dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that this relation can not be the exact match: one of the reasons for this is delayed production of proteins, and dependence of the cell cycle progression from post-translational protein modifications. The transcriptomic dynamics is relatively slow, and activation of protein synthesis is switched on in advance, leaving time for producing enough proteins needed at a certain stage of the cell cycle molecular program. Same is true for the process of degradation of RNAs involved in cell cycle: a cell needs enough time after mitosis to degrade all cell cycle-related transcripts. The suggested formalism is not limited to transcriptomic data. It looks promising to analyze the geometrical properties of cell cycle trajectory measured in unsynchronized cell populations profiled at various levels of molecular description, including epigenetics and protein expression, when the datasets of sufficient volume and quality will become available.
FUNDING This work was supported by the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ), The work was supported by the  Ministry of Science and Higher Education of the Russian Federation  (Project No. 075-15-2021-634 ) and by  European Union 's  Horizon 2020 program  (grant No. 826121 , iPC project). These funding sources had no role in the design, execution and interpretation of the results of this study.
Increasing doses of 238D2, 3SM5, and 4N1H, but not control anti-ovalbumin Nb, dampened this recruitment, consistent with antagonistic activity. We performed the same β-arrestin assay on 2 other GPCRs to ensure the specificity of the three Nb for CXCR4 (Figure  A4 ). Neither 238D2 nor 3SM5 or 4N1H inhibited β-arrestin 2 recruitment to the vasopressin 2 receptor (Figure  A4A ) or to the luteinizing hormone receptor (Figure  A4B ). These data demonstrated that our database-recovered Nbs mimic 238D2 in all pharmacological aspects studied (full cross-functionality) and could therefore be used as surrogates of 238D2 to antagonize CXCR4.
The data (Ab sequences and described off-targets) used for this study were extracted from the PDB. For the substitution task, which consists in finding a new antibody that binds to the target of the query antibody, the database can be greatly increased by the addition of NGS data that can be found in the NCBI Sequence Read Archive (SRA). However, to detect an off-target for a query antibody, the major limitation of our method is that we need an antibody binding this off-target at the right epitope in our database. The performance of the off-target detection task hence depends on the number and diversity of antibodies in the database used for searching. The PDB, restricted to Abs (about 6000 variable domains), is clearly not sufficient to exhaustively predict off-targets and to evaluate the safety of an Ab.
We are continuously extending our own databases by collecting information from filed patents to increase our collection of Ab sequences with known targets. Our database of antibodies having known cognate targets now contains more than 50,000 non-redundant antibodies, covering more than 5000 targets. Among these targets, about 1500 are human proteins, and another 1000 correspond to non-human mammalian proteins. Abs are undeniably much more specific for their targets than small chemical compounds. However, much evidence shows that Abs do have off-targets. The notions of Ab specificity and Ab cross-reactivity were enunciated in the 1950s, when the variety of the Ig repertoire started to be grasped and opposed to the variety of the antigen determinants in nature  [19]. In the late 1980s, it was evaluated that up to 3.
MAbCross Itemsets Similarity For each variable domain sequence (query Ab and Ab of the search database), all CDRs fragments were delineated using Chothia's numbering  [9]. Residues are binned into six categories according to their chemical properties: small (AGSTCP), aromatic (YW), aliphatic (ILFMV), polar (NQ), negatively (DE), and positively (HKR) charged. The secondary structure of each amino acid is made according to Kabsch's and Sander's dictionary of protein secondary structures (DSSP). These two pieces of information are aggregated in a single code for each amino acid of the CDRs The similarity between two encoded sequences is then computed using the method introduced by Egho et al. [11]. Briefly, the methods consist in enumerating all subsequences common to the two compared encodings.
Biolayer Interferometry (BLI) All the measurements were performed with the Octet RED96 System (Pall Forte Bio, Fremont, CA, USA), in Pall kinetics buffer at 30 • C and by shaking at 1000 rpm. Biotinylated AviTag-238D2 (100 nM) was immobilized for 300 s on streptavidin-coated sensors (SA) and left for equilibration for 120 s in a kinetics buffer. Typical capture variability within a row of eight tips did not exceed 0. 2 nm. Binding was assessed at 50, 30, 20, 15, 10, 7. 5, and 5 µg/mL of influenza A H1N1 (A/SOLOMON ISLANDS/3/2006) for 300 s. Parallel correction was carried out by subtracting the association of H1N1 on immobilized non-relevant biotinylated protein. Data were analyzed using Octet Software version 9. 0. Experimental data were fitted with the binding equation describing a 1:1 interaction.
Funding: This publication was funded with support from the  French National Research Agency  under the program " Investissements d'avenir", Grant Agreement MabImprove LabEx   ANR-10-LABX-53 ;  ANR  (Contract n •  ANR-2011-1619 01 ); Région Centre ARTE2, MODUPHAC (32000514), MABSILICO (32000797) grants and GPCRAb (32000593) grant from the  ARD2020 Biomédicaments program.
The program is called CUTABI (CUrvature and Torsion based Alpha-helix and Betasheet Identification) and identifies residues in α-helices and β-sheets using solely α-carbon coordinates. This algorithm is ideal for the difficult cases in which the content of secondary structure is not directly determined. The first of these papers (RT_1) by Guzzo et al. (  2021 ) deals with α-synuclein and three single point mutations (A39P, A53T and E46K) of this protein related to familial forms of Parkinson disease (PD). The authors addressed the problem of identifying differences in conformational tendencies, possibly related to different tendencies to aggregate in β-sheet structures, that is the hallmark of PD. They calculated twodimensional probability density maps and evaluated their differences by molecular dynamics trajectories.
Motivation: DataSHIELD is an open-source software infrastructure enabling the analysis of data distributed across multiple databases (federated data) without leaking individuals' information (non-disclosive). It has applications in many scientific domains, ranging from biosciences to social sciences and including high-throughput genomic studies. R is the language used to interact with (and build) DataSHIELD. This creates difficulties for researchers who do not have experience writing R code or lack the time to learn how to use the DataSHIELD functions. To help new researchers use the DataSHIELD infrastructure and to improve the user-friendliness for experienced researchers, we present ShinyDataSHIELD. Implementation: ShinyDataSHIELD is a web application with an R backend that serves as a graphical user interface (GUI) to the DataSHIELD infrastructure.
Introduction Data privacy continues to be a central concern in contemporary research  1. There are many ethico-legal considerations, including requirements under General Data Protection Regulation (GDPR), which must be taken into account when planning and configuring an analysis involving sensitive data,  2  in particular mitigating the risk of individual identification. Such considerations have a huge impact on the feasibility and time required to carry out multicohort studies and genomic studies, which rely on obtaining permissions to access and share sensitive data. DataSHIELD is an open-source software infrastructure aimed at facilitating an effective and appropriate response to such challenges  3, 4. To achieve this, DataSHIELD represents an infrastructure where the researchers only ever receive sufficient statistics (low-dimensional data transformations/aggregations containing all of the information needed to drive whatever analysis is required) from each of the different data servers, and the servers themselves manage their data using Obiba's Opal technology  5.
The data owners/custodians manage these servers and have sole control of the disclosure filters that are applied to the outputs, as well as the set of DataSHIELD functions that can be used on their data. This enables researchers to perform analyses on federated data without the need to possess physical copies of the data from each source. On the DataSHIELD website [https://www. datashield. org/help] the reader can find information on how to: i. conduct basic statistical analyses; ii. administrate the servers; iii. deploy DataSHIELD functions and packages. Recently, DataSHIELD has seen a major upgrade focused on expanding the scope of which types of data can be analysed  6  , which results in the ability to analyse high-volume, potentially non-tabular data such as genomics data structures, among many others.
Many science fields can now make use of this extension, and therefore it is important to make DataSHIELD easier to use for non-technical users. The DataSHIELD infrastructure includes a series of R packages that enables the remote and non-disclosive analysis of sensitive data [https://www. datashield. org/help/com munity-packages]. The software described in this article uses a subset of functionalities from the dsBase  7  package for data shaping, analysis and presentation methods, the dsSurvival  8  package for survival analysis and the dsOmics  9  package for omics analysis. We present ShinyDataSHIELD, an R Shiny 10 application that enables interaction with the DataSHIELD analysis infrastructure via a web application, providing capabilities to perform federated non-disclosive analysis for non-technical users.
Resources functionalities i. Genomics. Genome-wide association studies (GWAS) can be performed using two types of resources: VCF (Variant Call Format) files and PLINK containers. The VCF files are analysed using BioConductor libraries (GWASTools 12 mainly), and PLINK containers are analysed using the PLINK software  13. The GWAS results can be visualized as a table or as a Manhattan plot  14. ii. Omics. Perform association analysis can be performed using Limma  15. The accepted resources for this analysis are ExpressionSet and RangedSummarizedExperiment containers  16.
Miscellaneous functionalities The miscellaneous functionalities are not part of DataSHIELD, they are part of the software that improves the user experience. There is a built-in plot editor that allows some simple customization for the generated plots. This editor has been built using the ggplot2 17 and the ggthemr 18 R packages. The options that the plot editor offers are: (i) change text size; (ii) change x-axis text angle; (iii) add title, subtitle and caption; (iv) custom labels for x-and y-axes; (v) custom legend label (if there is a legend to be customized); and (vi) colour themes. ShinyDataSHIELD has been implemented using a modular approach where all the modules have the same design, so there is no confusion when using different functionalities. Each one of the different modules performs a single task: a module for statistical modelling, a module for descriptive analysis etc.
This guarantees that the application will be easy to upgrade and the source code will be easier to read for future maintainers. The language for interacting with the modules follows a similar pattern, which in our application is the following. a. When entering a module, the researcher must select the tables or resources to be used. Internal checks are always performed on the selected items to ensure the functions of the module will not crash. b. The buttons are disabled until the researcher performs an operation that requires them, e. g. the visualization buttons for the survival models are not available until a survival model is fitted. This shared language ensures a consistent experience. Operating in the background, R Shiny provides executive control of the functions in the different DataSHIELD packages, thus providing a seamless experience for the researcher, who simply receives the aggregated results as tables and figures interacting with a web application.
Use In this section we will explain how to use the software. As previously mentioned, there is a common structure across all the functionalities described, which provides for a userfriendly experience. With this provision, the typical experience of a ShinyDataSHIELD user should be both easy and intuitive. A key component of our software is that there are multiple checks that display human-readable error messages, helping the users understand the reason something is not working. If the reader wishes to reproduce the displayed screenshots using our software, he/she can refer to the online user guide. The first step is to define which Opal servers will be used, the credentials to be applied to them and which tables or resources have to be loaded into the remote R sessions hosted at the Opal servers.
It is in this step that we define whether we want to use a single data source or multiple sources. To define multiple data sources, we just have to add more servers. This allows us to perform pooled analysis-combining inferences across all the specified servers. Once we have performed the connections, we only have access to the particular set of datasets we have selected. If we wish to use different ones, we have to disconnect and reconnect again specifying the new set of datasets we now require. This step is illustrated in Figure  1. The next step is to select which of the array of data available on each server should be used. This can sound counterintuitive, given we just selected the data to load in the previous step. However it adds flexibility, e. g. we can load multiple datasets and study them separately without having to disconnect.
Moreover, it is at this point that the data are checked for integrity for doing pooled analysis. This ensures that the tables contain equivalent variables. This step is illustrated in Figure  2. With this last step completed, we are inside the module that we have selected. Now we can finally use our data to undertake the desired statistical data analyses. All the modules have multiple functionalities, e. g. the descriptive analysis module has different visualization options (see Section 3. 3. in the user guide bookdown), all the functionalities available displayed along the navigation tabs of each module (see Figure  2 ). Linear, generalized linear and survival models can also be fitted using our Shiny app as described in Section 3. 4. in the bookdown. There are other modules that have functionalities that can only be used when a certain action has been performed.
Discussion ShinyDataSHIELD is a novel R Shiny application that enables federated non-disclosive analyses for non-technical users. The goal of our software is to make the DataSHIELD infrastructure more accessible to researchers without R skills, as well as providing a platform for researchers experienced in DataSHIELD to perform quick hypothesis prototypes and quick analyses without the burden of writing a new analysis pipeline within R. For reference, even a basic pipeline aimed at fitting a linear model and necessarily incorporating appropriate check code, may typically require anywhere from 25 to 50 lines of code. However, despite benefiting from the simplicity provided by ShinyDataSHIELD, the researcher is not freed from the need to ensure that he/she is correctly interpreting the statistical results obtained from the application and that all models are built upon correct assumptions.
Our software is designed around core DataSHIELD functionalities and will be expanded as new functionalities and packages are available. With the current version of the Shiny app, the plot editor can only be used for customizing boxplots, but we aim to make this functionality available for all other types of plots in a future release. Also, new functionalities will be added if researchers request them. Moreover, other research groups could take the source code and it to suit their particular needs. Since our software wraps DataSHIELD functions, periodical revisions from the maintainers will be required when new versions of DataSHIELD are released, to ensure that no wrappers are broken. In conclusion, helping researchers to adopt non-disclosive methods for potentially federated analyses is inevitably a challenging task.
When using DataSHIELD via its traditional integrated development environment, this involves learning DataSHIELD syntax. By providing a user-friendly Shiny R-based tool that can simplify the procedures and shorten the learning curve, we believe that this article and the technical work program underpinning it can contribute towards an accelerated adoption of such methods as well as demonstrating the capabilities of DataSHIELD. We hope that this will encourage researchers interested in the technology to explore its capabilities and test them out for themselves. Given ongoing developments in ShinyDataSHIELD as well as rapid evolution of the DataSHIELD infrastructure itself, ShinyDataSHIELD will be actively maintained and upgraded in the years to come, so that all the novel functionalities introduced by the DataSHIELD community can be used and be utilized in on our application.
Funding This research has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements No 874583 (ATHLETE) and No 824989 (EUCANConnect); also through  Centro de Excelencia Severo Ochoa   2019-2023 Program  ( CEX2018-000806-S) ; also has received funding from the project  PID2021-122855OB-I00  funded by  MCIN / AEI / 10. 13039/501100011033 / FEDER , UE; and through the Figure 2 Selecting the tables to use. We can see, following Figure 1, that we have three available tables.
11r further information we have made available an online user guide with different use cases and technical information [https://isglobal-brge. github. io/ShinyDataSHIELD_bookdown/]. All the functionalities use DataSHIELD disclosure controls. All graphical representations preserve data privacy as they are generated through anonymization techniques or disclosure controls11. iii. Statistical modelling: There are three classes of statistical models that can be fit. a. Generalized linear models (GLM): GLM models can be fitted using pooled techniques or meta- analysis techniques (study-level meta-analysis fitting will also yield a forest plot of the results as well as the regression results table). For both approaches, the user can specify the error distribution to be ei- ther Gaussian, Poisson or binomial for linear, Poisson and logistic regressions, respectively.
• Other methods than deep learning are not explored anymore because deep learning performance seems impossible to exceed, leading to a loss of diversity in our research field that may be problematic when the limit of this technique will be reached. sets of the medical segmentation decathlon challenge  [13, 14]  7 that aims at segmenting organs or tumours. It is also possible to use Monai on other data sets but this requires additional work. Monai provides low-level functions and classes that must be combined in a Python script to learn a classification or a segmentation task, or to train a generative adversarial network (GAN). Attribution methods are also available: class activation mapping (CAM), gradient-weighted CAM and occlusion sensitivity, which allow interpreting the trained network results.
A large diversity of transforms, loss functions, metrics and optimizers are provided. Finally, with the support of Nvidia, Monai provides multi-GPU parallel processing. The other main Python library for deep learning in medical research is TorchIO  [15]. This library does not implement deep neural network training, but implements a large variety of transforms for 3D image preprocessing and/or augmentation to prepare data for deep learning use. As with Monai, two public data sets are integrated with the library for ease of use: IXI 8 and EPISURG  [16]. An interface to manage custom data sets is also provided. Finally, Nobrainer 9 , focuses on learning brain segmentations and is documented by Jupyter notebook tutorials. Other initiatives were launched but seemingly abandoned, such as for example NiftyTorch 10 , MildInt 11  [17]  or pymia 12   [18].
As the name suggests, it is meant to work on the outputs of Clinica  [8] , an open-source software platform for reproducible clinical neuroimaging studies. The core of Clinica is a set of automatic pipelines for multimodal neuroimaging data preprocessing with standard tools of the community (such as SPM 13 , FreeSurfer 14 or ANTS 15 ). ClinicaDL takes as inputs these preprocessed images and convert them into tensors to train deep neural networks. Thus, the combination of these two tools allows performing an end-to-end neuroimaging analysis, from the download of raw data sets to the interpretation of trained networks, including neuroimaging preprocessing, quality check, label definition, architecture search, and network training and evaluation.
Formatting and preprocessing of neuroimaging data One di culty faced by data scientists is the manipulation of raw neuroimaging data sets as their organization can be quite di cult to understand. Moreover, raw images coming from di↵erent scanners may need some preprocessing to be handled by deep neural networks. These preprocessing steps 13 https://www. fil. ion. ucl. ac. uk/spm 14 https://surfer. nmr. mgh. harvard. edu/fswiki/FreeSurferWiki 15 http://stnava. github. io/ANTs are easier to perform and manage when data are organized in a standard manner. To allow any researcher to completely or partly reproduce the steps performed in a study with ClinicaDL, we decided to work with data set structures, described in the following sections, whose specifications are exhaustive. Moreover, our framework is adapted to the use of 3D images, as it allows the user to choose their own way to cut the image in smaller pieces (patches or slices) to ease network training (see ClinicaDL modes in section 2.
BIDS format Clinica and ClinicaDL follow the Brain Imaging Data Structure, described in  [7] , to organize their datasets. The BIDS standard provides a list of specifications  16  , which specify how files in a BIDS data set should be organized, named and formatted. It is widely adopted by the neuroimaging community, and more and more databases try to distribute their data in BIDS format or approaching (see the OpenNeuro 17  platform for a list of BIDS formatted data sets). However, some databases still use a custom format that can be quite di cult to exploit. This is the case for example of the Alzheimer's Disease Neuroimaging Initiative (ADNI)  18  , the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL)  19  , or the Open Access Series of Imaging Studies (OASIS)  20  , three databases used for the characterization of Alzheimer's disease dementia and its prodromal stage, or of the frontotemporal lobar degeneration neuroimaging initiative (NIFD), a database including patients with frontotemporal dementia (available from the same platform as ADNI and AIBL  21  ).