dataesr/scientific-paragraphs-categorization huggingface dataset:
In summary, the observed changes in contraction due to perturbations of motor activities, as well as stabilizing and destabilizing filaments, are well predicted by a model in which contraction is driven by disassembly, but not by a motor-driven model. While in many aspects the two models are similar (e. g. both models include the 'fluidization' of the network by disassembly), the principal difference is that in the disassembly-driven model disassembly and contraction are directly coupled. In silico reconstitution of the contractile system predicts a 'depolymerization harnessing factor' essential to drive contraction Our data suggest that the F-actin network transporting chromosomes in starfish oocytes contracts by a mechanism mediated by filament disassembly. To explore the mechanism at the filament level, we implemented an agent-based model in the Cytosim software  (Nedelec and Foethke, 2007).
Image processing and data analysis Image processing was done using Fiji  (Schindelin et al. , 2012). Figures were assembled in Adobe Illustrator. Data analyses were performed using Matlab (MathWorks). Image data are shown after brightness and contrast adjustment and application of a 2D Gaussian blur filter (sigma value: 0. 2-1. 0). Some image data were registered using the 'StackReg' plugin implemented in Fiji to correct for oocyte movements during experiments. Panels show either single Z-slices, maximum or sum intensity projections as indicated in the figure legends. Colored time projections were done using the Time-RGBcolorcode macro in Fiji. Chromosome tracking and data analysis were done by in-house routines written in Matlab. Segmentation and tracking algorithms are described elsewhere  (Monnier et al.
After drug addition, we measured the size of contracting old network, and derived the contraction rate similar to Figure  2E. Surface curvature measurements were performed using an algorithm written in Matlab and described elsewhere  (Bischof et al. , 2017). The strength of surface contraction waves was defined by the maximum variance of the radius of curvature. The Matlab script is available in the Supplementary file 6. Statistical tests used were unpaired nonparametric tests (Mann-Whitney and Kruskal-Wallis) implemented in Matlab.
The disc was populated initially by 5000 filaments of 1. 5 mm long, 20,000 cross-linkers and 20,000 motors for the motor-driven mechanism. For the disassemblydriven mechanism, we added 40,000 end-tracking cross-linkers to the simulated system. All filaments disassembled at the same rate from one end while the other end remained stable. The disassembly rate was set at 0. 0015 mm/s to match the experimentally measured contractile characteristics. To simulate the polymerization of newly-assembled filaments at the edge of the nuclear region, filaments were added with an initial length of 1. 5 mm at the boundary at a rate of 12 filaments per second. Because there is no edge or steric interactions, adding new filaments per se does not produce compressive force on the old network. Cytosim is an Open Source project hosted on https://github.
Acknowledgements We would like to acknowledge members of the  Lenart lab  for reagents and support,  Johanna Bischof  in particular for sharing plasmid constructs. We would like to additionally acknowledge the essential support of  EMBL 's  Advanced Light Microscopy Facility, Protein Expression and Purification Facility , IT support, and  Laboratory Animal Resources ,  Kresimir Crnokic  in particular. The work was supported by the  European Molecular Biology Laboratory , and specifically by the  EIPOD Interdisciplinary Postdoctoral Programme  to PB and JB, co-funded by the  Marie-Curie Actions of the European Commission. SD was in part supported by the  Center for Modeling in Biosciences (BIOMS).
Verification Experiments and Analysis All models, policies, and mechanisms presented above have been finally implemented within a new version of a parameter-configurable traffic simulator QoS-CITS (v2. 1), which is a testbed fully designed and developed by ourselves with C# language on Windows platform, and where, several existing traffic models have been also adopted, especially the Ackermann steering model, Krauss car-following model. With this simulator, a plenty of experiments are conducted.
Background and objective: Dose-finding, aiming at finding the maximum tolerated dose, and pharmacokinetics studies are the first in human studies in the development process of a new pharmacological treatment. In the literature, to date only few attempts have been made to combine pharmacokinetics and dose-finding and to our knowledge no software implementation is generally available. In previous papers, we proposed several Bayesian adaptive pharmacokinetics-based dose-finding designs in small populations. The objective of this work is to implement these dose-finding methods in an R package, called dfpk. Methods: All methods were developed in a sequential Bayesian setting and Bayesian parameter estimation is carried out using the rstan package. All available pharmacokinetics and toxicity data are used to suggest the dose of the next cohort with a constraint regarding the probability of toxicity.
Stopping rules are also considered for each method. The ggplot2 package is used to create summary plots of toxicities or concentration curves. Results: For all implemented methods, dfpk provides a function ( nextDose ) to estimate the probability of efficacy and to suggest the dose to give to the next cohort, and a function to run trial simulations to design a trial ( nsim ). The sim. data function generates at each dose the toxicity value related to a pharmacokinetic measure of exposure, the AUC, with an underlying pharmacokinetic one compartmental model with linear absorption. It is included as an example since similar data-frames can be generated directly by the user and passed to nsim.
Particularly in the case of small populations, such as rare diseases or paediatrics, it should be useful to take into account all the infor-mation collected during the trial, and to try to utilize the PK measurements within the dose-finding design. Only few attempts have been described in the literature so far and, usually, the methods were built for a very specific situation  [5] [6] [7] [8]. Moreover, no software implementations are publicly available. In this article we present the new R package dfpk (short for dose-finding Pharmacokinetics), which provides the Bayesian adaptive PK-based dose-finding designs in small populations proposed by Ursino et al. [8]  through the freely available R software  [9]. The six methods detailed in  [8]  have been implemented in dfpk. For each of them, two functions are provided: (i) a function to determine the next recommended dose (during the trial) or the recommended MTD (at the end of the trial) and (ii) a function to run simulations of phase I studies to design a new trial.
Interactive graphical representations of the dose-concentration curve, of the dose allocation process in the trial and of the dose-toxicity response are also provided by the package. The paper is organised as follows. Section 2 introduces the statistical methods proposed by Ursino et al. [8]  , along with the description of the suggested scenarios to be simulated. Section 3 outlines the structure of the package and the main functions of the paper ( sim. data , nextDose and nsim ) with practical examples. Section 4 & 5 include conclusion, discussion and recommendations.
Simulating data for trial design The package implements several examples reproducing the scenarios proposed in Ursino et al. [8]  to evaluate the method performance using simulated data. In these settings, toxicity is linked to a PK measure of exposure, namely to AUC, and we used a simulation setting similar to the one in  [12]. A first order absorption, linear, one compartment PK model was used to simulate PK data. In this model, the concentration at time t after administration of a dose d k of the drug at time 0, can be written as a function of k a , the absorption rate constant for oral administration, CL, the clearance of elimination, and V, the volume of distribution, as follows: c(t ) = d k V k a k a -CL/V e -(CL/V ) t -e -k a t , (8) Individual CL and V are sampled from log-normal distributions with mean CL pop (Lh -1 ) and V pop ( L ), respectively, and standard deviation ω CL = ω V , while k a is fixed in this study as limited information concerning the absorption phase was available in the original dataset to estimate its inter-individual variability.
R-functions Package dfpk implements all the dose-finding methods described in Section 2. In Fig. 1  , an overall explanation of the main functions in the dfpk package is given. The aim of the package is to assist the design of phase I clinical trials. During the trial, the patient data already accrued ("Trial data" in Fig. 1 A ) can be used in the nextDose function in order to determine the next Bayesian parameter estimation is carried out using the rstan package while the ggplot2 package is used to create plots. Three S4 classes are implemented in the package, "dose" , "dosefinding" and "scen", in order to store the outputs of the main R functions nextDose , nsim and sim. data , respectively. The classes are detailed described in the Appendix B. The package dfpk is available on the CRAN archive and can be easily installed on the fly through the URL https://cran.
Dose-finding methods ( nextDose ) The nextDose function is used to perform parameter estimation at each step during a dose-finding trial. It gives the recommended dose to administer to the next cohort of patients, or the final estimated MTD if applied at the end of the trial. It can be used during an ongoing clinical trial or with a simulated dataset, as described later in the Section 3. 2. The description of the input arguments used in the nextDose function are provided in Table  1. The user has to choose the dosefinding method from the available set in the model parameter. Then, he/she should provide the parameters required by the selected method, as specified in Table  2  , while the others are set automatically to NULL. Any argument not specified by the user will be set to the corresponding default choice  [8].
Demonstration The following illustration shows how to generate the datasets of the first scenario described by Ursino et al. [8]. In this example, we set the number of trials to 10 (i. e. TR = 10), the threshold of toxicity value to 10. 96, the dose levels to (12. 6, 34. 655, 44. 69, 60. 807, 83. 689 and 100. 371 mg) which are used to obtain the true probabilities of toxicity, 48 evenly spaced sampling time points between 0 and 48 h, a sample size of 30 and k a = 2 , CL = 10 and V = 100 as illustrated below: Each trial's result stored in a list of the R "scen" object, gen. scen , which regroups the subject's PK parameters, the concentration measurements for all patients and the simulated toxicities values at each dose level. Here, we provide some sim. data results for the first trial: Based on the above AUC with the parameter and the chosen toxicity threshold τ T = 10.
Dose-finding simulation ( nsim ) The function nsim simulates a single or n prospective clinical trials. In the simulated trial, the dose is escalated stepwise cohort by cohort until the first toxicity response is observed and then the chosen dose-finding method design is applied (two-stage design) as suggested in Ursino et al. [8]. In addition to the input arguments of nextDose , the nsim function has the following additional arguments: (i) N , the total sample size per trial, (ii) cohort , the cohort size, (iii) TR , the total number of trials to be simulated, (iv) simulatedData , a list for each trial containing previous simulated datasets as in 3. 2. 1 , (v) icon , a vector containing the index of real blood sampling that enables the user to use all concentration points, previously simulated, or only a subset of them and (f) AUCmethod , a string number specifying the estimation method for AUC; valid choices are 1 for a "compartmental method" (see  [8]  for details) and 2 for non-compartmental method (defaults to 2).
Conclusion The dfpk package implements novel methods for dose-finding phase I clinical trials incorporating PK in the dose-toxicity relationships  [8]. In this package, each method can be used during a prospective adaptive trial, where the dose for the next cohort of patients depends on the outcomes of the previous cohorts, in order to estimate the recommended dose for further clinical trials. It can also be used to perform simulations before the beginning of trial in order to study the robustness of the method to the different parameters setting choices. Running simulations is also useful to calibrate some parameters, but it takes time, therefore we suggest to run simulations on a dedicated server. The package is user-friendly and several flexible inputs are allowed: for instance the user can generate by her/himself scenarios (simulated datasets) and pass them on to the function nsim , or change the hyper-prior parameters of the prior distributions used in the Bayesian regression.
The choice of the prior distributions is crucial. In this package, we set as default the prior distributions suggested in  [8]. These prior hyperparameters were chosen after sensitivity analysis to give good performance in most cases. However, we suggest setting the prior hyperparamenters using preclinical information or other external pertinent information if this is available. A scen is a S4 class to save and show a dataset simulated by the function sim. data. We can look in detail at the structure of the class scen as follows: Thanks to S4 classes, the user can easily create his/her own datasets. For example, he/she can create a new object for each simulated trial, named UserData, and store it in a scen-class by a similar way using the command new in the following way: and then add in a list, along with the other simulated datasets.
A detailed definition of each slot is presented in the Table  B. 2. Once again, user can access to the slots and apply the generic functions and methods in this class by exactly the same way as in dose-class. Assume that we run 10 (i. e. TR = 10) simulated datasets using the function sim. data and simulatedData is a scen object then: 3. Lastly, a third S4 class is available in the package dfpk, called dosefinding , which is created to store all the dose-finding results that are simulated through the R function nsim.
WMHs are very variable in size, appearance and location, and therefore the categorical nature of the Fazekas scale has limitations for studying their progression in relation with other clinical parameters. WMH volume has been demonstrated to correlate with severity of symptoms, progression of disability and clinical outcome  [6, 14, 48]. Accordingly, determining WMH volume has been of interest in clinical research as well as in clinical trials on diseasemodifying drugs  [48, 69, 11, 1]. For some studies, lesions have been traced manually (sometimes with the help of semi-automated tools for contour detection) slice by slice. This process can easily become prohibitively expensive for even moderately large datasets. It is therefore obvious that the accurate automatic quantification of WMH volume would be highly desirable, as this will undoubtedly lead to savings in both time and cost.
Although the method was originally proposed for ischemic stroke, tumor and brain injury segmentation on MR images, it can be easily adapted for differ-ent tasks using their provided package DeepMedic 2. Similarly, Ghafoorian et al. [27]  proposed a CNN architecture that considered multi-scale patches and explicit location features while training, and later was extended to consider non-uniform patch sampling  [28]. Their best performing architecture shares a similar design with the architecture proposed by Kamnitsas et al. [38, 39] , in which it trained independent paths of convolutional layers for each scale. Using multi-resolution inputs  [39, 27, 28]  can increase the field of view with smaller feature maps, while also allowing more non-linearities (more layers) to be used at higher resolution, both of which are desired properties.
Data sampling and class imbalance Generally, in the segmentation of pathologies, healthy tissue is present in far larger quantities than pathological. For example, in WMH segmentation the number of voxels labeled as WMH (regardless of the underlying pathology) is very small compared to those labeled background/healthy tissue, which leads to a significant class imbalance (∼99. 8% of the voxels in the dataset used in this work are labeled as background/healthy tissue in our training set). Hence, although dense training (where whole images or slices are used) is a staple in computer vision with natural images  [47] , it is less intuitive for WMH segmentation. Therefore, patch sampling is used in this work in order to alleviate the class imbalance problem. There are several techniques that could be used to sample patches for training.
For example half of the samples could be extracted from locations centered on healthy tissue and half centered on WMH tissue  [39] , however this strategy does little for the class imbalance when large patches are being considered, as individual patches tend to still be highly class imbalanced at a voxel level. Another option, is to sample patches centered at WMH locations only, which in fact reduces the healthy tissue class to ∼90%. However, both strategies, in combination with the proposed architecture that has a field of view comparable to sample size, would lead to a location bias, where WMHs are always expected in the center of the patch. Instead, we propose that after defining a random subset of WMH voxels from which to extract training patches, a random shift ∆ x,y of up to half the patch size be applied in the axial plane before patch extraction to augment the dataset.
Model training An important factor during CNN training is the definition of the loss function that will guide the learning process (Section 2. 2). Here, we experimented with several recently proposed loss functions that were used to train the proposed WMH segmentation CNN using FLAIR images as input. In order to directly compare the effect of different loss functions, Dice score results from evaluating the CNN after different stages of training were calculated, see Figure  3. Here, the horizontal axis indicates number of training epochs while the vertical axis indicates the Dice score achieved on either the train (top row) or test (bottom row) datasets. In this work, an epoch is defined as transversing the complete set of training of patches once. It must also be noted that Dice results displayed here are calculated on the whole brain MR volumes, not on the extracted patches.
We believe both these factors had a significant impact in the differences be-tween these methods, specially in the stroke lesion class. Training data was augmented by applying random flips to training patches, however we did not find this had a clear effect on results. An important aspect to note is that WMH segmentation is notoriously challenging: For example, Bartko  [5]  and Anbeek et al. [4]  consider similarity scores of 70 to be excellent, while Landis and Koch  [44]  consider scores of 40, 60 and 80 to be moderate, substantial and near perfect, respectively. With this in mind, we can consider average Dice scores for WMHs generated by the proposed uResNet, as well those from DeepMedic and LPA to all be substantial, with LGA generating only moderate results. It is important to note that LGA is at heart an unsupervised method and that data was only used to tune its κ parameter.
Only uResNet and DeepMedic are capable of distinguishing between different types of lesion, and in this regard only uResNet produced an average stroke Dice score that could be considered moderate. might still be simpler, thus leading to an observed faster convergence. This observations need to be interpreted with care. We believe that the Dice overlap scores that our method achieves are close to expected intra-rater variability, hence the lack of observed difference in performance between one and two convolutions in residual blocks, might come down to limitations of the data itself. Training with patches that always contain a diseased label in the center would bias towards labeling this region of a patch as diseased during inference. Patch center shifting alleviates this problem due to the distribution of probability to observe a lesion across the whole field-of-view.
Acknowledgments The research presented here was partially funded by  Innovate UK (formerly UK Technology Strategy Board ) grant No. 102167  and by the 7th  Framework Programme  by the  European Commission  (http://cordis. europa. eu; EU-Grant-611005-PredictND -From Patient Data  to Clinical Diagnosis in Neurodegenerative Diseases). Additionally, data used in preparation of this work was obtained under funding by the  Row Fogo Charitable Trust  (AD. ROW4. 35. BRO-D. FID3668413 ) for MVH, and the  Wellcome Trust  ( WT088134/Z/09/A ). hyper-parameters, which were the best results obtained in our experiments.
In recent years, multiple C-N coupled models, such as DyN-LPJ  (Xu-Ri and Prentice, 2008) , O-CN  (Zaehle and Friend, 2010, 2011) , Land Surface Processes and exchanges model of the University of Bern (LPX-Bern 1. 0;  Stocker et al. , 2013) , CLMCN-N2O  (Saikawa et al. , 2014) , and LM3V-N  (Huang and Gerber, 2015)  were developed by integrating a prognostic N cycle into different land surface models and simulate N2O emissions from land ecosystems. Unsurprisingly, these models generated divergent estimates of global terrestrial N2O budgets and spatiotemporal patterns mainly due to differences in model input datasets, model structure, and parameterization schemes. What are the major contributing factors responsible for the changing patterns of terrestrial N2O emissions? How can we narrow down the model-estimated bias or uncertainties? What are the knowledge gaps in fully accounting for the N2O processes? Here, we attempt to answer these questions through the establishment and designing of the global N2O Model Inter-comparison Project (NMIP).
During the past two decades, carbon-related Model Intercomparison Projects (MIPs) have been established to evaluate model uncertainties in simulating the terrestrial carbon dynamics. For example, the Vegetation-Ecosystem Modeling and Analysis Project (VEMAP) was a pioneer MIP activity, driven by a common model input database and was established to provide multimodel ensemble estimates of carbon fluxes and storage in response to changing climate and atmospheric CO2  (Melillo et al. , 1995; Schimel et al. , 2000). More recently, a number of CO2oriented MIPs and synthesis activities were implemented, such as the North American Carbon Program site and regional synthesis (NACP;  Schwalm et al. , 2010; Richardson et al. , 2011; Schaefer et al. , 2012)  and its extended  Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP; Huntzinger et al.
Key model input datasets To minimize the uncertainty that results from input datasets, the NMIP provided consistent model driving datasets for all modeling groups. The datasets include potential vegetation, climate, atmospheric CO2 concentration, atmospheric N deposition, synthetic N fertilizer applications in cropland and pasture, manure N production and applications in cropland and pasture, and historical distribution of cropland at a spatial resolution of 0. 5° by 0. 5° latitude/longitude (Table  1 ). Half-degree resolution is appropriate for studies at a global scale, considering that most of model input data are available and many previous MIPs at a global scale were conducted at this resolution. Here we briefly describe these input data sets and their sources. 2) Atmospheric CO2: Monthly atmospheric CO2 concentration from 1860 to 2015 was obtained from NOAA GLOBALVIEW-CO2 dataset derived from atmospheric and ice core measurements (https://www.