dataesr/scientific-paragraphs-categorization huggingface dataset:
Humanization of 35PA80 6. 20 Because the affinity maturation consisted the introduction of mutations, the new antibody could have been potentially more immunogenic for humans. To improve the potential tolerance of 35PA83 6. 20 antibody, we performed its super-humanization (Table  3 , Figure  2 ). The closest human germline-gene sequences of 35PA83 6. 20 sequences were identified with IMGT ® database and the amino acids diverging between the germline sequences and the 35PA3 6. 20 sequences were defined. For each humanized residue (17 positions) an antibody variant was synthesized/produced and tested in ELISA and surface plasmon resonance. As many variants had to be screened, a limited number of dilutions were realized for each variant. As a consequence, relevant affinity (KD) was not calculated; the selection of variant was done on the basis of the Koff only.
In-vivo Protection Conferred by 35PA83 Because we initially planned the clinical development of 35PA83 6. 20 and because this antibody could have been a national countermeasure, the in-vivo protection data had not been initially published. The protection mediated by 35PA83 IgG was assessed in New Zealand rabbits infected with the virulent 9602 strain and tested in prophylaxis and therapeutic schemes. The protection mediated by 35PA83 IgG as a prophylaxis or as a therapy, alone or in combination with relevant antibiotherapy, was also assessed in Fisher 344 rat model intoxicated with LT and in A/J mice model infected by the vaccinal Sterne 7702 strain. To respect ethical animal experimentation, only a limited number of animals were used. Indeed, additional and more complete in-vivo experimentations were planned during the pre-clinical development (data not shown).
In-Vivo Protection Conferred by 35PA83 Because we initially planned the clinical development of 35PA83 6. 20 and because this antibody could have been a national countermeasure, the in-vivo protection data had not been initially published. The protection mediated by 35PA83 IgG was assessed in New Zealand rabbits infected with the virulent 9602 strain and tested in prophylaxis and therapeutic schemes. The protection mediated by 35PA83 IgG as a prophylaxis or as a therapy, alone or in combination with relevant antibiotherapy, was also assessed in Fisher 344 rat model intoxicated with LT and in A/J mice model infected by the vaccinal Sterne 7702 strain. To respect ethical animal experimentation, only a limited number of animals were used. Indeed, additional and more complete in-vivo experimentations were planned during the pre-clinical development (data not shown).
A stability assay was realized in the final packaging by different methods: optical density (at 280 and 400 nM), dynamic light scattering, high pressure size exclusion chromatography, SDS-PAGE, reverse phase-high performance liquid chromatography, Isoelectric focusing, SPR and research of subvisible particles (data not shown). For each time-point, the sample was compared to the parental antibody (t = 0). In normal condition, no stability issue was observed after at least 24 months at 5 • C. This antibody would represent a medical countermeasure for the French Armed Forces, and could be shipped all over the world. During the transport, particularly in intertropical area, a cold chain failure could occur. A stability assay at +25 • C and +40 • C was thus also realized. Under these conditions, a stability issue was observed within 3 months.
Formulation and Stability In 2011, a 10-year pre-clinical and clinical phase one development of 35PA83 6. 20 was funded by the Direction Générale de l'Armement (under reference 2010. 94. 092). The development was sub-contracted with the Laboratoire Français du Fractionnement et des Biotechnologies (LFB). During the first step of clinical development, the antibody was expressed as IgG under Good Manufacturing Practices (GMP) conditions. The antibody was expressed in the rat YB2/0-E cell line, a property cell line of LFB. Several buffers were tested to ensure protein stability, and the buffer containing 30 mM acetate, 245 mM Mannitol and 400 ppm Polysorbate 80, pH 5. 0 (+/-0. 2) was selected. A concentration of 9. 8 g•L -1 of 35PA83 6. 20 was reached in this liquid buffer. A stability assay was realized in the final packaging by different methods: optical density (at 280 and 400 nM), dynamic light scattering, high pressure size exclusion chromatography, SDS-PAGE, reverse phase-high performance liquid chromatography, Isoelectric focusing, SPR and research of subvisible particles (data not shown).
20 has affinity and neutralization properties at least equivalent to those of the best previously described antibodies. The in-vivo protection of IgG 35PA83 6. 20 was evaluated in three animal models of anthrax, but here, we only presented the data obtained with the WNZ rabbit model, which is more relevant than the rat and the mice models. In a prophylactic study, all the WNZ rabbits having received a dose of 2. 5 mg•kg -1 of IgG 35PA83 6. 20 before an intranasal challenge with 100 LD 50 of spore survived. In a similar experiment, Raxibacumab was used at a dose of 40 mg•kg -1 to reach the same protective results  [36]. In a therapeutic study, the same dose of IgG 35PA83 6. 20, injected 6 h after a challenge of 80 or 200 LD 50 , allowed a survival rate of 100% and 80%, respectively. These results cannot be compared with those published for Raxibacumab, because therapeutic assays were realized under different conditions.
Affinity Determination The antibody affinities were measured by surface plasmon resonance using a BIAcore 3000 (GE-Healthcare/cytiva), instrument. The toxin was coated at a maximum of 1100 resonance units (RU) on a CM5 chip (GE-Healthcare/cytiva) via amine coupling, ac-cording to manufacturer's instructions. A volume of 100 µL of at least six dilutions of the antibody in HBS-EP buffer (GE-HealthcareMcytiva), were tested. Generally, sample dilution concentrations ranged from 2 µM to 0. 1 nM. A 30 µL•min -1 flow rate was maintained during the run. After each scFv dilution tested, chip was regenerated with 1. 5 µM glycine buffer (GE-Healthcare/cytiva), run for 30 s at 10 µL•min -1. Affinities were calculated using the BIAevaluation software (GE-Healthcare/cytiva) according to Langmuir adsorption model and results were verified by internal consistency tests.
Humanization The human germline antibody-sequence closest to 35PA83 sequence was identified online using IMGT ® database. The amino acid diverging between 35PA83 and the germline sequence were identified. Each amino acid diverging between the sequences represents a potential hotspot for T-epitope, and should be removed. Antibody variants containing one or several identified mutations were expressed and purified by Proteogenix. The mutations inducing stability issue during the antibody expression were not selected. The affinity of the expressed antibody variants was determined by surface plasmon resonance. The mutations inducing a significant decrease in affinity were not selected. The mutation inducing no or low decrease in affinity were selected and combined in new variants, until a variant containing a maximum of mutation was obtained.
Pharmacokinetic Studies The elimination half-life of IgG 35PA 83 was evaluated in WNZ rabbits. Six WNZ rabbits (Charles River laboratories, L'Arbresle, France) received 5 mg•kg -1 of the antibody by intravenously injection via ear vein. Blood (2 mL) of each animal was collected by sampling in the ear artery at the following time: pre-injection, 0. 5 h, 1 h, 3 h, 12 h, 24 h, 72 h, 96 h, 144 h, 216 h, 360 h, 504 h and 672 h. The IgG 35PA 83 concentrations were determined for all animals and for each bleeding day by ELISA (see above). Pharmacokinetics was evaluated by non-compartmental analysis of the test item plasma concentration data using non validated computer software (WinNonlin, version 5. 0, Pharsight Corp. , Mountain View, CA, USA). The area under the curve (AUC) was calculated using the linear trapezoidal method (linear interpolation).
Acknowledgments: We acknowledge all the LFB staff involved in the clinical development of 35PA83 6. 20. Particularly, we acknowledge  Christian BEHRENS  and  Aurélia CHEVALIER  for their project management of  ATHENA  project. We acknowledge  Didier HILAIRE  and  Julien THOUROT  for the  ATHENA  project management at  DGA. We acknowledge  Estelle MOSCA  and  Valérie GROS  for their technical assistance in the project.
Materials and methods Subjects. Data were collected from 26 healthy subjects (age range 20-32, 10 females, 16 males). The study was performed under institutional guidelines for experiments with humans, adhered to the principles of the Declaration of Helsinki, and was approved by the ethics committee of the Georg-Elias-Mueller-Institute for Psychology at the University of Goettingen. All the subjects were right-handed, had normal or corrected-to-normal vision and received financial compensation for their participation. The informed consent was obtained from all subjects and/or their legal guardian(s). Subjects received detailed written instructions for the task. They were asked to paraphrase the instructions in their own words to make sure they understood the task. The experiment was carried out in one single appointment that lasted between 2.
Therefore, the same target movement can be subjected to perturbations in each of the three planes parallel to the surfaces of the cube. This allowed us to compare perturbations in different body-centered dimensions without changing movement start and end positions, thereby avoiding potentially confounding effects of posture. Within each block of trials, the order of the targets was pseudo-randomized such that all eight targets were presented within each set of eight successful trials. These 8-trial sets counted as epochs and the model fitting analysis was carried out on epochs rather than on single trials. Data preprocessing. Data were stored for offline analysis performed in MATLAB® R2018a (MathWorks, Inc. , Natick, Massachusetts, United States). Data plots were generated with gramm, a MATLAB plotting library  50.
For this, we used the probabilistic programming language Stan 51 , through its MATLAB interface. Stan allowed us to perform Bayesian inference with its implementation of a Hamiltonian Markov chain Monte Carlo method using a NUTS sampler algorithm  52  for fitting the model parameters to the empirical data. The underlying trial-by-trial adaptation model for the movement angle corrections is the following. For the trial n in subject j and perturbation plane p , the measured movement angle y jp (n) is: with x jp (n) being the two-dimensional hidden internal state (planning angle). C is equal to  [1, 1]  to express the measured movement angle y jp (n) as the sum of the two hidden states. ε u,jp (n) is the measurement noise of the movement due to limited sensory accuracy of the agent, described by a normal distribution with zero mean: ε u,jp (n) ∼ N 0, σ u,jp.
For this, we generated N = 100 artificial experimental runs using an underlying two-state time course with the same number of baseline, perturbation, and washout trials as in our experiment. For each simulation, each parameter was drawn from a random normal distribution approaching the values we observed in our data: as ~ N(0. 93,0. 03), af ~ N(0. 55,0. 2), bs ~ N(0. 06,0. 04), bf ~ N(0. 18,0. 08), planning variability ~ N(1. 5,1), measurement variability ~ N(6,3). We then compared the accuracy of parameter estimations from different fitting methods, namely an EM algorithm  45  and a non-hierarchical and a hierarchical HMC algorithm. To quantify whether our two-state hierarchical algorithm introduces spurious correlations among the learning rates and the Kalman gain, we applied it to two versions of the surrogate datasets: One dataset was built with 200 completely uncorrelated samples, whereas in a second data set, we imposed the slow learning rate (bs) equal to 0.
54E+04 and 3. 58e+04, respectively), confirming the advantage on using a two-stage model. To validate our hierarchical fitting algorithm, we tested whether model parameters are better reconstructed by our two-state Bayesian HMC hierarchical algorithm compared to a state-of-the-art EM algorithm  45. We also compared the HMC hierarchical model with the corresponding non-hierarchical HMC model. For this, we generated multiple surrogate two-state adaptation datasets with randomly sampled parameters (see Methods). We then tested how well the three algorithms retrieve the parameters of the surrogate datasets. The hierarchical algorithm better reconstructs all parameters (error is smaller and correlation is higher) (Fig. 4 ). We therefore decided to use a two-state model and independently fit the parameters for each perturbation plane.
4444E-05, null value probability coronal-sagittal = 0. 0376, null value probability  Comparison of hierarchical and non-hierarchical fitting algorithms. Parameter estimation quality for our HMC hierarchical fitting procedure (orange), an HMC non-hierarchical fitting procedure (red), and a EM fitting procedure  45  (blue). For each random parameter setting of the surrogate two-state datasets, we represent the error in parameter reconstruction (estimated minus set parameter) for each fitting procedure, including the distributions of the errors, average and 95% confidence intervals of the squared error; and the correlation (average d 95% CI) between the estimated and set parameters (CC). sagittal-horizontal = 0. 0213). These results indicate that we can assume with a high level of certainty (> 95%) that the learning coefficients differs among the perturbation types.
To test if the above-mentioned mechanism of OI can explain the observed differences between the perturbed planes in our experiment, we also correlated planning and measurement variabilities with the slow learning rate bs for each experimental run in our dataset (Fig. 5B ). Correlations between planning variability and the learning rate, as well as between measurement variability and learning rate, were low (respective R2: 0. 13 and 0. 11), but with a trend consistent to the OI predictions, i. e. , low measurement noise and high planning noise correlate with higher values of the slow learning rate. When combining the contribution of the two noise parameters into an estimate of a Kalman gain for each subject, the slow learning rate was explained much better (R2 between K and bs: 0. 48), with a high Kalman gain being associated with high learning rate.
targets, we found similar results as for the model trained on epochs (R2 between K and bs: 0. 29, Supplementary Fig. 4 ). We also validated the ability of our method to extract correlated learning parameters whenever present. For this, we simulated an additional set of data where the slow learning rate bs was imposed to be 0. 3 times the steadystate Kalman gain (see Methods). Figure  6A  shows that we were able to reconstruct such correlation with our method. Conversely, when no correlation between the slow learning rate and the Kalman gain was introduced in the dataset, the method does not introduce spurious correlation. Figure  6  overall confirms the validity of our method for the study of correlations between two-state model parameters.
Figure2. Quantification of adaptation level. (A) Example trajectories from one subject. Each row/color of the graph corresponds to different planes of rotation. The trajectories are projected onto the plane which is perpendicular to the axis of visuomotor rotation for representation and analysis of the data in each of the three conditions (coronal, horizontal, sagittal). The trajectories show similarities across the three planes. (B) Within each plane, the trajectories are quantified along the two task relevant dimensions, the center-to-target dimension and the dimension orthogonal to it in the same plane, which we refer to as "perturbed" dimension. To compare data across tasks, each trajectory was resampled and rotated into this reference frame. At half of the normalized trajectory, the adaptation angle was calculated and used for model fitting.
Acknowledgements German Research Foundation (DFG, Germany , Grant Number  FOR-1847-GA1475-B2 ), received by  AG. German Research Foundation (DFG, Germany , Grant Number  SFB-889 ), received by  AG. Federal Ministry for Education and Research (BMBF, Germany , Grant Number  01GQ1005C ), received by  AG. Federal Ministry for Education and Research (BMBF, Germany , Grant Numbers  01GQ0814 ), received by AG.
Study design and patients This is a prospective multicenter observational cohort study. Patients admitted between December 7th, 2021 (week 49/21) and May 1st, 2022 (week 17/22) in one of the 20 participating ICUs (17 from the Greater Paris area and 3 from the North-East of France) were eligible for inclusion in the SEVARVIR cohort study (NCT05162508) if they presented the following inclusion criteria: age ≥18 years, SARS-CoV-2 infection confirmed by a positive reverse transcriptase-polymerase chain reaction (RT-PCR) in nasopharyngeal swab samples, admission in the ICU for acute respiratory failure (i. e. , peripheral oxygen saturation (SpO 2 ) ≤90% and need for supplemental oxygen or any kind of ventilator support), patient or next of kin informed of study inclusion. Patients with SARS-CoV-2 infection but no acute respiratory failure or with a RT-PCR cycle threshold (Ct) value >32 in nasopharyngeal swabs were not included.
The study was approved by the Comité de Protection des Personnes Sud-Méditerranée I (N°EudraCT/ID-RCB: 2021-A02914-37). Informed consent was obtained from all patients or their relatives. Demographics, clinical and laboratory variables were recorded upon ICU admission and during ICU stay. Patients' frailty was assessed using the Clinical Frailty Scale  18. The severity of the disease upon ICU admission was assessed using the World Health Organization (WHO) 10-point ordinal scale  19  , the sequential organ failure assessment (SOFA  20  ) score, and the simplified acute physiology score (SAPS) II score  21. Acute respiratory distress syndrome (ARDS) was defined according to the Berlin definition  22. The primary clinical endpoint of the study was day-28 mortality.
SARS-CoV-2 variant determination Full-length SARS-CoV-2 genomes from all included patients were sequenced by means of next-generation sequencing. Briefly, viral RNA was extracted from nasopharyngeal swabs in viral transport medium using NucliSENS® easyMAG kit on EMAG device (bioMérieux, Marcyl'Étoile, France). Sequencing was performed with the Illumina® COV-IDSeq Test (Illumina, San Diego, California), which uses 98-target multiplex amplifications along the full SARS-CoV-2 genome. The libraries were sequenced with NextSeq 500/550 High Output Kit v2. 5 (75 Cycles) on a NextSeq 500 device (Illumina). The sequences were demultiplexed and assembled as full-length genomes by means of the DRAGEN COVIDSeq Test Pipeline on a local DRAGEN server (Illumina). Lineages and clades were interpreted using Pangolin and NextClade, before being submitted to the GISAID international database (https:// www.
gisaid. org). Full-length Omicron genome sequence analysis yielding high coverage (>90%) were deposited in Genbank (accession numbers OP160034 to OP160218). For mutational pattern analysis at the amino acid level, only highquality sequences, i. e. , sequences covering ≥90% of the viral genome, were considered. For variant comparison (Delta vs Omicron), mutation-specific RT-PCR was performed whenever the full-length genome was insufficiently covered by sequencing. For this, a multiplex mutation-specific RT-PCR kit (ID TM SARS CoV-2/VOC Revolution Pentaplex, IDSolutions, Grabels, France) was used to search for the presence of Spike amino acid mutations K417N, E484K, and L452R.
To illustrate differences in phenotypes of patients infected with variants Omicron or Delta, we performed an exploratory unsupervised clustering analysis using the Kohonen's selforganized map (SOM) methodology  23  , allowing us to build 2-dimensional maps from multidimensional datasets. In a nutshell, each map is divided into districts in which patients are located by the SOM algorithm on the basis of their characteristics: patients with similar features are closely located on the maps, while patients with distinct profiles are farther from each other, hence allowing to identify key differences or similarities among them. The SOMs were obtained with the Numero package framework for the R statistical platform  24  after variables with missing information were imputed using the k-nearest neighbors (k-NN) approach and principal component analysis adapted for mixtures of qualitative and quantitative variables was applied (PCAMix)  25, 26.
Acknowledgements The authors would like to thank all study investigators,  Dr Pierre-André Natella ,  Ms Nolwenn Bombenger  for taking care of regulatory aspects,  Ms Clélia Chambraud  for taking care of data management,  Mr Mohamed Ader  for clinical data abstraction, the nurses and physicians who took care of the patients, the laboratory staff who took care of virological samples and the patients and their family for agreeing to participate in the study. The SEVARVIR study has been funded by the  EMERGEN consortium -ANRS Maladies Infectieuses Emergentes  ( ANRS0153 ).
Data availability Source data are provided with this article. Due to the sensitive nature of the data, all the clinical datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request (S. F. ). Full-length Omicron genomes were deposited in Genbank (accession numbers OP160034 to OP160218). Source data are provided with this paper. J. -M. P. , C. R. , and S. F. were responsible of virological analyses; N. D. P. , E. A. , J. M. P. , and S. F. wrote the first draft of the article; All authors revised and approved the article. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. S. F. is the guarantor.
Many studies have detected a relationship between diarrhea morbidity rates with the changes in precipitation, temperature, floods, droughts, water shortage, etc. But, most of the authors were cautious in their studies, because of the lack of empirical climate-health data and there were large uncertainties in the future projections. The study aimed to refine the link between the morbidity rates of diarrhea in some Egyptian governorates representative of the three Egyptian geographical divisions with the meteorological changes that occurred in the 2006-2016 period for which the medical data are available, as a case study. Medical raw data was collected from the Information Centre Department of the Egyptian Ministry of Health and Population. The meteorological data of temperature and precipitation extremes were defined as data outside the 10th-90th percentile range of values of the period of study, and their analysis was done using a methodology similar to the one recommended by the WMO and integrated in the CLIMDEX software.
Meteorological data In this study, we use the assimilated ERA-Interim  (Berrisford et al. 2011)  daily data (maximal and minimal temperatures, precipitations) of the period 2006-2016 for which the parallel medical data are available. The meteorological observations have been performed routinely for decades by the EMA in its network of surface stations. As these data were not distributed to the large public and therefore rarely used, an effort was made recently to check the quality of the hourly meteorological measurements performed from 1 January 2004 to 31 December 2010 at 8 of the EMA stations  (Korany et al. 2016). These high-quality data were then deposited in a public repository (https:// www. panga ea. de/) from which they can be downloaded at no cost. The datasets contain the hourly values of the air temperature from which the maximum temperature (Tmax) and minimum temperature (Tmin) can be extracted, but not the precipitation.