dataesr/scientific-paragraphs-categorization huggingface dataset:
Peptides were analyzed on a Q-Exactive Plus mass spectrometer coupled to a Proxeon 1000 Nano-LC (ThermoFisher). Peptides were separated by chromatography using the following specifications: acclaim PepMap100 C18 pre-column (2 cm, 75 μm i. d. , 3 μm, 100 Å); Pepmap-RSLC Proxeon C18 column (50 cm, 75 μm i. d. , 2 μm, 100 Å); 300 nL/min flow rate, 98 min gradient going from 95% solvent A (water, 0. 1% formic acid) to 35% solvent B (100% acetonitrile, 0. 1% formic acid) followed by column regeneration (total time 120 min). Peptides were first analyzed in positive mode in the Orbitrap cell at a 70,000 resolution with a m/z range of 375-1500. MS/MS data were acquired in the Orbitrap cell in a Top20 mode with an AGC target of 3. 10 6 for full MS. Fragments were obtained by Higher-energy C-trap Dissociation (HCD) activation with a collisional energy of 27%, a quadrupole isolation window of 1.
4 Da, and an AGC target of 2. 10 5. MS/MS data were acquired in a data-dependent mode with a dynamic exclusion of 30 s. Monovalent peptides or peptides with unassigned charge state were excluded from the analysis. The maximum ion accumulation times were set to 50 ms and 45 ms for MS and MS/MS acquisition respectively. Label Free quantitation was performed with Progenesis QI (Waters) using HI-3 method for protein quantification. Data were processed with Proteome Discoverer 2. 2 software (ThermoFisher Scientific). The mass tolerance was set to 6 ppm for precursor ions and 0. 02 Da for fragments. Proteomic data analysis. The MASCOT software (Matrix Science, v. 2. 4) was used for protein identification on the Homo sapiens and Bos Taurus Swissprot databases (2019). Post-translational modifications were searched in dynamics parameters: oxidation (M) phosphorylation (S/T/Y), acetylation (Protein N-terminal).
The maximum number of missed cleavages was limited to two for trypsin digestion. P-values of peptides were calculated using the percolator algorithm and a 5% threshold was applied. Filters used in the MASCOT software correspond to: proteins identified with a minimum of 2 peptides, AND a MASCOT score > 40, AND P < 0. 05. Extracellular proteins were identified using the extracellular compartment database of the Proteome Discoverer software. Bioinformatics analysis and figures were developed with R Software (v. 3. 6. 2)  75. Heat maps were generated with the 'pheatmap' package  76  , where correlation clustering distance row was applied. PCA was calculated from the abundance of the extracellular proteins identified in each replicate and the results presented using the 'ggbiplot' package  77. The 'packcircles' package  78  was used to represent protein abundance, where the circle area is proportional to each protein abundance.
Statistical analysis. The statistical analysis was performed with Prism GraphPad Software (v. 7. 0). Data are expressed as mean ± standard deviation. All data passed the D' Agostino and Pearson or the Shapiro-Wilk normality test (α = 0. 05). When comparing groups, multiple comparison two-way ANOVA corrected with Benjamini, Krieger and Yekutieli test were used. For comparing time variability within group, three-way ANOVA corrected with Benjamini, Krieger and Yekutieli test were used. For Lucifer yellow assay, Boneferroni-Dunn t-test method was applied. P < 0. 05 was used as a threshold for statistically significant results.
Acknowledgements This project received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant agreement no. 760928  (BIORIMA). DSG was supported by a fellowship from the  Ecole Doctorale MTCI, Université de Paris. Confocal microscopy images were performed at the  Plateau Imagerie, BFA, Université de Paris. We acknowledge the  Proteomics platform at Institut Jacques Monod, Université de Paris , for support with mass spectrometry analysis.
Literature Review The main principle of econometric modelling  [5] [6] [7] [8] [9]  is in determining the functional form of a relationship or a set of relationships that exist between two or several variables. The two main elements, cross-sectional data focused on information about homogeneous objects and time series data focused on the description of dynamic processes, form the two types of datasets for econometric analysis, their combination being panel data (several time series data sets taken together). The term "econometrics" has been proposed by Ragnar Frisch in 1926 (Norway). Today econometrics is used to empirically test and reformulate economic theories, especially in macroeconomics. The first macroeconomic model using econometrics was created by Jan Tinbergen for the  Netherlands (1936) , who later applied this technique in creating a model for the US and the UK.
From this perspective, there is more generality in the picture of the interdependency of volumes and prices presented by an econometric input-output model than in standard neoclassical approaches because it is not necessary to base the analysis on the restrictive assumptions of general equilibrium  [25]. In econometric input-output models, the release of a closed modelling concept is compensated by the emphasis of the empirical database. The INFORGE model shares the proposal by Selten  [26] , who comes to the conclusion that it is better to use empirically tested ad-hoc assumptions than unrealistic principles of high generality and elegance. In the INFORGE model, the agents follow empirically tested routines  [27]. So, for example, instead of equilibrium prices, we use the mark-up hypothesis.
Methodology We have chosen to construct a dynamic multivariate econometric model that combines elements of long-term and short-term dynamics as this approach seems most suited to address the changes in wealth that took place over the chosen period of 1914-2015. We have used the advanced OxMetrics software package developed at Oxford University for estimating the coefficients in our model. The model consists of two submodels, one describing the long-term changes and the second one-the short-term fluctuations. The two subsidiary models were then re-integrated into a single overall model. We were estimating a model expressed in the following way in the general case: W = α × X β × Y γ × Z δ (1) which naturally led to the log-linear specification: LogW = α + β logX + γ logY + δ logZ. ( 2 ) The wealth function, due to its exponential form, was modelled in logarithms, which allowed us to use a linear model.
The data we have used for the econometric modelling was obtained from the Federal Reserve Bank of St Louis. Analysis of the time series variables for the 1914-2015 model using the Augmented Dickey-Fuller (ADF) test revealed that, on the one hand, the difference in Log WEALTH (p-value < 0. 0001), the change in US house price index (p-value < 0. 0001), the change in Log Market Capitalization (p-value < 0. 0001), the change in debt-to-GDP ratio (p-value = 0. 009), the change in oil price expressed in 2015 USD (p-value = 0. 038) and the change in birth and death rates spread (p-value = 0. 006) were found to be stationary, as their p-values were smaller than the confidence level. On the other hand, the inflation time series was found to be non-stationary (p-value = 0. 055) and unemployment was found to be non-stationary too, as the p-value is equal to 0.
Full-Wave Simulations The effect of the finger touching the scatterer were investigated in simulations using time-domain solver of the full-wave commercial software CST Microwave studio. The human finger can be considered as a multilayer lossy medium composed of different tissues (skin, fat, blood, and bone)  [25]. Each layer had its own permittivity, conductivity, and The loop resonators (represented with geometrical parameters in Figure  4 ) can be modelled as a coplanar stripline of length L with short circuits (SC) at both terminations. At radio frequency, the shorted ends depart from ideal SC due to magnetic energy stored behind the termination. This can be modelled by an additional length ∆L as in  [22] , and L is the total effective length (see Equation (  1 )). The resonant frequency of the loop is then classically obtained from the study of the resonance equation associated to the equivalent circuit  [22]  which gives L + 2•∆L = m•λ g /2 = L (1) where λg is the guided wavelength and m is an integer.
Full-Wave Simulations The effect of the finger touching the scatterer were investigated in simulations using time-domain solver of the full-wave commercial software CST Microwave studio. The human finger can be considered as a multilayer lossy medium composed of different tissues (skin, fat, blood, and bone)  [25]. Each layer had its own permittivity, conductivity, and density. The hand model used for the simulation was taken from the CST human voxel family. The voxel model was made of 2 to 5 million voxels with a resolution of 2. 08 × 2. 08 × 8. 0 mm 3  [26]. The permittivity of the tissues was calculated by the Cole-Cole relaxation model. The model used permittivity values of 61. 3, 12. 4, 5. 4, 41. 4 and loss tangent values of 0. 500, 0. 229, 0. 186, and 0. 418 for blood, bone, fat, and skin, respectively  [27] [28] [29] [30].
The backscattered fields from the three realized tags were measured for the frequency position coding for identification; the working frequency band (3 GHz to 5. 6 GHz) was divided into 17 frequency slots. The slots were non-uniform to account for the decrease of the quality factor of the resonators with respect to the increase in frequency. The frequency slots are represented in Figure  12  with a width from 110 MHz to 250 MHz for lower to higher frequency. The measurement results of the three tags as well as their respective ID are presented in Figure  12. In real environment, the data were subject to noise and the presence of ripples made decoding difficult. From this example we saw that a strategy must be employed to retrieve the correct code. The first action to be taken was to select peaks based on peak prominences in order to discard secondary peaks (ripples), as represented by the inline of Figure  12.
Methods Study design. A retrospective study was undertaken in the IVF unit, Bretonneau University Hospital, Tours, France, between January 2011 and December 2019. Over these 9 years, we have collected all IVF attempts (including conventional IVF and intracytoplasmic sperm injection (ICSI)) while excluding oocyte donation and ICSI with testicular extractions. All methods were carried out in accordance with relevant guidelines and regulations. Our experimental protocol was approved by ERERC (Espace de Réflexion Ethique de la Région Centre) which is the Ethics Committee for research involving human subjects in our hospital. Informed consent was obtained from all subjects. Data were collected from 4952 patients, representing 21,301 normally fertilized zygotes and 14,157 blastocysts developed at day 5 (D5) or day 6 (D6).
s -1 ) and AIx (%); (5) inflammation biomarkers with CRP (mg. L -1 ); and (6) quality of life with EQ5D (/100). Statistical analyses were performed using R (R Development Core Team, 2020) and SAS JMP Pro (JMP R , Version 16. 1. 0. SAS Institute Inc. , Cary, NC, 1989-2021), where p < 0. 05 was considered statistically significant. All tests were two-sided.
Transmission electron microscopy (TEM) Droplets of dilute levan-Ag/AgCl suspension were deposited on freshly glow-discharged carbon-coated copper grids and allowed to dry after negative staining with 2 wt% uranyl acetate. The specimens were observed with a JEOL JEM-2100-Plus microscope operating at 200 kV and images were recorded using a Gatan Rio 16 camera. The size of the AgNPs was measured from the images using the ImageJ software.
Acknowledgments A B H would like to thank  Søren Christensen  and  Dr Kjeld Bøhm Andersen  for assistance with tape casting,  Leonardo Soares de Oliveira  for assistance with the  NaNbO 3 XRD data , and funding from  Independent Research Fund Denmark , Grant No. DFF-6111-00440 ,  Siemensfonden  and  Brødrene Hartmanns Fond.
MUTATION DETECTION BY NCOUNTER The nCounter Vantage 3D DNA SNV Solid Tumor Panel enables detection of 97 driver mutations in 24 clinically relevant genes (online Supplemental Table  4 ). For mutation detection using nCounter, 5 mL of purified cfDNA and a reference DNA (NanoString Technologies, provided with the panel) were subjected to a 21-cycle preamplification step in a Verity thermal cycler (Applied Biosystems), according to the manufacturer's instructions. Amplified DNA was denatured at 95 C for 10 minutes and hybridized at 65 C for 18-24 hours with the SNV pool, which contains mutationand exon-specific probes that bind to DNA independently of the presence of mutations. Capture, cleanup, and digital data acquisition were performed using the nCounter Prep Station TM and Digital Analyzer TM (NanoString Technologies) (Supplemental Fig.
DATA ANALYSIS Count values were exported to Excel 2016 (Microsoft) using nSolver software v. 4. 0 (NanoString Technologies). For each mutation, samples with count values lower than the reference DNA were automatically considered negative and excluded from further analysis. The reference consists of a wild-type DNA that does not harbor any mutation and allows estimating the "background noise" counts for every mutation targeted by the kit. The remaining mutation-specific counts were normalized using the geometric mean of the exon counts of the corresponding gene in the same sample. The same procedure was applied to the count values derived from the reference DNA. Finally, the mutationspecific normalized counts of the samples were divided by the corresponding normalized counts of the reference DNA and the result was subjected to a base-2 logarithmic transformation to obtain the log MUT values.
It requires a substantially lower amount of material, has a 24-48 h turnaround time with relatively short hands-on time, sample preparation is simple compared to NGS, and data analysis is straightforward and does not require bioinformatics expertise (Supplemental Fig. 1 ). The main disadvantage of the nCounter platform is that, not being a sequencing technique, it cannot detect mutations other than those contained in the SNV panel, although the panel can be customized. Detection of mutations by nCounter can be particularly useful in some settings. Examples include liquid biopsies with small volumes and/or low concentrations of cfDNA, such as CSF samples  (8)  or pleural and peritoneal lavages  (16) ; liquid biopsies where NGS has failed; or patients with cancer who are in urgent need of genetic testing to determine whether they are eligible for targeted therapies.
Acknowledgments: We thank  Ariadna Balada-Bel ,  Marı ´a Jose Ćatalan ,  Marı ´a Gonzalez-Cao ,  Margarita Magem ,  Xavi Gonzalez ,  Veronica Pereira , and  Rich Boykin  for their contributions. The investigators also wish to thank the patients for kindly agreeing to donate samples to this study. We thank all the physicians who collaborated by providing clinical information.
For instance, Bell and Tagami  [23]  studied the RuO2 reduction for 10 -2 < p(O2) < 1 atm. They determined that the RuO2 decomposition (RuO2 → Ru 0 + O2 + ε(RuO2(g), RuO3(g), RuO4(g))) occurs at 1542 °C at p(O2) = 1 bar and 1403 °C at p(O2) = 0. 21 bar. In addition, they measured the equilibrium vapor pressure and calculated the heat of formation and standard entropy of RuO2(s), RuO3(g) and RuO4(g). Jacob et al. [25]  described the thermodynamic parameters (Gibbs energy, enthalpy and entropy) of the Ru-O system and provided a critical literature review. Later, Brunetti et al. [24]  measured and modeled the RuO2 dissociation for 10 -4 < p(O2) < 10 -6 bar. All these data are considered in the Calphad assessment developed by Gossé et al. [26] , which was used for the presented thermodynamic calculations (see § 2.
-RuO2(s) is considered a stoichiometric phase; its Gibbs energy is as follows: ° ! " -∑ # ° 298. 15 = + + + ∑ , (2) where G RuO2 is the Gibbs energy of RuO2 (rutile phase) and xi is the atomic fraction of the pure element i in the stoichiometric phase. The other parameters are the same than those described above. -O(g), O2(g), RuO(g), RuO2(g), RuO3(g), and RuO4(g) are part of the gas phase, which is considered an ideal mixture of species; its Gibbs energy is as follows: = ∑ # ° + $ ∑ # # + $ % & & ' ( , (3) where G φ is the Gibbs energy of the gas phase, xi is the atomic fraction of the species i in the gas, Gi is the Gibbs energy of the species i, R is the ideal gas constant, p is the pressure of the gas phase and p0 is the reference pressure set at 1 bar. All previous mentioned species were considered in the studied Ru-O system, and the development of the databases and optimization of parameters a, b, c and dn were made in  [26, 30, 31].
RuO2 reduction in air The Ru-O Calphad database used for thermodynamic modeling agrees well with the literature data  [26]. In addition, the experimental results presented in this paper agree well with both the literature and calculation data. Thus, the RuO2-to-Ru 0 reduction (Treduction) can be predicted well, and the 1400 °C, a more significant reduction phenomenon was highlighted, with a significant amount of Ru 0 formed. In the 1280-1400 °C range, the oxygen pressure of the glass melt is higher than the RuO2/Ru 0 equilibrium calculated. In the thermodynamic model, no interaction between the glass and ruthenium is considered, except the p(O2). Therefore, the glass melt must have an impact on the ruthenium redox. To explain this discrepancy, between observations and thermodynamic prediction at T < 1400 °C, two hypotheses are proposed: 1-Iron impurity (Fe 2+ ) may reduce RuO2 in a small amount (Equation  9 ): 4Fe FGG O + Ru FGH O : = 2Fe : FGGG O ? + Ru = (9) The maximum content of Fe 2+ in samples G5 is around 20 % of the total iron content, or 10 -5 mol of Fe 2+.
The fourth item is especially relevant to safety in the context of close proximity operations, for example, docking. Especially in the future, if in-orbit servicing will be performed to increase the spacecraft lifetime, docking becomes an issue needing international agreements. However, the locations of the current operational spacecraft are not always known to a great accuracy, even if it seems to be easy to track the positions of ASO. We present a case example using ASTRIAGraph, 40 which collects position information from multiple sources and maps their combined information into a graph database. One of the issues with ASO location knowledge is that the different sources of information about where these objects are located are not always consistent or in agreement, indicating that the information is to be regarded as an opinion.
Acknowledgements 1 , 2 , 3 , 4 , 5 , 6Authors' contributions: Section 1: All, with special contributions from M. P. , J. T. , A. S. , and A. P. Section 2: J. T. and A. S. Section 3: M. L. , J. T. , and M. P. Section 4: M. N. , M. J. , and M. P. , all. Section 5: A. P. and T. S. Section 6: All, with special contributions from M. P. , A. S. , and A. P. The views expressed in this article are made exclusively in personal capacity. The work of M. P. is supported by the  European Research Council   Consolidator grant  PRESTISSIMO (grant no. 682068 ), and  Academy of Finland Centre of Excellence in Research of Sustainable Space  (grant no. 312351 ).
Our study focuses on the mechanical behaviour of 9 mm ammunition materials, namely a lead alloy core and a steel alloy jacket. For this purpose, a preliminary study (not presented here), was carried out on both the lead core and the steel jacket separately and the parameters for each constitutive model were determined. Lead-steel cylindrical samples, extracted from the ammunition, have been used for the validation of the entire constitutive model. By utilizing those samples, a high degree of the ammunitions material properties have been retained. SHPB tests have been carried out in multiple conditions, varying the striker speeds and temperatures. Additionally, the tests were recorded with an ultra-high speed camera. Strain gages were used to record signals along the input and output bars. Those measurements have been compared to numerical results using Finite Element code (ABAQUS® Explicit).
Concurrently, the steel jacket material surrounding the lead core has also been investigated, leading to the identification of the Johnson-Cook model parameters  [8]. This paper focuses on the mechanical behaviour of the core-jacket assembly at high strain rates under compression loading. By testing the assembly as a whole, crucial material interaction are also studied. Lead-steel cylindrical samples extracted from the ammunition have thus been utilized for the validation of the entire constitutive model. SHPB tests have been carried out in different conditions, varying striker speeds and temperatures, in order to verify if strain rate and temperature sensitivities are correctly taken into account in both material models. Material interactions have also been considered in the model. The resulting signals and deformation faces have been compared to numerical results using Finite Element code (ABAQUS ® Explicit).
Comparison between experimental and numerical results In this section, the numerical model of the previously presented tests is detailed. The finite element commercial software Abaqus® Explicit is used. The setup is fully modelled in a 3D environment. The striker, the input and output bars are meshed by linear hexaedric elements. Finally, a linear elastic model is assumed for their behaviour (respectively steel, steel and aluminium). The initial speed of the striker is given as the initial condition. The samples are composed of the two materials (lead and steel), each part meshed by linear hexaedric elements with a 0. 32 mm size, a static friction coefficient of 0. 6  [10]  between the lead core and the tombac (CuZn) plated steel jacket is supposed. The cylindrical sample is presented on the Fig 1.
Conclusion The mechanical behaviour of the 9 mm ammunition, consisting of a lead alloy core and a steel alloy jacket, was investigated in this paper. For this purpose a preliminary study, not presented here, was carried out on both the lead core and the steel jacket separately. That data was then used to identify the parameters for constitutive models of each material. SHPB test were performed on the lead core and steel jacket assembly, extracted from the ammunition, to validate the constitutive modelling of the ammunition as a whole. The experimental study concluded that the assembly was sensitive to strain rate and temperature. A numerical model of these tests was conducted in ABAQUS© Explicit. The comparison of the experimental and numerical strains of the input and output bar gauges pointed out a good correlation between the numerical and experimental results.
Gene therapy reprograms the transcriptional signature of FA HSPCs towards the transcriptional profile characteristic of healthy HSPCs Next, we investigated whether changes in the transcriptional program of gene-corrected FA-A HSPCs resembled profiles characteristic of healthy HSPCs. To this end, scRNAseq data from GT-treated FA CD34 + cells was compared to that obtained from a healthy donor (HD) CD34 + cells. As shown in Fig. 2A  ectopic expression of FANCA (FANCA + cells) was detected in every CD34 + cell cluster. In Fig. 2B  we confirmed the heterogeneity of FANCA expression levels in the different HSPC subpopulations, either from GT-treated FA patients or HDs. Despite this heterogeneity, in eight out of the twelve HSPC subpopulations, physiological levels of FANCA mRNA were significantly higher in HD samples compared to the ectopic expression of FANCA in corrected FA HSPCs (Fig.