dataesr/scientific-paragraphs-categorization huggingface dataset:
Effectiveness of vaccine combinations ChAdOx1-S-nCoV-19 and BNT162b2 vaccines (hereafter referred to as ChAd and BNT) have been shown to confer strong protection against SARS-CoV-2 infection, and to induce robust immune responses directed against SARS-CoV-2 spike protein, when administered in a homologous setting  [3] [4] [5]. However, the effectiveness of a ChAd/BNT combination as prime/boost vaccination has not been formally tested in vaccine trials. In order to compare the risk of SARS-CoV-2 infection following heterologous ChAd/BNT or homologous BNT/BNT vaccination schedules, we extracted from the occupational medicine database of the University hospital of Lyon (Hospices Civils de Lyon, HCL), France, the number of individuals who received each vaccination regimen (which started in January 2021 in both groups), and the number of infections documented by positive SARS-CoV-2 RT-PCR that occurred at least 2 weeks after the boost in each group (Table  1 ).
Antibodies and virus neutralization To understand the immunological basis of this difference, we constituted a cohort of healthcare workers (HCWs) without comorbidity (Covid-Ser-Vac) in which half of HCWs received a homologous BNT/BNT combination within the recommended 4-week interval between the two doses, while the second half received the BNT boost after receiving the first ChAd dose approximately 12 weeks before. In both groups, the mean age was 41 and 34 respectively, and about 70% of participants were women (Table  2 ). Three blood samples were drawn, prior vaccination, prior second dose and 4 weeks after the second dose. Immunological analyses were performed longitudinally as depicted in Extended Data Fig. 1  and a linear regression model was used to compare immune parameters between groups, to adjust for age differences.
Ethical statement and cohort description The use and analysis of data from the occupational health medical file were authorized after a regulatory declaration to the National Commission for Information Technology and Civil Liberties (CNIL) according to the reference methodology (declaration MR004 n° 20-121 of April 30 th 2020). The declaration of infection by the virus is compulsory for all staff in order to obtain daily allowances without loss of salary during the imposed quarantine. Covid-Ser-Vac: Clinical data were recorded by a trained Clinical Research Associate using Clinsight software (version Csonline 7. 5. 720. 1). Blood samples were processed and stored at the Centre de Ressource Biologique Neurobiotec, 69500 Bron. Sixty naïve HCWs for COVID-19 vaccinated with Pfizer BNT162b2 and/or ChAd vaccines were included in a prospective longitudinal cohort study conducted in HCL (Lyon, France).
Blood sampling was performed before vaccination, before the second dose of vaccine and 4 weeks after the end of vaccination schedule. Absence of previous SARS-CoV-2 infection was confirmed using the Wantai Ab total assay in the pre-vaccine sample. Demographic characteristics and delays between doses are depicted in Table  2  and Supplementary Figure  1. Written informed consent was obtained from all participants; ethics approval was obtained from the national review board for biomedical research in April 2020 (Comité de Protection des Personnes Sud Méditerranée I, Marseille, France; ID RCB 2020-A00932-37), and the study was registered on ClinicalTrials. gov (NCT04341142).
Cells were then washed, fixed with Cytofix/Cytoperm (BD Biosciences) and stained with PE-conjugated anti-IFNγ (BioLegend). Negative controls without peptide stimulation were run for each sample. All results were acquired on a BD LSRFortessa (BD Biosciences) flow cytometer using the BD FAC-SDIVA v8. 01 software and analyzed using FlowJo version 10. 6. 1 software.
Cells were washed in PEB, and resuspended in a PEB dilution (1/500) of the fixable viability dye eFluor™ 780 (eBiosciences 65-0865-18). They were next washed and fixed at 4% paraformaldehyde (PFA) for 20 min at 4 °C in the dark before a final wash and resuspension for analysis. Cells were then acquired on a Cytek Aurora spectral flow cytometer equipped with five lasers operating on 355nm, 405nm, 488nm, 561nm and 640nm using the SpectroFlo V2. 2. 0 (Cytek) software. Data were analyzed using FlowJo10. 6. 1 software (Becton Dickinson).
Data collection For analyses of infections (Figure1), Health Care Workers at the Lyon University hospital (HCL) who received each vaccination regimen were monitored for infections starting in January 2021 in both groups. Data in Figure  1  show the infections that occurred after the 14-days postboost period, up to the end of the recording (08/15/2021) For immunological analyses (Figures  2 3 ), clinical data were collected using the Clinsight software, during January-April 2021. Blood samples were processed and stored at the Centre de Ressource Biologique Neurobiotec, 69500 Bron. Serological and immunological analyses were performed at the Lyon-Sud hospital or at the Centre International de recherche en infectiologie (CIRI) in Lyon.
Acknowledgements This study was supported by  IDEX Lyon  ( HPI-2019 ) operated by the  French National Research Agency  ( ANR-11-IDEX-0007 ),  Inserm-Transfert , and institutional grants from  INSERM ,  CNRS ,  UCBL1 , and  ENS de Lyon. We thank all the staff members of the occupational health and medicine department of the  Hospices Civils de Lyon  who contributed to the sample collection. We thank all  Clinical Research Associates , for their excellent work. We acknowledge the contribution of  SFR Biosciences  ( UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) AniRA-cytometry ,  E. Devêvre ,  S. Dussurgey  and  V. Malassigné. We thank  Karima Brahima  and all the members of the clinical research and innovation department for their reactivity ( DRCI, Hospices Civils de Lyon ). Human biological samples and associated data were obtained from  NeuroBioTec (CRB HCL ,  Lyon France ,  Biobank   BB-0033-00046 ).
Data availability The data that support the findings of this study are included in the "Source data" file available online. Viral sequences are available on GISAID (GISAID accession numbers: EPI_ISL_1707038 19A (B. 38); EPI_ISL_1707039 Alpha (B. 1. 1. 7); EPI_ISL_768828 Beta (B. 1. 351); EPI_ ISL_1359892 Gamma (P. 1); EPI_ISL_1904989; Delta (B. 1. 617. 2). Source data are provided with this paper.
28 < η < -2. 97 and 4. 61 < η < 4. 92. The V0 detector was used for triggering and event selection. It is composed of two scintillator arrays, located on both sides of the nominal interaction point and covering the pseudorapidity intervals -3. 7 < η < -1. 7 and 2. 8 < η < 5. 1. The results presented in this paper were obtained from the analysis of the data sample of pp collisions at √ s = 5. 02 TeV collected in 2017. The events used in the analysis were recorded with a minimum bias (MB) trigger which required coincident signals in the two scintillator arrays of the V0 detector. Events were further selected offline in order to remove background due to the interaction between one of the beams and the residual gas present in the beam vacuum tube and other machine-induced backgrounds  [68]. This selection was based on the timing information of the two V0 arrays and the correlation between the number of hits and track segments in the two innermost layers of the ITS, consisting of Silicon Pixel Detectors (SPD).
In order to maintain a uniform acceptance in pseudorapidity, events were required to have a reconstructed collision vertex located within ±10 cm from the centre of the detector along the beam-line direction. Events with multiple primary vertices reconstructed from TPC and ITS tracks, due to pileup of several collisions, were rejected. The rejected pileup events amount to about 1% of the triggered events and the remaining undetected pileup is negligible in the present analysis. After the aforementioned selections, the data sample used for the analysis consists of about 990 million MB events, corresponding to an integrated luminosity L int = (19. 3 ± 0. 4) nb -1 [69]. The Monte Carlo samples utilised in the analysis were obtained simulating pp collisions with the PYTHIA 8. 243 event generator  [70, 71]  (Monash-13 tune  [72] ), and propagating the generated particles through the detector using the GEANT3 package  [73].
2. A systematic uncertainty ranging from 2% to 10% was assigned. This source of systematic uncertainty was found to be mostly correlated with the signal extraction procedure. The correlation was evaluated by repeating the computation of f non-prompt varying the fit configurations used for the raw-yield extraction, as described above. For the analysis of prompt D + and D + s mesons, the systematic uncertainty on f prompt was estimated by varying the FONLL parameters (b-quark mass, factorisation, and renormalisation scales) as prescribed in  [79]. It ranges between +1 -1 % and +6 -7 % depending on the D-meson species and p T interval. The systematic uncertainty on the track reconstruction efficiency was evaluated by varying the trackquality selection criteria and by comparing the prolongation probability of the TPC tracks to the ITS hits in data and simulation.
In the case of non-prompt D mesons, the extrapolation factor was evaluated using the FONLL predictions for the beauty-hadron production and PYTHIA 8 to describe the H b → D + X decay kinematics. Besides the uncertainties of FONLL, for the non-prompt D-meson extrapolation factors two additional sources of systematic uncertainties were considered, i. e. the uncertainty on (i) the beauty fragmentation fractions f (b → H b ) and (ii) the branching ratios of the H b → D + X decays. The former was estimated considering an alternative set of beauty fragmentation fractions measured in pp collisions  [74]  reported in Table  2 , while for the latter the branching ratios implemented in PYTHIA 8 were reweighted in order to reproduce the measured values reported in Ref. [74]. In addition, it was verified that the extrapolation factors computed with the PYTHIA 8 decayer were compatible with those resulting from the usage of the EvtGen package  [89]  for the description of the beauty-hadron decays.
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL) ,  State Committee of Science and World Federation of Scientists  (WFS), Armenia;  Austrian Academy of Sciences, Austrian Science Fund (FWF) : [M  2467-N36 ] and  Nationalstiftung für Forschung ,  Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (Finep) ,  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  and  Universidade Federal do Rio Grande do Sul (UFRGS), Brazil; Ministry of Education of China (MOEC)  ,  Ministry of Science & Technology of China (MSTC)  and  National Natural Science Foundation of China (NSFC), China; Ministry of Science and Education and Croatian Science Foundation , Croatia;  Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear  ( CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the Czech Republic, Czech Republic ; The  Danish Council for Independent Research | Natural Sciences , the  VILLUM FONDEN  and  Danish National Research Foundation (DNRF) ,  Denmark ;  Helsinki Institute of Physics (HIP) , Finland; Non-prompt and prompt D-meson production in pp collisions at √ s = 5.
Please do not adjust margins To improve the structural model and assess its veracity, geometry optimization calculations have been performed using the Vienna Ab initio Simulation Package (VASP) code (ESI †). 31, 32 The structure of 1-Mo8 containing the 1o conformers has been optimized by using the unit cell parameters obtained at 123 K. It has then been used as a starting guess to reach the metastable structure containing all DTEs in their closed-ring form. This latter has been fully relaxed and optimized thanks to a guided optimization procedure, which consisted in gradually imposing the S⋅⋅⋅S distances to achieve (Fig. S17 , ESI †). Noticeably, the position of POMs, DTEs and DMF units in both calculated structures perfectly fit with those determined experimentally, and non-covalent interactions are reproduced with very good accuracy (Fig.
Acknowledgements This work was supported by the  CNRS , the  Université de Nantes , the  Université de Rennes 1 , the  Ministère de l'Enseignement Supérieur et de la Recherche , the  Japan Society for the Promotion of Science  and the  LUMOMAT  project supported by the  Région des Pays de la Loire. Prof. P. Mialane  and  Dr. T. Devic  are gratefully acknowledged for their help in X-ray diffraction study.
A new type of RF To identify the most important predictive features, we developed a new class of information criteria to estimate the probability of a feature making a perfect prediction (PP) and how a PP can be achieved. We therefore go against the concepts of Breiman's RFs by combining perfect trees (PTs) in massive quantities to approach the exhaustiveness of the possibilities and activation paths leading to an event. We define PTs as tree classifications without any classification errors, with a sensitivity and a specificity equal to 100%. For each forest, the number of variables included in each neuron of each tree is therefore fixed in advance. To build a tree, the variables are randomly selected from the variables in the database. Unlike in Breiman's method, the observations are not bootstrapped.
NIC score This hierarchical strategy would not allow us to consider all the NICs we have developed. Therefore, we propose a simple score that considers the three dimensions of the NICs, excluding NIC5 and NIC7, because NIC5 requires normalization into a new range from 0% to 100% and NIC7 is nested into NIC8. We penalize this score by NIC2 and NIC6, which assess the probability of null predictive information (NIC2) and the probability of a contrary effect (NIC6). Finally, the NIC score is calculated as follows: NIC score ¼ NIC1 À NIC2 À NIC6 þ NIC8 þ NIC9 þ NIC10 2. 2. 5 Datasets 2. 2. 5. 1 Breast Cancer Wisconsin (Diagnostic) dataset. The Breast Cancer Wisconsin dataset, which includes 569 complete observations, was obtained from Kaggle  (Blake and Merz, 1998). Ten realvalued features were computed for each cell nucleus.
For each feature, the means, extrema and standard errors were collected, leading to 30 features. 2. 2. 5. 2 Markers of taxane sensitivity in breast cancer. The public dataset GSE22513 involves expression profiling by Affymetrix V R arrays and includes fourteen patients with two replicates each and 54 675 features  (Bauer et al. , 2010). The objective of this study was to identify molecular markers of the pathologic response to neoadjuvant paclitaxel/radiation treatment.
Diagnostic performance The analysis used all 569 complete observations in the Breast Cancer Wisconsin dataset  (Blake and Merz, 1998). For each tree, 15 of the 30 available features were selected at random. In total, 27 276 trees including 15 features selected at random were built, leading to 14 368 PTs (52. 68%) (Supplementary Data 4). Features were classified according to the Gini index or NIC1. At each step, the most important feature was introduced into a LR model, and the Akaike information criteria (AICs) were compared (Fig. 2 ). The LR models were compared using the ten most important features according to each information criterion (Supplementary Data 2). These results achieved better selection performance based on the AIC for the RFPT model than the RF model.
Selection of probes using the NIC score The results published in the literature with the GSE22513 dataset suggest that a pathological complete response (pCR) to paclitaxel was encountered in aggressive breast tumors and that an immune response and paclitaxel resistance were encountered in less aggressive tumors  (Bauer et al. , 2010). For the forest including one probe per node, 8 363 591 trees were built, of which 112 481 were PTs (1. 34%). Each probe was, on average, selected at random 153 times. Among the 54 675 probes, 141 always led to PTs (NIC1 ¼ 100%) that never had all null coefficients (NIC2 ¼ 0%). Among these 141 probes, 65. 24% (92/141) produced trees with a unique solution (NIC10 ¼ 100%), and only 12 probes produced PTs with one node. Based on these results, the selection of the top 12 probes (BBOX1 (205363_at), ZNF711 , MAP2 (210015_s_at), CMC4 (210212_x_at), ADAMTS3 (214913_at), ACOT9 (221641_s_at), TOMM5 (225036_at, 228053_s_at), ERAP2 (227462_at), FAM3D (207781_s_at) , OTULIN (228382_at), TLCD2 (241359_at)) was proposed.
Discussion We developed a new paradigm for RFs that differs from Breiman's approach, which is a forest that includes only perfectly classified trees (Nguyen and Antonioli, submitted for publication). Using a neuron in each node, we simultaneously adjusted the effects of each feature relative to other features into a decision tree. These innovations were accompanied by the development of new statistical information criteria (NICs) and a score (NIC score) that takes into account the three dimensions (performance, proximity and simplicity) explored by these NICs. We used different datasets to analyze the performance and reproducibility of the feature selection and illustrated the use of this new approach with a complex omics dataset. A biochemical reaction involving molecules is written as a perfect model that has no room for an error term because there is no variability in perfectly identified molecules and all the information is known.
Therefore, the idea was to start from known results and then describe and assemble the combinatorial possibilities to obtain results without making any errors. The problem is the exponential Exp(n) number of combinations to be analyzed, where n is the number of variables, and randomness is used to minimize bias. NICs are PT ratios, and thus, the amount of information that can be analyzed increases with the number of features included in the neurons. In the example of GSE22513 breast cancer data, this probability was 1. 34% with one feature and exceeded 99% with 15 variables per neuron. The possibility of obtaining a large quantity of PTs was found in all the databases we analyzed. These results have at least two consequences. The first is that this perfect tree approach is possible as long as we have enough features.
These features can simply be intermediate features that may be essential, or even limiting, features in the cascade of events. It is therefore very dangerous to eliminate these features from subsequent analyses. This phenomenon is why analyses should start with only one variable per neuron to measure the potential of each variable independently of others. Thus, we were able to verify that in the presence of one of the 16 important and selected variables in the GSE22513 dataset, the coefficients of the 15 other variables could all be equal to zero in a perfect tree. However, if neurons containing one feature allow us to first select candidates, as in bivariate analysis, then neurons with several features allow us to confirm predictive features after adjusting for other features. Another advantage of the use of neurons that include multiple features is the identification of associations of features that, when taken together, would provide the best possible prediction.
The results showed that the performance of our approach presented a slight advantage to that of the RFE-SVM procedure. However, more datasets have to be used to better quantify the potential gain. Beyond the quantitative aspect of performance, we have opened up a field of research into criteria for evaluating the pathophysiological proximity of features to a biological event of interest and the complexity of their relationships. With these two pieces of information, we were able to propose a mapping of the cascade of features leading to the event of interest. The mechanism explaining the effectiveness of paclitaxel is not fully understood  (Lohard et al. , 2020; Weaver, 2014). Our analysis focused on the genes presented by the authors, and all the selected features were concordant with the hypothesis that high tumor aggressiveness is associated with a high level of pCR and, conversely, that low tumor aggressiveness is associated with a low level of pCR.
Our analysis demonstrated that the probes and genes we selected using the initial 2010 dataset were fully consistent with the current hypotheses of paclitaxel resistance, although they had not been identified by the authors in 2010. We also identified a promising new probe for which limited biological information (TLCD2) was available. Based on our results, this probe (241359_at) should be biologically explored to confirm its role in taxane sensitivity. The modifications concern the neuron itself and include new functions regarding conditioning activation and changes in the propagation mode. These new architectures will make it possible to discover new combinations of features and thus enrich the model information. Another step is to improve the development of the NIC score. However, the major change will involve the use of the convolutional neural network architecture of deep learning by using patterns to summarize small chains of pathophysiological cascades involving at most two levels (i.
Molecular Modeling of Drugs with HPβCD The crystal structure of β-CD was downloaded from the Cambridge Structural Database (CSD reference code (BUVSEQ02)  (21) , and modified using Avogadro software to obtain the molecular structure of 2-HPβCD at C6  (22, 23). Generalized Amber force field (GAFF) was used to minimize the resulting structure  (24). Ligand structures were constructed using MarvinSketch  (25)  and minimized using MMFF94s force field  (26). All minimization runs were implemented via Open Babel software with 10,000 steps using the steepest descent algorithm  (27). Molecular docking was carried out using AutoDock Vina software (28) implemented through PyRx GUI  (29). All figures were generated using UCSF Chimera  (30). Through these methods, the binding of two molecules of HPβCD together to form a dimer was simulated and compared to the binding of HPβCD to each of the different drug molecules.
Nevertheless, in spite of the short period of time since the importation of Charolais animals from France, a marked differentiation (fixation index (F ST ) = 0. 049) is observed between CHFR and CHCU cattle. Genetic and phenotypic differentiation can be caused by genetic drift, i. e. , as a consequence of the importation of a small number of individuals and by selection of beneficial variants that promote adaptation to the tropical environment. A number of statistics have been proposed to distinguish changes produced by genetic drift from those due to adaptation (e. g. ,  [9] [10] [11] ). Because SNP array genotype data are biased and have a low resolution, for our study, we obtained whole-genome sequence data from 72 animals, including 12 CHCU animals, to provide an unbiased estimation of the population structure and to fine-map regions that could have played a role in the adaptation to tropical conditions of the Chacuba population.