dataesr/scientific-paragraphs-categorization huggingface dataset:
[51]  and from the diatom Fistulifera solaris  [52]. For α/β hydrolases, amino acids at positions X 1 and X 2 are variable: at X 1 , the most common residue is a histidine representing 33% of the sequences, while at X 2 a majority of sequences displays a hydrophobic residue (I, L, M, V, A). For patatin-like phospholipases, X 1 is either A, S or T while X 2 is A, T or G which is also common for this class of proteins  [35, 53]. Based on the alignment, two sequences, the α/β hydrolase Phatr3_J40695 and the class 3 Phatr3_EG01544 did not seem to contain the active pentapeptide, however, closer inspection of their sequence revealed that they contained respectively a GVSHG and a GRSTG pentapeptide motif (not apparent in Figure  1 ). Hence, they were kept in the dataset. For GDSL lipases, the N-terminal motif (GDS at position 354) is conserved across sequences, except for Phatr3_J47870 which contained a GDE motif lacking the catalytic serine (Figure  1D ).
However, no tool is currently able to predict a localization to lipid droplets. Hence, such a prediction can only rely on previous studies and known addressing mechanisms. Four studies aiming at determining the LD proteome of P. tricornutum have been conducted, using a nitrogen starvation as the stress condition permitting an accumulation of TAG and the subsequent formation of LDs  [10, 11, 66, 67]. In these proteomic datasets, except for one α/β hydrolase, Phatr3_J43352 which was found in the LD proteome of P. tricornutum Pt4 strain  [10] , no annotated TAG lipase has been reported. This result is in line with our previous assumption of candidates being class II LD-associated proteins based on their general hydrophilic character and their cytosolic subcellular localization. In various species, lipases have been shown to be co-located with LDs, permitting direct lipolysis as it is the case for the human adipose triglyceride lipase  [36] , for the sugardependant-1 (SDP-1) lipase in germinating seeds of Arabidopsis  [35] , or for two TAG lipase of the green alga Chromochloris zofingiensis expressed in yeast cells  [65].
However, no tool is currently able to predict a localization to lipid droplets. Hence, such a prediction can only rely on previous studies and known addressing mechanisms. Four studies aiming at determining the LD proteome of P. tricornutum have been conducted, using a nitrogen starvation as the stress condition permitting an accumulation of TAG and the subsequent formation of LDs  [10, 11, 66, 67]. In these proteomic datasets, except for one α/β hydrolase, Phatr3_J43352 which was found in the LD proteome of P. tricornutum Pt4 strain  [10] , no annotated TAG lipase has been reported. This result is in line with our previous assumption of candidates being class II LD-associated proteins based on their general hydrophilic character and their cytosolic subcellular localization. As almost no lipase was found in LD proteomes, lipases could be produced and translocated to LDs only in situations where TAG are degraded.
In our dataset, a prediction of acylation and prenylation sites using GPS-lipid shows that 36 out of 57 proteins have at least one site for these modifications (Table  S4 , Figure  4 ): 70% are sites of S-palmitoylation (Figure  4A ), a modification known to enhance the hy-drophobic character of a protein allowing better interactions with membranes  [70]. This could be an important mechanism of protein-LD interaction during lipid catabolism. All the four families of proteins include predicted sites for such a modification, but two proteins have more than four predicted sites: the patatin-like lipase Phatr3_J46193 (six sites) and the class 3 lipase Phatr3_J47614 (nine sites) (Figure  4B ). The latter was also one of the proteins with a slightly positive (hydrophobic) GRAVY score, suggesting a possible interaction with membranes and/or LDs.
to the LDs. Cytosolic proteins can associate transiently to LDs through structural features such as proline rich regions and amphipathic helixes, or after post-translational modifications such as acylation or prenylation  [64, 69]. In our dataset, a prediction of acylation and prenylation sites using GPS-lipid shows that 36 out of 57 proteins have at least one site for these modifications (Table  S4 , Figure  4 ): 70% are sites of S-palmitoylation (Figure  4A ), a modification known to enhance the hydrophobic character of a protein allowing better interactions with membranes  [70]. This could be an important mechanism of protein-LD interaction during lipid catabolism. All the four families of proteins include predicted sites for such a modification, but two proteins have more than four predicted sites: the patatin-like lipase Phatr3_J46193 (six sites) and the class 3 lipase Phatr3_J47614 (nine sites) (Figure  4B ).
Transcription Data Reveal Contrasting Regulation across All Candidate Lipases It is well-known that nitrogen starvation induces TAG accumulation in diatoms, mainly through a recycling of polar lipids  [75] , however the role of TAG lipases in this accumulation process is mostly unknown. As their main function is to degrade TAG, expression of these proteins is expected to be downregulated during a nitrogen stress, and to be upregulated when nitrogen is resupplied. To identify in our dataset which candidate lipases are responsive to stress conditions, the regulation of their transcripts, expressed as a log2 value, was retrieved from three experiments that specifically investigated the response of the whole cell to nitrogen starvation over time  [75] [76] [77]  The regulation of each putative lipase at every time point of the three experiments is reported in Table  S5 , and the highest regulation level observed over all datasets is shown in Figure  5.
Overall, only nine proteins showed a maximum regulation of their transcripts higher than an absolute log2 > 2 (Table  1 ). These highly regulated lipases were chosen as the most plausibly involved in lipid metabolic processes and hence selected for the modelling of their 3D structure. For each protein sequence, the Alphafold algorithm was run twice on Colabfold, once with a search for templates for modelling, once without. For each protein, the best model of the two runs was retained according to Colabfold internal metric (predicted local Distance Difference Test, plDDT). The positions of catalytic and possible oxyanion hole residues were investigated, and structures are reported in Figure  6. Structure inspection of the upregulated α/β hydrolase Phatr3_J40695 revealed that its pentapeptide GVSHG motif was outside of the α/β hydrolase fold domain, and that no potentially catalytic histidine could be found in a localization near the serine.
The gene encoding this protein was created in the latest It is especially the case for the downregulated Phatr3_EG02408, which clearly displays two domains separated by a long poorly predicted sequence of approximately 300 amino acids (Figures  6  and 7B ). The gene encoding this protein was created in the latest genome annotation of P. tricornutum by merging two genes (Phatr2_31407 and Phatr2_31408) from the previous annotation, a process carried out when two closely located genes are regulated similarly in transcriptomics data and encode protein domains often found together in other known-proteins  [48]. The dataset by  Matthijs et al. (2016)   [77]  was using the Phatr2 annotation and both genes showed different regulation levels, the Phatr2_31408 part being much more downregulated than the Phatr2_31407 one (at 8 hrs of nitrate starvation, a log2 of -3.
Sequences were first submitted in the Pfam database (http://pfam-legacy. xfam. org/, accessed on 1 June 2022), and a threshold E-value of 10 -5 was used to consider a hit as a significant annotation. Then, as lipases should include a GXSXG motif (excepting GDSL lipases), sequences not containing this motif were excluded from the dataset. Finally, as α/β hydrolases can have a range of hydrolytic activities (not necessarily toward lipids), proteins annotated as such were submitted to the ESTHER database (https://bioweb. supagro. inrae. fr/ESTHER/general?what=index, accessed on 1 June 2022). Enzymes for which the best hit was annotated with a precise function (i. e. , proline iminopeptidase, pheophytinase. ) other than lipase were excluded from the analysis.
Multiple Alignment of Sequences Sequences from the identified proteins with a possible TAG lipase function were aligned using the software MAFFT (Multiple Alignment Program for Amino Acid or Nucleotide Sequences) with default parameters (MAFFT flavor auto, Gap extension penalty 0. 123, Gap opening penalty 1. 53, No matrix) through the NG phylogeny platform (https://ngphylogeny. fr/, accessed on 3 June 2022).
Tertiary Structure Prediction and Quality Analysis The translated amino acid sequence of the most regulated candidate proteins were used as inputs for tertiary structure prediction by Alphafold2 through the Colabfold interface with the "template_mode" option set as pdb70 (https://colab. research. google. com/github/sokrypton/ColabFold/blob/main/AlphaFold2. ipynb, accessed on 29 August 2022)  [95]. Alphafold2 is the first algorithm that predicts protein tertiary structure to a precision close to experimental models  [96]. It is based on deep neural networks trained with structures available on the Protein Database (PDB). Briefly, the algorithm searches for homologues of the primary sequence in databases and generates a multiple sequence alignment (MSA). In the case of Colabfold, the database is a combination of BFD and MGnify databases expanded using metagenomics sequences from different environment and domains of life  [95].
VMD 1. 9. 3 was used to visualize the structures and to highlight residues. Ramachandran plots were traced in Z-lab (https://zlab. umassmed. edu/bu/ rama/, accessed on 30 August 2022). Energy scores of the structures were obtained using the Protein Structure Analysis-web tool (https://prosa. services. came. sbg. ac. at/prosa. php, accessed on 2 September 2022)  [81]  and data extracted using WebPlotDigitizer (https: //automeris. io/WebPlotDigitizer/, accessed on 2 September 2022).
The three datasets exposed P. tricornutum cultures to a shift from standard nitrate-containing f/2 medium to nitrate deprived f/2. Those of Matthijs et al. (2016) [77] and Alipanah et al. (2015) [76] describe the response of the Pt1 ecotype (CCAP 1055/1) to a nitrogen stress in batch cultures during respectively 4 to 20 h and 48 to 72 h. The transcriptomic dataset of Scarsini et al. (2022) [75] monitors the response of the Pt4 ecotype (UTEX 646) during 15 days in a progressive transition to nitrogen deprivation.
The three datasets exposed P. tricornutum cultures to a shift from standard nitrate-containing f/2 medium to nitrate deprived f/2. Those of Matthijs et al. (2016) [77] and Alipanah et al. (2015) [76] describe the response of the Pt1 ecotype (CCAP 1055/1) to a nitrogen stress in batch cultures during respectively 4 to 20 hrs and 48 to 72 hrs. The transcriptomic dataset of Scarsini et al. (2022) [75] monitors the response of the Pt4 ecotype (UTEX 646) during 15 days in a progressive transition to nitrogen deprivation.
Materials The inclusion procedure and data collection will be carried out online on a mobile and computer website application (IPrevApp, https://iprevapp. emse. fr) hosted on a local host server AlmaLinux V. is implemented to perform the ML analysis using the open-source scikit-learn libraries V. 1. 1. 2, 30 on highperformance computing hardware (2 CPU Intel Xeon Gold 6132, 14-core, 2. 6 GHz). All statistical analyses regarding the primary and secondary aims will be carried out using the Statistical Software R. 31  Population recruitment and inclusion/exclusion criteria At the beginning of the 2022-2023 athletics season, the FFA will disseminate information about the IPredict-AI study through their website, social media and by email to all athletes licensed at the FFA for competition. These emails will include an invitation to participate in this study at an individual level, as well as a description of the study purpose and procedure, the participation right, a letter of information and a link to register on the website application.
Data collection All data will be collected through questionnaires on the website application. To minimise filling errors, we will use only close-ended questions (slider, single-choice, multiple-choice, drop-down list). To avoid missing values during the filling process of a new observation, all questions in the questionnaires will be mandatory. After being included in the study, athletes will have to fulfil a baseline questionnaire that contains their demographic characteristics (age, weight, height, sex, primary athletics discipline) and their history of injuries and illnesses during the previous athletics season (2021/2022) (table  1 ). During the athletics season and throughout the study (from the end of the inclusion to the end of the study follow-up, ie, 38 weeks), the daily follow-up will consist of two questionnaires: one in the morning and one in the evening.
These questionnaires will collect parameters related to the athletics activity (eg, volume, intensity, types), athletes' psychological state (eg, anxiety, stress, self-regulation, emotions), sleep, injuries, illnesses and, only for the 34 last weeks, the level of I-REF use and the frequency of I-REF view. All these parameters are shown in table  1. The daily questionnaires will take between 0. 5 and 5 min to be filled out and will be displayed automatically in the website application. They will be accessible during the whole day from 3:00 a. m. to 24 hours. Every night at 3:00, the answers will be reset for the next day. In this study, missing data will necessarily result from athletes not responding to questions. When omissions occur, no imputation strategy will be used, the questionnaire will be considered incomplete.
In addition, some questions and thus variables (eg, competition, training session, stress event) will depend on responses to other questions. Sometimes there will be no response to a certain question and thus no value for a related variable, but this cannot be considered a missing observation. These variables will take a predefined default value within their initial response range (eg, if the athlete replies that he/she did not go to training, the database will record 0 hours of training). The variables which cannot take a default value will be replaced by a constant one (eg, '-1'), which reflects the absence of a daily life event. The response proportion to the questionnaire will be calculated at the individual athlete's level and expressed as the number of responses obtained in the questionnaires divided by the total number of expected responses.
Open access The often-denounced black box effect of ML models is partly resolved by models that have good explainability. 35 36 Recent research conducted by Lundberg et al  37  has provided a tailor-made method for decision trees, hereby strengthening their use. Baseline and daily questionnaires data will be used as input features (X ) (table  1 ). The output parameter (Y ) will be the new injury event (ie, ICPR, as a binary outcome). Our modelling process aims to establish a binary classification based on X from the day of starting data collection to the day of the last model training in order to obtain a predicted classification value of Y (ie, 0: not injured, 1: injured) for the following day. Then, we will estimate the predicted class probabilities ranging between 0 and 100 of this binary classification, which will represent the risk of injury.
For the frequency of I-REF viewing, we will consider that an athlete checked the I-REF when he/she enters the I-REF module in the application. This variable will be monitored automatically. Strategies to limit the bias We will develop strategies to limit filing errors and missing values (see the 'data collection' section). Strategies will also be implemented to limit bias in the predictive ML model and in the ROC-AUC score estimation (see the 'Daily individualized feedback on ICPR risk estimation (I-REF)' section). In order to limit the bias implied by the non-randomisation, the key variables, that have been shown to be associated with injury risk: sex, age and history of ICPR during the previous season, will be included in the analyses as independent variables. Consequently, there will be no stratification as these key variables will be already included in the analyses.
Statistical methods We will perform the statistical analyses using a well-known software, namely R (V. 3. 6. 3 (2020-02-29, Copyright 2020 The Foundation for Statistical Computing (Comprehensive R Archive Network, http://www. R-project. org)) and the R library 'survival'). 31 or the primary outcome, we will use a linear regression model where the dependent variable will be the ICPR burden and the independent variables will be the level of use of I-REF, sex, age and history of ICPR during the previous season. Using this model, we will explore the relationship between the ICPR burden and the level of use of I-REF by calculating the model coefficient for the variable 'level of use of I-REF' and its 95% CI. For the secondary outcome (i), we will adjust a logistic regression model where the dependent variable will be the occurrence (or non-occurrence) of at least one ICPR over the season.
We will calculate the OR with 95% CI as a measure of association. For the secondary outcome (ii), we will use survival analysis. First, non-athletics-related ICPR will be analysed as competing risks (using R package 'cmprsk') to explore whether there were significant differences between the athletes who showed higher and lower use of I-REF in terms of the cumulative incidence of ICPR occurred during athletics and outside of athletics. Second, we will adjust a Cox proportional hazards model where the dependent variable will be the time to the first ICPR. We will calculate the HR with 95% CI as a measure of association. Participants will be considered as rightcensored (ie, will have incomplete data on the study outcomes at the right side of the follow-up period) if they stop completing the questionnaires, if they have an ICPR that occurred outside of athletics activity or if they have not had an injury at the end of the follow-up.
Ethics and dissemination This prospective cohort study was reviewed and approved by the Saint-Etienne University Hospital Ethical Committee (Institutional Review Board: IORG0007394, IRBN1062022/CHUSTE). Participants' study information will not be released outside of the study without the written permission of the participants. During the study, participants will give their personal information and complete study questionnaires through a secured individual account on a website application. The database will be hosted on the physical server of Mines Saint-Etienne secured by a private network and individual professional access (login, password). Only P-ED, PE, LN, and the engineer who developed the application will have access to the database before, during and after the trial. Any extraction from this database will necessarily involve the anonymisation of the subjects.
The raw database will not be made available online. The results of the study will be communicated through articles in peer-reviewed journals following the STROBE items  26  to produce the manuscripts, and in international scientific congresses. Individuals who have contributed to the design and implementation of the protocol will be eligible to be included in publications as coauthors. The results of the study will also be part of the doctoral thesis of P-ED. In addition, the participants of this study will be informed about the results of the study. Dissemination of the results to the end-users, with the aim of knowledge translation will also be made.
Acknowledgements The authors would like to thank  Colin Riviere  for the website application development,  Diana Rimaud ,  Arnauld Garcin  and the  University Hospital of Saint-Etienne  for their help in the ethical approval,  Crane Rogers  for his English corrections and edits and  Ugo Rochet  for the creation of the  IPredict-AI study 's on October 3, 2023 by guest. Protected by copyright. http://bmjopen. bmj. com /BMJ Open : first published as  10. 1136/bmjopen-2022-069423  on  16 May 2023. Downloaded from.
Case of the COVID-19 Pandemic: Estimation of NPI Intensities and Identification of the Strategies Chosen by Several Countries We estimated the time evolution of NPI intensities ν(t) for several countries in order to find a match with one of the previous strategies discussed in the previous section. For that purpose, we used incidence (and equivalently the total number of new cases) and hospital admission (if available) data collected from John Hopkins University's Github repository (https://github. com/CSSEGISandData/COVID-19/blob/master/csse_COVID_19 _data/csse_COVID_19_time_series/, accessed on 1 March 2023) and Our World in Data (https://ourworldindata. org/COVID-vaccinations, accessed on 1 March 2023). We simulated the evolution of the incidence using the SEIRS model defined in Section 2 with a time-varying NPI intensity ν(t).
The degradation of three antibiotics (sulfamethoxazole, trimethoprim, and ofloxacin) and one synthetic hormone (17 α-ethinylestradiol) was investigated in three in-vitro biotransformation models (i. e. , pure enzymes, hairy root, and Trichoderma asperellum cultures) for anticipating the relevance of the formation of transformation products (TPs) in constructed wetlands (CWs) bioaugmented with T. asperellum fungus. The identification of TPs was carried out employing high-resolution mass spectrometry, using databases, or by interpreting MS/MS spectra. An enzymatic reaction with β-glucosidase was also used to confirm the presence of glycosyl-conjugates. The results showed synergies in the transformation mechanisms between these three models. Phase II conjugation reactions and overall glycosylation reactions predominated in hairy root cultures, while phase I metabolization reactions (e.
ABs and their TPs were analyzed in positive ionization mode, whereas EE2 and its TPs were analyzed in negative ionization mode. The HESI parameters were as follows: 40 arbitrary units (AU) sheath gas; 15 AU auxiliary gas; 300 • C capillary temperature; 200 • C heater temperature, and the electrospray voltage was set at 3. 0 kV for the positive and 2. 5 kV for the negative ionization modes. The S-lens radio frequency (RF) level was set at 100 AU. Full scan data were acquired at a resolution of 70,000 full width at half maximum (FWHM) with an automatic gain control (AGC) of 1 × 10 6 10e 6 , 250 ms of the maximum ion injection time, and a scan range of 100-1000 m/z. Moreover, for MS 2 , data-dependent acquisitions (DDA) and data-independent acquisitions (DIA) were achieved at a resolution of 17,500, two absolute collision energies (20 eV and 40 eV), an isolation window of 1 m/z, an AGC of 5 × 10 4 , 150 ms of the maximum ion injection time, and a scan range of 50-1000 m/z.
Data Analysis A one-or two-way ANOVA was performed using STATISTICA 6. 0 (StatSoft, Tulsa, OK, USA), and significant differences between treatments were calculated by Tukey's test with a significance level of 0. 05 (p < 0. 05). Before the significance test, normality and homogeneity of variance were verified using the Shapiro-Wilk and Levene tests, respectively. If the assumption of homogeneity of variance was not confirmed, the data were transformed using an appropriate function. Data acquisition and processing were carried out using Xcalibur™ version 2. 2. 1 (Thermo Fisher Scientific, Les Ulis, France) with Qual and Quan browsers. To compile the list of possible TPs for further DDA, raw files from Orbitrap were uploaded to Compound Discoverer 3. 1 (Thermo Fischer Scientific, San Jose, CA, USA) and processed with the Environmental Untargeted Metabolomics workflow.