 9375 × 0. 9375 mm; slice thickness = 1 mm; slice number = 160; FOV = 240 mm).
Image preprocessing and analysis Preprocessing and analysis was carried out with the FEAT module included in the FSL (FMRIB Software Library) software  (Smith et al. , 2004). The first 20 s, corresponding to signal stabilisation, were discarded. Preprocessing included motion correction (using the MCFLIRT algorithm) and co-registration and normalisation to a common stereotactic space (MNI template). Before group analyses, normalised images were spatially filtered with a Gaussian filter (FWHM = 5 mm). Statistical analysis was performed by means of a general linear model (GLM). Two regressors of interest were defined at the single-subject level analysis (memory-evoking blocks and non-memory-evoking blocks) and the GLM was fitted to generate activation maps of each condition compared to baseline and the comparison between conditions.
Bado et al. (2014)  examined 18 healthy subjects and found greater activation during a 'relax and stay awake' condition than during recall of both emotional and neutral autobiographical memories in the subgenual anterior cingulate cortex, the ventral striatum and the hypothalamus/septal area, and additionally in the inferior parietal lobe bilaterally during recall of emotional memories. Collectively, these findings support the idea that autobiographical recall is associated with de-activations, and there are hints of an overlap with the findings from our own group of healthy controls. The only other relevant data come from a study by DuPre et al. (2016) in which 31 healthy adults viewed emotional pictures and engaged in autobiographical recall, prospection or theory of mind reasoning based on their content.
Data from this study are publicly available at NeuroVault (https://neurovault. org/collections/ 1866/). Contrasting self-generated thought (i. e. combining all three task conditions) to a baseline consisting of viewing a scrambled image followed by a button press, clusters of de-activation were seen in the parietal and temporal regions not dissimilar to those we observed in our study, as well as in the occipital cortex. If replicated, the current study's finding of intact default mode activation but failure of de-activation outside the default mode network in schizophrenia would appear to have two main implications for the pathophysiology of the disorder. The first is that, rather than there being dysfunction of the default mode network specifically in schizophrenia, it may be that there is a more general problem with de-activation, which manifests itself in different regions (i.
Data description Four S. officinalis individuals collected from natural populations from the island of Crete, Greece, were reared in closed tanks and experimentally exposed to elevated temperatures approximate an extreme climate event naturally occurring in the sponge's habitat during summer. The 50 m 3 rearing tanks contained natural seawater collected from a pristine open-sea area, with temperature and salinity adjusted to reflect typical local conditions for the time of year (24 °C and 39 ppt, respectively). Two experimental tanks were employed, one as control (24 °C) and one as treatment with increasing temperature (up to 30 °C). Five sampling points initiated after 5 days acclimatization in the tanks and over a span of 6 days, resulted in 20 samples. RNA was extracted with TRIZOL (TRIzol ™ Reagent, Thermo Fisher Scientific, Cat.
number 15596026) following the manufacturer's protocol. The quality control of the RNA revealed a unique profile. Apart from the expected 28 s, 18 s ribosomal bands two extra bands, possibly of 23 s, 16 s characteristic of the microbial ribosomal RNA, appeared at the agarose gel, which reflected a remarkably large proportion of prokaryotes in the extracted RNA (data file 2). For the library preparation we used the TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, Cat. Number 20020594) and followed the protocol of the manufacturer for sequencing using the shortest possible fragmentation time and applying 13 cycles instead of 15 in the amplification library PCR at the very last step of the protocol. In total, 20 RNA libraries were sequenced on an Illumina HiSeq 2000 platform. Τhe amount of prokaryotic RNA in our extraction urged us to implement extra steps for excluding the prokaryotic sequences from our dataset (data file 3).
Sequencing yielded on average 12,933,232 raw paired reads per library (data set 1). Raw reads were quality controlled using multiple software in a workflow described in  [12]  and run through bash scripts (data file 4 and 5). The used software included scythe (version 0. 994 BETA; https ://githu b. com/vs. buffa lo/scyth e), sickle (version 1. 33; https ://githu b. com/najos hi/sickl e), prinseq (version 0. 20. 4; http://prins eq. sourc eforg e. net/) and trimmomatic version 0. 32  [13]. The quality-controlled data were used to build an initial Trinity (v2. 1. 1) [14]  assembly (data file 6). However, given that a great percentage of sponge transcriptome is comprised of bacterial sequences, we downloaded all bacterial sequences from NCBI (data file 7) and removed all reads (2. 2 to 17. 6% of the reads of each sample) that were successfully mapped on them using riboPicker (ribopicker-standalone-0.
4. 3 version; https ://sourc eforg e. net/proje cts/ribop icker /files /stand alone /; command ribopicker. pl -c 47 -i 75 -l 40 -z 3). Then, we built another assembly with the remaining reads (data file 8). The reconstructed transcripts were then used for a similarity search through NOBLAST  [15]  against the Swiss-Prot database (e-value: 1. 0E-5). Transcripts that had as best hit prokaryotic sequences (17. 1% of the assembly) were eliminated leading to the final assembly (data file 9). Their corresponding reads were eliminated from the bam files as well (data file 10) and were excluded from downstream analyses.
Acknowledgements The authors thank  Vasso Terzoglou ,  Katerina Oikonomaki  and  Eliza Kaitetzidou  for recommendations during RNA extraction and  Alexandros Tsakogiannis  for useful discussions on transcriptome analysis. Sequencing service was provided by the  Norwegian Sequencing Centre  (http://www. sequencing. uio. no), a national technology platform hosted by the  University of Oslo  and supported by the  "Functional Genomics" and "Infrastructure" programs of the Research Council of Norway  and the  Southeastern Regional Health Authorities.
Availability of data materials The data described in this Data note can be freely and openly accessed on figshare (https ://doi. org/10. 6084/m9. figsh are. 10001 870. v3) and SRA (https :// www. ncbi. nlm. nih. gov/sra/?term=SRP15 0632). Please see Table  1  and reference list for details and links to the data.
rs2070600 NM_001136. 4:c. 244G>A p. Gly82Ser Polymorphism This SNP (single-nucleotide polymorphism), often referred to as Gly82Ser or G82S, is the most described SNP within the AGER gene. It is reported in the Human Gene Mutation Database (HGMD) (http://www. hgmd. cf. ac. uk/ac/index. php) as being associated with microangiopathy in type 2 diabetes  [28]. In the general population, the G allele frequency is 92. 8% and the A allele frequency is 7. 2%. In the CEU population, the G and A allele frequencies are 92. 4% and 7. 6%, respectively; in the EAS population, they are 78. 1% and 21. 9%, respectively (1000 Genome data). The rs2070600 SNP or Gly82Ser polymorphism is located in exon 3 of the AGER gene. The rs2070600 SNP probably emerged after migrations from Africa, reaching the highest frequencies in Asia  [29].
8% and is 19. 2% for allele C. The G allele frequency in the EAS population is 88. 6% and is 11. 4% for allele C (1000 Genome data). This SNP is located in the 3 ′ downstream region of the AGER gene. In studies on children with type 1 diabetes, the CC genotype of rs9469089 was linked to increased blood concentrations of sRAGE. It was also described as protective; in fact, rs9469089 was associated with a decreased risk of type 1 diabetes  [22, 38]. 7. 12. rs3530981 NM_001136. 4:c. 143G>A, p. Arg48Gln Polymorphism. Its allele frequency is not available on the 1000 Genome database, perhaps because of its poor frequency. It is a very rare SNP. This SNP has been proposed as a risk predictor only recently  [80, 81]. Polymorphism. The G allele frequency in the general population is 99. 9% and is 0.
1% for allele A. The G allele frequency in the CEU population is 100%. The G allele frequency in the EAS population is 100% (1000 Genome data). It is a very rare SNP. The rs35802968 SNP was less studied and did not correlate with any risk of developing any disease  [80, 81]. 7. 14. rs35795092 NM_001136. 4:c. 267C>G, p. Val89= Polymorphism. The C allele frequency in the general population is 93. 2% and is 6. 8% for allele G. The C allele frequency in the CEU population is 94. 9% and is 5. 1% for allele G. The C allele frequency in the EAS population is 99. 6% and is 0. 4% for allele G (1000 Genome data). The rs35795092 SNP was reported in two studies but was never associated with any pathological risk  [72, 80]. 7. 15. rs17846806 NM_001136. 4:c. 341G>A, p. Arg114Gln Polymorphism. Its allele frequency is not available on the 1000 Genome database, perhaps because of its low frequency.
Acknowledgments This study has been funded by the project " Nature, Society and Monumentality: High Resolution Archaeological Investigations on the Megalithic Landscape of Antequera " ( HAR2013-45149-P) (2014-2017 ), sponsored within the  National R&D Plan of the Ministry of Economy and Competitiveness (Spanish Government). This study was undertaken as part of the project " Societies ,  Territories and Landscapes in the Prehistory of Antequera (Malaga) " ( 2013-2018 ), approved by the  Ministry of Culture of the Andalusian Government. We would like to thank  Lucy Shaw Evangelista  for her kind revision of the English translation of the text and various suggestions as to its writing.
Methods This study was a randomized controlled clinical trial, conducted from June 2015 to December 2017. All participants were recruited at the outpatient clinic of the Prof Edgard Santos University Hospital in Salvador, Brazil, from June 2015 to July 2017 and provided written consent to participate. Prior to the inclusion of the first volunteer, the research protocol received full approval from our institutional review board (ICS/ UFBA Nb. 943738) and was registered in the ClinicalTrials. gov database (CT Identifier NCT03171077). The study was performed in accordance with the Declaration of Helsinki and current national regulations for human research. Demographic data were collected to characterize the sample, namely, sex, age, laterality, affected body parts, type, and time of stroke. An improvement in the sensorimotor function was the primary endpoint, assessed by computing the score change in the Fugl-Meyer Assessment (FMA), which is a stroke-specific performance-based 155-item quantitative scale.