dataesr/scientific-paragraphs-categorization huggingface dataset:
Polymers Thickness(µm) ( ) PEEK 50 3. 3 5 × 10 -3 [7] Kapton 125 3. 4 1 × 10 -2 [8] PEN 125 3. 2 4. 2 × 10 -3 [9] The numerical simulations on Comsol Multiphsics software have shown that the distribution of the magnetic field in the micro-coil is homogeneous in the three substrates (Figure  2 ). The values of the inductance and the resistance are illustrated in the following table  (Table 2 ). We chose to work with the kapton substrate because it has a good thermal stability and has more chemical resistance (Figure  2 ).
The PhoneX case study PhoneX  [18]  is a central telecommunication system model describing a protocol to establish sessions between phones. It was initially used as a reference to investigate the test case generation capacities of the platform Diversity 6  by the Ericsson company. In our context, PhoneX is interesting since it allows the number of communicating actors to be parameterised, even though there is only one time constraint in the subsystem models. Fig. 1    Then it joins PhoneX central by sending to it src and dest (caller reaches Initiating state). Caller returns to Idle state when it receives an error code from PhoneX (PhoneX cannot establish a call due to violated condition of call establishment) or a signal to terminate the call from the active session (due to a call rejection by called client).
The fourth column provides the time 11  needed to analyse all local traces. For each multi-trace, we generate 1000 mutated tuples of traces and we count the ratio of multi-traces that are faulty with regards to communication policy (before last column). Finally, in the last column, we give the average time to check the communication constraint of the mutated tuples. Experiments have been performed on a 3. 10Ghz Intel Xeon E5-2687W working station with 64 GB of RAM on Linux Ubuntu 14. 04. Table  2 : Experimental data for correct multitraces and their mutants Among classical solvers, we get best results with the Yices SMT solver  [5]. The efficiency of Yices for solving constraints of the form d i + x > d j + y where x and y are concrete durations together with the fact that constructing the set of constraints from a tuple is linear explains that communication checking is more efficient than unit testing.
The authors focus on the construction of analogue-clock and digital-clock test cases. The question of communications is supported by a compositionality result saying that correctness up to tioco is preserved by parallel composition of timed automata provided that they are input-enabled. Similarly, in  [22] , local testers that can exchange synchronisation messages are derived from a global timed automaton. Thus, all these works are rather interested in the issue of test case generation, assuming testing hypotheses on communications between components, while we leave aside this question to focus on the analysis of traces with almost no hypotheses on internal communications. Lastly, the use of constraint solvers is often advocated when dealing with software and system testing issues  [7]. As an example of usage for the verdict computation in MBT,  [12]  uses SAT solvers for generating checking sequences from finite state machines.
(a)). PhoneX central behaviour (Fig 4(a)). At the Idle state, PhoneX may receive a call and reach Calling or get notified of the ending of an already Fig. 3: TIOSTSs G src and G dest of Caller and Called clients active session and return to Idle. At Calling, PhoneX may start a new session (src, dest) and return to Idle provided that dest is a registered and allowed-tocall number in the Client database and there is no active session with called client dest. Otherwise, PhoneX may also return to Idle when dest is not registered in Client database, or calling dest is not allowed, or called client dest is busy. Session behaviour (depicted in Fig 4(b)). When a new session is started, a Session TIOSTS is instantiated. At the Idle state, Session receives src and dest numbers, it then reaches Starting.
Equilibrium simulations of an elastic shell Stress analyses of closed cylindrical and toroidal shells subject to internal pressure were computationally undertaken with finite-element simulations using Abaqus FEA (Dassault Systems, Providence, RI). Explicit details of these simulations are discussed in the Supplementary Methods. Abaqus input files were created with MATLAB, with shells being discretized uniformly into approximately 10,000 S4R elements, assigned material properties faithful to that of the E. coli cell wall, and equilibrated with respect to a range of internal pressures and material parameters. The axial and circumferential stress resultants were then extracted from the deformed state and used to compute the areal strain.
Numerical solutions of the growth equation Numerical solutions of the growth equation were found in MATLAB by discretizing the integral as a Riemann sum and subjecting two time series, one for each of the lengths of the inner and outer edges of the cell, to the specified difference equation. The curvature along the midline is obtained as where L(θ, t) is the pole-to-pole length at angle θ and time t and r is the cell radius. Discrete simulations of the growth process were also undertaken, as discussed in the Supplementary Methods, and are summarized in Supplementary Fig. 5  and Supplementary Video 11.
To study MreB localization in confinement, cells were grown in microchambers for approximately two doubling times (~80 min at 37°C or ~160 min at 30°C) before imaging. At this time, cells had an average length of ~10 µm (Supplementary Table  2 ). Subsequently, cells were extracted in the same way as described above. Extracted cells were sandwiched between a flat 1% M63 agarose pad and a cover glass for cell immobilization and microscopy. We acquired imaging data for 132 non-fusion cells over 24 replicate experiments, out of which 60 were analyzed (Fig. 3 ), and approximately 70 cells under osmotic shock over 6 replicate experiments, out of which 49 were analyzed (Supplementary Fig. 11 ). As discussed below, the cells which were analyzed were selected because their curvatures were quantified accurately by our image analysis.
Shape analysis Cell identification, outlining, and skeleton detection for the length and curvature measurements was performed using a custom-made, MATLAB-based software package called CurvatureTracker, which is available upon request. We found pre-existing software, such as MicrobeTracker,46 to not provide adequate tools for automating the collection of curvature data required in this work. CurvatureTracker uses Sobel edge detection, morphological opening, cluster analysis, and thresholding for an initial guess, followed by a custom optical flow algorithm for detecting the cell in subsequent frames. An orientation for the skeleton, required for a parametric fit, was induced using a nearest-neighbors algorithm and connecting disparate connected components according to local orientation and distance.
A sweepline algorithm was used to determine the overall orientation. To measure the straightening rate, we analyzed a total of 60 cells which CurvatureTracker was able to track perfectly. CurvatureTracker oftentimes failed to track image sequences with multiple cells, excessive noise, poor resolution, or cells growing out-of-plane. By parametrically fitting the set of points T lying on the midline to a ninth-degree polynomial (x(t), y(t)) using MATLAB's native fit function, we calculated the cell length using MATLAB's native hypot function and the curvature according to Since the ninth-degree polynomial fit may capture excessive curvature fluctuations within the midsection of a cell, we repeated our analysis with a global arc fit to the midline. We found similar results for the global fit, which suggests a lack of substantial cell-substrate pinning or single-cell curvature fluctuations (Supplementary Fig.
Acknowledgements F. W. was supported by the  National Science Foundation Graduate Research Fellowship  under grant no. DGE1144152. L. D. R. , S. v. T. , and A. A. were supported by the  Volkswagen Foundation. G. O. and S. v. T. were supported by funds from the  European Research Council  ( ERC-2015-STG RCSB 679980 ), the  LabEx IBEID (Integrative Biology of Emerging Infectious Diseases) program , the  Mairie de Paris "Emergence(s) "  program , and the  ANR  " Investissement d'Avenir Programme " ( 10-LABX-62-IBEID ) to S. v. T. J. P. acknowledges funding by a  Delta ITP   Zwaartekracht grant. A. A. was supported by the  Alfred P. Sloan Foundation. We thank  J. Hutchinson  for numerous discussions on shell theory;  J. Hutchinson ,  E. C. Garner , and  C. Wivagg  for comments on the manuscript;  L.
The reliability of molecular phylogenies is strongly dependent on the quality of the assembled datasets. In the case of eukaryotes, the selection of only one protein isoform per genomic locus is mandatory to avoid biases linked to redundancy. Here, we present IsoSel, a tool devoted to the selection of alternative isoforms in the context of phylogenetic reconstruction. It provides a better alternative to the widely used approach consisting in the selection of the longest isoforms and it performs better than Guidance, its only available counterpart.
Introduction The alternative splicing, a process by which a single coding gene may lead to different transcripts and thus to different protein isoforms, is common in Eukaryotes. For instance, about 20% of plant genes  [1]  and 90% of human genes  [2]  undergo alternative splicing. In molecular phylogeny, the construction of homologous sequences datasets-usually performed by similarity-based procedures-does not allow distinguishing among the various isoforms and all of them are gathered during the process. However, most of the time only one isoform is kept for phylogenetic analyses, because they carry redundant information. Furthermore, due to the fact that some exons are present in some isoforms and absent in others, aligning them frequently leads to the introduction of many gaps in Multiple Sequence Alignments (MSAs)  [3].
Presently, only one software dedicated to isoform selection is available: PALO  [10]. It selects the combination of isoforms that are most similar in length. However, PALO only works on sequences from the Ensembl database and its unique selection criterion is based on sequence length. In that context, we developed IsoSel (Isoform Selector), a tool designed to the selection of protein isoforms specifically designed for phylogenetic reconstruction. IsoSel is based on the same approach as the one used by Guidance  [11] , a tool devoted to the assessment of protein MSAs reliability. The two main differences between IsoSel and Guidance are the availability of a broader range of amino acids substitution models in IsoSel and the introduction of different options allowing to penalize short and long sequences.
IsoSel can also provide an output file giving the corresponding selected isoform for each genomic locus. Most frequently, the sequences selected by IsoSel allows to build phylogenetic trees that are better than those obtained with the sequences selected by Guidance, this when considering a tree length criterion and the number of Duplication-Loss (DL) events inferred in a tree reconciliation. Lastly, IsoSel is a standalone program that does not require the availability of interpreted languages such as Perl and Ruby. It is therefore easier to install and to use.
Algorithm The first step of an IsoSel run consists in the alignment of an input protein dataset, using either CLUSTALO  [12] , MAFFT  [13]  or MUSCLE  [14] , to generate what we call a reference alignment. We selected those three programs because they allow to input a user-provided guide tree when building a MSA, which is required during the second step of the algorithm (Fig  1 ). The second step is the generation of a set of perturbed alignments through a bootstrap approach. Let a be the reference alignment obtained during step 1, ℓ the length of this alignment and n the number of bootstrap replicates set by the user. First, n alignment replicates are generated by the standard bootstrap procedure (i. e. , random sampling with replacement of ℓ sites among a). For each bootstrap replicate, a distance matrix is then computed by IsoSel, this using one of the amino acid substitution models implemented in the program: Poisson (with or without Gamma correction), PAM (or its Kimura approximation), JTT (or its Gamma-corrected Poisson approximation), BLOSUM62, WAG or LG.
From this distance matrix, the BioNJ  [15]  algorithm is used to infer a tree. Finally, the input dataset is realigned using this tree as guide tree, this with the same MSA program as the one used the first step. The n resulting realignments of a represent the perturbed alignments. The third and final step is the computation of the Sum-of-Pairs (SP) score  [16]  using the perturbed alignments. As described in the original publication, the SP score is used as a comparison metric between two MSAs (Fig  2). Let m be the number of sequences of the input dataset and b (1 b n) one of the n perturbed alignments generated during the second step. For each sequence i (1 i m) of length l i from b, the pair score of the amino acid at position k (1 k l i ) is computed as: R ðbÞ ik ¼ 1 m k À 1 X m k j¼1;j6 ¼i p ijk ð1Þ where p ijk is equal to 1 or 0 whether or not the amino acid at position k of sequence i is facing the same amino acid at the position k 0 (1 k 0 l j ) of sequence j in the reference and perturbed alignment (Fig  2 ).
Special options IsoSel comes with a lot of available options and parameters (for the substitution models, the Gamma correction, the alignment programs, etc. ) and we will not list them all. But some of those options are of special interest as they can allow to improve greatly the results depending on the dataset contents. Below is a short description of those key options and in which context they should be used. The -gap option. The standard SP score described in the previous section does not take into account the number of gaps present at a given site of a. For that purpose, we have implemented an option (-gap) which penalizes the sequences introducing gaps in the reference alignment. With this option, p ijk is weighted by the number of gaps present at position k in a: R ðbÞ ik ¼ 1 m k À 1 X m k j¼1;j6 ¼i p ijk m g ð4Þ where m g = m -m k is the number of gaps at this position in a.
If there is no gap, then m g = 1. Here, p ijk is the same as before excepted that it is fixed at -1 if there is no other residue at this position in the reference alignment. By construction, S i values are computed using the same equation as before but they are much smaller. Nevertheless the alternative transcript selected is always the one with the highest score. This option is suited for datasets containing alternative isoforms resulting from intron retention or exon skipping events that are restricted to a reduced set of taxonomic groups. The -short option. If the option -gap penalizes too long isoforms, the option -short was designed to penalize short isoforms, even if they are well aligned. In this case, S i is no longer calculated by dividing the sum of residue scores by the length of the sequence, but by the length of the reference alignment: S i ¼ 1 ' X l i k¼1 R ik ð5Þ As for the -gap option, the S i values obtained are, by construction, smaller.
This option is suited for datasets with isoforms corresponding to partial sequences or when a very short isoform is specific to a given taxonomic group. The -DS and -WOT options. The bootstrap approach, with the computation of a set of perturbed alignments, is very time consuming when the number and/or the length of the sequences increase. To allow a faster approach, the -DS option (for "Distance Scores") approximates the SP score by the mean of observed divergence (p-distance) between sequence i and the other input dataset sequences. Unlike the other scoring schemes, there is no bootstrap resampling procedure and the isoform selected is the one with the smallest distance (i. e. , the sequence selected is the one that it is the more similar to the others). In order to take into account the difference of lengths between isoforms, the -DS option uses a modified p-distance in which a gap is considered as a supplementary character state.
In that way, considering two isoforms that only differs by an exon skipping event, their modified p-distance will not be near to one as it is with the standard p-distance. A bias towards the systematic selection of smaller isoform is thus avoided. Let d ij be the modified p-distance between sequences i and j (1 i, j m). The score S i corresponding to the sequence i is then computed as: S i ¼ 1 m s X i2O;i6 ¼j d ij ð6Þ where O corresponds to the subset of sequences without isoform and m s its cardinal. The higher S i , the more distant to the other sequences of the input dataset is the sequence i. In combination to the -DS option, the -WOT option (for "With Other Transcripts") computes the mean of distances on the entire input dataset. Then Eq (6) becomes: S i ¼ 1 m s X m i¼1;i6 ¼j d ij ð7Þ This option is suited for datasets containing a majority of homologous genes generating alternative isoforms.
The -auto option. Best options for IsoSel are dataset dependent, therefore we have implemented an automated mode (-auto) allowing to estimate them. Under this mode, if the reference alignment has > 35% of sites containing > 80% gaps, the S i scores are computed with the -gap option. If there are > 600 sequences in the input dataset or if the alignment length is > 10000 AA, then the option -DS is selected. In all other cases, the default parameters are used.
Input and output IsoSel minimal input requirement is an unaligned set of protein sequences in Fasta format. The output is a text file containing the scores for each input sequence (Fig  3 ). Optionally, the user can provide a file in which the information on transcripts locus tag is given. In this case, IsoSel will also create a file in Fasta format that will contain the filtered dataset (i. e. , in which only the isoform having the best score for a given gene is kept). An example dataset containing two files is included in the program distribution. The first one, named example. fasta, contains 58 homologs of the human AKTS1 protein taken from Ensembl and from a local database of complete eukaryotic proteomes. Among those 59 sequences, 26 correspond to alternative isoforms whose genomic origin is indicated in the second file, named isoforms_locus_tag.
Datasets for program testing We randomly sampled 200 human proteins among the 20201 available in UniProtKB release 2016_05. For each sequence, we searched for its homologs into two collections using BLASTP  [17] , this with a similarity threshold set at E 10 -30. The first collection corresponded to a subset of Ensembl release 80 containing 32 species while the second was made of 84 complete eukaryotic proteomes taken from GenBank release 70 (S1 and S2 Tables, respectively). Among the 200 human proteins used as seed for BLASTP, 12 corresponded to ORFans and 32 returned less than 20 homologous genes. Moreover, two BLASTP runs contained only one IsoSel minimal input requirement is an unaligned protein sequence dataset in Fasta format (example. fasta). The two output files generated contain the alignment (output.
0  [18]  with 30 bootstrap replicates and default parameters. The resulting filtered datasets, containing only one sequence per genomic locus, were aligned using MAFFT (automatic algorithm selection and a maximum of 20 iterations). From each MSA, two trees were inferred to compute the tree length and the DL score. The first tree was inferred by SeaView  [19]  using the p-distance and the BioNJ algorithm. Then, the sum of the branch lengths was computed and all tree lengths obtained are listed in S4 Table. For the second gene tree inference, we run BMGE to select conserved blocks of the MSA (BLOSUM30 substitution matrix, 40% of gap allowed and a minimal block length of three amino acids). Then, the selection of evolutionary models (Bayesian Information Criterion) and the tree inference was computed by maximum likelihood using IQ-TREE  [20]  with default parameters and 1000 replicates for the Shimodaira-Hasegawa-like approximate likelihood ratio test (SH-aLRT).
Finally, the resulting gene tree was rooted and reconciled with a reference tree of eukaryotic species  [21, 22]   Among the 154 sets, we used the one built with the human protein WDR18 (UniProtKB accession number Q9BV38) as a case study. For this sequence, the BLASTP search in the two collections led to a set of 73 homologous protein sequences. Among them, fourteen (19. 2%) resulted from alternative splicing events (S3 Table ). of isoforms from this set was performed by the longest sequence criterion and by IsoSel (default parameters). For both selections (containing 63 sequences) we applied the methodology described above for computing maximum likelihood trees. The two filtered alignments contained a total of 349 and 354 conserved sites, respectively. In both cases, the evolutionary model selected by IQ-TREE was LG+F+Γ 4  [24].
Tree length criterion There is presently no gold standard to evaluate the quality of a MSA relatively to a phylogenetic criterion. However, and Nei  [26] , stated that the tree with the smallest sum of branches length is most likely to be the true one. We therefore used this criterion to roughly estimate the quality of the MSAs. We compared the length of the trees generated with the Iso-Sel filtered datasets to those obtained by keeping a random isoform, the longest isoform or by a selection with Guidance. For that purpose, we used the 154 sets of eukaryotic protein families obtained through the procedure described above. For more than 76% of the test datasets, the use of IsoSel led to a shorter tree than the one obtained using the other approaches (S4 Table  and Fig 5A). The longest and the random isoforms selections led to the smallest trees in only three and five cases, respectively.
Moreover, for 129 and 127 of the 154 datasets (corresponding to 83. 7% and 82. 4% of the total), using IsoSel with the -auto option led to shorter trees than the random and the longest isoform selections, respectively. A Wilcoxon paired test showed that these results are highly significant (both P < 2. 2 × 10 -16 ). In 119 cases (78. 8%), one of the IsoSel options led to a shorter tree than the Guidance selection, which is also highly significant (P < 5. 27 × 10 -12 ).
DL score criterion Another criterion of gene tree quality is its congruence to a reference species tree, especially in the case of eukaryotic species where horizontal gene transfers are rare events. We therefore performed a tree reconciliation for each of our datasets using Notung. The DL score provided by this program is proportional to the minimum number of duplications and losses needed to reconcile a gene tree with a species tree. Therefore, the lower the score is, the closer the gene tree is to the species tree. For 122 datasets (corresponding to 79. 2% of the total), one of the IsoSel options allowed to obtain a tree with an equal (20 cases) or better (102 cases) DL score than the one inferred after the selection of the longest isoform. The comparison with the random selection gave similar results as IsoSel was better in 117 cases and equal in seven (corresponding to 75.
9% and 4. 5%, respectively). Finally, in 128 cases (84. 7%), IsoSel gave better results than the Guidance selection and performed equally in thirteen cases (8. 6%). All those results are highly significants (all Wilcoxon paired tests led to P < 10 -8 ). Globally, for 63. 5% of the datasets, the use of IsoSel led to equal or less discordant gene trees than those obtained using the other approaches (Fig  5B ).