dataesr/scientific-paragraphs-categorization huggingface dataset:
Stratum size impact according to data size and dimension Experiments were conducted using data sizes up to 5 million items with an input space dimension up to 80. The main conclusions to be drawn from the results, given in A, are: The accuracy does not depend on the dataset size nor on the input space dimension. The variations are only due to the kmeans random initialization. The ratio of the sample size to the whole size results clearly increases with the stratum size, it decreases with the dataset size and does not depend on the space dimension. For a given stratum size, the number of samples does not depend on the dataset size, it is related to the data structure. The time ratio increases with the stratum size while it decreases with the number of items in the dataset and, to a lesser extent, with the input space dimension.
Stopping criterion influence The threshold q th is the stopping criterion. To assess its influence on the process, extensive tests with a synthetic benchmark database  [59]  were conducted with different stratum sizes. As the conclusions were the same for all the stratum sizes, only one experiment, with a stratum size of 504, is reported. The dataset contains 6751 patterns in 10 dimensions. It is structured in 9 Gaussian clusters with different levels of separability. q th ranges from 0. 6 to 0. 999 and the algorithm was evaluated for 2 to 11 clusters. Table  2  shows the Adjusted Rand Index (ARI) values for each configuration. The average ARI for all configurations with a given threshold is in the last column. It is shown that the ARI are all high and differ slightly with the number of clusters.
Numerical experiments The proposal is now tested using synthetic and real datasets. The experiments compare the sample size, the running time and the partitions that result from a clustering applied to the whole set to the ones yielded by the same clustering algorithm applied to the sample. The final sample is given either by the proposal, the stratum sampling algorithm run on the whole set or alternative progressive sampling strategies. Three are considered: in the first one the final sample is the union of the samples from all strata, in the two remaining ones the stratum size follows an arithmetic or a geometric progression. The accuracy is measured by the ARI, the Ratio and Time indices are the ones used in the previous section. The ARI returns a value of 1 when two partitions are in complete agreement, 0 when the partitions are expected by chance and a negative value when the partitions are in greater disagreement than would be expected from chance.
Despite its well known limitations, the k-means algorithm is mainly used in this study as it allows for extensive tests even on large datasets. Moreover the objective is not to find a good partition but to compare the partitions built from the whole set or samples given by alternative sampling methods. The sampling algorithm used in all these experiments was ProTraS  [32]. The default value used in this paper is 0:1. The threshold used as a stopping criterion for the algorithm is q th ¼ 0:99.
The synthetic and real world datasets Some are from the data clustering repository of the computing school of Eastern Finland University, 1 while others come from a benchmark data for clustering repository 2 or were proposed in the published literature. These data are quite interesting as their structure is known and, in most cases, they are easy to display. They are used to ensure that the proposal is able to handle complex data of varying shapes and densities. The data characteristics are summarized in Tables  3  and 4.
Comparison with the clustering algorithm applied to the whole set The results are given in Table  5  for the synthetic datasets and Table  6  for the real ones. The two tables have a similar structure: each value is the average of ten runs for each configuration with a number of clusters between 2 and 10. The mean (resp. sd) is the mean of the mean (resp. sd) of the column. In these tables, the same four stratum sizes used in Table  1  are considered. To alleviate the notations the stratum size is referred to as the minimum number of points for the smallest cluster, i. e. 2; 10; 50 or 100. The main trends that can be analyzed from these tables are that the stratum size has a strong influence on the results: large sizes yield a better accuracy but a slower running time and a higher sample size ratio.
With the real world datasets the stratum size has a major impact on the accuracy. This is because these data are not as structured as the synthetic ones and a larger size is needed to make the strata representative. The time ratio is less than 1, meaning that the proposal is faster than the alternative without any sampling, for the two smallest stratum sizes in average for the two kinds of datasets. This result should be carefully analyzed as the datasets have a highly variable number of records, from 1351 for S9 to more than one million for R10. The progressive sampling algorithm is useful only to manage large datasets, otherwise applying a sampling algorithm to the whole set is smarter. Nevertheless, all these datasets were used as classical benchmarks to ensure that the proposal is able to select a representative sample set.
When the data size is too small, not only are all the strata processed but, to reach a good accuracy more partitions are generated (line 22 of Algorithm 2). When dealing with large datasets the time ratio is always less than 1, even for large stratum sizes, e. g. R2; R4; R9; R10. On the other hand, when the data are structured in groups the results are quite good even with small stratum sizes. This is the case for S18 and S20. The latter, processed using the smallest stratum size (258 points), yields an ARI of 0:942 with a sample size ratio of 0:006, meaning that the sample size is 240, and a time ratio of 0:028, meaning that the proposal is 35 times faster.
The proposal is run with a ProTraS cost set at 0:1 and the parameter is the stratum size. For each dataset, the process was run 10 times for each number of clusters, between 2 and 10. The values are averaged over all the number of clusters and all the runs for each dataset. These results are then averaged for all the datasets, either synthetic or real world, in Table  7  for the synthetic data and Table  8  for the real world ones. Compared to ProTraS applied to the whole set, the proposal yields comparable accurate results, especially with a 0:1 cost, which is the ProTraS parameter for the proposal. That result was expected as ProTraS is used by the proposal. That also means that the progressive sampling scheme does not harm the performance. The sample set given by ProTraS is smaller but for the lowest cost (0:02), which also corresponds to a higher accuracy.
The computational time required by the proposal is smaller for the real world datasets. Moreover the use of ProTraS with the whole set is restricted due to memory constraints: the limitations come from the dataset size but also from the number of representatives. This is not the case for the proposal. When the sample set is the union of all the representatives of the strata, Full, the proposal seems to be more efficient. Considering the real world data, the most accurate configuration for the Full scheme is with the parameters 0:1 for ProTraS and 50 for the stratum size. The accuracy is comparable to the proposal with the smaller stratum size. In this case, the sample set is 6 times smaller and the algorithm is twice as fast. When the stratum size follows an arithmetic or geometric sequence, the sample size is significantly higher than the one yielded by the proposal and the required processing time is noticeably increased.
The poor performance of the competitive progressive sampling frameworks may be explained by the lack of quality control: they just add randomly chosen representatives without considering the ones already selected. The proposal thus gives smaller sample sets in less time to get a comparable, or even higher, accuracy. The comparison is now illustrated using the R10 real world dataset. Fig. 4  shows the evolution of the quality criterion, q, for the four stratum sizes until the threshold, q th ¼ 0:99 is reached. The colors for increasing sizes are: Green, Grey, Red and Blue. The abscissa is the sample set size. The smaller the stratum size, the smaller the sample set. Fig. 5  shows the evolution of the sampling cost when ProTraS is applied to the whole set R10. The ordinate is the ProTraS cost that is based on distances between the original items and their representatives  [32].
But the opposite does not hold: the ARI may be very high with a large sampling cost when the groups are well separated. The ARI is the basis of the comparison, with the corresponding sample size and time ratio. For the R10 data, with more than 1M of data patterns in 9 dimensions, ProTraS and the proposal reach comparable high ARI with a similar number of prototypes (from 2000 to 14000). For both of them the increase in ARI (from about 0:9 to 0:960) is very small compared to the increase in the sample size. The main difference is in the running time. ProTraS is comparatively slower: it selects a 14000 sample set in a time ratio of about 0:86 compared to about 0:5 for the same sample set size using the proposal. With progressive sampling schemes based on an arithmetic sequence, ARI values range from 0:887 to 0:960 with a sample size from 11000 to 40000.
Conclusion Progressive sampling allows for managing large datasets while smart sampling algorithms are restricted to moderately-sized ones due to memory or time constraints. The proposed framework is independent of the sampling algorithm and of the clustering algorithm. This is an important characteristic as many studies are limited to the classical k-means. In this work, the sampling algorithm is ProTraS and tests were carried out with a hierarchical clustering algorithm and, more extensively, with the famous k-means. The progressive sampling framework is easy to use. It requires two parameters. The first one is the stratum size. It is defined in a clustering context to yield statistically representative strata: the larger the better the representativeness. Experiments showed that small sizes also yield good results with structured data.
The second parameter defines the stopping criterion: it is the quality representation of the already selected items for a new stratum. This is an important feature of the framework: the new items are not randomly added to the sample as is usually the case. They are chosen because they improve the quality of representation. A careful study showed that the behavior of the algorithm is stable for a wide range of values of this threshold. The tests conducted on synthetic and real world datasets proved that the progressive sampling framework yielded similar results to the sampling algorithm applied to the whole set. This means that the sampling scheme does not decrease the performances despite the dataset splitting. The framework can be used with small datasets but, in this case, it is slower than the sampling algorithm applied to the whole set.
With large datasets the advantage of using the framework becomes obvious as the runtime is a function of the stratum size instead of the whole set size. The progressive framework proved highly efficient with large datasets and/or clustering algorithms with a quadratic, or higher, time complexity. The framework is smarter than other classical progressive and stratification strategies as it includes some intelligent mechanisms that select only the necessary representatives and at low computational cost. The neighbor search was optimized but time improvement is still possible thanks to approximate search techniques, e. g. using a k-d tree based algorithm  [68]. The current framework addresses only one aspect of big data issues: the number of records. Dealing with higher dimension spaces would require additional effort to cope with the curse of dimensionality  [69] : feature selection, input space transform or the use of non euclidean distances could be considered.
Generic progressive sampling algorithm 1: Input: X Whole dataset 2: Output: S The sample set 3: Select initial sample S 4: Compute Q ðSÞ Quality of sample S 5: repeat 6: Select additional sample S i 7: S ¼ S [ S i 8: Compute Q ðSÞ 9: until (Q(S) satisfies the stopping criterion) 10 return S.
Algorithm 1. 1 Generic progressive sampling algorithm 1: Input: X Whole dataset 2: Output: S The sample set 3: Select initial sample S 4: Compute Q ðSÞ Quality of sample S 5: repeat 6: Select additional sample S i 7: S ¼ S [ S i 8: Compute Q ðSÞ 9: until (Q(S) satisfies the stopping criterion) 10 return S.
Pathogenicity of LQT2 variants Over 1000 mutations in hERG related to LQT2 have now been described in various databases. These include Clinvar at the international level, but also Bamacoeur, a French network (http://www. filiere-cardiogen. fr/), at the national level. Following clinical diagnosis, tens of new variants are identified yearly. Next generation sequencing demonstrated the extraordinarily high variability of the human genome and in particular the abundance of rare variants (<0. 1%) modifying the protein sequence without functional consequences. 11 In this context, allele frequency can no longer be considered as evidence for variant pathogenicity. In 2015, the American College of Medical Genetics and the Association for Molecular Pathology (ACMG-AMP) published international guidelines to classify variants in five classes from pathogenic (class 5) to benign variants (class 1).
Optimised protocol analyses in R For optimised voltage-clamp protocol analysis, a R automated routine has been developed to perform rapid and reliable analyses (R script accessible at dx. doi. org/ 10. 17504/protocols. io. bte7njhn and Supplementary File 3). This pipeline allows direct analysis from pClamp acquisition files (Supplementary Material Online, Figure  S5 ). Briefly, each depolarisation step has been isolated using a time conversion rule and stimulation steps of each protocol have been superimposed to fully analyse hERG properties (three human ventricular APs, steady-state activation, steady-state inactivation and kinetics of activation, inactivation, recovery from inactivation and deactivation).
Modelling In 2017, the crystal structure of hERG was resolved (PDB ID#5VA1, 5VA2 and 5VA3). We decided to take advantage of the availability of this structure to visualise in the protein the different locations of the considered mutations. Using UCSF Chimera, an open access software, we evaluated for each mutation the impact of the surrogate residue on the rest of the protein. We are aware that this approach is approximate since, on the one hand, it is based on a given state of the protein and that, on the other hand, the latter presents absent parts which we do not know if they can be impacted by the different mutations. Nevertheless, by making it possible to categorise these different mutations, it helps, along with electrophysiology, to anticipate the expected impact. To evaluate this impact for each mutation considered, we propose to address to these mutations a score of 0, 1 or 2 for three different criteria A, B and C where: Criterion A: evaluates the change in occupied space (In this criterion, a score of 0 = no change, 1 = small change, 2 = large change).
0 versus pH 7. 4) allows for fluorescence quenching of the channels expressed at the membrane, without affecting fluorescence levels in internal compartments. We named this construct the Opti-hERG-pHluorin sequence. The resulting sequence was cloned in the pCDNA5/FRT/TO plasmid between the HindIII and XhoI restriction sites. Missense mutations were selected from a French hospital patient database (Bamacoeur from the National Cardiogen network including the CHU Nantes Hospital, France). Several criteria of selection were used: (i) a rather large domain coverage, (ii) a mix of previously characterised (eight out of 13; R35W, C64Y, I96T, R176W, R328C, R534C, G548S, A561V) and not characterised (five out of 13; T74R, K93E, D591H, R835P, P1026L) variants, D591H and P1026L being recently identified VUS, of major interest for the cardiologists of our network (Table  1  and Figure  S1(B) ).
Modelling tool for evaluating the impact of a variant on hERG channel structure The crystal structure of hERG was resolved in 2017. 20 It therefore represents an opportunity to evaluate for each variant the impact of the surrogate residue on the rest of the protein. To this end, we used the open access UCSF Chimera software to assign a score of structural impact based on three criteria (first, space occupied by the new amino acid; second, the modification of charge, polarity or water affinity; and third, modifications of interactions with nearby residues). For each of these criteria, a score of severity was assigned from 0 (no change) to 2 (maximal change) (see description in Methods section and Supplementary Material Online). The final score is the sum of the three criteria scores. It should allow ranking of each variant from 'no impact' (score of 0 to 1) to 'high impact' (score of at least 4).
The efficacy of this method is excellent (100% success rate on 70 variants generated so far) and considerably speeds up the generation of a database of hERG variants. Of note, one approach that consists in codon optimisation, to reduce the G/C content without altering the amino-acid sequence, was not chosen because of earlier evidence that it affects channel expression levels. Indeed, an average G/C content reduction from 66 to 51% leads to marked decrease in translation efficiency and a ∼3-fold decrease in protein expression. 40, 41 Second, we introduced a pHluorin tag to the C-terminus of the hERG channel to evaluate the percentage of channels that reach the plasma membrane. The approach was validated for the first time on this channel type and shown to reliably explain most of the data from current density measurements.
Perspectives and limitations of the present study In this study, we have defined and optimised conditions for high-throughput screening of hERG variants. This was a prerequisite step for handling hundreds of variants that have been listed in various databases. What would the follow-up steps be? First, from our Cardiogen database, we will select the most clinically relevant hERG variants and generate a library of plasmids coding for these variants, using the Gibson assembly method described herein. Second, we will adapt the optimised biophysical protocol to an automated high-throughput patch-clamp system by taking into considerations the specificities of the automated patch-clamp system. It will also be an opportunity to perform the recordings at 37 • C, as mentioned above. Of note, the optimised protocol may need some adjustments to work at physiological temperature, namely with regard to step durations.
This work was supported by the Fédération Française de Cardiologie (DENJOY -Grands projets -2019 to I. D. , M. D. W. and F. L. ), the Agence Nationale de la Recherche (grant number ANR-11-LABX-0015 to M. D. W. and F. L. ; grant number ANR-21-CE17-0010-CarDiag to I. D. , M. D. W. and F. L. ), the Fondation Leducq in the frame of its program of 'Equipement de recherche et plateformes technologiques', the Région Pays de la Loire (nouvelle équipe, grant number 2016-11092/11093 to M. D. W. ) and the European FEDER (grant number 2017/FEDER/PL0014592 to M. D. W. ].
Grant/Award Numbers:  ANR-11-LABX-0015 ,  ANR-21-CE17-0010-CarDiag ;  Fondation Leducq : 'Equipement de recherche et plateformes technologiques';  Conseil Régional des Pays de la Loire , Grant/Award Number:  2016-11092/11093 ;  European Regional Development Fund , Grant/Award Number:  2017/FEDER/PL0014592.
The use of high segmentation and the dual scintillator, provides particle discrimination, and aims to identify and reduce backgrounds. Moreover, the materials used, the robustness and compactness are also attractive for future reactor monitoring applications. After demonstrating the applicability of the composite scintillator technology, a full-scale prototype module, SM1, with a fiducial mass of 288 kg was operated near the BR2 reactor in 2015. Based on the performance of the prototype module  [34] , improvements were made to the original detector design before proceeding to the construction of a 1. 6 ton detector in 2016-2017. The SoLid detector installation was completed in February 2018 and was successfully commissioned near the BR2 reactor in spring 2018. In this paper, we will first give a complete description of the SoLid detector: its detection principle, its mechanical design, the construction phase and the quality assurance process.
The SoLid detector currently includes a total of 5 detector modules, accounting for a total of 50 detector planes and corresponding to a fiducial mass of 1. 6 ton. The front and back planes of the detector are capped with a HDPE reflective shielding with a thickness of 9 cm. Under normal detector operations all modules are closely grouped together with an average spacing of 0. 5 mm between two modules. The outer layer of active cells have neutron screens without a reflective backing (red). The active volume is surrounded by the HDPE neutron reflector (black),the aluminum frame for mechanical rigidity and attachment of the electronics, and two tyvek sheets for light isolation. (Right) Sketch and dimensions of a 10 planes detector module mounted on its trolley (blue).
Each cube was weighed with a digital scale with a precision of 1 mg, before and after being equipped with neutron detection screens and wrapped with Tyvek. The two neutron detection screens for each detection cell were also individually weighted. Each detection cell was marked with a bar code sticker that allows for tracking of the production history in a dedicated SQL database. This database includes the bare and wrapped weights of each cell. During a period of 8 months a total of 13228 cubes were washed, inspected, wrapped and catalogued. Only 3% of all produced PVT cubes were rejected due to quality issues. The accuracy of the weights, combined with the tracking of the production batches revealed a small shift in cell mass during the production process,  which falls well within the tolerances used in the cell quality control.
The mass distributions of the PVT and neutron detection screens of the 50 detection planes are shown in Fig. 4. The mean weight of all PVT cubes equals 119. 7 g with an RMS of 0. 1 g, which allows to control at per mille level the proton content. The difference in mass between the neutron detection screens with and without reflective backing can be observed in Fig. 4. Each of the 50 detection planes was assembled and equipped by hand in its aluminium frame. The position of each MPPC in the detector is stored in the construction database, together with its breakdown voltage.
Quality assurance Before being integrated in a detection module, each detection plane was tested on the so-called Calipso test bench, shown in Fig. 5  and described in detail in  [50]. This test bench consists of a robot that can position a calibration source in front of a SoLid plane with millimetre accuracy. A polyethylene (PE) neutron collimator is added when performing neutron calibrations, in order to increase the neutron capture rate. In addition, a dedicated 22 Na self-triggering calibration head was designed for the calibration of the energy response of the PVT. The Calipso test bench served primarily as an automated quality control system. As such it provided an early detection of typical construction quality issues such as missing neutron detection screens, bad fibre connections, malfunctioning MPPCs and wrong cabling which were all resolved before integration in a detector module.