Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features. Traditional access control models, while successful in solving various problems in some situations, are designed to offer a link between information associated with an access control rule logic and a resource to which access is requested. The implementation of an access control model can be manipulated in a variety of ways, ranging from an unforeseen situation involving poorly designed access policies to malicious entities gaining access to a group of existing accounts. As a result, traditional access control models, which are based on static and predefined policies, cannot manage unanticipated scenarios and situations . As a reDynamic access control techniques, as opposed to static policies, make access decisions based on real-time and contextual features. Trust, risk, context, history, and operational needs are examples of real-time features ,4. A risThe risk estimation process is an important stage for implementing a risk-based access control model. This process is based on calculating the likelihood of data leakage and the value of that data. The primary goal of the risk estimation process is to devise a method for ranking risks in order of priority and using risk numeric values to make access decisions in a given situation ,8,9. SevThe fuzzy logic system was one of the risk estimation techniques offered in the literature to overcome the lack of datasets for estimating the security risk value associated with access requests. As a result, the authors put the fuzzy logic system into action, and the findings showed that it generates correct and realistic risk values for access control operation, as presented in . The fuzThis paper proposes a novel NFS model to build the risk estimation process in the risk-based access control model for the IoT system. The proposed NFS model has been implemented to evaluate security risk values associated with each access request. The results demonstrated that the NFS model provides an efficient and accurate risk estimation technique that can adapt to the changing conditions in the IoT environment. The proposed NFS model was evaluated against access control scenarios of a children’s hospital. Using the contextual and real-time features involving time and location, the results demonstrated that the proposed NFS model can be applied to provide dynamic and contextual-aware access decisions.Proposing the NFS model to overcome flexibility and scalability issues associated with the fuzzy logic system for the risk estimation in the risk-based access control model;Identifying and implementing the most effective learning algorithm for the NFS model by comparing three different learning algorithms;Evaluating the accuracy and applicability of the proposed NFS model through providing access control scenarios of a children’s hospital.The contribution of this paper can be summarized as follows:The remainder of this paper is organized as follows: Risk-based access control models are used primarily to provide the required flexibility to the access control process. Several researchers discussed creating a risk-based access control model to overcome flexibility and the inability to handle unpredicted situations. McGraw  introducChen et al.  presenteThe essential step to developing a risk-based access control model is the risk estimation process, which assesses the risk value associated with each access request. This calculated risk value is then utilized to decide on whether to grant or deny access. It is difficult to assign a quantitative or numeric value to risk without a dataset to characterise its likelihood and impact. The fuzzy logic system was one of the risk estimation strategies provided by the literature to overcome the lack of datasets. For instance, Chen et al.  designedNi et al.  used a fBased on the recommendations of the literature review, the fuzzy logic system was implemented in  to resolTherefore, the solution was to integrate an Artificial Neural Network (ANN) with the fuzzy logic system which creates NFS. NFS is an ANN technique, which is functionally equivalent to the fuzzy logic system. It combines the parallel computation and learning capabilities of an ANN with the human-like knowledge representation and explanation abilities of the fuzzy logic system. As a result, the ANN becomes more transparent, while the fuzzy logic system becomes capable of learning. In addition, the NFS can be trained to develop IF-THEN fuzzy rules and determine membership functions (MFs) for input and output variables of the system ,18.Among all methods of integrating fuzzy systems and ANNs, a hybrid NFS has the most potential. This is because it makes the best use of the advantages of fuzzy systems and ANNs. Hybrid systems have the property of constantly being treated as systems of fuzzy rules with ANNs used to tune MFs in preconditions and conclusions of rules based on the set of learning. A NFS has been utilized to estimate risk in various domains. For example, Kristjanpoller and Michell  utilizedOne of the major issues to develop an efficient and effective risk-based access control model is to create an accurate and reliable risk estimation technique that can resolve issues associated with existing techniques in terms of scalability and inability to learn. This paper utilizes the NFS model to add the learning capability to the risk estimation technique to build a dynamic and efficient risk-based access control model for IoT applications. To the best of the authors’ knowledge, no research exists that utilizes the NFS model in risk-based access control models. Therefore, this paper provides a novel NFS model to implement the risk estimation technique of the risk-based access control model.NFS is the result of integrating an ANN with the fuzzy logic system. It integrates the human-like reasoning of fuzzy logic systems with the learning and connectionist of the ANN . The NFSThe integration of the ANN with the fuzzy logic system resolved some of these issues. The resultant NFS combines parallel computation and learning abilities of the ANN with the human reasoning of fuzzy systems and clarity of systems representation. Therefore, the ANN becomes more transparent, and the fuzzy logic system becomes capable of learning . The intUnauthorized information disclosure is one of the critical challenges in the IoT system that need to be addressed. Current traditional access control models cannot resolve this challenge, since these models are built using static and predefined policies that always give the same result in different situations ,31. TherThe risk-based access control model is one of the dynamic models that use the security risk value associated with each access request as a criterion to determine the access decision. It performs a risk analysis to estimate the security risk value for each access request, and then it uses the estimated risk value to decide to either grant or deny access ,6.A dynamic risk-based access control model for the IoT was proposed by the authors and discussed in ,9,32. ThThe hybrid NFS was utilized to implement the risk estimation technique of the risk-based access control model. Implementing the hybrid NFS was performed in two separate stages. The fuzzy logic system was first implemented, then the ANN was used to train it. The proposed hybrid NFS model was built by implementing the fuzzy logic system and neural network in two separate phases. In the first phase, the fuzzy logic system was implemented to utilize the effectiveness of converting linguistic expressions into quantitative values, which could be used to produce numeric risk values to make access decisions. The implementation of the fuzzy logic system was discussed in detail in ; therefoTo implement a Mamdani Fuzzy Inference System (FIS), there are five main stages, which include fuzzification, MFs, fuzzy rules, rule aggregation, and defuzzification. To determine the required parameters to implement the fuzzy logic system of the risk estimation technique, 20 IoT security experts from inside and outside the UK were interviewed, as discussed in .The first stage was used to convert the input and output variables of the system into linguistic expressions (fuzzy sets), which is called fuzzification. Three fuzzy sets were used to represent each risk factor and five fuzzy sets to represent the output risk. The user context, action severity, and risk history were represented by “Low”, “Moderate”, and “High” fuzzy sets. The resource sensitivity was represented by “Not Sensitive”, “Sensitive”, and “Highly Sensitive” fuzzy sets. Conversely, the output risk was represented by using five fuzzy sets: “Negligible”, “Low”, “Moderate”, “High”, and “Unacceptable High” .The second stage to implement a Mamdani FIS was to specify the MF that represented the relationship between the input risk factors and output risk. A Triangular MF was selected as the appropriate MF to provide a proper representation of the expert knowledge and facilitate the calculation process. The third stage was fuzzy rules, which are the knowledge base that is used by the fuzzy model to generate the output. Fuzzy rules are used to define the relationship between the output risk and input risk factors. It builds input combinations with the corresponding output in the form of IF-THEN statements. As there is no available dataset, there is no way to ensure correct and precise fuzzy rules. Hence, IoT security experts were used to build fuzzy rules based on their knowledge and experience . Since tThe fourth stage was rule aggregation, which combined the outputs of all fuzzy rules. The max (maximum) aggregation operator was used to combine output rules into one fuzzy set. The fifth and final stage was defuzzification. The output risk of the fuzzy model should be back to be a crisp value. The centroid method was selected to defuzzifize the output. The MATLAB fuzzy logic toolbox was utilized to implement the fuzzy logic system of the risk estimation technique, as shown in After the fuzzy logic system was implemented, a dataset consisting of 160,000 records was created. Each record contained values of the four risk factors  and the output risk value.The second phase to implement the proposed NFS model was the neural network. A Multi-Layer Perceptron (MLP) model was utilized to implement the neural network. A MLP is a feed-forward neural network that is used to explore complex and nonlinear models. It is based on a supervised learning technique that needs the desired output for each input to be known to calculate the error . The MLPThe input layer represents the risk factors: user context, resource sensitivity, action severity, and risk history. The output layer represents the output risk value resulting from the risk estimation process. Conversely, the middle layer is the hidden layer that is responsible for carrying out computations and updating weights between different connections. One of the challenges associated with implementing the NFS model was determining the appropriate number of hidden layers and the appropriate number of neurons for each hidden layer. The number of hidden layers needed depends on the complexity of the relationship between the input and the target parameters. It represents a major impact on the learning process. However, a Feed-Forward Back Propagation (FFBP) network encompassing more than one hidden layer is very rare . Hornik,In addition, determining the optimal number of neurons in the hidden layer plays a significant role in the implementation of the NFS model. If an insufficient number of neurons are used, the model will be unable to model complicated data, and the resulting fit will be poor. Conversely, using a large number of neurons in the hidden layer affects its performance on new data, and its ability to provide a generalized model will be compromised . Indeed,In the next section, training the NFS model using various learning algorithms will be discussed to determine the appropriate learning algorithm as well as the appropriate number of neurons in the hidden layer that provides the best performance.Several experiments were carried out to train the proposed NFS model of the risk estimation technique to increase the accuracy of the output risk. Furthermore, several experiments were performed to determine the number of neurons in the hidden layer with various learning algorithms. All training and experiments were coded and executed using the MATLAB software. All experiments and measurements were coded using MATLAB on Intel(R) Core (TM) i7-2600, 3.40 GHz CPU, with 16 GB RAM, running Windows 10.Training set: This set contained 96,000 records (60% of the dataset) to train the NFS model.Testing set: This set contained 32,000 records (20% of the dataset) to test the NFS model.Validation set: This set contained 32,000 records (20% of the dataset) to validate the NFS model.Implementing the NFS model of the proposed risk estimation technique requires a dataset or examples for training. After implementing the fuzzy logic system, a dataset consisting of 160,000 records was created. To avoid possible bias in the sample data to the NFS, the dataset was randomized and divided into three sets using the cross-validation method.n is the total number of data, iO is the observed value, and iP is the predicted value.Commonly used performance evaluation metrics in forecasting problems were utilized to compare and evaluate the accuracy of the NFS model . The NFSTo reach network generalization and a good fit with all the data points, the proposed NFS model of the risk estimation technique was trained using three learning algorithms, Levenberg–Marquardt (trainlm), Conjugate Gradient with Fletcher–Reeves Resrarts (traincgf), and Scaled Conjugate Gradient (trainscg), to determine the optimal learning algorithm that guarantees network generalization with the minimum error (lowest RMSE and MSE) and the maximum fit (highest R).The Levenberg–Marquardt (LM) algorithm is an iterative method that locates a local minimum of a multivariate function. It is expressed as the sum of squares of several non-linear and real-valued functions. The LM algorithm is widely adopted in various domains to deal with data-fitting applications. It has become a standard method for nonlinear least-squares problems. The LM algorithm can be considered as a combination of the steepest descent and the Gauss–Newton method and is one of the fastest learning algorithms .The LM algorithm with one hidden layer was utilized to train the proposed NFS model of the risk estimation technique. Several experiments were carried out to determine the number of neurons that produce the lowest error and the best fit with the learning process. The NFS model was trained using the LM learning algorithm with increasing the number of neurons in the hidden layer from 100 to 1000, and MSE, RMSE, and R values were recorded, as shown in The results demonstrated that increasing the number of neurons in the hidden layer led to decreasing both MSE and RMSE values for training, testing, and validation data and increasing the value of R. The results demonstrated that the NFS model had the lowest MSE and RMSE error values for training, testing, and validation at 1000 neurons. Furthermore, the NFS model had the highest value of R, 0.9985, at 1000 neurons, which is an adequate correlation that indicates the NFS model was well trained and fitted with the learning process, as the value of R is very close to 1.Since the best results in terms of MSE, RMSE, and R were obtained using 1000 neurons in the hidden layer, the proposed NFS model was implemented and trained using the LM learning algorithm with 1000 neurons in the hidden layer. The performance graph of the MSE values of training, testing, and validation data is shown in A gradient-based learning algorithm is one of the most commonly used error minimization techniques. It is a gradient descent local optimization algorithm that includes the backward error correction of the network weights . The conThe CGF learning algorithm with one hidden layer was utilized to train the proposed NFS model. Several experiments were carried out to determine the number of neurons that produced the lowest error and the best fit with the learning process. The NFS model was trained with increasing the number of neurons in the hidden layer from 50 to 1200, and the MSE, RMSE, and R values were recorded, as shown in The results of training the NFS model using the CGF learning algorithm showed unstable behaviour when increasing the number of neurons in the hidden layer. For example, the MSE value of the training dramatically decreased from 25.27 at 50 neurons to reach 21.04 at 100 neurons. This decrease continued to reach 20.59 at 200 neurons. Then, the MSE value of the training data increased dramatically to reach 26.75 at 300 neurons. Then, the MSE reached its lowest value at 400 neurons. Increasing the number of neurons in the hidden layer from 400 to 1200 showed the same unstable behaviour. However, the MSE value at 400 neurons produced the lowest error. This was the same scenario for the RMSE value for training, validation, and testing data, where it produced the lowest RMSE values at 400 neurons. For the value of R, the results showed the same behaviour of the MSE and RMSE values, in which the lower the error, the higher the correlation. The highest value of R (0.976) for the proposed NFS model was realized at 400 neurons in the hidden layer.Since the best results in terms of MSE, RMSE, and R were obtained using 400 neurons, the proposed NFS model of the risk estimation technique was implemented and trained using the CGF learning algorithm with 400 neurons in the hidden layer. The performance graph of MSE values of training, validation, and testing data is shown in Conjugate gradient methods need a line search at each iteration, which is computationally expensive, as it requires that the network response to all training inputs be estimated multiple times for each search. The Scaled Conjugate Gradient (SCG) learning algorithm was developed by Moller in 1993 . It was The SCG learning algorithm with one hidden layer was utilized to train the proposed NFS model of the risk estimation technique. The NFS model was trained with increasing the number of neurons in the hidden layer from 50 to 1200, and the MSE, RMSE, and R values were recorded, as shown in Since the best results in terms of the MSE, RMSE, and R were obtained using 1000 neurons, the proposed NFS model of the risk estimation technique was implemented and trained using the SCG learning algorithm with 1000 neurons in the hidden layer. The performance graph of the MSE values for training, validation, and testing data can be shown in A comparison between three learning algorithms that were utilized to train the proposed NFS model is shown in As shown in Utilizing the risk-based access control model to decide whether granting or denying access needs a risk estimation technique that produces a quantitative and numeric risk value for each access request. However, there was no available dataset to describe the risk probabilities and impacts for a set of specified incidents. In the absence of a dataset, the fuzzy logic system using the Mamdani FIS was utilized with the input of IoT security experts to implement the risk estimation process . One of After training the NFS model using three learning algorithms , the results demonstrated that implementing the proposed NFS model with the LM learning algorithm to implement the risk estimation technique provided less processing time, as it used only one-sixth of the time used by the Mamdani FIS, as depicted in Healthcare is one of the main applications of the IoT system ,46. It iThis section discusses applying the proposed risk-based access control model and the risk estimation technique using the NFS model. Different access control scenarios will be presented to evaluate the applicability of the proposed model to real-world scenarios.A closed world scenario involving a healthcare provider, such as Mount Cedar (MC) children’s hospital , was utiConsider the MC hospital has now received a four-year-old child called Harry, who was brought into the MC’s first aid clinic by his mother, Eva, late Wednesday evening. The admitting staff observed that Harry suffered from several bruises all over his body, a fractured rib, and a distorted shoulder . Let us When the examination results returned, Dr Chris wrote the diagnosis and the required medication for Harry and called social workers and policemen to investigate the incident, as he suspected a child abuse has occurred. Therefore, one of the social workers who is responsible for helping the children in case of abuse and a police officer requested to access Harry’s medical information for investigation purposes.The child who needs treatment;Doctors who are responsible for providing care to the child;Nurses who are responsible for helping the doctors;Social workers who are responsible for helping the children in case of trauma or abuse;Policemen who are responsible for investigating and establishing possible criminal charges and responsibilities in cases of trauma or abuse.The MC is a children’s hospital. Actors involved in this scenario include:There is no available dataset that describes risk likelihood and its impact nor has similar work done before to use the NFS model in the risk estimation in risk-based access control models. Some parameters will be assumed based on the literature review and logical flow of the scenarios. Applying the proposed risk-based access control model on the healthcare access control scenario requires defining values of the four risk factors  for each access request. For the action severity, three actions were assumed, involving read/view, write, and delete. The delete operation is not permitted for all actors involved in this scenario, as the hospital keeps track of all the medical histories of patients, so there is no need to delete any data. As discussed earlier, there are various actors involved in this scenario, in which each actor has a different role in the hospital. The proposed risk-based model should validate its applicability in this scenario by allowing or denying tasks for each role. Generally, only doctors can perform both read and write operations on the EPR, while other actors, including nurses, social workers, and policemen, can only read/view the EPR. For the resource sensitivity, two sensitivity levels were assumed: sensitive and not sensitive. However, all data/resources involved in this scenario were assumed to be sensitive.To define the value of the action severity, Sharma et al.’s  formula a et al.  have sugAs shown in The output risk value was used to assess the security risk value associated with the access request and used to make the access decision. The risk values of this scenario were categorized into three groups, as shown in All access control scenarios of the MC children’s hospital were implemented, and the output risk value was evaluated using the proposed NFS model with the LM algorithm, as shown in Applying the proposed risk-based model on access control scenarios of the MC children’s hospital demonstrated it can provide an effective access control model that can use contextual and real-time features to provide access decisions. It solves issues associated with static policies that always give the same result in different situations. For example, it allows access if the actor is located in the hospital location. It also solves issues associated with misuse and credential loss by allowing access by actors in person.A risk-based access control model is one of the dynamic models that utilizes real-time and contextual features to make access decisions. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. The risk estimation process is one of the essential stages to implement a risk-based access control model for the IoT. Although the fuzzy logic system provides accurate and realistic risk values for access control operations, it has some limitations. For example, the scalability of the fuzzy logic system seems to be doubtful, since it requires a non-trivial time to estimate security risks of access control operations. Furthermore, the fuzzy logic cannot learn or adjust itself to a new environment. Therefore, this paper proposed a novel NFS model to build the risk estimation technique that evaluates security risks associated with access requests. The proposed NFS model was trained using three learning algorithms, LM, CGF, and SCG, to determine the optimal learning algorithm with the minimum error (lowest RMSE and MSE) and the maximum fit (highest R). The results demonstrated that the LM algorithm was the optimal method to implement the NFS model of the risk estimation process. The trained NFS model with the LM learning algorithm proved that it provides a more efficient processing time, which can provide a timeliness risk estimation technique for various IoT applications. Besides this, adding the learning capability will make the risk estimation technique able to adapt to changes in the IoT environment, which results in more accurate and realistic risk values. The proposed NFS model was also evaluated against access control scenarios of a healthcare system , and the results demonstrated that the proposed NFS model provides dynamic, flexible, and accurate access decisions using the contextual and real-time features involving the time and location associated with each access request. For future work, deep learning techniques will be investigated for the risk estimation process. With the availability of a large dataset in this research, deep learning algorithms are expected to provide better results in terms of accuracy and performance. In addition, investigating utilizing risk with user trust to provide access decisions should be performed.


In the sequential approach, the volitional input disabled the autonomous control. In the simultaneous approach, the volitional input to a specific degree of freedom (DoF) activated autonomous control of other DoFs, whereas in the continuous approach, autonomous control was always active except for the DoFs controlled by the user. The experiment was conducted in ten able-bodied subjects, and these subjects used an SA prosthesis to perform reach-and-grasp tasks while reacting to audio cues . The results demonstrated that, compared to the manual baseline , all three SA modalities accomplished the task in a shorter time and resulted in less volitional control input. The simultaneous SA modality performed worse than the sequential and continuous SA approaches. When systematic errors were introduced in the autonomous controller to generate a mismatch between the goals of the user and controller, the performance of SA modalities substantially decreased, even below the manual baseline. The sequential SA scheme was the least impacted one in terms of errors. The present study demonstrates that a specific approach for integrating volitional and autonomous control is indeed an important factor that significantly affects the performance and physical and cognitive load, and therefore these should be considered when designing SA prostheses.Semi-autonomous (SA) control of upper-limb prostheses can improve the performance and decrease the cognitive burden of a user. In this approach, a prosthesis is equipped with additional sensors  that provide contextual information and enable the system to accomplish some tasks automatically. Autonomous control is fused with a volitional input of a user to compute the commands that are sent to the prosthesis. Although several promising prototypes demonstrating the potential of this approach have been presented, methods to integrate the two control streams  have not been systematically investigated. In the present study, we implemented three shared control modalities (i.e.,  To increase the autonomy of affected users and to meet their requirements  control was first developed in other fields of assistive robotics   learning to classify objects according to the grasp types that are appropriate for the object  using the “Wizard-of-Oz” paradigm  an ideal autonomous control module built using a state-of-the-art motion capture system, and (3) a control fusion module that integrates the decisions from the two control streams  to produce the final command that was transmitted to a prosthesis. The “ideal” modules mimicked the state-of-the-art autonomous  of the left-hand prosthesis . The control fusion module processed volitional and autonomous commands according to one of the predefined shared control schemes, as explained in Subsection Control Fusion Module.This module comprises (1) a custom-made mechanical adapter to map the subject's hand and wrist movements into the prosthesis functions (DoF selection) and (2) a myoelectric (EMG) armband to provide a proportional control signal see .mechanically. The adapter identified the movements through push-buttons triggered by performing selected gestures , as illustrated in class by physically performing a given hand motion. The push-buttons were connected to an Arduino Nano board that streamed their states to the host PC via a Bluetooth connection at 100 Hz.The state-of-the-art approach in myocontrol is to use the EMG pattern classification to estimate the subject motion intention and map it into the prosthesis functions. This method allows intuitive control, but is also prone to misclassifications. To implement a reliable command interface, we did not rely on interpreting the myoelectric activity; instead, a custom-made adapter was produced to fully encompass the subject's hand and detect the movement intention via a Bluetooth connection. To smoothen the signal, the root mean square (RMS) of EMG was computed over 150 ms windows with an overlap of 10 ms between consecutive windows. Then, the RMS from the eight EMG channels was averaged to estimate the overall magnitude of muscle activation along the forearm. The average RMS was normalized to 80% of the maximum voluntary contractions (MVC) and mapped to the normalized movement speed of the selected DoF.In addition, the myoelectric activity was recorded using eight surface EMG electrodes placed around the forearm  to implement a proportional speed control of the selected DoF. The Myoband streamed the EMG data at 200 Hz to the host PC target object's desired grasping configuration (flexion/extension and rotation) was calibrated according to the user preference at the beginning of each session, as explained in subsection Experimental Protocol.The ideal autonomous control system automatically preshaped the prosthesis' wrist (rotation and flexion/extension) during the object reaching phase and simultaneously maintained the hand of the prosthesis opened at 90% of the full aperture. To this end, the module used prosthesis position, orientation, and internal states , along with the position and orientation of the target objects  to calculate the commands for each prosthesis DoF . Typicalideal) manner, the artificial exteroception block employed a state-of-the-art motion capture system , which tracked the prosthesis and pose of the target objects and streamed it at 100 Hz. Similarly, the artificial proprioception was achieved using (1) the motion tracking to infer the prosthesis position and orientation in space, (2) prosthesis position encoders to retrieve the state (angle) of each prosthesis DoF, and (3) the pressure sensor of the prosthesis thumb to detect contact with the target object. The tracking information  was extrapolated using quadratic splines based on the data from the previous 2 s to compensate for the occasional marker occlusions.The velocity of each prosthesis DoF was set proportional to the speed at which the subject moved the lower arm (to which the prosthesis was attached) toward the target object, thereby mimicking the smoothness of natural movements during pre-shaping  to the target object position into three phases : (1) iniPoseFinal is the normalized final DoF angle; Posecurrent is the normalized current DoF angle; Disttotal and Disttraveled are the total and traveled distances, respectively; and Forearmspeed is the speed at which the prosthesis moved toward the target computed as the derivative of its position.where pre-grasping phase, the autonomous system compensated for the subject's variations in the limb position to maintain the desired grasping configuration, which minimized the difference between the desired grasping configuration and the actual prosthesis state at any given time, further ensuring a steady pose of the hand with respect to the target object regardless of the forearm movements. The orientation (represented in quaternion) of the prosthesis (QProsth) (Equation 2) was computed using the orientation of the forearm (Qforearm) and the current state of the prosthesis wrist DoF according to the coordinate system of the prosthesis socket (QProsthwrist).Finally, in the QProsth) was then compared to the desired grasping configuration (QDesiredGraspConf) to determine the difference in orientation that had to be compensated (QCompensation) (Equation 3).The current orientation of the prosthesis ((QCompensation) was transformed into a three-angle representation, and the autonomous control module activated each DoF accordingly. The actuation speed in this phase was set to the maximal speed (78°/s in rotation and 90°/s in flexion). The prosthesis DoFs were driven at their maximum speed to ensure that the required orientation was attained at the earliest to ensure that the prosthesis was prepared for the imminent grasping action. The movements in this phase were typically triggered if the automatic controller did not have enough time to fully complete the preshape during the preceding flight phase and/or if the subject performed unexpected movements .Then, the compensated orientation simultaneous SA, sequential SA, and continuous SA.Three different shared control schemes were implemented : simultasimultaneous SA scheme, the autonomous system complemented the subject's actions only while the user actively controlled the prosthesis using a volitional control interface. Whenever a DoF was controlled manually, the autonomous system would control the remaining DoFs. For example, while the user controls the prosthesis aperture, the autonomous module adjusts its wrists.In the sequential SA scheme, the autonomous system controlled the prosthesis only while there was no volitional input. Unlike the previous approach, the autonomous module was deactivated when the subject controlled the prosthesis aperture. After the volitional input was stopped, the autonomous module resumed the control of all the prosthesis DoFs. To avoid the oscillation between two control modalities, the autonomous system re-activated 1.5 s after the last volitional input.In the continuous SA scheme, the autonomous system continuously controlled all DoFs, but relinquished the DoF actuated volitionally by the subject. Therefore, if the subject controlled the flexion/extension, the autonomous module drove the rotation. When the subject stopped generating volitional commands, the prosthesis wrist was fully controlled by the autonomous module. As in the sequential scheme, the control was switched to autonomous after a delay of 1.5 s.In the The experiments were performed in 10 able-bodied subjects , within the age group of 24–55 years, all right-handed. All subjects signed a written consent form approved by the ethical committee of the University Medical Center Göttingen (22/04/16).The experiment involves reach-and-grasp tasks wherein the subject received specific instructions regarding the techniques by which an object can be grasped using the left-hand prosthesis while reacting to acoustic stimuli .The experimental setup from the subject's perspective is depicted in During the execution of the reach-to-grasp task, a pseudo-randomly occurring acoustic stimulus (a continuous monotonous beep of 400 Hz) was played through the computer speakers. Each stimulus was played continuously until the participant reacted to it by pressing a button that was fixed on the right side of the table with her right hand, that is, the hand that was not used for prosthesis control. After the participant had reacted to the auditory stimulus, a new cue appeared after a time interval that was randomly selected to last between 0.8 and 1.6 s. The audio cues continued to (re)play until the user grasped the target object.desired grasping configuration during the reach-to-grasp task.The experiment started by calibrating the SA system to match the individual preferences of each subject, including (1) the responsiveness of each DoF of the prosthesis to volitional input and (2) the prosthesis To calibrate volitional control, the participant was asked to trigger each movement using maximal contraction (MVC), followed by a light contraction (activation threshold). The myoelectric signal range between the measured activation threshold and 80% MVC for the given movement was then mapped to the full range of prosthesis velocity for that DoF. Finally, the subjects were asked to perform 20 grasps using the ideal volitional interface to familiarize themselves with it. During these interactions, minor adjustments were performed in control responsiveness as per their requirements.desired grasping configuration for the given object and grasping side. This was repeated for all combinations of target objects and grasping sides, and a lookup table of the desired grasping configurations was created. The autonomous module used the lookup table to set the final flexion/extension and rotation .The subject was asked to control the prosthesis using a volitional interface to grasp both target objects placed on one of the marked positions for every grasping side  to adjust the desired grasping configuration. The states of the prosthesis DoFs (flexion/extension and rotation) and the orientation of the subject's forearm at the moment of contact were recorded as the simultaneous SA, sequential SA, and continuous SA). Each scheme was employed under three conditions, in which the autonomous controller navigated the hand to the desired configuration with no error (SA baseline) and with moderate and large systematic errors, respectively. The systematic error was implemented as a constant offset added to the decisions of the autonomous controller, 30° for moderate and 60° for large errors. Additionally, the subjects performed the experimental task using the ideal volitional control , resulting in 10 conditions.After calibration, the subject started the experimental task . Before commencing each trial, the two DoFs of the prosthetic wrist were rotated 80° from the final position in a random direction . Such random alterations in the initial prosthesis' orientation were used as a pragmatic solution to enforce a multitude of different pathways that the prosthesis had to transverse in order to reach its final orientation, without having to resort to the (tedious) reorientation of the target object itself. Indeed, the combination of four possible object grasp-directions  and four possible perturbations in the prosthesis' initial orientation  yielded 16 unique combinations in the orientation of the prosthesis at the beginning and end of the trial . The subjects performed reach-to-grasp tasks using three SA control schemes . The subjects performed the blocks in a random order. The shared control modality was announced to the subject each time it changed. A break of 10–15 min was made in every six blocks; hence, there were three breaks in total.The following outcome measures were employed: (1) the duration of the trial as a measure of performance, (2) the usage of volitional control quantifying the “amount” of a subject's intervention during the task , and (3) the user's reaction time to auditory cues as a measure of their cognitive load. Trial duration was defined as the duration between the start signal and the moment of contact with the target object (grasp onset). Volitional control usage was calculated as the accumulated time of volitional control throughout the trial. Finally, the reaction time was defined as the period from an acoustic stimulus until the subject pressed the button.p-values were corrected using the Bonferroni-Holm correction. The results in the text are reported as median (interquartile range).The first two trials of each block were regarded as an adaptation to a new condition, and these were discarded from the analysis. Therefore, out of 240 performed trials, 200 were used for the data analysis. For each condition, the average values of the outcome variables were computed for each participant. Because none of the outcome variables passed the Kolmogorov-Smirnov test, the difference between the conditions was assessed at first using the Friedman test, and thereafter, in the case of a positive result, using the Wilcoxon signed-rank test for realizing the pairwise comparison. To account for a large number of statistical tests, the During the experiment, volitional and autonomous controllers shared the control of the prosthesis. The pose of the different DoFs of the prosthesis during three trials  is illustrated in sequential SA compared to simultaneous SA modality. The addition of systematic errors impacted all three modalities by increasing the task completion time, but the increase was not equal across the modalities. Moreover, the difference in performance between the modalities became more pronounced as the error increased. The increase in task completion time [median (interquartile range)] was significantly higher (p < 0.05) for simultaneous SA [+6.3 (6.3) s] and continuous SA [+9.1 (3.9) s] compared to the sequential SA modality [+3.1 (1.5) s]. Overall, the sequential control was faster than simultaneous SA and continuous SA modalities in both moderate and large error conditions. This increase in the time required to complete the task when using SA control corrupted by the systematic error also impacted the comparison with the manual control baseline. In the moderate error condition, the simultaneous modality demonstrated worse time performance than the manual baseline, while in the large error condition, all modalities were slower than the manual baseline.Subjects grasped the cylinder and the box object equal number of times  and the grasp distribution of the four grasping directions was uniform - 50 grasps were performed from the left, right, top, and front sides of the two objects. On average, per condition, there were 9.9 unique combinations in the orientation of the prosthesis at the beginning and end of the trial . A summary of the results for all outcome measures across different conditions is depicted in simultaneous SA and continuous SA modalities became similar in the moderate error condition and eventually longer in the large error condition compared to the manual baseline. However, the sequential modality resulted in a reduced volitional control usage with respect to the baseline in both no error and moderate error conditions and a similar duration in large error conditions. When comparing the SA modalities to each other, the simultaneous SA modality required more volitional control in no-error and moderate error conditions than the two other modalities, which were similar to each other. In large error conditions, sequential SA modality required less volitional control compared to the other two similar modalities. From moderate to large error conditions, the use of volitional control increased significantly more (p < 0.05) for the continuous SA [+3.4 (2.0) s] than for the sequential SA modality [+0.7 (0.9) s].Volitional control usage exhibited similar trends compared to those observed for the trial duration . Comparecontinuous SA modality seemed to be the most sensitive as the reaction time was different for each combination of error conditions. For the sequential SA scheme, the time increased in large and medium error conditions with respect to no-error, whereas for the simultaneous SA approach, only the large error condition increased with respect to time. Across the modalities, the only difference was registered for the large error condition, where sequential SA control resulted in the smallest time, similar to that achieved in the manual baseline. The reaction time in the other two modalities were higher than those in the manual baseline.The subject's reaction time  was simiWe investigated three different schemes for the integration of volitional and autonomous control in a SA prosthesis. To avoid confounding factors related to the performance of specific implementations, an experimental assessment was conducted using “ideal” solutions. The results, collected from 10 subjects, demonstrated clear differences between the SA modalities vs. the manual baseline  and across the SA modalities in performance, volitional control usage, and reaction time. The differences were exacerbated when systematic errors were introduced in the autonomous control.desired hand configuration). In this case, the SA modalities were consistently faster than the pure volitional control . Several studies have demonstrated that SA systems can outperform conventional EMG-based controllers . Notably, the volitional controller in the present study was implemented as an “ideal” class selector; it is still to be investigated how SA control would compare to an “ideal” regression-based approach, which could be realized using a joystick type interface providing simultaneous control of multiple DoFs.In the no-error condition, the subject and the autonomous agent had similar goals  to trigger the autonomous controller to adjust the wrist DoFs. Conversely, in the other two SA modalities, the autonomous control was active without triggering, and the subject only had to bring the prosthesis into a graspable position and then close the fingers. Therefore, the longer task completion time suggests that the SA scheme with triggering  negatively affects not only the volitional control usage but also the time performance. While triggering was used as a predominant approach in the studies that implemented SA prostheses, some recent approaches eliminated this step  condition. In this case, the desired hand configuration) of the user and the autonomous agent , the results indicate that the design of the shared control modality can have a critical impact on the task performance as well as the physical and cognitive workload. This situation can arise because of erroneous decisions of the autonomous system .Nevertheless, the results also reveal that some SA modalities are inherently more robust in these situations. The In shared control, conflicts between the human and the robot should be minimized, by modeling robot actions based on human behavior; and in case of conflicts, the robot should ensure that the human has the time and ability to influence the robot's actions.” The current results demonstrated that these points impact the performance, physical, and cognitive workload and need to be addressed while designing a collaborative control , the performance of the SA modalities decreased substantially, even below the manual baseline. However, the most robust approach was the sequential scheme wherein the subject control completely disabled the autonomous controller. This implies that such a scheme is likely the method of choice while implementing upper-limb prostheses equipped with SA control.This study investigated the impact of shared control modalities on performance and workload while using a SA prosthesis. The results indicate that all SA modalities outperformed pure volitional control under ideal conditions. However, The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethical Committee of the University Medical Center Göttingen. The patients/participants provided their written informed consent to participate in this study.JM, MABC and MM conceptualized the study. JM and MABC developed the system and wrote the manuscript draft. JM conducted the experiment and performed the data analysis. SD, AS, and MM contributed to the study design and revised the manuscript. All authors approved the submitted version.This work was supported by the German Ministry for Education and Research (BMBF) under the project INOPRO (16SV7657) and in part by the Independent Research Fund Denmark .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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, as high levels of inbreeding could lead into whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies but discovering highly variable regions across species’ genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify, and design PCR primers for amplification of SSR loci from a multi-individual microsatellite library. By combining commonly used programs for data cleaning and microsatellite mining, this pipeline easily generates, from a fastq file produced by high-throughput sequencing, standard information about the selected microsatellite loci, including the number of alleles in the population subset, and the melting temperature and respective PCR product of each primer set. Additionally, potential polymorphic loci can be identified based on the allele ranges observed in the population, to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers significantly reduces processing time in comparison to manual analysis while keeping the same quality of the results. The elapsed times at each step can be longer depending on the number of sequences to analyze and, if not assisted, the selection of polymorphic loci from multiple individuals can represent a major bottleneck in population studies. While traditional studies covered a gene region and/or a pathway with limited number of genes, next generation sequencing (NGS) has pushed the trend towards whole-genome analysis and population genetics, where the genome of several individuals of a species can be characterized at the same time2. In this field, molecular techniques such as genotyping by sequencing (GBS) and marker-assisted selection (MAS) have gained prominence for not requiring a reference genome4 but also due to the possibility of characterizing a whole species at a lower cost, providing comprehensive information for both evolution studies and conservation efforts6.At the Omics’ era, the cost of sequencing and time required for getting useful information from different organisms, even uncultured, has been drastically reduced with the advances in technology8. SSRs are repeated DNA motifs that occur in non-coding regions, evenly distributed throughout the genome. They are excellent markers for genotype identification, genetic diversity, and genetic-phenotype mapping, at both species and population levels, due to their high levels of polymorphism10. Traditional methods normally use a single individual per species for microsatellite library development. The number of microsatellite loci genotyped afterwards needs to be limited as a balance between the cost associated with microsatellite design and optimization, since the microsatellite found for a single individual may not be polymorphic in the population so the efforts in sequencing afterwards could be in vain. In this work, we aimed to design and implement an automated pipeline for screening polymorphic SSRs using as input raw reads from a multi-individual sample. The tool can detect the variation in SSRs loci present in the population and design optimal primers per SSR marker. Highly polymorphic markers can then be used to genotype other individuals from the same species.Genetic polymorphisms, such as single nucleotide polymorphisms (SNPs) or simple sequence repeats (SSRs), also known as microsatellites, have served the field of population genetics20. Since most of the pipelines aim is not to find polymorphic SSRs, they do not make use of enriched libraries although they could perfectly process this type of data since the difference lies only in the proportion of sequences with repetitive patterns versus the total number of sequences. More recently, the MiMi11 pipeline has been developed to work directly from raw sequencing data of multiple individuals and, based on microsatellite prediction and calculation of allele frequencies, it can identify good priming regions. However, this pipeline requires quite a bit of data manipulation by the user, as quality trimming and microsatellite screening need to be performed externally  before importing the results into the MiMi pipeline for microsatellite selection and primer design. Moreover, to the best of our knowledge, none of these tools takes into consideration several important parameters in SSR identification and primer design from a singleplexed multi-individual library. The possibility of developing SSR loci from multiple samples in a single reaction is particularly desirable, as it reduces the overall costs of microsatellite development per species. Another concern about the existing tools is their dependence on external software and the need for the user to install all dependencies manually. A comprehensive description of the existing programs can be found in Table Currently, there are a limited number of programs that can detect microsatellites directly from sequencing output, or that incorporates data cleaning and primer-designing processes in their workflows Table . To perfhttps://github.com/FilAlves/micro-primers).To tackle such limitations, we developed Micro-Primers, a tool that integrates a set of external programs into an automated pipeline, thereby allowing the perfect communication between programs through conditional formatting of input/output and reducing end-user data manipulation. Micro-Primers presents both a simple and intuitive GUI, with the basic parameters to be set and run in just a few clicks, and a command-line mode, where the user can manage the whole set of parameters or even switch on/off steps they do not want to be applied to their data. Overall, Micro-Primers represents a unique and easy framework for microsatellite development from multi-individual libraries  in three pipelines from Table 21 for each SSR recovered from the multi-individual sample. From left to right, the first column, in red, is the unique name of each cluster (ID) composed by a prefix (set by the user), the number of the loci and the primer pair number. Lines sharing the same loci number represent different primer pairs for the same SSR loci. The second column has the length (size) of the sequence resultant from PCR amplification using the respective primer pairs. The third and fourth columns are the forward primer sequence and its melting temperature. The fifth and sixth columns refer to the equivalent information for the reverse primer. In the seventh column, the specific motif found is shown with the number of repeats present in the representative sequence. The column identified as ‘Range’ shows the length span of the alleles detected for the same SSR. The nineth column contains the total number of alleles for the specific SSR loci. The tenth column indicates the potential number of alleles to be found in the population estimated from the difference between the longest and shortest alleles found. The eleventh column indicates the best combination of primer pairs for each loci (coded as “|BEST|”) as provided by Primer3 and the last column contain the representative sequence for the SSR loci from which the primers where designed.The execution of Micro-Primers pipeline produces a single output file in plain text with useful information for the amplification of the SSR loci based on its representative sequence. Figure 22 and sequenced on an Illumina MiSeq v2 kit .The Micro-Primers pipeline was tested with a bat dataset from two different populations, Namibia and Botswana, with a total of 15 and 21 individuals respectively . Samples were pooled, enriched for di- and tetra-repeat motifs separately, following a protocol modified from Garrett et al.The analysis was performed in a single core of an Intel i7 Octa-Core processor and 64 Gbytes of main memory and took 2 min for Micro-Primers to finish. It should be noted that the only processing step demanding high RAM memory is data trimming, carried out by the component; otherwise, minimal resources are needed. In addition, four different parameters configurations were tested to check the performance of the pipeline and evaluate the differences in the number of microsatellite loci detected. The maximum expected number of alleles to be found in each locus is 72 since the species is diploid. The pipeline’s execution was modified by changing the parameters at the GUI settings tab or modifying the Primer3 settings file, and the number of sequences remaining after each step is presented in Table As observed in Table At the end of the pipeline, changes in the maximum difference of melting temperature between primers in Primer3 (MAX DIFF TM) induces variation in the number of primer pairs designed, as expected. Higher values in this parameter increase the capacity of Primer3 to find primer pairs in a sequence but, contrary to expectations, they may not necessarily be the most suitable and therefore fewer primers are selected at the end.Four random SSR loci out of the 23 found by Micro-Primers using the default configuration were validated by searching for each of the predicted alleles. Validation consists of retrieving from the original raw data (before any processing) the sequence including the repeated pattern to count the different alleles. The selected loci were Bats1, Bats8, Bats13 and Bats20 with 19, 10, 7 and 5 alleles, respectively. Alignments of the repeated region for the 4 SSR loci are shown in Fig. 23 in fasta format. Our efforts in comparing pipelines were hampered by data incompatibility:Micro-Primers performance was compared with three of the pipelines shown in Table The SSREnricher was not able to process the bat data despite the input file was in fasta format, demanding the input to come from Trinity software output. Trinity is an assembler, and the single action an assembler can conduct is to create consensus sequences, thereby reducing the number of microsatellite alleles. Since our sequence data was originated from enzyme digestion, independent fragments could not be assembled with other fragments.The GMATA was executed with the clean joined pairs in a fasta format. The program run and managed to find microsatellites but the primer design could not be performed, and thus no plots were generated. Moreover, despite what is indicated in the program description, there is no function to perform the analysis of polymorphism. The pipeline originated a file with individual SSR prediction for each sequence and another with the overall statistics.The MiMi was the only software that was able to run with the bat dataset . The pre-processing of the data prior to MiMi execution  on the Galaxy server took more than 5 h and required a great amount of data manipulation by the user to upload the data, run each of the programs and download the results. Once the appropriate input was obtained, the MiMi program was able to process data in few seconds. It detected five microsatellite loci but all with low quality, thereby originating no results at the end. The five SSR loci detected were not comparable with the Micro-Primers results.24, and the manual selection of candidate loci for primer design in Primer3. The use of multi-individual assays reduces laboratory and sequencing costs, but it hampers data processing since results need to be compared to extract useful information and avoid replicated information across the samples, which likely explains why this procedure has not been fully embraced by researchers yet. Most selected SSR loci and subsequent PCR amplifications would likely be mediocre if manual SSR selection is applied, but also if clustering of flanking regions and the number of putative alleles is not considered, thereby generating markers with low variability across populations. The preparation of a microsatellite library from a single individual is easier and cheaper than from a multi-individual sample, particularly if using an enrichment step, and the posterior analysis deserves less analytical effort. However, processing a single individual hardly results on the detection of highly polymorphic loci in comparison to a multi-individual library, although the latter requires a more complex data handling.Nowadays, it can take up months for laboratories and researchers to analyze and select SSR loci and design primers for their projects. Usually, it requires the analysis of pre-processed sequencing data from individual samples in programs such as MISAThese technical constraints could be easily overcome by the implementation of an automatic tool, allowing users to test distinct parameters and select the most suitable SSRs for their experiment in a few seconds. Towards this end, we have designed and implemented an automatic pipeline for microsatellite screening and primer design from multi-individual sequencing data, Micro-Primers, which can be applied on sequencing output from libraries produced by restriction enzyme digestion or from mechanical shearing experiments. There is a great advantage of implementing this tool due to its ability to surpass time-consuming steps such as the editing of input/output formats between successive programs, or the categorization and allele counting for every single SSR. First, all microsatellites individually identified need to be grouped by their ’true’ locus, considering that they are originated from different individuals and can be represented in multiple sequences. After the identification of all copies of the same microsatellite loci, the tool counts for the number of different unique alleles for the subsequent selection. This particular task can be very tedious if performed manually, as some of the locus can be represented by many elements.SPECIAL SEARCH option, which considers the maximum and minimum allele found in the SSR loci, incorporates in the decision-making the assumption about unsampled alleles for the species.Micro-Primers has a default configuration that has demonstrated to achieve a good balance between the number of loci and primer designed, however users should modify these parameters to adequate the results to their requirements. For example, the inclusion of the 26 software, the creation of clusters based on the flanking regions can be arbitrary to some extent, due to the occurrence of amplification and sequencing errors that were not discarded at the first steps. Because of it, the group of sequences composing each cluster will depend on the seed sequence selected to originate each cluster; (c) the selection of the representative cluster for primer design is arbitrary. A cluster can be discarded whenever the representative does not produce suitable primer pairs in Primer3 , however its effect was found to be marginal; and (d) Micro-Primers uses the starting position of the SSR loci provided by MISA to design primers in Primer3. However, in some situations, this position is incorrectly assigned , and thus unsuitable primer sets can be generated. We recommend users to always validate the primer sets given using the microsatellite sequence provided, or, whenever needed, to manually redesign primers by finding suitable binding positions in the sequence . Yet, only 2 out of the 23 results in the default configuration were affected by the creation of primers inside the repeated area itself. This can be avoid activating the option prior to run to eliminate results with any primer inside the SSR pattern but we would be losing good SSR loci that can be just solved re-designing a new primer pair. In case of libraries produced by restriction enzyme, like the one used in the experiment, the possibility of having errors of increasing or reducing the number of repeats at the merge step by FLASH is controlled by the selection of a proper restriction enzyme with a non-repeated pattern, so the SSR repeat will never be in the ends of the pair reads.It is recommended that users scrutinize the final output of Micro-Primers prior to laboratorial processing. Despite the appropriate settings imposed, we observed various sources of variation associated with the different programs used, occasionally producing divergent or imprecise results. The observed bias on the final output is not related with the performance of Micro-Primers as it may also occur during manual processing. Specifically, (a) since most SSR sequences occur on the overlapping region, the software FLASH can produce some length variation while merging the paired-end reads by not recognizing the accurate position of the repeated pattern where sequences should be merged, thus creating ‘fake’ alleles; (b) based on the CD-HITThe use of Micro-Primers provides an unprecedented availability of candidate SSRs at a more reliable and faster pace than before, and retrieves relevant SSR information from multi-individual libraries not considered in other pipelines. The development of automatic pipelines is highly relevant for the scientific community since it can speed up the process and overcome the biases associated with the manual processing, while allowing the user to test various parameter choices by automatically running the process several times on the same dataset.We presented a novel, integrated and user-friendly pipeline for microsatellite searching and primer design from multi-individual sequencing data named Micro-Primers. Experimental results showed that Micro-Primers takes about 2 min to execute an entire analysis (300 k reads), from raw data to primer design, using just a single core of an Intel i7 Octa-Core processor with 64 Gbytes of main memory. The accuracy of Micro-Primers was tested identifying each of the predicted alleles for 4 SSR loci in the raw data prior to any processing.To the best of our knowledge, this is the first pipeline to completely automatize the process for multi-individual libraries, allowing any researcher to make a fast characterization of a species prior to any extensive genotyping. It can be used through a GUI or command-line and, since it is implemented in Python, it is portable enough to be used in any kind of operating system, such as Windows, Linux or MacOs. Due to its simple design, it can be easily implemented in any type of computational language and library, or even incorporated in more complex software systems.The main motivation of Micro-Primers is to eliminate issues regarding computational time and work, by performing an automated selection of candidate microsatellite loci and PCR primers. As such, several tools and scripts were integrated within Micro-Primers for discovering SSRs and designing the respective primers for further in vitro amplification. Micro-Primers has been installed and tested in Linux and MacOS. The installation can be done by cloning the software directly from Github or through conda installation where all the software versions and dependencies are installed automatically. Instructions are available at Micro-Primers Github page.The Micro-Primers pipeline was written in Python version 3.6. The two main internal components were implemented using the following scripts: (1) install.py that includes all necessary pre-requisites for a proper installation of Micro-Primers, and (2) micro-primers.py, which is the main script that defined the pipeline. Analysis settings are described in the config.txt file, and parameters can be modified by the user accordingly to their own needs. The folder software, provided together with the Python scripts, holds all the scripts and external software employed by micro-primers.py.27 for the removal of the sequencing adapters; (2) Cutadapt28 for the removal of the technology-specific adapter; (3) FLASH for the merging of paired-end reads (R1 and R2); (4) MISA for the SSR searching; (5) CD-HIT for the removal of redundancy; (6) Primer3 for primer design.The Micro-Primers pipeline integrates several external components, such as: (1) Trimmomatic22 or just from mechanical fragmentation methods. The enrichment protocol must be performed after digestion, so the target SSR motifs are the most represented strands in the final library. A fragment size selection is then performed on the enriched library to keep only fragments of an average length lower than the maximum sequencing length, to allow both paired-ends reads overlap when merged later on. The final fragment size is important for microsatellite screening and must comprise the full SSR pattern (variable in length) and the two flanking regions with fair length for primer design.To run Micro-Primers, users only need to provide two FASTQ files corresponding to both ends of a paired-end sequencing run. The program can run as well in case of single-end sequencing by introducing the same file as forward and reverse since in the merge step they will be reduced to the original one. Samples should come from a library of pooled (untagged) individuals of the same species, so that the microsatellite selection can be optimized. SSR selection will be performed based on the number of alleles of each SSR loci, so the more heterogeneous the sample is , the better the outcome is expected to be. Reads can come from a microsatellite library built using a restriction enzyme and following an enrichment protocol such as the one described in-help” to the micro-primers.py script. Upon execution, Micro-Primers will follow the flowchart described in Fig. Additionally, prior to the execution of Micro-Primers, all the external components must be installed through the script install.py (only the first time). Once everything is set Micro-Primers can be launched executing the script micro-primers.py and the GUI will appear with the available options. For a complete list of parameters and to enter in command-line mode the user only needs to add the option “Sequences belonging to the same cluster are sorted and number of different alleles in the cluster are computed. Only clusters  with a minimal number of alleles (set by the user) are chosen, from which a random sequence among variants is selected as the representative of each locus. Every representative is then parsed into primer3 and, accordingly to the primer’s specifications set by the user, an output file will be generated that includes both primer information and number of alleles per SSR locus see Fig. .As described previously, the basic parameters required by Micro-Primers must be set in the different tabs of the program interface. In the main screen the input pair-end files and a prefix for the results should be indicated. The “Settings” button contains three sections with different parameters to be considered for the pipeline execution:Section 1: Pre-processing: the user must indicate the sequence of the adapters used after the restriction enzyme action and the pattern remaining in the sequence after the digestion. If the data was not produced by restriction enzyme this option can be disabled at this section as well.Section 2: Primers: here one or more SSR types can be excluded from the available options: c (compound), c* (compound with imperfection) and p1 to p6 (repeated motif of 1–6 nucleotides). A tuned Primer3 setting file can be selected in case the default one included in Micro-Primers is not convenient.Minimum number of alleles in cluster” which indicates the minimum number of alleles for a SSR locus to be selected and it is based on the observed alleles . In opposition, the parameter “Minimum distance between alleles” indicates the minimum potential number of alleles desired for each locus, assuming that not all alleles are represented in the multi-individual sample. Considering the difference between the alleles with higher and lower number of repeats, only loci that satisfy the minimum number of alleles indicated in this parameter are kept. This parameter is used only when the option “Special Search” is enabled (ON). Other parameters in this section are “Minimum flank region length” which will discard every sequence with at least one flanking region shorter than the indicated, and “Minimum motif repetition” that sets the minimum number of repetitions in a SSR to continue in the pipeline.Section 3: Alleles: this section has important parameters to filter the SSR results produced by MISA software. Here we can find the “Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


The aim of this study is to (1) evaluate the usage of orthobiologics in the Netherlands, (2) to clarify the reasons for the use or non-use of orthobiologics, and (3) the most addressed disease for use of orthobiologics.The authors created a 19-quenstion online survey comprised of both closed-ended and open-ended response questions in order to examine the use of and the indication for orthobiologics. The survey was sent to all the members of the Dutch Orthopaedic Association and Netherlands Association of Sports Medicine of which 15% responded.The majority of the 265 respondents (65%) did not treat patients with or refer patients for treatment with orthobiologics. The most important reasons for not using orthobiologics were the lack of scientific evidence, the lack of good experience, and the lack of insurance coverage. Of the physicians that used orthobiologics, the most used hyaluronic acid (76%) and platelet-rich plasma (27%). Orthobiologics were most used for knee osteoarthritis and medial or lateral epicondylitis.Although some orthobiologic treatments might be effective and the research interest is growing, our study shows that the majority of orthopaedic and sport physician clinicians in the Netherlands still does not make use of orthobiologics as a treatment option, but almost a third of them is contemplating to start using orthobiologics.III.The online version contains supplementary material available at 10.1186/s40634-021-00380-9. The growing interest is also shown by the recent orthobiologics Initiative (ORBIT) of the ESSKA . Orthobiologics are biologically derived materials which aim to promote regeneration of tissues that are the focus of orthopaedic surgery. These biomaterials are naturally present in the human body. Urist et al. was one of the first to describe such a substance over 50 years ago, named bone morphogenic protein (BMP), which would assist bone regeneration . There aAlthough some basic science studies showed promising results of orthobiologics in tissue repair and regeneration and some studies claimed it might inhibit the progression of osteoarthritis, there is no clinical evidence to support this theory . The curFor MSC the rationale is that it has immunomodulatory properties, which modulates inflammatory responses and prevent immune responses mediated disease . MSC canIn the recently published Osteoarthritis Research Society International (OARSI) guidelines the use of intra-articular MSC and PRP were discommended, and intra-articular HA was conditionally recommended in individuals with knee OA, but is not recommended for individuals with hip or polyarticular OA . This waIn the light of a worldwide growing interest and usage of orthobiologics the aim of this study is to (1) evaluate the usage of orthobiologics in the Netherlands, (2) to clarify the reasons for the use or non-use of orthobiologics, and (3) the indications of use for orthobiologics.Between February 2020 and July 2020, an online survey was conducted among orthopaedic surgeons, orthopaedic surgery residents, sport physicians, and sport physician residents in the Netherlands. The design and report of our study followed the guidelines for reporting medical surveys outlined by Artino et al.  In May 2n = 1828).Sample size was calculated using the Raosoft® software at the developer’s website. The required sample size was estimated at 90% confidence level with an estimated 50% response distribution and a margin of error of ±5%. The required minimum sample size was determined to be 236 [The authors created a 19-quenstion Dutch survey, comprised of both closed-ended and open-ended response questions in order to examine the use of and the indication for orthobiologics. The respondents were asked to fill out two general questions: the first to identify their profession  and the second to identify the frequency of cases where they treated a patient with or referred a patient for treatment with orthobiologics . If the respondents never treated with or referred a patient for treatment with orthobiologics three questions followed: the first to identify the reasons for not using orthobiologics, the second to see if there were contemplating to start using orthobiologics, and the last open-ended question to give the opportunity for remarks or suggestions.The respondents that use orthobiologics as a treatment answered 14 closed-ended and open-ended response questions to clarify to following study topics: the type of orthobiologics they used ; the frequency and dosage of use; the indications for use; and the physician’s satisfaction regarding the treatment.P values of < 0.05 to be significant.Frequencies were calculated for each closed-ended question on the survey. The chi-square test was used to explore potential associations between selected categorical variables. Among respondents two sub-analyses were conducted to compare Orthopaedic surgery physicians (surgeons and residents) versus sport physicians, and non-residents versus residents. We used STATA 13.0  to perform our statistical analyses, and considered n = 8) or had no clinical activities .Our survey was sent to all 1457 members of the NOV and 371 members of the VSG. Of all recipients 275 responded to our questionnaire (15%). Of the 275 respondents 198 (72%) were orthopaedic surgeons, 35  were orthopaedic surgery residents, 24  were sport physicians, 2  were sport physicians in training, and 6 had other medical professions . Ten respondents  were dropped from the further analysis because they were no longer practicing orthopaedic surgery (n = 93) used or referred patients for treatment with orthobiologics or HA. There was no statistical difference in use or non-use between Orthopaedic surgery physicians versus sport physicians  or non-residents versus residents .Of the remaining 265 respondents, 35,1% , whereas sport physicians more often used or referred patients for treatment with ACS . For the other treatments there were no statistical differences. Between non-residents versus residents, there was no statistical difference in use of the different discussed treatments.Of the different treatments HA is used the most . Orthopaedic physicians more often used or referred patients for treatment with HA than sport physicians (n = 172) were the lack of scientific evidence , the lack of good experience , and the lack of insurance coverage .The most important reason of physicians for not using orthobiologics or HA  not using or referring patient for treatment with orthobiologics or HA, is contemplating to start using orthobiologics.The main finding of this study was the majority of the 265 respondents  among Dutch orthopaedic surgeons and sports physicians does not treat patients with or refer patients for treatment with orthobiologics or HA. The most important reason of physicians for never using orthobiologics are the lack of scientific evidence , and the lack of good experience . The relatively low percentage of use might fit with the inconclusive literature. The most used treatments were HA and PRP  and the most frequent mentioned indications for the use were osteoarthritis of the knee , a jumpers knee , and lateral and medial epicondylitis .In line with our findings the literature shows that HA is one of the most used orthobiologics used for OA of the knee . The ratPRP is produced by centrifugation (“spinning down”) of a small sample of the patient’s blood to isolate and concentrate the platelets. Both platelets and plasma (the fluid portion of blood) contain a number of proteins that can potentially decrease inflammation, improve pain, and aid in tissue healing. PRP has been used for many soft tissue injuries, including ligament, tendon, meniscus, cartilage, and muscle injuries. The most common indications for use are: knee OA, Jumpers knee and epicondylitis of the elbow . A studyThe clinical studies for treatment with MSCs are scarce and focusses mainly on the treatment of osteochondral defects, OA and tendinopathy. Treatment with MSC holds a great potential because they have the potential to contribute to tissue regeneration directly by differentiation into damaged cell types or indirectly by stimulating angiogenesis, limiting inflammation, and recruiting local tissue-specific progenitors, but there is no high-quality evidence available at this time to support the routine use of stem cell therapy for any musculoskeletal injuries . The appOur study has several limitations. Firstly, a general limitation attributed to this survey research is the possible oversimplification of the studied topic by studying the use of orthobiologics in general. Ideally the survey would have been contracted using Likert scale response options but due to variety of orthobiologic types questioned and for the sake of simplicity and pragmatism multiple choice questions were used. Also, it is hard to meticulously analyse the use of specific types of orthobiologics due to the lack of clear practice guidelines and the difference of concentrations of product. Moreover, due to the potential oversimplification it might have been unclear to the respondents if the survey was focussed solely on injections with orthobiologics or also on the perioperative use. Secondly, the national aspect of this survey to might be of influence. The differences in availability and coverage of these treatments may vary between countries. Thirdly, an important concern in survey research are the problems related to validity and reliability of results. Even when questions are correctly formulated and well-intentioned, there is always a chance of being unclear or misunderstood. The main strength of our study is the fact it is the first study that evaluates the use and reasons for (non-)use of orthobiologics in daily clinical practice in the Netherlands. Fourthly, almost a third of the respondents who use orthobiologics did not provide specific information about the type of orthobiologic they used.Despite the fact that the usage of orthobiologics in the Netherlands is low at this moment, the recent surge in research should be watched closely, because the concept holds a great potential. Future research should strive for standardization of treatment, description of indication (e.g. stage of disease) and focus on the clinical outcomes after use. Another interesting research topic would be to compare the practice variation in use between different countries. This study in combination with the ORBIT (orthobiologics Initiative) of the ESSKA  could be a first step.Although some orthobiologic treatments might be  effective and the research interest is growing, our study shows that the majority of orthopaedic and sport physician clinicians in the Netherlands still does not make use of orthobiologics as a treatment option, but almost a third of them is contemplating to start using orthobiologics.Additional file 1 : Supplementary Table 1 – questions survey.


During the reproduction period, female sea turtles come several times onto the beaches to lay their eggs. Monitoring of the nesting populations is therefore important to estimate the state of a population and its future. However, measuring the clutch size and frequency of sea turtles is tedious work that requires rigorous monitoring of the nesting site throughout the breeding season. In order to support the fieldwork, we propose an automatic method to remotely record the behavior on land of the sea turtles from animal-attached sensors; an accelerometer. The proposed method estimates, with an accuracy of 95%, the behaviors on land of sea turtles and the number of eggs laid. This automatic method should therefore help researchers monitor nesting sea turtle populations and contribute to improving global knowledge on the demographic status of these threatened species.Chelonia mydas) using accelerometers. First, we trained a fully convolutional neural network, the V-net, to automatically identify the six behaviors shown during nesting. With an accuracy of 0.95, the V-net succeeded in detecting the Egg laying process with a precision of 0.97. Then, we estimated the number of laid eggs from the predicted Egg laying sequence and obtained the outputs with a mean relative error of 7% compared to the observed numbers in the field. Based on deployment of non-invasive and miniature loggers, the proposed method should help researchers monitor nesting sea turtle populations. Furthermore, its use can be coupled with the deployment of accelerometers at sea during the intra-nesting period, from which behaviors can also be estimated. The knowledge of the behavior of sea turtle on land and at sea during the entire reproduction period is essential to improve our knowledge of this threatened species.Monitoring reproductive outputs of sea turtles is difficult, as it requires a large number of observers patrolling extended beaches every night throughout the breeding season with the risk of missing nesting individuals. We introduce the first automatic method to remotely record the reproductive outputs of green turtles ( Estimation of parental investment in sea turtles relies primarily on the measurement of reproductive outcomes of females. Without parental care, female sea turtles favor energy investment in pre-ovipositional allocations and lay several nests of 50 to 130 eggs per breeding season depending on the species ,3,4. TheMeasuring the clutch size and frequency of sea turtles is tedious work that requires rigorous monitoring of the nesting sites throughout the breeding season. The most common method is based on capture–mark–recapture design: patrols of at least eight hours are carried out every night to survey the nesting sites and identify every female that comes ashore, using a Personal Integrated Transponder(PIT) tag or an unique numbered flipper tag ,6,7,8. HFew studies have explored the use of new technologies to record reproductive outcomes of nesting sea turtle populations. For example, Blanco et al.  used ultAccelerometer is a low-cost miniature sensor that can provide high-frequency information about the body movements and postures of animals to which it is attached. It measures static and dynamic acceleration and enables researchers to remotely deduce behaviors for animals that are difficult to observe. Over the past few years, there has been an explosion of its use on both terrestrial and marine species , for whiThe aim of this experimental study is to evaluate the extent to which the accelerometer can remotely measure reproductive output of sea turtles. First, we deployed accelerometers on 14 nesting green turtles and visually monitored their behavior simultaneously. Next, we used this dataset to validate the identification of their nesting behavior from acceleration signals and train a powerful supervised learning algorithm to perform it automatically. For this purpose, we tested a fully convolutional neural network that had already proven effective in automatically identifying the underwater behavior of green turtles . Finally®, Douglas, Isle of Man, British Isles), the presence of a Passive Integrated Transponder (PIT) or injected a new one into the right triceps of the unknown turtles. We measured their Curved Carapace Length (CCL) and Curved Carapace Width (CCW) as described in Bonola et al. [The field work was carried out in April 2019 at Awala-Yalimapo beach , French Guiana, South America. We deployed CATS  devices including tri-axial accelerometers on 14 free-ranging adult female green turtles during the nesting process. The acceleration was recorded at a frequency of 20 Hz for the three body axes of the sea turtle . The devices were fixed to the turtle’s carapace by four suction-cups allowing us to rapidly operate with minimum disturbance. It took less than a minute to attach the device. In most case, we spotted the turtle going up the beach and waited for its first sand-sweeping to start see , and dura et al. . In paraThe nesting behaviors of the sea turtle are similar between the species and the different phases and action patterns were precisely described in several ethograms ,27,28,29In order to automatically identify the six nesting behaviors from the accelerometer, we trained a fully convolutional neural network: a V-net. The latter was originally developed by Milletari et al.  for biomOnce the V-net has predicted the six behavioral categories, it became possible to automatically extract the predicted Egg laying stage and to estimate the number of laid eggs. The laying process is associated with a very slight back and forth movement of the sea turtle’s body which can be visualized on the X-axis of the accelerometer. Its visualization synchronized with the observed number of laid eggs in the field suggested that a peak on the X-axis acceleration signal corresponded to a contraction . Thus, tBinarize the behaviors sequence: label “1” is assigned to the behavior Egg laying while all the others are labelled as “0” a;Perform a convolution of the binarized sequence with a Gaussian mask whose standard deviation is empirically chosen. The convolved signal is represented in blue as the ‘Smoothed density’ b;Choose a minimal threshold (threshold = 0.7), and extract the acceleration values associated to the part of the convolved signal which is greater than it b.To automatically extract the accurate Egg laying part from the V-net predictions, we first discarded the false positive identifications, which generally corresponded to very short sequences distributed in the nesting sequence. For this purpose, we performed the next algorithm with each step depicted in At this point, as it has been concluded that X-axis acceleration contained the largest amount of information for estimating the number of eggs laid, the next analysis was only performed on this axis. In order to augment the precision of peak detection, we firstly smoothed the extracted Egg laying signal using a narrow Gaussian mask. Moreover, we observed a decrease of the average values of the signal all over the laying process, with lower peaks at the end, making their identification difficult compared to the higher peaks at the beginning. We corrected this by subtracting from the trend from its signal, estimated by a second-degree polynomial, adjusted by least-squares approximation. The data are also centered with respect to its average values inside the Egg laying category.To estimate the number of peaks over the X-axis, assumed to be related to the number of turtle contractions, we ran over the signal a rolling window with a width approximatively equal to the distance between two picks and detected the local maximum for each window. To avoid detecting the same maximum several times, we kept the value only if it was located in the very middle of the rolling window. Finally, a threshold parameter , we calculated the mean number of eggs laid per contraction per turtle and obtained an average of 1.6 (standard deviation = 0.05). For each turtle, we multiplied the estimated number of contractions by this mean to obtain the estimated number of eggs laid. The mean number of eggs laid per contraction should be reconsidered in a larger population to improve its accuracy.We tested the entire procedure (from the V-net identification to the estimation of number of laid eggs) on the eight green turtles distributed in the training/validation/testing dataset for which the number of laid eggs has been observed.The V-net predicted the six behaviors  with an accuracy of 95%. It correctly identified 97% of the Egg laying dots, corresponding to the highest Recall index , Table 2The correct identification of Egg laying allowed its automatic extraction with sufficient precision to estimate the number of contractions. Thus, from the V-net predictions, we succeeded in estimating the number of eggs with a mean relative error of 7%  with an accuracy of 0.95 and a precise detection of the Egg Laying process . In a second step, we estimate the number of laid eggs from the predicted Egg Laying sequence and find the reproductive outputs with a mean relative error of 7%. The main aim of this method is to support field monitoring of nesting sea turtles by providing a remote method and thus reducing the monitoring effort. In the interests of improving our knowledge of sea turtles, we expect that this method will be a valuable tool for measuring maternal investment in sea turtles and understanding the parameters that influence it.The V-net is a powerful algorithm that successfully identifies the six behaviors of the nesting process of the green turtles from the accelerometer with an accuracy of 0.95. Similarly, Nishizawa et al.  performeThe main confusion from the V-net concerns Covering and Sand-sweeping. The visualisation of the activity budget shows that this misclassification appears between the end of Covering and the beginning of Sand-sweeping. This confusion is mainly due that nesting turtles express rear flipper sweeping movements in the two stages . In factAfter this step, the second challenge was to automatically estimate the number of eggs laid, which would thus make it possible to determine the maternal investment during one nesting season.This study is the first to propose a fully automatic method to remotely estimate the number of laid eggs from a bio-logger. The precise detection of the Egg laying process allowed us to automatically extract the associated acceleration signals and estimate the number of eggs laid. We succeeded in estimating the number of eggs laid with a mean relative error of only 7%. However, it remains difficult to identify the main causes of error considering underestimates of the number of eggs laid for some individuals and overestimates for others . The parhttp://www.technosmart.eu, accessed on 15 February 2022) making this long tracking feasible. Therefore, this long term monitoring of sea turtles from bio-loggers during the whole breeding period would allow researchers to know precisely the clutch frequency, its clutch size and variation during the breeding season for a representative part of a population, and therefore the estimation of their maternal investment, while reducing the patrol time.The main aim of the proposed method is, therefore, to support field nesting sea turtles’ monitoring while reducing the monitoring effort, via the remote monitoring of nesting sea turtles for estimation of maternal investment. In particular in French Guyana, given that we know the average number of spawns per individual per season for green turtles and the average delay between two successive nesting processes , it would become possible to equip several dozen females with bio-loggers at the start of the breeding season and recover them at the estimated end of their nesting season. Therefore, we would go from exhaustive monitoring 7 days a week during 6 months to 30 days of patrols . Although further research is needed to determine the impact of equipment attached to turtles, the miniaturization of the accelerometer allows for miniature loggers  and the consequences on their maternal investment during reproduction periods. This challenge seems very accessible in the near future.