dataesr/scientific-paragraphs-categorization huggingface dataset:
". In experimental linguistics, repeated measurements within subjects or within items are also now usually accounted for with random effects  (Baayen et al. , 2008). At the statistical level, including random effects is a more reliable strategy than for example averaging values over subjects or items  (Baayen et al. , 2008). Such a strategy to bypass the independence problem indeed leads to reduced datasets and a significant loss of information. Random effects fall into random intercepts and random slopes, and with the latter, the impact of predictors entered as fixed effects can be further analyzed across groupings of observations  (Barr, 2013). More generally, as underlined by  Drager and Hay (2012) , random effects are not only a tool to get more accurate models; actually looking at the conditional modes of their levels can provide useful information.
The AIC and BIC values reported for the various models in the next sections have all been rounded up or down to the closest whole number. Two identically reported values may therefore be in fact slightly different. Turning our attention to our test case, we can compute a LMM with the lmer() function provided in the lme4 package  (Bates et al. , 2015)  -one of the better-known packages offering this possibility. lmer() takes as inputs the dataset and a formula specifying the predicted variable, the fixed effects and the random effects of the desired model, and outputs estimates for the various parameters of this model. The underlying algorithm uses either a maximum likelihood (ML) or a restricted/residual maximum likelihood (REML) approach. The second differs from the first in the way the variance components that belong to random effects are estimated: REML accounts for the loss in degrees of freedom corresponding to fixed effects, while ML does not.
This is true if no random slope is simultaneously considered, since covariance between the random slope and the random intercept must then be estimated unless it has been constrained to take a 0 value. We did not consider random slopes in our models, both for the sake of simplicity and because we hypothesized that the impact of the fixed effects did not vary across the linguistic families. We are aware though that this choice could be contested  (Barr, 2013). Table  1  summarizes the output of the model. The lmerTest package is loaded so that the lmer() function returns p-values with Wald t-tests. There are two options to approximate the used degrees of freedom: the Satterthwaite approximation, and the Kenward-Roger approximation which is a slightly more conservative option. Likelihood-ratio tests (LRT), which compare the likelihood of the initial model with that of a model where a target fixed parameter has been dropped, are another option to assess significance.
With respect to count data, a number of articles have been published in ecology to discuss log transformation, and overall favor not transforming the data, although linear models with a log transformation often seem robust with large datasets, and may be more resistant to false positives, also known as type I errors  (O'Hara and Kotze, 2010; Ives, 2015; Warton et al. , 2016). Looking beyond the frequentist framework, Bayesian approaches to predictive uncertainty allow construction of credible intervals in untransformed units from a regression model with a transformed dependent variable  (Gelman and Hill, 2007; Korner-Nievergelt et al. , 2015). Within the frequentist framework, other modeling options are available, and are described in the next sections. Given the inadequacy of the previous LMM with respect to our test case, it makes sense to consider such options.
Phonemic inventory size falls into the domain of count data, and it makes sense therefore to consider distributions over positive integers rather than over real numbers. The Poisson distribution is the better known option in such cases. In cases where the counts are small, i. e. , close to 0, considering a distribution over real numbers would be dangerous, since predictions of the related model could be non-sensical negative values. A distribution over positive real numbers seems more appropriate, but exponential distributions like inverse-Gaussian or Gamma are not suited to count data close or equal to 0, unless in very specific cases. When count values are far from 0, however, continuous distributions may be considered, as it is the case for Phonemic inventory size -the smallest value is 11, the largest value 156, and the median 33.
They may then give better results than discrete distributions. Given these considerations, we thus fitted to our data a Poisson regression, an inverse-Gaussian regression, and a Gamma regression, each time with an identity link function. This choice was motivated by the positive skewness of the distribution of inventory sizes. We used the glmer() function of the lme4 package  (Bates et al. , 2015) , in which a few distributions of the exponential family can be specified, including the three previous ones. glmer() takes the same inputs as lmer() plus the chosen distribution. The inverse-Gaussian distribution turned out to give the lowest deviance, which was much lower than that of the Poisson regression  (10,693 vs. 11,653). The corresponding results (with restricted maximum likelihood -REML) are reported in Table  2.
346). Random slopes can also be considered. One then speaks of generalized additive mixed models (GAMM), or "mixed GAM. " Significantly in a GAM(M), the smooth function of a predictor is estimated while taking into account all other predictors, whether smoothed or not. In R, common packages for GAM(M) are gam, mgcv, or gamm4  (Wood, 2011) , with differences in the underlying MLE algorithms. In mgcv, the function gamm() calls to the lme() function of the package nlme to estimate random effects, while gamm4() calls to lmer() or glmer(), all these secondary functions being related to LMM or GLMM. As said earlier, random effects can also be specified directly with a penalized smoothing function. It can be noted that the mgcv package enables the use of other distributions than those already mentioned, such as the Tweedie distribution, the zero-inflated Poisson distribution etc.
An example of the use of GAMLSS is given by  Zha et al. (2016)  in their analysis of motor vehicle crash data. The predicted variable consists in count data of crashes in highway segments in the United States over the course of several years. As previously stated, the Poisson regression is what usually comes first to mind when count data needs to be assessed. However, as seen for phonemic inventory size, the overdispersion is very high for the number of crashes. The negative binomial distribution better accounts for overdispersion, but by using GAMLSS,  Zha et al. (2016)  show that a Poisson-Inverse Gaussian provides a better fit and similar predictive performance. They thus suggest that it should be used in subsequent studies to obtain better estimates of the role of predictors. Another example is response times in psycholinguistic experiments.
As for the overall philosophy of GAMLSS, it is interesting to quote  Stasinopoulos et al. (2017, p. 26-27) : "GAMLSS provides greater flexibility in regression modeling, but with this flexibility comes more responsibility for the statistician. This is not a bad thing. The philosophy of GAMLSS is to allow the practitioner to have a wide choice of regression models. " In R, GAMLSS are available through several packages. The main package is named gamlss, but associated packages such as gamlss. add, gamlss. cens, gamlss. mx, gamlss. spatial etc. allow extending the main functionalities: generation of censored or truncated versions of the main distributions, additional smooth functions such as neural networks or decision trees, use of mixture distributions etc. Models built with the aforementioned lmer(), glmer() or gam() functions can all be reproduced within the GAMLSS framework.
Investigating the Marginal Distribution of Phonemic Inventory Size A first step in contemplating the use of GAMLSS to study phonemic inventory size is to pay a closer look at the distribution of the latter. The distribution of the dependent variable independently from any predictor is called the marginal distribution. The histDist() and fitDist() functions of the gamlss package come in handy to investigate what theoretical distribution comes closest to the empirical one. The first one takes as its main inputs a vector of values and the name of a distribution, and returns how well the values fit the distribution, as expressed by the global deviance, the AIC and BIC of the fit. The second allows one to find the best fit among a list of distributions, and also returns the AIC of the different fitting attempts.
We used these two functions to compare different distributions. On the one hand, we considered distributions adapted to count data available in the gamlss. dist package (loaded by default with the gamlss package). There are over 25 available distributions, among them: -The 1-parameter Poisson distribution (PO); -The 2-parameter negative binomial distribution; the types I and II parametrizations (NBI and NBII) available in gamlss led to the same result, and we took the type I; -The 2-parameter Poisson-Inverse Gaussian distribution (PIG); -The 3-parameter Delaporte distribution (DEL); -The 3-parameter Sichel distribution; we considered the second parametrization (SICHEL) offered in gamlss in order for the mean of the distribution to be equal to µ. We also checked all the distributions adapted to positive real numbers.
(2011). All in all, with respect to our test case, whether language contact significantly affects phonemic inventory size through borrowing remains to us an open question. What geo-linguistic measures best capture language borrowing is a connected question that requires further investigation. Finally, we argue that linguistics and psycholinguistics could benefit from the use of GAMLSS when regression models are envisaged to explore a phenomenon. The adequacy of the Delaporte distribution to model phonemic inventory size in no way means that this distribution in particular is the solution to a large number of problems. Rather, we have tried to highlight the reasoning that led us to consider this distribution, and why other options -LMM, GLMM, GAMM, GAMLSS with other distributions -were not as much appropriate.
Besides psycholinguistics, work in preparation suggests that another variable which can benefit from GAMLSS is speech rate. Indeed, speech rate -the number of syllables uttered by second -presents interesting variations between speakers and languages  (Pellegrino et al. , 2011; Coupé et al. , 2014) , but distributions in speakers and languages also suggest meaningful patterns of skewing, where the amount and orientation of skewing is connected to the mean value of the speech rate. More generally, we have little doubt that many other variables, either continuous, discrete or count data, can benefit from both the smooth functions and distributions of GAMLSS.
CONCLUSION Various statistical tools are available to linguists willing to explain how a given linguistic variable varies across its domain. We highlighted how GAMLSS models, which are still very rarely used in the language sciences, could be put to use to depict 'complex' variables such as phonemic inventory size. This seems especially relevant when non-linguistic causes of linguistic diversity such as climatic or sociodemographic factors are considered, since their study can often be conducted with regression models. The distributions offered by GAMLSS can be more appropriate from a methodological point of view, and both the possibility to include additive terms and the possibility to model the scale and shape of the distribution in addition to its location can be put to use to better understand the behavior of a system.
Flow cytometry Cells were suspended in PBS (Ca 2+ /Mg 2+ free) containing 0. 5% FBS and 5 mM EDTA and stained with Vybrant DyeCycle Green stain at a final concentration of 5 µM for 15 min at 37°C. Samples were then filtered through a 30-µm filter (Sysmex; Partec CellTrics) and placed on ice. Stained populations were individually profiled in a Bio-Rad S3 flow cytometer. For population analyses, ∼50,000 cells were profiled per sample. In addition to nuclear dye detection, cytoplasts were also identified from intact cells based on distinct side-scatter profiles. Periodically, this was used to assess cytoplast population purity. FlowJo (v10. 1r5) software was used for graphic visualization of population distributions and extraction of statistical values. All fluorescence threshold values were designated based on unlabeled and labeled cells.
Cell viability Intact cells and cytoplast populations were stained with 1 µM calcein-AM (to label live cells), 0. 2 µg/ml Hoescht 33342, and 0. 04% (vol/ vol) Trypan blue (to label dead cells) for 10 min in a tissue culture incubator. Dyes were washed out after staining, and images were acquired on an Axiovert 200M microscope with AxioVision software (Zeiss). Calcein-AM and Hoescht 33342 were imaged under fluorescent excitation/emission wavelengths suitable to each fluorophore, whereas trypan blue was imaged via transmitted light. Images were analyzed via automated particle counting with ImageJ software and verified by manual counting of randomly selected images from different time points. Stained populations were used for a single time point and were not restained for later time points. Viability data for intact cells and cytoplasts were derived from at least three independent experiments.
Directional migration assays Directional migration assays were performed as previously described  (Wu et al. , 2012). In brief, polydimethylsiloxane (PDMS; Sylgard 184; Dow Corning) microfluidic molds were cast from a custom silicon wafer. The microfluidic device was used to establish a gradient across a defined central chamber that was amenable to both cell migration and live-cell monitoring. For most experiments, intact cells and cytoplasts were plated together. Cells were allowed to spread for 2-3 h prior in the PDMS molds, and experiments were performed for 8-16 h under humidified 5% CO 2 at 37°C. Image acquisition was performed with MetaMorph imaging software (Molecular Devices), with images being acquired every 10 min from multiple stage positions. For chemotaxis, 10 µg/ml FN was used to coat the central chamber.
For experiments where intact cells and cytoplasts were coplated, cells were distinguished based on the presence of the nucleus, as determined by staining at the end of the experiment with Vybrant DyeCycle Green nuclear stain. For all direct migration, single-cell tracking was manually performed in ImageJ using the Manual Tracking plugin. To obtain FMI, persistence, and velocity values, raw tracking data were analyzed with the "Chemotaxis Tool" plugin (Ibidi) in ImageJ. Rose plots were generated using the "secplot" script for MAT LAB (http :// www. mathworks. com /matlabcentral /fileexchange /14174 -secplot).
Nuclear density was measured for both intact cells and cytoplasts at the end of experiments via sampling three random regions per nuclear image with a 100 µm × 100 µm-square region. These values were then averaged and reported per scratch assay run. Scratch closure was measured for each hour over a 16-h experiment by manually outlining the open cleft area using ImageJ. Scratch closure rates were measured from three or four fields of view per dish. Closure rates were quantified relative to the starting area of the cleft (from t = 0). All data were collected from at least three independent experiments.
Biaxial cyclic strain assay The biaxial strain was performed essentially as described elsewhere  (Uzer et al. , 2015). In brief, REF52 intact cells and cytoplasts were plated at a density of 30,000 cell/cm 2 per well in six-well BioFlex collagen-I coated plates (BF-3001C; Flexcell). After plating for either 3 or 18 h, cells were subjected to dynamic uniform biaxial cyclic strain at 5% magnitude at 10 cpm for 20 min using the Flexcell FX 5000 under conditions of 37°C and 5% CO 2. Control plates were handled the same but without strain application. Immediately after strain, whole-cell lysates were prepared and probed for phospho-FAK, total FAK, and GAP DH via Western blot analysis. Blots were analyzed using ImageJ. Data were derived from three independent experiments.
Acknowledgments We thank  David Scott  for critical discussions,  Kellie Beicker  and  Timothy O'Brien  for exploratory efforts,  Irina Lebedeva  for technical assistance, and members of the  Burridge and Bear laboratories  for support. We thank  Jan Lammerding 's laboratory for providing the lamin cell lines. We apologize to all authors whose work could not be cited due to space constraints. This work was supported by  National Institutes of Health  grants  GM029860  ( K. Burridge ),  GM103723  ( K. Burridge ),  GM111557  ( J. E. Bear ),  AR066616  ( J. Rubin ), and  GM109095  ( G. Uzer ) and  National Science Foundation  grant  1454257  ( B. Hoffman ). The authors declare no competing financial interests. Author contributions: D. M. Graham designed and performed most experiments, analyzed data, and prepared the manuscript.
In a SEBOMD simulation, a Born-Oppenheimer molecular dynamics calculation is run, meaning that only the positions of the nuclei are defined and, at each step of the molecular dynamics, a fully converged self-consistent field (SCF) calculation is performed to obtain molecular orbitals, energy, and forces. 4] [45]  To enhance the speed of the QM calculation, a linear scaling Divide & Conquer (D&C) algorithm can be used for large systems. 46, 47  have implemented the SEBOMD approach within the Amber biomolecular package,  48  more particularly in the open source AmberTools package that is part of Amber. Here, we highlight some specific features of our implementation. First, we briefly describe the implementation of SEBOMD as a module in the sander program. Second, we present how the total energy can be conserved in NVE simulations.
Third, a simple way to accelerate SEBOMD simulations is given. Fourth, different Periodic Boundary Conditions (PBC) methods to handle long range electrostatics interactions are introduced. Fifth, the speed-up of SEBOMD simulations using the linear scaling Divide & Conquer algorithm with parallel computers is presented. Finally, we provide a comparison of SEBOMD simulations using either standard full Fock matrix diagonalization or approximate linear scaling D&C method. The SEBOMD module Implementation in the sander program SEBOMD has been implemented in the sander program as a component of the free open source AmberTools package that is part of Amber. 48, 49 The sander program is in charge of performing molecular dynamics simulations. So far, it could run full MM and QM/MM MD. With SEBOMD, we have added the capability of running full QM simulations with or without periodic boundary conditions.
At each step of a molecular dynamics simulation, the energy and forces calculations for a given set of coordinates are directed to a SEBOMD module that will compute these quantities at the semiempirical NDDO level. Currently available NDDO hamiltonians are MNDO, 50 AM1,  51  PM3, 52 RM1, 53 PM3/PDDG, 54 AM1/d-PhoT,  55  and AM1/d-CB1. 56 MAIS  57, 58  and PIF  [58] [59] [60] [61] [62]  corrections to the PM3 Hamiltonian are also provided. SEBOMD simulations can be run with sander using usual thermodynamical ensembles: NVE, NVT, or NPT. At each step of an MD simulation, an MM energy calculation is still computed and is mixed with the SEBOMD energy if requested, as controlled by a λ parameter (keyword: lambda): E pot = (1 -λ )E MM + λ E QM (1) where E pot is the total potential energy, E MM is the MM potential energy, and E QM is the semiempirical QM potential energy.
Buffer regions are used to determine what information from the local density matrices should be used to recover the entire, global density matrix. In our implementation that is originally based on the DivCon99 software,  68  we use the dual buffer region scheme as suggested by Dixon and Merz, Jr. 47  Because the subsystems are mostly equal in terms of size, the CPU times corresponding to the diagonalizations of the local Fock matrices are similar and linear scaling is achieved by the fact that the number of subsystems scales linearly with the size of the system. When no external force is applied to a system, a molecular dynamics simulation should conserved the total energy. This corresponds to the NVE thermodynamical ensemble where the number of particle N, the volume V and the total energy E being the sum of the kinetic energy and the potential energy are kept constant along the simulation.
In contrast to classical MM molecular dynamics, conservation of the total energy in Born-Oppenheimer molecular dynamics not only depends on the time step that is used but also on the precision with which the SCF is converged at each step of the simulation. This is due to the fact that forces are not directly derived from the potential energy equations but make use of the SCF convergence in their calculation to avoid the costly evaluation of the derivatives of the density matrix elements. Therefore, to ensure a proper energy conservation during a SEBOMD simulation, one must pay a special attention to the convergence of the SCF at each step of the molecular dynamics. In many quantum chemistry software, the SCF convergence is achieved when the electronic energy between two steps of the SCF cycle does not change for more than a certain threshold.
e. , electrostatic embedding scheme). Polynomial extrapolation of the guess density matrix was used to accelerate the convergence of the SCF calculations. SEBOMD analysis was performed using the cpptraj 75 module of Amber. The radial distribution functions (RDFs) for water obtained with the three SEBOMD simulations are represented in Figure  3. As it is not the scope of the present study, no comparison with any experimental values are provided here. All simulations present nearly identical curves for the oxygen-oxygen, hydrogen-oxygen, and hydrogen-hydrogen RDFs. As a result, we can safely conclude that using the Divide & Conquer scheme does not impact the structure of liquid water compared to using a full diagonalization scheme. Similarily, using a relaxed convergence setup (dpmax= 10 -5 a.
Conclusions Semiempirical methods are fast approximate quantum methods that are suitable for long molecular dynamics runs. In the Amber biomolecular package, we have implemented a module that is capable of running SEBOMD simulations: SemiEmpirical Born-Oppenheimer Molecular Dynamics. At each step of a SEBOMD simulation, the semiempirical quantum wavefunction is fully converged through a Self Consistent Field process. In our implementation, we have taken care of the SCF convergence to ensure the conservation of the total energy when no external forces or restraints are applied (i. e. , in the NVE thermodynamical ensemble). This is triggered by the dpmax keyword. A safe setting for dpmax is 10 -6 a. u. but SEBOMD simulations can be made faster using a dpmax value of 10 -5 a. u. without modifying results such as the structural properties of liquid water.
An additional way of accelerating SEBOMD simulations is to use guess density matrix extrapolation which reduces the number of SCF cycles made at every MD step. Finally, the Divide & Conquer scheme provides a fast and parallel way of finding the NDDO energy of "large" molecular systems in SEBOMD simulations without compromising the results obtained with this approximate linear scaling quantum method as compared to the traditional full Fock matrix diagonalization scheme. With the SEBOMD module implemented in the Amber biomolecular package, it is now possible to routinely perform full-QM molecular dynamics simulations of systems containing hundreds to thousands of atoms in reasonable amounts of CPU time for simulation lengths up to the nanosecond timescale using parallel computers. Fellowships and Grant Programmes Department for financial support.