dataesr/scientific-paragraphs-categorization huggingface dataset:
The code named allen_creator. py is located in the folder TVB folder/tvb/adapters/creators/. The code is composed of a function "launch" that calls several functions (all written in the file allen_creator. py) that perform all the actions described above. To work outside TVB, users need to copy and paste all the functions that they need from the allen_creator. py. The volumes and the connectivities used in the present work have a resolution of 100 m and each connection strength is defined as the ratio between projection density and injection density. The areas included in the parcellation have a volume Ͼ2 mm 3 and they have Ͼ50 voxels infected in at least one injection experiment. The connectome obtained is in the TVB data folder (/TVB_Distribution/tvb_data/ lib/python2. 7/site-packages/tvb_data/mouse/); the instructions to obtain it through the TVB Jupiter interface are in script Figure  1 -1 (Extended data).
Functional connections Functional connections in the simulated time series are explored from both spatial and temporal point of views using, respectively, the FC and the FCD. The ij-th element of the FC matrix is calculated as the Pearson correlation between the BOLD signal of the brain region i and of the brain region j. To estimate the FCD, the entire BOLD time series is divided in time windows of a fixed length (3 min) and with an overlap of 176 s; the data points within each window centered at the time t i were used to calculate FC(t i ). The ij-th element of the FCD matrix is calculated as the Pearson correlation between the upper triangular part of the FC(t i ) matrix arranged as a vector and the upper triangular part of the FC(t j ) matrix arranged as a vector. To observe signal correlations at frequency greater than the typical one of the BOLD signals, the sliding window length is fixed to 3 min, since, as demonstrated by  Leonardi and Van De Ville (2015) , the nonspurious correlations in the FCD are limited by high-pass filtering of the signals with a cutoff equal to the inverse of the window length.
We set the pre-expression monitor to keep track of the local field potential, defined in the Epileptor as -x 1 ϩ x 2 , as well as the slow permittivity value z. We define the time at which seizure initiates in a large brain region, as for example the olfactory cortex, as the mean of the seizure onset time of all the network nodes composing that region (see Table  1 ); to evaluate the chronological order of areas recruitment we define the seizure onset latency of a region as the difference between the time at which the seizure initiates in that region and the time at which the seizure has started in the epileptogenic zone, i. e. , the hippocampal regions. The script to simulate epileptic activity in mouse brain is in Figure  4 -1 (Extended Data).
The y-axis shows the average time of recruitment in arbitrary units of these regions after triggering a seizure in the left hippocampus in silico. Note the good match between simulated and experimental data. Extensive names of the region composing each group are illustrated in the table in Table  1. C, The time distance from seizure onset in the left hippocampus is given by the color scale and plotted in the brain volume for each region. The Ipython script to obtain the results in the figure is insight. However the differences in brain activity between the two conditions are revealed at the network level when computing the FCD (Fig. 3B ). The functional connections that emerge in the epileptic brain are not correlated in time resulting in a suppression of the switching behavior of the FCD, as compared with the control connectome (Fig.
Both resting state dynamics and seizure propagation, as demonstrated above, satisfy these assumptions. On the other hand, for example, fine grained spike timing effects would not well described within TVMB's mathematical framework. The wide range of possibility offered by rodent experiments will easily accommodate the validation of the parameterization required by all the modeling approaches contained in the software. This validation sometimes can proceed at a qualitative level for phenomenological models, such as the Kuramoto model of synchronization, but many detailed biophysical models allow for quantitive comparison with empirical data, such as spike timing  (Brette and Gerstner, 2005). At the whole-brain level, TVMB allows for direct comparison with common modalities such as EEG, MEG, and fMRI, or common statistics thereupon such as FC; these comparisons allow for the characterization of parameter values in terms of their fit with empirical data and thus biological validity.
On the other hand tracer-based connectome are built by averaging experiments over many mice, and the definition of the seed region is based on stereotaxic coordinate rather than on anatomy. A comparison of the effects in simulating brain network using one or the other kind of connectome is needed especially since the only data available in human are dMRI data. TVMB is an actively developed software, with new versions released regularly with new features. Among those targeted specifically for the mouse, the module which builds connectivities from the public Allen data will continue to evolve as the available dataset becomes richer. For example, when cortical layer annotations become available, it will be possible to construct mouse connectivities in which the cortical layers are distinct, allowing for example manipulations of inter-layer interactions.
However, it does not fit well with the deployment stack of a BPMS that include web servers and databases. In this paper, we propose a method that allows to distribute process execution on a set of cloud computing resources, and to adjust the resources based on the load that the customers require. We call a customer a tenant, and we ensure that all the processes of a tenant are executed on a BPMS installation. We consider that resources are paid by discrete time units. We assume that we know for each tenant what will be its maximal resource consumption time unit per time unit. More precisely, we want to take into account the knowledge we can get from the business dimension, i. e. the number of task execution per hour. In our previous work  [2] , we proposed to optimize resource consumption from a timeslot to the next.
We focus here on the scheduling and resource allocation parts, with an emphasis on the multitenancy functionality. Hoenisch et al. [4]  proposes an interesting approach : the Service Instance Placement Problem, a cost optimization model concerning the assignment of process instances to VM, scheduling of service invocations, and the provisioning of VM. The authors consider the underline structure of the BPM processes and propose to optimize the cost while taking into account penalties for violated deadlines. However, they consider only the BPM engine's CPU and RAM capacity in their model -and not the database tier-and do not consider multitenancy or migrations. Other previous attempts have the same drawbacks such as  [5] ,  [6] ,  [7]. Hachicha et al. [8]  addresses multi-tenant BPMaaS with the concept of configurable resource assignment operator.
It consists of an enrichment of the process with meta informations on the required resources for the tasks execution. However, it needs to add informations in the BPM schema, thus needing to alter BPM engine and the processes of the customers and do not propose a resource allocation and scheduling method. Another attempts to tackle the multi-tenant problem is done in  [9]  by Sellami et al. They propose a multi tenant approach based on customizable thresholds, however, it does not take into account migration cost or the database tier. There are many papers on Virtual Machine assignment or reassignment in data centers such as  [10] ,  [11] ,  [12] ,  [13] , however as in datacenters the physical machines are already reserved, only the scheduling part is studied and for the reassignment papers, the migrations are usually counted as part of the objective function instead of a separated entity.
The BPM execution model In this section we introduce the model for the BPMS execution we want to optimize. Our model relies on a few assumptions regarding the BPM system. First, it must be multi-tenant, i. e. several customers (or tenants) can share the same BPMS installation. Second, it is possible to migrate a tenant from one installation to another with minimal disruption. The main issue here is the data migration from a database to another  [14, 15]. We also only consider that IaaS providers bill the computing resources per studied timeslot (for instance per hour). Main public cloud providers like AWS, and IBM Bluemix, follow this pattern per hour, while Google Compute Engine or Azure propose also a per minute billing. The operation of a BPMS requires a complex production software stack.
It may combine a BPM engine, load balancers and relational databases. They are often deployed on distinct hardware instances or virtual machines from the BPM engine, mostly for performance reasons. We call "cloud configuration" the set of resources that we use to execute process instances for a group of tenants. Last, we assume that we know the usage requirement for each tenant timeslot by timeslot in term of the maximum task throughput per second. This metric has the advantages of being representative of the system usage of both the database tier and the application server tier. We must also know the capability of each cloud configuration type in term of BPM task throughput. We aim at minimizing the cloud resource cost while ensuring that the throughput for each tenant is at the required level.
To test our solution we made a few assumptions and relied as much as possible on datasets that gives us realistic foundations for the resolution of the model. As we can see in the model part, we needed to have a good estimation of the customer loads, timeslot by timeslot on one side, and of the price and capability in task throughput per timeslot of a cloud configuration on the other side. We have then compared several segmentation methods on the same datasets of customer loads and cloud configurations to find the best ones. In the next part we describe the datasets.
Datasets In order to get meaningful cost and task throughput for our cloud configurations, we have used the data obtained in our test framework experimentation  [19]. In this paper, we have set up an experiment on AWS with a BPMN process composed of 20 consecutive automated tasks launching a Fibonacci script. We launched tests on several r3 (storage-oriented) family instance types for the database, and c4 (CPU-oriented) family instance types for the application server. We used the BPM system BonitaBPM 7. 3. 2 3  in its Open Source version. We compared the obtained throughput with the price of each cloud configuration. Results are described in table  1  For the customer load part, we wanted to test multiple tenant quantities (5, 10, 25, 50 and 100), having different throughputs based on real data from BonitaBPM customers.
Software and methods Our goal here is to determine between the different methods and for different number of migrations, which one gives the better migration strategies i. e. which one between top-down and bottom-up algorithms, grouped and individual strategies, and ConstantMaxPieceWise, ConstantPieceWise and Linear Regression fitters gives the best results. As our metrics are hourly based on the different configurations and tenant loads, we used hourly timeslots, more precisely 48 timeslots. The initial setting is the following : for each tenant, we select the least expensive resource that is suited for the maximum required throughput on the study time. We name this method adapted heuristic. For the segmentation part we implemented a fixed and updated version of the Alchemyst library 4. This library implements top-down and bottom-up algorithms, with mean constant and linear regression fitters.
We also compared a subset of our test dataset with the solving of the linear model described in chapter 3 during a limited time, for the lowest number of tenants. For this we used the solver Gurobi  [21]. Table  3  summarize the different parameters we used in this experiment. Experiments have been executed on c4. xlarge (CPU optimized) instances on Amazon Web Services. In the next part, we discuss our results. Table  3. Synthesis of the experiment dataset and algorithm used values.
This is our next step. We also think that can still improve our results using metaheuristics. Even if they are much better than with a naive approach, there is room for improvement as shown by our experiments with a solver for 5 tenants. Last, we want to test this heuristic with customer data, where the results should be better considering the load patterns we can identify (day/night cycle, working hours, lunch time, etc. ). The authors would like to thank Gurobi for the usage of their optimizer, and Amazon Web Services for the EC2 instances credits (this paper is supported by an AWS in Education Research Grant Award). The data and the results are available at : http://doi. org/10. 5281/zenodo. 401374. The source code of the framework is not free for now, except for the segmentation library, available at https://github.
LETTER RESEARCH Reconstructing the flowering-time genetic network in sunflower is of particular interest, because it is a key trait in crop production and the best-adapted flowering time has been selected in each cropping area during the breeding phase. Taking advantage of a recently developed database of flowering-time gene networks in Arabidopsis thaliana  20  , we identified 485 orthologues and in-paralogues (that is, paralogues post-dating speciation) for 270 flowering-time genes in the sunflower genome (Extended Data Fig. 5 , Supplementary Data 7 and Supplementary Note 6. 2). There were several sunflower in-paralogues for 180 Arabidopsis genes, illustrating the complexity of regulatory networks in sunflower. Previous investigations of flowering-time architecture in the sunflower  21  , using more limited genomic data, focused on the transition from the wild sunflower to early domesticates.
To find evidence of selection during sunflower breeding, we mapped resequencing data of 80 genotypes and measured differentiation (F st ) between oil and non-oil (for example, confectionary) types of domesticated lines (Supplementary Note 8. 2). Genes of the oil metabolic network were enriched in the top differentiated genes, suggesting that we had successfully identified relevant candidates for oil improvement. We found 46 oil genes in 32 genomic regions corresponding to previously identified QTLs for seven oil-related traits (Supplementary Note 8. 2). Nine of these genes were highly differentiated between highand low-oil lines (Extended Data Fig. 6c ), including FAD2-1, which has been shown to be under selection during post-domestication  24. Another, HPPD, had already been found to co-localize with a QTL for the vitamin E precursor tocopherol  25.
Assembly. The PBcR wgs8. 3rc1 assembly pipeline  32  was used to perform the correction of reads, WGS 8. 3 to assemble the corrected reads and quiver  33  to polish the consensus sequence after the construction of the pseudomolecules (see below). However, to overcome challenges associated with the sunflower genome assembly, substantial parameter tuning, code modification and software development were required and these are described in Supplementary Note 1. 3-1. 7.
The plant early release of BUSCO (release July 2015)  35  was run on the set of predicted transcripts, and it detected 92% of complete gene models (590 complete single copy and 291 duplicated, respectively) plus 10 additional fragmented gene models. Protein-coding genes were annotated using a three-step process, taking into account reciprocal best hits in the SwissProt and TAIR10 (ref. 36) databases (12,360  sunflower proteins), protein-domain content using Interpro (26,646 sunflower proteins), and similarity with plant proteomes (Ensembl release 30) or coverage of the transcript with RNA-sequencing data (1,200 predicted proteins with similarities in other plant proteomes without expression support, 1,832 with similarities in other plant proteomes with expression support and 8,542 gene models supported by expression data, but without significant hits with other plant proteomes).
We generated 58 paired-end RNAsequencing libraries to measure expression in 11 sunflower organs, the responses to hormonal and osmotic and salt treatments in roots and leaves, as well as response to variable water status (Supplementary Note 4. 1). Library sequencing was done with Illumina HiSeq, reads were mapped with the glint software (https://forge-dga. jouy. inra. fr/projects/glint) and only the best scoring pair(s) of reads was(were) kept. Expression measurements and normalization were performed as described in Supplementary Note 4. 2. Organ-specificity was measured by computing a specificity index, Tau  47  , on the normalized expression score. We identified sets of organ-specific genes and regulators (transcription factors and lncRNAs) (Extended Data Fig. 4  and Supplementary Note 4.
jouy. inra. fr/projects/glint) and SNP calling with VarScan 49 (Supplementary Notes 5. 1, 5. 2). Identification of flowering time orthologues and in-paralogues. Flowering time genes in A. thaliana were retrieved from a recently developed database, FLOR-ID  20  , which includes 295 protein-coding genes and 11 miRNA genes and describes their interactions. We built gene clusters for a set of seven species, namely H. annuus, A. thaliana, Cynara cardunculus, Oryza sativa, Hordeum vulgare, Brassica rapa and Populus trichocarpa, chosen to be consistent with a previous study that identified orthologues for more than 30 flowering-time genes in the sunflower  21  , adding the proteome of the recently sequenced member of Asterids II C. cardunculus  16. To identify orthologues and in-paralogues (that is, paralogues post-dating speciation) of A.
thaliana genes, we built and visually examined trees for the clusters defined above (Supplementary Note 6. 2) and manually screened BLAST reports on the sunflower genome browser. We identified 485 orthologues and in-paralogues (Supplementary Data 7). A genome-wide association study of flowering time was performed on a set of 480 hybrids obtained from 72 inbred genotypes (Supplementary note 6. 1), and colocalization of flowering-time orthologues with flowering time QTLs was assessed with bedtools  50. Analysis of paralogues dating from the most recent whole-genome duplication (WGD-2). Correlation of expression between WGD-2 paralogues was assessed quantitatively by measuring the Pearson correlation coefficient and qualitatively by counting the number of pairs of paralogues that belong to the same co-expression modules based on a weighted gene co-expression network constructed with WGCNA (Supplementary Note 7).
Acknowledgements We thank  G. Kuhn  for sharing his expertise in PacBio sequencing and  H. Witsenboer  for his help with the production of the Fingerprint-based physical map; the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing help and computing resources, the common services of the LIPM for their support, and  Genome Quebec Innovation Centre and Canada's Michael Smith Genome Science Centre for 454  and Illumina sequencing;  M. Scascitelli ,  M. Stewart ,  D. Ebert ,  J. Roeder ,  H. Shaffer ,  E. Gudger ,  B. Hsieh ,  S. Jackson ,  S. Rounsley ,  C. Feuillet ,  B. Barbazuk  and  M. Barker  for their help and advice during the  Genome Canada/Genome BC  project; and  D. Swanevelder  for contributing to the sequencing of the sunflower association mapping populations; members of the  International Consortium for Sunflower Genomics resources  ( 2012-2015 ):  Advanta, BASF, Biogemma ,  Dow ,  KWS ,  Pioneer  and  Syngenta  companies and their sunflower project leaders;  F.
Bonnafous  for the development of the statistical pipeline for GWAS and  P. Castellanet ,  C. Henry ,  M. Laporte ,  J. Piquemal ,  M. Coque  and  T. André  for the coordination of flowering time phenotyping on the sunflower hybrid panel (GWAS). This project was funded by the  French National Research Agency  ( SUNYFUEL/ANR-07-GPLA-0022  and  SUNRISE/ANR-11-BTBR-0005  projects), by the  Midi-Pyrénées Region , the  European Fund for Regional Development , the  French Fund for Competitiveness Clusters (FUI) , the  Genoscope SystemSun project, Genome Canada  and  Genome BC's Applied Genomics Research in Bioproducts or Crops (ABC) Competition , the  NSF   Plant Genome Program  ( DBI-0820451 ) and the  International Consortium for Sunflower Genomics Resources.
In this study we present a method using whole cell MALDI-TOF MS and VITEK MS RUO/SARAMIS as a rapid epidemiological screening tool. MRSA was used as a model organism for setting up the screening strategy. A collection of well-characterised MRSA strains representing the 19 most common Pulsed-Field Gel Electrophoresis (PFGE)-types in the region of South-West Sweden for the past 20 years was analysed with MALDI-TOF MS. A total of 111 MRSA strains were used for creating 19 PFGE-specific Superspectra using VITEK MS RUO/SARAMIS. Prior to performing the final analysis, the 19 Superspectra were combined into ten groups displaying similar peak patterns, hereafter named BMALDI-types^. Two-hundred fifty-five MRSA strains were analysed to test the constructed Superspectra/MALDI-type database. Matches to the Superspectra above a threshold of 65% (corresponding to the number of matched peaks in the Superspectrum) were considered as positive assignment of a strain to a MALDI-type.
Introduction Whole cell Matrix-Assisted Laser Desorption/Ionisation -Time Of Flight Mass Spectrometry (MALDI-TOF MS) has been a major breakthrough for microbial identification in the clinical microbiology laboratory  [1, 2]. It has been shown to be able to process microbiological identification much faster and in some cases with higher resolution than traditional phenotyping. The combination of a rapid analysis and low cost makes it a valuable tool for processing large numbers of samples. VITEK MS Plus (bioMérieux, Marcy l'Etoile, France) is one of the commercial systems used for species identification with MALDI-TOF MS in microbiological diagnostics. It consists of two databases that use different software, the closed IVD database, mainly used for routine identification, and the Research Use Only (RUO)/ SARAMIS databases.
The RUO/SARAMIS system is an open database in which the user can add spectra and curate the database(s). It employs the concept of Superspectra for identification, which is a composite spectrum containing conserved and specific peaks identified in a population spectra from well-characterised reference strains  [3]. Peaks are weighted to account for specificities at different taxonomic levels, for example, family, genus, and species levels or like in this study, the type level. The successful implementation of MALDI-TOF MS in the clinical microbiology laboratory and the ease and speed of the analysis have spurred further investigations of how to exploit MALDI-TOF MS for more detailed analyses, i. e. at subspecies levels. There are increasing numbers of studies investigating the potential levels of resolution able to be elucidated by MALDI-TOF MS for epidemiological purposes as well as for typing with regard to virulence or antibiotic resistance, such as the typing of Clostridium difficile  [4] , Haemophilus influenza type B capsule typing  [5] , outbreak typing of Shigatoxigenic Escherichia coli  [6]  and subtyping of Streptococcus equi  [7].