dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING L. P. S. is a  Sir Henry Wellcome   Postdoctoral Fellow  funded by  Wellcome  [ 220422 / Z / 20 / Z ]; R. C. M. was supported by funding from  Wellcome  [ 106918 / Z / 15Z ]; E. P. C. R. acknowledges support from the  Fondation pour la Recherche  M édicale [ EQU201903007835 ];  Laboratoire d'Excellence IBEID: Integrati v e Biology of Emerging Infectious Diseases  [ ANR-10-LABX-62-IBEID ]; the computational aspects of this r esear ch wer e supported by the  Wellcome Trust   Core Award  [ 203141 / Z / 16 / Z ];  NIHR Oxford BRC. Funding for open access charge: Block grant funding from  University of Oxford , applicable to  Wellcome -funded r esear ch.
Rapid searching for homologous complexes is now supported by annotated databases of such predicted relationships  (70). The processes of scoring and ranking models derived from template-based docking conform to the same principles as for classical docking but with the potential advantage of having to score and rank fewer models. However, there is an obvious caveat: If the modeled complexes do not comply with the principle of conservation of homologous interfaces, then the native complex will not be sampled. This contrasts with the ab initio methods, in which there is always a chance, provided that flexibility does not dominate, of at least having a near-native model in the complete list of models generated. Moreover, there are clearly certain categories of interactions that are not conducive to this form of docking; the classic example is antibody-antigen complexes, where evolutionary relationships between the binding partners are not expected to be prevalent, thereby limiting the options to ab initio docking methods  (40).
CAPRI CAPRI is a community-wide experiment inspired by CASP. It was established in 2001  (51)  to offer computational biologists the opportunity to test their algorithms in blind predictions of experimentally determined 3D structures of protein complexes, the targets, provided to CAPRI prior to publication. Experiments focusing on this prediction task were attempted only twice before, including once in 1996 by CASP  (51) , attracting only limited interest. Since its inception, CAPRI prediction rounds have been managed in collaboration with the Protein Databank Europe at the European Bioinformatics Institute (EBI) (https://www. ebi. ac. uk/pdbe/complex-pred/ capri/). Since the rate at which structures of protein complexes are being determined and offered as targets for prediction is slower than for single chain proteins, CAPRI runs prediction rounds on a rolling basis as targets become available  (51).
THE BREAKTHROUGH OF DEEP LEARNING-BASED PREDICTION METHODS: THE CURRENT STATE OF PLAY The past few years have witnessed a breakthrough in modeling the 3D structure of proteins. This breakthrough can be attributed to two primary factors. The first is the extraordinary growth in protein sequence databases  (131)  coupled with a less prolific, yet notable, growth in the database of experimentally determined structures  (136) ; both types of databases are freely available in public depositories. The second is the progressive introduction of cutting-edge methods in deep learning to a maturing protein modeling field  (5, 126, 129). A key role was also played by the community-wide initiatives that enabled the critical evaluation of the recent breakthrough methods for predicting the structure of single protein chains, recorded in the CASP13 and CASP14 prediction seasons  (16).
Predicting the Structure of Single Chains For the reasons discussed above, deep learning, a subfield of machine learning that utilizes multilayered artificial neural networks to extract patterns within large data sets without the need to explicitly define features of the data  (73, 117) , was uniquely primed to make substantial contributions to the protein structure prediction field. Taking advantage of this, the DeepMind scientists designed and employed their prediction engine, AlphaFold2  (56) , in the 2020 CASP season  (55). This engine was trained on approximately 170,000 experimentally determined protein structures in the Protein Data Bank (PDB)  (8)  and a massive data set on multiply aligned protein sequences of related proteins, many of unknown structure  (131) , to produce models that rival in accuracy experimentally determined protein structures, surpassing the results of their first program, AlphFold1  (119) , which was shown to perform well 2 years earlier  (118).
The power of AlphaFold2 lies in its novel multicomponent architecture that jointly embeds features from multiple sequence alignments and a residue pair representation, encoding spatial relationships between residues, and integrates graph-based components with attention learning  (60). The pipeline also involves iterative refinements of predicted residue interactions based on their predicted interactions with other residues, enabling it to encapsulate structural features to a higher level of accuracy in a fully differentiable end-to-end deep learning method  (56). To enable the community to benefit from their monumental advance, DeepMind made their source code for the trained model of AlphaFold2 freely available to anyone wishing to make new predictions (https://github. com/deepmind/alphafold).
Following suit, the group of David Baker released their deep learning protocols for protein prediction and design, RoseTTAFold, an algorithm exploring similar ideas to those of AlphaFold  (6). In a further move to accelerate scientific research, DeepMind recently partnered with the EBI to create AlphaFold-DB  (135) , which provides access to predicted structures of single protein chains for the human proteome and other key organisms, as well as to the majority of the manually curated Uniport entries (SwissProt), further extending the coverage to over 200 million catalogued proteins. This new treasure trove of structural data and the associated software tools have had a watershed effect on the field of computational and experimental structural biology, generating a flurry of studies  (16). Examples include the optimization of multiple sequence alignments fed into AlphaFold2  (12)  and a community-wide study evaluating various aspects of the structural information that AlphaFold produced and the applications that it enables  (1).
Tackling this limitation is an important goal that is receiving increased attention, as further noted below. Notwithstanding these limitations, ready access to the predicted protein structures in AlphaFold-DB and to the freely available code and resources, such as ColabFold  (90) , that can be used to predict new structures with AlphaFold2 or RoseTTAFold efficiently with more modest computational resources is having a resounding impact on experimental determination of protein structures. Rather than making experimental structural biology obsolete, it is offering new opportunities like never before. Combining these opportunities with the recent spectacular advances in cryo-EM techniques is propelling the field to new levels. In several instances, hard-tosolve X-ray or cryo-EM structures have been elucidated by using AlphaFold models in molecular replacement protocols  (66, 89).
Two examples of such instances are illustrated in Figure  3. In one, the AlphaFold2-predicted structures of the L-ring and P-ring proteins of the flagella LP ring from Salmonella (Figure  3a ) reproduce quite accurately the experimentally determined structures of these proteins (Figure  3b ) when they are part of the assembled flagella ring (54) (PDB code 7BGL) (Figure  3c ). The second example concerns the TnpA transposase from Bacillus thuringiensis, a protein displaying a high degree of structural plasticity. The recent cryo-EM structures of this protein were determined to high resolution in the apo state and in complex with transposon (DNA), where TnpA forms dimers adopting significantly different conformations (121) (Figure  3e, f  ). AlpfaFold2 predicts a single structure for the TnpA chain that accurately reproduces the structure of the TnpA protomer in the complex with transposon (Figure  3d ).
POSTSCRIPT As this review was being completed, the first results of the CASP15 blind prediction experiment, held in May through August 2022, became available, shedding further light on where the field is headed. These results indicate a consolidation of the successful performance of the prediction of single protein chains heralded by AlphaFold2 from DeepMind in 2020. This consolidation was achieved by different groups (DeepMind did not participate in the 2022 experiment) employing various implementations and different parameters of AlphaFold2, as well as bolstering and tweaking the contents of the multiple sequence alignments. Most notable, however, is the substantial progress recorded in modeling protein-protein interactions and complexes, evaluated in collaboration with CAPRI. The primary driver of this success has been a similar creative employment of AlphaFold2 and AlphaFold-multimer inference engines, often modifying them to sample a much larger number of models (137a) and/or using multiple sequence alignments augmented with sequences from various sources.
(b) DockQ values for the best models as a function of target difficulty. Individual color-coded plots refer to best models evaluated in individual CAPRI assessment periods and CASP-CAPRI prediction rounds between 2009 and 2020, as indicated in the legend. Examples of targets of different difficulty levels are shown together with their Protein Data Bank (PDB) codes. Arrows indicate the rounds in which they were offered; numbers shown in parentheses following the PDB codes refer to the models of individual interfaces of the targets in question. Target difficulty is based on sequence and structure similarity to other proteins with known experimental structures.
ACKNOWLEDGMENTS S. V. and D. K. acknowledge support from grants  R35GM118078 ,  R01 GM140098 , and  RM1135136  from the  National Institute of General Medical Sciences  and grant  DMS 2054251  from the  National Science Foundation. P. A. B. acknowledges support from the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001003 ), the  UK Medical Research Council  ( FC001003 ), and the  Wellcome Trust  ( FC001003 ). We thank  Rouslan Efremov , author of the cryo-EM structures of TnpA, for disclosing these structures prior to publication and offering the TnpA-transposon complex as a target to CAPRI.
Subjective estimates of body condition have been widely used for a relatively long time to assess fat in the live animal. Murray (1919)  defined body condition as 'the ratio of the amount of fat to the amount of non-fatty matter in the body of the living animal. In the method used in the experimental farm from which we collected the historical database analysed in this study, the prominence of the spinous processes of the anterior lumbar vertebrae is assessed by palpation. The BCS was assessed by a trained operator according to an adaptation of the original grid described by  Russel et al. (1969)  which was further divided into a 1/10 scale, i. e. , from 0 to 5 with 0. 1 increments. In the specific case of females, this information indicates a strong link with her reproductive performance, and the potential percentage of open females, lambing interval, and lamb vigour at birth being among other parameters, all closely related to the body condition of the ewe both at lambing and during the breeding season and lactation periods  (Debus et al.
Animals and experimental data The datasets used in the present study have been previously described in detail  (Macé et al. , 2018 (Macé et al. , , 2019)). Briefly, the experimental animals belonged to the Romane meat sheep breed. The monitored ewes (n = 1478) were reared under extensive conditions on the rangelands of the INRAE La Fage experimental farm (Causse du Larzac 43°54'54. 52"N; 3°05'38. 11"E, Roquefort du Soulzon, France). Ewes performed one productive cycle per year. The biological productive cycle length was thus 365 days. Data were collected for the period 2002 to 2015. In this study, the BCS was used as the main indicator to illustrate the BR dynamics (i. e. the capacity of ewes to mobilize and restore BRs). Ewes were measured for BCS, with eight measurements collected regularly during each female's productive cycle according to a physiological stage schedule.
The concept of the expected trajectory of   performance is already described in the literature  (Nguyen-Ba et al. , 2020). In the database used, the ewe population attained at most three productive cycles, and the total BCS variations was calculated as the sum of variations, for. ∈ {1,2,3} The general model for one productive cycle could then be written as Equation  1. Equation 1   ()/ = Accretion -mobilization For productive cycle , periods of BR mobilization and accretion are both assumed to  start from the point when starts to decrease ( , considered as the starting point      ) of the perturbation being studied. When the animal is no longer able to meet the energy demands induced by pregnancy and lactation using only the energy available from ingested feeds, it will mobilize the energy stored in its adipose tissues (i.
𝐵𝐶𝑆(𝑡 𝑗 )   is the number of observations for a given animal (at most N = 24). Other parameters of the model are time-related and , which are determined using a data analysis    Δ  procedure for automatic detection of the perturbation within each productive cycle. The dataset involved ewes with 1, 2 or 3 parities, and the data analysis procedure allowed automatic determination of the number of parities to be considered for each ewe, and the beginning and the duration of the associated perturbation. Global sensitivity analysis  (Saltelli et al. , 1999; Taghipoor et al. , 2016)  was used to calculate the fraction of the BCS variance explained by and. The sensitivity     indices were calculated for a hypothetic example of BCS variations for only one parity, for the BCS output at three time points before the end of perturbation , and three time   points after (details are presented in supplementary code S1 and Table  S1 ).
Modelling procedure: Automatic detection of perturbation period The beginning and end of each productive cycle ware taken to be from one mating to the subsequent one. When all data were missing in a given period, it was taken as a missing productive cycle. In this section, a data analysis procedure based on functional data analysis (FDA) was developed to automate detection of perturbations within each productive cycle. In this procedure, the beginning of a perturbation is where the (   ) observed BCS started to deviate from the initial value of ( The expected value of BCS is determined depending on the observed (  ) maximum value of BCS ( ) for each individual. For ewes with then     < 3. 4 the of value of , otherwise for ewes with , the value of   = 3. 5   < 4   = 4. The value of 3. 4 was chosen because it was the common observed maximum BCS value for most of the ewes in the dataset.
, 2018). In the construction of PhenoBR, the recovery period of each productive cycle is over  when the next productive period starts. A major challenge was then to ( =   + 1  ) determine the recovery period when second or third productive cycles were missing for individuals from the original database. For this, it was assumed that in the absence of , the recovery from the productive cycle would be over 150 days after the post-  + 1   weaning period (i. e. the period corresponding to the next mating). This procedure allowed the number of productive cycles and the associated and     to be determined for each animal. All missing values of BCS were replaced by values of the function. Fig 2 illustrates the perturbation periods as determined by the () data analysis procedure against a reproductive cycle as reported in the original dataset.
Results The PhenoBR fitting procedure to estimate parameters and converged for the       1474 ewes in this study ( = 0. 54±0. 22 units of BCS, Equation  4 ). Four ewes with   only one parity and less than four datapoints were removed, due to insufficient data for estimation of model parameters. The minimum and maximum of the residuals were 0. 06 and 1. 35 units of BCS, respectively (supplementary figure  S1 ). The dataset used in this study contained ewes with different number of parities (from 1 to 3). To minimize the intervention of the user, PhenoBR enabled us to identify automatically the number of reproductive cycles per ewe (1, 2 or 3), and in each cycle to detect the time of the beginning , and the length of the BR mobilization periods ( =212. 3±51. 0, S2 ). Results of the sensitivity analysis showed that the BCS variance is mainly explained by the parameter during the mobilization period, and   by the parameter during the recovery period.
Discussion The aim of the present study was to propose a metric of ewes' capacity to adapt to their increasing and fluctuating energy requirements through several productive cycles. We exploited the existing database derived from historical and dynamic measurements of BCS in Romane meat ewes reared in an INRAE experimental farm in France to develop a mathematical model, called PhenoBR. This model converts the individual time series data of BCS into a small number of biologically meaningful synthetic parameters to characterize body reserve dynamics of ewes. Overall and individual body reserve trajectories had been previously described but without characterizing the individual capacity of ewes for BR mobilization and accretion  (González-García et al. , 2014; Macé et al. , 2018)  , estimated in the present study, was consistent with previous results and (Δ  ) biological knowledge showing that BR mobilization lasted approximately 180 days in our experimental conditions (i.
This is likely the case for the first BCS point in one part of our ewes present in the database (supplementary Table  S2  and S3 ) for which the first BCS could be overestimated in comparison with the same BCS or energy balance status in the following parities. The slope between this first BCS value before mating and the following may be biased, which could affect results in our model (i. e. most drastic BCS variation). Our results are consistent with those reported by  Macé et al. (2018)  showing similar effects of parity and age at first lambing when considering BCS changes between successive physiological stages. The BR mobilization and accretion rates estimated in the present study indicated that ewes were able to mobilize more BR as litter size increased, while the BR accretion was less marked for ewes with larger litter size.
Positive correlations were found for BR mobilization rates between parities, (  ) suggesting that ewes maintained their biological capacity for BR mobilization across productive cycles. However, correlations between parities for the other two parameters ( suggested that BR accretion rate and BR mobilization duration varied across    , Δ  ) productive cycles. This could be due to higher environmental effects of each cycle (i. e. feed availability, number of lambs previously suckled, etc. ) on BR accretion rate and BR mobilization time. Correlations were also found between BR mobilization rate and BR accretion rate suggesting both processes are biologically linked as previously claimed by  Chilliard et al. (1998). This result was consistent with high genetic correlations found previously between BR mobilization and accretion in the same dataset, which indicated that ewes exhibiting high level of BR mobilization also exhibited high level of BR accretion  (Macé et al.
Acknowledgements The authors are indebted to  Sara Parisot  and  Paul Autran , current and former heads, respectively, of the  INRAE La Fage  experimental farm and all the staff, and particularly to  Sébastien Douls ,  Julien Pradel ,  Fabien Carrière  and  Didier Foulquié  for animal management and care as well as for their key role in the collection of experimental data. They are also indebted to  Marie-Christine Batut  for management of the database GEEDOC. The preprint of this article is deposited in Zenodo public repository and is part of the PhD thesis of T. Macé (Macé, 2020;Macé et al. , 2020). Financial support This work was part of the PhD thesis of T. Macé, supported by  INRA division of Animal Genetics  (GA) and Occitanie region. The development of the model and online software was supported by  INRAE division of Animal Physiology and Livestock Systems (Phase)  through  PhenoBR  project.
Introduction and motivation Single-cell RNA sequencing (scRNA-seq) aims to quantify the gene expression of individual cells. Each run of scRNA-seq generates large amounts of noisy and sparse data, making rigorous data analysis and biological interpretation challenging. The main steps of a scRNA-seq analysis are quality check, normalization, denoising and dimensionality reduction, clustering of cells, and identification of differentially expressed genes. All these steps are usually coupled with extensive data visualization. In bioinformatics, R and Python languages are frequently used to develop pipelines for the analysis of biological datasets. The Seurat  [1]  and Scanpy  [2]  packages, developed in R and Python, respectively, have been widely adopted for scRNA-seq data analysis. These packages perform the different tasks necessary for scRNA-seq data analysis.
For example, they allow the users to normalize raw counts (gene/feature counts matrix), apply Principal Component Analysis (PCA) for dimension reduction and different clustering methods to identify clusters of cells. However, using such packages requires prior knowledge of R or Python. This prerequisite expertise in computer programming hampers the engagement of biological experts in scRNA-seq data analysis, which limits reliable and robust biological interpretations. In this context, several web tools have been developed  [3] [4] [5] [6] [7]. These tools are accessible but present some limitations. First, some tools are restricted to few steps of the scRNA-seq analysis pipeline. For example, visnormsc performs only the normalization  [8]. Second, web tools follow a strict and previously-defined architecture, thus preventing the users with expertise in computer programming customizing or extending the tool.
Moreover, web tools process and analyze data on their hosted servers and often lack well-defined policy for data protection and integrity, which raises concerns related to data security. In addition, scRNA-seq dataset are large and can crash web applications, forcing the users to re-run the analyses from the beginning. Frameworks such as snakemake  [9] , nextflow (https://www. nextflow. io/index. html), Galaxy (https://usegalaxy. org/), and Konstanz Information Miner (KNIME)  [10]  can also be employed to implement workflows composed of several data analysis steps. For example, WASP is a snakemake workflow dedicated to scRNA-seq data analysis  [11]. Importantly, the installation, execution, and modification of such frameworks require programming expertise. KNIME is a free and open-source data analysis framework with graphical interface.
Implementation and scRNAseq_KNIME workflow architecture scRNAseq_KNIME workflow is developed in KNIME. In KNIME, a specific task, for example reading/writing files, is defined by introducing a node. A node is both a graphical entity and a basic processing unit. Some nodes are associated with predefined tasks, for example a node dedicated to reading CSV files. Other nodes are associated with tasks that can be defined by writing scripts in different languages, such as R, Groovy, Matlab and Python. The scRNAseq_KNIME workflow is implemented with a modular structure (Supplementary figure  1 ), in which each module is composed of KNIME predefined nodes and nodes defined by custom R scripts (Figure  1A ). The nodes are connected to each other through edges defining their relationships. The input module takes 10x genomics files (barcode, genes/features and matrix files) as input.