dataesr/scientific-paragraphs-categorization huggingface dataset:
For the 8bpp case, the algorithm shows a constant, near linear speed-up curve. In both datasets, the speed-up shows an increasing behavior for up to 256 cores with no reason to doubt its consistency for a higher number of cores, with an execution time of 9. 38 and 62. 35 seconds for Naples and ESO, respectively. However, for ESO 16bpp and 32bpp the speed-up flattens sooner, stabilizing at 64 and 16 cores, respectively. With a high gray-level depth, the number of tuples is increasing sharply, resulting in larger merge time. The effect observable here is the Amdahl speed-up boundary for a constant workload. When these results are compared with the Moschini algorithm, the proposed algorithm always provides faster execution times. Unfortunately, for the dataset ESO 32bpp it was not possible to derive any conclusions since the Moschini algorithm did not terminate.
A more detailed analysis of the execution time for the different phases of the algorithm (see the Algorihtm 2) is depicted in Figure  6. The results are related only to a single dataset case (ESO 16bpp) because of space considerations. Each set of rows depicts a a specific number of nodes (i. e. , 1 node, 2 nodes, 4 nodes and 8 nodes). The time distribution for increasing number of threads is shown in each row. For the single node case, the computation of the local max tree is the most time-consuming phase. This is a shared-memory scenario where the three phases concerning the management of the tuples do not take place. When the number of threads increases, the second most costly phase is the local apply. The local merge needs to be considered only beyond four threads. The same conclusions can be derived for the remaining nodes configuration.
However, since it is a distributed memory environment, the phases connected with tuple handling are also present. In the two-node case the tuple generation phase and the global apply are mostly present, the weight of the tuple resolution becomes more pronounced the higher the node count. This behavior can best be explained by the algorithm complexity, explained in Table  1 , showing a logarithmic scaling with the number of processing cores. As has been shown here, the parallel implementation allows to achieve a significant processing time gain when compared with serial processing. In Table  3  the processing times for the different datasets are presented. When considering ESO 32bpp, which is the more challenging dataset used in this work, the proposed algorithm computes the max-tree in ≈27 minutes (with 24 cores) while Berger converges only after ≈3 hours.
Last but not least, comments should be made regarding the memory consumption of the proposed algorithm. Considering the usual trade-off between memory consumption and computational time, the experiments show the proposed algorithm is more memory efficient and takes shorter computational time than Berger and Moschini. Table  4  and Table  5  scrutinize the memory consumption (in GB) for the different algorithms with Naples and ESO 16bpp images, respectively, when computing on a single node. For each given number of threads, it can be noticed that the average and the maximum memory usage of all the tasks in the job are always lower for   the proposed algorithm. This means that the algorithm is able to scale in terms of memory consumption and communication cost with respect to large datasets and the number of parallel cores.
g. growth/shade tolerance), as the ForClim parameters are mostly derived from observed/ measured traits; and (ii) the model has originally not been developed to study diversity-productivity questions, and can thus be viewed as an independent tool, as illustrated in former studies  29, 30. Climate data. To represent the anticipated change in climate in the 11 sites (see Table  S1  for a detailed description of the site conditions), climate data for the 21 st century were obtained from the Landscape Dynamics Unit at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). Data were spatially interpolated at 1 km resolution. Three Regional Climate Models (RCMs) nested in the General Circulation Model ECHAM5 were used to derive climate data based on the IPCC 63 AR4 SRES scenario A1B, providing us with the alternatives KNMI (Royal Netherlands Meteorological Institute KNMI RACMO2), SMHI (Swedish Meteorological and Hydrological Inst.
SMHI RCA30), and MPI (Max-Planck-Institute for Meteorology MPI CLM). To derive anomalies, we used the periods 1950-2000 for KNMI, 1961-2000 for SMHI and 1960-2000 for MPI from the WorldClim dataset  64  as baseline periods and the period 2090-2100 as representative for future conditions. Averaged changes in mean annual temperature and annual precipitation sums between the two periods are shown in Table  S3. Note that we chose to use one SRES scenario but several RCMs because variability was greater between RCMs than between scenarios from IPCC  63. Similarly to Rasche et al. 65  , we averaged the nine cells covering and surrounding each of our 11 locations. We used simulated time series data with stationary climatic conditions (i. e. no trend across time, but including inter-annual variability) for both baseline and anticipated climate, to run the simulations over 2,000 years because the main objective of this study was to explore how climate change affects the DPR from a theoretical point of view, and not to produce robust predictions of changes in DPR in the next century.
Thus, overall we ran 30 + 435 + 13 × 500 + 435 + 30 + 1 = 7,431 simulations for each site and for one climate dataset, differing in their initial species composition. Analyses. First, we tested how climate change affected mean forest productivity through direct and indirect effects by comparing the simulations between baseline and RCM conditions with the same original tree species composition. To do so, we partitioned the change in productivity (ΔP) as follows: where ΔP c-f denotes the difference in productivity between current (c) and future (f) climate scenarios according to the absence (c, f = 0) and presence (c, f = 1) of species. Thus, ΔP 1-1 is the difference in productivity between future and baseline climate conditions of those species present under both conditions, ΔP 1-0 is the loss in productivity due to species unable to grow under future conditions (ΔP 1-0 < 0), and ΔP 0-1 is the gain in productivity due to species able to colonize the patch and grow under future conditions only (ΔP 0-1 > 0; Fig.
Acknowledgements We thank  Niklaus Zimmermann  and  Dirk Schmatz  ( WSL ) for providing the climate change scenario data. We are grateful to  G. Kunstler ,  M. Loreau  and  F. Valladares  for helpful discussions and comments on these results. XM is also grateful to  Baptiste Hautdidier  for his help on figure design and to  Louise Riotte-Lambert  for her suggestions on a previous version of the manuscript. This study was mainly funded by a grant from the  EU-FP7  project  BACCARA  (contract no. 226299 ). XM also benefited from the  ANR  project  BioProFor  (contract no. 11-PDOC-030-01 ) and from the  BGF  project  DISTIMACC  (contract no. ECOFOR-2014-23 , funded by the  French Ministry of Ecology and Sustainable Development  and the  French Ministry of Agriculture and Forest) , and RGV from the  Biodiversa  project  EC21C  (contract no.
Samples were collected either as part of the Tara Oceans or Tara Pacific expeditions. Field permits were obtained as part of the Tara Oceans and Tara Pacific expeditions and the approving bodies were the respective governments and/or ministries for the environment of the countries where the samples were collected. The authors of this work are part of the Tara Oceans and Tara Pacific expeditions, and thus, participated in sample collection. Tara Expeditions are global scientific voyages aimed at probing morphological and molecular diversity, evolution, and ecology of marine plankton (viruses, bacteria, archaea, protists, and planktonic metazoans) in the photic layer of the world oceans to research how they are impacted by Earth's climate changes. Tara meteorological, oceanographic, biochemical, and plankton morphological data are archived in the comprehensive Sample Registry of the PANGAEA database (https://www.
pangaea. de). The Sample Registry links physical samples to metadata about sampling and analysis methodology performed on each sample. Whilst the Tara Pacific registry is still under construction at the time of writing of this manuscript, the Tara Oceans registry can be accessed at https://doi. pangaea. de/10. 1594/PANGAEA. 842237. Six CO samples from three genera, 2 Porites lobata (samples: CO-0000150 and CO-0000151), 2 Pocillopora meandrina (samples: CO-0000208 and CO-0000209), and 2 Millepora platyphylla (samples: CO-0000302 and CO-0000303), were collected using SCUBA off the coast of Panama in July 2016. Corals were stored in DNA/RNA Shield (Zymo Research, Irvine, CA, USA). Two CSW and one SRF-CO samples were also collected off the coast of Panama in July 2016. CSW samples were collected (15-17 L) from water surrounding two P.
Sequence data analysis-primer pair comparisons To assess which of the three primer pairs performed the best, each primer pair was assessed for specificity (i. e. preferential amplification of Symbiodinium DNA) and sensitivity (i. e. ability to amplify Symbiodinium from Symbiodinium-rare environments). Additionally, potential within-Symbiodinium taxonomic biases were assessed by comparing the proportions of sequences for each primer pair that belonged to each of the nine major lineages of Symbiodinium, clades A-I  (Pochon & Gates, 2010). Our assumption was that if no or only small clade biases exist, the proportion of sequences from each clade should be similar between primers. Consequently, a large deviation in clade proportions by any one of the primer pairs was regarded as indicative of taxonomic bias.
The specific PCR conditions used for each primer pair are given in Table  1  with PCR reagents as detailed above. The sequences returned from each PCR were annotated using blastn and the NCBI 'nt' database according to their closest match to one of the following categories: Symbiodinium, dinoflagellate, stony coral, Hydrozoan, uncultured eukaryote, other, or 'no match'. To do this, the. fasta file produced from the initial QC from each sample was run against NCBI's 'nt' database with the max_target_seqs argument set to 1 and an output format string of '6 qseqid sseqid staxids sscinames sblastnames evalue'. For sequences to be categorised as Symbiodinium, an e-value >50 was required (representing approximately a 100% coverage match at 80-85% nt identity) in addition to the closest match being of Symbiodinium origin.
Additionally, closest match subject sequences were screened for two sequences in particular (JN406302 and JN406301), which are mis-annotated as Symbiodinium (highly divergent from any other Symbiodinium sequences) in the 'nt' database. Thus, query sequences matching these sequences were annotated as 'Unclutured eukaryote. ' Notably, before controlling for this, 58% of all 'Symbiodinium' sequences amplified by the ITSintfor2 and ITS-DINO primer pairs (0% for the SymVar primer pair) in the SRF-OO samples matched these sequences as their closest match (Fig. S2 ). blastn was also used to associate Symbiodinium sequences to one of the nine clades. To do this, a. fasta file was created for each sample that contained all sequences that had previously been categorised as Symbiodinium. This file was used as an input for blastn with the max_target_seqs argument set to 1 and an output format string of '6 qseqid sseqid evalue pident gcovs'.
Sequence data analysis-optimising the SYM_VAR PCR protocol Having determined SYM_VAR as the most effective primer pair for the amplification of the Symbiodinium ITS2 region, we undertook a second sequencing effort to optimise the PCR protocol. Specifically, we investigated: (1) how consistent the PCR was (i. e. PCR-to-PCR effect) by conducting PCRs with the exact same reaction conditions but sequenced on different sequencing runs, (2) whether increasing the annealing temperature could increase sensitivity in non-coral sample environments, and (3) whether a decrease in the number of PCR cycles would still allow for a satisfactory amplification of Symbiodinium whilst decreasing chimeric sequence return. PCR conditions were the same as for the SYM_VAR_5. 8S2/SYM_VAR_REV primer pair (Table  1 ), but with a 30 s denaturing step, following the manufacturer's recommendation (Phusion High Fidelity PCR; ThermoFisher Scientific, Waltham, MA, USA).
ACKNOWLEDGEMENTS Tara Pacific  consortium acknowledgement: We are keen to thank the commitment of the people and the following institutions and sponsors who made this singular expedition possible:  CNRS ,  CSM ,  PSL ,  KAUST ,  Genoscope/CEA ,  ANR-CORALGENE ,  France Genomique  (specifically grant number  ANR-10-INBS-09 ), agnes b. , the  Veolia Environment Foundation ,  Region Bretagne ,  Serge Ferrari ,  Billerudkorsnas, AmerisourceBergen Company, Lorient Agglomeration, Oceans by Disney , the  Prince Albert II de Monaco Foundation, L'Oreal, Biotherm, France Collectivites, Kankyo Station, Fonds Francais pour l'Environnement Mondial (FFEM) ,  Etienne Bourgois ,  UNESCO-IOC , the  Tara Foundation  teams and crew. Tara Pacific  would not exist without the continuous support of the participating institutes.
This paper presents a 1. 80GHz class-A Microwave power amplifier (PA). The proposed power amplifier is designed with single-stage architecture. This power amplifier consists of a bipolar transistor and improved by Collector-Feedback Biasing fed with a single power supply. The aim of this work is to improve the performance of this amplifier by using simple stubs with 50Ω microstrip transmissions lines. The proposed PA is investigated and optimized by utilizing Advanced Design System (ADS) software. The simulation results show that the amplifier achieves a high power gain of 13dB, output power rise up to 21dBm and good impedances matching ;For the input reflection coefficient (S11) is below than -46. 39dB. Regarding the output reflection coefficient (S22) is below than -29. 898dB, with an overall size of about 93 x 59mm².
SIMULATION RESULTS AND PERFORMANCE ANALYSIS This section presents the simulated results of proposed microwave power amplifier operating at DCS band. It was implemented based on Agilent's Advanced Design System (ADS) software. From the simulation S-parameter. As can be observed, the proposed amplifier has -46. 39dB input return loss (S11) and -29. 898dB output return loss (S22) at 1. 80GHz. Figure  4  shows the simulated input/output return loss. While, the power gain (S21) has a maximum value of 13dB with a unilateral transmission coefficient (S12) less than -19. 5dB in the operating frequency band. The unilateral transmission (S12) is important to quantify in PA performance analysis as practically some signal transmitted back to the input from the output (Ideally should be no back reflection).
Introduction Nucleic acid sequences not only carry genetic information, but can also adopt various structures that go beyond double-helical DNA or stem/loop RNA combinations. These shapes and their dynamics could code for another level of genetic/genomic information. More and more light has been recently shed on alternative or unusual nucleic acid structures as sequences prone to 'unorthodox' conformations are proposed to have nucleic acid-related functions  (Bacolla et al. , 2004; Kouzine et al. , 2008). Guanine quadruplexes (G4) are a family of alternative nucleic acid structures that have attracted attention because of their high structural stability under physiological conditions  (Davis, 2004)  and the widespread distribution of sequences compatible with G4 formation  (Hansel-Hertsch et al.
, 2016) , we published the R-code for the algorithm allowing anyone to use G4Hunter. But I came to realize that many people are reluctant to code or might need G4Hunter for a simple task; such as calculating the G4Hunter score for an oligonucleotide or retrieving the G4Hunter predicted hits for their favourite gene or transcript. Therefore, I have developed small applications in the Shiny/R framework that allow such tasks with a user-friendly interface. For whole genomes scans, scripts are available within the original publication for G4Hunter  (Bedrat et al. , 2016).
Materials and methods Fours applications have been written in Shiny/R starting from the scripts published  (Bedrat et al. , 2016). All apps require the Shiny library. Additional libraries required are: Xvector, Biostrings  (Huber et al. , 2015)  and GenomicRanges  (Lawrence et al. , 2013). All source code are available on GitHub (https://github. com/ LacroixLaurent) and can be run on a personal computer via RStudio (https://www. rstudio. com/). Setting up the apps on an Rstudio server is out of the scope of this article.
The output can be exported to a text file (tab-separated values) that can be directly opened with Microsoft Excel. There is no limit for the size of the input sequence using the manual entry option and sequences up to several megabases are well tolerated by the app. For fasta file entry, there is a limit set by the default settings of the shiny environment: the file can be up to 5 Mb. However, this option can be modified (see the README file from the source code for details).
Acknowledgements I thank  J. L. Mergny ,  P. Martin ,  G. Micas ,  A. Sahakyan ,  D. Morgan  and colleagues from  Olivier Hyrien's lab (IBENS, Paris)  for discussion and  V. Besic  for English improvement. The app G4HunterSeeker was kindly made available through a web server by  M. Dunning  and  A. Pajon  ( CRUK, Cambridge, UK ): https://bioinformatics. cruk. cam. ac. uk/G4Hunter/.
Proposed SA optimization. Scores in this proposal are obtained using RAxML  [3]. As an inference of a bootstrapped and rooted phylogenetic tree may take times, and as we need to compute several trees, each calculated state is tagged so that it is never recomputed without an explicit user demand. Associated and detailed results are buffered on disk. Then a simple, reliable, and not really space-characteristics dependent solution is the synchronization of some SAs after the end of a Markov chain  [34]. In order to do so, a batch of SAs is launched with the same configuration. After a chain, each running SA shares its own best known solution to a server. Then, it demands to this server if a better state has been found before starting the next chain. Finally, each SA halts after n local non optimizing chains.
The large number of obvious errors in the NCBI annotated complete chloroplastic genomes can be explained by the large variety of annotation tools used during sequence submission, most of them being not specific to this kind of genomes (unlike DOGMA), to a misuse of these tools, or due to errors in manual annotations. The absence of a clear norm in the gene naming process adds difficulties, so that the sole method to provide accurate annotations to these 780 complete genomes was to constitute a basis of knowledge, with a subset of well curated genomes that represent well the plant diversity. And, to blast each genome against the basis, which is indeed what is done by DOGMA. We finally have written a script that automatically send requests to the DOGMA web service, and recovers the annotated genomes.
Due to this automatic process, the gene name spelling issue is resolved, and we can recover the clusters of homologous coding sequences by simply considering gene names. By applying the same tool for coding sequence prediction and naming process, we have resolved the problem of quality variability in annotations. And as DOGMA has been specifically designed for chloroplasts, errors in sequence positions have been reduced as possible. At this stage, and using our script on DOGMA web service, we have then a collection of 780 complete and "well" annotated chloroplastic genomes, from which gene names can be used to recover core and pan genomes of any subset of genomes.
Early analysis on SA computed problem: an illustration An example of a SA batch run (three clients on the first family described previously) is depicted in Fig. 7. For easy understanding, only some outputs have been reported in the figure. On the lower part, all moves of the simulated annealing are reported with their nature : synchronized move in yellow (i. e. , copy, from a shared memory, of the best known solution found in the three SAs), move with an accepted status in orange, and rejected moves in black. Active genes are filled squares and not selected ones are white squares. Other important data for analysis are reported, such as: temperature, accepted score of other SAs (green and purple), and Hamming distance between two consecutive positions (moving behavior indicator). On the upper part, a graph of accepted scores from the three SAs is provided, with the temperature variations due to move iterations (a lower score is a better one).
Pinus data set has got maximum bootstrap b larger than what has been obtained using the genetic algorithm, while Picea and Trebouxiophyceae have got the same values of b than with genetic algorithm. Further comparison results between GA and both versions of BPSOs are provided in Fig. 9. According to this figure, we can conclude that the two approaches lead to quite equivalent bootstrap values in most data sets, while on particular subgroups obtained results are complementary. In particular, BPSO often produces better bootstraps than GA (see Magnoliidae or on Bambusoideae), but with a larger number of removed Fig. 7  Illustration of clade analysis with a 3-parallelized SA genes. Finally, using 15 particles instead of 10 does not improve so much the obtained results (see Fig. 9  and Table  6 ).
Multi turn simulation studies One of the main peculiarity of the proposed machine is the multiple interactions of the primary beam within the target. Therefore, a dedicated multi turn simulation scheme has been set up, in order to follow the evolution of the beam at every interaction in the target and to optimise its parameters. The simulation starts with the initial 6D beam distribution, taken from the equilibrium emittances. This distribution is tracked for a whole turn up to the target using both Accelerator Toolbox and MAD-X PTC for comparison. Then, the behaviour of the positrons inside the target is simulated by means of FLUKA  [11]  [12] and GEANT4Monte Carlo codes, which exit distribution is sent back to the tracking program until the next interaction in the target. So, at each pass through the target the positron beam undergoes two processes.
First, we introduce the basic scheme of the simulation with the study of ecological interactions between co-evolving virulent phages and bacteria under phage and antibiotic pressure in structured environments. Then we introduce lysogeny and super-infection exclusion in the model to study the role of prophages as competitive weapons. We show that our model provides better fit to previous experimental results than earlier models. Finally, we introduce transduction and the way we encode individual genomes in the model to elucidate how bacteria may obtain novel adaptive genes from sensitive bacteria by autotransduction. We use eVIVALDI to explore and quantify the different mechanisms of phagebacteria interactions and to gain insights on how the structure of the environment can affect these interactions and the community dynamics.