dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , O. sulphurea [O], T. terscheckii [T]; and T. terscheckii 2 × alkaloids [T2]) to perform two pairwise comparisons (DEG I: T vs O; and DEG II: T2 vs T) for differential gene expression analyses (see below). We extracted total RNA from 10 batches of 3 rd instar larvae for each genotype (biological replicates), using a TRIzol/ RNeasy protocol specific to Drosophila. We used the Illumina paired-end library (insert size: 150-450 bp) and sequenced in a HiSeq 2000 platform with 101-cycle reads, obtaining a mean of 16 Gbp of raw reads per genotype and treatment (NCBI Accession: PRJNA314520).
Alkaloids identification. To thoroughly characterize the alkaloid fraction of T. terscheckii, we collected fresh samples from five individuals at different times of the year to account for temporal variation in alkaloid concentration. The plant material was collected in the northwest of Argentina, in the Valle Fertil Natural Park (public land of the province of San Juan) and the identification of the cactus was performed by Alejandro Saint-Esteven et al  85. We isolated alkaloids from plant tissues through the alkaline-CH 2 Cl 2 extraction method  16, 40. The chemical profile of the extracted fraction was accomplished by GC-MS (Thermo Scientific EM/DSQ II-Trace GC Ultra AI3000). Alkaloids identification was performed by comparing the retention times and mass spectra with reference standards and database spectra (UMYMFOR and NIST).
Raw reads of our nine transcriptomes (three genotypes per treatment) were quality controlled using FASTQC v. 0. 10. 1  87  and filtered for quality scores ≥ 25 and minimum lengths ≥ 25 bp, resulting in a mean of 12 Gbp per genotype in each treatment. We used the program RSEM v1. 2. 30  88  to estimate gene expression levels in fragments per kilobase of transcript per million mapped reads of protein-coding genes using the reference genome of D. buzzatii  39. To analyze the differential gene expression, we used the NOISeqBIO method of the R package NOISeq v2. 18. 0  89  with a false discovery rate (FDR) < 0. 01, and applied a posteriori filter using a custom script to further reduce possible spurious "noise". The statistical strategy of NOISeq considers the differences in both the mean expression level and in the order of magnitude to measure changes in gene expression between conditions, and thus identify significantly differentially expressed genes.
Positive selection analyses. For the analyses of positive selection, we employed two alternative approaches: the degree of genetic differentiation and the extended haplotype homozygosity. For the genetic differentiation test, we used the method implemented in the TreeSelect software  98  to contrast whether allele frequencies in any of the evaluated populations are significantly differentiated from the putative ancestral genetic background. We performed the TreeSelect test in the Central Andean population, using the reference population of Han Chinese 96 in all cases, and one reference population of South America (Yukpa or Bari for Data-set 1, and Yanesha or Wichi for Data-set 2; Table  S9 ). Thus, in each data set we obtained two Log Ratio Test (LRT) scores for each SNP. For the extended haplotype homozygosity test, we calculated iHS scores for each SNP of our target population using the rehh package of the R software  99.
Datasets were filtered by MAF < 0. 01 and missing genotype > 2%. We considered the mean and the median of |iHS| or LRT scores (at the SNP level) as summary statistics at the protein-coding gene level. In order to exclude possible stochastic signals of non-selective events, such as genetic drift or demographic fluctuations, we implemented a genome scan approach 100 by taking into account the gene-level background for protein coding regions. We computed the median and the mean summary statistics for both |iHS| and LRT scores and estimated gene-level empirical distributions for each genomic background as previously implemented  76. Empirical P-values were calculated using the gene-level score distributions generated from the genes in the background genome set. Finally, LRT and |iHS| based scores were combined using the Fisher combination test  101  : Z F = -log P |iHS| + P LRT Z F ∼ X 2 (4) where P i stands for the empirical P-value obtained from the test i.
A given gene was considered to be under positive selection when its associated Z F score was significantly different from 0 with a type I error of 5%, using each of the four South American reference populations separately. In addition, we performed a complementary test to consider evidence of selection when at least one, two and three Z F scores were significant. We generated custom scripts to estimate the P-values by calculating the proportion of the 1,000 control gene sets exhibiting a greater number of signals of selection than observed in our set of candidate genes (see Data availability).
Acknowledgements This research was supported by the  National Council of Scientific and Technical Research of Argentina (CONICET)  and funded by the  National Agency of Scientific and Technological Promotion  ( PICT 2017-0220 ;  2010/2795 ) and the  University of Buenos Aires  ( UBACyT 2018 ). Additional support for sample collection was provided by a grant from the  Stanley J. Glaser Foundation  and the  Dr. John T. Macdonald Foundation Department of Human Genetics. The human genotype datasets used for human analyses described in this manuscript were obtained directly from the authors except the data from Moreno et al. ( 2013) which was retrieved from dbGaP at http:// www. ncbi. nlm. nih. gov/ proje cts/ gap/ cgi-bin/ study. cgi? study_ id= phsxxx. v1. p1 through dbGaP accession number phs000750/GRU.
It relies on the use of a simulation tool named CLIFS (Crop LIvestock Farm Simulator) which is devoted to mixed crop-livestock farms. The approach is specifically intended to be used with farmers and transferred to farm advisors. Earlier versions of CLIFS were tested on samples of 2 to 20 family farms in a range of contexts, and in both tropical and temperate environments (Le  Gal et al. , 2013; Ryschawy et al. , 2014; Sempore et al. , 2016). This article presents the current version, developed in Microsoft Excel®, which can be downloaded freely at the following address: https://doi. org/10. 18167/DVN1/NZHWQQ. A User Manual (UM) provides a full description of the software and its equations to which the reader can refer for additional information (Le Gal, 2021; https://doi. org/10. 18167/agritrop/00577).
The Initial Scenario (IS) is based on an analysis made with the farmer about the current situation on his/her farm. This analysis allows the advisor to better understand the farmer's objectives and strategies, to characterise the farm's structure, operations and performance, and to calibrate certain CLIFS input variables that are difficult to access, such as pasture productivity. Depending on the data available, it may take several loops to arrive at a representation of the farm that the farmer thinks to be consistent with reality. This representation is then considered to be valid for the next steps in the process. This validation stage is important to ensure that the rest of the process is based on knowledge that is shared by the farmer and the advisor, and to enable the farmer to understand the structure of CLIFS and the calculations made.
Overview CLIFS translates the generic representation of a mixed crop-livestock farm (Fig. 1 ) into a spreadsheet-based simulation tool (Microsoft Excel®). The format makes it usable and accessible to a wide range of users. The overall structure of the tool, along with its calculation procedures and output variables, have been designed so that the farmers involved can understand them while also providing a representation that closely matches the farms which the farmers can validate. This led to limiting the number of variables to be characterized on each farm and to excluding the integration of crop and livestock models, which are furthermore often unavailable in many contexts. The only equations related to biological processes concern: (i) for all ruminants, excreta production as a function of their live weight to calculate the supply of organic manure as a function of herd structure and management (Table  5 ); (ii) for cattle only, energy and nitrogen requirements for animal maintenance, gestation of breeding females, and production of milk and meat per head (Table  2 ) in order to link these productions to the rations distributed, with the choice between two calculation systems independent of the user's working language: French (INRA, 2007) and American  (NRC, 2001).
3, F. 4 and F. 5 for green forage, hay and silage, and crop residues, respectively); (iii) the annual balance of organic fertiliser based on the manure produced by stabled animals and the use of the organic fertiliser on crop fields (UM F. 7). In addition to these three balance sheets, the economic results (variable and fixed costs, gross and net margins) corresponding to each farm configuration are calculated (UM F. 8). However, CLIFS does not include a labour balance sheet due to the time required to estimate labour supply and demand with a precision that makes sense to farmers. CLIFS is available in English, French, Spanish and Portuguese for names and titles that cannot be modified by the user. However, users can enter in their own language the contents of the choice lists linked to certain variables.
General structure CLIFS consists of a series of Excel® sheets grouped into three linked modules (Fig. 3 , and UM C. for a full list of the sheets): (i) Parameters (UM D. ) with seven sheets grouping variables whose values are identical for a set of farms, such as the characterisation of animals' feed and requirements; (ii) Input variables, specific to each farm, with an "animal" submodule of 16 sheets characterising the dairy, growth and fattening ruminant enterprises and the batches of monogastric animals (UM E. 1 to E. 8); and a "crop" sub-module of six sheets characterising the crop blocks making up the farm. Each block is defined by the combination of one to two food, cash or forage crops, the technical practices applied and the associated yields. It may or may not correspond to physical crop fields (UM E.
The structure of Excel® in sheets and tables limits the number of elements that can be characterised per activity (UM B. 3). The sizing was determined to (i) make it easier to read the tables on the screen, and (ii) represent many different production systems, combining cattle, small ruminants and monogastrics for livestock, and food, cash and forage crops (including permanent grasslands) in pure, associated or catch crop form for crops. CLIFS uses different time steps depending on the process represented (Table  3 ). These time steps are those used by farmers to manage their production activities while being aggregated to limit the amount of data needed to be entered. For example, diets are entered in kg per day, which can be modified each month, i. e. , 12 values to be entered per feed. Similarly, the units used vary according to the variables (UM B.
Characterizing scenarios CLIFS limits the use of mechanistic equations to excreta production for ruminants, and to the relationship between feed and production for cattle. The latter equations are assumed to be valid for any type of context where animals have a live weight of between 400 and 800 kg  (INRA, 2007) , and to be sufficient for farm-level estimates that do not require the same degree of precision as a feed rationing tool. Aside from these situations, or when the data needed for the calculations are unavailable, users can switch to a simplified use of the simulator where the link between diet and production is based on their own expertise and not on calculations. This option, and the fact that CLIFS essentially only mobilizes data describing the farm's resources and how these are used, provides sufficient flexibility for the software to be used in many contexts.
These considerations can include the effects of adverse climatic and health events to assess the capacity of the simulated configurations to absorb such shocks. The third point concerns the characterisation of the technical innovations integrated into the prospective scenarios when the farmer is unfamiliar with them. The data needed to describe the innovations are then obtained from scientists and experts in the field. However, their validity in the case under study cannot be guaranteed. In such situations, it is useful to develop a set of scenarios in which yields are varied within a range considered plausible in the context studied to assess the variability of the resulting technical and economic results. Unlike other whole-farm models  (Pissonnier et al. , 2017; McDonald et al. , 2019) , the balance between supply and demand for labour has not been integrated into CLIFS due to the complexity of calculating this at the scale of a farm where there are a multitude of tasks, some seasonal, some routine, multiple possibilities of division of labour within the farm  (Davison, 2019) , and multiple viewpoints on this question depending on the farmer involved.
Conclusion The farmer support approach built around CLIFS software is specifically dedicated to the strategic questions that mixed crop-livestock farmers ask themselves about their future. The various experiments conducted with family farms in the tropics and in France show that the tool meets the expectations of farmers and enables them to better understand possible changes in their production systems. CLIFS was designed to adapt to a wide range of situations and geographical contexts while remaining simple to use, particularly with regard to the variables to be filled in on each farm. Its transfer to advisory structures remains to be tested, but this simplicity should be an asset in this effort. The use of the tool also could evolve towards being linked with environmental assessment methods in order to think about crop-livestock combinations on farms that limit environmental impacts and improve certain components such as biodiversity.
Acknowledgements We would like to express our thanks to the many producers, researchers, experts and students who participated to the design, development and experimentation of the CLIFS approach in the various studied contexts. We thank  Grace Delobel  for translating the paper into English. This work was supported by the following research projects:  SIRMA (Water Saving in Irrigation Systems in North Africa ), financed by French institutional co-operation and North African countries in Morocco,  PEPITES  project  (Ecological, Technical and Social Innovation Processes in Conservation Agriculture) ANR-08-STRA-10 in Brazil and Madagascar, and MOUVE ANR-2010-STRA-005 in France , both financed by the  French National Research Agency , the  TFESSD (Trust Fund for Environmentally & Socially Sustainable Development)  of the  World Bank in Peru.
Then, 600 ng of Cy3-labeled cRNA was hybridized on the microarray slides. Immediately after washing, slides were scanned on an Agilent G2505C Microarray Scanner using Agilent Scan Control A. 8. 5. 1 software, and the fluorescence signal was extracted using Agilent Feature Extraction software v10. 10. 1. 1 with default parameters. Raw data (median signal intensity) were filtered, log2-transformed, corrected for batch effects (microarray washing bath and labeling serials), and normalized using the quantile method. All genes analyzed in the present study are listed in Supplemental Table  S2 , together with respective forward and reverse primer sequences in Supplemental Table  S3.
Recording pipettes were slowly advanced towards individual striatal MSN within the slice under positive pressure and visual control. On contact, tight gigaohm (GΩ) seals were achieved by applying negative pressure. The membrane patch was then ruptured by suction and both membrane current and potential were monitored using Multiclamp700B and Digidata 1440A by Axon Instrument (Molecular Devices, San Jose, CA, USA). Whole-cell access resistances, measured in voltage clamp, were in the range of 8-20 MΩ. A concentric bipolar electrode (Phymep, Paris, France) was placed on afferent fibers to evoke EPSCs (at 0. 1 Hz), recorded in MSN under voltage-clamp configuration with membrane potential clamped at -70 mV. Current over voltage (I/V) curves were acquired in I = 0 (free) current-clamp mode. All data were sampled at 20 kHz and filtered at 1 kHz.
Series resistance was measured throughout the experiment with a -5 mV step lasting 50 ms. Synaptic events were stored by using p-CLAMP 10. 6 (Axon Instruments, Burlingame, CA, USA) and a posteriori analyzed offline on a computer with Axograph software (Axon Instruments, Burlingame, CA, USA). Only data from putative GABAergic MSN were included in the present study, identified immediately after rupture of the GΩ seal, by evaluating responses to the injection of both hyperpolarizing and depolarizing currents, as described previously  [81].
2008a ) used to generate the final RSM probability map. The Auto-RSM framework being computationally expensive, a clustering approach is used to divide the set of targets into multiple subsets. For each subset, the cluster center is identified and the Auto-RSM framework is applied onto it to provide the optimal parametrisation for the entire cluster. The obtained optimal parametrisations are also compared to unveil potential 1 https://exoplanet-imaging-challenge. github. io/ commonalities and understand their relationship with the ADI sequence characteristics. Detection maps are then computed via the RSM approach, relying on these optimal parametrisations. The detection maps are used to identify potential planetary companions, and a new companion characterisation framework based on the RSM approach is introduced.
2003; Rousset et al. 2003) , having poor performance below 0. 5. The underlying goals are to characterise the disks architecture and properties, and statistically link these properties to the stellar age, spectral type, and potential presence of companions. This paper contributes to the achievement of these objectives by applying the RSM detection algorithm  (Dahlqvist et al. 2021b ) on the datasets, to detect potential planetary candidates. The RSM detection algorithm was designed to unveil point-like sources and is therefore not fitted to detect extended features such as debris disks. The detection of companions can bring valuable information to better understand the secular interactions between debris disks and companions, and whether such interactions are always needed to explain particular signatures in disks such as azimuthal asymmetries, warps or sharp edges (see  Mouillet et al.
The observations were grouped in two programs, 46 sources were imaged during P96 (1 October 2015-31 March 2016) and 9 during P97 (1 April 2016-30 September 2016). Due to adverse observing conditions, multiple observation sessions were required for some targets, leading to an actual dataset of 73 ADI sequences. Table  1  provides details on the set of targets, including the number of observation sequences acquired for each target (epoch). The distances, magnitudes, and spectral types were taken from the HIPPPARCOS and Gaia catalogues (van Leeuwen 2007; Gaia Collaboration 2016). The target Fomalhaut C, part of the SHARDDS sample, was excluded from our analysis because of poor observing conditions for all three epochs (dataset published in  Cronin-Coltsmann et al. 2021).
Pre-processing and extraction of environmental data The first reduction steps consist in applying standard calibrations to the raw IRDIS images (sky subtraction, flat-field correction, and bad-pixel correction), and registering the frames. This was done using a dedicated pipeline in python 2. The frame registration was done using the four satellite spots imprinted on the IRDIS images by a specific waffle pattern applied on the deformable mirror of SPHERE  (Delorme et al. 2017a; Galicher et al. 2018). The ouput of the pre-processing consists of a temporal cube of frames (individual detector integrations), cosmetically cleaned and recentered, called hereafter an ADI sequence. This cube is accompanied by the corresponding list of parallactic angles for the dedicated high-contrast image processing steps (see  Sect.
Clustering In order to take full advantage of the RSM algorithm, we rely on the Auto-RSM optimisation framework (see  Dahlqvist et al. 2021b ) to define the optimal sets of parameters for the PSF-subtraction techniques and the RSM algorithm itself. This optimisation pipeline being computationally expensive, we propose to apply it on a subset of targets representative of the whole dataset. The obtained optimal parametrisations can then be used to compute the RSM detection maps for all targets. Dahlqvist et al. (2021b)  showed a relatively high degree of similarity in the optimal parametrisations of both the PSF-subtraction techniques and the RSM algorithm, when using ADI sequences generated with the VLT SPHERE instrument. Dividing the SHARDDS dataset into multiple subsets should nevertheless allow us to account for small variations in the optimal parametrisations depending on the ADI sequence characteristics.
The subdivision of the SHARDDS dataset in multiple subsets is based on a set of observables characterising the ADI sequences. The subdivision itself is done via the K-means algorithm (MacQueen 1967), a centroid-based clustering procedure aiming to find the centroids that minimise the within-cluster sum-of-squares, also called inertia. The K-means algorithm was selected as it provides a good estimate of the centroids position. This is a key element to define properly which ADI sequence within a cluster is the most representative. These centroids being often not associated to a sample, we define the most representative ADI sequences as the ones closest to the cluster centroids. Once defined, the Auto-RSM optimisation framework is applied on the selected set of ADI sequences. The optimal parametrisations are then used to compute the RSM detection maps for the remaining ADI sequences of each cluster, following the standard RSM map procedure.
The number of images included in the ADI sequences was identified as a key metrics for the definition of the optimal parametrisation during the development of the Auto-RSM framework. We have therefore decided to divide the SHARDDS dataset into two subsets before applying the clustering algorithm. We defined a threshold of 151 frames to separate the two subsets, as this value ensures that the standard deviation of the number of images within each subset is equivalent. This ensures a similar distribution in terms of sequence size within the two subsets.
We therefore selected for both subsets a number of clusters equal to four, implying a total of eight ADI sequences on which Auto-RSM will be applied. The eight cluster centroids, as well as the composition of their respective clusters are presented in Table  2. After the subdivision of the dataset into eight clusters, we made several consistency checks by relying on principal component analysis to reduce the dimensionality of our set of observables and eliminate residual correlations between the variables. We tested the K-means algorithm with different numbers of principal components and retrieved almost every time the same set of clusters. Figure  2  illustrates the repartition between the different clusters in the space formed by the first two principal components. As can be seen, the different clusters are relatively well defined except for cluster 2-2 and 2-4, for which a larger set of principal components are necessary to make a clear distinction.