dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The study enrolled 24 critically ill patients. The patients' demographic and clinical characteristics are presented in Table  1. Concentration-time data were obtained from the patients on the fifth day of the administration, with patients providing one or two plasma samples within a dosing interval. A two-compartment model with a linear elimination from the central compartment, a linear inter-compartmental distribution between the central and peripheral compartment (∆-2*LL: -34. 6; ∆AIC: -29. 3; ∆BIC: -27. 3), and with the inclusion of initial conditions to describe the steady-state conditions achieved prior to the dosing interval, improved the model (∆-2*LL: -59. 4; ∆AIC: -54. 1; ∆BIC: -51. 9), compared to a one-compartment model. The inclusion of creatinine clearance (CRCL) normalized to 60 mL/min/1.
Neither of the proposed therapies (4 g 6qh or 4 g 8qh) achieved the optimal FTA (>85%) for patients with a creatinine clearance higher than 130 mL/min. The fractional target attainment is presented in Table  3. Neither of the proposed therapies (4 g 6qh or 4 g 8qh) achieved the optimal FTA (>85%) for patients with a creatinine clearance higher than 130 mL/min. Shaded area indicates optimal FTA of greater than or equal to 85%. An external validation was performed on the final covariate model using two non-Brazilian, critically ill patient populations. The model was applied to data obtained from a study in (A) critically ill Australian patients, and (B) critically ill Indigenous Australian patients (Figure  3 )  [16, 17]. In the external validation a lower precision (root mean square prediction error, RMSPE) was observed when testing concentrations that were within the concentration range of the original dataset (<100 mg/L; Table  4 ).
Shaded area indicates optimal FTA of greater than or equal to 85%. An external validation was performed on the final covariate model using two non-Brazilian, critically ill patient populations. The model was applied to data obtained from a study in (A) critically ill Australian patients, and (B) critically ill Indigenous Australian patients (Figure  3 )  [16, 17]. In the external validation a lower precision (root mean square prediction error, RMSPE) was observed when testing concentrations that were within the concentration range of the original dataset (<100 mg/L; Table  4 ).
Patients and Study Setting Patient data were retrieved from a previously described prospective, randomized clinical trial  [33]. The study was performed at the ICU of a medium-sized hospital in the Midwest region of the state of Minas Gerais, Brazil. Patients were eligible to be included in the study if they were aged >18 years, with a confirmed or suspected infection with indications for use of the antibiotic piperacillin/tazobactam. Exclusion criteria included: pregnant women, individuals positive for human immunodeficiency virus (HIV) or hepatitis B or C virus, patients with a known allergy to the piperacillin/tazobactam, patients who had previously been enrolled in this study, patients with serum creatinine >2 mg/dL or elevation superior to twice the baseline value. Patients enrolled in the study received an empiric dose of 4 g q8h as an intermittent infusion over approximately 30 min or using 2 g q6h or 3.
Population Pharmacokinetic Modelling A population pharmacokinetic model was developed using Pmetrics version 1. 5. 0 (Laboratory of Applied Pharmacokinetics and Bioinformatics, Los Angeles, CA, USA) in RStudio (version 0. 99. 9. 3) as a wrapper for R (version 3. 3. 1), Xcode (version 2. 6. 2) and the Intel Parallel Studio Fortran Compiler XE 2017. One or two compartment structural models were constructed using the nonparametric adaptive grid (NPAG) algorithms within Pmetrics. A stepwise approach was followed in the model-building process as described below: (i). Determination of the structural base model-One or two compartment structural models were tested using the concentration-time data. The elimination of piperacillin from the central compartment was modelled as a linear process, as were the intercompartmental rate constants.
(ii). Selection of the best-fit statistical error model-Additive (lambda) and multiplicative (gamma) error models were tested using a polynomial equation for standard deviation as a function of observed concentration, Y. (SD = C 0 + C 1. Y), with observation weighting performed as error = SD. gamma or error = (SD 2 + lambda 2 ) 0. A Bland-Altman analysis was performed, using BlandAltmanLeh package for R, to describe the difference between observed and predicted concentrations with prediction error versus predicted concentrations plotted, for internal validation, as well as to compare our model to two other patient populations.
5 to 1 h; each patient had 20 blood samples collected in two dosing intervals. For the external validation the Brazilian model was used as a prior. Then a Bayesian posterior simulation was performed calculating a posterior for each subject. The linear regression, the goodness of fit and the coefficient of determination for the observed and predicted concentrations in the external validation were assessed using Pmetrics. Finally, prediction errors were evaluated to outline bias (calculated as mean prediction error [MPE]), precision (root mean square prediction error (RMSPE)) and their respective 95% CI as described by  Sheiner and Beal (1981)   [35]. Two ranges of concentrations were tested: (a) full data from external studies; (b) just concentrations within the prior/base model. The acceptance criteria to establish model validity was set to a bias of 20%, which has also been applied in a study by  Guo et al.
The fractional target attainment (FTA), during the steady state dosing interval, was calculated for P. aeruginosa based on the MIC distribution of the European Committee for Antimicrobial Susceptibility and Testing (EUCAST) database (available at www. eucast. org accessed on: 25 October 2021). FTA was calculated considering MIC distribution within the susceptibility range defined by clinical breakpoints (16 mg/L for P. aeruginosa). Doses were considered optimal if the FTA was greater than 85%.
In our paper, we collected data from Unilend, an ORA-based platform which pioneered P2B lending in France in 2013 and introduced a BA (Autolend) in 2016. This provides an interesting before/after context to investigate the impact of automation through the introduction of a BA in the context of a P2B platform. We find that this BA had a positive impact on the number of bids and bidders in the auction and also reduced the time necessary to collect funds. In this sense, introducing the BA improved the general efficiency of the auction process by lowering transaction costs. It also impacted the level of interest rates. However, we find that this impact was mediated by the rating given to the project by the platform: after the BA was introduced, well-rated projects benefited from an extra saving on interest rates.
Data collection We collected exhaustive data about publicly available projects on the Unilend platform from December 6, 2013, to September 25, 2018. Over the period, 463 projects tried to raise funds, of which 451 were    6 This upper bound has a legal origin (French 2014 Crowdlending Regulation). 7 If the financial target is not met, the whole campaign is canceled. successful. 8 We focus on those projects and for each project, we consider the following set of information at the time of the crowdlending campaign: bidding process and outcome information (these data are measured after the auction process occurred), campaign characteristics, company specific information (sector and financial information 9 ) and macroeconomic 10 and time indicators. In some analyses, the number of observations was reduced to 385 because of missing data.
g. , EBIT, debt) from the most recent fiscal year before the campaign. 10 We extracted from Bloomberg database the rate of return of French Treasury Bonds with a 36-month duration to control for variations of the interestrate level in the economy. We chose 36-month duration since is the closest one to the average project duration on Unilend (40. 82 months). 11 The company turnover for the previous year or two was occasionally missing, notably for recent companies but also if data from previous fiscal year were not provided. H1b. The introduction of Autolend has a positive impact on the number of bidders. Since the use of Autolend decreases the screening and bidding costs, we expect the arrival of new bidders. We also expect that those bidders already operating in the platform are able to invest in more projects.
Robustness checks We performed several types of robustness checks. 23 With respect to all hypotheses (H1-4), we considered an alternative measure of the rating in the form of a less precise rating signallow (0) if the project received the lowest rating (3 in the original rating scale) or high (1) if the project received a higher rating. This might be relevant if lenders are more severe as they discriminate between projects based on rating. We also introduced loanTarget in absolute terms (instead of using a log scale). We also combined these two changes. Our results are preserved in all these specifications. With respect to Hypotheses 1a, 1b and 3, we also considered the absolute number of bids, bidders and lenders, respectively, instead of the number relative to the loan target. We used Poisson 17 Estimations have been performed using the Stata software.
Full Stata code is available on request. 18 Note that one may also say that introducing Autolend "reversed" in some sense the tendency for bidders to bid on well-rated projects. Pre-Autolend, an increase of 1 digit on the rating scale led to 126. 9 additional bids. Post-Autolend, those additional bids were canceled out, because Autolend induced -283. 4 fewer bids, which overcame the initial effect. 19 As above, recall that this effect is measured relative to the loan target and that the absolute effect of Autolend on the total number of bidders should be multiplied (on average) by the loan target. 20 Considering the post-Autolend average loan duration (37. 17 months), a large increase of, e. g. , +23 months (i. e. , 37. 17 + 23 leading to the maximal loan duration of 60 months) generates 8.
These results show that introducing a BA improved the overall efficiency of the ORA but that it was not neutral towards borrowers and may, combined with a rating-based DSS, increase discrimination among projects. It also sheds light on the potential consequences of a disconnection between perceived and actual risk levels. This research can be extended in two directions. In this paper, we used project-level data and focused on the crowd's decisions and on aggregate outcomes. It would also be fruitful to consider more granular data documenting user behaviors. For instance, this research provides an indirect measure of portfolio diversification and it would be interesting to use a lender-based dataset to analyze the impact of the BA on risk exposure conditional on lenders' financial literacy.
Acknowledgements We thank  Editor-in-Chief James R Marsden  and two anonymous referees for their careful reading of our manuscript and their insightful comments and suggestions. We are also grateful to  L. Arena ,  R. Belo ,  S. Brana ,  D. Lahet ,  N. Lesur  and  P. Sevestre  for fruitful comments and discussion. We also thank the participants of the " Fin&Tech" research project , the AIM 2018, ECIS 2018, JMA 2018, ZEW ICT 2018 conferences and CREM and GREDEG seminars for feedback. Editing support from  C. Buchanan  is kindly appreciated. All remaining errors are ours.
For comparison we also considered classic summarization based on the site frequency spectrum (SFS) and a discretized distribution of the decay in linkage disequilibrium (LD-decay), as these carry information about temporal changes in population size and selfing rates  (Tang, et al. 2007; Boitard, et al. 2016). We therefore evaluated the efficiency of three sets of summary statistics: SFS/LD, TMwin, and SFS/LD/TMwin (see methods). To test whether our approach can distinguish a transition to selfing from a reduction in population size, we conduct an ABC model choice analysis using two competing models (Figure  3A ,B): model 1 where the selfing rate changes from σANC to σPRES at tσ and with N constant, and model 2 with population size changes from NANC to NPRES and σ constant. We simulate datasets under model 1 and evaluated the ability of tsABC to identify the correct model for transitions of varying ages, using different sets of summary statistics to summarize the genetic data (see Appendix B).
We simulate 100 datasets under model 1 (Figure  3A ) with values of tσ ranging from 1,000 to 200,000 generations, and used tsABC to re-estimate posterior distributions for tσ and the other parameters of the model (Figure  3F -H, Figure  S9 ). Estimations are obtained using the same three summarization strategies used for the model choice (SFS/LD, TMwin, SFS/LD+TMwin). The age of a shift to selfing could be well estimated using the TMwin approach, while the SFS+LD approach over-estimated tσ almost over the complete range of values (Figure  3F, G ). Combining SFS, LD, and TMwin does not further improve the accuracy of the estimations (Figure  3H ). We note that the parameters N and σPRES (i. e. the population size and the current selfing rate) are both better estimated with TMwin than with SFS/LD, except for transitions younger than 10 4 generations ago where σPRES is slightly better estimated with SFS/LD, Figure  S9D ).
Modelling of a transition to selfing using forward and coalescent simulations To model a transition from outcrossing to predominant selfing, we considered a single population composed of N diploid individuals. At each generation, each offspring is generated by selffertilization of a single individual or by outcrossing with probabilities σ and 1-σ, respectively, where σ is the selfing rate. Unless stated otherwise, transitions to predominant selfing were modelled by allowing the selfing rate to change instantaneously from σANC to σPRES at time tσ. The mutation and recombination were set to 1x10 -8 events per generation per nucleotide. When needed the population size was allowed to change instantaneously from NANC to NPRES at tN. This model was implemented using the Wright-Fisher (WF) simulation mode in slim3  (Haller and Messer 2019) ; scripts to simulate genetic data using this model are available on our git repository (https://github.
Analysis of TMRCA-segments in simulated data For the forward and coalescent simulations, we wrote functions to obtain the lengths and the time to the most recent ancestor (TMRCA) of TMRCA-segments. TMRCA-segments are sets of contiguous nucleotides in a sample of size two that share the same MRCA. The joint distribution of TMRCA and lengths of those segments (TL-distributions) was used to describe the consequences of a transition to selfing at the genomic level and how it differs from a change in population size (Figure  1 , Figure  S1 , Figure  S3 ). TMRCA-segments were analyzed by identifying consequential recombination events in the history of the sample (i. e. events that lead to the inclusion of a new MRCA) and the corresponding breakpoints represented the boundaries of the successive segments.
Then, the TMRCA of each segment was obtained by identifying the MRCA of each segment. We also calculated the transition matrix of TMRCA of successive TMRCA-segments along the genome (TMtrue, Figure  1D, E ). For this, we discretized TMRCA values and counted the frequencies of segment transitions along simulated sequences for each combination of discrete TMRCA values. TMRCA were discretized using a similar approach as in MSMC  (Schiffels and Durbin 2014)  with the lower boundary of bin i given by, -8N x log(1-i/m), where N is the population size and m the total number of bins. The relevant code can be found at https://github. com/laurentlab-mpipz/struett_and_sell-inger_et_al. git.
Simulations with background selection (BGS) Simulations with BGS were conducted with slim3. We used the distribution of fitness effects (DFE) estimated by DFEalpha for A. thaliana published by  Hamala and Tiffin (2020). The DFE was used to assign negative selection coefficients to simulated coding non-synonymous genetic variants only (i. e. we did not simulate negative selection on functional non-coding regions). We took care of simulating realistic proportions and spatial distributions of coding sequences by using the positional information of CDS from the annotation of the reference genome of A. thaliana  (Arabidopsis Genome 2000). Except for the DFEs and genetic structure all other parameters and dataset dimensions were identical to the simulations without negative selection.
tsABC was conducted on the A. thaliana data, by using 12 samples for 5 independent loci of 1 Mb length. The mutation and recombination rates were set to 6. 95e-9  (Ossowski, et al. 2010 ) and 3. 6e-9. The recombination rate is the genome-wide average provided by  Salomé, et al. (2012). We simulated a total set of 130,000 vectors of summary statistics. Parameter estimates were conducted as described for the ABC performance analysis. We provided the mode of the average posterior distributions as a final result. In addition, we used a subsample of 20 sequences of the Arabidopsis data per genetic cluster to estimate demography and the transition to selfing using teSMC in the one-transition mode. Data and scripts can be found at (https://github. com/laurentlab-mpipz/struett_and_sellinger_et_al.
Data availability A complete detailed description of the teSMC and tsABC methods is in the SI text appendix A and B. Scripts for all figures, simulations, and the tsABC workflow can be found on github (https://github. com/laurentlab-mpipz/struett_and_sellinger_et_al. git). The simulator used to simulate selfing transition as well as teSMC are available on github (https://github. com/TPPSellinger/eSMC2). A tutorial is also provided to simulate and analyze data.
The yellow dashed lines indicate that, for a Bayes factor of approximately 3 (BF=√10), tsABC identifies the right model 80% of the time, for transitions as old as 51,000 generations (corresponding to 2. 5Ne generations in coalescent time units, where Nσ is the effective population size of the selfing population). F, G, H: Parameter estimation accuracy for the age of a transition to selfing (100 simulated datasets) under a model with constant population size (N= 40,000) and a change in selfing rate from σANC = 0. 1 to σPRES = 0. 99. Coloured lines represent average quantiles for 100 posterior distributions.
G, H, I: Re-estimation of the ancestral selfing rate on 100 datasets simulated under a model with constant population size (N = 40,000) and a change in selfing rate from σANC = 0. 1 to σPRES = 0. 99. Coloured lines represent the average quantiles for 100 posterior distributions corresponding to the given credible intervals. The panels should be compared with the case without linked negative selection in Figure S11. Coloured lines represent the average interpercentile ranges for 100 posterior distributions obtained with tsABC.
The output is a set of databases that model the distribution of the transient faults at the circuit layer, depending on the cell type, size, inputs and radiation scenario. The transient faults are Single Event Upsets (SEU) and Multiple Event Upsets (MEU) for sequential cells and Single Event Transients (SET) for combinational cells. Such technology-and circuitlayer analysis is required once per fabrication technology and radiation scenario. The second step is the gate-level analysis, based on statistical fault injection through a single-cycle simulator, in order to characterise with significant statistical confidence the propagation of radiation-induced faults (modelled at the circuit level during the first step). This simulation is able to analyse logical masking and latching window masking, since the hardware design frequency, the area, the delay, the type of cells and the netlist of the hardware design are taken into account.
The output is a set of databases describing singlebit and multi-bit error patterns that avoided masking and finally latched in the hardware design registers. Such gatelevel analysis is applied once per hardware design. The third step analyses the impact of single-bit and multibit error patterns, occurring at microarchitecture layer, on the system execution, taking into account the application workload. This is achieved initially through fault injection using a fast CABA simulator executing the application code. To further improve the analysis time at this step, FLODAM develops a fault injection emulation tool running on an FPGA platform. The microarchitecture-level analysis is applied per application workload.
B. Technology and Circuit Analysis The FLODAM flow leverages MUSCA-SEP3  [5]  and AT-MORAD  [16]  towards the development of the DiaQuant tool in order to analyse the physical impact of radiation to the hardware circuit, therefore considering the environment and the physical circuit of the hardware design. As an output, DiaQuant creates a set of databases of fault models per technology cell, along with their probability to occur depending on the cell type, size and inputs. Figure  2   The environmental models are based on generating environment databases according to the considered missions (e. g. , aircraft trajectories, ground trajectories, orbits) and space weather conditions (e. g. , solar activity, solar flare). The physical models correspond to the device description, including the circuit layout, fabrication technology, semiconductor active zones, passivation, metallization layers and package.
Depending on i, E i , the size of the transistor and the positioning of the particle with respect to the Drain-Grid-Source topology, the most relevant induced current is identified. After this analysis, DiaQuant selects a current from an I(t) database, depending on the characteristics of the transistor to be impacted (size, type), the particle and its characteristics, to be injected at the circuit layer. Following the aforementioned approach, a set of databases is generated with fault models per cell from the technology library used for the considered design, and for the different particles encountered in atmospheric and space environments.
Therefore, the selection of the cell, where the fault is injected, it driven by the area of the cell. If the selected cell is of sequential type, the fault is injected directly to the pipeline register. If the selected cell is of combinational type, an SET is inserted into the netlist. The time offset, when the SET is inserted, is randomly chosen within a clock cycle, since radiation may affect the hardware at whatever time instance. The SET duration is given from the databases obtained during the technology and circuit layer analysis. Then, the SET is injected and the output is latched by register at the output of the design stage. If the result in the register is different than the golden reference, the injected fault has led to a single-bit or multiple-bit error. The number and the position of faulty bits are logged, in order to create a set of databases with error patterns.
A. Technology and Circuit Layer Results Thanks to the DiaQuant tool, we can obtain fault models for 28nm FDSOI technology cells considering different radiation scenarios. In this use case, we considered several flights departing from Paris to New York, Los Angeles, Johannesburg and Sao Paulo, and vice versa. Each flight is described from a set of realistic flight plans from the Eurocontrol Demand Data Repository (DDR) databases. Each flight plan is characterised by the information regarding the timing (date, time) and the trajectory (latitude, longitude and altitude). Figure  4  represents the set of considered flights. We can observe the diversity of the flight plans for a given flight, e. g. , the maximum latitude of a flight from Paris to Los Angeles is between 67°and 45°N orth, which implies a potential variability in the radiation impacting the aeroplane during the flight.