dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The Puéchabon experimental site belongs to the  OSU OREME  ( UMS 3282 ) and the  SOERE F-ORE -T, supported annually by  Allenvi  and the  French national research infrastructure ANAEE-France  ( ANR-11-INBS-0001 ). This research was supported by the  "Fond Stratégique Forêt Bois" (FSFB)  funded by the  French ministry of Agriculture and Food  through the  INNOV ' ILEX  project ( MAA 2017 ). The authors are indebted to  Serge Rambal  and  Richard Joffre  who designed and initiated the long-term rainfall exclusion experiment in Puéchabon, and to  Alain Rocheteau ,  David Degueldre  and  Karim Piquemal  whose efforts greatly contributed in maintaining it throughout the years. SNP genotyping was performed at the  Genome Transcriptome Facility of Bordeaux (Grants from Investissements d'Avenir, EquipEx Xyloforest   ANR-10-EQPX-16-01 ).
We compare the trajectories of the particles obtained directly from the GP simulations with those predicted by the MFM. We solve the coupled ODEs of the MFM by using the specialized ODE solver ODE45 in MATLAB. We also include the short-range repulsion forces as now they are relevant for the cases where our model is tested beyond the formal limit of applicability, namely, when the inequality ξ R d does not hold. For comparing the above two solutions, we define an average error  (24)  where (x s , y s ) are the GP simulated coordinates of the particle and (x p , y p ) are the coordinates from the simulated ODE. We choose N t points on each trajectory which are evenly spaced  in time. The measure of error, , is the average distance the predicted value is away from the true value over the entire run; we present this in units of ξ.
ACKNOWLEDGMENTS S. N. is supported by the  Chaire D'Excellence IDEX (Initiative of Excellence)  awarded by  Université de la Côte d'Azur, France. M. -E. B. was supported by  Agence Nationale de la Recherche  through the project  QUTE-HPC   ANR-18-CE46-0013. This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreements No. 823937  in the framework of the  Marie Skodowska-Curie HALT  project and No. 820392  in the FET Flagships PhoQuS project. Computations were carried out on the  High Performance Computing Cluster  and supported by the  Scientific Computing Research Technology Platform at the University of Warwick. We also acknowledge the following computing grant:  GENCI  ( Grand Equipment National de Calcul Intensif ) No.
The multi-threaded implementation of LCIO LCIO (Linear Collider Input-Output) defines the event data model for linear collider studies and provides the persistency implementation via the SIO (Simple Input Output) sub-package. The Marlin framework uses LCIO, both as input and output format as well as its transient event data model. As with most of the software written by HEP scientists in the early 2000s, it has not been designed with multi-threading in mind. The first task to make MarlinMT work with LCIO was to refactor parts of the code in order to make multi-threading possible. Apart from removing some global states, it was also necessary to re-implement the SIO persistency layer of LCIO from scratch. The original user API was restricted to simple read and write functions that always handled one full event in memory in one pass.
Histogramming in a multi-threaded environment One of the core functionality needed in every HEP-framework is the booking and filling of histograms. In the Marlin framework both tasks were implemented independently in every Processor individually, using ROOT histograms. This implementation is inherently not thread-safe and therefore a solution for MarlinMT had to be be found. Other HEPframeworks typically implement one of the following two approaches: • Split and merge method: create one thread-local copy of the histograms per module, fill these copies independently in each thread and eventually merge them into one before writing to disk. While thread-safety is guaranteed by construction, the memory for the histograms is duplicated for every thread. This can turn out be a problem for applications with very large numbers of histograms, such as online data quality monitoring.
• to reduce the synchronisation phases when using the single shared memory layout, fill calls can be buffered in a thread-local buffer that is flushed into the histogram using the mutex only when it is filled up. The BookStore component itself does not depend on MarlinMT and can be made available for other multi-threaded applications. In order to measure the performance of the different approaches, we have created a Processor that books 1000 histograms with 1000 bins and fills them from an LCIO data file using the copy approach and the shared approach with (shared) and without buffering (lock). The resulting total processing times per core are shown in Figure  4 -right as a function of the number of cores/threads used. All three approaches show very similar behavior, where the copy turns out to be the slowest, presumably due to more frequent cache misses.
Conclusion MarlintMT is a multi-threading re-implementation of the Marlin framework which is used in the linear collider community and beyond. The scheduler allows for processing events in parallel in different threads on modern multi-core architectures. The performance of Mar-linMT has been benchmarked, using a CPU crunching module. It shows good scaling of the total processing time with the number of cores/treads used. The development of the Mar-linMT framework also triggered new developments in the LCIO EDM and persistency library. Besides making the underlying I/O implementation SIO thread-safe, a new feature for lazy unpacking was introduced. This allows to postpone the uncompressing and unpacking of the event data to the worker threads after the data has been read from the file. This reduces the amount of sequential code, that is run in MarlinMT and improved the scaling performance considerably.
In order to address the problem of histogramming in a multi-threaded environment a new component the BookStore has been developed. This component allows to choose at initialization time between three different ways of handling the access to the histograms when filling them in the parallel modules: a local copy per Processor or filling a shared histogram instance, either in single fill mode or via buffered bulk-filling. The implementation is based on the, yet experimental, ROOT 7 histogram library with a conversion to the commonly used ROOT 6 histograms at the end of the program. The BookStore has been in implemented as an independent, reusable solution which can also work in other applications. The next important step will be to make the large number of Marlin processors that are intesively used in various future collider studies thread-safe and adapt them to the new MarlinMT API.
Transcriptomic Profile We determined up-and down-regulated genes following treatment and selected those specific to each toxin. Following OA treatment, there were 36 and 18 specifically up and down-regulated genes, respectively, while they reached 745 and 572 specific to DTX-1, and 127 and 149 specific to DTX-2, respectively. The biological and molecular processes affected specifically by OA, DTX-1, and DTX-2 were addressed using the GoMiner application and the Database for Annotation, Visualization and Integrated Discovery (DAVID) Functional Annotation Tool. For the genes specifically regulated by OA, no significant Gene Ontology (GO) term was depicted for the up-regulated genes, while 16 biological processes were significantly enriched for the down-regulated genes (Supplementary Table  S2 ).
We also identified nine significant cellular components related to the nucleus and five significant molecular functions related to transcription. Our data suggest that genes specifically up-regulated by DTX-1 encode mainly proteins involved in the regulation of transcription. Using DAVID, the analysis outlined 13 significant terms mostly including the PI3K-Akt and MAPK signalling pathways (Supplementary Table  S3 ). These biological pathways, through the involvement of transcriptional factors, affect the regulation of numerous processes such as cell proliferation, differentiation, inflammation, and apoptosis, in agreement with the GoMiner results. Among the major proteins involved in these signalling pathways, Ras, NFκB, MEK, and JNK are coded by genes belonging to the DTX-1 specific up-regulated cluster.
The 572 genes specifically down-regulated by DTX-1 provided a list of 13 biological processes and five cellular components, without any enriched molecular function (Supplementary Table  S2 ). Both the biological processes and the cellular components listed were mainly related to nucleosome/chromatin/chromosome assembly and to DNA replication. The additional analysis using DAVID revealed three significant terms: "mismatch repair", "nucleotide excision repair", and "DNA replication" (Supplementary Table  S3 ). All included four genes coding for proteins involved in DNA replication. Our results suggest that genes specifically down-regulated by DTX-1 encode proteins related to DNA repair and replication. For the genes specifically up-regulated by DTX-2, there were seven biological processes significantly enriched, but no cellular components and no molecular functions were depicted (Table  S2 ).
Cytotoxicity Evaluation by Neutral Red Uptake Assay After 24 h of treatment with the toxins, cells were incubated for 2 h with 0. 004% neutral red solution prepared in FCS-free medium before a solubilising step (acetic acid:ethanol 50% (1:99; v:v)). Absorbance was measured at 540 nm with a microplate-reading spectrofluorometer (FLUOstar OPTIMA, BMG Labtech, Champigny-sur-Marne, France). For each independent experiment (biological replicate), the median of three technical replicates was calculated, and then expressed in percentage to that of the vehicle control. The IC 50 was determined using GraphPad Prism software (version 5. 0, GraphPad Software Inc. , La Jolla, CA, USA).
Microarray Experiments and Data Analysis Based on cytotoxicity evaluation, Caco-2 cells were exposed for 24 h to the highest sub-toxic concentration inducing no significant alteration of cell viability: 13. 8 nM OA, 6. 9 nM DTX-1, and 27. 7 nM DTX-2. Thereafter, total RNA was isolated using the NucleoSpin RNA II kit according to the manufacturer's instructions with a final elution volume of 10 µL of RNase free water (Macherey-Nagel, Hoerd, France). RNA was quantified with the BioSpec-Nano (Shimadzu, Marne la Vallée, France), and RNA integrity was assessed with the "Experion RNA StdSens analysis" kit using the Experion automated electrophoresis system (Bio-Rad, Marnes-la-Coquette, France). Only RNA with an RNA quality indicator ≥ 9 was used for further experiments (Experion software 3. 0; Bio-Rad).
The dataset, including the 26 960 post-filtering probes, was labelled "filtered data" and deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus database through the accession number GSE159293. For each toxin, from the "filtered data", the differentially expressed genes compared to the vehicle control were selected at p < 0. 05 (Student t-test) and with a fold change (FC) greater than two (for "up-regulated genes") or less than 0. 5 (for "down-regulated genes"). Among these genes, those specifically altered by only a single toxin (and not by the other two) were distinguished, leading to six clusters: for each toxin, "specific up-regulated genes", and "specific down-regulated genes". An analysis of these clusters was performed using the GoMiner tool (http://www.
ncbi. nlm. nih. gov/BLAST/) with, for each gene, at least one primer designed to span an exon-exon junction. All primers were purchased from Sigma-Aldrich (Lyon, France), and additional information regarding the target genes and oligonucleotide primers is listed (Supplementary Table  S1 ). Using NormFinder software (version 0. 953; Aarhus, Denmark), the gapdh gene was chosen as the reference gene since it did not exhibit any significant variation in expression among all the samples. Quantitative PCR was performed on a Chromo4Real-Time Detector in low-profile 8-white tubes strips (Bio-Rad). SYBR Green chemistry was used. Reactions were performed on three technical replicates in a total volume of 10 µL containing 1X Power SYBR GREEN PCR Master Mix (Applied Biosystems, Foster City, CA, USA), 300 nM each primer, and 0.
2 or 0. 4 ng cDNA for moderate or poorly expressed genes respectively. Negative quantitative PCR controls of RNase-free water were included in each run for contamination assessment. The thermal cycling conditions were 94 • C for 10 min, followed by 40 cycles of denaturation at 94 • C for 15 s, annealing at the determined temperature for 15 s, and polymerisation at 72 • C for 30 s. Opticon Monitor software (version 3. 0; Bio-Rad) was used for the quantitative analysis, and melting curve analysis was used to check the specificity of each amplicon. Threshold Cqs were calculated from a baseline subtracted curve fit. For contamination assessment, the results revealed that the ∆Cqs of the samples were at least 5 compared to the various controls (extraction, RT, no-reverse transcriptase, and qPCR).
For nuclear identification, cells were incubated for 5 min with DAPI 1 µg/mL/0. 05% Tween in PBS Immunostaining with EdU was performed 30 min before the end of phycotoxin exposure using a Click-iT TM Plus EdU Cell Proliferation kit, according to the manufacturer's instructions (Thermo Fisher Scientific, Courtaboeuf, France, C10637). Plates were scanned with a Thermo Scientific Arrayscan VTI HCS Reader (Thermo Fisher Scientific), and 10 fields (size of each field: 660 × 660 µm) were analysed per well. Using nuclear DAPI staining and the Cell Cycle Analysis module of BioApplication software, cells were classified in the different cell cycle phases and expressed in relation to the total cell number. Phospho-histone H3 and EdU (for cell cycle progression), and phospho-MEK2 (for the MAP kinases pathway) were quantified in the nuclei using the Target Activation module.
Statistical Analysis Statistical analyses were performed using GraphPad Prism software (version 5. 0). For cell counts, data were analysed using the one-sample t-test, with "100" as the theoretical mean. Means were considered significantly different from 100 at p < 0. 05. For IC 50 values, RT-qPCR results, and HCA data, the homogeneity of the variances was verified. Thereafter, an analysis of variance was performed. When the concentration effect was significant (p < 0. 05), the values were compared. For IC 50 , values were compared with each other using Bonferroni's test. For RT-qPCR and HCA, values were compared to the vehicle control using Dunnett's test. Differences were considered significant at p < 0. 05. The values presented are means ± SEM.
, 2017). Neurons were selected as points of maxima in these images and manually curated. Fluorescence traces were extracted from circular regions of interest (ROIs) with a 5 mm radius around the selected centroid locations. Post experiment analysis was performed on calcium traces extracted using the Suite2P pipeline  (Pachitariu et al. , 2017). In brief, videos were motion corrected, neurons were detected, their calcium traces were extracted across time and these traces were corrected for any neuropil contamination. To ensure data quality and the stability of cells across our recording epochs we ran Suite2P separately on the baseline, stimulation and all epochs together (baseline, pre, stimulation and post). We manually curated the output of the baseline epoch, removing ROIs which did not correspond to cell soma or had insufficient signal.
We then filtered for neurons which had a corresponding ROI throughout the session recordings (centroid location within 4 pixels) and selected these ROIs from the collectively processed data to ensure that we were looking at cells which were recorded stably and not lost to brain movement. On average 2. 8 ± 3. 4 target neurons did not pass our stability filter after each stimulation session. There was no difference in this number between session types (p = 0. 9, Kruskal-Wallis test). In order to minimize this, following place cell analysis and targeting, any slight shift in the field of view was corrected if needed by matching it to an average image from the beginning of the baseline epoch. All analysis was performed using custom software written in MATLAB (MathWorks).
Place cell identification and classification Place cells were identified from extracted calcium traces similarly to previous work  (Dombeck et al. , 2010; Danielson et al. , 2016). Data from all trials were velocity filtered for periods when animals were running over 5 cm/s. For each ROI DF/F was calculated across time and drift in the baseline fluorescence was removed by normalizing by the 8th percentile value of a 10 s window around the imaging frame. For each virtual reality trial the DF/F value for each ROI was extracted as a function of virtual space and the average DF/F over space was calculated for 2. 27 cm spatial bins and smoothed with a Gaussian kernel with an SD of 3 bins. Place field criteria were then applied to these traces as reported previously  (Dombeck et al. , 2010) , but without the bootstrapping measure to minimize the time between baseline and stimulation and with a minimum field peak value of 20% DF/F to reduce the occurrence of any false positives.
A 5 ms gap between stimulation to clusters allowed SLM phase mask switching and galvo-galvo scanner movement. The 5 clusters were cycled through twice for a total of 1000 ms of stimulation over 1045 ms. We quantified the optical resolution of our stimulation pathway by taking a Z-stack of a 1 mm fluorescent bead using a single stimulation beamlet and measuring the signal along each axis at different distances from its peak intensity. We generated physiological resolution curves by targeting light at different displacements (as in  Marshel et al. , 2019 , over a range of ± 30 mm for X/Y and ± 55 mm for Z) from the soma of C1V1 and GCaMP6f expressing neurons and then stimulating for 30 trials using the same protocol as our experimental data and with an 10 s delay between trials. We then extracted the average DF/F value for each neuron over a 100 ms window after the stimulation and normalized this by the maximum across all stimulation displacements.
Network activity factor analysis After removal of imaging frames acquired during stimulation, DF/F traces were z-scored within each epoch and spatially binned. Traces were concatenated across all trials in a session to form a data matrix of cell activity. Factor analysis was performed using the MATLAB function factoran (10 factors), with the regression method for estimating factor scores (MathWorks). Changes in network activity due to stimulation were quantified by the Euclidean distance in latent space between the trial-averaged trajectories from stimulation and control (Pre) epochs of each session  (Rickgauer et al. , 2014). Within stimulation conditions, Euclidean distances at each spatial bin following the stimulation zone were compared to averages over the ten spatial bins preceding the stimulation zone using Wilcoxon sign-rank tests.
ACKNOWLEDGMENTS We thank  Juan AgulloCampello ,  Sander Tanni , and  Francesca Feletti  for help with surgeries and behavioral training,  Caswell Barry  for advice on the experimental design,  Edgar Baumler  for histology and figure preparation,  Anna Simon ,  Claudia Racca , and  Matilda Cloves  for assistance with immunohistochemistry, and  Sami Mollard ,  Henry Dalgleish , and  Ago Lajko  for help with data processing and analysis. We are grateful to  Arnd Roth ,  Liset M. de la Prida ,  Kimberly Young ,  James Priestley , and  Nat Kinsky  for helpful discussions and comments on the manuscript. We thank  Adam Packer  for his valuable advice and support in setting up the all-optical system. We are grateful to the team at  Thorlabs, Inc. for technical support. This work was supported by grants from the  Wellcome Trust ,  Gatsby Charitable Foundation ,  ERC ,  MRC , and the  BBSRC  as well as a PhD fellowship from the  Boehringer Ingelheim Fonds  (to M.
Cell replenishment We then grouped the genes by function, using the OrthoDB, Uniprot and Interpro databases (Table  1 ). Consistent with the significant GO term 'signal transduction', we found genes for neurogenesis, the Notch signalling pathway, cell cycle and cell-cell adhesion, including the previously described cadherin gene (AAEL023845). Together, this gene profile suggests that cell replenishment pathways could be important for Wolbachia-mediated dengue blocking. It has been found that the Notch signalling pathway confers resistance to the dengue virus in A. aegypti by controlling host cell regeneration rate whereby mosquitoes with faster cell regeneration in the midgut are significantly more resistant to the dengue virus  (Taracena et al. 2018). Moreover, the depletion of cadherin proteins is known to downregulate the Notch signalling pathway and so cell replenishment  (Hatakeyama et al.
A. aegypti genes that contain single nucleotide polymorphisms (SNPs) that were significantly differentiated between mosquito populations selected for high vs. low Wolbachia-mediated dengue blocking  (Ford et al. 2019). The genes are listed if they have been annotated and are grouped based upon their function according to OrthoDB, Uniprot and Interpro databases. It is unclear whether neurogenesis specifically plays a role in Wolbachia-mediated blocking or whether the gene annotations exhibit a bias from Drosophila developmental studies and these genes instead function in cell proliferation and differentiation more generally  (Taracena et al. 2018). The Zika virus has, however, been found to dysregulate neurogenesis in humans through the Notch signalling pathway  (Ferraris et al. 2019)  and by degrading adherens junction proteins  (Yoon et al.
Cytoskeleton/transport The dengue virus utilises the cytoskeleton to enter/exit the cell and recruit resources to RCs  (Lindsey et al. 2018). Simultaneously, Wolbachia utilise the cytoskeleton for successful transmission during mitosis and meiosis and so may interfere with the virus life-cycle  (Lindsey et al. 2018 ). In our dataset, we identified many genes with putative links to the cytoskeleton, including genes related to microtubules (AAEL002173), actin (AAEL018215) and associated motors (AAEL019696). Of particular interest is the gene TRAF3interacting protein 1 (AAEL002173). TRAF3 has been implicated in an antiviral response in the sand fly, Lutzomyia longipalpis (Martins-da-Silva et al. 2018).
Conclusion From reviewing A. aegypti genes isolated by artificial selection, we have been able to present new hypotheses for the mechanism of Wolbachia-mediated dengue blocking. Signal transduction and the regulation of transcription were significantly enriched in this dataset, corresponding to large sets of genes for cell regeneration and the transcriptional pausing pathway. Our data led to two main hypotheses: 1) efficient regeneration of mosquito cells increases resistance against flaviviruses and Wolbachia could promote this by modulating adherens junctions and Notch signalling; and 2) Wolbachia could trigger the host's transcriptional pausing pathway to either prime the hosts' anti-viral response or inhibit viral transcription. Consistent with previous studies, we also found evidence that oxidative stress and the cytoskeleton are likely to be involved in blocking.