dataesr/scientific-paragraphs-categorization huggingface dataset:
Thereafter, VPS13M1 was duplicated in Bryophytes and the Angiosperms' VPS13M2 proteins lost their C-term   SfVPS13B ). The 3D structures were obtained mainly using AlphaFold2  (Jumper et al. , 2021)  but also RoseTTAFold  (Baek et al. , 2021)  for PsVPS13M. The C2 domain of VPS13M paralogs were included in the prediction. The different modules of the VAB domains are represented by colors and numbered from 1 to 6, the C2 and DysF domains found in VPS13Ms are in raspberry and limon, respectively. β-tripod domain (Figure  7 ). In addition, VPS13M2 was also duplicated in some angiosperm species (S. moellendorffii and Z. marina). These results illustrate the complex evolution of VPS13 proteins in terms of paralog number (gain and loss) and domain organization.
In Viridiplantae, our most curated dataset, the VPS13 gene copy number varies from three in Chlorophyceae to six in the Charophyte K. nitens and the Bryophyte S. fallax (Figure  7 ). Huge variations in sequence length were also observed. By analyzing RBG repeats, we showed that sequence length is not always correlated to an increase in RBG repeats. Tunnel length and the addition of new domains are not directly correlated to the protein sequence length neither, as exemplified for VPS13X proteins ranging from 3122 to 4070 aa (Figure  4 ) though harboring the same RBG number and domain organization. Our analysis showed that general VPS13 protein domain and structure organization is widely conserved among Viridiplantae with the occurrence of a N-term Chorein, a VAB and an ATG2_C domain, most of them also having a C-term PH domain (Figure  3 ).
This suggests that the β-tripod domain emerged after the separation of Rhodophytes and Viridiplantae 1. 5-2 Ma  (Bowles et al. , 2023). In addition, our analysis did not reveal any relationships among VPS13M and another VPS13 groups. As it possesses 12 RBG repeats like VPS13S, it would be parsimonious to hypothesize that it could come from a duplication of VPS13S ancestor followed by the rapid acquisition of the extra-domains. From an interrogation of the InterPro database (Paysan-Lafosse et al. , 2023), we found that IPR009291 domain (Vacuolar protein sorting-associated protein 62, aka β-tripod domain) is present in ≈6000 sequences belonging mainly to Viridiplantae (≈3000 sequences) and fungi (≈2000 sequences). In fungi, none of the sequences containing a β-tripod domain was associated to a known VPS13 domain.
Its extension requires a massive vesicular lipid and protein transport to the pollen tube tips to allow the delivery of components required to sustain its growth, including lipids  (Adhikari et al. , 2020). AtVPS13M2 accumulates at the pollen tube tip secretory vesicles and might play a role in the establishment of polarity and in cell wall deposition during pollen tube growth  (Tangpranomkoorn, 2022 , BioRvix, DOI:10. 1101 / 2022. 11. 01. 514778). Pollen tube growth is common in Angiosperms but also found in some Gymnosperm species that have usually shorter pollen tubes that grow more slowly  (Fernando et al. , 2005; Adhikari et al. , 2020). The only Gymnosperm species included in our dataset is Thuja plicata in which pollen germination and pollen tube growth was reported  (Land, 1902). TpVPS13M2 still has a C-term β-tripod domain, thus it is difficult to speculate about the link between the presence of this domain and an essential role in the pollen germination process.
Sequence Retrieval and Selection For Viridiplantae, all the sequences from Embryophytes (land plants) were retrieved from the Phytozome database  (Goodstein et al. , 2012) , which contains assembled genomes from more than 300 Viridiplantae species. For Glaucophytes, Rhodophytes, Chlorophytes, Prasinodermatophytes, and Charophytes, sequences were retrieved from five databases: Uniprot (The UniProt  Consortium et al. , 2023) , Joint Genome Institue (JGI)  (Nordberg et al. , 2014) , Phycocosm  (Grigoriev et al. , 2021) , Eukprot V3  (Richter et al. , 2022) , and the National Center for Biotechnology Information (NCBI)  (Sayers et al. , 2022). To find as many VPS13 homologs as possible, BLAST searches  (Altschul et al. , 1990)  were primarily performed using the well-described Arabidopsis thaliana sequences (M1: At4g17140, M2: At1g48090, S: At5g24740, and X: At3g50380) as queries.
For Embryophytes, BLASTPs were performed with AtVPS13M1 and AtVPS13X aa sequences, whereas for other organisms, BLAST searches were performed from the four A. thaliana sequences because of the putative higher divergence of sequences compared to the plant ones. As orthologues of HsVPS13B were recently supposed to be present in some protists  (Levine, 2022) , HsVPS13B was BLASTP searched as well. Sequences with at least one VPS13 domain annotation, such as VPS13_N, VAB, Chorein or VPS13_C, were retrieved to generate data in Table  S1. Then, a curation was performed by selecting sequences that were at least 2400 aa-long to constitute a dataset of 383 sequences (with addition of the four HsVPS13 and ScVPS13), used for ML phylogenetic analyses (Dataset 1). Some sequences were split up in two or three pieces and were merged to obtain likely full-length sequences: Closterium fusion = GJP45422 + GJP45421 + GJP45420; Klebsormidium nitens fusion = GAQ85408 + GAQ85409; Sphagnum fallax fusion = Sphfalx12G108100.
1 + Sphfalx12G108200. 1, Thuja plicata fusion = Thupl. 29379161s0002. 1. p + Thupl. 29379161s0001. 1. p. Then, a subset of organisms was chosen in each taxonomic clade to obtain a reduced dataset of 104 sequences to perform ML and Bayesian phylogenetic analysis (Dataset 2). We selected species for which we had the highest number of sequence and eliminate protein models potentially emerging from the same gene but corresponding to splicing variant by multiple alignments of sequences within each species. When two proteins likely originating from the same gene were discovered, the longest isoform was kept for the analysis. Presence of at least two canonical VPS13 domains (Chorein, VAB, ATG2_C, PH) was also used as a criterion to select sequences. HsVPS13A/B/C/D and ScVPS13 were included in this dataset.
Domain and RBG Predictions Domain prediction was performed using HHPred  (Zimmermann et al. , 2018)  on all the sequences of Dataset 2 in order to infer domain information in the Bayesian analysis. The full-length sequences were used for analysis and domains predicted with at least 50% confidence were kept. Because of the huge protein size, HHPred usually failed to predict all the putative domains and 2nd and 3rd analysis were performed in regions in which no domains were predicted at a first place. Once again, domains predicted with at least 50% confidence were kept. Domains found in the sequences are summarized in Tables  S2  and S3  in the form it was coded for the Bayesian analysis. RBG domain prediction was performed by HHPred in the targeted sequences by using as queries RBG domain sequences of AtVPS13 paralogs as well as HsVPS13B described by  Levine (2022).
Maximum Likelihood Analyses ML analyses were performed with datasets 1, 2, and 3. The full-length protein sequences were aligned with the software MAFFT  (Katoh and Standley, 2013)  performing 16 iterations of refinement. The obtained alignment files were processed with the trimAL  (Capella-Gutiérrez et al. , 2009)  program to remove all sites with a percentage of gaps higher than 10% (gap score = 10). After trimming, the size of sequences in the final alignments is around 900-1000 aa and include mainly amino acids from conserved domains (Chorein-RBG2-RBG10-VAB-RBG11-RBG12-ATG_C-PH). The phylogenetic reconstruction was performed with the software FastME  (Lefort et al. , 2015)  using the LG model and 5000 bootstrap iterations. Nodes with at least 4000 boostraps (80%) were considered as well supported.
Bayesian Inference Two independent Metropolis-coupled Markov Chain Monte Carlo (MC 3 ) analyses were performed using the final curated datasets in the MrBayes3. 2 software  (Ronquist et al. , 2012). Two partitioned datasets were used for Bayesian inference, each including a binary column indicating the result of domain annotation reported above and a second partition for the amino acid sequence, for the Archaeplastida (Table  S2 ) and for Viridiplantae (Table  S3 ) containing 104 (Standard:1-42,PROTEIN:43-1166) and 79 (Standard:1-25, PROTEIN:26-1332) entries, respectively. The domain annotation was mathematically formalized into a 1/0 code (presence = 1, absence = 0) and each domain accounted as a position in the alignment. One cold chain and either seven (temperature parameter 0. 01, Dataset 2) or three "heated" (temperature parameter 0.
1, Dataset 3) chains drove the analyses. The analyses were allowed to switch among all the substitution models implemented in the software to identify the best model without any a priori (aamodelpr = mixed). 90,000 and 35,000 generations with sampling every 100 generations were set for Dataset 2 and Dataset 3, respectively. The standard deviation of the split frequency between the two parallel analyses at the end of each analysis were 0. 068996 and 0. 006487 for Dataset 2 and Dataset 3, respectively. The first 25% of the trees produced were discarded in order to let the analyses stabilize (burnin = 0. 25). The convergence of the runs was estimated and the potential scale reduction factor (PSRF + ) were all around 1 for all the parameters indicating that the associated chains converged to one target distribution.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by the  French National Research Agency  in the framework of the  "investissement d'avenir" program  ( ANR-15-IDEX-02 ) and of the  ANRJCJC MiCoSLiT  ( ANR-19-C13-0013 ) to MM. It also received funding from  GRAL , a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alps  ( ANR-17-EURE-0003 ).
Risk management The decisions that we make at each step of the review have associated risks that we summarize as follows. • Missing papers risk: The building of the set of relevant articles depends on the inclusion criteria. To limit the risk of missing relevant papers, we define specific criteria to ensure that the chosen papers represent the main trends to answer the research questions. Moreover, having a comprehensive data set refined to the only relevant papers limits this risk. • Partial paper analysis risk: The risk is that the analysis could be incomplete or oriented. We limit the incompleteness risk because we consider two points of view in our study. As stated earlier, we consider first which MAS model dimensions are relevant in the design of an APTS (with the vowel approach). Then we consider how the properties of a MAS model benefit the APTS functionalities.
Data set building criteria We consider the following criteria for a paper to be included in the study: Publication type. With the massive number of publications (conferences, journals, reports, white papers, PhD theses, etc. ) in the ITS field, we have to adopt an editorial position to discuss a coherent subset of works in detail. We decided only to consider peer-reviewed publications and, among them, only those published in peerreviewed journals indexed by Scopus or Web of Science databases. Publication period. The papers have to be published between 1990 and 2020. This period seems to be large enough to be representative and limits the number of missing papers. Research question relevance. The papers must be related to the ITS field and based on a multi-agent approach. This criterion is used to design our requests with keywords combining the two terms or expressions: • (i) "transport" OR "traffic", AND • (ii) "multi-agent system" OR "multiagent system" OR "agent".
Knowledge base building We first build the data set (using research engines) and then apply the inclusion criteria. The count of the journals to which at least one selected paper belongs gave 58 journals (cf. Annex 1): 29 journals in the ITS field, 20 journals in the AI field; and 9 journals in a related field, called in the following, Computer Science and Industrial Informatics (CSII). We executed the search for the last time on 28 February 2021, and we thus consider the journal papers concerning the period 1990-2020. The number of all published papers for the considered journals over time, particularly the papers on ITS and MAS, grows exponentially (e. g. on ITS and MAS, from a unique paper (1990) to 82 papers for 2020). The total number of papers is 119 819 while the number of papers matching our request is 731.
Finite Element Modeling Simulation For each case, the geometry developed for the specific coils used to treat that specific aneurysm (same coil configuration for in vivo and in vitro deployment) was uploaded into the FEM software as discrete deformable wire geometries. Each coil geometry was meshed into beam elements, with a diameter of 𝐷 2 and an element length (𝑑𝑙) determined by the curvature of the pre-shape such that 𝑑𝑙 = 𝐷 3 /12, ensuring a minimum of 36 elements per circle, ensuring the unforced angle between beam elements is no greater than 10° while the beam length is still at least 10 times the beam diameter. (21)  We used shear-flexible Timoshenko beam elements as they are more appropriate for handling large deformations. (16, 22)  Contact is modeled using the built-in general contact algorithm, which assumes hard contact in the normal direction and a "penalty" method for the tangential contact.
The FEM coil geometry occluded the neck in that region and did not demonstrate the same jet effect, leading to a much lower average WSS. Even in this worst-case scenario, however, the CFD simulation using the FEM coil geometry still significantly outperformed the porous media approach. In this work we have demonstrated the marked improvement in CFD accuracy using coil resolved simulations over the standard porous media method. Due to the large hemodynamic differences between coil resolved and porous medium CFD approaches, we recommend that future CFD based research on coiled aneurysm hemodynamics replace the homogeneous porous medium approach with a more accurate coil geometry representation when possible. We recognize that when dealing with large datasets or when computational resources are limited, conducting FEM simulations may be infeasible due to the computational cost associated with resolving the coil geometries.
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification Xavier The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advantages of quantum algorithms on artificially generated datasets, and initial experiments with small binary classification problems have yielded comparable outcomes to classical algorithms. However, it is essential to investigate the potential quantum advantage using realworld data. To the best of our knowledge, this study is the first to propose the utilization of quantum systems to classify neuron morphologies, thereby enhancing our understanding of the performance of automatic multiclass neuron classification using quantum kernel methods.
We examined the influence of feature engineering on classification accuracy and found that quantum kernel methods achieved similar performance to classical methods, with certain advantages observed in various configurations. The field of quantum computing originated with a question posed by Feynman, namely whether it was feasible to simulate the behavior of quantum systems using a classical computer or whether a quantum computer would be required instead  1. Since quantum algorithms were initially developed, scientists have been searching for their optimal applications and output. The first two quantum algorithms were published by Shor  2  and Grover  3  , who documented that applying them to factorization and database search theoretically offers an advantage compared with classical computing.
The number of features determined the number of qubits, and a quantum circuit used to implement the feature map was of a depth that was a linear or polylogarithmic function of the dataset's size. Thus far, the studies that have been conducted to support the advantages of a quantum feature map have carefully selected synthetic datasets or applied it to small binary classification problems. Despite the fact that research on cortical circuits has been conducted for over a century, determining how many classes of cortical neurons exist remains an ongoing and uncompleted task. Moreover, the continuous development of techniques and the availability of an increasing number of phenotype datasets have not led to the maintenance of a unique classification system that is easy to update and can consider the different defining features of neurons specific to a given type.
Results Classification using feature selection. In this study, to classify 14 classes of digitally reconstructed neuron morphologies based on the 43 morphological features described in Fig. 1 , we applied several classic and quantum kernel algorithms by combining different feature rescaling and selection techniques to reduce the dataset to five features. We also considered four popular kernels to benchmark the performance of the classical SVM method (RBF, Linear, polynomial and sigmoid). Eight quantum kernel algorithms were studied. We named the first one q_kernel_zz, which applies a ZZFeatureMap and an encoding function described by Havlíček et al. ,  4  who defined a feature map on n-qubits generated by the unitary. The second algorithm (q_kernel_default) applies PauliFeatureMap (paulis = ['ZI' , 'IZ' , 'ZZ']) with a default data mapping φ S.
In addition, we used five encod- ing functions presented by Suzuki et al. 8  that we named q_kernel_8, q_kernel_9, q_kernel_10, q_kernel_11, q_kernel_12. The last quantum algorithm (q_kernel_training) trains a quantum kernel with quantum kernel alignment (QKA),  16  which iteratively adapts a parametrized quantum kernel to the dataset and simultaneously converges to the maximum SVM margin. In addition, selected algorithms were run by selecting 10 and 20 features. We used up to 20 qubits. The accuracy of the classification was assessed by performing cross-validation on the training dataset. Figure  1  presents the feature's importance using XGBoost, decision tree, and random forest. Table  1  presents the five SVMs with classical kernels that provided the best cross-validation scores when applied to sample 5.
The classical algorithm that provided the best score (score = 0. 91 ± 0. 001) was the SVM with a radial basis function kernel and the combination of the Yeo-Johnson technique for data rescaling with the embedded decision tree classifier as a feature selection technique (selection of five features). When we used the trained model on the test dataset, the classification accuracy was 0. 92. The SVM with a radial basis function used with the quantile-uniform technique for feature rescaling and an embedded decision tree classifier for feature selection also provided a cross-validation score of 0. 91 ± 0. 004. When we used the trained model on the test dataset, the classification accuracy was 0. 91. Among all of the feature rescaling techniques, quantile transforms that map a variable's probability distribution to another probability distribution provided the optimal classification accuracies together with Yeo-Johnson.
When we used the trained model on the test dataset, the classification accuracy was 0. 93. Furthermore, q_kernel_training associated with the quantile-Gaussian technique and embedded decision tree classifier performed better than classical top SVMs with a cross-validation score of 0. 92 ± 0. 001. When we used the trained model on the test dataset, the classification accuracy was 0. 92. In terms of quantum kernel methods, q_kernel_training and q_kernel_zz provided the most accurate results among the quantum algorithms. The combination that provided the minimum cross-validation score was that of MaxAbsScaler, XGBoost, and q_kernel_10, which had a cross-validation score of 0. 55 ± 0. 005. Figure  2  presents the cross-validation scores of q_kernel_training run on five qubits with a combination of the quantile-Gaussian technique for data rescaling and a decision tree for feature selection; classical SVM with RBF kernel using the combination of Yeo-Johnson for data rescaling and a decision tree for feature selection; and q_kernel_zz run on five qubits using the combination of the quantile-uniform technique and a decision tree, which were applied to the samples described in Fig.
(o) Feature's importance using XGBoost, decision tree, and random forest applied to the entire dataset (sample 5). (p) Definition of the 43 morphological features extracted for each neuron. They were extracted using the L-Measure tool,  40  providing quantitative morphological measurement from neuronal reconstruction (http:// cng. gmu. edu: 8080/ Lm/ help/ index. htm). (q) Dataset with the number of neuron morphologies for multiclass classification. From the 27,881 extracted neurons, 22,691 neurons (sample 5) remained after the application of Mahalanobis distance transformation and the suppression of all neurons with a soma surface equal to 0. for feature selection) using 20 as well as 10 selected features, which were applied to all of the samples described in Fig. 1. For q_kernel_zz, we used 20 and then 10 qubits.
After rescaling data with each of the techniques described in the method section, we combined the outputs with feature extraction techniques to dimensionally reduce the number of features of the dataset to two principal components. Table  1  presents the SVMs' cross-validation scores with classical and quantum (two-qubit) kernels. The results from applying the algorithms on sample 5 are displayed. We present the five algorithms that provided the best cross-validation accuracy scores. Here, the five algorithms that provided the optimal accuracy were SVMs with classical feature maps, which indicates that feature extraction techniques may not be the most suitable techniques for the quantum kernel methods used in this study. SVM RBF with Yeo-Johnson for data rescaling and LDA for feature extraction had a cross-validation score of 0.
Furthermore, many disease processes that affect the nervous system exhibit selectivity and may only involve certain neuronal types while sparing others. To a larger extent, the shape and extent of the dendritic tree define the neuron's role in the organization of the nervous system as a whole, since the neuron determines the field from which it receives its input. Dendrites form relatively late in the development of neurons, and always preceded by the outgrowth of axons and axon connection. The high-throughput generation of data is expected to enable the learning of a systematic taxonomy from data by considering molecular, morphological, and electrophysiological features  21  ; however, this huge amount of data requires appropriate storage and processing. In a transition in neuroscience over the last decade, data scientists in computational modeling and database experts have gained a major place in the field by extracting knowledge and finding regularity within the data.