dataesr/scientific-paragraphs-categorization huggingface dataset:
For comparison with the experimental daia, the activity retained in RoB was extrapolated from that measured in muscle. A fraction of HTO activity in each of these systemic compartments is exchanged with a companion compartment representing the OBT content of the organ. The excretion of HTO mainly takes place in urine; however, a significant portion of it is also cleared by the exhalation of water vapor and loss through skin (sweat plus insensible loss) and a smaller fraction is excreted in faeces  [30]. The systemic and GI tract (GIT) transfer rates for the model are indicated in Table  3. These were determined by fitting the experimental data to the model. The fits obtained with SAAM II by minimising the deviation of the model to the activity data (points) are illustrated in Figure  S5. The adjustment of the model to the datalooks reasonable, taking into account the dispersion of the data.
Specific values were derived from the biokinetic model for lung absorption parameters (Table  4 ). These specific absorption parameter values can be and were extrapolated to humans. In conjunction with the start-of-art dosimetric models of the ICRP, specific equivalent and effective dose coefficients were evaluated with the dedicated ICARE software. A specific committed effective dose coefficient of 5. 6 x 10-12 Sv Bq-1 was assessed for the inhalation of tritiated steel particles that may be applied, for instance, to workers during dismantling operations. Such results may valuably be considered in future revisions of ICRP dose coefficients for tritium. The dose coefficient evaluated here is slightly lower than that formerly assigned to other tritiated compounds (2. 4 x 10-11 Sv Bq-1) and is also lower than that for HTO (2.
After the organic content was measured to be 4% [loss on ignition: SS-EN 15935:2012 (SIS 2012)], the sample was treated for organic matter destruction  [SS-ISO 11277:2020 (SIS 2020) ]. Soluble salts were removed through washing by centrifuge drainage for 15 min at a relative centrifugal force (RCF) of 400 g [SS-ISO 11277:2020 (SIS 2020)] until the measured electrical conductivity was 2. 1 mSm -1 (far below the limit of 40 mSm -1 ). For chemical dispersion, sodium hexametaphospate was used following  Gee and Or (2002) , and physical dispersion was achieved by using an horizontal orbital shaking table at 125 min -1 overnight. The sedimentation measurement lasted 12 h, and the suspension was subsequently sieved using the wet sieving method to obtain the data for the larger fractions in the range of 63 μm < d particle < 2 mm, directly after the measurement  [SS-ISO 11277:2020 (SIS 2020) ].
To be noted is that for all magnifications, the field of view (FoV) was smaller than the actual sample size, hence only covering a central subvolume within the clay sample. The 10-mm diameter and 20-mm tall cylindrical sample was cored from a larger STII sample tube retrieved from the Utby test site in Sweden. The sample was mounted in the XCLAY miniature triaxial apparatus  (Birmpilis 2020)  to control the stresses, avoid loss of water, and monitor the porewater pressures during the scan. In all cases, the 3D data are reconstructed from the projections using parallel filtered back-projection with Paganin phase retrieval with a δ=β ratio of 500  (Paganin et al. 2002)  using the ESRF reconstruction software PyHST2  (Mirone et al. 2014). This reconstruction technique retrieves phase contrast from the data using a fixed sample-detector position at the expense of a small loss in image sharpness.
The measurements were performed using a beam with an energy of 29. 6 keV. For each scan, 3,009 projections with a pixel size of 25 nm and a FoV of 64 × 54 μm were recorded over 360°u sing a PCO edge 5. 5 scientific CMOS camera with an acquisition time of 400 ms per projection. The 3D volumes were obtained after a two-step data processing procedure. First, the phase retrieval calculation was done using an in-house developed Octave script  (Cloetens et al. 1999 ) with an iterative calculation based on a Paganin-like approach  (Paganin et al. 2002)  and δ=β of 190, where δ = refractive-index increment and β = absorption index. In the second step, the reconstruction was performed using a filtered back projection with the ESRF reconstruction software PyHST2  (Mirone et al. 2014).
Analyses of Image Data The product of the acquisition and reconstruction from the nano and microtomographies, as described in the previous subsections, are 3D images (stacks of 2D images) representing a scalar "CTvalue" as a rescaled greyscale quantity, approximately proportional to the local X-ray attenuation. The software used to process the data was the open-source code Fiji (ImageJ)  (Schindelin et al. 2012 ) and Python-based scripts for further image analysis, as elaborated in the following subsections. A specific processing workflow outlined in Fig. 1  was followed to statistically quantify the structural components that could be distinguished in the 3D tomographies. First, the greyscale images were numerically rescaled and converted into 8-bit 3D arrays. Although the original image size is presented for qualitative interpretation, a subset of the original data with dimensions 1,000 × 1,000 × 1,000 pixels was cropped from each image for the quantitative analysis to reduce the computational expense when producing statistically meaningful results.
Image Preprocessing After cropping the image volume, the initial preprocessing comprised of filtering greyscale images to reduce the noise levels and mitigate, to some extent, the artifacts from reconstruction. Two image filters were selected interactively depending on their effectiveness with noise reduction for each data set; that is, either a Gaussian Blur filter or a bilateral filter was used. The selection was assessed visually. The Gaussian blur filter is a convolution filter for smoothing and has the significant advantage that the filter template is weighted toward the center of the filtered volume. An example of the effectiveness of the Gaussian blur filter is indicated in Fig. 2. The bilateral filter is another nonlinear filter for which the intensity magnitude of a pixel is based on the weighted average of the intensities in the neighboring pixels.
Segmentation Image segmentation was used to identify groups of objects and obtain their geometrical properties. The first step in the segmentation procedure is the binarization-the transformation of the greyscale image to a black and white binary image-for which intensity levels lower and higher than a certain threshold value are respectively set to black (0) or white (1) (Fig. 3 ). In the present research, the threshold value is manually picked for each data set when inspecting the resulting binarized image stack. The watershed algorithm is a classical method for image segmentation and the creation of unique labels for each individual grain. Consequently, geometrical properties, such as volume, shape, and particle size, as well as the definition of the particle orientation (linked to a reference system) can be quantified for further statistical analyses.
In this workflow, the SimpleITK library  (Beare et al. 2018 ) called from the Software for the Practical Analysis of Material -spam-toolkit version 0. 5  (Stamati et al. 2020 ) was used for the image segmentation and subsequent labeling. Furthermore, before further analyses, the labeled images were corrected for oversegmentation to prevent the overestimation of local maxima in a single grain that typically leads to multiple labels in very large grains. This oversegmentation algorithm detects overly long contacts and merges the labels that share those contacts. In addition, the labels that crossed the boundaries of the image were removed because of their complete geometry and, hence, the associated statistics are unknown. Finally, in the last step, a screening filter removed all labels with (particle) volume <200 pixels because those small particles are sensitive to segmentation artifacts that skew the subsequent analyses.
The pore space visible in Fig. 8  by the black spots with low intensity is most probably linked to the macropore structure of natural sensitive clays (e. g. , Delage 2010). Only the nanoscale data appears to indicate a (clay) particle assembly comprised of small and larger particles that form the pore space. Although the speckle representing the clay platelets can be discerned in the data, individual clay platelets are not identified. The average intensity in these speckled grey clay areas found throughout the image is higher than that of the pore space, thus indicating that smaller particles are in the system that are only partially resolved. SEM imaging was utilized to verify the information on the particle shapes and dimensions as well as the expected emerging fabric. Fig. 10  presents the SEM images at three magnifications of Utby clay (from the same site and depth as the other samples).
Acknowledgments We would like to acknowledge the  ESRF  grants  ma3227  and  ma3735  for the beam time at ID19 and support for this series of experiments. Furthermore, we would like to thank  Jaime Segura-Ruiz  for supporting this project and ID16B beamline for in-house beamtime access. Financial support from the  Swedish Research Council (Vetenskapsrådet)  under Grant No. 491401 , FORMAS under Grant No. 2016-01070 , and Trafikverket in the framework  Branschsamverkan i Grunden  are greatly acknowledged. GB acknowledges the financial support for the experimental campaign from Chalmersska forskningsfonden. The short duration visitor grant from  TEC-21  that enabled the collaboration that led to these experiments is acknowledged. Finally, the work presented herein is part of the activities within the  International Research Centre for Clay Micromechanics : https://www.
This decorrelated path was used as the starting point for the production TPS simulations. Settings. The TPS simulations were performed with OpenPathSampling (0. 1. 0. dev-c192493). 39, 40 Multiple state TPS (MSTPS)  16  was performed with an all-to-all flexible length ensemble, excluding self-transitions. All-to-all means all transitions connecting two states are allowed. A self-transition is a path that starts in a state and returns to that same state after crossing the boundaries set by the state definitions. We used the one-way shooting algorithm, 41 with uniform shooting point selection. For the S1 simulations, 1000 shooting trials were performed, while for each of the S2 simulations, 2000 shooting trials were performed. Analysis. All analysis of the TPS simulations was performed using the tools included in the OpenPathSampling package,  39, 40  extended with custom Python code.
From the probabilities, the switching rate from i to j, k ij , can be calculated by = + + k n n t t ij ij ji i j P P i j (2) The values for n and t are taken from the MSTPS simulations. This analysis is adapted from ref 24. 43 RESULTS AND DISCUSSION Identification of Conformational States. The crystal structure of GppNHp bound HRas (PDB: 4EFL)  21, 22  was used as a structural template to model the sequence of WT and Q61L KRas-4B with GTP bound. With these two structures we performed four 100 ns MD simulations to explore the conformational space of KRas, for both WT and Q61L. These simulations resulted in the characterization of two stable states for S1, S1-D33 and S1-open and two stable states for S2, S2-GTP and S2-open. After initial TPS simulation ended up with trajectories that did not end in any of these states, a third state was found for S1, S1-30-32, as well as S2, S2-α3.
The latter is characterized by the water flow decrease until the drainage flow stops during summer. The hydrological behavior of agricultural drainage networks is highly dependent on hydrometeorological data and soil hydro-physical properties. To understand the hydrology of drained agricultural lands, several numerical models have been developed to approximate the underlying dynamics and physical processes. The appropriate modeling of the perched water table formed in drained soils is complex, timeconsuming and still require multiple input data and parameters. In contrast, some simplified models simulate the outputs from a limited input dataset but are not able to describe the physics of hydrological and hydraulic processes  (Tournebize et al. , 2004; Henine et al. , 2010; Henine et al. , 2014).
Furthermore,  Skahill and Doherty (2006)  proposed a simple methodology to address the local minima issue. Based on a local search algorithm, their methodology consists of starting the minimization process with different parameter values to increase the probability of reaching the global minima. The same concept has been deployed when calibrating 3D morphodynamic model data  (Shoarinezhad et al. , 2020)  using the PEST parameter estimation package  (Welter et al. , 2012) , thus showing that the optimization process is substantially affected by the local minima issue. However, the presence of multiple local minima is, essentially, a sign of ill-posedness in terms of non-uniqueness (equifinality thesis,  Beven (2006) ). In these circumstances, one should look for the mean of the posterior distribution rather than for one of its modes.
The outlet of the subsurface drainage network has been monitored by the water level measurement device. Then, the water level has been transformed into discharge value using calibrated rating curve. The rainfall has been measured near the study site using a rain gauge. Daily potential evapotranspiration (PET) data have been extracted from the SAFRAN database  (Vidal et al. , 2010). PET values have been calculated according to the Penman-Monteith formula. Available data cover September 1st, 2008 to August 31st, 2013 (Fig. 3 ). Among all recorded data, two wet hydrological years, 2010/2011 and 2012/2013 can be distinguished, accumulating respectively 172 mm and 217 mm of drained water. The three remaining dry years account for a combined 209 mm, nearly equal to the drained water accumulated in just a single wet season.
The adjoint model is generated using the automatic differentiation (AD) tool TAPENADE  (Hascoet and Pascual, 2013) , developed at Inria (https://team. inria. fr/ecuador/en/tapenade/). The AD engine processes the Fortran code which implements the mapping from the model inputs to the valuable function J(U). For the AD procedure to succeed, one has to ensure that all operators of the code are differentiable. This is not always the case, especially with conceptual models. For example, the flow routing scheme originally used in AIGA model (described in Jay-Allemand et al. (  2020 )) was not differentiable. Since the need for a more accurate routing scheme was obvious, the new one has been developed taking into account the differentiability requirement. Concerning SIDRA-RU model, a brief inspection of the model equations (see Appendix A) shows that it is conditionally differentiable.
Local adjoint sensitivity analysis (LASA) Let us consider a model M that transforms input vector I into model output vector X: M(I) = X (1) In the particular case of the SIDRA-RU model, input vector I includes the vector of parameters U i and the climate forcing datasets (i. e. rainfall and potential evapotranspiration): I = {P(t), PET(t), U } (2) In this study, rainfall P and evapotranspiration PET are considered to be known with high accuracy; thus, their values are not estimated. The input parameters vector U is defined as follows: U = {S, l, K sat , μ, A, C, P, S inter , SSDI, α, β} (3) Where: SSDI = S max -S inter. The model response X represents the simulated temporal evolution of drainage discharge Q: X = Q(t) t∊[0, T] (4) Since in the current SA framework the model output is directly and completely observed, no observation operator is required, and the corresponding observations are hereafter denoted X *.
Study regions and study sites This study was carried out in two regions, Pescara province in Italy, and the Lviv region of Ukraine. Regions were selected due to historical records on dog population management from the Veterinary Services -Pescara Province Local Health Unit and local Communal Enterprise in Lviv. Both regions had ongoing dog population management through a combination of catch-neuter-release (CNR) and sheltering. Data was available for 42 out of 46 municipalities in Pescara and for the entire city of Lviv. Four areas in each study region were selected to have similar: (i) number of inhabitants in each town/suburb; and (ii) profiles in terms of size, structure (e. g. residential/industrial), and household numbers (assessed visually prior to fieldwork). In Pescara, a study site refers to a rural town/village in the Pescara province.
g. the front yard of a house); (ii) not on a lead; and (iii) not associated with a person (i. e. not on a lead but under the watch and responsibility of a person). All information was logged on the Animal-id. info app (animalid. net). To reduce inter-observer variation, fieldworkers undertook training prior to fieldwork on how to score the body condition of dogs. Fieldworkers photographed every observed dog using a Nikon D3400 camera for subsequent identification of individuals. Photographs were taken to include details of both sides of the dog's body, its legs, head, and tail. This study was conducted by observing and photographing free-roaming dogs from a distance in public areas of Pescara, Italy and Lviv, Ukraine. All data was collected visually, i. e. no dogs were handled during this study.
Removal entry probabilities thus have no biological meaning  [48] [49] [50]. Instead, we followed Royle & Dozario (2008)  [48]  and  Kery & Schaub (2011)   [49]  by deriving the 'entry probability' for each time point t, defined as the fraction of real individuals in the augmented data set that entered at each time point. We calculated per capita probability, describing the fraction of new recruits at primary period t per individual dog alive and in the study site at primary period t. Calculation of per capita entry probabilities are detailed in the supporting information. We estimated survival probability, which was also a confounded variable, as it was a function of both the probability of an individual remaining in the study site (i. e. not emigrating) and remaining alive, leading to its more popular name of "apparent survival".
All model parameters had 'weakly informative' prior distributions and all individuals started in the not-yet-entered state. The model was written in Stan  [51]  and run in R version 3. 6. 1  [52]  using the "Rstan" package version 2. 21. 3  [53]  with four Markov chain Monte Carlo chains of 2,500 iterations of warmup and 2,500 iterations for sampling, giving 10,000 posterior samples for inference. The Stan model used the forward algorithm to marginalise out the latent, discrete states for each individual. Convergence was assessed by inspecting the Rhat values (values less than 1. 05 suggest convergence) and effective sample sizes (values over 1000 suggest good precision of the tails of distribution). Random intercepts were included for apparent survival, recruitment, and detection to describe intra-country variation across study sites and primary periods, and intra-site variation across dogs.
Spatial correlations (correlations in parameter estimates given the distances between study sites) and temporal correlations (correlations in parameter estimates given the time differences between primary sampling periods) were captured by using Gaussian process prior distributions on the sites and primary periods random intercepts (squared exponential and periodic kernel functions, respectively; see model code in https://github. com/lauren-smithr/Smith-et-al-Population-dynamics-free-roaming-dogs). Due to the hierarchical structure, survival, recruitment, and detection results were partially pooled across dogs, sites, and primary period times. Parameter estimates were converted from the log odds scale to the probability scale using the inverse logit function:  (-1) () = exp () (1 + exp() ) , where  is the posterior value on the logit scale.
Discussion This study provides some of the first estimates of health, welfare, population size, and rates of recruitment and removal for free-roaming dog populations in Europe. This study is also the first to use Pollock's robust design mark-recapture to estimate free-roaming dog population dynamics. We found high population turnovers in both Pescara and Lviv, with removal rates of 7% per month in both locations, and recruitment rates (per capita entry probabilities) of 5-6% per month. Few juveniles were observed in either location, indicating recruitment through abandonment or immigration. This detailed demographic data provides critical information for planning effective dog population management and informing population and infectious disease modelling. Population size reduction is an important indicator of effective dog population management.
Acknowledgements We are grateful to  VIER PFOTEN International  for funding this research. We thank  Sarah Ross  and  Alesya Lischyshyna  for project and logistical support;  Marta Kopach ,  Viktor Kopach ,  Vasil Dub  and all at Animal-id. info for providing technology to aid in data collection; all field assistants who helped to collect the data - Dr Helen Gray ,  Dr Mary Friel ,  Annika Geijer-Simpson ,  Phoebe Abrahams , and  Sarah Ross ; the local Communal Enterprise in Lviv, and the  Veterinary Services -Pescara Province Local Health Unit  who provided data on dog population management in the study areas.
Models Latent factor regression models evaluate associations between the elements of a response matrix, Y, and variables of interest, called primary variables, X, measured for n individuals. The response matrix records p markers, which can represent any type of omic data (genotypes, DNA methylation, etc), collected for the individuals. The X matrix can also incorporate nuisance variables such as observed confounders (age, sex, etc), and its dimension is n × d, where d represents the total number of primary and nuisance variables. Latent factor regression models are regression models combining fixed and latent effects as follows Y = XB T + W + E. (1) Fixed effect sizes are recorded in the B matrix, which has dimension p × d. The E matrix represents residual errors, and has the same dimension as the response matrix.
Conclusion. Removing variation due to unobserved confounding factors is extremely difficult in any type of association study. Assuming that a small proportion of all markers correlate with the exposure or phenotype, we addressed the confounding issue by using sparse latent factor regression models, providing mathematical guarantees that global solutions of least squares estimation problems are proposed. Sparsity constraints in our algorithm allowed the selection of markers without any need for statistical testing. The application of our method to real data sets highlighted new associations with relevant biological meaning. The methods are reproducible and are implemented in the R package lfmm.
, 2010). LASSO regression models did not include any correction for confounding, and strong biases were expected in effect size estimates. The LASSO models were implemented in the R package glmnet, and the regularization parameter was selected by using a 5-fold cross validation approach  (Zeng et al. , 2017). We also used Bayesian Sparse Linear Mixed Models (BSLMM) implemented in the GEMMA software  (Zhou et al. , 2013). BSLMM is a hybrid method that combines sparse regression models with linear mixed models. BSLMM uses a Markov chain Monte Carlo (MCMC) method to estimate effect sizes. The MCMC burn-in period and sampling sizes were set to 10,000  (Zeng et al. , 2017). To determine the proportion of non-zero effect sizes, two parameters were tuned (pmin and pmax). Those parameters correspond to the logarithm of the maximum and minimum expected proportions of non-zero effect size.