dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Emmanuel and Integragen  for their assistance. The results published here are in part based upon data generated by TCGA pilot project established by the  National Cancer Institute (NCI)  and  National Human Genome Research Institute (NHGRI). Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome. nih. gov. Access to the TCGA raw sequencing data was obtained under project # 10312. We thank  Cecile Vicier  from  Inserm   U981  and the  Department of Medicine of Gustave Roussy  for helping with clinical data acquisition. We thank  Dr. Giuseppe Narzisi  for providing access to Scalpel 0. 5. 2 and for his invaluable support in interpreting its output. We thank  Nikolaus Schultz  and  Ritika Kundra  from  Memorial Sloan Kettering Cancer Center  for hosting the mutation profiles in cBioPortal.
This work was supported by the  Breast Cancer Research Foundation ,  Fondation ARC, Fondation Lombard-Odier "Philanthropia," Odyssea, Operation Parrains Chercheurs, Dassault Foundation ,  French NCI: INCa-DGOS-INSERM 6043 , and  SIRIC Socrate. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. TB is a board member for  Roche ,  Novartis ,  Pfizer , and  AstraZeneca , and received non-financial support from  Roche ,  Novartis , and  AstraZeneca. TB also received grants from  Roche  and  Novartis.
Sequence data have been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number (https://ega-archive. org/; EGAS00001001695); the somatic mutations and copy number alterations are available at cBioPortal (http://www. cbioportal. org/) under the descriptor "Mutational profiles of metastatic breast cancer (France, 2016).
Most of the collections from this second project were from highland (ca. 1600-2200 m elevation) wet-forest remnants, with a smaller number of samples from mid-altitude dry savanna and woodland. Over 700 insect species were reared from fruits and much of the data from the two projects has been published previously (e. g. Copeland et al. 2002 , White et al. 2003 , Copeland et al. 2004 , Copeland et al. 2006 , Copeland et al. 2009 , Adamski et al. 2012 , Razowski and Brown 2012 , Brown et al. 2014). Surprisingly, Megastigmus were reared from several species of wild fruits. Megastigmus specimens were also recovered, though rarely, from Malaise trap samples from an unrelated project, also conducted by the second author. In this paper, combining the use of morphological and molecular tools, we report range expansion of some previously described species, add two plant families of Angiosperms to the list of known hosts of seed-feeding Megastigmus, describe eight new species from Kenya, and provide a key to females of all known Afrotropical species.
Fruits were usually held for up to two months. Emerged adult insects were held for 1-3 days before being killed. Beentje (1994)  and  Agnew and Agnew (1994)  were used for preliminary plant identifications. Identifications made by the second author were confirmed or corrected by Quentin Luke of the East African Herbarium. Plant names and authors correspond to those found in The African Plant  Database (2015)  and the Missouri Botanical  Garden (Tropicos 2015). Voucher specimens of plants are deposited in the collection of the International Centre of Insect Physiology and Ecology (ICIPE) in Nairobi. Independent of the wild-fruit surveys, at various times between 2005 and 2014, the second author surveyed Kenyan insects using Townes-style Malaise traps  (Townes 1972). Traps were run in a total of 76 locations representing diverse habitats, mostly throughout the southern half of the country.
Microvision Cartograph® software was used to combine pictures. In a few specimens, head, antenna and thoracic sculpture were examined using a Cambridge StereoscanB 90 scanning electron microscope equipped with OrionB® image processing software. When less than three specimens were available per species and sex, these specimens were kept intact and mounted on triangular cardboards. Photographs and measurements of body and body parts (wings, antenna, head, thorax, gaster and ovipositor) were made as above without dissecting the mounted specimens. Morphological terminology follows  Roques and Skrzypczyńska (2003). The following abbreviations are used in the text: F1, F2, … Fn: Segment number of antennal funicle; OOL: Ocellocular line: the minimum distance between the eye margin and the adjacent posterior ocellus; POL: Posterior ocellar line: the minimum distance between the posterior ocelli; T3: first apparent tergite of gaster; T4: second apparent tergite; T5, T6, T7, T8, T9: subsequent gaster tergites.
DNA sequence analysis • DNA extraction, molecular markers, amplification and sequencing In order to include the Kenyan species in an existing phylogeny of Megastigmus, we used mtDNA sequences previously obtained by  Boivin et al. (2014)  for 25 species of seed chalcids associated with different host families (Pinaceae, Cupressaceae, Rosaceae, Anacardiaceae; GenBank accession numbers KF531833 to KF531858. Total genomic DNA was isolated by crushing, individually, whole adult females. A segment of the cytochrome oxidase I (COI) gene was amplified by PCR using the primers "Clyde and Bonnie" following  Boivin et al. (2014). Whenever possible, the amplification was tested for one to five individuals per species, but for several species, only one specimen had amplifiable DNA although other specimens of the same species were collected.
When these two primers did not allow sequencing, we used Bonnie and another primer C1-J-2183 ("Jerry", 5  '-CAACATTTATTTTGATTTTTTGG-3'; Simon et al. 1994) , which supplied a shorter dataset but on a larger number of species. PCR products were purified using Nucleospin gel and PCR clean-up kit (Macherey-Nagel, Düren, Germany). Sequencing was performed using the big-dye terminator sequencing kit (PE Applied Biosystems) and carried out with an ABI 3500 Genetic Analyzer (Applied Biosystems, Foster City, California, USA). Specimens were sequenced in both directions. In addition to the COI gene, a nuclear fragment, the D2 region of the 28S ribosomal subunit (rDNA), was used to build a phylogenetic tree of the studied Megastigmus species. Nuclear primers, D1F (5'-ACCCGCTGAATTTAAGCATAT-3') and D3R (5'-TAGTTCACCATCTTTCGGGTC-3'), previously used for reconstructing a molecular phylogeny of Megastigmus spp.
• Phylogenetic analyses For all datasets, sequences were aligned using Clustal W  (Thompson et al. 1994)  as implemented in BioEdit 7. 05  (Hall 1999). COI was aligned unambiguously and all sequences were truncated to the same length (810 bp for the longest fragment and 417 bp for the shortest). All sequences were translated into amino acids using MEGA 6  (Tamura et al, 2013)  to check that no stop codons occurred  (Tamura et al. 2013). Genetic distances were calculated on the two COI datasets with MEGA 6 using Kimura-2-parameter (K2P) distance model. Interspecific sequence divergences based on K2P distances were calculated for all species, and mean interspecific K2P divergences were calculated from the pairwise comparisons within and between the clades identified according to host families.
icipeensis. DNA from the other species was amplified with the different sets of primers and, although we obtained data from more species with the shorter fragment than with the longer, we studied and compared the two datasets because some short sequences showed the presence of overlapping fragments (for less than 2% of the short sequence) which could be due to heteroplasmy or numt's (nuclear copies of mitochondrial DNA), even if they were of the correct length and reading frame and contained no stop codons. These trends were observed in M. lanneae and M. ozoroae and their occurrence seemed to be species specific. These problems (no amplification and/or overlapping fragments) could be due to the preservation quality of the specimens. For M. transvaalensis, we used sequences from specimens previously obtained from Morocco, because we failed to amplify any Kenyan specimens.
Megastigmus copelandi, M. laventhali and M. smithi were not analyzed molecularly because of the limited number of available specimens necessary to define holotype and paratypes. For the nuclear marker 28S, one specimen per species was successfully amplified for all the species analyzed in the COI dataset except M. icipeensis, and an alignment of 924 bp including gaps was obtained. The inclusion of the nine Kenyan species in an already existing molecular phylogeny  (Boivin et al. 2014)  and their resulting phylogenetic position confirmed without ambiguity that none of the Kenyan species was synonymous with previously examined species. After grouping Megastigmus species according to their host plant families (Pinaceae / Cupressaceae / Taxodiaceae / Rosaceae / Anacardiaceae / Malvaceae / Rhamnaceae), we calculated within-and between-group mean distances for the two COI datasets (Table  4 ).
In both cases lower values were observed within groups than between groups, with values ranged from 6% to 7. 9% for COI-long fragment and from 4. 6% to 6. 8% for COI-short fragment (with no values for Malvaceae, Rhamnaceae or gall-former because there was only one species per group). Values within the Anacardiaceae group were quite similar for both datasets (6. 8% and 6. 3%), and are consistent with the values observed within the Holarctic groups (Pinaceae / Cupressaceae / Taxodiaceae / Rosaceae). Between groups, similar values were observed between Holarctic groups, as well as between the species related to Malvaceae and the Holarctic groups, whereas pairwise K2P distances showed higher values for the other host families. In the COI-long fragment dataset, the values between M. somaliensis and the two other species related to the genus Juniperus (Cupressaceae) were 3.
5%. The two specimens identified as M. pistaciae (one from Greece and one from Kenya) diverged by 4. 5%, suggesting that the two are probably sibling species. The use of maximum likelihood and Bayesian inference methods resulted in similar information about phylogenetic relationships within the genus Megastigmus. Therefore, we present only the phylogenetic trees built with the Bayesian inference method (Figure  14 ). The phylogenetic reconstruction was carried out by adding, where possible, one sequence (selected at random when there was more than one specimen) per Kenyan species to the sequences of the 25 Megastigmus species considered in  Boivin et al. (2014). The current dataset thus included 32 taxa for the COI-long fragment, 35 for the COI-short one and 34 for the 28S marker. To obtain phylogenetic trees including as many taxa as possible, after alignment we compared the sequenced regions common to all taxa for analysis; i.
However, the phylogenetic position of M. helinae (Rhamnaceae), M. grewianae (Malvaceae) and M. zebrinus (gall-former) was unstable and unresolved. Similarly, the position of M. icipeensis (caught in malaise trap) remained unclear. For this last species, as for M. hypogeus and M. ozoroae (both associated with Anacardiaceae), mtDNA amplification produced only the shortest COI fragment. The study of this gene fragment was very useful because some species were only available in this dataset, although it is recognized that longer fragments are usually more reliable in identification of insects  (Aly 2014)  and that the presence of numts can skew the phylogenetic relationships, leading to mtDNA divergence that is often lineage specific  (Song et al. 2008). Details for each species studied both morphologically and molecularly are provided in the key below.
Acknowledgments This work was partly supported by grants awarded to  R. Wharton ,  Texas A&M University, USA , from  USAID  (no. PCE-G-00-98-0048-00 ) and  USDA/CSREES/IFAFS  (no. 00-52103-9651 ) in collaboration with the  International Centre for Insect Physiology and Ecology (ICIPE)  and its  African Fruit Fly Initiative. The authors are grateful to the government of Finland for partial funding of our research through a grant to ICIPE (the  CHIESA  Project;  Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa ). Many thanks to the  Kenya Wildlife Service (KWS) , and in particular to  Dr Richard Bagine , the  KWS Head of Research , for permission to sample in Kenyan national parks and reserves. Thanks also to the director,  Kenya Forest Service  for permission to sample in the forests of  Taita Hills.
52 • N, 12. 63 • E; 50 m a. s. l. ) on the island of Lampedusa are used in the analysis. The combination of these different data sets suggests that SO 2 and ash, despite the initial injection at about 7. 0 km altitude, reached altitudes around 10-12 km and influenced the column aver-age aerosol particle size distribution at a distance of more than 350 km downwind. This study indicates that even a relatively weak volcanic eruption may produce an observable effect on the aerosol properties at the regional scale. The impact of secondary sulfate particles on the aerosol size distribution at Lampedusa is discussed and estimates of the clear-sky direct aerosol radiative forcing are derived. Daily shortwave radiative forcing efficiencies, i. e. radiative forcing per unit AOD (aerosol optical depth), are calculated with the LibRadtran model.
We exploit the information coming from different observation and modelling sources: (a) quantitative information on the emissions, obtained from satellite observations; (b) information on the volcanic plume horizontal and vertical dispersion, obtained by a combination of satellite observations and Lagrangian modelling; and (c) information on the downwind time-dependent modifications of the aerosol optical and micro-physical properties, obtained by surface remote sensing observations at the island of Lampedusa and the corresponding radiative forcing, obtained with a radiative transfer model. Lampedusa was downwind the plume for this event, due to the prevailing dynamics, and at a relatively large distance (about 350 km). The paper is organised as follows. In Sect. 2 we introduce the instruments, the data and the methods used in our work.
Work is ongoing to provide these further information layers for future studies. 2. 2 Satellite data 2. 2. 1 Sulfur dioxide retrieval and SO 2 /ash emissions rate inversion by MODIS (MODerate resolution Imaging Spectroradiometer) TIR (thermal infrared) data The MODIS is a multi-spectral instrument onboard both the NASA (National Aeronautics and Space Administration) Terra and Aqua polar satellites (http://modis. gsfc. nasa. gov/). The MODIS instrument has 36 spectral bands from the visible to the TIR, a swath width of 2330 km and a repetition cycle of 1 or 2 days. The spatial resolution in the TIR channels used for the sulfur dioxide total column retrieval is 1 km × 1 km. The SO 2 total column retrieval by MODIS is based on the SO 2 wide absorption around 8. 6 µm (MODIS channel 29). The retrieval scheme used in this study is based on the volcanic plume removal (VPR) approach developed for the Etna volcano  (Pugnaghi et al.
Look-up tables of single scattering optical properties for several mineral compositions (andesite, ash, basalt, hematite, obsidian, quartz) and sulfate aerosols are used. The optical properties are calculated with Mie theory using the com-plex refractive indices reported in the HITRAN (HIgh resolution TRANsmission molecular absorption) database  (Rothman et al. , 2013). Within each considered pixel, τ a and r e are obtained from the observed brightness temperatures at 11 and 12 µm through linear interpolation from the set of precalculated brightness temperatures. For a given pixel, a solution is sought separately for each particle type; the retrievals are then averaged.
92 ± 0. 44 for the whole month of October 2013. The Ångström exponent values observed on 26-29 October are very high for Lampedusa (see e. g. Meloni et al. , 2007) , especially in the early fall season. In the data set of aerosol optical properties at Lampedusa, covering the period 2001 to present, there is no other case with similar values of the Ångström exponent and fine-to-coarse AOD fraction in late October. This case stands out as an anomaly in the Lampedusa record and suggests an unusually large population of fine particles. A further indication of the aerosol size distribution changes occurring at Lampedusa is shown in Fig. 12 , where average volume size distributions are shown before, during, and after the eruption event. The coarse mode, which is predominant in the period 1-24 October and is still evident on 25 October, falls quickly on 26 October, while the fine mode becomes more important (more than 2 times larger than in the pre-eruption period), with increasing mode radius (from a pre-eruption value of about 0.
1 ). Conversely, the discussion in Sect. 4 (the height-resolved SO 2 dispersion analysis indicates that SO 2 concentrations are significantly higher than background levels in the upper troposphere over Lampedusa), the comparison of micro-physical aerosol properties at Lampedusa with data in the literature and the indications of favourable conditions for the formation of new particles through gas-to-particle conversion suggest that the secondary sulfate particles may be the primary cause for the observed aerosol optical properties and their evolution following the eruption.
, 2016). Correspondingly, the impact of volcanic aerosols (from passive degassing or moderate to explosive eruption) in the central-southern Mediterranean is only episodic. A stronger long-term impact is expected at the same distance in the eastern direction (ventilated for about 80 % of the time in the same period, considering south-east to north-east quadrants). The FLEXPART simulations, SEVIRI cloud top pressure and MODIS ash/SO 2 data used in this work are proprietary and can be provided on request by email (psellitto@lmd. ens. fr). The aerosol data from the Lampedusa ground station are available via the AERONET website (http://aeronet. gsfc. nasa. gov/). This work contributes to WP-4 of ChArMEx on aerosol-radiation interactions. Edited by: W. Lahoz.
Acknowledgements. The RELEASES file used for our FLEX-PART simulations has been produced with the aid of a modified version of the mk_releases. f routine, initially developed by Nina Kristiansen and Andreas Stohl (NILU -Norwegian Institute for Air Research). Measurements at Lampedusa were partially supported by the Italian Ministry for University and Research through the NextData Project. Damiano Sferlazzo guaranteed the continuity of the observations at Lampedusa, and his contribution is gratefully acknowledged. The work of Stefano Corradini has been partially funded by the EU 7th Framework Program under the grant 606738 (APhoRISM -Research, Technological Development and Demonstration Activities). This work has been partially supported by the EU Seventh Framework Program under the grants 603557 (StratoClim) and 308665 (Med-SuV).
NASA's Solar Probe Plus (SPP) mission will make the first in situ measurements of the solar corona and the birthplace of the solar wind. The FIELDS instrument suite on SPP will make direct measurements of electric and magnetic fields, the properties of in situ plasma waves, electron density and temperature profiles, and interplanetary radio emissions, amongst other things. Here, we describe the scientific objectives targeted by the SPP/FIELDS instrument, the instrument design itself, and the instrument concept of operations and planned data products.
In each panel, the green curve represent a nonlinear least square fit to the Helios data using a simple power law. The vertical dashed lines show the distances of 54, 20 and 10 R s. The power law for the magnetic field from the Helios data is compared to a Parker spiral field that fits the data between 0. 3 and 1 AU (Fig. 1a ). The Parker spiral is represented by the red curve: we used an average solar wind speed of 400 km/s and B 0 ≈ 4 nT is obtained from fitting the curve to the data. The power-law for the solar wind speed from the Helios data (green curve in Fig. 1b ) is compared the equatorial speed from a model by  Cranmer et al. (2007)  (blue curve). There are other, empirical models for the radial profile of the solar wind speed (i. e. Sheeley et al. 1997). The red curve represents an empirical optimum Sheeley-like model which fits best the Helios data.
Digital Fields Board (DFB)- The Digital Fields Board (DFB) is responsible for conditioning, digitization, and processing signals from the five voltage sensors and four search coil magnetometer (SCM) coils over a frequency range of DC to ≈60 kHz, as well as production of a calibration signal to the SCM. These nine analog inputs are processed by the DFB into twenty-five digital data streams, which are then used to produce a range of timeand spectral-domain data products. The signals measured by the FIELDS five voltage sensors are amplified by the preamplifiers and are passed to the AEB boards for low frequency signal conditioning before entering the DFB. Four analog voltage signals arrive directly from the SCM sensor, three from the low frequency (LF) SCM windings, and one from the medium frequency (MF) winding.
These nine inputs are processed by the DFB into twenty-six digital data streams, which are then used to produce a wide range of time-domain and spectral-domain data products. A more detailed description of the DFB for Solar Probe Plus can be found in  Malaspina et al. (2016). Voltages measured by the five FIELDS antennas encounter the FIELDS preamplifiers and the AEB boards before entering the DFB. The four SCM signals arrive directly from the SCM sensor, three from the low frequency (LF) SCM coils, and one from the medium frequency (MF) coil. DFB analog conditioning includes separation into DC-coupled and AC-coupled signals, production of differential signals, application of anti-aliasing filters, and application of gain stages. Figure  14  summarizes these steps graphically. The five antenna signals are divided into DC-coupled and AC-coupled using a singlepole high-pass filter (-3 dB at 100 Hz).
SCM-LF data streams start in the high-speed digital filter cascade and are passed to the low-speed digital filter cascade upon reaching 18. 75 kS/s in order to make the SCM-LF data available to all low-speed and high-speed time-and spectral-domain data products. Further details on the digital filters appear in  Cully et al. (2008)  and  Ergun et al. (2014). The DFB uses the output of these cascading digital filter banks to generate survey waveform data, which has a low cadence (relative to the ADC sampling frequency), but which has continuous coverage over the Solar Probe Plus orbit. The DFB also produces competitively selected snapshots of high-speed waveform data using the DFB burst memory (DBM). DBM snapshots are Nx ~3. 5 s long and are comprised of six (selectable) waveform channels captured simultaneously, where N is determined by the selected sample rate 150/2 N kS/s.
The DBM is capable of producing far more data than the DCB internal memory can store. Therefore, data is down-selected using a competitive buffer scheme. In this scheme, there is one circular ingress buffer constantly being filled, several hold buffers which retain high-quality data, and an egress buffer which is read out to the DCB. Because emptying the egress buffer is a slow process relative to filling the ingress buffer, data in the ingress buffer is periodically assigned a buffer quality. Each time an ingress buffer quality is calculated, it is compared with the hold buffer quality values. If the ingress buffer quality exceeds a hold buffer quality, the ingress buffer is promoted to hold buffer status. The displaced hold buffer data will either be demoted to a lower hold buffer, or else it will be discarded.