dataesr/scientific-paragraphs-categorization huggingface dataset:
Please visit https://doi. org/10. 1130/ GEOL. S. 12837788 to access the supplemental material, and contact editing@geosociety. org with any questions. The CREST software is accessible from https:// chevaliermanuel. wixsite. com/webpage/crest/. The mean annual temperature reconstructions are available at https://doi. pangaea. de/10. 1594/PANGAEA. 915923. The R scripts used to analyze the data and create the figures are available from https://github. com/mchevalier2/ Papers/tree/master/Chevalier_etal_MD962048. 3 ) is within the modern value calculated for the catchment as a whole (20. 9 °C, σ = 2. 5 °C), highlighting the fact that the pollen and associated MAT reconstructions reflect the catchment's average temperature.
ACKNOWLEDGMENTS Chevalier was supported by the  Swiss National Science Foundation  ( SNF/FNS)  ' HORNET'  Project (# 200021_169598 ). Funding for this work was received from the  European Research Council (ERC)  under the European Union's  Seventh Framework Programme  (FP7/2007-2013)/ ERC Starting Grant "HYRAX," grant  agreement no. 258657. We gratefully acknowledge three reviewers for their constructive comments.
Literature search and study selection We performed a systematic literature search in June 2016 to compile peer-reviewed articles. We used the scientific search engine Scopus, employing the following search terms: "livestock", "feeds" OR "forage" OR "fodder", and "Africa". We complemented this search with references cited in the primary literature and unpublished studies obtained from the authors' personal networks. For inclusion into this review, we only selected studies that met the following criteria: (1) The study reported empirically measured, original data on one of the target impact indicators (see section 2. 2), excluding simulated data or data cited in reviews or secondary articles; (2) the article examined at least one tropical forage technology (grass, herbaceous or dual-purpose legume, leguminous multipurpose trees, and shrubs hereafter called "shrubs") but not cereal crop residues, concentrates or tree products-if the technology was a dual-purpose legume, it was only included if the forage or livestock impact was assessed as well; (3) the article focused on ruminant livestock, excluding monogastrics; (4) the study reported data from experimental, "improved" treatments and a control treatment; (5) the reported data was continuous and numerical thus not reported in scores, ranks, percentages or as graphs; (6) the study was written in English; (7) basic experimental information was available in section 2; and (8) the study was conducted in Sub-Sahara Africa.
Data was extracted from the 72 selected papers into a Microsoft Access database. In addition to impacts, we extracted experimental and technology descriptions including type of technology and forage species, cropping system, management, and number of replications (N). Figure  3  summarizes the number of studies and pairs of observations (treatment-control) per impact dimension and indicator (columns) and technology groups (rows) that are reported in this review. Improved germplasm effects on forage productivity and quality, cropping system integration effects on soil quality, household economics, and food crop productivity were shown as overall average impacts as well as by technology subgroup (plain color). The technologies falling under improved management and cropping system integration were considered to be too diverse to be presented as average impact values across all technologies.
(2012) , including analyzing the heterogeneity of data with random-effect models, sensitivity analysis, and investigation of publication bias. We quantified the effect of forage technologies on the impact indicators (see section 2. 2) calculating response ratios (RR) for individual observations: RR ¼ X E X C where X E is the impact indicator value for the forage technology treatment, and X C is the impact indicator value for the control treatment. For most observations in our dataset, the original studies did not report measures of variance. Consequently, we relied on a nonparametric approach to weighing observations instead of using the inverse of the pooled variance. Effect sizes were weighed by replication to assign more weight to wellreplicated studies: W R ¼ N C *N E N C þ N E ð Þ where W R is the weighing factor by replication, N C the number of treatments per control, and N E the number of replicates per experimental treatment.
Forage biomass productivity is assessed in various ways in terms of establishment time, cutting interval, cutting height, and reporting times (per harvest, season, year). This made results less comparable across sites and studies. There is a need in SSA for implementing both proposed standards in forage agronomy, and capacity building in forage agronomy. However, investment in forage research has been low, with for example only seven forage agronomists out of the 545 agricultural scientists in Kenya  (Murithi and Minayo 2011). Further statistical analysis of the heterogeneity of forage productivity data could identify explanatory variables for the observed site-year variability, relying on additional metaanalysis methods such as random-effects models and sensitivity analysis as proposed by  Philibert et al.
Acknowledgements This paper has benefitted from various contributions:  John Yumbya Mutua  ( CIAT ) for mapping the study sites,  Celine Birnholz ,  Jessica Mukiri , and  Alex Nduah  ( CIAT ) for database and reference checks,  Kees-Jan van Groenigen  for advice on metaanalysis methods,  Di Prestwidge  ( CSIRO ) for help with database management and queries with Microsoft Access, and  José Luis Urrea Benitez  ( CIAT ) for assistance in figure design and format. We thank two anonymous reviewers and the editor for constructive comments that helped to substantially improve the manuscript. In: Whitebread E, Pengelly B ( 2004) Tropical legumes for sustainable farming systems in southern Africa and Australia, 115 pg. 126. Mutegi JK ,  Mugendi DN ,  Verchot LV , Kung'u JB (2008) Combining napier grass with leguminous shrubs in contour hedgerows controls soil erosion without competing with crops.
Results Menorin, FAM151A and B are members of the GDPD/PLCD superfamily. We analysed the amino acid sequence of FAM151B to identify similarities with proteins of known function. A search of the UniRef 50 database  6  shows the FAM151 family to be widely distributed in animals, including C. elegans, as noted above, and Drosophila, and is annotated in Pfam as containing the domain of unknown function (DUF) 2181. We used the remote homology detection server HHPred 7 using a profile from multiple FAM151 alignments to find more divergent homologues. HHPred searches with FAM151 family matched the Phospholipase D domain of a spider (Sicarius terrosus) toxin (PDB-ID: 4Q6X) with a highly significant E-value of 1. 4 × 10 -5 8. Moreover, in support of this, the next most statistically significant matches were to additional members of the Phospholipase D and glycerophosphodiester phosphodiesterase (GDPD) families (Fig.
This may mean that the photoreceptor cells develop and can function normally but degenerate very quickly after eye opening. By P21 there is little to no a-wave amplitude when stimulated with both 10 cd. s/m 2 and 3 cd. s/m 2 flashes (Fig. 4b ). This is replicated in the light adapted response showing that cones are also being lost. the program Belvu using a colouring scheme indicating the average BLOSUM62 scores (which are correlated with amino acid conservation) of each alignment column: red (>3), violet (between 3 and 1. 5) and light yellow (between 1. 5 and 0. 5)  31. Sequences are named according to their UniProt identification. (b) Structural superimposition of two members of the PLC-like phosphodiesterases superfamily: GDPD and PLCD1. Active site residues are labelled and coloured according to their reference protein sequences: human FAM151B, GDPD (PDB-ID: 3QVQ) and PLCD1 (PDB-ID:1djy) in red, blue and green, respectively.
Material and Methods Sequence analysis. The computational protein sequence analysis began by performing a JackHMMER iterative search  24  beginning from the human FAM151B protein sequence, against the UniRef50 database 6. The FAM151 family is widely distributed in animals, including nematodes (C. elegans; UniProt: O45879) and hexapods (D. melanogaster; UniProt: Q9VLU9). This reproduces the family phyletic distribution reported in Pfam (Family: DUF2181/PF10223)  25. Next, we took advantage of profile-versus-profile (HHpred)  7  to search PDB70 database for more divergent FAM151 homologues using as input a profile generated from the multiple protein sequence alignment of FAM151 family. The PDB70 database contains profile hidden Markov models (HMMs) for representative sequences, clustered to 70% maximum pairwise sequence identity to reduce redundancy, drawn from the PDB (Protein Data Bank)  26.
| Variant annotation To overcome the fact that the germline counterpart was not available, variants were filtered to retain exonic and pathogenic acquired mutations based on a methodology previously described  19  according to an in-house pipeline (Supplementary material and methods). Briefly, we first restricted the analysis to variants with a sequencing depth ≥100× and supported by ≥5 mutated reads. Minimum variant allele frequency (VAF) was set to 2%. Highly recurrent hotspot mutations in CLL were identified based on bibliography and by systematic screening of databases for cancer mutations (COSMIC, IARC TP53 databases) for all variants. Validation of non-hotspot mutations needed either positive prediction of pathogenicity by the bioinformatic scores SIFT and/or CADD in order to control the false discovery rate of new variants.
Variants with no available classification by SIFT or CADD scores were also evaluated but all tolerated variants were not considered. We then systematically screened databases for polymorphisms (dbSNP, GnomAD, 1000 genomes). All variants with a minor allele frequency (MAF) in the general population ≥0. 01 were considered as polymorphisms. For other variants with an entry in dbSNP, GnomAD or 1000 genome databases but with MAF <0. 01 or with absence of available annotations were specifically reviewed by two of us (JC, DR) to strictly retain highly plausible mutations only.
| Statistics The chi-square test was used to evaluate the difference between categorical covariates for TMB or the eight gene estimator subgroups when sufficient patient numbers were achieved. For small sample size, chi-square test was replaced by Fisher's exact test. The Mann and Whitney test was used to compare lymphocyte blood counts between treatment naïve patients of both series. The effects of genetics (IGHV mutational status, individual gene mutations), cytogenetics (trisomy 12, del(11q), del(13q) and del(17p)), TMB (two subgroups with a threshold at two or more mutations), and the eight gene estimator (two subgroups: mutated vs unmutated) covariates on treatment-free survival (TFS) were examined among previously untreated Binet stage A patients using the Cox proportional hazard model  24  processed with the Survival R package (https://github.
25  Roughly, a pair (i,j) is concordant if xi < xj and Ti < Tj. The c-Harrel index corresponds to the proportion of concordant pairs. Here, xi and xj take the value 0 or 1 according to the criteria studied (mutational status of a given gene, mutational complexity, Binet stage A vs B…). The C-Harrel concordance index can also be interpreted as a summary measure of the area(s) under the time dependent ROC curve(s). 26 With each variable being coded as 1 (present) or 0 (absent), the C-Harrel concordance index was calculated with the "rcorr. cens" command of the Hmisc package (https://github. com/harre lfe/Hmisc).
This work was supported by Ligue contre le Cancer (Programme National Equipe Labellisée et comités Haute-Vienne et Corrèze de la Ligue) as well as partially funded by the hematology laboratories of Limoges and Rennes University Hospital Centres. We thank both the hospital sequencing functional unit and the university Genolim platform of Limoges (Limoges, France) as well as the BioGenouest GEH sequencing platform (https://geh. univ-renne s1. fr/) from the UMS Biosit (Rennes, France). We thank Dr Jean-Baptiste Woillard (Pharmacology department of Limoges University Hospital Centre, France) for statistical advice and Dr Jeanne Cook Moreau (UMR CNRS 7276/INSERM 1262 CRIBL, University of Limoges, France) for careful English editing.
In the laboratory, orthomosaics and DEMs were produced by the Photoscan PRO software, v. 1. 4. 3 from Agisoft. Picture alignment and subsequent sparse cloud construction were strongly constrained by the distances between GCPs. This step, which helps to structure the 3D model, was useful here, as wind causing movement of long grass (which dominates the landscape studied) may lead to the accumulation of slight alignment errors between pictures. The workflow then consisted in densifying the cloud, producing height field models, DEMs, and orthomosaics, at a fixed resolution of 5 cm / pixel for Tsatsiin Ereg and 8 cm/pixel for Jargalant (see  [22, 25]  for more details about the photogrammetrical workflow). It is worth mentioning that the use of GCPs precisely georeferenced with differential GPS, for example, would have been optimal for accurate positioning of the maps produced.
Hyperparameter tuning and metrics for model evaluation Basically, a good practice to evaluate the capacity of the models produced to generalize to unseen data is to split the supervised data into two groups, one for training (here 70% of the dataset), with the remainder (30%) as a test dataset  [36]. However, depending on learning strategies, as mentioned above, several model hyperparameters (some of them tackling overfitting via regularization) have to be fine-tuned. This optimization step is operated by a brute force search on a grid of possible hyperparameter values, using inner cross-validation on the training set. For final model evaluation, two strategies are possible. The first simply applies the model to the test dataset. This method provides a single evaluation value, which is unbiased, as these data were not used to build the models  [44].
Practical implementation Computation of GLCM and TPI used the glcm package and a homemade script, both written for the free R software (https://www. r-project. org/). Colour manipulation, application of machine learning algorithms to standardized data, and georeferencing of the maps produced used the scikitlearn 0. 20. 3, opencv 4. 1. 1, rasterio 1. 0. 22, gdal 2. 4. 2, and scipy 1. 2. 1 libraries for Python 3. 7. 1 (https://www. python. org/). Final results, expressed as georeferenced polygon vector layers, were integrated using the free QGIS software (https://www. qgis. org). Point pickup to produce the training dataset used ImageJ (https://imagej. nih. gov/ij/), or a homemade snippet based on OpenCV.
Pixels were manually picked out on the orthomosaic to train the model with 363 positive and 1010 negative cases. Such a dataset may appear oversized and time-consuming for a procedure requiring manual operation. This is especially true when repeated several times for large sites, covering several square kilometres. However, the objective here was to evaluate the influence of the size of the training set, in terms of performance, which therefore required a vast number of samples. RGB as input features. Results appear to be good, most of the time > 93%, up to 98. 5%, whatever the algorithm applied to the RGB information alone (Table  1 ). These good scores are all the more remarkable in that they include some rather basic procedures, such as naive Bayes, nearest centroid, and, to a lesser extent, linear discriminant analysis, which is among the four best results (Table  1 ).
Interestingly, the operator can also evaluate orthomosaics the same day, to detect possible technical problems, and so perform the operation again if necessary. Note that several isolated rocks are recorded. Depending on the final objective, these positive cases could be easily removed, either by hand (taking typically less than 30 min), or automatically using an algorithm, taking into account the local density of positive cases, and/or the distance to the nearest positive neighbours. One final test was performed to examine if the time spent acquiring the training dataset could be significantly reduced. Tiles n°5 and n°3 were treated with their specific training dataset, but also with that acquired to process tile n°4 (Fig. 6 ). Good results were anticipated for n°5, since the images were captured the same day, approximately at the same time, and thus in the same lightning and topsoil moisture conditions, but there was some doubt about tile n°3, which exhibits greener images (like tile n°6), probably because the images were taken after a rainy event.
With the increasingly high resolution of images, and technological progress making it possible to collect hundreds of images for each flight, applying machine learning algorithms becomes indispensable. The operator intervenes significantly during two crucial steps. The first is the manual selection of the training dataset. In the examples presented, it was very important to sample different kinds of stones, but also grass, car tracks, bare soil, etc. , because if the training data is inadequate or not adequately representative, poor classification results are to be expected. The second step concerns feature engineering. While the selection of the best learning model and hyperparameter tuning can be performed almost automatically, the workflow consisting in reformatting, processing, enriching, calibrating, and finally selecting features requires some experience.
Acknowledgements This research was funded by the Join mission Mongolia -Monaco, and the project ROSAS (uB-FC and RNMSH). We are grateful for helpful comments by an anonymous reviewer and the editor, which have greatly improved the manuscript. Texture features encompass contrast, homogeneity, and entropy. In bold, the results corresponding to the four feature combinations: RGB, texture (CON + HOM + ENT), RGB + texture, RGB + texture + TPI, presented in Fig. 3.
As expected, the relative amount of reads mapping to spike-in RNAs increased upon blocking transcription when compared to those mapping to endogenous mRNAs (Supplementary Figure  2a ). However, spike-in normalization of read counts introduced a significant source of technical variability. To avoid this issue, we decided to use spike-in read counts to identify stable endogenous transcripts that exhibited almost no decay upon transcription inhibition over the three-hour time course of our experiments (see methods section), both in resting and activated cells (Supplementary figure  2b ). We then used these stable transcripts to normalize abundance of all transcripts from our libraries using the DESeq2 package  4. Analysis of our datasets allowed us to monitor the fold change in RNA abundance after one and three-hour transcription blockade and to obtain a proxy of decay rates in resting and activated T cells.
The length of untranslated regions and ribosome density define the extent of TDD Having uncovered a global impact of mRNA translation on mRNA decay, we next investigated which mRNA features could explain the extent of TDD. To this aim, we built a random forest model to predict the observed TDDindex based on transcript-related information such as total transcript length, length of the 5'UTR, coding sequence and 3'UTR length (obtained from our PAS-Seq data), ribosome-density (obtained from our ribosome profiling data), density of m6A sites (experimentally obtained from T CD4+ mouse cells  8  ) or the number of upstream Open Reading Frames (uORF), among others. The model was trained with two thirds of our dataset while the remaining third was used as a test set. In the test set, the trained model was able to capture 32% of the variance and yield a Spearman correlation coefficient of 0.
; https://doi. org/10. 1101/2020. 10. 16. 341222 doi: bioRxiv preprint translation-dependent mRNA decay observed in our datasets. Nevertheless, other features such as exon count and the nucleotide composition of the coding sequence and 3'UTR could also play a significant role in mediating translation-dependent mRNA decay. Translation-independent mRNA decay is associated with small transcripts with few exons and a biased nucleotide composition in their coding sequence When applied to the TIDindex, random forest analysis was able to explain 40% of the variance and yield a Spearman coefficient correlation of 0. 69 between the predicted and observed TIDindex values of the test set. In this case, the number of exons per transcript as well as total transcript length were among the two most important factors able to predict the extent of TID in resting T cells (Figure  5a ).
Since our datasets allow us to discriminate between TDD and TID, we tested the relationship between codon usage and the two mRNA decay pathways. The relationship between codon usage and transcript stability has been recently studied using a specific metric known as the "Codon occurrence to mRNA Stability Correlation coefficient (CSC)"  15. Briefly, the CSC is defined as the Pearson correlation between the frequency of each codon in mRNAs and the half-lives of the mRNAs. Here, rather than calculating a correlation with mRNA half-lives, we implemented the TDD-CSC and TID-CSC metrics by calculating the Pearson correlation between codon frequency and TDD or TID index, both in resting and activated T cells. TDD-CSC and TID-CSC values are within similar ranges (between -0. 2 and 0. 2, see figure  6a  and 6b ) to those obtained from the literature using mRNA half-lives as input  [15] [16] [17]  , thus confirming the relevance of this approach to TDDindex and TIDindex.
Fur hermore, the frequency of ribosome collisions has been recently reported to be positively correlated to ribosome density in a transcriptome-wide manner  26  and in some instances to recruit the RNA decay factor SKIV2L 20 , suggesting that ribosome collisions could be responsible for the correlation between ribosome-density and TDD observed in our datasets. Moreover, there is evidence showing that ribosome collisions can result in the recruitment of the ubiquitin ligase ZNF598 to the collided ribosomes, thus triggering transcript degradation  [27] [28] [29]. Interestingly, ZNF598 is usually found is substoichiometric amounts compared to ribosomes  28. This would imply that not all collision events result in mRNA degradation, thus potentially explaining the lack of a clear cleavage at stalling sites observed by Arpat and colleagues in their study  26.
In this pathway, long 3'UTRs were shown to decrease accessibility of mRNA decay factors recruited by non-optimal codons to the 3' poly(A) tail (distance model of codon-dependent mRNA decay). If a similar mechanism is responsible for protecting mRNAs from the translation-dependent mRNA decay observed in our datasets, it would imply that decay occurs through transcript deadenylation rather than an endonucleolytic cleavage. In contrast to 3'UTR length, the role of 5'UTR length in modulating TDD is likely to be indirect, possibly through the regulation of translation initiation, which impacts ribosome density in the coding sequence. Long 5'UTRs could restrict ribosome loading and the rate of translation initiation through secondary structure or the presence of upstream ORFs and thus decrease the density of ribosomes along the CDS, leading to low TDD.