dataesr/scientific-paragraphs-categorization huggingface dataset:
A note on model selection for the NT matrix While selecting a model of sequence evolution is facilitated by using model-selection methods (e. g. ModelFinder), choosing the right model-selection strategy on complex datasets such as mitochondrial genomes remains challenging. The use of informationtheoretic methods  (Posada & Buckley, 2004 ) such as corrected Akaike information criterion (AICc), and Bayesian information criterion (BIC) is now common practice to distinguish and select several phylogenetic models. In the first phase of our phylogenetic analyses using IQ-TREE, we used eight different strategies for analyzing the NT matrix and rank each phylogenetic model using either AICc or BIC criteria (Table  1 ). The implementation of the GHOST model has led to the lowest supports and topologies highly different from the others, possibly due to repeated numerical underflow errors.
But GHOST is also a mixture model and some recent discussions  (Crotty & Holland, 2022 ) questioned the use of information-theory methods to compare partition and mixture models. Similarly, models involving edge-unlinked partitions have low AICc or BIC scores, likely due to the great amount (several thousands) of parameters to evaluate. Interestingly enough, GHOST and edge-unlinked models are the only two strategies that can take heterotachy  (Lopez et al. , 2002)  into account. Our results stress out the need for alternative heterotachy models that are less parameter-rich and more suited for datasets with high number of taxa. Obviously using a single partition over the entire NT matrix is unrealistic, even using a FreeRate model  (Le et al. , 2012)  with 10 categories to take more site heterogeneity into account.
e. RAxML, IQ-TREE or MrBayes). We suspect that for some partitions, i. e. the ones with high-evolving rate sites such as 3 rd codon positions, the methods fail to correctly assess the substitution rates. A careful look at some partition parameters in MrBayes show that a lot of them are not converging, even at the end of the analyses. In spite of the obviously overparametrized model that was used and unrealistic branch lengths, the topologies (which are the prime interest of our study) remain stable across analyses or within the tree space explored by MrBayes. It is now generally accepted that over-parameterization should be favored over underparameterization  (Abadi et al. , 2019; Fabreti & Höhna, 2022) , and studies with similar datasets (i. e. mitochondrial genomes spanning a large taxonomic diversity) have also faced the same issues of unrealistic branch lengths but reliable topologies (e.
g. Song et al. , 2016; Uribe & Zardoya, 2017). Our eight NT matrix topologies (Fig. 1 , Supp. Mat 2) show few very important differences (i. e. , apart from already known unstable regions of the tree) but over-partitioned datasets and overly complex models can return unrealistic branch lengths, even when AICc or BIC scores are good. Caution must be used before running any kind of meta-phylogeny analysis (e. g. , dating or diversification analyses) that will use branch length information and we recommend carefully checking the branch lengths to eventually detect unrealistic branch lengths.
Input for neogastropod classification With 42 families of neogastropods and tonnoideans, representing almost 60% of the families currently considered as valid in WoRMS, our dataset is the most complete published so far. Many of the previously recovered relationships are recovered here and new ones are revealed (Table  2 ). At the superfamily level, some superfamilies are recovered monophyletic and supported, often confirming previously published phylogenies, although based on a much more reduced sampling  (Choi et al. , 2021; Osca et al. , 2015; Uribe et al. , 2021; Wang et al. , 2021). For example, the Conoidea, represented by many mitogenomes covering almost all the family-level diversity of the group, is found monophyletic. However, some unsupported relationships between families are probably resulting from long-branch attraction (LBA), as suggested in  Uribe et al.
Acknowledgments The material used to produce the new transcriptomes was collected during the KANACONO expedition in New Caledonia (convention  MNHN-Province Sud ,  APA_NCPS_2016_012 ;  PI N. Puillandre  and  S. Samadi ), with support from the  Laboratoire d'Excellence Diversités Biologiques et Culturelles (LabEx BCDiv ,  ANR-10-LABX-0003-BCDiv ) and the project  CONOTAX , funded by the  French Agence Nationale de la Recherche ,  France  ( ANR-13-JSV7-0013-01 ). This expedition operated under the regulations then in force in the countries in question and satisfy the conditions set by the  Nagoya Protocol  for access to genetic resources. Additional samples were collected during the expedition to Central Vietnam, supported by the  Coastal branch of Russian-Vietnamese Tropical Research and Technology Center (Nha-Trang ).
Ecological infrastructure data Ecological infrastructure data were mapped using an image-based approach, supported by a Geographic Information System (GIS). All the homogeneous patchescorresponding to vegetation remnants (i. e. patches clearly different, in origin and dynamics, from the surrounding agricultural landscape), were manually and individually digitised through a visual analysis of the Esri World Imagery layer (1:1000 scale). The Minimum Gap (MG) considered between EI patches was 10 m, while the Minimum Mapping Unit (MMU) was 200 m 2  (Ferreira et al. , 2005; Wasser et al. , 2015). These thresholds represent the minimum distance between EI patches and the smallest patch size, respectively. The EI vegetation patches were then classified according to the vegetation physiognomy, i. e.
, those dominated by trees and shrubs (hereafter referred to as "woody EI") and those where trees were absent or rare, including open areas with scarce vegetation or dominated by herbaceous plants and low bushes (hereafter referred to as "non-woody EI"). We also classified EI vegetation patches according to their location, i. e. , those located contiguous to a watercourse (hereafter referred to as "riparian EI") and those that were non-contiguous to a watercourse (hereafter referred to as "terrestrial EI"). Information concerning the geographic location of water features (rivers and water channels) were obtained by manually improving a Portuguese river network layer, produced based on a 25 m resolution DEM (available at the Portuguese Environment Agency). Small tributaries and headwater streams were added to the previous vector data using visual analysis of high spatial resolution (0.
Acknowledgements We thank  António Faro  and  Susana Rocha  for their help during fieldwork. We are grateful to the landowners for granting us permission to sample, and to both farmer associations (Agrotejo and  Associação de Regantes e Beneficiários do Vale do Sorraia -ARBVS)  for all the help and support during fieldwork. This study was funded by  Project Optimus Prime  ( FCT-PTDC/ASP-AGR/29771/2017 ) and Project  CERES Interreg  ( IV-B SUDOE-SOE2/P5/ F0551 ). GD was supported by national funds via FCT ( PTDC/ASP-AGR/ 29771/2017  and  UIDP/00239/2020 ). AF and VZ were supported by national funds via FCT ( PD/BD/142884/2018  and  PD/BD/142882/ 2018 , respectively). This research was funded by the  Forest Research Centre , a research unit funded by  Fundação para a Ciência e a Tecnologia I.
Agricultural practices of smallholder farming systems of Latin America can play an important role in reducing their exposure to the risks associated with climate extremes. To date, however, there is no systematic analysis of scientific evidence for the extent to which these practices can provide the multiple benefits needed for smallholders to adapt to climate extremes. In this paper, we searched scientific databases to review scientific evidence of the benefit provided by twenty-six practices in crops commonly farmed by smallholders in the region and highly relevant for their food and nutrition security; namely, coffee, maize and beans. We reviewed scientific documents (n = 304) published in the period 1953-2021 to register evidence of the practices' effects on fiftyfive benefits. Our analysis of these documents found measurement records (n = 924) largely based on field experiments (85%).
Solving approach The proposed model is non-linear due to the multiplication of the price by the production quantity in the objective function. In its current form, our model cannot be solved with commercial optimization softwares, like Cplex, due to the non-linearity of the objective function. Moreover, the use of non-linear solvers is not an interesting option since these solvers often have a weak performance when there are many constraints in the model. In this section, we linearize the original model and show that the obtained model can be effectively solved to optimality with Cplex in a very small time. In practice, the olive oil prices are expressed in € with a maximum of two decimals. Hence, if we express the prices and costs of our model in cents, then we can consider prices (p o and p n ) as integer variables without losing optimality.
Besides the consequences of retrotransposition, long interspersed element 1 (L1) retrotransposons can affect the host genome through their antisense promoter. In addition to the sense promoter, the evolutionarily recent L1 retrotransposons, which are present in several thousand copies, also possess an anti-sense promoter that can produce L1 chimeric transcripts (LCT) composed of the L1 5 UTR followed by the adjacent genomic sequence. The full extent to which LCT expression occurs in a given tissue and whether disruption of the defense mechanisms that normally control L1 retrotransposons affects their expression and function in cancer cells, remain to be established. By using CLIFinder, a dedicated bioinformatics tool, we found that LCT expression was widespread in normal brain and aggressive glioma samples, and that approximately 17% of recent L1 retrotransposons, from the L1PA1 to L1PA7 subfamilies, were involved in their production.
These tools include the LIONS suite  (21) , a tool to identify transposable element-derived oncogene transcripts  (22) , and Chimeric LINE Finder (CLIFinder), a bioinformatics tool we developed to identify chimeric reads that correspond to potential LCTs in RNA-seq data  (23). Glioma is the most frequent primary malignant brain tumor. The most aggressive forms, mainly glioblastoma multiform, are molecularly defined by the presence of wild-type isocitrate dehydrogenase 1 and 2 (IDHwt)  (24, 25). IDHwt glioma is a major source of morbidity and mortality, because it is almost always fatal. Despite aggressive treatment, these cancers are highly recurrent, leading to a median survival time after diagnosis that does not exceed 18 months. It has been proposed that therapeutic resistance and tumor relapse rely on a subpopulation of cells within the tumor with stem cell characteristics, called glioma stem cells (GSC)  (26, 27).
Most chimeras including L1PA1 to L1PA7 elements correspond to LCTs First, we generated Ribo-Zero stranded paired-end RNA-sequencing (RNA-seq) data to analyze the whole transcriptome (total RNA) of three healthy brain and 8 IDHwt glioma samples with CLIFinder. As the L1-ASP is active in the more recent subfamilies  (5, 7, 19) , we used this approach to identify chimeric transcripts involving one of the 9123 elements with a 5 UTR from the L1PA1 to L1PA8 subfamilies (Supplementary Material, Table  S1 ). Although we obtained 90 M of reads, each RNA-seq dataset covered only partially the whole LCT transcriptome of that sample. Accordingly, the number of detected LCTs increased with the number of samples analyzed, indicating that for extensive genomewide LCT identification sequencing data from several samples must be pooled (Supplementary Material, Fig.
To this aim, we designed a RT-PCR 5 L1 ). The chimeras Id_1087 (L1PA2 subfamily) and Id_179 (L1PA7 subfamily) correspond to LCTs with a transcription start site (TSS) in the L1-ASP-450 region. The chimera Id_2900 (L1PA8 subfamily) initiates upstream the two L1-ASP regions and therefore, it is not considered to be an LCT. (D) Results obtained for the 36 tested chimeras distributed according to the L1 subfamilies. The TSS of each chimera is defined according to the PCR results obtained with the L1 downstream and upstream primers. Chimeras are defined as LCTs when no amplification was obtained with the L1 upstream primer on the tumor cDNA samples. (E) The long-read SMRT sequencing dataset was used to generate a heatmap showing TSS occurrence along the full-length L1 element associated to the 226 putative LCTs defined by CLIFinderin GSCs.
Twenty-two LCTs were not detected in the long-read SMRT sequencing dataset and are shown as white horizontal lines. Among the other 204 LCTs, 84. 80% had at least one read with a TSS included in the 0-500 bp 5 UTR region, and this percentage increased to 98% when considering TSS in the whole 5 UTR. walking approach using three primers, located at different positions of the L1 5 UTR, and one LCT-specific primer (LCT-Unique Sequence = LCT-US), located in the unique adjacent sequence, to delineate the area where each chimera initiated (i. e. +200 or +450 in the 5 UTR or upstream) (Fig. 1B ). We tested 36 chimeras (7 from L1PA1, 6 from L1PA2, 4 from L1PA3, 3 from L1PA4, 4 from L1PA5, 3 from L1PA6, 5 from L1PA7, and 4 from L1PA8) in two glioma samples (GS#1 and #2). We could amplify 19 of them using the two primers located in the first 500 bp of the 5 UTR, but not with the "upstream" primer, for instance chimera 1087 and 179 (Fig.
To evaluate whether this observation can apply to a whole population of chimera, we also analyzed by Ribo-Zero RNA-seq two IDHwt GSC lines (GSC1 and GSC2). By pooling the data of these two samples, we could identify by CLIFinder 226 chimeras that involved L1 elements from the L1PA1 to L1PA7 subfamilies. Then, to determine the proportion of chimeras that corresponded to LCTs, we mapped their TSS along the L1 sequence using a long-read single molecule real-time (SMRT) sequencing dataset generated from three GSC lines (GSC1, GSC2 and GSC6). Among the 226 chimeras identified by CLIFinder, 22 were not detected in this dataset. The remaining 204 had at least one TSS within the L1 sequence, including the L1 coding region. Nevertheless, we observed a TSS hotspot within the 900 bp of the L1 5 UTR (Fig.
Most LCTs are unspliced at their 5 end and can be polyA and non-polyA transcripts The size of the 1509 LCTs ranged from 132 to 45 574 bp. The median size of the LCTs identified by only 1 read (n = 453) was 306 bp, and 77% of them were smaller than 400 bp. As the L1 antisense 5 UTR contains two donor splicing sites, we evaluated the splicing status of these transcripts by analyzing three parameters: the genomic distance between the L1 side and the unique sequence for each chimera, the total chimera size, and the position of the splice junctions identified by TopHat around the L1 first nucleotide in the 11 samples (Supplementary Material, Fig. S4 ). We found evidence of splicing for 44 LCTs (including for the 17 spliced LCT-ESTs already described). This indicated that most LCTs identified by CLIFinder resulted from continuous transcription from the L1-ASP to the unique adjacent sequence and that only 2.
In this case, we could amplify 22 of the 24 LCTs (Fig. 2A ), suggesting that at least 11, and up to 13 of the 24 studied LCTs were not polyadenylated. In a more systematic approach, we compared the data obtained by Ribo-Zero RNA-seq of GSC1 and GSC2 samples with those obtained by polyA RNA-seq of GSC2 and GSC6 samples. The number of LCTs detected per sample was always higher in the Ribo-Zero RNA-seq datasets (Fig. 2B ). Similarly, the total number of LCTs identified was significantly higher in the two GSC samples analyzed by Ribo-Zero RNA-seq than in the two samples analyzed by PolyA RNA-seq (n = 226 versus n = 36) (Fig. 2C ). Specifically, for the GSC2 sample (analyzed using both RNA-seq approaches), CLIFinder identified 160 and 12 LCTs associated with recent L1 retrotransposons in the Ribo-Zero and PolyA datasets, respectively, among which five were shared (Fig.
2D ). The 13-fold increase in LCTs detected in the Ribo-Zero datasets is not explained only by the 2-fold higher sequencing depth (90 M versus 40 M of reads), and supports the hypothesis that a relevant LCT subset is not polyadenylated. Importantly, most LCTs detected in the GSC samples by both RNA-seq approaches were also detected in the pooled RNA-seq data from the eight IDHwt glioma samples (Fig. 2C ), highlighting that LCTs are recurrently expressed in glioma samples from different patients.
LCTs are expressed in healthy brain and aggressive glioma As previous studies suggested that some LCTs could be tumor-specific  (7, 19, 20) , we asked whether LCT expression differed in glioma and healthy brain samples. As shown before (Supplementary Material, Fig. S1 ), extensive genome-wide LCT identification required to pool data of several samples. In agreement, CLIFinder analysis of the RNA-seq data from three healthy brain and eight IDHwt glioma samples did not allow assessing the LCT transcriptional status in these two tissue types. Therefore, we first quantified, by RT-PCR, the expression of the 31 validated LCTs in 10 brain control and 41 IDHwt glioma samples. All 31 LCTs were expressed in both control and glioma samples (Fig. 4A ), although at different levels. Specifically, 16 were upregulated (e.
However, this does not preclude the possibility of downstream splicing events in the LCT transcripts, as observed in most of the previously characterized LCTs. Moreover, 89% of the LCTs we identified in GSC lines are also found in glioma samples isolated from different patients, demonstrating that our approach allows revealing a LCT tumor-type specific signature. Altogether, these observations validate our approach as relevant to investigate genome-wide LCT expression from L1-ASP in a tissue type. It should be noted that due to the CLIFinder settings used here, our conclusions are valid only for LCTs that initiate at ASP regions located in the first 500 bp of the L1 elements. However, our analysis of long-read sequencing datasets revealed that LCTs may also initiate in the coding sequence of L1.
List of ASP-containing L1 elements The coordinates of L1 elements were obtained from the RepeatMasker database deposited in the UCSC Table  Browser. Recent L1 elements were selected based on their RepName (L1P1, L1HS, L1PA2, L1PA3, L1PA4, L1PA5, L1PA6, L1PA7, L1PA8). This list was then filtered using repLeft and repStart <400 and repEnd >600 to retain only L1 elements with an ASP. The final list is in Supplementary Material, Table  S1.
Illumina RNA-sequencing and LCT identification using CLIFinder Total RNA was isolated from frozen tissue samples and frozen cell pellets as previously described  (35). Strandoriented RNA-seq was performed using total RNA (n = 3 brain control, n = 8 IDHwt, and n = 2 GSC samples: GSC-1 and GSC-2) and also polyA mRNA from the GSC-6 and GSC-2 samples  (34). CLIFinder was used as previously described (23) to extract chimeras from stranded paired-end RNAseq data. The analyses were based on the reference annotated human genome GRCh37/hg19. The reference set of L1 sequences used was a text file containing FASTA sequences that corresponded to the first 500 bp of the 9123 L1 elements (L1PA1 to L1PA8) with a 5 UTR sequence in the human genome. These sequences were extracted from the Repeat Masker database with UCSC tools.
As the reference file contains all potential targeted sequences, the parameters used to select chimeric cDNA of potential interest were: • 5 end read sequence in which at least 50 bp that matched a L1 reference sequence (1 mismatch was tolerated to take into account single nucleotide polymorphisms that may be present also in L1 sequences). • The associated 3 end read sequence should contain at least one unique sequence (i. e. not annotated by Repeat Masker) of ≥30 bp. Finally, a maximum insert size of 50 kb after alignment to genomic DNA was tolerated between the pairedend reads retained (to allow the identification of spliced chimeras). In the 11 RNA-seq datasets, CLIFinder identified 1677 chimeras associated with the L1 subfamilies L1PA1 to L1PA8 (Supplementary Material, Table  S2 ). Among these chimeras, 37 were transcribed in the same orientation as the associated L1, and 69 were associated with L1 elements without conserved 5 UTR, therefore resulting in 1571 chimeras that potentially corresponded to LCTs.
Whole gene expression (fpkm) determination Illumina stranded RNA-seq reads from three controls and eight IDHwt glioma samples were used to determine the normalized gene expression (fpkm), as previously described  (34). Briefly, reads were mapped to the human genome (hg19) using TopHat2 (version 2. 1. 0) and a transcript annotation file from GENCODE (Release 19). Gene expression levels were determined with Cuffquant and Cuffnorm from the Cufflinks suite (version 2. 2. 1).
Data access Data are available at the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under the following accession numbers: GSE123892 for the stranded total RNA-seq of control samples and IDHwt gliomas; GSE161438 for the stranded total RNA-seq of GSCs; GSM4907328 and GSM180209 for the stranded polyA RNA-seq of GSCs; GSE190930 for the PacBio sequencing of GSCs; and GSE123682 and GSE161275 for the Cytoscan HD data.
Chimera validation The LCT-Unique Sequence (LCT-US) primers (using the Primer 3 software: http://primer3. ut. ee/) were designed for the 36 chimeras selected according to their detection by CLIFinder using high or low reads number, they tumorspecificity or not, and their localization (intragenic or intergenic). This primer was combined with a L1 common primer (localized at the beginning of the 5 sequence of L1 elements). Due to sequence homology, a common primer could be designed for the L1PA1 to L1PA7 subfamilies and a specific L1 common primer for the L1PA8 subfamily. Then, chimera detection was validated by RT-PCR using RNA from two glioma samples (GS#1 and #2) already used for RNA-seq. Brief ly, 1 μg of total RNA was treated with DNase I (Promega) according to the manufacturer's recommendations, and then reverse transcribed using Random Hexamers (Invitrogen) and the SuperScript III Reverse Transcriptase Kit (Invitrogen).
RT-PCR-based 5 L1 walking approach to localize the TSS of the validated chimeras RNA available in large amounts and of good quality (RIN > 8) from two glioma samples was used for these experiments. Additional L1 primers, downstream and upstream of the L1-ASP +450 location, were designed using the Primer3 software. Due to the high sequence homology displayed by the 5 UTR sequence of the L1PA1 to L1PA6 subfamilies, the same L1 downstream and upstream primers were used for chimeras associated with L1 elements from these six subfamilies. However, as some mismatches could impair PCR amplification (when too numerous or when occurring at the 3 end of the primer), three different L1 downstream primers (PA1 to PA6) were designed and alternatively used. Specific L1 downstream and upstream primers were designed for the L1PA7 and L1PA8 subfamilies.
Primer sequences, mismatch number between L1 primer/L1 sequence, and PCR product amplification sizes and results are given in Supplementary Material, Table  S3. As previously described, 1 μg of total RNA was treated with DNase I and reverse transcribed with SuperScript III using random hexamers. For each sample, a control was prepared without reverse transcriptase. PCR amplifications were performed as previously described, using the relevant LCT-US primer and L1 downstream or upstream primer. In the PCR program, the extension time at 72 • C was adjusted in function of the expected amplification product size (40 s and 1 min for L1 downstream and L1 upstream primers, respectively). As all tested chimeras corresponded to continuous transcription events from the L1 sequence and the adjacent unique sequence, genomic DNA was used as positive control and as size marker.