dataesr/scientific-paragraphs-categorization huggingface dataset:
SIMULATION-BASED EVALUATION TOOLS Simulation-based evaluation tools were first developed by Bayesian statisticians and are now increasingly used to evaluate NLMEM. They rely on the concept of the posterior predictive check,  11  whose principle is that if a model correctly describes a dataset, the data simulated under that model would be similar to the observations. Hence, to evaluate a model with these methods, one needs to simulate a large number (K) of Monte Carlo samples under the tested model, using the design of the observed dataset then compare a statistic computed from the observed data with that computed from the simulated data. The chosen statistic can be a PK parameter calculated from noncompartmental analysis, for instance, area under the curve, half-life, steady-state concentration, maximum or minimum concentration,  12, 13  or other statistical inferences, such as mean prediction errors (residuals), root mean square prediction errors,  11, 13  or objective function value.
g. , 2. 5, 50, and 97. 5th percentiles), are calculated for each of the K simulated datasets. The 95% CI for each of the selected percentiles is then easily obtained from the distribution of the K percentiles computed for the K simulated datasets. If the model is correct, the observed percentiles should be close to the predicted percentiles and remain within the corresponding CI. However, an appropriate VPC may still result from models in which individual unexplained variability is misspecified, either because it is absorbed by other model components (interoccasional variability by residual variability, for instance) or because it is contributing little to the overall variability (e. g. , residual variability in the presence of large interindividual variability). An important property of VPC is that it conserves the original units (e.
The numeric version of VPC, known as the numeric predictive check (NPC), is also used in model evaluation. 9, 22 It summarizes the information of several "scatter VPCs" evaluated at different selected PIs, for instance, the 0, 20, 40, 50, 60, 80, 90, and 95% PI. 9, 22  NPC calculates the percentages of outliers for each selected PI, which are the observed data above and below different PIs. By providing the same calculation for each of the K simulated datasets, we can obtain a CI for the percentages of outliers. The observed percentages can be compared with the empirical CI using a coverage plot. Just as in a VPC plot, a trend in the NPC coverage plot would indicate a misspecification of the structural, interindividual variability, or residual error model (Supplementary Figure  S9 ). As NPC evaluates model misspecification on several PIs, it may provide additional information compared to the VPC, which only presents one selected PI.
Study design and data simulation We considered the same design as the one used in the original study. 24 There were 27 male and 5 female patients (N 5 32). Body weight ranged from 40-102 kg. The administered doses were calculated on a 1. 5 mg per kg basis. Central compartment drug concentrations (mg/L) and PCA (PCA unit) were measured as described in the original report. A dataset was simulated using the design and models described above.
3 (http://www. iconplc. com/innovation/solutions/nonmem/). For parameter estimation, the PK model was first fitted to the simulated concentrations. The population PK parameters were then fixed at their estimated values to perform data fitting for the PCA observations. Thus, only the PD model was misspecified in this example. Parameter estimation was performed using the Quasi-random Parametric Expectation Maximization algorithm  26  in Phoenix NLME 7. 0 (https://www. certara. com/software/pkpd-modeling-and-simulation/phoenixnlme/) keeping all default settings. For simulation-based evaluation graphs, a thousand replicates were performed.
As indicated by their name, simulation-based metrics requires data simulation. However, many design and data features, such as adaptive design, response-guided treatment, changes in dosing regimen to limit adverse effect or to maintain drug concentrations within the therapeutic window (therapeutic drug monitoring), missing data, or dropout, etc. , may be difficult to reproduce through simulation. If these features are neglected, simulation-based graphs may show significant trends, even though the underlying model is adequate to describe the data. 20, 31, 32 2] [33]  As a large number of simulations is required (usually !1,000 simulations),  34  the computation of these metrics can be a time-consuming process, especially with complex models and large datasets, and, therefore, may not always be feasible for evaluating models when the timelines for decision-making are tight, especially in an industrial setting.
Acknowledgments. This work was performed by members of the  Model Evaluation (MoEv) Group of the ''ISoP Best Practice Committee. ''The authors would like to thank other members of the group who participated in the outline of this first tutorial:  Malidi Ahamadi ,  Brian Corrigan ,  Kevin Dykstra ,  Marc Lavielle ,  Robert Leary ,  Don Mager ,  Peter Milligan ,  Flora Musuamba Tshinanu ,  Rune Overgaard ,  Marc Pfister ,  Richard Upton , and  Byon Wonkyung. The authors would like also to thank  Ren e Bruno , chair of the  ISoP Best Practice Committee , for his involvement in the preparation of the tutorial.
4  Continued See supplementary table 1, Supplementary Material online for the list of accession numbers of sequences used and supplementary figure  1 , Supplementary Material online for sequences of the P. pacificus proteins. (B) Inferred gene structure of Ppa-daf-21. CRISPR/Cas9 mutation was introduced in the second exon, which interferes with both identified isoforms. (C and D). Pleiotropic effects of the Ppa-daf-21 knockout. (C) On the top, a homozygous mutant. Such animals are small and clear. On the bottom, a normally looking heterozygous or genetically wild-type individual from the same brood. (D) On the left, a seven-day-old homozygous mutant. Mid-body region, sagittal plane. The gonad is underdeveloped. Proximal part of the gonad is encircled (distal parts are not visible in this plane).
Geometric Morphometrics Analysis Worms were mounted on microscope slides on 4% agar pads containing 0. 3% NaN 3. Animals were turned right side up and covered with a cover slip. Correct positioning of the head was verified by checking if amphid openings were seen close to the central axis. Animals were examined using a 100x/1. 4 oil objective. A stack image of the head region was taken, and coordinates of 20 landmarks in XY plane were recorded using FIJI software  (Schindelin et al. 2012). Detailed descriptions of landmarks are available in supplementary table 1, Supplementary Material online. Procrustes alignment and PCA were done in R using geomorph package (Adams and Otarola-Castillo 2013; R Development Core Team 2013). Sum of variances was calculated using the MATLAB package MDA  (Guide MUs 1998; Navarro 2003).
CRISPR/Cas9 Knock Out As a first step, we searched for homologs of Hsp90 in P. pacificus genome. Protein sequences were retrieved from Genbank and the http://pristionchus. org databases. Alignment was done using MUSCLE  (Edgar 2004 ). Conserved sites were manually selected using SeaView  (Gouy et al. 2010). Phylogenetic trees were inferred using PhyML  (Guindon and Gascuel 2003)  with the LG þ G substitution model. Branch support values were assessed using the approximate likelihood ratio test  (Anisimova and Gascuel 2006). Ppa-DAF-21 was identified as the closest homolog of Drosophila melanogaster Hsp83 protein, which was knocked out in earlier studies  (Rutherford and Lindquist 1998). Exon 2 of the Ppa-daf-21 gene was selected as CRISPR/Cas9 target due to high sequence conservation. To verify if the mutation in this locus would interfere with the function of the protein product, we performed Rapid Amplification of cDNA Ends experiment and identified two isoforms both containing the targeted exon.
The cross-entropy (CE) method is a simple and versatile technique for optimization, based on Kullback-Leibler (or cross-entropy) minimization. The method can be applied to a wide range of optimization tasks, including continuous, discrete, mixed and constrained optimization problems. The new package CEoptim provides the R implementation of the CE method for optimization. We describe the general CE methodology for optimization and well as some useful modifications. The usage and efficacy of CEoptim is demonstrated through a variety of optimization examples, including model fitting, combinatorial optimization, and maximum likelihood estimation.
cemethod. org/ and http://www. montecarlohandbook. org/. Since R (R Core Team 2016) has become an essential tool for statistical computation, it is useful to provide an accessible implementation of the CE method for R users, similar to R packages for simulated annealing  (Xiang, Gubian, Suomela, and Hoeng 2013) , evolutionary methods  (Mullen, Ardia, Gil, Windover, and Cline 2011) , and particle swarm optimization methods  (Bendtsen 2012). Some advantages of the CE method are: • The CE method is a global optimization method which is particularly useful when the objective function has many local optima. • The CE method can be used to solve continuous, discrete, and mixed optimization problems, which may also include constraints. • The CE code is extremely compact and is readily written in native R, making further development and modifications easy to implement.
• The CE method is based on rigorous mathematical and statistical principles. Our aim is not to replace the standard optimization solvers such as optim and nlm but to provide a viable alternative in cases where standard gradient or simplex-based solvers are not applicable (e. g. , when the optimization problem contains both discrete and continuous variables) or are expected to do poorly (e. g. , when there are many local optima). The rest of this paper is organized as follows. In Section 2, we sketch the general theory behind the CE method, which leads to the basic CE algorithm. In Section 3, we describe a variety of optimization scenarios, including continuous, discrete and constrained mixed problems, to which CE can be applied effectively. The description and usage of the CEoptim package  (Benham, Duan, Kroese, and Liquet 2017)  are given in Section 4.
Constrained optimization The general optimization problem (1) also covers constrained optimization, where the search space X could, for example, be defined by a system of inequalities: G i (x) ≤ 0, i = 1,. , k. (6) One way to deal with constraints is to use acceptance-rejection: Generate a random vector X on a simple search space that contains X , and accept or reject it based on whether the sample falls into X or not. Alternatively, one could try to sample directly from a truncated distribution on X , e. g. , using Gibbs sampling. CEoptim implements linear constraints for continuous optimization of the form Ax ≤ b, where A is a matrix and b a vector. The program will use either acceptance-rejection or Gibbs sampling to sample from the multivariate normal distribution truncated to the constraint set.
05i (x) is the solution to (9) for time t = 0. 05i, and x = (a, b, c, V 0 , R 0 ) is the vector of parameters. The true parameter values are here a = 0. 2, b = 0. 2, and c = 3. The initial conditions are V 0 = -1 and R 0 = 1. Estimation of the parameters via the CE method can be established by minimizing the leastsquares performance S(x) = 400 i=0 (y i -V 0. 05i (x)) 2 , ( 11 ) where the {y i } are the simulated data from the model (  10 ). Note that we assume also that the initial conditions are unknown. We use the deSolve package  (Soetaert, Petzoldt, and Setzer 2010)  to numerically solve the FitzHugh-Nagumo differential equations (  9 ). Hereto, we first define the function FN. R> FN <-function(t, state, parameters) { + with(as. list(c(state, parameters) ), { + dV <-c * (V -V^3/3 + R) + dR <--1 / c * (V -a + b * R) + list(c(dV, dR)) + }) + } The following function ssres now implements the objective function in (11).
R> ssres <-function(x, fundf, times, y) { + parameters <-c(a = x[1], b = x[2], c = x[3]) + state <-c(V = x[4], R = x[5]) + out <-ode(y = state, times = times, func = fundf, parms = parameters) + return(sum((out[, 2] -y)^2)) + } CEoptim could be used with µ 0 = (0, 0, 5, 0, 0) and σ 0 = (1, 1, 1, 1, 1). Constant smoothing parameters α = 0. 9 and β = 0. 5 were used for the {µ t } and the {σ t } respectively. To see the progress of the algorithm we set verbose to TRUE. The other arguments remain default. R> library("deSolve") R> library("CEoptim") R> set. seed(123405) R> times <-seq(0, 20, by = 0. 05) R> data("FitzHugh", package = "CEoptim") R> res <-CEoptim(ssres, f. par = list(fundf = FN, times = times, y = ySim), + continuous = list(mean = c(0, 0, 5, 0, 0), sd = c(1, 1, 1, 1, 1), + smoothMean = 0.
, X n ) ∼ Ber(p), where p = (1, p 2 ,. , p n ). In this case the updated success probability for the ith component is the mean of the ith components of the vectors in the elite set. As an example, consider the network from  Knuth (1993)  describing the coappearances of 77 characters from Victor Hugo's novel Les Miserables. Each node of the network represents a selected character and edges connect any pair of characters who coappear. The weights of the edges are the number of such coappearances. Using CEoptim, the data can be loaded via the command data("lesmis", package = "CEoptim"). The network is displayed in Figure  4 , using the graph analysis package sna  (Butts 2008). R> library("sna") R> library("CEoptim") R> data("lesmis") R> gplot(lesmis, gmode = "graph") For any fixed cost matrix costs and cut vector x, the objective function of the max-cut problem can be written as: R> fmaxcut <-function(x, costs) { + v1 <-which(x == 1) + v2 <-which(x == 0) + return(sum(costs[v1, v2])) + } To optimize this function with the CEoptim package, we specify the following arguments: discrete$probs = {(0,1); (0.
5, 1, 1. 5), oma = c(1, 1, 1, 1)) R> for (i in 1:5) { + plot(X[i, ], type = "h", lwd = 4, col = "blue", ylim = c(0, 1), + xaxt = "n", yaxt = "n", ylab = "", + main = paste("t=", i -1, sep = "")) + axis(2, at = 0. 5, labels = 0. 5) + } R> for (i in 1:5) { + plot(X[1 + 4 * i, ], type = "h", lwd = 4, col = "blue", + ylim = c(0, 1), xaxt = "n", yaxt = "n", ylab = "", + main = paste("t=", 1 + 4 * i, sep = "")) + axis(2, at = 0. 5, labels = 0. 5) + } Based on the output above, the two groups of characters are indicated in Table  1 : We ran the program 1000 times with different inital random number generator seeds. In 312 cases the optimal solution (535) was found. The frequency of the results of CEoptim is given in Figure  6.
Dirichlet data Suppose that we are given a random sample of data from a Dirichlet(α) distribution, where α = (α 1 ,. , α K+1 ) is an unknown parameter vector satisfying α i > 0, i = 1,. , K + 1. Recall that the pdf of a random vector Y = (Y 1 ,. , Y K ) ∼ Dirichlet(α) is given by f (y; α) = Γ( K+1 i=1 α i ) K+1 i=1 Γ(α i ) K i=1 y α i -1 i 1 - K i=1 y i α K+1 -1 , for y i ≥ 0, i = 1,. , K and K i=1 y i ≤ 1, where Γ is the gamma function. The conditions on α provide natural inequality constraints: G i (α) ≡ -α i ≤ 0, i = 1,. , K + 1. We will use the CE method to obtain the maximum likelihood estimate by direct maximization of the log-likelihood for the Dirichlet distribution given the data. The CE parameters are the initial mean vector µ = (0, 0, 0, 0, 0) and the standard deviation vector σ  = (10, 10, 10, 10, 10).
Concluding remarks CEoptim provides an R implementation of the cross-entropy method for optimization. The versatility and effectiveness of this new package have been illustrated through a variety of optimization examples involving continuous, discrete, mixed and constrained optimization problems. We have demonstrated how this simple algorithm can be of benefit in statistical inference, including model fitting, regression, maximum likelihood, and lasso methods. CEoptim is available from the Comprehensive R Archive Network (CRAN) at https: //CRAN. R-project. org/package=CEoptim.
Here, we aim to circumvent this problem by reconstructing and analyzing the internal nasal morphology of MHs and Neanderthals, including its mucosa surface, to compare the performance among the Neanderthal nasal model and MHs from two different climates/populations. Then, we simulate the respiratory cycle under several conditions using computational fluid dynamics (CFD) methods  (21, 22). To complement this performance approach from a model-bound evolutionary perspective, we test if nasal variation on different clades containing species inhabiting in cold climates is compatible with neutral versus nonneutral evolution. This twofold approach resembles previous works in the larger field of evolutionary biology  (23, 24)  and biological anthropology (25) that explicitly used mixed datasets with different variables and samples to evaluate adaptive processes from the performance and evolutionary analyses perspectives.
monkeys including a cold-adapted species (Macaca fuscata, at least in relation to the climate conditions of the clade's common ancestor), and a composite sample of Hominins including Australopithecus, Paranthropus, and Homo genera (SI Appendix, Table  S5 ). This dataset provides a phylogenetic context where some members of a clade evolved in cold climates whereas others do not (Fig. 3 ), thus maximizing the chances of detecting potential nonstochastic signals not due to acrossclades spurious comparisons (see refs. 10, 30, and 31). Lande's test predicts that, under a scenario of evolutionary divergence driven exclusively by stochastic factors, among-population variation should be proportional to within-population variation  (1, 28, 29). Results presented in Table  1  and Fig. 3  suggest that, whereas the covariation patterns of the skull as a whole are compatible with evolution under stabilizing selection, the nasal structures seem to have evolved mainly by stochastic forces.
Both specimens were selected among many Neanderthal remains because they offer the largest conservation of the internal bony structure of the nose  (36) , thus providing a minimal osseous "template" against which to extrapolate the modern human mucosa coverage (see below). CT scans were explored using the 3D Slicer software (v 4. 1. 0; https://www. slicer. org) to place a 3D configuration of 11 landmarks (SI Appendix, Table  S1 ) located exclusively on the osseous tissue (Fig. 1  and SI Appendix, Fig. S1 ). Even when landmark coverage in both external and internal nasal structures is limited by the conservation state in both Neanderthal specimens, this landmark configuration enables a proper coverage of the lateral walls, the roof top, the floor, and the nasal concha in all of the 38 + 2 analyzed specimens.
1 ). Both individuals were chosen as representatives of their populations (southwestern European versus northeastern Asian ancestry). Reconstruction of the Internal Nasal Cavities in MHs. We used a minimum of 140 sections from the CT images of both subjects to construct an anatomically correct 3D model of the nasal and pharyngeal cavities. The images were obtained at intervals of 1 mm from the plane delimited by the nasal apertures to the posterior end of the pharynx, at the level of the posterior nasal spine. The 3D geometry of the nasal and pharyngeal cavities including the mucosa extension was reconstructed based on the DICOM (digital imaging and communications in medicine) data of the CT scan sections by using the imaging software 3D Slicer (https://www. slicer. org). The CT scans belonging to both specimens were segmented by manual outlining of the nasal boundaries on successive images to yield a surface triangulation for each image dataset.
The data subset selected into 3D slicer was further exported to meshlab code (www. meshlab. net/) to make a starting boundary mesh. Although this mesh is useful for geometry visualization purposes, it is inadequate for CFD analysis. The last steps on mesh-healing, triangle-shape optimization, adapting density to the CFD requirements, discrimination of surfaces with different boundary condition (for the calculus stage), and filling the volume with shape-and density-controlled tetrahedra, was made on Meshsuite  (38) , adapted ad hoc for this kind of domain and the meshes. After necessary smoothing and artifact correction, the resulting average mesh size is h = 1. 3 × 10 -4 m for both specimens. The meshes representing the nasal cavity for the southwestern Europeans and northeastern Asian models with different levels of refinement ranged from 50,000 to 240,000 nodal points, giving differences of 1-2% in the variables between the coarser and the finer mesh.
This represents the geometric soft counterpart of the osseous nasal cavity that respects the real extension of the mucosa into the nasal cavity. Thus, taking the southwestern European nasal cavity surface as the starting shape, we used Landmark software  (40)  to warp the surface using the vector of Europeanaverage Neanderthal shape changes imported previously from MorphoJ (see above). Note that, in relation to the placement of Neanderthals on the morphospace, the SWE-NEA difference has no significant effects on the selection of SWE or NEA as the starting shape (Fig. 1 ). Since the right side of the Neanderthal specimens is best preserved than the left one, we inferred just the right side of the Neanderthal internal nasal anatomy. The resulting Regression of between-group on within-group variances as a test for genetic drift.
γ is the recovery rate of the infected compartment. We evaluated mobility matrices under two hypotheses. The first made no assumption regarding specific overall probabilities of mobility from each location, allowing the rate α to be adjusted. The second assumed the overall probability of mobility from each location to be approximated by CDRs. This CDRs mobility matrix resulted from the processing of cell phone network metadata of 2. 9 million  (12) dS i dt = -βS i (t)   (1 -α)I i (t) + α � j� =i π ij I j (t)   dI i dt = βS i (t)   (1 -α)I i (t) + α � j� =i π ij I j (t)   -γ I i dR i dt = γ I i subscribers of the largest mobile phone operator in Haiti called Digicel (60% market share). Voice call and written messages metadata were collected daily from October 15 to December 19, 2010  [4, 10, 15].
Accuracy of the transmission model assuming various mobility patterns for the 2010 Haiti cholera epidemic Statistical dispersion measures comparing population heterogeneity for both spatial definitions are presented below (Table  1 ). The coarse pattern (which includes 78 aggregated units) displays greater population heterogeneity. Transmission parameters estimated with the Metropolis-Hastings Markov Chain Monte Carlo algorithm using a CDRs-based fine-scale mobility matrix (n = 140) are presented in Table  2. CDRs data were used to calibrate the transmission parameters. These parameters were then entered in new models that lacked mobility patterns. Table  3  shows the AIC results obtained with different mobility matrixes, assuming that the rate of mobility from each location remains unknown.
Funding This work was conducted with the support of the program " Investissements d' Avenir " of the French Government in the form of an A*MIDEX grant (n°ANR- 11-IDEX-0001-02 ). This work was granted access to the  HPC resources of Aix-Marseille Université  financed by the project  Equip@Meso  ( ANR-10-EQPX-29-01 ) of the program " Investissements d' Avenir " supervised by the  Agence Nationale pour la Recherche (Mésocentre). K. S. received grants from  ADEREM (Association pour le Développement des Recherches Biologiques et Médicales ) and OpenHealth Institute. XL acknowledges the support of the  Natural Science Foundation of China  ( 71522014 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.