dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank our colleagues  Mieke Van de Walle  for her input during her Master thesis,  Wilson Ardiles  for sequence analyses, and  Stephane Rombauts  for help with bioinformatics. The  FF laboratory acknowledges Mathias Brault  for guidelines to construct the similarity tree, as well as the support of the IPS2 imaging facility, the  LabEx Saclay Plant Sciences-SPS  and of the Plant Phenotyping Pipeline-3P LIdEx projects.
This work presents the supervision strategy in an Arduino card PV generator with storage connected to the low voltage grid. The studied system is composed of a photovoltaic generator, a boost converter, a buck-boost converter and a singlephase inverter. The power of the PV module depends on atmospheric conditions. Batteries are often confronted with overload problems and underload. The objective of this article is to manage the charging and discharging of the batteries, taking into account their protection against overload and under load and supervise the system. For this, supervision algorithms implemented in Arduino developed. Simulation results under Matlab show that the Arduino board has ensured the protection of the batteries and system supervision.
82% to 1. 76% of system. The technique discussed in the monitoring system proposed in  [6]  uses an Arduino microcontroller. This approach clearly detect the location of the error according to information acquired and collected. To manage energy production of a multi-source system, Multi-Agent System (MAS) is proposed in  [7]  which are a connection of several fields such as artificial intelligence, distributed computer systems and engineering. The results showed that the SMA is very flexible due to their distributed structure and can also adapt to changes in energy systems to renewable sources. In  [8] , a centralized power supervisor is established to determine the reference power levels depending on the conditions of supply and demand. The simulation results obtained in Matlab / Simulink environment show the effectiveness of the proposed control scheme.
). We call it ''magic card''. The Arduino system gives us the opportunity to combine the performance of programming to those of electronics. The Arduino board is programmable in a language similar to C that many researchers do not mastered. And in this paper we have proposed a novelty that is to use matlab language to drive the Arduino card (AC). And the latter is used for optimal power management and both ensure proper regulation of BS. And in this paper we have proposed a novelty that is to use matlab language to drive the Arduino card (AC). And the latter is used for optimal power management and both ensure proper regulation of BS. And in this paper we have proposed a novelty that is to use matlab language to drive the Arduino card (AC). And the latter is used for optimal power management and both ensure proper regulation of BS.
The Arduino board has its own software under which we can write purely Arduino program. But in this work in a spirit of curiosity and the fact that many researchers use matlab for simulations, we implemented the strategy in matlab algorithm. Arduino is always the control board and becomes an outer periphery of matlab. For that to happen, we first connected the card to the PC via a cable and then wrote 00 a ¼ Arduino; 00 on the command window matlab. They also allow you to quickly know the features of the card.
Results and discussions As we know Arduino card is a programmable microcontroller card capable of performing a variety of tasks such as control of home appliances-lighting, heating and store the results and also to communicate with the outside world. This means creating a program for controlling appliances based on the received data. For that several programming languages are used, such as Python, Java, C??, etc. In this project we used the Matlab development environment installed in a computer. The latter sends an instruction to the microcontroller which acts on the electric device by sending a signal PWM (a digital signal) which, at a given frequency, a duty ratio which varies with the given time ''orders it receives''. Figure  11  below shows the GUI of the Matlab software that contains all the instructions of our program.
X-ray diffraction (XRD) For XRD experiments, threads were glued across a hole on a rigid plastic frame. Drying was minimized by placing threads between a Kapton foil with a hydrated tissue present. Wide and small angle X-ray diffraction (WAXD and SAXD) measurements were conducted at BESSY II Berlin (Elektronenspeicherring Gesellschaft m. b. H. , Berlin, Germany) MuSpot beamline. 31 he X-ray wavelength was 0. 82565 Å and the beam diameter was 50 mm. The diffraction patterns were collected on a 2D CCD detector (MarMosaic 225, Mar USA, Evanston, USA) with a pixel size of 73. 24 mm and a frame size of 3072 Â 3072 pixels. Diffraction patterns were processed with dpdak, an open source XRD analysis tool. 32 Confirmation and refinement of the structure were done using the HELIX software,  33  which is a simple tool simulating diffraction patterns of helical structures from a set of parameters introduced by the user.
Acknowledgements We thank the crew of  Alchimie-Mediterranee  ( A. Garcia ,  C. Dumas ,  L. Gilletta de St-Joseph ) for the collection of Pinna nobilis byssus and members of the  Observatoire Oceanologique de Villefranchesur-Mer  ( R. Lasbleiz ,  K. Testard ,  D. Luquet ) for their logistic support. We also thank  J. M. Squire  ( University of Bristol ) for his help with the HELIX analysis software,  J. Choi  for help in acquiring A. pectinata and P. fucata threads,  R. Pitschke  ( MPIKG ) for assistance with acquiring TEM images,  J. Steffen  ( MPIKG ) for the composition measurements and  Stefan Siegel  and  Chenghao Li  ( MPIKG-MuSpot ) for technical support. This research was supported by the  Max Planck Society , the  Deutsche Forschungsgemeinschaft (DFG)  ( HA6369/4-1 ), the  Observatoire des Sciences de l'Univers Terre-Homme-Environnement-Temps-Astronomie  (OSU-theta, 2017), the  European Marine Biological Resources Center (EMBRC ,  AAP2017 ) and the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT & Future Planning  ( NRF-2015K1A3A1A59074243 : KONNECT).
thaliana with K = 6 ancestral populations and σ = 1. 5 for the range parameter. Using the Benjamini-Hochberg algorithm to control the FDR at the level 1%, the program produced a list of 12,701 candidate SNPs, including linked loci and representing 3% of the total number of loci. The top 100 candidates included SNPs in the flowering-related genes SHORT VEGETATIVE PHASE (SVP), COP1-interacting protein 4. 1 (CIP4. 1) and FRIGIDA (FRI) (p-values < 10 -300 ). These genes were detected by previous scans for selection on this dataset  (Horton et al. , 2012). We performed a gene ontology enrichment analysis using AmiGO in order to evaluate which biological functions might be involved in local adaptation in Europe. We found a significant over-representation of genes involved in cellular processes (fold enrichment of 1.
ACKNOWLEDGEMENTS The authors are grateful to  Edoardo M. Airoldi  and three anonymous reviewers for their constructive comments. This work has been partially supported by the  LabEx PERSYVAL-Lab  ( ANR-11-LABX-0025-01 ) funded by the  French program Investissement d'Avenir. Olivier François  acknowledges support from Grenoble  INP  and from the  Agence Nationale de la Recherche , project  AFRICROP ANR-13-BSV7-0017.
Under these conditions, we numerically compute the absorbed power in the device under test. As reported recently by Zolotavin et al. [17] , the absorption of the laser by the metal structure acts as a local source of heat, creating an inhomogeneous temperature distribution in the system. The deposited power is modeled as a cylindrical heat source Q, appearing in the heat transfer equation: m p ( ) T C k T Q t ρ ∂ = ∇⋅ ∇ + ∂ (1) where ρ m is the mass density, C p is the heat capacity at constant pressure, k is the thermal conductivity, and T is the temperature. Heat transfer works quite efficiently at the nanoscale and allows for a stationary treatment  [18]. We therefore neglect the time-varying term on the left side of Eq. (  1 ). The flow of power generates a temperature map that is numerically calculated in the stationary state through a finite element approach using the commercial software COMSOL.
Acknowledgments: The work was funded by the  European Research Council  under the European Community's  Seventh Framework Program   FP7/2007-2013 Grant Agreement 306772. Samples were fabricated using the nanofabrication facility of the technological platform ARCEN-Carnot financially supported by the  Région Bourgogne Franche-Comté. The authors thank  Irene Alda  for her critical reading of the manuscript.
Average hospital stays are 6. 9 days for acute care and 36. 5 days for rehabilitation. An active surveillance policy for MDRO carriage among high-risk patients is implemented: nasal swab for methicillin-resistant Staphylococcus aureus (MRSA), and rectal swab for Gram negative resistant bacteria and vancomycin-resistant enterococci. Systematic screening is performed at hospital admission for all patients coming from acute or long-term care facilities, and for community patients previously known as carriers. Moreover, weekly screening is performed in our intensive care and surgery departments. All hospitalized patients with positive blood cultures for bacteria were identified from the microbiology laboratory database, and microbiological data was obtained and reviewed. Patients with MDRO carriage (at least one site) during the last 3 months until day of sepsis were included.
Medical charts were reviewed using a standardized data set to collect: demographic characteristics (age, sex, comorbidities, risk factors, etc); clinical, biological, and microbiological data (clinical and severity signs, laboratory tests, organisms identified), and outcomes of each episode. Blood cultures were performed using aerobic and anaerobic blood culture vials incubated in a Bactec FX instrument (Bactec Ped+ and Lytic/10 Anaerobic/F, BD Diagnostics, Le Pont de Claix, France). The positive blood culture vials were subcultured on blood and chocolate Polyvitex agar plates. All isolates were then identified using MALDI-TOF mass spectrometry (Maldi Biotyper 3. 0, Bruker Daltonique, Marnes la Vallée, France). Antimicrobial susceptibility testing was carried out using the agar disk diffusion method (Bio-Rad) or an automated broth microdilution method (Phoenix, BD Diagnostics, Oxford, UK).
Background and Aims Large clades of angiosperms are often characterized by diverse interactions with pollinators, but how these pollination systems are structured phylogenetically and biogeographically is still uncertain for most families. Apocynaceae is a clade of >5300 species with a worldwide distribution. A database representing >10 % of species in the family was used to explore the diversity of pollinators and evolutionary shifts in pollination systems across major clades and regions. • Methods The database was compiled from published and unpublished reports. Plants were categorized into broad pollination systems and then subdivided to include bimodal systems. These were mapped against the five major divisions of the family, and against the smaller clades. Finally, pollination systems were mapped onto a phylogenetic reconstruction that included those species for which sequence data are available, and transition rates between pollination systems were calculated.
However, to date there has been no attempt to quantitatively synthesize what is currently known about the family as a whole. In this study we have assembled a large dataset of floral visitors and pollinators for the family, and used this to address the following questions: How much do we currently know about the diversity of pollination systems in the family? How is that diversity partitioned between the major clades of the family, and what are the evolutionary transitions between the major groups of pollinators? Do these pollination systems vary biogeographically? Answering these questions will provide important insight into the diversity and evolution of pollination systems in a large clade of flowering plants, establish the ground work for more detailed future studies within the family, and provide a baseline for understanding pollination diversification in other major clades of angiosperms.
MATERIALS AND METHODS Published studies of pollinators and pollination of Apocynaceae were located by using keyword searches (Apocynac* or Asclepiad* and Pollinat*) of the major scientific depositories (e. g. Web of Science), building on the earlier literature searches of  Meve and Liede (1994)  and  Ollerton and Liede (1997). In addition, we used our network of contacts to locate observations published in regional journals that are not always easy to obtain (e. g. Nakahama et al. , 2013)  and to locate data in reports, theses and dissertations, as well as data held by some of the authors of this study but so far unpublished. Some of the unpublished data came from targeted fieldwork on particular groups of Apocynaceae from un(der)-studied parts of the world and from citizen science projects (see Supplementary Data S1).
Database construction Data on flower visitors and pollinators of species of Apocynaceae were brought together into a single database that included details of the taxonomic placement of the species (subfamily or major division, tribe, and subtribe, as appropriate) following  Endress et al. (in press). Plant names were updated as required and noted in the database (Supplementary Data S2). Flower visitors were accorded a code (based on  Ollerton and Liede, 1997)  depending upon the quality of the data on their effectiveness as pollinators, as follows: 0 -the plant is an obligate selfer (very uncommon in Apocynaceae); 1 -identity of the pollinator proven -visitors with pollinia/pollen attached and observed to bring about pollination of a flower under natural conditions; 2 -identity of the pollinator inferred -visitors observed with pollinia/pollen attached, under natural conditions; 3 -identity of the pollinator inferred from circumstantial evidence, e.
g. visitors observed on flowers, but evidence of picking up pollinia/pollen is missing, under natural conditions; 4 -the flower visitor is a nectar or pollen thief, a herbivore, a predator, or a parasite of insects in the flowers. Where pollination or visitation was observed outside of the plant's natural range, the letter A was appended to the number code (e. g. 2A). Where pollination or visitation was observed outside of the animal's natural range, the letter B was appended to the number code (e. g. 2B). In the database the code '3(2)' indicates that although the data do not quite reach the standards of evidence required to assign them to code 2, additional evidence (e. g. details of floral phenotype) strongly supports the case for the visitors being pollinators. These were treated as code 2 in the analyses.
Details of the higher taxonomy (e. g. order, family) of the flower visitors were included, as well as the locality of the study (country) and a reference. This database will be made freely available and will be regularly updated as new information becomes available. (Ollerton and Liede, 1997). Pollinators were initially grouped into seven single taxon categories: [bee, wasp, butterfly, moth (hawkmoth + settling moth), fly, beetle, bird] plus an insect generalist category (see below). These categories were then used in our assessments of the diversity of pollinators within the family and across biogeographical regions, and for mapping pollination systems onto the phylogeny (see Figs  3, 5  and 6 ). For other analyses (see Fig. 4 ) species of Apocynaceae for which good data/evidence was available were then categorized into broad unimodal (bee, fly, wasp, bird, etc.
Phylogenetic reconstruction and mapping of pollination systems Maximum likelihood reconstruction of ancestral states and estimation of evolutionary rates among states were conducted with the rayDISC function in the corHmm package  (Beaulieu et al. , 2013)  for R (R Core Team, 2017), following  Fishbein et al. (2018). The root state was treated as equally likely for all characters. Three classes of models were fitted: all rates equal (ER), transition rates varying across all combinations of states that were equal forward and backward (SYM), and transition rates varying across all combinations of states that differed forward and backward (ARD). The best fitting model for each character was selected by likelihood ratio tests, and the set of adequately fitting models was found by comparing corrected Akaike Information Criterion (AICc) scores.
Ancestral state reconstructions were depicted on the Apocynaceae phylogeny using the plot. phylo function in the ape v. 4. 1 package  (Paradis et al. , 2004)  for R  (R Core Team, 2017). Two data sets were analysed, a 'full' data set of 237 species, which included species where the identity of pollination systems was suspected, but not confirmed; and a 'reduced' data set of 135 species, for which the most confident information about pollinator type (code 1 or 2 as described above -see Supplementary Data S2B) was available. We note that the calculated transition rates may only be accurate if diversification rates are not affected by the pollination state. However, we currently do not have sufficient data to fully test this and it is a question that must be revisited in future analyses. The base phylogeny was a chronogram (branches scaled by time) estimated from 21 concatenated plastid loci for 1041 species  (Fishbein et al.
Quantity and quality of available data, and the diversity of pollinators within Apocynaceae The Pollinators of Apocynaceae Database currently contains 5061 observed interactions between pollinators and species of Apocynaceae, mainly within their natural areas, but also on some species that have been cultivated or naturalized outside of their native range (Supplementary Data S2A, 3A). From these data, 567 species can be categorized into broad pollination systems which correspond to a >10 % sample of the family (~5350 species), with representatives from all the major groups and most of the tribes and larger subtribes, although sampling is sparse or non-existent in some lineages (Supplementary Data S3C). Particularly well represented are some subtribes of Asclepiadeae and Ceropegieae (Asclepiadoideae), and the Rauvolfioid tribes Plumerieae, Aspidospermateae and Carisseae (Supplementary Data S3B, 3C).
A B C D E F G H I J K L The geographical distribution of the data is both widespread and patchy with some countries being very well represented and others less so. In part this reflects the high diversity of Apocynaceae in those countries, but not completely, as some species-rich regions are not represented in the Database (Fig. 2 ). The 567 species of Apocynaceae were divided into two categories: those to which we can firmly attribute a pollination system and those where we suspect (but cannot confirm) the pollination system (Supplementary Data S3A). The following analyses have been performed using only the more restricted dataset of firm attributions, comprising 294 species. The majority (73 %) of species observed so far are pollinated by a single broad taxonomic group of animal pollinators, including bees and wasps (Hymenoptera), butterflies and moths (Lepidoptera), flies (Diptera), beetles (Coleoptera) or birds (Aves).
4 , Supplementary Data S3). Birds, particularly sunbirds (Nectariniidae) and hummingbirds (Trochilidae) are frequent flower visitors to Apocynaceae but the degree to which they rob nectar from otherwise insectpollinated flowers is unclear. If the birds recorded as visitors to flowers in the early diverging groups are legitimate pollinators then bird pollination may have arisen several times, often bimodally with insect generalist pollination. Within Asclepiadoideae bird pollination has been confirmed from Astephaninae where pollinia transfer occurs on birds' tongues  (Pauw, 1998). Whether this can also occur with free pollen from Rauvolfioids or Apocynoids remains to be determined.
The second most frequent transition (at least 5× greater than the remaining) occurs between hawkmoth and settling moth pollination. All pollination types have non-zero transition rates to at least two other categories, although some systems are more constrained. Transitions away from beetle pollination almost always occur to wasps, and the reverse is almost as pronounced. The next most restricted pollination types are butterfly, which has a low rate of transition only to bee or moth, and fly pollination, which has a low rate of transition to only hawkmoth or general insect pollination. These patterns are largely consistent with those found with the full dataset of 238 species with less stringent criteria for attributing pollinators (Table  S3 ). Across the Apocynaceae, pollination systems have been regularly lost and gained over time (Fig.
Biogeographical patterns of plant-pollinator interactions Our data allow broad comparisons of plant-pollinator interactions for species in four regions: Asia, Africa, North and Central America, and South America (Fig. 6 , Supplementary Data S4). Compared to the spectrum of pollinators recorded for the family as a whole, some striking patterns are apparent. Fly pollination is much more frequent in Africa and Asia in comparison with the Americas, although this may be affected by the large amount of recent work on Ceropegia and its relatives (see  Ollerton et al. , 2017  for a summary) as the large subtribe Gonolobinae, restricted to the Americas, is also mainly fly-pollinated (see below). In the Americas, bee and insect generalist pollination are more common compared to the other regions (Fig.
DISCUSSION The evolutionary and biogeographical patterns of plant-pollinator interactions evidenced in Apocynaceae show a complex interplay of constraints and flexibility that we are just beginning to appreciate. Apocynaceae exploit pollen vectors from most of the main animal groups known to act as pollinators (as recently summarized by  Ollerton, 2017)  with the exception of lizards and mammals, and, with some rare exceptions, birds. In addition, pollination by wind and water is unknown, and obligate selfing extremely rare. Mapping these pollination systems onto the phylogeny of species within the Pollinators of Apocynaceae Database, and subsequent ancestral state reconstruction (Fig. 5 ), shows that certain clades are associated with a rather conservative range of pollinators, e. g. fly pollination in Stapeliinae.
Maximum likelihood estimates of ancestral states of pollinator type for the reduced data set depicted on the chronogram in Supplementary Data S6 (Fig. S2 ). Pollinator types are indicated as in the key with polymorphic states indicated by additional intermediate shades of colour. Probabilities of states at ancestral nodes are indicated by pie charts. Best-fitting evolutionary models and rates are given in Supplementary Data S6 (Tables  S1, S5 ). Major clades are indicated by tick marks or arrows and labelled as follows: Apocynoids-Periplocoideae-Secamonoideae-Asclepiadoideae (APSA); subfamilies: Periplocoideae (P), Secamonoideae (S) and Asclepiadoideae (A); tribes: Asclepiadeae (ad), Ceropegieae (ce), Echiteae (e), Marsdenieae (ma), Mesechiteae (ms), Plumerieae (p) and Tabernaemontaneae (ta); and subtribes: Asclepiadinae (an), Cynanchinae (cy), Gonolobinae (g), Metastelmatinae (mt), Oxypetalinae (o) and Tylophorinae (ty).