dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations There are some limitations that need to be discussed. As we observed, most apps were in English. It can be debated that the bias toward English is due to the use of English keywords. However, it is clear from the list that apps in languages other than English were also obtained using these keywords. This can be attributed to the English keywords used by developers while uploading their apps on Google Play Store. To cross-validate, we ran a search with the keywords translated in Mandarin (Chinese) and Spanish (two of the most spoken languages worldwide  [36] ) and no extra apps were recorded. "Number of downloads" displayed on Google Play Store indicates the number of unique downloads by different users. Use of "number of downloads" to assess dissemination of the apps might be insufficient.
Years of gestation may be needed before an emerging technology achieves widespread market acceptance and commercial success. By satisfying the users' needs of niche markets, a novel technology improves before reaching a broader population of mainstream users  (Adner and Levinthal, 2002). During the subsequent growth phase, the mass market adopts the technology, which becomes compelling, attracts more investments, and sees overall improvements thanks to new features  (Klepper, 1997; Perez, 2010). Last, the positive change rate decreases as the technology matures and approaches its diffusion limits due to market saturation  (Perez et al. , 2010; Abernathy et al. , 1978). The empirical literature of these quantitative approaches uses different private-and open-source datasets to measure technological change.
, 2022; Zhang et al. , 2022; Li et al. , 2019b; Mikheev, 2020; Feng et al. , 2022; Hong et al. , 2021)  are particularly prolific. For instance,  Guo, Wang, Tian, and Xian (2019)  analyze 1666 scientific publications to detect trends in network technologies and information systems  (Guo et al. , 2019). With a literature-growth approach and a co-citation analysis, they detect exponential patterns (capturing the introduction and the initial stages of the sigmoid process) in technologies. Similarly,  Priestley, Sluckin, and Tiropanis (2020)  investigate a longitudinal dataset of 20493 Internet-related US patents between 1990 and 2013. They find that the accumulation of corporate Internet inventions follows a sigmoid growth process  (Priestley et al. , 2020). Son, Kim, Kim, Han, and Kim (2010)  extract topics and primary contributors of peerreviewed publications.
Data We extract data from open scientific works (i. e. , scholar articles consisting of working papers, preprints, technical reports, post-proceedings, and publications) labeled e-prints and uploaded on the arXiv repository. The latter is a free distribution service and open-access archive for academic articles related to various technical fields, including computer science (uploaded e-prints are not peer-reviewed). First, we download the entire arXiv repository (1 858 293 files, corresponding to 3 TB of text in pdf) through a mirror of the database found on kaggle. 2 The data encompasses all e-prints uploaded since the inception of the arXiv repository  (August 14, 1991)  until December 31, 2020. Next, for each CSTCC, we (i) count the number of e-prints through time, (ii) extract the share of e-prints that include security attention, and (iii) extract the opinion expressed by authors.
Technological change process To model the technological change, we use a non-linear optimizer, the Levenberg-Marquardt algorithm, to fit a logistic function on the historical observations of e-prints for each CSTCC)  (Moré, 1978). We use the Python scipy package and its. optimize. curve_fit method. 10 We make this choice because the logistic function generalizes the sigmoid and therefore offers more flexibility to capture the common process underlying each CSTCC. It is also a bounded and differentiable function with a single inflection point  (Han and Moraga, 1995). Therefore, we define the technological development function,   (), with  ∈   , 𝜎 𝑥 (𝑡) = 𝐿 𝑥 1 + 𝑒 -𝑘 𝑥( 𝑡-𝑡 0 𝑥 ) , ( 2 ) where: - 0 is when the inflection point is reached (corresponding to the maximum of the first derivative of the function, i.
This demonstrates that this omitted variable does not affect the test of H3a. Future research could enhance the selection of keywords related to security attention by implementing other information retrieval methods such as tf-idf or Key-BERT to capture the recurrence of words related to security, and use the most recurrent ones as filters to capture the security attention in e-prints. Finally, our classic lexicon-based approach to capture opinion could be enhanced with machine-learning approaches (e. g. , decision-trees, support-vector machines, or neural networks). Such approaches would yield a higher precision for sentiment analysis. However, researchers willing to use such sophisticated NLP methods may face issues finding labeled datasets. For instance, BERT  (Tenney et al. , 2019)  and XL-Net  (Myagmar et al.
However, in these studies, the proposed approach led to low sequencing yields of tRNA molecules (~20-40× lower than expected for a DRS run) and did not report whether extant in vivo tRNA abundances and/or tRNA modifications were recapitulated using this method. Here we present Nano-tRNAseq, a nanopore-based approach that allows accurate and direct measurement of native tRNA molecules. The library preparation benefits from the 3′ CCA overhang typically present in the mature tRNAs to incorporate a double ligation of RNA adapters at both the 5′ and 3′ ends of the tRNA molecules, which leads to an improved proportion of basecalled and mapped tRNA molecules. Moreover, we show that MinKNOW, the ONT proprietary software https://doi. org/10. 1038/s41587-023-01743-6 be accurately basecalled and mapped, would enable us to capture the entirety of the tRNA sequence.
Mapping parameters significantly affect tRNA read mappability The alignment of native tRNA reads is challenging due to their short and highly modified nature. Indeed, native tRNAs contain a large proportion of mismatched bases, often originating from inaccurate basecalling of modified bases in DRS datasets  94, 98, 102. As a consequence of these miscalled bases, the commonly used long-read mapper minimap2 with recommended settings (-ax map-ont -k15) aligned only a fraction (2. 56%) of the reads (Fig. 2a-c  and Supplementary Table  2 ). We further tested a range of minimap2 parameters and observed only incremental improvements in mapping and an increase in false alignments (antisense mapped reads served as a proxy of mismapping) (Supplementary Table  3 ). To improve the mappability of Nano-tRNAseq reads, we next tested the mapping algorithm BWA, a short-read mapping algorithm commonly used to map Illumina reads  103, 104.
Custom MinKNOW improves yield and tRNA abundance estimates A surprising feature of our initial tRNA sequencing runs was the low amount of sequenced reads. Although pore clogging caused by tRNA structure might partially explain the low sequencing yield  92  , we also noticed that the MinKNOW software classified a high proportion of reads as 'adapter-only' reads in real time. Hence, we hypothesized that a considerable fraction of tRNA reads might be discarded by the MinKNOW software due to their short signal lengths, as they resemble 'adapter-only' reads. The MinKNOW software is responsible for analyzing the continuous electrical current (signal intensity) measured at each pore, reporting the signal regions that correspond to 'reads' into FAST5 files, which are then basecalled to generate a FASTQ file (Fig.
1a ). To examine whether tRNA linearization might improve sequencing yield, we tested a range of commercial reverse transcriptases and incubation conditions on both IVT and native tRNAs and examined their cDNA outputs using TapeStation (Extended Data Fig. 5a ,b and Methods). We found that both Maxima and SuperScript IV at 60 °C offered the best performance in the production of full-length cDNA products, and we opted to use https://doi. org/10. 1038/s41587-023-01743-6 and mismapped reads (purple; reads mapping to antisense strands were used as a proxy to assess mismapping) (Supplementary Table  4 ). The proportion of mapped reads (d) and alignment identity (e) for each template from the bar plot in c, using either minimap2 or bwa mem -W13 -k6-T20. We should note that minimap2 alignment identity in S.
The digested sample was combined with 30 µl of formic acid, added to the column and collected in a fresh collection tube by centrifuging at 100g for 10 minutes. The flow-through was re-applied to the column and centrifuged at 100g for 10 minutes. Now bound to the column, the sample was washed with 40 µl of 0. 1% formic acid by centrifuging at 100g for 5 minutes. Next, 40 µl of 60% acetonitrile was added to the column, and the sample was eluted by centrifuging at 100g for 5 minutes. The CRG/UPF Proteomics Facility conducted LC-MS/MS of S. cerevisiae tRNA modifications. In brief, 125 ng of each digested and desalted sample was analyzed by LC-MS/ MS using a 40-minute gradient on an Orbitrap XL. As a quality control, ribonucleoside standards were run between samples to prevent carryover and to assess the instrument performance (see Supplementary Table  19  for raw data and Supplementary Table  20  for normalized data).
We should note that the sequence of 5′ and 3′ RNA adapters were included in the respective references when mapping the tRNA reads. The effect of 5′ and 3′ RNA adapters length on the mappability was tested by shortening the respective adapter sequence from the alignment reference with a step of 5 nt (Supplementary Table  6 ). All reference files used in this work are available in the GitHub repository (https://github. com/novoalab/Nano-tRNAseq).
Analysis of tRNA abundances tRNA abundances were quantified using the get_counts. py script (available on GitHub: https://github. com/novoalab/Nano-tRNAseq). Unique (mapping quality above 0) primary alignments were considered. Differentially expressed tRNAs were inferred using DESeq2 (ref. 151). Volcano plots were generated using the EnhancedVolcano package  152. Differentially expressed tRNAs were defined as those having adjusted P < 0. 01 and absolute log 2 expression fold change greater than 0. 6.
Analysis of differential tRNA modifications Differential tRNA modifications were measured using differential basecalling errors (mismatch, insertion and deletion) for each tRNA nucleotide. The sum of basecalling errors was calculated by subtracting the frequency of the reference base from 1. The frequency of the reference base equals the number of reads with a basecalled equivalent to the reference base, divided by the depth of coverage for that position. Only uniquely aligned reads (primary alignment with mapping quality above 0) were considered. To ease the above calculations, we developed a script (get_sum_err. py), which reports the sum of basecalling errors and frequencies of A, C, G, T, deletions and insertions for every position of tRNA reference as well as plotting heat maps that order tRNA isoacceptors from highest to lowest expressed (plot_heatmap.
py). The script is available at https://github. com/novoalab/Nano-tRNAseq. For heat maps, only tRNAs whose sequences were consistent between the tRNAdb2 and MODOMICS databases could be used, with the exception of His-GTG, whose sequence varied between tRNAdb2 and MODOMICS databases. The disparity was manually resolved by replacing the first base in the MODOMICS alignment with a gap. The difference in Ψ55 modification stoichiometry between WT and Pus4 KO was quantified using NanoRMS  98  , using a supervised k-nearest neighbor (KNN) classification algorithm, incorporating signal intensity and trace features. Ψ55 sites with coverage lower than 5 reads in either the WT or Pus4 KO condition were excluded from the NanoRMS analysis. The script is available at https://github. com/novoalab/nanoRMS.
Adjusting MinKNOW parameters to capture small RNAs Sequencing runs were conducted without live basecalling, and the bulk dump raw file was recorded for a subset (channels 1-50 for runID 4_NanotRNAseq_IVT + tRNAphe) or all 512 channels (all remaining runs). MinKNOW version 21. 06. 0 was used for sequencing and running the simulations with distinct MinKNOW parameter settings from raw data dumps. The sequencing simulations were performed with default and custom MinKNOW configurations. By default, MinKNOW defines adapter duration as up to 5 seconds and the strand (an actual read) as at least 2 seconds. Thus, the RNA molecule has to spend up to 7 seconds in the pore to be classified and reported as an actual read. The motor protein (RNA helicase) used in DRS experiments has an average speed of 70 nt per second; thus, 7 seconds corresponds to roughly 490 nt.
Acknowledgements We thank all the members of the  Novoa laboratory  for their valuable insights and discussions. We thank  V. Malhotra  for providing us with the S. cerevisiae Pus4 KO strains used in this work. We thank the  CRG Protein Technologies Unit  for producing recombinant E. coli  T4 RNA Ligase 2. We thank  A. Delgado-Tejedor  for help setting up simulations on bulk data from S. cerevisiae Pus1 and Pus7 KO Nano-tRNAseq runs. M. C. L. is supported by an  FPI Severo Ochoa  fellowship by the  Spanish Ministry of Economy, Industry, and Competitiveness (MEIC). L. P. P. is supported by funding from the  European Union 's  H2020 Research and Innovation Programme  under  Marie Sklodowska-Curie  grant agreement number  754422. I. M. is supported by  'la Caixa '  InPhINIT PhD fellowship  ( LCF/BQ/DI18/11660028 ).
This work was supported by funds from the  Spanish Ministry of Economy, Industry, and Competitiveness (MEIC)  ( PID2021-128193NB-100  to E. M. N. ). This project has received funding from the  ERCEA program  ( ERC-StG-2021  under grant agreement number  101042103  to E. M. N. ). We acknowledge the support of the MEIC to the EMBL partnership,  Centro de Excelencia Severo Ochoa  and  CERCA Programme/ Generalitat de Catalunya.
Data availability Basecalled FAST5 nanopore data have been deposited in the European National Archive (ENA) under accession PRJEB55684. From these data, both basecalled FAST5 and/or FASTQ files can be acquired  153. FASTQ from HydraPsiSeq data  121  has also been deposited in ENA under accession PRJEB55684 (ref. 153). A description of all the runs used in this work is included in Supplementary Table  7  and Supplementary Table  27. The list of tRNA modifications present in S. cerevisiae tRNAs was obtained from MODOMICS (https://iimcb. genesilico. pl/modomics/ sequences/) and was retrieved on 21 September 2021. tRNA expression estimates from Illumina-based S. cerevisiae tRNA sequencing were obtained from following sources: mim-tRNAseq (Gene Expression Omnibus: GSE152621), tRNA-HydroSeq (Supplementary Material of the publication) and ARM-seq (Supplementary Table  2  of the publication).
Code availability The reference FASTA, alignment and modification BED files, custom MinKNOW configurations and all code used to analyze the runs are publicly available on GitHub (https://github. com/novoalab/ Nano-tRNAseq)  154. The NanoRMS script is available at https://github. com/novoalab/nanoRMS (ref. 98). Data are presented as mean ± SEM for n = 2 biological replicates.
New complete evaluation of 235 U(nth,f) proposed to JEFF-4 In the recent work  [15] , we proposed a new consistent evaluation of independent and cumulative yields with minimal impact from models. This analysis is based on the following decomposition of independent fission yields: 𝑌𝑌(𝐴𝐴, 𝑍𝑍, 𝑚𝑚) = 𝑌𝑌(𝐴𝐴) • 𝑃𝑃(𝑍𝑍|𝐴𝐴) • 𝐼𝐼𝐼𝐼(𝐴𝐴, 𝑍𝑍, 𝑚𝑚)R (1) Where 𝑌𝑌(𝐴𝐴) is the mass yield (post-neutron), 𝑃𝑃(𝑍𝑍|𝐴𝐴) the charge distribution and 𝐼𝐼𝐼𝐼(𝐴𝐴, 𝑍𝑍, 𝑚𝑚) the probability populating the metastable of the 𝑋𝑋 𝑍𝑍 𝐴𝐴 nucleus. Fig. 10. Comparison of the conservative evaluation (see §2) of 235 U(nth,f)  [15]  with the GEF model  [14]  and the JEFF-3. 3  [12]  and ENDF\B-VIII. 0 evaluation  [19]. For 235 U(nth,f), the EXFOR database  [16]  is sufficiently complete to perform a mass yield evaluation without any model input.
Multiplicity of mass yield evaluations The 235 U(nth,f) mass rate measurements are sufficiently complete to cover the fission product mass range (> 99. 5%) to ensure the self-normalization of the fission yields. Nevertheless, the dataset provided by experimentalists are not in agreement and a crossnormalization of fission rates per mass is required  [17]. We have shown that the use of one reference measurement to cross-normalized all the datasets (before absolute normalization) introduces different results according to the reference dataset. Fig. 14  illustrates the dependence in the choice of reference dataset that maximizes or minimizes the information provided. The quantification of information is deduced from the Shannon entropy  [18]  This result corresponds to non-ergodic analysis paths.
Several analysis approaches The test and the sorting of datasets are the key point to obtained mean values per mass representing the data selected. Considering that the experimental information is reduced to mean values and the standard deviations for the majority of datasets, we assume a Gaussian distribution for each mass yield (per experiment). Then, the maximum of likelihood method is equivalent to the minimum of the generalized least square 𝜒𝜒 𝑔𝑔 2. The analysis is performed in two steps. First, we compute the mean value for each mass by using all the measurements. Then we test if the evaluation based on these mean values is compatible at 3 𝜎𝜎 with all the datasets used. If the test fails, a regularization is necessary. In this work, we have developed 4 different regularization. The two global ones are: -Strict solutions: the sorting of mass measurements follows the main contributors 𝜒𝜒 𝑔𝑔 2 up to obtain an acceptable 𝜒𝜒 𝑔𝑔 2 , i.
e. with a 𝑃𝑃 -𝐶𝐶𝑉𝑉𝑣𝑣𝑣𝑣𝑣𝑣 > 1 -𝐶𝐶𝐶𝐶  [20]. Different sorting of data are possible and we kept the two possible solutions, called "strict-1" and "strict-2"; -Conservative solutions: we apply the same statistical test after adding an uncertainty of 2. 5% for all masses to obtain an acceptable 𝑃𝑃 -𝐶𝐶𝑉𝑉𝑣𝑣𝑣𝑣𝑣𝑣 (See  [15] ). [15] , the two last regularizations are new additional calculations. We observe that the uncertainty of the Best Estimated (B. E. ) method or the Student approach are very similar and included between the conservative and strict methods. These two local regularizations produce the most realistic solution considering all the data adjusted on the database.
6,209 In our analysis tools, we are able to integrate the available experimental correlation matrices and to deduce the dependence of the evaluated correlation matrix to the lacks of experimental correlations, testing the effects of additional systematic uncertainties. We conclude that the evaluated uncertainties increase of 1% for 3% systematic uncertainty added. This effect is due to the cross-correlation between datasets in the renormalization process. To compare the correlation matrices of all analyses, we have calculated the Shannon entropy associated to the different correlation matrices  [18]  by considering the previous kinds of regularizations. Table  1  presents the Shannon entropy calculations for the ten following cases. The information quantity distributed is very stable in contrary to the previous method using a reference dataset (Table  1 ).
Probability density functions (PDF) of fission rates The evaluation of fission yields involves mixing of fission rates from different datasets assuming a Gaussian distribution for each measurement. The generalized least squares method (𝜒𝜒 𝑔𝑔 2 ) is equivalent to the maximum likelihood method for Gaussian distributions associated to measurements. If there is no bias, this method leads to a minimal variance (Cramer Rao theorem) and the covariance matrix corresponds to the Fischer matrix associated to data  [20]. Then, the mean value of data (maximum likelihood) is also distributed according to a Gaussian distribution. To test this property, we developed a Bayesian Monte Carlo (BMC) considering the different approaches and the empirical mean calculation to illustrate the PDFs of the evaluated values.
Fig. 16  presents the PDF for a regular dataset (A=81 u) and Fig. 17  for an un-regular dataset (A=84 u). In the first case, the "Strict", "Student-like" and "without regularization" are similar. The empirical mean value is not the minimal variance estimator and the conservative assessment take into account a penalty to obtain an acceptable 𝜒𝜒 𝑔𝑔 2 test for all datasets. In the second case (Fig. 17 ), when the 𝜒𝜒 𝑔𝑔 2 (𝐴𝐴) test is not acceptable, we see clearly the bias in the mean values according to the regularization methods applied. In particular, we see that the mean value without any regularization underestimates the uncertainty for the dataset selected and presents a bias in comparison to the strict method. The Strict method applies a sorting on available data which generates two solutions for this mass A=84.
The conservative method corresponds to a Gaussian reduction method for an incompatible initial dataset, hence the name used. These BMC calculations illustrate that the most accurate assessment naturally corresponds to the selection of compatible data and leads to a Gaussian PDF associated to the mean rate. Fig. 17. PDF of evaluated mass rate before absolute normalization for a rejected 𝜒𝜒 𝑔𝑔 2 (𝐴𝐴) test.
Conclusion and Perspectives The proposed analysis method of several fission observables allows us to determine a consistent evaluation of the independent and cumulative yields. In this global approach, we conclude that the sorting of the data is an essential milestone to reach the best estimation of fission yields, limiting the bias and minimizing the uncertainties. If there is no bias in the very accurate measurements of chain yields, then the consistency of our work with the JEFF-3. 3 library allows us to conclude that the strict evaluation corresponds to the best assessment of the 235 U(nth,f) mass yields. Nevertheless, this force us to exclude a part of the available measurements. On the other hand, to limit the potential bias of evaluation, the conservative method proposed a synthesis of the actual experimental knowledge taking into account the inconsistency of the datasets used.
Figs. 22 & 22 23 present selected chain yields for current evaluations. The mass A=153 illustrates the impact of the sorting of data to obtain an acceptable statistical compatibility test of datasets. For this mass, two groups of two measurements exist. The recursive process of renormalization, test, sorting of data, renormalization, test (etc. ) according to the major contributor of 𝜒𝜒 𝑔𝑔 2 generates several compatible data groups providing the different evaluations.
General analysis and requirements gathering To define the requirements of our application, the infarction code applied in our country  [12]  was considered. The infarction code is an urgent protocol that includes the activation of a series of assistive devices that allow emergency care in acute phase of disease when a patient is suspected of having a heart attack and is a candidate for immediate reperfusion. Since not all the hospitals of our country have a catheterization laboratory, the protocol aims to ensure the proper attention of the patient until the target hospital (the one with the laboratory) is reached. The aim of ODISEA is to complement this protocol with an application that provides expert physician oversight and support from the moment that the code is activated until the patient arrives to the catheterization laboratory to perform primary angioplasty.
Application user profiles From the infarction code description, the three user profiles that have to be supported have been identified. ODISEA users will be: (i) the EMS professionals; (ii) the primary care professionals (from a primary care center of a hospital with no catheterization laboratory), and (iii) the nurses and doctors, from the catheterization laboratory of the infarction reference hospital. All these users will have access to the application via username and password.