This paper develops an improved model predictive controller based on the online obtaining of softness factor and fusion velocity for automatic train operation to enhance the tracking control performance. Specifically, the softness factor of the improved model predictive control algorithm is not a constant, conversely, an improved online adaptive adjusting method for softness factor based on fuzzy satisfaction of system output value and velocity distance trajectory characteristic is adopted, and an improved whale optimization algorithm has been proposed to solve the adjustable parameters; meanwhile, the system output value for automatic train operation is not sampled by a normal speed sensor, on the contrary, an improved online velocity sampled method for the system output value based on a fusion velocity model and an intelligent digital torque sensor is applied. In addition, the two improved strategies proposed take the real-time storage and calculation capacities of the core chip of the controller into account. Therefore, the proposed improved strategies (I) have good performance in tracking precision, (II) are simple and easily conducted, and (III) can ensure the accomplishing of computational tasks in real-time. Finally, to verify the effectiveness of the improved model predictive controller, the Matlab/simulink simulation and hardware-in-the-loop simulation (HILS) are adopted for automatic train operation tracking control, and the tracking control simulation results indicate that the improved model predictive controller has better tracking control effectiveness compared with the existing traditional improved model predictive controller. The urban rail transit system with automatic train operation system has the advantages of safety, stability, economy, and comfort, and it has become one of the most popular and efficient means of the urban public transportation . The traIn recent years, many improved algorithms have been applied in the automatic train operation tracking control field, such as robust adaptive automatic control, model predictive control, online learning, iterative learning control, matter-element model, etc. ,11,12,13Among numerous algorithms, Model Predictive Control (MPC) is one of the most effective control algorithms, which is characterized by good robustness, fast tracking speed, accurate tracking target speed, etc. . A lineaIt is necessary to conduct further study on the basis of previous research findings, and key parameters adjusting and improving the sampling accuracy should be taken seriously. A method for calculating the traction characteristics of a traction motor is proposed . A new mTraditional simulations based on a pure software environment cannot truly reflect the actual automatic train operation process, and the situation representing the actual automatic train operation experiment is difficult to implement because it is expensive, has restricted experimental conditions, high construction difficulties, and high security protection requirements. Hardware-in-the-loop simulation (HILS) is a new simulation technology for solving this difficult issue ,26. At pAn improved model predictive controller based on online obtaining of softness factor and fusion velocity for automatic train operation is developed. The following summarizes the main contributions of this paper.(I)An improved whale optimization algorithm (IWOA) based on the Tchebycheff decomposition method, convergence factor nonlinear decline, and genetic evolution measurement is proposed to solve the optimization of the softness factor adaptive adjusting parameters appropriately.(II)Aiming at improving the tracking control performance for automatic train operation, an improved model predictive controller based on online obtaining of the softness factor and fusion velocity is developed for automatic train operation tracking control effectively.(III)To further verify the effectiveness of the developed model predictive control controller, the scenario about rail transit line No.12 in Dalian, China is chosen for simulation test. The results of the Matlab simulation and hardware-in-the-loop simulation (HILS) show that the tracking controller proposed in this paper has good tracking control performance.The paper is organized as follows. t represents the sample time of control process, and The integral of time multiplied by the absolute value of error (ITAE) is the frequently used evaluation index for tracking control performance . The spei th optimization index; t represents the actual running time of the train; s represents the actual position of the train; a represents the actual acceleration of the train; M is the mass of the train; D is the actual running distance; s; s; u represents the train control quantity; E represents the energy consumption during the train operation process [As automatic train operation has its own unique characteristics and requirements, the multi-objective performance index is more appropriative, and it used universally. The computation model of multi-objective performance index follows,Pk=∑i=14ω process .In addition, security index should be taken into account as well. Traveling over the velocity limit is the main risk and non-negligible factors can cause an unsafe environment. The computation formula of security index DMC MPC uses three methods, including the DMC predictive model, rolling optimization, and feedback correction, to control the controlled object .The DMC predictive model is one of significant models for DMC MPC. The unit step response model reflecting the dynamic performance is adopted as the DMC predictive model for controlled object, and the predictive value of system output is obtained by the step response characteristic for controlled object.N, then the N sampled values of the controlled object unit step response can be used to describe the dynamic response characteristics of the system. The specific calculation formula for the predictive value of system output is as follows,A represents the dynamic matrix. The specific dynamic matrix A and the specific calculation formula for the element of i represents the element index of k represents the initial point of DMC predictive model [If the model length is ve model ,32.i th softness factor  is as follows.Feedback correction is an important component of DMC MPC; it is used to reduce the influence of system disturbance for the control system, so as to achieve the ideal control effectiveness. The specific calculation formula of the error between actual system output value and the predicted output value in the present control period  is a control method that considers the step response characteristics and fuzzy logic for the train operation mechanism of the control object. The fuzzy DMC model predictive controller is widely used in automatic train operation due to its characteristics of simple design scheme and high tracking precision. The fuzzy DMC model prediction controller is mainly composed of four function chips , and it is used to realize four function modules of Fuzzy DMC MPC . The schematic diagram of the Fuzzy DMC model predictive controller for automatic train operation is shown in As can be seen from i-th sampling point; i-th sampling point; i-th sampling point; i-th sampling point; i-th sampling point; i-th sampling point; i-th sampling point; i-th sampling point; isT represents the actual motor torque sampled by torquemeter of the i-th sampling point; is,Fη represents the force comprehensive transmission efficiency of the i-th sampling point; mrR represents the radius of motor rotor; wisv,s); Δt represents the sampling time-interval, Δt is 500 μs in this paper; Δs represents the sampling displacement-interval; iss represents the actual train instantaneous displacement sampled by displacement pickup of the i-th sampling point; and isλ = {is,vλ, is,Fλ, is,sλ} represents the synthetic weight of the velocity sampled by different ways.According to the multi-objective performance index for automatic train operation tracking control, the fusion velocity model based on online obtaining is necessary to take into account the energy consumption, running time, comfort, and parking accuracy. Taking into account the sampling effect, hardware technology (storage and computing ability), funds, space, and other factors, three kinds of velocity sampling sources are sufficient  and are selected and synthesized. The fusion velocity computation model based on online obtaining is established as follows,Synthetic weight is vital for real-time sampling precision in the tracking control process, the importance of each velocity sampling sources needs to be considered, so as to give the appropriate synthetic weight. The synthetic weight indicates the importance of the real-time velocity obtained by different speed sampling sources. Yet, the selection of the synthetic weight by traditional methods lacks the specific theoretical basis, so there is certain subjective limitation in actual applied. As automatic train operation tracking control is an extremely complex issue, there is a trajectory characteristic for automatic train operation tracking control curve dominated by velocity target curve, train parameters, line conditions and running requirements, and the traditional methods for setting synthetic weight based on experience empower is subjective and blind, so it is necessary to be improved. In this paper, an synthetic weight empower using entropy method is applied for automatic train operation tracking control. First, the whole tracking control curve is divided by a position according to trajectory characteristic and line conditions; second, a large number of real-time data, including velocity, force, and position information for the whole tracking control process, should be sampled to prepare for calculation; finally, the entropy method is used to calculate the synthetic weight of each divided subinterval of tracking control curve.Entropy is a measure of uncertainty for information calculation. The entropy weight method is utilizes the entropy characteristics and assigns a weight to each index in an event by calculating the entropy value. The entropy weight method is an objective weight empower method, because it simply depends on the discreteness of data itself. The specific steps of computational process for entropy weight method are as follows.A certain number of samples (as many as possible) must be collected to prepare for the calculation, and their index values also needed to be recorded.m represents index number; n represents the number of samples; j-th processed index value of the i-th sample after normalization; j-th original index value of the i-th sample before normalization; To eliminate the negative influences caused by the difference between units and magnitude orders, the index values must be normalized. The calculation formulas for the normalization can be expressed as follows,j-th processed index value of i-th sample after filtering out the zero value; The normalized index values are necessary to filtered out the zero value further, so as to avoid illegal logarithmic function (ln(0)) in next subsequent calculation processes. The specific formula for filtered out zero value is as follows,j-th index weight value of i-th sample in j-th index; j-th index entropy value; k represents the entropy coefficient, the value is the reciprocal of Then, the entropy values of each index values are necessary to be calculated. The specific formulas for calculating the entropy values are as follows,j-th entropy redundancy value of j-th index, it indicates the difference degree of this index; j-th index entropy value; j-th weight value.Finally, the index weight value could be calculated. The specific formula for calculating the index weight values is as follows,x from If the factor of the velocity sampling sources sampled inaccurately is not considered, the improvement effect for automatic train operation tracking control will inevitably be restricted. To improve the real-time sampling velocity precision, a corrected method of real-time sampling velocity for automatic train operation tracking control using auxiliary corrective velocity sampling source is popularly applied in various types of urban rail vehicles. The specific evaluation and corrected formulas are as follows,Reference velocity The softness factor is a key parameter for DMC MPC; it plays plays an important role in balancing the degree of robustness and rapidity for the DMC MPC tracking control system. If softness factor bustness . Thus, bConsidering the trajectory characteristic and tracking control condition for automatic train operation, the softness factor adaptive adjusting model based on online obtaining is established as follows,The whole tracking control curve must be divided into several different types of subintervals by position according to the trajectory characteristic and line conditions. The specific types of subintervals are described as follows.Type 1: The vibrating area nearby inflection point of tracking control curve.In this area, there is the strong velocity fluctuation in the velocity trajectory. Thus, aiming at improving the system robustness as much as possible, softness factor Type 2: The smooth area of tracking control curve.In this area, there is no obvious velocity fluctuation in the velocity trajectory. Thus, aiming at improving the system rapidity as much as possible, softness factor Type 3: The connected area in the middle of smooth area and the vibrating area of the tracking control curve.In this area, the system rapidity and rapidity are taken into account for softness factor In addition, although in the same type of subintervals, the softening factor Aiming at solving this control problem with fuzzy characteristic, an fuzzy adaptive adjusting method for online obtaining softness factor b represents the gain coefficient; it determines the shape of the softening factor The error between the output value and the reference target value of system  are decision variables.Optimization algorithms are used to obtain a set of adjustable parameters for the satisfactory tracking control effect in actual automatic train operation scenarios. The specific softness factor adaptive adjusting parameters optimization model is as follows,i th objective, Objective decomposition is an effective method to solve the multi-objective optimization problems. The Tchebycheff decomposition method is selected in this paper among many objective decomposition methods . The spep represents the behavioral selection probability of humpback whales, B represents a constant, which is used to define the shape of spiral; l represents the random number in t is the current iteration number; a represents convergence factor; A and C represent the correlation coefficients respectively; Whale optimization algorithm with strong global optimization ability is chosen in this paper. Whale optimization algorithm (WOA) is a new metaheuristic optimization algorithm learned from whale predatory behavior. There are two operators (position update and prey searching) in the computation process of the whale optimization algorithm . The spea, correlation coefficients A, and C is as follows,The specific calculation formulas for convergence factor After the position updated, prey searching is implemented by means of random individual positions. The specific calculation formula for prey searching of the basic whale optimization algorithm is as follows,a will reduce the population diversity maintenance ability, so that the algorithm can easy to fall into local convergence in the late iteration. Aiming at solving this problem, the strategy of cosine decline combined with chaotic random method for convergence factor nonlinear decline is proposed in this paper. The specific calculation formula is as follows,a, a, The relatively fixed method of linear decline of convergence factor a, and it is helpful for maintaining the population diversity, thus the algorithm global convergence performance will be improved [Compared with the linear decline strategy, the decline rate of the convergence factor is significantly different in the whole iteration cycle caused by the nonlinear decline strategy with a certain degree of chaos uncertainty for convergence factor improved .According to the Tchebycheff decomposition method, the aggregate function value is the fitness index for the multi-objective optimization algorithm. After the computation process of each iteration, the newly generated non-dominated solutions of the current population are put into the elite archive. The archive must kept within a certain size by some elite individuals with small differences from other elite individuals, so as to avoid computational burden of the algorithm. According to the updating rules of the whale optimization algorithm, the reference point The specific steps of improved whale optimization algorithm proposed in this paper are as follows.Step 1: Initialization.Initialize the whale population  If the current iteration number is greater than 1, the weight terature , in the For any solution target a;The strategy of cosine decline combined with chaotic random method is used to calculate convergence factor The updating rules of the whale optimization algorithm are used to update each individual whale.Step 3: The archive and genetic evolution mechanism.The Pareto front of the current whale population is obtained, and it is used to expand the Some elite individuals with small differences from other elite individuals are deleted, until the size of Three operators  of the genetic algorithm are applied in the whole whale population, so as to further improve the population diversity maintenance ability.Step 4: Termination judging.A with reference point  is as follows.A is any objective vector set in objective space a, Pareto superior z and a belongs to A, The hypervolume indicator is chosen as termination judging indicator. Using the hypervolume indicator of the dominated portion of the objective space as a measure for the quality of Pareto set approximations is appropriate and effective. The specific formula for hypervolume indicator for objective vector set e, αAz=0 .Thus, the hypervolume indicator indicates the dominated portion in objective space ndicator . As onlyThe schematic diagram of the hypervolume indicator for 3 objects by using equivalent volume model is shown in If the hypervolume indicator If the maximum unchanged number of the hypervolume indicator The flowchart of improved whale optimization algorithm proposed in this paper is shown in x in the whale population.Aiming at improving the global searching ability, the evolution law is must be considered in the computation process. The excellent individuals should have a small mutation probability, so that they can accumulate optimization results effectively, and the poor individuals should choose a large mutation probability, which can be fully eliminated, so as to enhance the capacity of exploration . The mutThis mutation probability calculation method has certain fairness, and the whale individuals have appropriate mutation probability according to fitness function value, so as to prevent the population controlled by advantage individuals and persist evolution opportunity for disadvantaged individuals.d is the differential vector; A multimodal crossover method is conducive to finding a more satisfactory optimal solution for the complex optimization issue ,45. The b is 1; shape factor for sigmoid function of mutation probability Aiming at verifying the effectiveness of IWOA proposed in this paper, standard test functions  are selected as optimization objects, and multi-objective particle swarm optimization based on decomposition (dMOPSO)  and multAs can be seen from Based on the traditional Fuzzy DMC model predictive controller, a new functional module is necessary to be realized the function of online obtaining of softness factor and fusion velocity. The schematic diagram of improved DMC model predictive controller for automatic train operation designed in this paper is shown in In In To more effectively test the performance of the tracking control algorithm in actual automatic train operation tracking control scenarios, the dSPACE HILS technology is adopted. In this way, the optimization algorithm or control algorithm needed to be verified is written into the chip of the optimizer or controller. The structure diagram of HILS platform used in this paper for automatic train operation tracking control scenario, and the physical diagram of controller cabinet and simulation cabinet for HILS platform are shown in In In The automatic train operation tracking control scenario for rail transit line No.12 in Dalian, China is chosen as the experimental simulation object. Rail transit line No.12 is a significant urban rail transit line with 40.38 kilometers from Hekou station to Lvshun New Port. The running simulation line of scenario about rail transit line No.12 is from Lvshun New Port to Tieshan Town, there are three long steep ramps and three velocity limit subintervals in running interval. The main parameters of the automatic train operation tracking control scenario are shown in N is 60; control length is 15; predictive length is 15. The addition adjustable parameters for online obtaining of softness factor are set by practical experience as follows; blur width b is 0.909, 0.911, and 0.913. The subinterval range obtained by practical experience, reference value of soften factor obtained by each optimization algorithms, synthetic weight of fusion velocity obtained by entropy weight method are shown in The basic DMC MPC parameters are set as follows; sampling time is 500 In The sampling process is not necessary in the Matlab/simulink simulation platform. According to the automatic train operation tracking control scenario of rail transit line No.12 in Dalian, China, the Matlab/simulink simulation results are obtained by using the fuzzy DMC MPC based on online obtaining of softness factor As can be seen from Compared with traditional fuzzy DMC MPC and DMC MPC I, DMC MPC II has several obvious superiorities in the matlab/simulation environment. However, as there is no hardware equipment in the actual automatic train operation tracking control scenario in matlab/simulation environment, the effectiveness of DMC MPC proposed in this paper must be further tested and verified.In this way, sampling accuracy must be taken into account. To further verify the effectiveness of the algorithm, according to the automatic train operation tracking control scenario of rail transit line No.12 in Dalian, China, the HILS results are obtained by using the fuzzy DMC MPC based on online obtaining of softness factor According to the HILS results of different algorithms from The above HILS results show that DMC MPC V is a tracking control algorithm with good practical tracking control effect for automatic train operation tracking control scenario.Tracking control optimization for automatic train operation is a sophisticated optimization problem, and the model predictive controller is widely used to solve this problem due to its advantages of strong robustness and good performance in tracking speed and tracking precision. Aiming at obtaining a more ideal tracking control performance for automatic train operation, an improved model predictive control algorithm and corresponding controller based on online obtaining of softness factor and fusion velocity for automatic train operation are proposed and developed, and an improved whale optimization algorithm based on Tchebycheff decomposition method was proposed for softness factor adaptive adjusting parameters optimization. This clearly shows that model predictive controller has been improved to a considerable extent in tracking control optimization for automatic train operation not only in the pure software scenarios but also in the hardware-in-the-loop simulation scenarios (the (I)Aiming at improving the efficiency of the whale optimization algorithm based on the Tchebycheff decomposition method, the strategy of cosine decline combined with chaotic random method for convergence factor nonlinear decline is proposed, so as to obtain more satisfactory softness factor adaptive adjusting parameters for tracking control.(II)Not only is an improved online adaptive adjusting method for softness factor based on fuzzy satisfaction of system output value and velocity distance trajectory characteristic adopted, but also a fusion velocity model and a corrected model of real-time sampling for automatic train operation tracking control are adopted. Thus, compared with traditional improved model predictive controller, the improved model predictive controller developed in this paper based on online obtaining of softness factor and fusion velocity could enable the train in the optimal working state as much as possible, so as to obtain a more ideal tracking control result with more satisfactory performance indexes, including energy saving, punctuality, parking precision and comfort, ITAE, and security index.(III)For any tracking control system, the accomplishing capacity of computational tasks real-time is significant important. The only purpose of the improved strategies is the online obtaining of optimal softness factor and fusion velocity, so as to obtain the more reasonable real-time control quantity According to the Matlab/simulink results and ATO HILS results , the improved DMC MPC based on online obtaining of softness factor and fusion velocity proposed in this paper has better tracking control performance, so it can obtain more ideal tracking control results. In actuality, it can also be designed by other ways for online obtaining of softness factor and fusion velocity. It is important to note that, as the computational tasks such as various matrix computation of basic DMC MPC are onerous, the designed scheme should be simple and appropriate caused by the limited computation margin in real-time.


To speed up the implementation of the two-dimensional materials in the development of potential biomedical applications, the toxicological aspects toward human health need to be addressed. Due to time-consuming and expensive analysis, only part of the continuously expanding family of 2D materials can be tested in vitro. The machine learning methods can be used—by extracting new insights from available biological data sets, and provide further guidance for experimental studies. This study identifies the most relevant highly surface-specific features that might be responsible for cytotoxic behavior of 2D materials, especially MXenes. In particular, two factors, namely, the presence of transition metal oxides and lithium atoms on the surface, are identified as cytotoxicity-generating features. The developed machine learning model succeeds in predicting toxicity for other 2D MXenes, previously not tested in vitro, and hence, is able to complement the existing knowledge coming from in vitro studies. Thus, we claim that it might be one of the solutions for reducing the number of toxicological studies needed, and allows for minimizing failures in future biological applications. Experimental development of two-dimensional (2D) materials is booming and the industrial applications are not only envisioned but also are becoming a reality. On the other hand, safety verification by systematic and in-depth studies pose a great challenge. The first stage of research into the safety of individual materials are the in vitro studies on mammalian cells. If material is nontoxic, they are moving forward into more advanced studies. In this regard, toxicity appears when the viability of tested cells decreases from 100% to below 70%. This means reduction over 30% which is significantly below the natural 20% variance of survival rate assumed for the living organisms.n+1XnTz perfectly matches the arrangement of its layered features. In this formula—M is early transition metal, X is carbon and/or nitrogen, n = 1, 2, 3, or 4, whereas Tz corresponds to functional groups terminating the surface  [3C2Tz phase in 2011 by Naguib et al. [One of the most perspective 2D materials that can be utilized in technological applications are the MXenes. During last years their development has risen exponentially. MXenes are defined as early transition metal carbides, nitrides, and carbonitrides. They have received much attention as their unique 2D crystal structure can be easily tuned to produce dramatic improvement in material properties . Therefob et al. . Since tb et al. , and newb et al. ).3C2Tz MXene and showed a potential threat related to the generation of reactive oxygen species (ROS) [3C2Tz or Ti2CTz) [In the case of MXenes cytotoxicity, the first studies concerned in vitro testing of multilayered Ties (ROS) . Further Ti2CTz) . Moreove Ti2CTz) . In addi Ti2CTz) .In this regard, we have come to the moment that innovative solutions are needed to extract the most promising representatives of MXenes with the highest potential for application and the lowest cytotoxicological threats. It becomes obvious that it is impossible to carry out screening investigations for all MXenes phases in reasonable time and manageable costs. The most time- and cost-consuming analyses are undoubtedly the biological studies, which are also inevitable to push through MXenes applications in industry. What is more, many certification procedures involve verification of the safety of market products containing the claimed nanomaterials. Accordingly, there is a strong demand for theoretical solutions that could overcome the problem of so many complicated analyses.One such solutions might be the machine learning (ML) procedure. Machine learning has so far proved its applicability for cytotoxicity studies of large number of various chemicals (see Reference ) as wellThe aim of this study is to provide the prediction of the potential cytotoxic behavior of MXenes materials based on ML model with some elemental information provided from experiments. We first determine the biological and physico-chemical features that describe each 2D material in relation to the tested cytotoxicity. On this basis, we determine the possible descriptors for predicting the cytotoxic behavior of MXenes compounds. In particular, the surface characteristics, morphology, and structure have been taken into account as inputs for our theoretical model. Next, the Random Forest  approachNote, that the theoretical methodology developed here can be further applied to other types of 2D materials, which exhibit diverse surface characteristics. The detailed information about the experimental data and elemental features used in this study are presented in Appendices In order to determine a good quality ML model, it is crucial to determine potential descriptors that can characterize the cytotoxic behavior and identify ML algorithms well suited for a given dataset.Descriptors, which are representative of the compounds properties play a crucial role in ML, thus, we decided to describe in details the rationale behind the particular choice of the descriptors used in our studies.xOy) present on the surface which are studied by the X-ray photoelectron spectroscopy (XPS). Based on these considerations, we aim to select particular experimental descriptors listed in At first, we choose only the MXenes compounds tested in vitro which are well experimentally characterised and appear cytotoxic or not. In this regard, the particular MXene exhibits cytotoxicity if it causes the reduction of viability of tested cells in vitro below 70% within the concentration range up to 250 Based on the previous experiments  that linNote, that the choice of the descriptors which represent the cytotoxic behavior is also limited to the experimental data available in literature. Therefore, the prediction might change whenever more experimental data are provided.What kind of datasets and descriptors: theoretical, experimental or combined ones are sufficient to build a good quality ML model?Could the cytotoxic behavior be predicted based on purely theoretical descriptors , and hence, does it mean that no experimental data need to be provided to predict the cytotoxic behavior?Does inclusion of the geometrical descriptors in the ML model improve qualitatively predictions of the cytotoxicity?The toxicological in vitro data for 2D MXenes is taken from recently published high-throughput screening experiments, therefore, it is reliable and convenient for comparison. We have decided to test independently three datatsets in order to answer following questions:Dataset I —the experimental data have been selected based on detailed description of the experiment, as well as detailed information about the structure, surface modification, in-depth characterization of morphology, and the cytotoxic effect of the MXenes compound on the cells tested in vitro. Those information have been collected from the literature listed in Dataset II —data taken from the two-dimensional database  concerniDataset III (combined set)—dataset consists of both Dataset I and Dataset II. The number of the records and elemental features are combined from those two datasets correspondingly.Three datatsets are listed below. For each of the datasets the ML models have been built separately and discussed in details in the corresponding subsections of Results.1 and L2 . This approach allows avoiding over-learning a model even for a large number of variables. The algorithm removes unimportant features for the model.Logistic regression  with regRandom Forest (RF)  is commoSupport Vector Machine (SVM)  uses onlExtreme Random Tree (ERT)  is an exIt is worth mentioning, that the first two datasets overlapped partially, namely, the geometry of each of the compounds in the first set is known. In addition, we do not know anything about the class of function describing cytotoxicity, so it is not possible to point out the class of algorithms that should be used. Thus, only the size and the type of the variables in the dataset determine our choice of ML algorithms. Detailed analysis of the applicability of machine learning algorithms can be found elsewhere . Below, Parametric models such as linear regression are used to help us understand a phenomenon by determining the functional dependences. In the case of non-parametric models such as Random Forest, the crucial issue is to identify the importance of features, and thus, it allows us to understand the studied phenomenon. Note, that other commonly used ML methods such as Kernel Ridge Regression (KKR)  or NeuraIn addition, our datasets face a commonly known issue, namely, the class imbalance problem. Significantly, this problem is widely reported for the toxicity of many other materials, where the size of the positive data (toxic samples) is considerably smaller than the negative data (non-toxic samples) (see Reference ). To solWe have used the Python programming language (version 3.6.4) with the scikit-learn  and XRT In this study, we make use of state-of-the-art machine learning (ML) methods to identify the cytotoxicity of experimentally synthesized as well as deeply characterized 2D MXenes.This section is divided into three subsections related to the prediction for the three datasets: experimental set, theoretical set and combined one, as it was mentioned in previous section. Then, the models are simplified, by selecting the most important features based on Random Forest algorithm or by the construction of new features from the given ones by the use of the Principle Component Analysis (PCA). The models are tested by 10-fold cross-validation, with the performance measured by class balanced accuracy score of correct predictions . The accOur theoretical investigations see  reveal tIn order to understand the cytotoxicity issue and its dependency on selected descriptors, feature selection and feature engineering techniques are applied further in this study. The crucial premise of feature selection is that the data contains some variables that are either redundant or irrelevant, and can thus be removed without much loss of information. Feature selection and feature engineering techniques are methodological tools that allow simplification of the model, and hence, they can facilitate the interpretation of the studied phenomenon. In order to select the most important features, the feature importance score is obtained from a random forest analysis. Feature importance shows how much weight the model assigns to the given descriptor during predictions, and thus, gives insight into which variables are crucial for predicting the cytotoxicity of MXenes materials. For comparison purposes, we also use feature engineering from PCA.The results reveal that three features are already sufficient for a good level of prediction accuracy, for all employed algorithms except regSVM-sig see .Moreover, all the models based on PCA show lower accuracy than RF, which means that there is a low correlation between the features. The PCA approach is based on correlation between the features, while feature importance from RF selects the most important, original variables. PCA does not improve the results, thus, the original variables selected by RF have been chosen.xOy and the Li atoms on the MXenes surfaces.In addition, the feature importance score shows that there are two crucial features, four equally important features, and the rest seem to be unimportant from the RF analysis see . The mosNote that the results presented here show that experimental data and descriptors are sufficient to build a good quality ML model for cytotoxicity predictions in MXenes materials.Here, the ML model is built based on theoretical data. We have tested the dataset with structural information of the compounds included, namely position and type of atoms. There are many methods available for building descriptors such as Atom-Centered Symmetry Functions (ACSF) , CoulombAll the results presented in this subsection reveal that taking into account geometrical descriptors from theoretical dataset of MXenes materials are not sufficient to build a ML model for cytotoxicity prediction of MXenes compounds. However, this theoretical set can be combined with experimental set, to determine whether such enlarged database improves model predictions, which is a subject of study in the next subsection.Our study reveals that including the information related to the geometry of the compounds see  does notxOy, Li on the surface of MXenes, and surface modification with external compounds  and hence, does it mean that no experimental data need to be provided to predict the cytotoxic behavior?Our results reveal that taking into account only geometrical descriptors from theoretical dataset of MXenes materials are not sufficient to build a ML model for cytotoxicity predictions. Thus, the experimental information such as material synthesis methodology and characterisations play a crucial role in building ML model concerning cytotoxic behavior of MXenes compounds.Does inclusion of the geometrical descriptors in the ML model improve qualitatively predictions of the cytotoxicity?The inclusion of geometrical features of MXenes do not qualitatively change the ML results based on experimental dataset.Here, we present the discussion concerning the results of employed datatsets in response to the following questions (previously stated in After successful verification of the ML model based on experimental dataset, we are able to predict toxicity for 2D MXenes not tested in vitro. To do so, we have searched the available literature covering the MXenes compounds for which all the elemental features listed in xOy on the surface has been reported. The presence of MxOy is the key toxicity-generating feature obtained from our studies.The ML models predict two of 2D MXenes can exhibit cytotoxic properties with a high probability of prediction equal to 0.9, while the rest of them are predicted to be non-toxic see . It is wOur results demonstrate that our ML model is able to complement the existing knowledge coming from in vitro studies. However, note that this prediction has to be viewed with some caution, knowing that traditional k-fold cross-validation is highly optimistic when evaluating machine learning models, due to the fact that materials datasets are rarely uniformly distributed.xOy on the surface [xOy passivation layer that occurs as a result of M reaction with oxygen and/or water [Our results indicate that knowledge of the surface and its modification might be crucial issue concerning the toxicity of the studied layered 2D materials, whereas geometrical descriptors may have little impact on the outcomes. These results are in the line with recent experimental findings concerning the presence of Ti surface ,58,59, a surface , as wellor water . In factFrom this regard, MXenes are the most interesting, because their surface is highly unstable and susceptible to oxidation in the ambient conditions . It has xOy and Lithium atoms on the surface, as well as surface modification with external compounds. Our detailed analysis reveals, that the crucial issue is what happens on the surface, while the structural information of the systems might have minimal impact on cytotoxicological aspects of MXenes materials.Here, we present the first theoretical study concerning the toxicological aspects of 2D MXene materials by employing various machine learning models. Our work demonstrate that the most important features potentially responsible for the toxicological properties are related to the presence of transition metal oxides MOur ML model successfully complement existing experimental studies, for which no cytotoxicological measurements have been carried out. In particular, we have predicted the cytotoxicity of 19 MXenes compounds, for which two of them are predicted to be cytotoxic with 0.9 probability. The rest of the compounds are predicted to be non-toxic and can be potentially applied in many technological areas ,66,67,68Moreover, our results show that the cytotoxic prediction of MXenes can be examined on the materials that are well experimentally characterised in terms of surface chemistry and the presence of oxides on the MXenes surface. Thus, we claim that it might be one of the solutions for reducing the number of toxicological studies needed, and allows for minimizing failures in future biological applications.In addition, the theoretical research methodology based on ML models developed here can be further applied to other types of 2D materials exhibiting complex structure and diverse surface characteristics, such as for example, novel 2D transition metal borides, so called MBenes  as well


African swine fever (ASF) is a highly contagious disease affecting all suids including wild boar. As the disease can damage commercial pig production and its circulation can threaten international trade, understanding the risks produced by free-living wild boar (as a wildlife reservoir) is important to ensure proportionate policies to exclude the disease, as well as an effective contingency response. The recent spread of the virus into Western Europe has produced concerns in many stakeholders including pig producers and national governments. Unlike in mainland Europe, where wild boar are widespread, in Britain, free-living populations have only recently re-established, and whilst these are still relatively small and isolated, they may provide a sufficient reservoir capable of sustaining disease and may thus present a continual source of infection risk to domestic pigs. This study focuses on one component of the risk produced by wild boar, specifically the distribution and persistence of virus in a landscape produced by the natural circulation of disease within wild boar. We used a spatial individual-based model run across a representation of a real landscape to explore the epidemiological consequences of an introduction of ASF into the Forest of Dean, currently hosting the largest population of wild boar in England. We explore various scenarios including variations in the prophylactic management of boar, as well as variations in reactive management (contingency response) following the detection of disease to evaluate their value in reducing this specific risk (presence of ASF virus of wild boar origin in the landscape). The abundance and distribution of wild boar is predicted to increase across our study extent over the next 20 years. Outbreaks of ASF are not predicted to be self-sustaining, with the median time to disease “burn-out” (no new infections) being 14 weeks. Carcass removal, as a tool in a package of reactive management, was of limited value in reducing the duration of outbreaks in this study. We suggest that useful predictions of some of the risks produced by ASF might be possible using only the distribution of the boar, rather than more difficult abundance or density measures. Where the disease may circulate in wild hosts, the risks this produces to commercial pig production must be also be considered. The description of these risks includes the character of outbreaks in wild boar  where free-living populations overlap with commercial or back-yard pig premises. Predicting what an outbreak of ASF in wild boar might look like can also help inform the design of policies to address these risks. Such policies should be proportionate and practical (African swine fever (ASF) is a highly contagious and virulent disease of suids, known to severely damage commercial pig production and infect free-living wild boar in Europe . It is rractical , followiractical , 4 and sractical , 6.The spread of ASF westwards across Europe has been thoroughly described and analyzed to support both local action and to adhere to international agreements on actions and trade . There hhttps://europa.eu/rapid/press-release_MEX-19-1431_en.htm).The current guidance produced by EFSA for the management of ASF in wild boar lists actions to contain the infection to a geographically discrete population of wild boar until the infection “burns-out”  within this prescribed zone, using intensive focal hunting and a non-hunted buffer to prevent the propagation of disease across the wider landscape . In paraThe situation in England differs sufficiently from that of continental Europe to suggest that additional predictive modeling is required to inform decision-making. In particular, differences include the character and ownership patterns of the landscapes supporting boar, along with attitudes toward them. The first of these (patterns of landscape) influences the demographic and spatial dynamics of the wildlife host, potentially altering the course of an unmanaged outbreak of ASF from that anticipated in Europe; whilst the latter (attitudes) may affect the acceptability or effectiveness of potential management tools. In England, landscapes commonly host fewer, smaller and more fragmented forested areas than those typically found in Europe, and are dominated by a fine-scaled mosaic of arable land and pasture interspersed with small woodlands, people, and infrastructure , all of which are likely to modify the movement behavior of individual boar as well as their population processes (population dynamics). The recent reintroduction of boar and the landscapes available to them have to date restricted them to a few discrete populations. The most significant of these, in the Forest of Dean, is thought to have only established relatively recently  and appeTo better understand the risks to commercial pig production posed by the incursion of ASF into a free-living population of wild boar in England  we developed a spatially explicit individual-based model (IBM) and used it to predict the course of disease in wild boar and explore the value of different prophylactic and reactive management actions to mitigate the risk. Specifically, we wished to test the hypothesis that the risks produced by ASF would be similar to those identified for foot-and-mouth disease (FMD) , and req2 of mixed woodland surrounded by a mosaic of arable, pastures and smaller woodlands largely in private ownership  is located in south-western England. It includes an extensively forested core area owned by the Forestry Commission (hereafter the FC estate) comprising a fragmented 75 kmwnership . Our stuFeral wild boar was first detected on the FC estate during the 1990's and has been monitored annually since 2013. Population management using targeted culls (restricted to the FC estate) has been ongoing since 2008 ; an incrad-hoc activity across the wider study arena. To simulate these activities, animals were removed from the model with fixed weekly probability . Full details of the core host model are available in Croft et al. , can be used to derive the probability that contact with a carcass will remain exclusively within a social group.Lange and Thulke  define t2), density (boar/km2) and, for simulations with ASF introduction, the time to disease freedom (weeks) and change in the cumulative size of infection; the latter represented by both the linear distance between the centroids of the patch hosting the focal case and the most distant patch of cumulative infection (km) and the cumulative area infected (km2).Populations were initialized according to a fixed distribution of boar approximating that reported for the FC estate in 2015 . For eacUsing our model we simulated different outbreaks of ASF, infecting an individual selected at random, assuming various timings of release , which produces a range of simulations run across populations of differing size and distribution. In this study, infection always occurred on the same day of the year (representing the 15th April), producing a seasonally fixed response. For each of these scenarios we tested the efficacy of different management options combining both removal (culling and hunting) and environmental decontamination . Initially, we varied hunting rate (p. hunted) with a fixed rate of culling (p. culled) within the FC estate equivalent to that applied in 2015  consideWe then considered the addition of carcass retrieval to control an outbreak by shortening the time that carcasses remain in the model. For each management scenario using removal alone, we tested reducing carcass persistence from 8 weeks [reflecting the average time mainly invertebrate scavengers take to completely consume a carcass ] to eith2 (more than double that of 2015). With moderate hunting (p. hunted = 0.0065), equal to the estimated culling rate in the FC estate ,2 . Howeverous work  on foot-Other than our description of ASF, which for this study follows the relatively deterministic approach of studies supporting the current EFSA guidance , there a2, reducing to nearer 2 km/month toward the population edge where densities were closer to that observed in Europe (5 boar/km2). This compares well with an empirical description of 1.5 ± 1.3 km/month for the same disease in boar in Poland .In all of our scenarios, even those simulating relatively large and extensive populations of boar [e.g., scenario (a) (i) after 20 years; Both the demographic and spatial dynamics of boar vary seasonally, mainly in relation to food availability, in ways that might alter the course of disease outbreaks. For example, plentiful food in autumn might reduce or delay density-dependent dispersal between patches   or tempoIn agreement with Croft et al. , this st2 ranging up to 130 km2). In reality, it is unlikely that the perfect hunting we simulate here (hunting = 1) could ever be realized in landscapes with complex fine-scale patterns of private ownership and variable densities of boar, and that target rates for hunting would need to be set appropriately high to realize any policy to prophylactically control wild boar. This highlights a critical gap in our knowledge of the costs (efficiency per hunting day) and consequences (disturbance) of hunting boar in England, how this is affected by the variation in method of hunting, mode (intensity and frequency), or the varying interests of landowners.We explored the importance of differing cull and hunting rates and the effect these have on the duration of an ASF outbreak. Our implementation of this  simulates the removal of boar in direct proportion to their density homogenously across the landscape. The culling and hunting rates do not describe the effort needed to remove wild boar. Thus, we do not consider the additional effort necessary where densities of boar are low or the terrain difficult . Whilst the additional costs and complexities of delivering an effective annual cull across the difficult terrain of the FC estate is borne by its state owner, there is likely to be substantial spatial variation in the rate of hunting by private landowners. This heterogeneity is likely to perturb natural source-sink dynamics between patches, either directly through disturbance or indirectly by stimulating movement produced by density-dependent dispersal, and consequentially leading to increased spread of disease. This principle has been recognized in the advice given to EFSA on the control of ASF in areas of Europe where wild boar are widespread, for member states to ban hunting close to the focus of disease . Unlike The model suggested that in all scenarios median disease burn-out occurred around week 14–15. The only benefits of an ongoing contingency response  are a reduction in the likelihood of long outbreaks (worst case) lasting more than 20 weeks. However, in this context, we note that our estimates of culling rates or their integer multiples, whilst “realistic” and derived from FC estate cull operations , 11, arehttps://europa.eu/rapid/press-release_MEX-19-1431_en.htm), was motivated by the observation that disease “burn-out” is relatively rapid and is likely to overtake the sum of delays produced by the detection and confirmation of disease, the deployment of assets to manage the disease in wild boar , and the efficient operation of an intense responsive cull.Similarly to other authors , 32, we per se. We suggest that measures of the distribution of boar  are the easiest measures by which the duration of disease circulating in wild hosts can be quickly assessed. Here, all of the reactive management responses appear to have limited value in reducing the duration of ASF in wild boar, though in part that is a consequence of the rapid burn-out of the disease in free-living boar even without intervention. We also explore the potential value of prophylactic management of boar populations, and outline some of the issues which may need to be considered if this is to be adopted as a disease management strategy. These include the distinctions between organized culls undertaken as a professional activity and voluntary hunting in its varied forms. The potential for mismatches between the local density of boar and the spatial heterogeneity in management effort, as well as how management itself may affect the behavior of boar are identified as substantial gaps in knowledge.This study predicts that ASF introduced to the free-living wild boar is very unlikely to become endemic in the Forest of Dean for some time, in contrast to the predictions made for FMD. Consequently, the duration of the risks to commercial pig production resulting from uncontained and unmanaged ASF virus circulating in this English landscape is limited. The last new infection of wild boar is likely to be around 15 weeks after the focal infection, and active disease in wild boar rarely persisted for more than 25 weeks in any individual simulation. This outcome is likely to be consequent on the spatially discrete (isolated) and limited size of the population of wild boar, even in scenarios projected after 20 years of population growth and natural spread. This and earlier work  both sugAll datasets generated for this study are included in the article/GS, JA, and SC conceived the main research idea. SC designed the methodology with contributions from JA. All authors contributed critically to the analysis and interpretation of the results, to the writing of the manuscript, and contributed to sourcing and collating of key input data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The design of surveillance strategies is often a compromise between science, feasibility, and available resources, especially when sampling is based at fixed locations, such as slaughter-houses. Advances in animal identification, movement recording and traceability should provide data that can facilitate the development, design and interpretation of surveillance activities. Here, for the first time since the introduction of electronic identification of sheep, the utility of a statutory sheep movement database to inform the design and interpretation of slaughter-house based surveillance activities has been investigated. Scottish sheep movement records for 2015–2018 were analyzed in combination with several other data sources. Patterns of off-farm movements of Scottish sheep to slaughter were described and the spatial distribution of several distinct slaughter populations, throughputs and catchment areas for Scottish slaughterhouses were determined. These were used to evaluate the coverage of a convenience-sample slaughter-house based survey for antimicrobial resistance (AMR). In addition, non-slaughter sheep movements within and between Scottish regions were described and inter-and intra-regional movement matrices were produced. There is potential at a number of levels for bias in spatially-associated factors for ovine surveillance activities based at Scottish slaughterhouses. The first is intrinsic because the slaughtered in Scotland population differs from the overall Scottish sheep slaughter population. Other levels will be survey-dependent and occur when the catchment area differs from the slaughtered in Scotland population and when the sampled sheep differ from the catchment area. These are both observed in the AMR survey. Furthermore, the Scottish non-slaughter sheep population is dynamic. Inter-regional movements vary seasonally, driven by the sheep calendar year, structure of the Scottish sheep industry and management practices. These sheep movement data provide a valuable resource for surveillance purposes, despite a number of challenges and limitations that were encountered. They can be used to identify and characterize the spatial origin of relevant populations and so inform the interpretation of existing slaughterhouse-based surveillance activities. They can be used to improve future design by exploring the feasibility and cost:benefit of alternative sampling strategies. Further development could also contribute to other surveillance activities, such as situational awareness and resource allocation, for the benefit of stakeholders. Slaughterhouses provide a relatively easily accessible captive livestock population from which information on visible conditions can be obtained, or samples can be taken . WhetherThe livestock populations that can be accessed at slaughterhouses are a natural subset of the general population. They are however a potentially biased subset due to their relative health and, in many cases, age at slaughter , 5. In aWith legislative requirements and technological advances in the identification and recording of livestock, it should now be possible to provide improved quantitative evaluations of livestock population demographics, locations and movements. This information could then be used to inform both disease control strategies and the design of monitoring, data collection and surveillance activities. For the most part, published literature of analyses of animal movement data has focused on network analysis and risk-based surveillance, predominantly in the cattle and pig sectors e.g., –11]. She. She11)]During the period 2002–2007, unpublished work by the then Veterinary Laboratories Agency  explored the use of the British cattle traceability system data to investigate the relationship of source farms to slaughterhouses. These analyses were extended to the ovine population of England and Wales in order to inform the design of a potential slaughterhouse survey for prion protein genotype frequency in the lamb population. The outputs of the spatial analyses were combined with slaughterhouse throughput data, qualitative information, and expert opinion (http://www.legislation.gov.uk/ssi/2009/414/made). Scottish sheep keepers are required to identify and register their animals. If the animals are to be moved off the holding, this movement must be recorded and reported to the Scottish Animal Movement Unit (SAMU). These movement data are held in a system known as the Scottish Livestock Electronic Identification and Traceability database (ScotEID).Since then the use of EID for individual sheep identification and improved movement recording has been introduced via European law . In ScotThe primary aim of this study was to assess the utility of the Scottish sheep movement data, currently collated and held in ScotEID, to determine whether they can be used to inform the design and interpretation of Scottish sheep slaughterhouse-based surveillance activities. A further objective was to apply this knowledge to an example, namely the 2017/18 sampling of sheep from one Scottish slaughterhouse for testing for AMR.All of the datasets used in this study were provided to the Scottish Government's Center of Expertise on Animal Disease Outbreaks (EPIC) by the agencies or institutions listed below and stored in the EPIC data repository; a centrally curated collection of data resources. This was established in 2011, to support research within EPIC as part of the Scottish Government's Strategic Research Programme.ScotEID is the livestock traceability system for Scotland managed by the Scottish Agricultural Organization Society (SOAS) on behalf of the Scottish Government. The primary functions of the ScotEID systems are sheep, pig, and cattle movement recording. All sheep movements that contain all or part of their movement within Scotland are recorded in ScotEID.https://www.gov.uk/guidance/register-land-you-use-to-keep-livestock).Each movement record registers the departure premises, read location and destination premises. These are identified by their County Parish Holding number (CPH). A holding is a place where livestock are kept or handled in pursuit of an agricultural activity. In addition to farms, holdings include other types of premises and/or land such as markets, lairages, slaughterhouses, ports and showgrounds  tag is scanned at a Critical Control Point (CCP), such as a market or slaughterhouse, there will also be a separate individual animal-level read record of their movement.https://www2.gov.scot/Topics/Statistics/Browse/Agriculture-Fisheries/Publications/JuneAgriculturalCensus. The data provide a snapshot of sheep numbers during the same month (June) each year.The Scottish June Agricultural Census  is sent annually to all holdings that complete a Single Application Form  and to a random sample of other agricultural holdings. Information is collected across all types of farms about the areas owned or rented, crops, livestock numbers (not including cattle), and labor. Once every 10 years a full census is held with the last one being in 2012. It is a legal requirement to complete the census . For further information please see Premises types and geo-referenced locations were collated from a number of other Scottish and British  sheep movement and demographic datasets using the unique CPH as the link between records. These data sources included: the Sheep and Goat Inventory  and the Animal Movement Licensing System . These locational data included GIS coordinates and were combined to form a consistent repository of data. This was used to provide geo-references for the descriptive spatial analysis and to determine premises types.The time period studied was the 4 year period 2015–2018, inclusive. Where illustrative single maps are provided they are for 2017.Data processing and analysis were carried out using a combination of bespoke applications written in C++ and R  with pacThe Total Sheep Population in terms of number of sheep per calendar year was extracted from the Scottish Agricultural Census. The spatial distribution of the total sheep population was mapped as the sheep density calculated at a parish level as sheep per hectare .Sheep movement data for the study period were extracted from the ScotEID database held in the EPIC data repository, reviewed and summarized. The number of movements and the number of sheep per movement in batch data were counted and compared with the individual animal-level data.p-value of the odds ratio was <5%. Batch sizes for moves to slaughter (section Scottish Sheep Slaughter Populations) were also analyzed as above.The data were split into four classes of movement types: the total movements, moves within Scotland, moves out of Scotland and moves into Scotland. The distribution of batch sizes in each class was visualized as a boxplot. The differences in the batch sizes between classes were compared using a generalized linear model (GLM) in R. Given that the batch sizes  are counts, they were modeled as Poisson distributed. The batch sizes from different movement classes were considered to be significantly different from the reference category if the Scottish holdings were defined by their CPH number and those that supplied sheep to any GB slaughterhouse were identified from the movement records. A sheep movement record was classified as contributing to the slaughter population when the destination location was any GB slaughterhouse.Batch movement data were joined to individual animal movement data through common variables and the resulting dataset used to determine the numbers of sheep received by each slaughterhouse. From batch data it can be determined how many animals moved to a slaughterhouse on a particular date. However, when the datasets were combined it was made possible to identify some animals that had their EID tags read at both the market and the slaughterhouse. In the batch data these were recorded as two separate movement records, thus effectively double-counting some of the sheep moving to a slaughterhouse e.g., Record 1: Holding = departure premises; market = read location; slaughterhouse = destination. Record 2: market = departure premises; slaughterhouse = read location; slaughterhouse = destination premises. By removing the second move from the analysis this reduced the chance of over-estimating the number of sheep going to slaughter.The number of batch movements and the numbers of sheep to each of the slaughterhouses were calculated for each year. The slaughterhouses were ranked and the annual throughput determined.Data were processed to a consistent form for each of the 4 years (2015–2018) and three major populations were described.Population 1 - The total annual Scottish Sheep Slaughter Population (SSSP) contains sheep originating from a Scottish holding (hereafter referred to as Scottish sheep) that have been identified as going directly to slaughter at any slaughterhouse throughout Great Britain (GB). This Population was made up of two subsets, namely:Population 2 - The Scottish Sheep Slaughtered in Scotland Population (SISP) contains Scottish sheep that have been identified as going directly to slaughter at a Scottish slaughterhouse.Population 3 - The Scottish Sheep Slaughtered outside Scotland Population (SOSP) contains Scottish sheep that have been identified as going directly to slaughter to a slaughterhouse outside of Scotland.There is one further minor group, Population 4 which consists of sheep supplied in a direct move for slaughter at some Scottish slaughterhouses  that originate from holdings out with Scotland. These sheep are part of the population of sheep slaughtered in Scotland but did not come from a Scottish farm so were excluded from this analysis.2 hexagons was created using the QGIS Processing plugin module, overlaid on the point data and trimmed to the coastline. The number of holdings in each hexagonal grid cell or the number of sheep that were supplied to slaughter from each grid cell were counted and density maps plotted using these calculations.Precise co-ordinates were used to plot holding locations, if they were available; if not, the parish centroid was used. Slaughterhouses and their direct supplying premises were plotted on the GB national grid with QGIS 2.14.8. A grid consisting of 115 kmSlaughterhouse catchment areas are defined as the spatial distribution of the holdings from which they receive their annual throughput.E. coli isolate per sample against an agreed panel of twelve active substances (From June 2017 to March 2018 pilot sampling at slaughterhouses was undertaken by Food Standards Scotland (FSS) to test for antimicrobial sensitivity testing (AST) by SRUC Veterinary Services (VS). The aim was to acquire 40 samples per species per month from cattle, sheep, pigs and poultry, with each species being sampled in a single week of each month, in rotation. For sheep, the sampling was done at a single slaughterhouse (Slaughterhouse A). For cost and logistical reasons this was a convenience sample with the sampling occurring on 1 day in the designated week, over a 4.5 h period. The sampler was instructed to try to obtain samples from a spread of farms. Individual fecal samples were obtained. These were sent with a copy of the accompanying Food Chain Information (FCI) form to SRUC VS (Inverness) for AST, by disc diffusion of one bstances . EuropeaThe ovine dataset consists of 388 records. Each record represents a single sheep fecal sample collected between June 2017 and March 2018. The samples are identified by a sample reference number, an Animal flock ID based on the eartag number (UKxxxxxx) and the holding CPH.The spatial distribution of the sampled holdings was compared to the distribution of holdings from the slaughterhouse catchment area. In order to establish the representativeness of the sampled holdings across the slaughterhouse catchment areas, the observed number of sampled holdings was compared to the number of holdings that would be expected if sampling was allocated proportionately based on spatial spread or clustering of holdings. This was achieved by determining which hexagons were adequately represented and which were over represented by the sampled holdings using chi square test. Considering that the data is large (chi square degrees of freedom = 213) and given that the Chi square is asymptotically normally distributed when the sample size is large, the cut-off point for significance was taken to be 2. Therefore, if the calculated Chi square is ≤ 2 the hexagon is deemed to be adequately sampled otherwise the hexagon is deemed to be oversampled.https://www2.gov.scot/Topics/Statistics/Browse/Agriculture-Fisheries/AgricMapRegions. Sheep holdings were plotted using GIS coordinates and the CPH numbers identified that fell within a particular region. The number of sheep moving into or out of each Scottish region was calculated for 2017 as a whole and as numbers per quarter. This analysis was restricted to sheep movements that had originated and terminated within Scotland only, with movements to slaughter omitted. Movement matrices, or “heatmaps,” were then generated from this data. Sheep numbers were plotted as quartiles of the observed distribution of sheep numbers.Sheep movement data extracts were generated and processed. A shapefile  of the SThe movement matrix represents sheep movements between 14 regions. The numbers of sheep moving between regions were plotted either as (i) the number of sheep moving from departure region “X” to destination “Y” as a percentage of the number of sheep moved from departure “X” to all 14 destinations or (ii) the number of sheep moving to destination “X” from departure “Y” as a percentage of the number of sheep moved to destination “X” from all 14 departure regions. Sheep numbers were plotted as quartiles of the observed distribution of sheep numbers.In order to confirm seasonal patterns of sheep movements, the number of sheep moving each month was counted and compared for each year. These counts included the total number of sheep moved throughout the year or just the movements to slaughter. The differences in where a slaughterhouse sources its throughput from at different times of the year was analyzed by counting the number of sheep supplied to the slaughterhouse for each quarter of the year . The number supplied from each grid cell was plotted on a hexagonal grid map as a percentage of the annual slaughterhouse throughput.Overall numbers of sheep in the Scottish June Agricultural Census sheep increased by 4% between 2015 and 2017 when numbers peak at just under 7 million . Sheep aWhen the number of sheep movements calculated from the batch records and from the individual read animal-level records were compared, there were, on average, 20% more sheep and 4.8% more batches recorded in the batch data for each year studied. There was an average of 15% of sheep found in batch data that did not have associated individual animal-level reads and an average of 4% individual animals were missing due to mis-reads.Over a third (35%) of holdings in the sheep movement data did not have any spatial data associated with the record. Depending on the year, the total number of premises moving sheep to and/or from Scotland varied between 15,107 and 15,906; the majority of premises involved in these movements were located in Scotland  moved entirely within Scotland. On average just under half (48.1%) originated in Scotland and moved out across the border, with only a small percentage of batches (2.2%) entering Scotland. However, this latter percentage more than doubled in 2017 and 2018, compared to 2015 and 2016 .In terms of individual animals, an average of 4,514,194 sheep moved annually, with the largest percentage (48.5%) moving entirely within Scotland. However, the percentage that moved into Scotland was greater than when considered as batches; with an average of 6.7% .p < 0.0001) when compared with movements within and out of Scotland  were located in Scotland. Although the number of slaughterhouses slaughtering sheep of Scottish origin does not change markedly there were some differences with regard to which of the slaughterhouses were operable in each year.Approximately a third of the Scottish sheep population went to slaughter in each year SSSP,  of the sAn average of 8,543 distinct Scottish premises per year supplied sheep to slaughterhouses in GB. A quarter of these premises (25%) supplied Scottish slaughterhouses only; approximately a quarter (26%) supplied non-Scottish slaughterhouses only, while the reminder—just under a half (49%)—supplied sheep to slaughterhouses in both Scotland and the rest of GB. Approximately half of the Scottish sheep slaughtered in a year (SSSP) are slaughtered within Scotland SISP, .The lowest number of sheep moves to slaughter occurred in the period May to June of each year . There wAlthough broadly similar, the distribution of the Scottish sheep slaughter population SSSP,  differedThe immediate origins of the SISP sheep that originated in Scotland and were sent to slaughter at a Scottish slaughterhouse—was not uniformly distributed across Scotland . In eachRanked by throughput of SSSP, in terms of sheep numbers per annum, the same four slaughterhouses topped the ranking throughout the study period .The top four Scottish slaughterhouses  together received just over four-fifths (81%) of the population of Scottish slaughter sheep (SSSP) that were slaughtered within Scotland (SISP); 2017 .Each Scottish slaughterhouse catchment area was different. There was some degree of overlap between some slaughterhouses and there were some slight variations in the distribution of the supply population over the study period. However, the catchment areas generally remain relatively discrete and stable over this 4 year period. The catchment areas of the top four ranked Scottish slaughterhouses supply 40, 15, 14, and 12% of the SISP respectively. In combination the resultant area gave substantial coverage of the mainland spatial distribution of the SISP compare . Some ofThere were seasonal differences in the spatial distribution of premises supplying sheep to slaughter. For example: the movement of all of the sheep supplied from the Western Isles to slaughterhouse A occurred in the third quarter (July to September), whilst sheep from North East Highland (Caithness area) and Orkney tended to move to slaughter in the 4th quarter  was the Scottish slaughterhouse with the highest throughput of sheep. Over the 4 year period of the sheep movements analysis, it averaged 19% of the total Scottish sheep slaughtered annually and 38% of those sheep slaughtered in Scotland (SISP), in terms of sheep numbers. The catchment area covered north-eastern Scotland and included some of the Borders and eastern Central Belt . This slBased on the CPH of the 388 fecal sample records, these samples came from 216 distinct holdings. Two samples had no CPH identifier. There were multiple samples from some holdings . Two holdings, each supplying one sample, were omitted from the analysis because they were located outside Scotland (Northumberland).When the spatial distribution of the sampled holdings was compared to the distribution of holdings from the slaughterhouse catchment area, 52% of the grid cells in the catchment area were deemed to be adequately sampled, 45% were not sampled and the remaining 3% were oversampled .The parts of the catchment area that were not sampled included the Shetland Islands and Western Isles, the western part of the northwest part of the northeastern area (around Inverness), the central east coast area (Angus and Fife) and the Central Borders area . The catExcluding moves to slaughter, it was estimated that over a million  sheep were moved within Scotland in 2017. In general, the volume of sheep movement off Scottish holdings was much higher than the on-movement. The largest number of sheep were recorded as moving from premises within three regions; Grampian, Dumfries and Galloway and Clyde Valley  Grampian and Dumfries and Galloway sent and received most sheep overall. In the third and fourth quarter, while they still sent the most sheep, Highland became the region receiving most sheep from other Scottish regions. Example 2: in the first quarter, sheep from the Shetland Islands only moved within the Shetland Islands and a small proportion of sheep moved into the Shetland Islands from elsewhere. However, in the second half of the year, although sheep from the Shetland Islands still moved predominantly within Shetland, they also moved out to other destinations. These were Grampian in both the third and fourth quarters, Tayside in the third and the Orkney Islands in the fourth quarter .In this study, for what the authors believe is the first time since the introduction of EID, the utility of national sheep movement data has been assessed to determine whether they can inform the design and interpretation of slaughterhouse-based surveillance activities. The conclusion that can be drawn from these analyses is that these data can be utilized in such a way, as illustrated by application of the analytical techniques developed during the assessment to an example: a survey for antimicrobial resistance in Scottish slaughter sheep.A primary challenge to the development of all operational animal health surveillance systems is the availability of and access to existing data. This is often related to issues of subject confidentiality. In this study, this has been addressed by the Scottish Government's Center of Expertise on Animal Disease Outbreaks (EPIC) data repository. From its inception in 2011, a centrally curated collection of data resources has been carefully established, extended, improved and maintained. Data are sourced from a number of data providers, both public and private and held in a secure data repository. User access requires legally binding data use agreements to be in place and is monitored and audited. The existence of this repository facilitated the study, as multiple data sources were required to address some of the challenges identified.A number of data limitations were encountered during the course of the study. These were: 1/ that there is no year-round centrally available record of individual sheep and their geo-location, until a movement occurs between premises and the EID is captured when read at a Critical Control Point; 2/ knowledge of the source and destination premises type and geo-location is compromised by missing data; 3/ accurate counting and identification of individual sheep within movements is difficult, even with EID; 4/ the age of the sheep moved is unknown, and 5/ only the origin from the move immediately prior to slaughter could be accurately determined. Some of these are inherent aspects of the technologies in use and data collection processes. Some are due to missing data, or gaps in the information available. As discussed below, these challenges limit the utility of these data both for this surveillance purpose, and for others, to varying degrees. Their existence highlights the need to take adequate care; both when using these data for analyses and when interpreting the outputs of any such analyses.https://www2.gov.scot/Topics/Statistics/Browse/Agriculture-Fisheries/Publications/SGAI-DAS). These provide snapshots of what is known to be a dynamic population. Numbers and the age-structure of sheep populations on holdings will vary between the two as the Census in June occurs after the peak lambing period (March-May). In December's Inventory, the majority of fat lambs will have gone to slaughter  and in December from the SGI  should have improved with the introduction of new technologies, such as EID, and regulations requiring their use. This is the case; however, these methods have raised new issues. These include additional costs to production, mis-reads due to loss or failure over time –34, as whttps://www.gov.scot/publications/sheep-goat-identification-traceability-guidance-keepers-scotland/pages/7/). However, it would still omit identification of the subset of the lamb population that do not go direct to slaughter from their holding of birth, such as store lambs and those lambs intended as replacement breeding stock that do not make selection for the breeding flock. A possible alternative might be to record whether the batch consists of lambs <12 months; sheep over 12 months, or a mix of both at a batch-level. Either of these approaches would be a substantial improvement. These data are rich and much more could be done with them, if sheep could be age-differentiated, as was the case with the Australian sheep movement data . A further problem arises when there are man-drawn borders within the one landmass with different recording systems on each side. Without access to the data from the other side, analyses of movements are compromised. Here, this occurs due to devolved responsibilities. In this study, some of the overall Scottish sheep out moves may have been to markets across the border, from whence they may have gone to other holdings, returned to slaughter in Scotland in population 4, or gone on directly to slaughter for which they would currently not be included in the SSSP as they did not go direct from a Scottish premises to a GB slaughter house. The extent of these effects cannot be quantified.The fifth challenge of being able to identify only the direct move reduces the accuracy of the derived slaughterhouse catchment areas. As mentioned earlier, there will be a subset of the lamb population that does not move direct to slaughter, while adult sheep may have been resident on a number of premises within their life-time. Such movements contribute to the spatial dynamics observed in the outputs of the within-country non-slaughter movement analysis. Unlike cattle, where the individual animal passport and tracing system facilitates the modeling of life-time pathways –40, the A key outcome of this study is the ability to use sheep movement data to start to identify and characterize potential sources of bias in slaughter-house based surveillance. The outputs indicate that any sheep slaughterhouse-based surveillance, conducted in Scottish slaughterhouses, that aims to be representative of the Scottish sheep slaughter population has the potential to produce biased estimates. If the disease, condition, or subject under investigation is systematically different between those holdings that only supply slaughter sheep to GB slaughterhouses out-with Scotland (SOSP) and those that contribute to the SISP, such a bias will exist. Given the spatial differences between the SISP and the SOSP holdings biased estimates are likely, particularly if management systems, breed, flock size, livestock or holding density, or weather conditions are associated with the disease, condition, or subject under investigation.E. coli i.e., in the frequency of occurrence of AMR to any of the active substances that were tested for. This could arise at three levels: firstly, due to the reduced coverage of the catchment area of this slaughterhouse; secondly, due to the difference between the catchment area and the slaughtered in Scotland sheep population (SISP), and thirdly because of the difference between the SISP and SSSP. The fact that there was no sampling in the AMR survey in the second quarter is probably of the least concern. This is the quarter with the least movement to slaughter; however, it could be one where older lambs, more likely to have been treated with antimicrobials may be sent to slaughter. Bias will occur if there are systematic differences between the levels of AMR in those sheep sub-populations from the areas not sampled and those sampled. If the areas missed were areas of more intensive, lowland sheep production, then it might be argued that the likelihood of AMR could be higher, due to increased antimicrobial usage during the lambing and/or fattening period, than in more extensive areas. This would lead to an underestimate of prevalence of AMR. And, vice versa, if the areas not sampled are those of extensive hill flocks with lower antimicrobial usage. The potential contribution to the existence of AMR in sheep fecal organisms from other spatially related factors such as the environment , interpreted the outputs  and jointly drafted the manuscript, with JS. JS devised and performed the data analysis, generated bespoke C++ applications for data handling and jointly drafted the manuscript with ST and produced figures. She is a member of the EPIC Data Team. JE provided statistical input and drafted the statistical methods. GF led the AMR survey . AR provided advice regarding data handling and extraction, plus contributed to the development of the bespoke C++ applications. He leads the EPIC Data Team and led development of the EPIC Data Repository. JE, GF, and AR reviewed and commented on the manuscript. GG acquired the funding for and initiated the development of the EPIC Data Repository. He reviewed the manuscript. All authors read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.