dataesr/scientific-paragraphs-categorization huggingface dataset:
Fourth Case Study For the purpose of completing the data obtained in the second case study, the mean scores (6. 03) obtained by the students of the experimental groups to which the innovative methodology was applied and who were tested with an adapted digital system (post-test mean) were compared with the mean scores (6. 10) obtained by those same students in the evaluation performed prior to the study and the intervention (pre-test mean). The student's t test was then applied; however, since related groups were involved, prior application of Levene's test was not an essential requirement. The analysis did not reveal any statistically significant differences between the experimental groups' pre-test and post-test mean scores: t (12) =. 61, p >. 05. (Table  8 ).
e. 3. 0, in the three dimensions that comprise the attitudinal questionnaire, items 1 to 35. It should be noted that analysis of the data shows low typical deviations in the assessments carried out by the students, at both the individual and the global level (1. 17) of the item, which indicates an average homogeneity and evident uniformity in the answers obtained. The following independent variables were used to perform the inferential study: The Age of Students has a statistically significant impact on the level of acceptance of an adapted evaluation system: F (2. 157) = 3. 03, p =. 05 with low-medium strength of association (0. 025)  (Cohen, 1988). The results of the post hoc analysis of the data obtained show that the groups of students aged 16 and 17 or more have higher scores than the other students in the statistic F corresponding to the factor Degree of Acceptance of an Adapted Digital Evaluation System.
154) = 3. 39, p <. 05 with a medium strength of association (. 07). A more detailed analysis of the data obtained reveals that the students of schools located in towns of more than 10,000 inhabitants and big urban environments obtain higher mean scores in factor F, corresponding to the Degree of Acceptance of the Technology, in comparison with students of schools located in towns with fewer inhabitants and environments of smaller municipalities. One hypothesis which may explain these results is the existence of a possible digital gap between urban centres with a high population density and rural centres or areas with a low population density. The Habitual Use of Educational Resources based on AR technology in the Classroom has a statistically significant impact on the Degree of Acceptance of an Adapted Digital Evaluation System: F (1.
These results support the data obtained in other related studies, such as the one conducted by  Cabero-Almenara et al. (2017)  in which a similar instrument was used. The data obtained in the first phase of the performance study reveal a positive improvement in the scores obtained, although it is not significant from a statistical point of view. This supports rejection of the hypothesis that the use of a methodology based on nonimmersive educational technology can have a significant impact on students' performance. The analysis of the data obtained in the second phase of the performance study suggests rejecting the hypothesis that the use of an innovative methodology, based on the combination of AR technology and an adapted digital evaluation system can have a significant impact on students' general performance.
These results apparently differ from other recent studies that suggest that the use of AR-based technology in Secondary Education led to improvements in the learning process and academic performance  (Garzón et al. , 2019). It should be noted that the data analysed show that the students who formed part of the experimental groups, to whom the key concept had been explained with the help of nonimmersive technological resources, obtained significantly higher scores, with a large effect size in comparison with the ones obtained by the students in the experimental groups who had had the key concept explained with the help of the AR-based application. This result suggests rejection of the hypothesis that the use of an innovative methodology based on the combination of AR technology and an adapted digital evaluation system can have a positive and significant impact on students' performance in general, in comparison with the use of other non-immersive technologies.
The analysis of students' evaluations, carried out by means of the attitudinal questionnaire, indicates uniformity and reliability. The evaluations provide an argument of validity to determine the acceptance of AR technology by the students, in accordance with the results obtained by other researchers in this field  (Scherer et al. , 2020). The data obtained from the analysis of the answers indicate that an innovative AR-based methodology to explain key concepts of a subject, combined with an adapted digital evaluation system, has a positive influence on students' level of motivation in general. These data complement the results obtained from quantitative and qualitative data by other researchers in similar studies conducted in the field of Secondary Education  (Serio, Ibáñez, & Delgado, 2013).
A positive impact is also generated on the degree of acceptance of AR-based technology in students in general, and it is very significant in the specific case of students with special educational needs, with an effect of low-medium strength. These findings coincide with the conclusions of similar studies carried out in this field  (Bacca, Baldiris, Fabregat, Graf, & Kinshuk, 2014; Baragash, Al-Samarraie, Ibrahim, & Alfarraj, 2020; Cabero-Almenara, Barroso-Osuna, Llorente-Cejudo, & Fernández, 2019; Wu, Lee, Chang, & Liang, 2013). The data analysed in this study regarding the factor structure of the attitudinal questionnaire reveal the existence of a third factor relating to the degree of acceptance of digital evaluation systems adapted to AR-based educational technology, which is positively valued by students in general.
The students from schools located in urban environments and towns of more than 10,000 inhabitants show a positive level of acceptance of the adapted digital evaluation system. These students show a greater degree of acceptance than the others of AR technology as an educational resource, suggesting the possible existence of a digital gap between students from urban areas and students from rural areas  (Alalwan et al. , 2020). The data obtained indicate that students can understand better key concepts explained by the teacher with the help of an AR application used as an educational resource, upholding the hypothesis that an AR-based technology used as a complementary educational resource can generate a positive impact on students' academic performance and comprehension. The results of the analysis of the data indicate the existence of a statistically significant effect between the personal use of AR-based applications by some students in their personal environment and their willingness to use this technology for educational purposes.
ACKNOWLEDGEMENTS This work has received financial support from the  Spanish Agencia Estatal de Investigación (AEI)  and the  European Regional Development Fund -ERDF  ( RTI2018-098795-A-I00  and from the  Xunta de Galicia  ( ED431F 2020/05  and  Centro singular de investigación de Galicia  accreditation  2019-2022 ,  ED431G 2019/03 ) and the  European Union (ERDF). R. G. -F. thanks  Ministerio de Ciencia, Innovación y Universidades  for a " Ramón y Cajal " contract ( RYC-2016-20335 ). Funded by:  European Regional Development Fund  Funder Identifier: http://dx. doi. org/10. 13039/501100008530  Award:  RTI2018-098795-A-I00  Funded by:  Regional Government of Galicia , Spain Funder Identifier: http://dx. doi. org/10. 13039/501100010801  Award:  ED431F 2020/05 ;  ED431G 2019/0  Funded by:  Ministry of Science and Innovation , Spain Funder Identifier: http://dx.
Amino acids located at the surface of the folded protein are interacting with the solvent and are typically less important for the protein function. It is indeed well-established that evolutionary rates differ between residues depending on their solvent accessibility  (Franzosa and Xia 2009; Ramsey et al. 2011; Moutinho et al. 2019). The precise relationship between solvent accessibility, evolutionary rates and amino acid position along the sequence has however never been ascertained. Such investigation has long been impaired by the quasi-systematic absence of N-and C-terminal regions in protein structure files, because those were either poorly resolved in electron density maps, genetically modified or removed prior to protein production. To circumvent this issue, we used the recently released dataset of 22,613 complete protein structures predicted by Alphafold  (Jumper et al.
Evolutionary rates correlate with protein domain density In order to better characterize these results, we analysed the contribution of protein domains in the observed evolutionary profile. Predicted protein domains indeed capture a large fraction of amino acids involved in structural and functional roles in protein sequences, and their prediction relies on sequence similarity and structural information  (Wang et al. 2021 ). Both of these features make them also good proxies for sites under evolutionary constraints. We computed, for several predicted protein domains databases, the distribution of protein domains by calculating the frequency at which a given position along the protein sequence is found inside a domain. We found that domains are strongly depleted at protein termini (Fig. 2C  and fig.
This may explain that while we see the same increase in dN/dS at the 5' end of CDS as in a recent study on Drosophila genes  (Davydov et al. 2019) , we do not see the same decrease in substitution rates. On a related matter, we wish to point out that our finding that the average dS is constant along CDS length (Figs. 1B, 1C ) should not be interpreted as meaning that dS does not vary or is not subject to site heterogeneity in individual genes, as this has been shown in many previous studies  (Rubinstein and Pupko 2012). Protein domains in the PFAM database are unlikely to cover all biological domains  (Sammut et al. 2008) , even in well-studied vertebrate genomes. That human protein sequences still contain non-annotated domains (false negatives) would be consistent with the observation that the dN/dS measured outside domains is lower in the centre of proteins (Fig.
2D ), as if some feature (e. g. non-annotated domains) would exert negative selection. However, the important point for the present study is that the PFAM database contains few false positives, i. e. regions incorrectly annotated as functional domains. This is supported by the observation that dN/dS inside domains is remarkably constant across the length of proteins (Fig. 2D ), suggesting that domain annotation by PFAM is highly specific. We may ask why, if protein termini are under low evolutionary pressure, are they not cropped by micro-deletions in the course of evolution? Requirements for reduced RNA secondary structures at the beginning of the CDS  (Gu et al. 2010) , presence of signal sequence with limited folding constraints but with key roles in the processing and traffic of the protein, such as signal peptides (von Heijne 1985), but also the necessary display of amino acids carrying specific post-translational modifications such as epigenetic marks in histones (e.
Coding Plants. The genomes were from the following Fabids species: Cucumis sativus, Medicago truncatula, Lotus japonicus, Glycine max, Phaseolus vulgaris, Phaseolus angularis, Lupinus angustifolius, Manihot esculenta, Populus trichocarpa, Prunus persica. Coding sequences from all 10 genomes were downloaded from the April 2021 release of the OMA database  (Altenhoff et al. 2021)  based on Ensembl Plants. A MSA on the amino acid sequences of each OMA family was then computed with FSA  (Bradley et al. 2009 ) with default parameters. Sequences were finally back-translated (treebest backtrans -t 0. 9) with the corresponding CDS to obtain the final aligned codons in nucleotides. Human-Mouse orthologs. Gene CDS were directly extracted from mRNA sequences (transcripts) downloaded from the October 2020 release of AniProtDB  (Barreira et al.
2021). A reciprocal blastp was performed (e-value 1. 10 -3 ) to select orthologs. Pairs of matching sequences were then aligned using the Needleman-Wunsh algorithm (needle, with -gapopen 10. 0 -gapextend 0. 5) from the EMBOSS package. (See Table  S1  for a summary of data set size (number of sequences) related to figures. ) Gap-less alignments. Algorithms introduce gaps in MSA to accommodate insertion or deletions of amino acids. They are more frequent at the edges than in the middle of CDS, and they may bias the counting of substitutions in these regions: for every gap, there is one less site available to count substitutions in a column of the alignment, thus decreasing statistical power and increasing noise. To quantify substitutions in an unbiased way, for some results we removed, in each alignment, sequences that introduce gaps.
Position-specific codon alignments and evolutionary rate computation. To compute molecular evolutionary rates presented in 3 panels of 50 codons each, the following procedure was followed. Two random CDS sequences beginning with a start codon ATG were chosen in each MSA, and the aligned codons at the same position in each pair were concatenated into a new, position-specific alignment. The procedure was applied for the first, middle and last 50 codons of each alignment. On each concatenated position-specific alignment, the dN and dS were computed using the YN00 model in Codeml from the PAML4 package  (Yang 2007 ) using the Bio. Phylo. PAML. codeml library. See Figure  S1  for a schematic representation of the procedure. Several codon models were tested (YN00, LWL85 and NG86), with similar results.
Parameters were LAV 2 300 model for gaps, submodel 4 0. 175 , insertrate 0. 010500, deleterate 0. 021000, and primate values for codon stationary frequencies. The template tree was taken from the real dataset, with one replicate for each gene tree. This resulted in 14,470 simulated alignments which were considered to be the "truth". All gaps were then removed to reconstitute the original CDS, which were translated before being aligned with mafft (--maxiterate 1000 --localpair) in amino acids and back-translated into codon alignments. There were thus two sets of 14,470 sequences: a "truth" set generated by INDELible and a "realigned" set. Smart  (Letunic et al. 2021) , SuperFamily  (Gough et al. 2001)  and Prosite  (Sigrist et al. 2013)  patterns. To compute random re-distribution of domains, the sizes of the inter-domain spaces for each protein were summed, then redivided in the same number of intervals but using randomly sampled boundaries.
These new inter-domain intervals were then re-inserted between the original domains without changing their order or content. Solvent accessibility. PDB-formatted 3D structures of the human and mouse proteomes predicted by AlphaFold  (Jumper et al. 2021)  were downloaded from the AlphaFold Protein Structure Database  (Varadi et al. 2022)  resulting in 23,391 human and 21,615 mouse structures. To obtain relative solvent accessibilities (RSA), we used pCASA  (Wei et al. 2017)  to compute the total accessible surface (m) and the average accessible surface (n) for each residue, and we took the ratio between the former and the latter: RSA = m / n. Signal Peptides. Signal peptides were identified in the primate sequences involved in gapless multiple sequence alignments (Table  S1 ) using SignalP (Almagro  Armenteros et al.
2019 ) resulting in 15. 82% proteins with a signal peptide. Pearson correlations in individual sequences. We started from the same 13,668 rodent phylogenetic trees and sequence families as described above from Ensembl version 104 (Table  S1 ), aligned with MAFFT as described. We restricted the alignments to those of at least 400 bp, containing sequences from at least three different species and with no gap. The few MSA where all dN/dS values are null and no correlation can be computed were also excluded, resulting in 7,812 MSA for the 5' end and 7,790 MSA for the 3' end, respectively. The dN and dS were computed using the YN00 model in Codeml from the PAML4 package  (Yang 2007 ) using the Bio. Phylo. PAML. codeml library. A Pearson coefficient from a linear correlation was computed for each alignment between the dN/dS values and their respective positions.
Positive selection. To estimate sites under positive selection, we used 13,555 rodent phylogenetic trees and sequence families as described above from Ensembl version 104 (Table  S1 ), aligned with MAFFT as described, where the MSA was at least 300 bp (13,301 MSA, Fig. 3B ) or at least 400 bp (13,020 MSA, Fig. 3C ). To compute positive selection in high and low RSA amino acids, we had to restrict the analysis to alignments for which we could unambiguously find a correspondence between the AlphaFold dataset and the Ensembl Dataset, thus ending with 8,371 MSA. We used Hyphy MEME (Mixed Effects Model of Evolution)  (Murrell et al. 2012; Kosakovsky Pond et al. 2020)  to detect sites evolving under positive selection under a proportion of branches. In all cases, only positions with a positive selection p-value lower than 0.
sequences, protein sequences and alignments. The sequence data comprises 4 sets covering different taxonomic groups: primates, rodents, plants, and Human-Mouse orthologs. Primate and rodent sequences were downloaded from the Ensembl database (Cunningham et al. 2021) as follows: Primates. The genomes are from the following species: Otolemur garnettii, Microcebus murinus, Propithecus coquereli, Prolemur simus, Saimiri boliviensis boliviensis, Cebus capucinus, Aotus nancymaae, Cercocebus atys, Mandrillus leucophaeus, Papio anubis, Theropithecus gelada, Macaca mulatta, Macaca fascicularis, Macaca nemestrina, Chlorocebus sabaeus, Colobus angolensis palliatus, Piliocolobus tephrosceles, Rhinopithecus roxellana, Rhinopithecus bieti, Pongo abelii, Gorilla gorilla, Pan troglodytes, Pan paniscus, Homo sapiens, Nomascus leucogenys, Carlito syrichta, Mus musculus (outgroup).
It was then possible to compute the dN/dS ratio for both datasets and compare the values at each position to measure the impact of the multiple alignment method. The same experiments were performed by realigning with FSA with and without HMM-Cleaner (Di Franco et al. 2019), with similar results. Other experiments were conducted by artificially lengthening (by a factor of up to 50) the length of the branches to obtain more distant sequences, but this did not change the conclusions. Flanking regions. To control for a potential bias due to annotation errors, particularly at the extremities of the CDS which are notoriously difficult to identify precisely, we computed dN/dS values on position-specific codon alignments that included Untranslated (UTR) sequences before the start codon and after the stop codon.
For this, all Nomascus leucogenys (Nleu 3. 0) and Pan troglodytes (Pan tro 3. 0) CDS sequences from Ensembl version 104 were downloaded with upstream and downstream flanking 30 nucleotides collected via BioMart. Amino acid translation, double blastp (-evalue 1e-3) and reciprocal best hits selection followed by alignment (mafft --maxiterate 1000 --localpair) and finally backtranslation were performed to obtain aligned nucleotide data. Only the 13,079 pairs of orthologs aligned with no gaps were retained in order to compute dN/dS in these CDS as well as in the 10 flanking pseudo-codon overlapping UTR sequences. Functional Domain distribution. Functional protein domains were downloaded via the Ensembl v101 API for 12,067 human protein sequences selected from the primate MSA for the computation of molecular rates, for four different databases: Pfam (Mistry et al.
Data acquisition setup The laboratory and testbeam measurements used a custom-designed setup for the DPTS sensor that supplies biases and control signals to the chip. The CML and analogue outputs of the chip were recorded on an oscilloscope, with a sampling rate of 5 GS/s and a bandwidth of 500 MHz. For all the temperature sensitive measurements, in particular those involving irradiated chips, the chip temperature was controlled using a water-cooled (+20 • C) aluminium jig in thermal contact with the chip carrier card and thus the chip. The bias settings of the chip are set to "nominal" values (defined by the design operating point) for all measurements unless otherwise stated or varied in the measurement. These settings are V casb = 300 mV, V casn = 300 mV, I reset = 10 pA, I db = 100 nA, I bias = 100 nA, I biasn = 10 nA, V h = 600 mV (the pixel pulsing amplitude) and V sub = V pwell = -1.
Pixel position decoding As described in Sec. 2. 2, the positions of the hit pixels are time-encoded in the CML output pulses in terms of PIDs and GIDs. An example of the measured PIDs and GIDs for all pixels, each pulsed 100 times, at V sub = V pwell = -1. 2 V and -3 V is given in Fig. 12. It can be observed that the data points form clusters, corresponding to 1024 pixels. Some of the clusters for the different pixels are very close (and look to be overlapping due to an artefact of the axis scales), demonstrating where the decoding errors can arise. Given that the time constants in the encoding are dependent on the production process and measurement conditions  [16] , a decoding calibration is needed to convert the measured PIDs and GIDs of the waveforms to pixel rows and columns. The calibration is performed by pulsing each pixel 100 times and measuring the PIDs and GIDs of the recorded waveforms.
Analysis tools and methods Data were processed in the Corryvreckan test beam reconstruction software framework  [19]  by fitting General Broken Lines  [20]  to clusters found in the reference planes and interpolating the tracks to the DUT(s). Event and track quality selection criteria were applied to ensure a clean data sample: precisely one track per event, reduced track χ 2 < 3, and track points on each reference plane. Pixels on the reference planes with too large hit-rate (more than 1000 times the average; <1 pixel per plane were affected) were masked. Furthermore, the tracks intersecting the DUT within two pixel pitches from the sensor edge were rejected. The DPTS hit pixel position was derived from the CML output corresponding to the assertion of the in-pixel discriminator (cf. Sec. 2.
Timing resolution The timing resolution was estimated by measuring the output signal coincidence of two DPTS using a 5. 4 GeV/c electron beam. The trigger signal and thus the time reference is given by the scintillators signal (cf. Fig. 15 ). Figure  19  (top), shows the correlation of the signal arrival time and ToT, i. e. signal amplitude, for one of the two DPTS chips. The influence of the chip readout scheme on the signal time response is notable at high ToT, where the distribution splits into two tails which correspond to odd and even columns   (cf. Sec. 2. 2). The correction for this effect is applied by subtracting the asymptotic value of the two tails from even and odd columns, respectively. The time walk observed for the lower input amplitude is corrected by fitting the data with the empirical function -3.
Acknowledgements The measurements leading to these results have been performed at the  Test Beam Facility at DESY Hamburg (Germany) , a member of the  Helmholtz Association (HGF). We would like to thank the coordinators at  DESY  for their valuable support of this testbeam measurement and for the excellent testbeam environment. P. Becht  acknowledges the support by the  HighRR research training  group [ GRK 2058 ] and funding by the  German Federal Ministry of Education and Research/BMBF  (project number  05H21VHRD1 ) within the High-D consortium. W. Deng  acknowledges the support by the  National Key Research and Development Program of China  ( 2022YFA1602103 ). This work has been sponsored by the  Wolfgang Gentner Programme of the German Federal Ministry of Education and Research  (grant no.