dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008)  and MoRDA  (Vagin & Lebedev, 2015)  are automated pipelines that use MOLREP to place matches from curated databases containing fragments, domains and homo-and hetero-oligomers. Dimple  (Wojdyr et al. , 2013)  is an automated procedure that aims to quickly arrive at a solved structure of a protein-ligand complex starting from an isomorphous crystal; the software will phase the data and produce preliminary maps, including a difference density map where omit density for a ligand might be found. 2. 3. 2. Experimental phasing. The steady increase in unique new domains deposited every year in the PDB, the availability of millions of predicted models in the AlphaFold Protein Structure Database  (Varadi et al. , 2022)  and the continuous improvement of fragment-based ab initio phasing methods mean that experimental phasing is increasingly becoming a last-resort approach to recovering phases; it also means that software will have to deal with the most difficult cases.
Validation and deposition Both the CCP4i2 and CCP4 Cloud interfaces include a validation and deposition interface developed in collaboration with the PDBe (the Protein Data Bank in Europe; wwPDB Consortium, 2019;  Armstrong et al. , 2020). The purpose of this tool is to prepare mmCIF files for deposition; additionally, it provides the convenience of letting users see what their preliminary wwPDB validation report  (Gore et al. , 2012 (Gore et al. , , 2017) )  would look like and allowing them to fix errors and notice interesting chemical features of a model before going through the actual deposition process. Also, in preparation for deposition, the model and structure factors are converted into an mmCIF, which in turn allows the wwPDB to pre-populate many of the required metadata for deposition, such as refinement statistics.
Further validation tools exist in CCP4 outside this online validation process. Protein model validation can be performed with a variety of tools. MolProbity analyses backbone geometry, rotamers and clashes, and produces a script file that will generate a menu within Coot containing lists of outliers. Coot itself contains a plethora of interactive and live-updated validation tools, ranging from MolProbity-equivalent metrics to other less frequently quoted metrics, for example the Kleywegt Plot, which can be of great value depending on the problem. The EDSTATS software  (Tickle, 2012)  provides a unique analysis of model-to-data fit, separating results by main chain and side chain and looking at difference density, with the results being able to point out common modelling problems, such as poorly fitting regions requiring a peptide flip.
Version 8. 0 of CCP4 has seen the gradual inclusion of PDB-REDO  (Joosten et al. , 2012)  functionality into the CCP4 interfaces; for example Tortoize  (Sobolev et al. , 2020) , a tool that analyses main-chain and side-chain geometry and reports Z-scores for every amino acid, is now integrated into the CCP4 validation tasks. The visual output of PDB-REDO calculations is displayed consistently across CCP4i2, CCP4-Cloud and the PDB-REDO website by encapsulating various interactive plots and tables in a self-contained single web component. Detection of errors, particularly sequence-register errors, by analysing the agreement between observed contacts and interresidue distances with the predictions from software such as AlphaFold2 (Sanchez Rodrı ´guez et al. , 2022) is available in ConKit  (Simkovic et al.
, 2017). The findMySequence software  (Chojnowski et al. , 2022)  uses machine learning for the identification of unknown proteins in X-ray crystallography and cryo-EM data, with the added benefit of detecting elusive register errors, which may have a detrimental effect on the quality of the rest of the structure. The Iris validation framework  (Rochira & Agirre, 2021 ) is a standalone tool that displays a variety of validation metrics as concentric circles, with modelling errors becoming visible as ripples in successive circles. Carbohydrate model validation, including protein glycosylation, can be carried out with the Privateer software  (Agirre et al. , 2015) , which in the MKIV version incorporates checks of glycan composition against offline mirrors of several glycomics databases  (Bagdonas et al.
Under the bonnet The dxtbx toolkit for DIALS  (Parkhurst et al. , 2014)  is included as part of the cctbx (Grosse-  Kunstleve et al. , 2002)  distribution; the clipper-python module  (McNicholas et al. , 2018) , a SWIG wrapper around the original C++ Clipper library, is also included and supports a number of functions of the CCP4i2 interface, including the Iris validation framework  (Rochira & Agirre, 2021). At a higher level, CCP4i2  (Potterton et al. , 2018)  provides code reusability via the command line, offering a mechanism for executing Pythononly pipelines without a running instance of the graphical user interface (headless mode). CCP4 Cloud projects and automatic structure-solution workflows can also be initiated from the command line using the 'cloudrun' utility; this is useful for performing serial computations for selected targets.
Future plans The transition towards web technologies, which is already under way with the introduction of CCP4 Cloud, will be A collection of newer representations included in the CCP4 Molecular Graphics project (CCP4mg). (a) PDB entry 2bn3 is a high-resolution model of insulin  (Nanao et al. , 2005) ; it is shown here as worms, with water molecules drawn as ellipsoids, both coloured and scaled by the anisotropic B factors of the model. (b) PDB entry 3v8x  (Noinaj et al. , 2012)  is a structure of human transferrin (chain B), drawn here as a solvent-accessible surface with N-glycans shown as Glycoblocks  (McNicholas & Agirre, 2017). (c) PDB entry 3c02, a structure of aquaglyceroporin from Plasmodium falciparum  (Newby et al. , 2008) , embedded in a lipid bilayer by CHARMM-GUI  (Jo et al. , 2008) ; lipids are shown as cartoons.
completed in the near future by the introduction of fully fledged model-building, visualization and figure-preparation web-browser interfaces to the existing Coot and CCP4mg engines. We also foresee an increase in the number of connections to theoretical modelling packages such as AlphaFold  (Jumper et al. , 2021)  and RoseTTAfold  (Baek et al. , 2021) , as well as deeper harnessing of the AlphaFold Protein Structure Database  (Varadi et al. , 2022).
funded by  The Royal Society  ( RGF/R1/181006 ). JoseJavier Burgos-Marmol and  Daniel J. Rigden  are supported by the  BBSRC  ( BB/S007105/1 ). Robbie P. Joosten  is funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 871037  (iNEXT-Discovery) and by  CCP4. This work was supported by the  Medical Research Council as part of United Kingdom Research and Innovation, also known as UK Research and Innovation :  MRC  file reference No. MC_UP_A025_1012  to  Garib N. Murshudov , which also funded  Keitaro Yamashita ,  Paul Emsley  and  Fei Long. Robert A. Nicholls  is funded by the  BBSRC  ( BB/S007083/1 ). Soon Wen Hoh  is funded by the  BBSRC  ( BB/T012935/1 ). Kevin D. Cowtan  and  Paul S. Bond  are funded in part by the  BBSRC  ( BB/S005099/1 ).
Robbie P. Joosten  has been the recipient of a  Veni grant  ( 722. 011. 011 ) and a  Vidi grant  ( 723. 013. 003 )from the  Netherlands Organization for Scientific Research (NWO). Maarten L. Hekkelman ,  Robbie P. Joosten  and  Anastassis Perrakis  thank the  Research High Performance Computing  facility of the  Netherlands Cancer Institute  for providing and maintaining computation resources and acknowledge the institutional grant from the  Dutch Cancer Society  and the  Dutch Ministry of Health, Welfare and Sport. Tarik R. Drevon  is funded by the  BBSRC  ( BB/S007040/1 ). Randy J. Read is supported by a  Principal Research Fellowship  from the  Wellcome Trust  (grant  209407/Z/17/Z ). Atlanta G. Cook is supported by a  Wellcome Trust   SRF (200898 ) and a  Wellcome Centre for Cell Biology core grant  ( 203149 ).
Isabel Uson acknowledges support from  STFC-UK/CCP4 : 'Agreement for the integration of methods into the CCP4 software distribution, ARCIMBOLDO_LOW' and  Spanish MICINN/ AEI/FEDER/UE  ( PID2021-128751NB-I00 ). Pavol Skubak  and  Navraj Pannu  were funded by the  NWO Applied Sciences and Engineering Domain  and  CCP4  (grant Nos. 13337  and  16219 ). Bernhard Lohkamp was supported by the  Ro ¨ntgen A ˚ngstro ¨m Cluster  (grant  349-2013-597 ). Nicholas Pearce is currently funded by the  SciLifeLab and Wallenberg Data Driven Life Science Program  (grant  KAW 2020. 0239 ) and has previously been funded by a  Veni Fellowship  ( VI. Veni. 192. 143 )from the  Dutch Research Council (NWO) , a  Long-term EMBO fellowship  ( ALTF 609-2017 ) and  EPSRC  grant  EP/G037280/1. David M. Lawson  received funding from  BBSRC   Institute Strategic Programme Grants  ( BB/P012523/1  and  BB/P012574/1 ).
Approaches based on Probability Density Function Differential Equation (PDF-DE) have been used for modeling premixed turbulent combustion under Eulerian frame reference, but their solution requires the diffusion phenomena to be closed. In this context, Stochastic Differential Equations (SDEs) are an alternative method to solve the PDF-DE under a Lagrangian reference. PaSR-SDE is a Matlab package that solves the Partially Stirred Reactor mathematical formulation that takes into account mixing models for the closure of the term describing the scalar diffusion, and it is also used as a numerical tool in evaluating the influence of several mixing models on the thermochemical properties in a premixed turbulent combustion situation.
Introduction Among the recent challenges in studying turbulent reactive flows is the development of computational models capable of adequately describing the interactions between combustion, turbulence, and mixing of reactants  [1] [2] [3] [4]. The Partially Stirred Reactor (PaSR) approach can take into account the coupling between a detailed description of the chemical process and the micro-mixing process between reactants and combustion gases, i. e. , the presence of fluctuations in thermodynamic properties during combustion, which is directly related to the influence The code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean. com/). More information on the Reproducibility Badge Initiative is available at https://www. elsevier. com/physical-sciences-and-engineering/computer-science/journals.
Sabel'nikov and Gorokhovski  [16]  proposed stochastic mixing models, the most complex and functional, the Extended Interaction by Exchange with the Mean model (EIEM) and the Extended Langevin Model (ELM). Both models replace the single time scale used in the IEM and Langevin models with a stochastic description of the frequency of turbulent motion and by adding a term that simulates the dissipation of the scalar field, which leads to a scattering of the phase space trajectories while ensuring the boundedness property. Therefore, it is possible to achieve a PDF relaxation from an inert scalar field to a Gaussian shape of a single transported scalar. Despite the significant increase in open-source codes for the simulation of reactive processes seen in the last decade, mainly driven by developers and users of Open FOAM  [17] , and Cantera  [18]  packages, the literature is still lacking in Matlab codes to simulate chemical processes with stochastic micro-mixing models.
Software details The PaSR-SDE package is a Matlab code that solves the governing equations of a PaSR considering for model closure one of the following micro-mixing models: IEM, EIEM, LM, and ELM. In this setting, the outcome of the combustion process is governed by the competition between the characteristic time scales of the chemical reaction, mixing, and the residence time of the gases inside the PaSR. The PDF transport equation is solved employing an equivalent system of stochastic differential equations (SDEs) using the Monte Carlo technique  [19, 20] , using the  [21]  implicit numerical method. This allows obtaining a characterization of the transported scalar in terms of statistical properties like mean, variance, kurtosis, or other high-order moments. The code has a modular structure, and each routine/function has a detailed description of its functionality via internal comments.
Impact overview PaSR-SDE was developed at the Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), between 2005 and 2007, during the first author's master course  [8] , under the supervision of the second author. The results of this dissertation, obtained with the code, were published in the Proceedings of the Combustion Institute  [9]. Over the years, the first version of the code became incompatible with newer versions of Matlab, requiring a review, reformulation, and updating of practically all code modules. Such changes were introduced in early 2023 by the third author, who introduced detailed comments in English in all the code modules, seeking to provide the end user with an accessible tool. The open-source movement has sparked a trend toward creating code that is not only functional but also educational  [23] [24] [25] [26] [27] [28] [29] [30] [31].
PaSR-SDE follows this trend, offering the following features: • Real-world applications: Simulating premixed turbulent combustion in a partially stirred reactor is a relevant and practical problem in energy and combustion engineering. By using PaSR-SDE, students can learn how to model and analyze real-world problems, which is a valuable skill in engineering and science; • Understanding of SDEs: Stochastic differential equations are widely used in engineering, physics, and finance. By using PaSR-SDE, students can see how an SDE solver is implemented, which is a fundamental skill for experts in these areas; • Hands-on experience with simulation: Once it is a Matlab code, PaSR-SDE provides a user-friendly interface for simulating premixed turbulent combustion, which is a complex process. By using the code, students can gain hands-on experience with simulation, which is a valuable skill in engineering and science; • Understanding of combustion dynamics: The simulation of premixed turbulent combustion involves the interplay between fluid mechanics, heat transfer, and chemical reactions.
By using PaSR-SDE, students can better understand these processes, which is essential for solving real-world problems in the energy and combustion engineering field. In comparison to other open-source codes that simulate the dynamics of a partially stirred reactor, such as CRFlowLib (C/C++)  [22] , ISAT-CK7 (Fortran)  [32] , and PyJac (Python)  [33] , PaSR-SDE sets itself apart by incorporating more advanced micro-mixing models such as EIEM and ELM. The authors believe that this decision brings two pedagogical benefits to the table when using PaSR-SDE as an educational tool: (i) providing students with a simulator that has more realistic physical chemistry, and (ii) allowing students to engage with stochastic differential equations as a modeling tool, which is not typically taught in standard numerical methods courses.
Despite the computational efficiency offered by CRFlowLib and ISAT-CK7, implemented in lowlevel languages, PaSR-SDE stands out for its ease of use, as the Matlab environment is usually more accessible for beginner students compared to C/C++ or Fortran. In conclusion, PaSR-SDE is an open-source research and educational tool that has the potential to be of great utility for the academic and industrial community working with the simulation of reactive processes.
Final remarks PaSR-SDE is a computational package that provides an extensive framework to analyze an ideal partially stirred reactor with several types of micro-mixing stochastic models. Due to its modular structure, the code can be used together with other codes/libraries to simulate combustion systems (e. g. , CRFlowLib  [22] ). Once very few open-source packages exist to simulate combustion systems stochastic micro-mixing models, this package may be a helpful research and educational tool that can contribute to the community of reactive flow simulation.
Acknowledgments The authors are indebted to the Brazilian agencies  National Council for Scientific and Technological Development (CNPq) ,  Coordination for the Improvement of Higher Education Personnel (CAPES) , and  Research Support Foundation of the State of Rio de Janeiro (FAPERJ)  for the financial support given to this research. During this work  L. F. Figueira da Silva  was on leave from  Intitute Pprime ,  Centre National de la Recherche Scientifique (CNRS).
• Easy definition of new processes, such as light interception, photosynthesis, growth, soil water transfer, etc… • Fast, interactive prototyping of models, with constraints to help users avoid errors, but sensible defaults to avoid over-complicating the model writing process • Automatically manage input and output variables, time-steps, objects, coupling of models with a dependency graph • Switch between models without changing any code, with a simple syntax to define the model to use for a given process • Reduce the degrees of freedom by fixing variables, passing measurements, or using a simpler model for a given process • Fast computation, with 100th of nanoseconds for one model, two coupled models (see this benchmark script), or the full energy balance of a leaf using PlantBiophysics. jl (Vezy & Treillou, 2023) , a package that uses PlantSimEngine • Out of the box sequential, parallel (multi-threaded) or distributed (multi-process) computations over objects, time-steps and independent processes (thanks to Floops.
It also requires unit testing to ensure that the implementation is correct, and integration testing to evaluate the model within its framework. This process is often tedious and error-prone, and requires a lot of time and effort. The need for a tool that can help researchers and practitioners prototype, implement and evaluate plant or crop models at any scale, without the hassle of technical computer science details, is therefore clear. PlantSimEngine addresses this need by providing a flexible and user-friendly framework for declaring processes and implementing models for their simulation. The package focuses on key aspects of simulation and modeling, such as ease of definition of new processes, fast and interactive prototyping of models, streamlined management of input and output variables, and the ability to switch between models without changing any underlying code.
It also provides a way to test the models or forcing processes to observations to reduce the number of degrees of freedom. It also offers fast computation, scalability, and composability, making it ideal for developing Functional-Structural Plant Models (FSPMs) and crop models. For example the simulation of a toy model for leaf area over a year at daily time-scale took 260 μs to perform (i. e. 688 ns per day) on an M1 MacBook Pro 14, and 275 μs (i. e. 756 ns per day) when coupled to a model for light interception based on the Beer-Lambert law of light extinction (see provided benchmark script), which shows that PlantSimEngine scales well with several models. These performances are on par with what could be expected if the package was developed using a compiled language such as Fortran or C, and is by far faster than any interpreted language implementation.
State of the field PlantSimEngine is a state-of-the-art plant simulation software that offers significant advantages or different pattern designs over existing tools such as OpenAlea  (Pradal et al. , 2008) , STICS  (Brisson et al. , 1998) , APSIM  (Brown et al. , 2014; Holzworth et al. , 2014)  or DSSAT  (Jones et al. , 2003). The use of Julia programming language in PlantSimEngine allows for quick and easy prototyping of models compared to compiled languages (STICS, APSIM, DSSAT) and can achieve significantly better performance than typical interpreted languages (e. g. Python used in OpenAlea), without the need for translation into another compiled language. For example PlantBiophysics. jl, a Julia package that implements ecophysiological models using PlantSi-mEngine. jl is 38649 times faster than the same implementation in R in plantecophys  (Duursma, 2015).
The package's impressive features are primarily provided by the Julia programming language. Julia is a high-level, high-performance, and dynamic programming language widely used in various scientific fields, and particularly for biology  (Roesch et al. , 2023). The Julia community has recently developed remarkable tools such as Cropbox. jl (Yun & Kim, 2022) , and CliMA Land  (Wang et al. , 2021). Cropbox. jl is a declarative crop modeling framework that share a common objective with PlantSimEngine: simplifying the process of defining models by eliminating the complexities of underlying simulations. While both frameworks can be applied across a wide range of scales, including land surface models (LSM), crop models, and functional-structural plant models (FSPM), they differ in their implementation strategies.
Cropbox. jl defines a domain-specific language (DSL) tailored specifically for crop modeling, providing an intuitive approach for model definition. On the other hand, PlantSimEngine prioritizes speed as a fundamental aspect, ensuring efficient computations and enabling scalability right from the start. This is accomplished through meticulous considerations, such as type stability and pre-allocation to optimize performance. Type stability in PlantSimEngine ensures that variables maintain consistent types throughout computations, which leads to efficient execution. Additionally, the compatibility of PlantSimEngine with MultiScaleTreeGraph. jl  (Vezy, 2023)  further enhances its scalability and facilitates multi-scale computations. MultiScaleTreeGraph. jl provides a powerful framework for representing and analyzing hierarchical structures, enabling PlantSimEngine to seamlessly handle complex systems spanning multiple scales.
e. models that make an explicit, hard-coded call to another model. PlantSimEngine leverages Julia's multiple-dispatch to automatically compute the full dependency graph at compile time, including hard-dependencies. PlantSimEngine also offers a simple and generic API to define methods for model calibration that are used to programmatically calibrate models and sub-models. Additionally, PlantSimEngine offers the ability to reduce the number of degrees of freedom by fixing variables, passing measurements, or using a simpler model for a given process, making it an easier and more flexible solution for plant simulation. PlantSimEngine's approach streamlines the process of model development by automatically managing model coupling, time-steps, parallelization, input and output variables, and the type of objects used for simulations (vectors, dictionaries, and multi-scale tree graphs).
Therefore, the number of different shear sites assigned to an IS will be proportional to the initial number of contributing cells in the population studied, thus the clonal abundance of each IS in the starting sample does not consider the biases introduced with PCR amplification. Only integration events identified by at least 2 independent DNA fragments were considered, in the analyses, to remove potential PCR artifacts. The resulting table with all independent AAV IS reads is available in the SRA BioProject (PRJNA832955).
Databases adopted for annotation of integration sites Reference Sequences (RefSeq), CpG, TSS, RAG signal sequence (RSS), TCR, and END-seq analysis were performed using bedtools closest  43  using the databases obtained as described below. RefSeq, TSS, and CpG databases were downloaded from   51  and are reported in supplemental Table  7. TCRs and END-seq databases were converted from mm10 coordinates to mm9 coordinates using genome liftover (https://genome. ucsc. edu/ cgi-bin/hgLiftOver).