dataesr/scientific-paragraphs-categorization huggingface dataset:
This integration yielded a substantial increase in the number of detected exons across all classes. Remarkably, even the low expressed exons showed a significant improvement in detection, with approximately 90% of these exons being identified. This plateau in detection efficiency was achieved at a relatively modest expression level, suggesting that our combined approach effectively captures a comprehensive set of exons across the transcriptome. (Fig. 4d ). To ensure the undetected status of these exons, we selected the top 50 most expressed and reproducible undetected exons and visually inspected their binding profiles and associated RNA-seq data using the Integrated Genomics Viewer (IGV) (see Supplementary Material). However, we found it very challenging to find a single example of an internal gene failing to show evidence of an EJC.
By integrating datasets obtained through different experimental methods, it becomes possible to overcome individual limitations and improve specificity, leading to a more comprehensive understanding of the targeted molecular features  [37]. In our study, we applied this strategy to map EJC binding sites into the Drosophila transcriptome. First, we isolated EJC-bound RNA fragments by performing eIF4A3 eCLIP. To improve the accuracy of EJC binding site discovery, we leveraged the high frequency of 5′ truncations relative to readthrough events by using JACUSA2 that prioritizes RT arrests over local enrichments. Depending on the RBP studied and its mode of RNA binding, it is important to adapt a proper peak caller pipeline to analyze CLIP data  [38]. Additionally, we re-analyzed the available EJC interactome datasets obtained by RIP-seq after protein-protein crosslinking.
These datasets were obtained independently of RNAprotein UV crosslinking, thereby avoiding the limitations and drawbacks associated with it  [27]. By integrating both analyzes, we substantially increased the robustness of the results. Ultimately, we made the remarkable discovery that nearly every spliced junction of significantly expressed transcripts is associated with an EJC. Therefore, EJC deposition is a universal mark of pre-mRNA splicing. The deposition of the EJC onto every mRNA spliced junction certainly constitutes an important attribute of spliced mRNAs. eCLIP-seq of eIF4A3 both in human  [26]  and in Drosophila (this study) gives an extremely sharp enrichment of EJC crosslinking sites at metaexonic level, peaking 27 nucleotides upstream of spliced junctions in both cell types. This evolutionary conserved, spatially restricted and splicing-dependent loading of the EJC makes the existence of non-canonical EJCs not very likely and probably attributable to non-reproducible false-positive EJC peaks originally detected  [16, 23].
Conclusions In summary, our study provides important insights into the deposition of EJCs in pre-mRNA splicing. By employing a combination of transcriptomic mapping strategies, including eIF4A3 eCLIP and re-analysis of available EJC interactome datasets, we have clarified EJC deposition onto the Drosophila transcriptome. While the limitations of current methods hinder the ability to quantitatively assess EJC occupancy rates per exon and the dynamic nature of EJC disassembly, our study shows that EJC deposition is a universal mark of pre-mRNA splicing. We also showed that EJC deposition by the spliceosome is a conserved mechanism leading to a very precise positioning of the EJC upstream spliced junctions. Being a repressor of cryptic splice sites and a cornerstone of mRNP 3D organization, a systematic deposition of EJCs along transcripts would ensure the integrity of multi-introncontaining transcripts and a homogeneous compaction of mRNP particles.
Read preprocessing and data mapping We performed demultiplexing of raw reads using a custom script that identifies sample 5′ end barcodes. After barcode removal, human datasets were mapped to the human genome (hg38, Ensembl 85, with processed transcripts and pseudo genes masked) using STAR (version 2. 7. 9a). Drosophila datasets were mapped to the Drosophila genome (BDGP6. 22. 96). We applied PCR duplicate removal on the demultiplexed data with umitools -dedup. Intersection to the genome of reference was performed with intersect-Bed module from Bedtools, after trimming the read to 1 nucleotide, corresponding to the 5′ end (the crosslinking position), against a homemade GTF composed only by one representative transcript per gene, selecting the isoform with the maximum number of exons, using the longest exonic size as a tiebreaker.
SRD development and meta-exons The Stop Rate Difference (SRD) score was created from the JACUSA2 output  [35]. We performed RT-arrest analysis independently on three replicates of Drosophila eIF4A3-eCLIP using a control dataset comprising the two unified SMInput sequencing results. We then identified positions where at least two replicates exhibited a significantly higher stop rate compared to the control, indicating specific EJC binding (P value < 0. 05 on the integrated chi-square test). To obtain the SRD score, we took the mean of the stop rates per nucleotide of the three replicates and subtracted the stop rate of the control of the same nucleotide. Subsequently, to obtain the SRD score per exon, we calculated the average SRD score of all significant positions within a 10-bp window from -23 to -32 upstream of the exon junction of each exon.
Acknowledgements We thank all  HLH lab  members for advice and discussions. Olivier Bensaude ,  Tommaso Villa  and  Pr. John T. Lis  for critical reading of the manuscript. We acknowledge the sequencing and bioinformatics expertise of the I2BC Highthroughput sequencing facility, supported by  France Génomique  (funded by the  French National Program "Investissement d' Avenir "  ANR-10-INBS-09 ).
| Kinetic modeling of folate degradation Individual folate change versus time can be described by Equation (1). d X ½ dt ¼ Àk X ½ ð1Þ where [X] represents concentration (molÁL À1 ), t the time (min), and k the reaction rate constant (min À1 ). The uncertainty of the rate constants obtained with the Levenberg-Marquardt algorithm was calculated by nonlinear error propagation using Matlab software (The Mathworks Inc. , USA). The rate constants k, were assumed to vary with the temperature according to the Arrhenius law (Equation  2 ). k ¼ k ref exp ÀE a R 1 T À 1 T ref ð2Þ where k ref is the rate constant at the reference temperature chosen in the middle of the studied temperature range (80 C), E a , T, and R, respectively, the activation energy (JÁmol À1 ), the medium temperature (K), and the gas constant (8.
314 JÁmol À1 ÁK À1 ). Kinetic constants, and activation energies were identified by nonlinear regression thanks to the Levenberg-Marquardt minimization procedure using the Matlab software (The Mathworks Inc. , Natick, MA, USA). Confidence intervals of Arrhenius parameters (Equation  2 ) were obtained via the bootstrap simulation method. The principle is the generation of a high number (500) of resamples of the kinetic dataset added with a Gaussian perturbation within the experimental standard deviations. These virtual data were generated via Matlab software (The Mathworks, Inc. , USA).
| Statistical analysis ANOVA analysis was done using Statistica 7. 1 software (StatSoft inc, Maisons-Alfort, France). toethanol, acting like an antioxidant, was added. Therefore, the rapid initial rate of THF degradation and the discrepancy from the firstorder trend could be explained by the oxygen consumption in the media for this molecule that is very prone to oxidation  (Blair & Pearson, 1974). In acidic conditions of pH 3, rate constants of 5MTHF and THF were similar with values between 3 Â 10 À4 to 2. 8 Â 10 À2 min À1 and from 4. 6 Â 10 À3 to 1. 7 Â 10 À2 min À1 , respectively. Paine (1978)  found the same range of rate constant for 5MTHF (pH 3) of 8. 3 Â 10 À2 min À1 at 100 C. However, the author found that THF was more reactive with rates of 0. 1 min À1 at pH 2 and 100 C. These variations may be due to the difference of oxygenation that is not always specified and buffer types that may affects (up to 5-fold) the reactivity of the reduced forms of folates  (Paine, 1978; Viberg et al.
ACKNOWLEDGMENTS This study was supported by grants from the  French Agence Nationale de la Recherche (ANR)  '' RNAgermSilence '' and  '' Heat-EpiRNA' ', from  MSD Avenir (ERiCAN,   Epigenetic Reprogramming of Cancer Cell Plasticity and Resilience ). The graphical abstract was created with BioRender. com, license IR254FJIJE. We thank  Claire Vourc 'h and  Auriane Rakitch  for critical reading of the manuscript.
Acknowledgments & Funding We thank  P. Mahou  and the  Polytechnique Bioimaging Facility  for assistance in imaging. The Polytechnique Bioimaging Facility is supported by  Région Ile-de-France (interDIM)  and  Agence Nationale de la Recherche  ( ANR-11-EQPX-0029 Morphoscope2 ,  ANR-10-INBS-04 France BioImaging ). This work was supported in part by an endowment in Cardiovascular Bioengineering from the  AXA Research Fund  (to AIB) and postdoctoral fellowships from the  Lefoulon-Delalande Foundation  and the  Bettencourt-Schueller Foundation  (to CL).
There are few recent and methodologically robust life expectancy (LE) tables for dogs or cats. This study aimed to generate LE tables for these species with clinical records from > , Banfield Pet hospitals in the USA. Using Sullivan's method, LE tables were generated across survey years -, by survey year, and for subpopulations defined by sex, adult body size group (purebred dogs only: toy, small, medium, large and giant), and median body condition score (BCS) over life. The deceased population for each survey year comprised animals with a recorded date of death in that year; survivors had no death date in that year and were confirmed living by a veterinary visit in a subsequent year. The dataset totaled , , unique dogs and , , unique cats. LE at birth (LE birth ) was. years ( % CI:. -. ) for all dogs,.
Accurately calculating the life expectancy of dogs and cats would be of greatest interest in helping to understand the benefits of preventative care and the success of increasingly advanced medical interventions. However, there are relatively few studies on lifespan or all-cause mortality in either of these species, especially in cats. Four studies in dogs have compiled age of death statistics from surveys of owners belonging to the UK Kennel club  (4, 5) , the Danish Kennel club  (6)  and owners on a UK insurance database or attending the Crufts dog show  (7). Analyses of the primarycare veterinary records of both dogs  (8)  and cats  (9)  in England have provided age of death data as the percentage of pets dying within pre-specified age brackets. Kaplan-Meier survival curves for dogs attending the Banfield Pet Hospital network of primary care hospitals in the USA have been published  (10) , and a study of life-insured Swedish cats generated survival curves for cats using a methodology of age standardized cox regression  (11).
This study used life table methodology to analyze survival, in part to provide information equivalent to that for large human populations, and in part because of the high dropout rates of animals (right-censored individuals relative to animals with a confirmed date of death) due to discontinuity in veterinary visits in the clinical dataset. Life expectancy tables for pets have been reported previously from a total of four studies in Japan and one in the UK  (12) (13) (14) (15) (16). The oldest of these studies generated a life expectancy table using the cemetery records of 4,915 dogs in the Kyoto region of Japan that died between  1981 and 1982 (12). Similarly there was a census of 12,039 dogs from cemeteries in Tokyo between 2012 and 2015  (14). The only life expectancy tables found for cats were also from Japanese cemetery data in Tokyo (3,936 cats identified from between 1981 and 1982)  (16).
Using such data sources comprising only dead animals for a life-table methodology is unsatisfactory because of a "hidden" bias of right censorship, since they cannot account for animals in a cohort still alive at the time of sampling (17). This limitation did not apply to the fourth Japanese study, which generated life expectancy tables from the insurance records of 299,555 dogs insured between 2010 and 2011 and followed for 1 year  (13). The first life tables for dogs in the UK were based on the VetCompass TM database of primary-care veterinary clinics, in which there were 30,563 dogs with at least one clinical record in 2016 and a confirmed death between January 2016 and July 2020  (15). Despite differences between studies in data sources and the computed life expectancy, they all found substantial differences in life expectancies between different dog sizes or breeds when this was investigated.
Other studies in dog populations comprising multiple breeds and both sexes have summarized overall ages of death without computing life expectancy tables  (4) (5) (6) (7) (8). Age of death was 10 years (median) in a Danish survey of 2,928 purebred and mixed-breed dogs in 1997  (6) , 10. 33 years (median) in a survey of 5,663 Kennel Club-registered dogs in the UK in 2014 (4), 11. 08 years (mean) in 3,000 British purebred and mixed-breed dogs  (7) , 11. 25 years (median) in 15,881 purebred dogs in the UK over the 10 year prior to 2003  (5) , and 12 years (median) in a UK VetCompass TM dataset of 5,095 dogs between 2009 and 2011  (8). Age-at-death data are scarce for cat populations comprising multiple breeds and/or mixed-breed cats. A study that used a random sample of 4,009 purebred and mixed-breed cats with confirmed deaths in the UK VetCompass TM database between 2009 and 2012 reported a median age of death of 14.
0 years [interquartile range (IQR): 9. 1-17. 0] (9). The more recent studies demonstrate the opportunities available in the current era of veterinary "big data", which is giving researchers access to reliable and extensive high quality data over long periods of time  (18). There is a need now to leverage the power of other large datasets to characterize life expectancy in countries with different disease risks, attitudes toward veterinary care, and accessibility of such care. Furthermore, life expectancy tables are needed for pets with and without specific known or hypothesized risk factors for disease, to help quantify the potential benefits of improving modifiable risk factors, and to support conversations with owners on proactive lifestyle choices for the health of their pets. Obesity in pets is a condition of particular interest.
Understanding the impact of obesity as either a risk factor for the development of specific diseases or as a consequence of them is important for preventative medicine, disease signalment and disease management. The current study aimed to generate life expectancy tables for dogs and cats in the USA, computed for each survey year between 2013 and 2019, and across survey years according to breed size, sex, and body condition score (BCS). Clinical data were obtained from the electronic medical records (EMRs) of healthy and sick dogs and cats attending Banfield hospitals throughout the USA. Computations followed Sullivan's method for life expectancy tables, which can also account for health status assessed as a cross-sectional variable  (32). This methodology is one of the most popular methods for life expectancy tables and is used by the World Health Organization.
). All hospitals were linked by a proprietary practice management system (PetWare R ) and data from all visits were uploaded to a central database, resulting in a large amount of aggregated, structured data. The dataset was cleaned by removing animals with no recorded birth date, anomalous birth or death dates (date of birth posterior to visit, or date of death anterior to visit), a visit age >30 years, or incomplete data for sex, age or BCS. The resulting dataset is referred to as the cleaned dataset. A deceased and survivor population for each survey year was identified from the clinical dataset as outlined in Figure  1 ; the sum of these two populations comprised the total study population for that year. Dogs and cats with no death date in the EMRs were only considered to be survivors for a specific year if there was a new hospital visit in any of the following study years.
Dogs and cats with no death date and that only visited the clinic in the course of 1 year, with no return visits in a subsequent year of the study, were excluded because they could not be confirmed as either a survivor in any year or a deceased animal. The return rate of animal visits across all study years was calculated to support the validity of the range of years selected for the analysis. On average, dogs returned in 111 days and cats in 146 days; median return rate was 70 days (IQR: 21-169) for dogs and 90 days  for cats. The Tukey's outlier detection method applied to the return rate in days yielded an upper fence (UF = Q3 + 1. 5 * IQR) of 391 days for dogs and 443 days for cats. At least two full years of data after a survey year were needed for life table calculation. It was decided to analyze life expectancy in the dataset from January 1st, 2013 to December 31st, 2019.
The last 2. 5 years of data (January 1st, 2020 to July 31st, 2022) were used to define if a pet without a death date could be confirmed as a survivor in any of the previous years. Subpopulations of each species were defined by sex (male or female), and median BCS was recorded for individuals between January 1st, 2013 and July 31st, 2022. The sterilization status was available in the clinical dataset, but it was not included in the study because of the way these data were collected. Pets that were sterilized at a certain age were identified as neutered from birth to death. This introduced a bias due to mortality in the first few months of life when pets were not yet spayed or neutered, which impacted the early mortality rate for entire animals. Body condition score was assessed on a 5-point scale between 2013 and 2018, and on a 9-point scale between 2018 and 2022.
Data from the 9-point scale were converted to the 5-point scale (Table  1 ). Separate analyses were conducted for purebred and mixed-breed animals. Mixed-breed dogs were recorded as such in the database, whereas cats were identified as mixed breed if they were recorded as Domestic Short Hair, Domestic Long Hair, or Domestic Medium Hair. In addition, the population of purebred dogs was subdivided into five size categories based on adult bodyweight: toy, small, medium, large and giant. Firstly, dogs weighing more than 70 and <0. 5 kg were removed from the uncleaned dataset of purebred dogs. Bodyweight outliers for each breed were then excluded using the Tukey's outlier detection method (lower fence = Q1-1. 5 * IQR; upper fence = Q3 + 1. 5 * IQR). The average adult bodyweight of each breed in the resulting dataset was calculated from dogs aged 2-5 years, and was used to assign a size group (Table  2 ) to each breed in the cleaned dataset.
Life expectancy e (x) was calculated n times, using the Monte-Carlo-derived d(x) in the previous equations. Confidence intervals (CIs) at the 95% level were calculated for each year using the mean e (x) and standard deviation [denoted as S LE (x)] for the n iterations: CI 95% (x) = e(x) ± 1. 96 * S LE (x). Life tables were generated for each species across all survey years, for each survey year, for the global population and the subpopulations defined in Section 2. 1. For each subpopulation, the variables P(x) and d(x) specific to that subpopulation were used to recalculate the intermediate variables, m(x), q (x), l(x), L(x) and T(x). Only data for purebred dogs were used for life expectancy estimations by dog size group.
Software applications Only structured data were extracted from the PetWare R database; there was no searching of free text entries. Software applications used for data processing and building life tables were Databricks (runtime 11. 0, with Apache Spark 3. 3. 0) on Microsoft Azure, with Python 3. 9. 5 (libraries Numpy 1. 20. 3, Pandas 1. 3. 4, Lifelines 0. 27. 2 and Plotly 5. 6. 0).
Results. Study population There were 64. 76 million PetWare R visit records for 9. 29 million unique dogs, and 9. 85 million visits for 2. 51 million unique cats. Of these animals, 1. 74% dogs and 1. 82% cats were removed to produce the cleaned dataset as described in the methods. The total population sizes (deceased and survivor combined) in the cleaned dataset are presented by survey year for each species in Figure  2A  and Supplementary Table  1. Pets had a mean 2. 5 visits during a survey year, and over the 9 years studied had a mean 6. 3 visits and a median of three visits (IQR: 1-8). The percentage of dogs returning within 2 years after the survey year increased from 74. 8% in 2013 to 77. 6% in 2017, and subsequently decreased to 75. 9% in 2019. For cats, the percentage of returns was 61.
2%, small 1. 5%, medium 2. 1%, large 1. 6%, giant 1. 0%, and mixed breed 1. 4%). In the cat population, the median BCS was 3 for 58. 5% of animals (purebred 62. 4%, 57. 7% mixed breed) (Figure  2D ; Supplementary Table  5 ) and the prevalence of obesity (median BCS 5) was 3. 6% (2. 9% purebred, 3. 7% mixed breed). The percentage of dogs with median BCS 3 increased each year from 68. 7% in 2013 to 74. 5% in 2019, while for median BCS 5 it decreased from 2. 1% in 2013 to 1. 3% in 2017, subsequently remaining constant until 2019. The percentage of cats with median BCS 3 increased from 53. 2 to 60. 3% between 2013 and 2019, whereas for median BCS 5 it decreased from 4. 3% in 2013 to 3. 5% in 2015, and then fluctuated between 3. 5 and 3. 3% until 2019 (Supplementary Table  6 ). In summary, the study dataset for dogs covered all bodyweight size categories; the large and small dog sizes had the greatest representation.
Less than 5% of dogs were of mixed breed, whereas in the cat dataset there were ∼5 times more mixed-breed cats than purebred. For both cats and dogs the sex ratio was well-balanced between males and females, and the vast majority of both sexes in both species were sterilized. Median body condition was ideal for nearly three quarters of dogs and obese for 1. 5%. In contrast, for cats, median body condition was ideal for slightly more than two thirds and obese for 3. 6%.
Life expectancies overall and by size group or breed heritage The overall LE birth for dogs reported here [12. 69 years (12. 68-12. 7)] was higher than that computed from the UK VetCompass TM dataset  [11. 23 years (11. 19-11. 27 )] (  15 ) and lower than that for dogs in two Japanese studies (13. 7 years)  (13, 14). The LE birth in another Japanese study of dogs that died between 1981 and 1982 was only 8. 3 years (12). This is more consistent with ours when consideration is given to the increase in life expectancy of dogs over time that we observed. The LE birth by survey year increased by 1. 3 months (0. 93%) in each successive sampling year (Figure  2 ). A backwards projection estimates LE birth to be 9. 1 years for dogs in 1981. For comparative purposes with other studies, we also calculated the mean and median age of death of dogs; these were 10.
Accidents and fights are also more common for males between 1 and 2 years of age than for females. Neoplasia and other diseases associated with chronic retroviral infection, and complications of urethral obstruction can be fatal  (49, 50). A preliminary investigation of the causes of death for male vs. female cats in the current study's database found the main contributor between birth and 10 years was urinary tract obstruction, which accounted for ∼15% of male deaths in the 3-4 years age interval (data not presented).