dataesr/scientific-paragraphs-categorization huggingface dataset:
The geographical distribution of spoligotype families followed known patterns, with T and LAM being most prevalent in Europe, Africa, and the Americas, AFRI in West Africa, and Beijing found across most geographic regions. Interestingly, there were 3,775 spoligotypes that were present in < 5 isolates, of which 1,647 (43. 6%) had an assigned SIT in the SITVIT2 database, indicating that these are valid spoligotypes, although rare. In the future, it may be possible to assign family names to these unique and valid spoligotypes if sample sizes are greater. The remaining spoligotypes (56. 4%) may represent novel forms or have been generated from isolates with spurious or low CRISPR locus coverage, although we employed quality control procedures on genome-wide coverage to minimize the latter. Generally, there was a strong association of spoligotypes to lineage with the majority of spoligotype families associated exclusively to one of the major lineages.
Conversely, there were some instances where a sub-lineage contained multiple major spoligotypes (e. g. , EAI2-Manila and EAI2-Nonthaburi, both lineage 1. 2. 1. 2), and hence the spoligotype represents the higher resolution unit for the related samples. In these cases, the sub-lineage system and corresponding SNPbarcode could be further refined to reflect this diversity. While current observations are based on large numbers of samples for the main lineages (L1-L4), fewer number of samples have been sequenced for the other lineages (L5-L9) and the relationships between WGS and spoligotype may not be as stable. These can be explored further through growing WGS datasets, and applications of phylogenetic analysis and in silico strain typing using the updated TB-Profiler tool.
Conclusions We have presented a method to predict in silico spoligotypes from WGS, called "Spolpred2", which is fast and accurate. This software is freely available as part of the TB-Profiler package. Spoligotypes are useful in tracking the epidemiological spread of MTBC, but do not necessarily agree with the lineage system at more refined resolution of sub-lineages. We have clarified this relationship, which adds to the power of using a dual approach to strain typing.
Material and methods Sequence data and processing. The input dataset consists of 28,436 isolates for which next generation sequences have been deposited on the ENA, and have been previously described elsewhere  13. All sequence data was aligned to the H37Rv reference genome (NC_000962. 3) using BWA mem software (v0. 7. 17). Variants were called using GATK HaplotypeCaller (v4. 1. 4. 1 -ERC GVCF) and merged using the GATK CombineGVCFs tool. Variants were filtered to remove indels, SNPs in pe/ppe genes and those that had > 10% missing genotypes across isolates. Filtered variants were transformed to a multi-fasta format file, which was subsequently used as the input to phylogenetic reconstruction by iqtree software (v2. 1. 2 -m GTR + G + ASC). Lineage assignments were generated using TB-Profiler (v4.
Spolpred2 algorithm. While the original algorithm relied on direct matching of hard-coded spoligotypes with input reads, the Spolpred2 spoligotype prediction tool is based on k-mer counting. The KMC3 tool  18  is used to count k-mers from either raw fastq, fasta, bam or cram format. A k-mer length equal to the length of the unique spacers (k = 25) is chosen. For bam and cram files, alignment against the H37Rv reference genome (AL123456. 3)  19  is assumed and only reads falling between positions 3,117,003 and 3,127,206 are analysed. A custom Python script then loads the counts and performs a direct look-up of the spacers, accounting for up to two mismatches. The presence or absence of a spacer is determined by comparing the counts against a minimum threshold. The threshold is selected to be 20% of the maximum spacer count.
The presence/absence vector represents the binary spoligotype and is converted into an octal form. A limitation is that in silico and in vitro spoligotypes do not always match, due to potential interrupted spacers or direct repeat sequences leaving short sequences that are not detected by the algorithm. To obtain a full and precise reconstruction of the CRISPR locus, the semi-automatized application "CRISPR-Builder-TB" can be used  11. Finally, the associated family and SIT are reported by performing a look-up in a CSV formatted file, which currently contains data for all isolates submitted to SITVIT2  20. The code was integrated into TB-Profiler (v4. 4. 2)  13  and can be invoked to perform spoligotyping only, or as part of the standard profiling pipeline, which also reports genotypic drug resistance and SNP-based (sub-)lineage profiles.
Using a standard laptop with 8 Gb ram, Spolpred2 can profile from bam and fasta format files with 1000-fold coverage in < 10 s, whilst perform the same task on raw fastq files with up to 500-fold coverage in < 30 s. This translates to a 22-and 500-fold speed improvement from fastq and BAM files, respectively, when compared to the original SpolPred tool. Association of spoligotypes to lineages. Spoligotypes were inferred using Spolpred2 software across 28,436 MTBC samples with WGS data, location, and drug resistance phenotypes. Lineages and sub-lineages were inferred using the TB-Profiler tool, which implements a published barcode 1. The number of (sub-)lineages within spoligotype families was estimated. As there were many spoligotypes in low numbers of samples and therefore offering little predictive power, those appearing in < 5 isolates were excluded.
Acknowledgements GN was funded by an  BBSRC -LIDO PhD  studentship. TGC and SC are funded by the  Medical Research Council UK  (Grant no. MR/X005895/1 ). CJM is supported by the  Academy of Medical Sciences (AMS) , the  Wellcome Trust , the  Government Department of Business, Energy and Industrial Strategy (BEIS) , the  British Heart Foundation and Diabetes UK  and the  Global Challenges Research Fund (GCRF)  via a  Springboard grant  ( SBF006\1090 ). The authors declare no conflicts of interest. The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript should be declared.
Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorders, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce Bioptim, an easy-to-use Python framework for biomechanical optimal control based on both direct multiple shooting and direct collocation, handling musculoskeletal models. Relying on algorithmic differentiation, Bioptim is fast and it interfaces several nonlinear solvers. The software is both computationally efficient (C++ core) and easily customizable, thanks to its Python interface. It allows to quickly define a variety of biomechanical problems such as motion tracking/prediction, muscle-driven simulations, parameters optimization, multiphase problems, etc. It is also intended for real-time applications such as moving horizon estimation and model predictive control.
Most of the time, when investigating complex biomechanics issues, we do not have this information. Concerning the non-linear solver, a variety of software exist and have been used to solve musculoskeletal NLPs. They can use different heuristics: interior point methods (Ipopt,  [20] ) or sequential quadratic programming (SNOPT  [21] , Acados  [22] ), but they are all gradient based. Therefore, derivatives of the NLP cost function and constraints are required to perform optimization. These derivatives can be obtained by finite differences (often implemented but inaccurate thus compromising convergence) or computed exactly using automatic differentiation (requiring to write all dependencies of the software in symbolic variables), using, e. g. , CasADi  [23]. To promote the use of musculoskeletal optimal control in biomechanics research, we identified a strong need for a dedicated tool, as shown by the recently launched SCONE (2018)  [24]  and OpenSim Moco (2020)  [25].
The biomechanics community being mainly composed of software users, such a tool should requests a flexible user interface written in a widely used high-level and if possible open-source language (e. g. Python) with a low-level core (e. g. C++) for efficiency. To develop such a software, four interrelated components are essential in our opinion: i) a musculoskeletal modeling software, with a visualization module (multibody kinematics and dynamics, muscle dynamics, etc. ), ii) a method for automatic differentiation, iii) a discretization approach, and iv) one or some nonlinear programming (NLP) solvers. General-purpose optimal control software (e. g. GPOPS-II  [26] , Muscod-II  [27] ,  [28] , Acado  [29] ]) address ii) to iv) but they need to be interfaced with a musculoskeletal modeling module and they do not provide any built-in biomechanics features (physiological cost functions, kinematic constraints, etc.
). SCONE is definitely oriented towards biological motion, but it relies on a single shooting transcription, which as shown to be limiting on complex problems. The aforementioned OpenSim Moco, is a welcome initiative that draws its strength from its integration with the widely used OpenSim. However, it faces the following limitations: i) it is interfaced with only one solver (IPOPT), ii) it only implements direct collocation as transcription method, preventing the possibility to use multiple shooting and iii) it is not as flexible as required by the community, since it requires C++ knowledge for the user to develop new features, such as new objective functions. The objective of the present paper is to introduce Bioptim  1  , an open source optimal control software dedicated to musculoskeletal biomechanics.
Bioptim is based on C++ code for computational efficiency but the user interface is written in Python for flexibility and ease-of-use. The OCP transcription can be either direct multiple shooting to preserve the possibility of using arbitrarily accurate ODE solvers for the integration, or direct collocation for the user to choose the most efficient formulation to solve its problem. In both cases, the forward dynamics function is fully parallelized over the shooting intervals for more efficiency. Bioptim's core is fully written in CasADi symbolics to benefit from algorithmic differentiation and to exploit CasADi's interface with several non-linear solvers (Ipopt, SNOPT). Moreover, Bioptim is interfaced with the cutting-edge solver Acados, a recent NLP solver dedicated to direct multiple shooting, intended for realtime applications.
A. Implementation and dependencies Bioptim is the top layer of a series of libraries (Biorbd: dynamics and musculoskeletal modeling; CasADi: automatic differentiation; Ipopt/Acados: optimization; Bioviz: visualization). Within this software suite, Bioptim's main role is to shape the problem to allow its dependencies to communicate efficiently, while providing an intuitive and flexible interface to the user (Fig. 1 ). Therefore, it was written in Python for its flexibility and its widespread use among researchers. However, all intensive calculations behind the interface are performed in C/C++, keeping Bioptim both fast and easy to customize.
III. EXAMPLES In Appendix, six examples are presented to illustrate the versatility of Bioptim and give a practical overview on how to use its main features. They include an excitation-driven pointing task and a moving horizon estimation of muscle forces with arm models, twisting somersault, walking gait and jumping with whole-body models and a stabilization task with a pendulum-on-a-spring model. The performances (number of solver epochs, convergence time, integration error, optimized objective, influence of the transcription method) of each of these examples are summarized in Tab. I. When possible, problems were solved with both Ipopt and Acados. The source code of these example is available here. Beyond what is presented in this paper, every feature of Bioptim is thoroughly illustrated by additional examples available online, in the getting started folder of the project (parameter optimization, custom objects, multi-phase problems, etc.
B. Automatic differentiation One of the reasons explaining the performances of Bioptim is the rewriting of the core software, RBDL  [33]  and Biorbd implementing the dynamics, into CasADi symbolics, as in  [34] , to automatically provide the exact Jacobians and Hessians of the resulting NLP. The gain in accuracy for the calculation of derivatives leads to shorter convergence times (due to much less iterations) and to optimal solutions reached with lower tolerances. This last aspect must be emphasized for complex motions (fast, highly dynamics ones), because, for instance when using Ipopt, an optimal solution obtained with an unscaled convergence criterion of 10 -2 is very unlikely to be dynamically sound; In other words, it would diverge when forwardly integrating the controls in a single-shooting manner.
C. Python based, but fast! Bioptim was thought as an interface, and was therefore written in Python to allow the user to easily combine existing cost functions or constraints and self-implemented ones, to switch from one solver to another, etc. We believe this feature to be of importance given that the biomechanics community is mainly composed of software users rather than developers. Therefore, providing a custom interface in Python rather than in C++, was a driving objective of our work to facilitate a rapid appropriation by the community. Since flexibility and ease-of-use should not compromise the performances, the integration is multi-threaded (openMP-based) and all the inside computations are expressed as C++ CasADi graphs, interfaced with C++ NLP solvers. These graphs can either be built in casadi.
MX() or casadi. SX(). The latter requires more RAM for building the problem but is faster to solve. While both may be used with Ipopt, Acados is only compatible with casadi. SX(). By leveraging the speed of casadi. SX() graphs, we were able to estimate muscle forces in real time using Acados on a standard laptop (Appendix E). For a more in-depth analysis of the real-time estimation capabilities of Bioptim, see  [2]. Alongside with the 3D visualizer Bioviz that animates the solution, Bioptim proposes a series of onlinegenerated figures, inspired by the real-time graphics from Muscod-II  [27] ,  [28] , to visualize the optimized variables at each iteration of the solver. This is made with minimal computational cost thanks to the multiprocessing Python toolbox. Our implementation leverages the Python pickle library for easily saving and loading OCPs for, e.
D. Fast vs robust NLP solvers Fast solvers, such as Acados, offer the opportunity to use multi-start approaches on complex problems, to circumvent the obstacle of local minima  [13] ,  [19]. It also allows to get meaningful initial solutions from simpler problems, for guiding the resolution of the harder problems. On the other hand, robust solvers, such as Ipopt, are convenient when the user lacks information about the sought solutions and thus cannot guide the solver through a good initial guess. For biomechanics applications, the complementary characteristics of the interfaced solvers is a really useful tool. Moreover, Bioptim's full compatibility with CasADi provides the opportunity to use any solver already interfaced with it, including thirdparty software such as SNOPT, WORHP  [35]  and KNITRO  [36]  (not tested yet).
D. Multiphase activation driven walking cycle This example is presented to introduce Bioptim's ability to deal with a multiphase locomotion estimation problem including muscle actuation and contact forces. The goal was to estimate muscle activations by tracking markers trajectories and ground reaction forces. The model was a 3D leg with 13 DoFs (6-DoF pelvis, 3-DoF hip, 1-DoF knee, 2-DoF ankle and 1-DoF toes), driven by 19 muscle activations and residual joint torques to compensate for potential muscle actuation weaknesses. The gait cycle was defined from the first heel strike to the end of the swing phase discretized into 90 shooting intervals. To approximate the natural rolling of the foot, the stance was divided into three phases (heel, flatfoot and forefoot contacts) of fixed duration deduced from experimental force platform data and markers position (0.
E. Moving Horizon Estimation of Shoulder Elevation This example is presented to introduce Bioptim's ability to provide real-time estimation of biomechanical variables. The goal was to perform a real-time estimation of dynamically consistent joint kinematics and muscle forces, using a moving horizon estimation (MHE) approach (i. e. an optimization approach that uses a series of measurements observed over time). A shoulder elevation motion was performed with a 4-DoF (q) arm actuated by 19 Hill-type muscle elements. The control inputs of the model were the muscle activations (a). The MHE implementation consists in splitting the OCP into a succession of smaller ones for processing fixed-size subsets of the tracking data moving forward in time. Each time one subproblem is solved, a new measurement is added, the oldest one is discarded and a new subproblem is defined.
At the molecular level, we demonstrated that gigaxonin controls the turnover of the Ptch receptor, to positively modulate Sonic Hedgehog (Shh) pathway activity, one of the key developmental machinery sustaining neuron and muscle fate in vertebrates  (Jessell, 2000; Te Kronnie & Reggiani, 2002). In the absence of gigaxonin in zebrafish, Shh activity is reduced, leading to an impairment of motor neuron stability and somitogenesis, and the abolishment of neuromuscular junction formation and denervation. In the field of NMDs, most therapeutic efforts have focused on gene therapy  (Juneja et al, 2019; Ravi et al, 2019) , and this approach is being considered for GAN with an ongoing phase I clinical trial using intrathecal administration of an AAV9-GAN product  (ClinicalTrials. gov Id: NCT02362438, 2015).
Zebrafish embryos' small size allows them to be placed in 96-well plates and to be easily treated by balneation. Finally, the transparency of the embryos facilitates investigations at the physiological level within tissues and is well-documented for neuronal, muscle, and neuromuscular systems  (Pappalardo et al, 2013). To date, more than 65 smallmolecule screens in zebrafish have been reported in the literature  (MacRae & Peterson, 2015). Overall, this approach led to the identification of about 10 drug candidates, which are currently tested in clinical trials in various fields of biomedical science  (Patton et al, 2021). Among them, the neuroleptic compound pimozide, which is under Phase 2 clinical trial (ClinicalTrials. gov Id: NCT03272503, 2017), was first identified from a high-throughput screen in a zebrafish model for ALS  (Patten et al, 2017) , hence confirming the great value of zebrafish in clinical translation.
To validate our phenotypic in vivo readout for extensive drug screening, we assessed the robustness of the assay through assignment of Z'-factor quality metrics. Z'-factor is a dimensionless number that discriminates the positive (i. e. , noninjected wild-type (WT)) and negative (i. e. , WT injected with MO) controls, used to quantify the range of motility measurements within the test. Z'-factor analysis of our data was satisfactory (Z' = 0. 57), within the range of other similar zebrafish screens (from -7. 1 to 0. 78;  Walker et al, 2012) , ensuring maximized throughput and reproducibility. Subsequently, we established experimental conditions, including: (i) an evaluation of potential plate effects (timing of analysis and position on the plate); (ii) different timing and methods of dechorionation; and (iii) the number of fish tested per drugs.
System biology approach identifies three functional groups and recurrence within Hits To better refine our Hits, we performed an additional selection step, using System Biology (Filter I). The screened Prestwick Chemical Library ® has a high chemical and pharmacological diversity. We first classified the 59 common Hits compounds according to their pharmacological class annotations, as described in the library database  (Fig EV3). This analysis evidenced an enrichment of the common Hits in the Cardiovascular system (from 17. 6; 4% to 23. 73%), in CNS (from 17. 65 to 18. 64%), and to a lesser extent the Neuromuscular system. Conversely, this approach ruled out several pharmacological categories, including Gastroenterology and Infectiology. At this decision-making stage, a common limit for such phenotype-based screenings is the difficulty to further determine the mechanisms of action of the Hits compounds  (Schenone et al, 2013) , as the approach is meant to identify disease-modifying drugs regardless of the knowledge of their molecular targets.
Therefore, to enrich our strategy, we conducted an in silico system biology approach (Filter I) to identify the targets of our Hits and predict potential mechanisms of action  (Fig 5). At first, a Drug-Target list was constructed with known Drug-Target Interaction data extracted from DrugBank for the 59 common Hits. This approach identified 95 poly-pharmacological targets for 44 common Hits. However, the database does not report any known targets for 15 of the 59 common Hits. The latter were classified as Unknown Hits and kept for additional filtering stages (Filter II). Next, using the STRING v. 11 database, we retrieved the interactions between the targets to extract functional associations, that is, links between targeted proteins that participate jointly in specific biological functions.
Development of a novel quantitative imaging methodology refines Hits restoring the stability of the neuromuscular junction To increase the stringency of the selection of our Hits, we designed an additional filtering method (Filter II), based on the cellular deficits identified in the gan zebrafish models (Fig  1 ). To test whether the selected compounds exert their positive effect through the stabilization of the axonal length and NMJ, we designed a cellularbased method to quantify these parameters in noninjected WT, nontreated, or treated gan morphants at 48 hpf. This was achieved with the development of a novel high-content pipeline for image acquisition and analysis, using the Opera Phenix™ High Content Screening System confocal and the Harmony software (methodology published in Lescouz eres et al, 2022).
Briefly, pMN axons and   S1-S4. Taken together, the restoration of the AChR clustering and area of colocalization with axonal projection is a hallmark of our Hits (Favorite and Unknown) in rescuing synaptic contacts in the gan morphants. While these data provide the statistical significance of entire groups (Favorites, Unknown versus noninjected, and MOinjected embryos), we evaluated the specific scores of individual Hits, to rank their beneficial value. Toward this aim, we integrated The 103 Hits identified in the gan MO-injected fish were further tested in the gan CRISPR zebrafish line at 5 dpf. Due to the great imbalance toward positive results (all 103 compounds are Hits), the statistical analysis used a normalization of Percent Activation (%). Readout signals were calibrated and normalized to a 0-100% effect scale, in which 0% represents no effect and 100% corresponds to the locomotion activity of WT fish (See details in Appendix Table  S1 ).