dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability The current version of Eco3M_MIX-CarbOx is available from the Zenodo website (https://zenodo. org/record/7669658#. Y_dAJ0NKg2w, last access: 23 February 2023) under the Creative Commons Attribution 4. 0 international licence. The exact version of the model used to produce the results in this paper is archived on Zenodo (Barré Lucille, Diaz Frédéric, Wagener Thibaut, Van Wambeke France, Mazoyer Camille, Yohia Christophe, & Pinazo Christel. (2022). Eco3M_MIX-CarbOx (v1. 0). Zenodo. https://doi. org/10. 5281/zenodo. 7669658) as are input data and scripts to run the model and produce the plots for all the simulation presented in this paper. Data availability Surface total chlorophyll concentration data are available on request on https://www. somlit. fr/. Temperature data is available on www.
t-mednet. org by filling out the request form for station and years pre-selected. Salinity data is available on https://erddap. osupytheas. fr. The non-processed atmospheric pCO2 data can be found on https://servicedata. atmosud. org/donnees-stations. Request for processed atmospheric pCO2 data should be addressed to alexandre. armengaud@airpaca. org and irene. xueref-remy@imbe. fr.
As shown in Fig. 1C , there is significantly higher expression of Etrop2 in the sporozoites, no expression at the NSO and GAM stages in agreement with previous transcriptomic data  (28, 29) , and moderate expression in all other explored stages. In the absence of synchronous development of E. tenella, we cannot rule out that ImmSCHZ and MatSCHZ can be partly contaminated by iSPZ and ImmSCHZ, respectively, resulting in slightly overestimated Etrop2 transcription in those stages. To highlight a possible regulation of Etrop2 transcripts, we investigated the protein expression on NSO, SO, eSPZ, MI, and MII. A Western blot analysis using an anti-Flag antibody confirmed that EtROP2 is expressed in the sporozoites and in sporulated oocysts that contain eight mature sporozoites (Fig. 1D ). This expression is restricted to the first infectious stage of the parasite, and its localization in the bulb of rhoptries suggests a role of EtROP2 during the intracellular development of the parasite.
Those results also confirmed that EtROP2 did not modify the invasion process: no difference was observed between the WT and the recombinant strains during invasion (Fig. S3 ). The active EtROP2 induces the p38 MAPK cellular pathway. To investigate the host genes possibly modified by EtROP2 after cell invasion; transcriptomic analyses were conducted on CLEC213 cells transfected with a control pcDNA-eGFP-Flag or with pcDNA-EtROP2-YFP-Flag plasmid 48 h after transfection. Among the 141 differentially expressed genes (DEG) in EtROP2 transfected cells, 15 were downregulated and 126 were upregulated (Fig. S4 , Table  S1 ). Fold changes of. 2 were considered significant and were analyzed using the KEGG pathway database: 61 genes presented a significant upregulation (and none were downregulated), revealing an enrichment of 40 pathways.
Signaling pathway identification was predicted using the KEGG pathway database (https://www. genome. jp/kegg/pathway. html). Plasmid construction. We produced plasmids expressing Etrop2 (ETH_00027700) under its wild-type form (WT, full-length, from Met1 to Glu671), inactive form (rDead), in which the aspartate from the HxD motif was mutated into alanine D513A using the Q5 site-directed mutagenesis (NEB corporation, Fond Du Lac, WI, USA, no. E0554S) according to the manufacturer's recommendations, and the truncated form, with the N-terminal extension deleted, resulting in a fragment (Met261-Glu671) encompassing the catalytic domain only (CD). For all constructions, Etrop2 was amplified by PCR using the polymerase Phusion high fidelity DNA polymerase (Fermentas Corporation Waltham, MA, USA) and cloned between the KpnI and BamHI sites on the pcDNA3 (Invitrogen) expression vector in frame with a Cter Flag-tag or in fusion with a Cter YFP-Flag-tag under the endogenous or actin promoter.
Plates were gently agitated during all incubation processes. Luminescence was measured with an EnVision multimode plate reader (PerkinElmer). All measures were performed in technical triplicates, except for the IC 50 determination, which was done in technical quadruplicates. The data were processed using GraphPad Prism 8 software to fit a sigmoidal curve that allowed the determination of IC 50 values. R-squared was determined as the goodness-of-fit measure to characterize how the sigmoidal curve model fit our experimental data (R-squared. 85%). RT-qPCR analyses. RT-qPCR was run to monitor the parasite stages across the life cycle. Cecal RNA extraction was done with a NucleoSpin RNA kit (Macherey-Nagel) on two chickens from the first to the seventh dpi, in order to avoid individual variability.
We used the RNA-seq pipeline (v0. 9. 16), which is available online (https://github. com/sequana/sequana_rnaseq). Reads were trimmed from adapters using Cutadapt (v2. 10) and then mapped to the Gallus gallus genome assembly from Ensembl using STAR (v2. 7. 3a). FeatureCounts (v2. 0. 0) was used to produce the count matrix, assigning reads to features using annotation GRCg6a with strand-specificity information. Quality control statistics were summarized using MultiQC (v1. 8). Statistical analysis of the count matrix was performed to identify differentially regulated genes, comparing cell expressed a parasite protein (EtROP2) with empty vector samples. Clustering of transcriptomic profiles was assessed using a principal-component analysis (PCA). Differential expression testing was conducted using DESeq2 library (v1.
ACKNOWLEDGMENTS We are grateful to the  Infectiology of Farm, Model and Wildlife Animals Facility (PFIE-EMERG'IN, IBiSA  platform) and especially to  Thierry Chaumeil ,  Fanny Faurie Sarcé ,  Olivier Dubes ,  Mylène Girault , and  Lorine Branger  for the animal care, for their help with the animal experiments, and for their dedication and help in planning the animal experiments during this special pandemic period. We also thank  Thibaut Larcher  and  Mireille Ledevin  from  INRAE-Oniris (APEX Platform)  for their expertise in histology and immunohistochemistry analysis. We thank  Yves Le Vern  for his precious help in cytometry analysis. We also thank  Alisson Niepceron  for her valuable technical advice in the production of parasite stages. We thank  Estelle Godet , at the  BOA UMR (INRAE) , for technical advice and support with RIN measures.
We are grateful to  G. Haustant ,  L. Lemée ,  M. Monot , ( Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  [ ANR-10-INBS-09 ] and IBISA) for the RNA sequencing analysis. We gratefully acknowledge  Julien Burlaud-Gaillard  from the microscopy IBiSA platform (PPF  ASB ,  University of Tours and CHRU of Tours ) for transmission electronic microscopy analysis. We also thank the  Cancéropôle Grand Ouest  ("Marines molecules, metabolism and cancer" network),  IBiSA  ( French Infrastructures en Sciences du Vivant: Biologie, Santé et Agronomie ), and  Biogenouest (Western France Life Science and Environment Core Facility Network  supported by the  Conseil Régional de Bretagne ) for supporting the KISSf screening facility ( FR2424 ,  CNRS  and  Sorbonne Université), Roscoff, France.
Planetary scientists recognize that the exploration of Venus can inform our understanding of exoplanets, and vice versa as discussed in this article. These linkages permeate the new decadal survey released by the United States of America's National Academies (National Academies of Sciences, Engineering, and Medicine 2021) as detailed in the introduction to 1 https://www-planets. lmd. jussieu. fr/ 2 https://ncar. ucar. edu/ 3 https://simplex. giss. nasa. gov/gcm/ROCKE-3D/ 4 https://github. com/storyofthewolf/ExoCAM this topical collection  (O'Rourke et al. 2023, this collection). Table  1  pulls verbatim excerpts from this new report identifying some of the observations of Venus and exoplanets that scientists consider most important in the near term. We can study Venus as "the exoplanet in our backyard" and obtain measurements, including in situ data, that are not feasible at planets orbiting distant stars.
2018). NIRSpec PRISM has a wavelength range of 0. 7-5. 0 µm encompassing major H 2 O and CO 2 features, and has been shown to be the optimal instrument for performing transmission spectroscopy in the NIR  (Lustig-Yaeger et al. 2019b). PSG is a publicly available online interface that couples radiative transfer models, planetary databases, and spectral databases. Exo-Venus transmission or emission spectra can be produced with PSG by superimposing an atmosphere onto a terrestrial exoplanet in the VZ. Kepler-1649b is used as the hypothetical exo-Venus, as its size is similar to that of Venus, with a radius of 1. 077 R ♀ (1. 017 R ⊕ ), and has a incident insolation flux that is 2. 21 times greater than that of Earth (Venus is 1. 9), albeit orbiting a much redder M-dwarf star  (Angelo et al. 2017).
The atmosphere used for the Pandexo simulated observations is the same as that used for Fig. 4. Given 30 transit observation of Kepler-1649b, the simulated JWST data is unable to resolve any of the major absorption features in the NIR. Furthermore, the large uncertainty in the data would make it difficult to differentiate the spectra from that of a flat-line, which may result in mistaking an exo-Venus as a planet with no atmosphere  (Lustig-Yaeger et al. 2019a). Increasing the number of transit observations would decrease the uncertainty in the data, however acquiring the JWST time needed to conduct these observations will be a challenge. The features being less than 5 ppm make them smaller than the predicted 20 ppm noise floor of the NIRSpec instrument  (Rustamkulov et al. 2022) , making them potentially undetectable by JWST given any amount of observations and only accessible with future observatories.
To address this question in some detail, it is important to outline what we know about the thermal-tectonic regimes and evolution of the Earth. We will then extrapolate this knowledge to the Earth-Venus divergence, and outline potential implications for exoplanets. The Earth is the only body in the Solar System for which significant information about its thermal, geologic, atmospheric, and tectonic evolution is readily accessible. Consequently, Earth derived data and observations are often used to inform general models of thermal evolution, which are then extrapolated to other bodies in our Solar System, and beyond. However, despite the Earth's large dataset, our knowledge and understanding of the Earth's thermal evolution remains largely opaque. For instance, while we know Earth is currently within a plate tectonics regime, its initiation and total life of activity are far from certain (e.
Note Open Access funding enabled and organized by Projekt DEAL. MJW acknowledges support from the  Goddard Space Flight Center Sellers Exoplanet Environments Collaboration (SEEC)  and  ROCKE-3D : The evolution of solar system worlds through time, funded by the  NASA Planetary and Earth Science Divisions Internal Scientist Funding Model. CG acknowledges the support of  Rice University  and the CLEVER planets group (itself supported by  NASA  and part of NExSS). ACP gratefully acknowledges the financial support and endorsement from the  DLR   Management Board Young Research Group Leader Program  and the  Executive Board Member for Space Research and Technology.
One pipeline merely signals sunny periods, while the other predicts available production (that is, excess production with respect to the predicted consumption), thanks to load-forecasting techniques. We show that our pipelines are efficient, even with them using real, and therefore incomplete, information, by validating them on semi-real data, coming from several publicly available datasets, coupled with a simulation environment. We intend to validate them on a real-world setting in the future but, due to the many constraints in setting-up a real-world experiment, we wanted to first test our pipeline in a simulated setting. Under reasonable user behaviour assumption, the simulator provides a first validation of our pipeline. Our work takes place as part of ongoing efforts to help energy communities, using renewable energies, reach their full potential.
This requires shifts in consumption patterns, with respect to usages linked with traditional energy sources. These changes in habit will only be possible if efforts are made to increase general consciousness about the culture changes implied and by providing accurate information to the users. We elaborate on these goals in the discussion section. We start by presenting related works, and stating our contributions, in Section 2. Next, we introduce our model in Section 3. We then present our numerical simulation results in Section 4. Finally, we discuss our results, and their practical implications for the realworld deployment of our pipelines in Section 5. The code for the numerical simulations is available in the following repository, https://gitlab. com/enogrid/open-source/realisticnudging-pipelines-for-energy-consumption (accessed on 30 June 2023).
Nudging Pipeline Therefore, the system "household + controller" is a controlled system, illustrated on Figure  1. The nudging pipeline, which represents the system, save for the human behaviour part, consists of the following several components. Production and consumption data are monitored by a smart sensor located on the household premises. Weather APIs provide us with accurate forecasts for the upcoming days, in particular, concerning solar irradiance. All information is gathered by the controllers, which use it to compute solar consumption-friendly periods. These periods are gathered into nudges, which are sent to the household people through emails, for instance. Finally, upon receiving the nudge, the household people update their consumption accordingly.
Numerical Simulations Set-Up Synthetic data preparation. We generated two synthetic datasets, representing two use cases: a commuting household (a commuting household refers to a group of individuals who live together in the same residence but travel to different locations for work or school, typically on a regular basis), and a non-commuting household (a non-commuting household refers to a group of individuals who live and work in the same place, without the need for regular travel to a workplace or school). The commuting household represents, for instance, the household of people going to work away from home but who have equipped their domestic electrical appliances with sensors, allowing them to launch, or stop, these appliances, remotely. The non-commuting household represents, for instance, the household of people working remotely.
For each household, we use as fixed consumption (that is, not shiftable), the notshiftable part of the consumption of a real energy profile (which is part of the Iris dataset  [38] ). To create shiftable consumption, we add real dishwasher consumption to the data every day in the morning, real washing machine consumption every three days in the evening, and a real water heater for domestic hot water consumption every day in the evening. Therefore, the shiftable part of the consumption occurs in the morning or in the evening, and is thus representative of that of a commuting household. For the "non-commuting household", we add an additional electric cooker every day between 12 p. m. and 1:30 p. m. , and an additional air conditioner between 12 p. m. and 4 p. m. As the people of the household are present during the day, they must cook their lunch, and they may use air conditioning to maintain agreeable temperatures within the house in the afternoon.
This may be the case, for instance, for a French household, endowed with air conditioning. While it is common in France to cook at lunch, air conditioning is not as widespread as in the United States of America, for instance, but its usage is nonetheless increasing. We classify air conditioning as non-shiftable in this context of someone working from home during the afternoon in a potentially badly isolated building. In such conditions, in order to maintain cool temperatures, the air conditioning system may need to run continuously. Real datasets preparation. For the simulations using real datasets, we select a subset of appliances, which we define to be "shiftable", and we fix their maximum shifting delay (see Table  2  for details). We divide each dataset into past data, on which we train our model, and future data, on which we conduct the simulation.
Finally, we renormalise the data sampling steps to 30 min steps. The AMPds (Almanac of Minutely Power dataset)  [39]  is a publicly available dataset created by Simon Fraser University's (SFU) Electricity and Power Research Centre in Canada. It offers high-resolution energy consumption data collected from residential households. The dataset covers the period from 2012 to 2014 and includes measurements of electricity, water, and natural gas usage at one-minute intervals for an individual building in Canada. It can be found on the project's official website http://ampds. org/ (accessed on 30 June 2023) or through various data repositories and academic platforms. The Iris dataset is a subset of a European database of residential consumption collected as part of the REMODECE (Residential Monitoring to Decrease Energy Consumption) project  [38].
The REMODECE project is an initiative focused on monitoring and reducing energy consumption in residential buildings. It aims to develop innovative solutions and technologies to promote energy efficiency and sustainability in the residential sector. The Iris dataset contains the energy consumption data of 98 houses for a span of one year. The data sampling step is 10 min. We make the appliance names homogeneous between the houses, as there are sometimes small variations in the terminology. Solar energy production. As part of our simulator, we generate an artificial solar production as follows. We first simulate a sunshine coefficient as the product between the following: 1. A value taking into account the time of day (representing the fact there is no sun at night); 2. A value taking into account the month of year (representing the fact that the sun shines brighter during summer than winter); 3.
Iris Dataset and AMPds Dataset, Individual Building Simulation We first run a simulation for an individual building of Iris and the AMPds building. We show on Table  5  the results of the simulation for Iris and AMPds. We see that both the weather and the combined controllers manage to improve the self-consumption rate of the building, taking it from 63. 13% to more than 66. 77% (weather) and 67. 9% (combined). Results are comparable for AMPds: we see that both the controllers improve the selfconsumption rates from 70. 86% to more than 70. 92%. The fact that the performance results of the two controllers are close is probably due to the fact that excess production occurs mainly when the absolute production is high (contrary to the use case of Section 4. 3). We note that even the worse results of the controllers are better than all but the very best results of the reference situation, in which there is no control, which shows the value of our procedure.
Iris Dataset, Collective Runs Then, we conduct simulations for all the buildings of the Iris dataset, and show the results on Table  6. The mean, and mean of the percentage of improvement, are averaged on all the buildings. We see that, on average, the weather and combined controllers improve the self-consumption rate by about 2%. This corresponds to roughly 17-22% of the OMEGAlpes performance. While the results of OMEGAlpes are much higher than that of the controllers, we believe it is mainly due to the limitless number of shifts in consumption it can perform and the fact that it uses per-appliance knowledge of the true consumption. Therefore, given these advantages, we believe reaching 22% of its performance, in the case of the combined controller, still shows the reasonable efficiency of our nudging pipelines.
To look at the influence of energy flexibility on the enhancements in the self-consumption rate, we conducted simulations on three distinct subsets of the Iris dataset, based on the numbers of shiftable appliances in each house: 1, 2, or 3. We show on Table  7  the results of the simulations. Unsurprisingly, the greater the number of shiftable appliances within the houses, the greater the self-consumption rate increases when nudges are sent (going from 1% for the first dataset, to 4% for the third one), under similar conditions, such as weather and house type.
Funding: This work was partially supported by the  ANR  project  ANR-15-IDEX-02  through the  eco-SESA  project, https://ecosesa. univ-grenoble-alpes. fr (accessed on 30 June 2023) and the  OTE-Observatory of Transition for Energy , https://ote. univ-grenoble-alpes. fr (accessed on 30 June 2023).
Introduction Endogenous retroviruses (ERVs) are transposable elements (TEs) related to infectious retroviruses, a family of RNA viruses defined by their ability to reverse transcribe their RNA genome into DNA. The resulting DNA is then integrated into the host cell genome. When integrated in the germline genome, they may emerge as ERVs: they become permanent elements in the host genome and are vertically transmitted. ERVs are particularly successful TEs in their ability to expand in the genome. In Human, ERV-related sequences occupy 9% of the genome, in Drosophila 11. 3%, in mice 12%, in rat 10. 2%, in microbats 5. 3%, in marmosets 7. 5% (http://repeatmasker. org/genomicDatasets/RMGenomicDatasets. html). Once integrated and endogenized, each ERV copy evolves independently and can be at the origin of the emergence of a new TE family.
96. Scatter plots were done with RStudio with antisense piRNAs or siRNAs mapping to the respective internal sequence of TEs (allowing 0-3 mismatches for piRNAs and 0-1 mismatch for siRNAs). All small RNA sequencing done for this study is available in Supplementary Table  S6. Small-RNA sequencing data are available in NCBI GEO database (GSE213368).
ACKNOWLEDGEMENTS We thank the  Bloomington Stock Center  for providing stocks. We thank  J. Brennecke  for providing antibodies. We thank flybase. org for providing databases. We thank  S. Chambeyron  and  K. Senti  for helpful discussions,  A. Molaro  and the members of the  Brasset laboratory  for comments on the manuscript. We thank  C. Vaury  for providing the RevI-H2 line. We thank  Y. Renaud  and  P. Pouchin  for helpful advices and development of the GReD bioinfomatic platform. We thank  Gentyane Facility  for the Quality Control of gDNA.
FUNDING This work was supported by grants from the  Agence Nationale pour la Recherche  ( CHApiTRE   ANR-20-CE12-0005 ,  BiopiC   ANR-21-CE12-0022 , and  EpiTET ANR-17-CE12-0030-03 ), the  La Fondation ARC pour la recherche sur le cancer  ( PJA20171206129 ). M. Y. was supported by the  Ministère de l'Enseignement Supérieur et de la Recherche  and the  Fondation pour la Recherche Médicale  ( FDT202106012950 ). This research was financed by the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
). The ZAM containing reads were then recovered with bedtools getfasta (https://bedtools. readthedocs. io/en/latest/content/tools/getfasta. html, 68   ). The ZAM insertion sites where determined using Blastn of the ZAM containing reads to the D. melanogaster Release 6 genome (http://flybase. org). The absence of empty sites, without a given ZAM insertion, was verified by Blastn of the respective empty insertion sites, recovered from the D. melanogaster Release 6 genome, to all ONT reads. Sequencing data are available in NCBI GEO database (GSE213456).