dataesr/scientific-paragraphs-categorization huggingface dataset:
In the plot PC1 vs. PC2 (Fig. 7a ) samples showed a greater variability along three principal eigenvectors, the Mg, Ni, and Fe\ \Si directions, arranging the dataset of the EDS analyses in three main populations. The plot PC2 vs. PC3 (Fig. 7b ) evidenced a difference in elemental composition for samples K1 and K2, showing a Fe-rich and Si-rich content, respectively. K-means clustering analysis confirmed that the number of clusters present in the dataset is three (for further details see Supporting Materials S6). On the basis of PCA results, EDS data were plotted on ternary diagrams, considering Si, Mg, Ni and Fe elements, as shown in Fig. 8. Triangular diagrams considering Si-(Mg + Fe)-Ni (Fig. 8a ) and Si-(Mg + Ni)-Fe (Fig. 8b ) are commonly used to describe the chemical weathering of Mg/Ni phyllosilicates (garnierite) from lateritic Ni-ore deposits (e.
Acknowledgments This study is a part of a multidisciplinary project " Amiantes et Bonnes Pratiques (ABP) " supported by the  Centre National de Recherche Technologique (CNRT-Nouvelle Calédonie ). JRP post-doc position is funded by the  University of Torino  ( CHI. 2019. 08/XXI "Development of innovative tools for the risk assessment of elongated mineral particles (EMP) in natural environment") and by the  INAIL -BRIC   2019  project (grant number  ID 57. 1 ). Micro-Raman spectra have been obtained with the equipment acquired by the  Interdepartmental Centre "G. Scansetti" for Studies on Asbestos  and  Other Toxic Particulates  with a grant from  Compagnia di San Paolo, Torino, Italy. The authors kindly acknowledge  Dr. L. Barale  for reviewing the manuscript.
Implementation of model analyses. The model was implemented in Python  (version 3. 7, Rossum, 1995) , with the package "simuPOP"  (Peng and Kimmel, 2005). The starting point of each replicate simulated was a population of 2000 individuals in the susceptible compartment. A proportion f avr of virulent alleles was introduced initially in the pathogen population at Hardy-Weinberg equilibrium, as standing genetic variation. For diploid individuals, homozygous avr /avr individuals could therefore be initially present, depending on f avr. All simulations were run with a fixed total carrying capacity for the host population size, K = K R + K S = 100 000, but a variable proportion of the size of the R compartment propR = K R K. Preliminary analyses were carried out to study demographic and genetic outcomes with varying parameters.
The influence of three parameters (propR, Ploidy and Cycle) on these two output variables was studied with 1000 replicates for each combination through 1100 generations. For these two output variables, GLM were performed with a Gamma link function. For each general linear model developed, a dominance analysis was performed with the R package "dominanceanalysis" (Bustos Navarrete and Coutinho Soares, 2020) to compare the relative importance of the input parameters on the five output variables described. Estimated general dominance were calculated using bootstrap average values with the corresponding standard errors for each predictor with 100 bootstrap resamples, with McFadden's indices  (McFadden, 1974). Calculations of a growth rate threshold r 0 were carried out on Python for several parameter combinations.
Acknowledgements We warmly thank  Josselin Montarry ,  Lydia Bousset ,  Jean-Paul Soularue ,  Frédéric Grognard ,  Bénédicte Fabre ,  Clémentine Louet ,  Pascal Frey , and  Cyril Dutech  for constructive comments on previous versions of the manuscript. We thank  Frédéric Fabre  for insightful discussions on sensibility analyses. We thank  AgroParisTech  for support. We also thank  Hirohisa Kishino ,  Loup Rimbaud  and one anonymous reviewer for detailed comments and suggestions which helped improve the manuscript. A previous version of this article has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (DOI:  10. 24072/pci. evolbiol. 100131 ).
Participants A total of 153 English-speaking participants were recruited via Amazon Mechanical Turk. All participants had U. S. based IP addresses. Per our preregistration, participants (N=29) whose accuracy was below 75% in any production or comprehension task across phases 2 and 3 were excluded. The data of the remaining 124 participants was analyzed (Adv-DN: 29, Affix-DN: 38, Adv-NC: 32, Affix-NC: 30). Participants were paid 5 USD for their participation which lasted approximately 25 minutes.
8 We fit separate models for comprehension and production data, but the analysis pipeline was the same. All analyses were carried out in the R programming language and environment (R 1 3 Core Team 2018), using the lme4 software package  (Bates et al. 2007 ). Responses were analyzed by modeling response-type likelihood using logit mixed-effect models. Fixed effects were Marker, Interpretation and their interaction. Models included random intercepts for subjects and items when possible 9. Interpretation and Marker predictors were sum-coded. P-values were obtained by a 2 likelihood ratio test comparing the full model with a simpler one in which the relevant predictor (i. e. , main effect or interaction) was removed. The likelihood ratio test indicates whether or not the full model provides a better fit to the data than the reduced alternative.
A mathematical tool for tuning the LoRa parameters could therefore be very convenient for efficient network design. Although the LoRa technology is well explained in the patent  [9] , rigorous theoretical studies of this technology are still missing. In  [18] , a mathematical approach allowed to rigorously study the modulation and demodulation processes of LoRa, but it lacks a theoretical analysis of the Bit Error Rate (BER). Previously in  [19]  and more recently in  [20] , exact expressions of the BER of LoRa over several channels models were presented, including Nakagami-m and Ricean fading channels. As explained in  [19] ,  [21] , computational issues remain with classical software (e. g. Matlab, Octave, and Python) due to high values of binomial coefficients that potentially lead to aberrant results.
Since N is high, especially for SF = 12, the binomial coefficient C k N -1 will suffer from loss of precision, typically when k is superior to 5 for SF = 12 when using Matlab. The drawback is that, although the approximation is tight for large values of z, the divergence is problematic: as z goes to zero, the approximation tends toward infinity for odd values of and toward zero for even orders. Nevertheless, a simple and efficient approximation based on a piecewise definition is given by: where z c is a threshold depending on the values of and SF that will be discussed in the next paragraph. The different values of z c are available in Fig. 2  and a first quick observation is that the piecewise approximation should be accurate for low values of. The next step is to compute the expectation of g(z) by using the approximation  (19) , leading to the following result (proof in appendix A): g(z) 1 if z ≤ z c g(z) if z > z c , (19) SER(γ ) = 1 + 1 N +1 k=1 C k N (-1) k × e -N •γ (k-1) k Q 1 2N k γ , kz c , (20) where Q 1 (•, •) denotes the Marcum function of order 1 defined by: Q 1 (a, b) ∞ b xe -x 2 +a 2 2 I 0 (ax)dx.
V. NUMERICAL RESULTS This section presents the results obtained with the proposed approximations, all tested with both Matlab/Octave and Python functions. The first subsection compares our solution with the state of the art for available channels, i. e. , AWGN and Rayleigh fading to highlight the accuracy gain. The second subsection validates the Nakagami and Ricean cases thanks to simulations, and the high-SNR approximation is also plotted. Finally we study the impact of fading channel parameters m and K on the BER.
VI. CONCLUSION While LoRa standard is now well known and defined in the patent, theoretical studies about performance are not so simple when real propagation environment is considered. Some works are available in the literature but the results are not tractable and cannot be computed with software such as Matlab, Octave or Python. In this paper, we have introduced a new approximation based on Marcum function for an AWGN channel, and we have derived Bit Error Probabilities of LoRa for various fading channels, namely Rayleigh, Ricean or Nakagami. The obtained expressions have been validated by simulations, and comparisons with existing solutions have proved that the proposed solution is more accurate, while being computable on Matlab, Octave and Python. These expressions are very useful to obtain performance estimation of LoRa communications, e.
Gene Expression Analysis To analyze the expression profiles of related genes to apoptosis in cancer cells exposed to different treatment, the RNA isolated using TRIzole reagent and its integrity was checked by gel electrophoresis. The isolated RNA was subjected to DNase digestion and used in RT-PCR to generate cDNA by using transcription reagents (Maxime RT PreMix) purchased from iNtRON Biotechnology (Korea). The PCR reactions, using a thermal cycler (PXE 0. 5 Thermo), were implemented according to Sun et al. [19]  using the pairs of primers listed in Tab. 1. The product of PCR amplification runs on a 1. 5% agarose gel and envisioned on Trans-illuminator UV. DNA bands were scanned with Gel-Pro program (version 3. 1 for Windows 3) to quantify the signal intensities. The proportion between the levels of the target genesamplification product and GAPDH gene was determined to standardize for initial variance in sample as an endogenous control gene for productivity of the reaction  [20].
[23]. The present study data demonstrated that, NP99 extract at 18 μg/ml dose significantly decreased the MCF-7 cells proliferation and viability percentage by about 58% and 73% after treated for either 48 or 72 h. Different concentrations and exposure times were used to examine the cytotoxic effect and IC 50 value of tannin nanoparticles (NP99) on MCF-7 cancer cell. The collected data, using Trypan blue staining, are listed in Tab. 2 which revealed that NP99 treatments significantly reduce cell growth in a dose and time-dependent when compared with control cells (Fig. 4 ). The recorded IC 50 values of NP99 were 24, 18 and 12 μg/mL after incubation for 24, 48 and 72 h, respectively. Moreover, the effect of NP99 on the viability of cancer cells was   4 ). These results showed a significant decrease in the viability of the cells after 24 h at all the tested doses of NP99 except the dose of 6.
Histological and Immunohistochemical Experiments Upon sacrifice, samples of each of the four liver lobes were cut and placed on microscopic slides. Cell death by necrosis was evaluated on hematoxylin and eosin (H&E), and collagen fiber deposition was carried out by the picro sirius red (PSR) staining. Immunohistochemistry (IHC) for plasma kallikrein was performed using a rabbit polyclonal antibody anti-plasma kallikrein (PA5-76711 Invitrogen), biotinylated goat anti-rabbit secondary antibodies were added for an hour while staining using 3,3'-diaminobenzidine (DAB), and counterstaining with hematoxylin (Leica Biosystem) was carried out as described previously  (Habib et al. , 2019). No staining was observed when omitting the primary antibody. Evaluation and quantification of necrosis and collagen fiber deposition were performed on photomicrographs of ten each per mouse, using ImageJ software (NIH, United States).
Statistical Analysis Statistical analysis was conducted using SPSS (Statistical Package for the Social Sciences) software and GraphPad Prism 8, (version 8. 4. 3 for Windows, GraphPad Software, La Jolla, CA 92037, United States). The test of normality was performed using the Shapiro test, while multiple comparisons between groups were conducted by one-way analysis of variances (ANOVA) followed by Sidak's multiple comparison test or the Mann-Whitney U test; *p < 0. 05, **p < 0. 005, ***p < 0. 0005, and ****p < 0. 0001 are considered statistically significant. Correlation analysis was carried out by the Spearman correlation of nonparametric test, while the significance of the coefficient of correlation, r, was determined by p < 0. 05.
Data collection Each participant's height and weight were recorded prior to being fitted with the Inertial Measurement Unit (IMU) (Xsens Awinda; Xsens Technologies B. V. , Enschede, Netherlands). The characteristics of IMU's and their suitability to extrication research are described in our previous work  [19]. The IMU sensor was attached to the head using a headband. The thorax was assumed to be rigid and sensors were positioned over the clavicular notch on the sternum, and over each scapula using a tight-fitting elastic vest. A sensor was positioned on the sacrum by attaching the sensor to shorts using hook-and-loop tening, to prevent upward travel, and securing the sensor against the body with an elastic belt. Orientation data were collected from each sensor via a wi-fi link and sampled at a rate of 40 Hz.
Data were captured and analysed using the Biomechanics of Bodies (BoB Biomechanics Ltd,, Bromsgrove, UK) software interface before being exported to Excel (Microsoft v. 16. 9) and SPSS (IBM v. 25, Armonk NY) for further analysis and reporting. Total excursions, standard deviation and confidence intervals are reported for each extrication type. P values were calculated using a two tailed t-test comparing each extrication method with the current standard (roof removal) extrication type. The study protocol was reviewed and approved by the University of Coventry Research Ethics Committee (reference number P88416) and the University of Cape Town, Human Research Ethics Committee (reference number 530/2021).
Results Data from a total of 230 extrications were successfully collected for analysis (95. 8% data capture success rate). Three of the six participants were female, with a mean age across all of the participants of 52 years (range 28-68) and BMI of 27. 7 (range 21. 5-34. 6). The results are summarised in Tables 1, 2 and Figs. 2, 3, 4, 5, 6. The mean movements across the four extrication types were 4. 4 mm (Cervical AP), 4. 2 mm (Cervical Lat), 7. 9 mm (Lumbar AP) and 7. 8 mm (Lumbar Lat). Mean cervical roll was 16. 6°, cervical pitch 12. 4° and cervical yaw 17. 1°. Mean lumbar roll was 16. 6°, lumbar pitch 16. 0° and lumbar yaw 25. 4°. For the cervical spine, the smallest overall movements were recorded during self-extrication (2. 6 mm AP and 2. 4 mm LAT). These were also the conditions producing  the smallest movements at the lumbar spine (4.
We successfully collected data from a large number of extrications to meet the pre-specified power calculation, supporting confidence in the reported results. Our volunteers were uninjured, fully conscious and had not recently experienced a motor vehicle collision and did not have 'true' entrapment requiring disentanglement, as such the applicability of these results to the injured post collision population needs careful consideration. The volunteers were subjected to multiple extrications across a short time; we could find no evidence of 'learning' in the movements recorded but this could have influenced our results unknowingly. The rescue personnel also performed multiple extrications over the day-a far greater exposure than in operational practice. We did see faster extrications as the teams became increasingly familiar both with the techniques and working together as a team.
In Silico Molecular Dynamics Simulation of the SARS-CoV-2 S/ACE2 Complex A model was prepared considering the receptor binding domain (RBD) of the wild type Spike protein S1 of SARS-CoV-2 complexed with human ACE2. The X-ray structure with the PDB ID 6M0J, with a resolution of 2. 45 Å was used  [11]. This structure contains the RBD of S1 and includes all the amino acid residues of the RDB, with well-defined packing between the residues on S1 RDB and ACE2. In addition, the following N-glycosylation sites were identified and were taken into account: Asn53, Asn90, Asn322 and Asn546 in ACE2; Asn343 in S1 RDB. Protonation states of all the amino acid residues were predicted using PropKA version 3. 0 at pH 7. 0  [12]. The system was prepared for molecular dynamics using the AMBER18 software package and Leap AMBER toolkit, with the ff14SB force field  [13].
Charges on the system were neutralized through the addition of counter-ions (Na + ) and the system was placed in a with TIP3P water box with a minimum distance of 12 Å between the protein-surface and the side of the box, using the Leap module of AMBER. The model was submitted to four consecutive minimizations stages to remove clashes prior to the MD simulation. In these four stages, the minimization procedure was applied to the following atoms of the system: 1-water molecules (2500 steps); 2-hydrogens atoms (2500 steps, divided in 1250 steps with the steepest descent algorithm and 1250 with the conjugated gradient algorithm); 2-hydrogens atoms (2500 steps, 1250/1250); 3-side chains of the amino acid residues (2500 steps, 1250/1250); 4-full system (10,000 steps, 5000/5000 steps). In stages 1, 2 and 3, the remaining atoms of the system were restrained to their initial position through a harmonic potential of 50 kcal mol -1 Å -2.
Coordinates were saved at each 10 ps. Final trajectories were analyzed in terms of backbone root-mean-square deviation (RMSD), hydrogen bond formation and clusters analysis. The cluster analysis tool in cpptraj was used to partition the trajectory into 8 clusters, based on RMSD relative to the backbone C-alpha carbons, using the average-linkage hierarchical agglomerative method approach with a 2. 0 Å cut-off. From the cluster analysis performed on the ensemble of poses generated from the MD simulations, one representative structure from each of the 8 dominant clusters was selected, for the subsequent step. The FPocket software  (14)  was then used to identify possible druggable pockets in the interfacial RBD-ACE2 region in each of the eight structures selected from the cluster analysis and in the initial model prepared from the X-ray structure.
This established a final concentration of 20 µg/mL for each bead. The plate was then incubated for 1 h at RT in the dark before the AlphaLISA signal was detected using an EnSpire Multimode Plate Reader (Perkin Elmer). Negative control with binding buffer was used to control the assay quality. Data were analyzed with GraphPad Prism 5. 01 software. To evaluate tolerance for dimethyl sulfoxide (DMSO), the assay was performed as describe above with addition of 0. 3 to 20% (v/v) of DMSO in the 3nM ACE2-Biot/S-RBD(His) 6 binding step. In these conditions, binding data with DMSO were compared to the control condition without DMSO.
12 nM to 100nM) for 300 s with a 2200 rpm shaking speed. Finally, the dissociation step of the bound S-RBD(His) 6 was monitored by dipping the biosensor back into the reaction buffer for 300 s. Control experiments were performed to measure non-specific binding and included binding of reaction buffer with loaded biosensor (data subtracted) and binding of 100 nM of S-RBD(His) 6 with no ACE2-Biot loaded onto the biosensor. The association and dissociation experimental curves were global fitted using a 1:1 binding model with the ForteBio Blitz pro 1. 1 software. Kinetic binding parameters (k a , k d ) and the equilibrium dissociation constant (K D ) were determined. Sensorgrams curves were plotted using Prism 5. 0 software (GraphPad Software, La Jolla, CA, USA).
As reported by the cross-titration experiments shown in Figure  2C , typical Gaussian interaction curves were obtained with a maximal binding signal of up to 1 million AS units using 0. 3-100 nM protein concentration. More accurate titration showed that a strong AS signal could be obtained using low protein concentrations between 0. 625-10 nM (Supplementary Data S1). From the perspective of testing potential drugs, we also analyzed the tolerance of the interaction to the presence of DMSO solvent. As reported in Figure  2D , DMSO concentrations up to 5% did not significantly affect the interaction signal, which appears suitable for main drug screens.
Values obtained with FPocket. 0. 3-100 nM protein concentration. More accurate titration showed that a strong AS signal could be obtained using low protein concentrations between 0. 625-10 nM (supplementary data S1). From the perspective of testing potential drugs, we also analyzed the tolerance of the interaction to the presence of DMSO solvent. As reported in Figure  2D , DMSO concentrations up to 5% did not significantly affect the interaction signal, which appears suitable for main drug screens. To monitor the dynamics of the RBD/ACE2 interaction and measure the binding parameters, we applied biolayer interferometry (BLI) to the complex. First, the binding of the RBD protein on the corresponding biosensor with increasing concentrations of protein was monitored (Supplementary Data S2). After determination of the optimal ACE2 binding to the biosensor, we applied increasing concentrations of S-RBD(His) 6 as described in the Materials and Methods section.
Binding kinetics were divided into three steps: baseline with reaction buffer measurement for 60 s and association and dissociation steps for both proteins. Control experiments were performed to measure nonspecific binding and included the binding of reaction buffer with a loaded biosensor (data subtracted) and the binding of 100 nM of S-RBD(His) 6 with no ACE2-Biot loaded onto the biosensor. As reported in Figure  3A , strong binding of S-RBD(His) 6 to ACE2 was observed. The kinetic binding parameters (k a , k d ) and the equilibrium dissociation constant (K D ) were determined after global fitting of the association and dissociation curves using a 1:1 binding model with ForteBio Blitz pro 1. 1 software. The K D was found to be approximately 0. 93 nM, as reported in Figure  3B , confirming previous data  [3].
line with reaction buffer measurement for 60 s and association and dissociation steps for both proteins. Control experiments were performed to measure nonspecific binding and included the binding of reaction buffer with a loaded biosensor (data subtracted) and the binding of 100 nM of S-RBD(His)6 with no ACE2-Biot loaded onto the biosensor. As reported in Figure  3A , strong binding of S-RBD(His)6 to ACE2 was observed. The kinetic binding parameters (ka, kd) and the equilibrium dissociation constant (KD) were determined after global fitting of the association and dissociation curves using a 1:1 binding model with ForteBio Blitz pro 1. 1 software. The KD was found to be approximately 0. 93 nM, as reported in Figure  3B , confirming previous data  [3].
Bio-Layer Interferometry (BLI) experiments were performed using Streptavidin biosensors and 1 µM of ACE2-Biot coated for 500 s in order to reach a binding value of ~2. 7 nm. Baseline with the reaction buffer was measured for 60 s. For the association step, each loaded biosensor was dipped into serial dilutions of S-RBD(His) 6 (from 3. 12 nM to 100 nM) for 300 s. The dissociation step of the bound S-RBD(His) 6 was monitored by dipping the biosensor back into the reaction buffer for 300 s. Control experiments were performed to measure non-specific binding and included binding of reaction buffer with loaded biosensor (data subtracted) and binding of 100 nM of S-RBD(His) 6 with no ACE2-Biot loaded onto the biosensor. Sensorgrams curves shown in (A) were plotted using Prism 5. 0 software (Graphpad Software, La Jolla, CA).
Selection of a cell line carrying 9 copies of the ACE2 gene, quantified by qPCR, was performed (Supplementary Data S3). The CoV-2 LV was shown to transduce only HEK293T-ACE2 cells and not wild-type HEK293T cells, leading to the integration and expression of the eGFP gene encoded by the LV (Figure  4A ). This confirmed that the transduction efficiency indicated by the percentage of eGFP-positive cells relies on the S/ACE2 interaction. In contrast, typical VSVg-pseudotyped LVs were able to transduce both 293T and 293T-ACE2 cells. Viruses 2021, 13, x FOR PEER REVIEW 11 of 17.