Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots. As production tasks become increasingly complex, dual manipulator arm collaboration can better meet practical production needs. The development of dual manipulator arm motion planning for collaborative tasks has been a topic of significant interest.In the existing methods for dual-arm motion planning, there are learning-based motion planning methods, intelligent optimization algorithm methods, and sample-based methods, among others. The use of reinforcement learning and deep learning in robotic arm path planning is being widely applied. Reinforcement learning ,2 is a mIn terms of planning the initial path of the robotic arm, the rapidly exploring random tree (RRT) algorithm and its variants are capable of searching for efficient paths in high-dimensional spaces and have been widely used in the field of robotic arm path planning . Li Q.  proposedHowever, the paths derived based on sampling algorithms such as RRT classes need to be trajectory optimized. Trajectory optimization is the basis for controlling the movement of the robotic arm, and the quality of the trajectory has an important impact on the completion of the operation. Paths based on sampling planning only have spatial information and cannot be used as input for motion control. Trajectory planning transforms paths into expressions about time while satisfying dynamical properties, such as velocity and acceleration range limits and smoothing. In addition, as a prerequisite for trajectory tracking control, trajectory planning affects the accuracy of robot trajectory tracking control ,24. Cui The following From (1)Dynamic A* evaluation function sampling.Maximum number of fixed nodes by node pruning.In terms of initial path planning, the following enhancements were made to the RRT*Smart algorithm:(2)Path detection and judgment in dynamic regions.Path repair.Goal-guided regrowth.Adaptation to dynamic environments involves:(3)Trajectory optimization using a combination of PSO and quintic non-uniform rational B-splines (NURBSs).Improvements in trajectory planning include:The RRT*Smart  algorithThe sampling mechanism of the RRT*Smart algorithm is suitable for optimizing the original paths but has insufficient exploratory capability. Dynamic A* evaluation function sampling has stronger exploratory capability. A maximum number of fixed nodes can be found by deleting the nodes that do not work for the optimal path in spatial exploration and reducing the nodes of the tree via node pruning, which improves the computational efficiency. Path collision detection in dynamic environments can be increased, and path repair and goal-guided regrowth can quickly generate new valid paths in dynamic environments. With trajectory optimization using a combination of PSO and quintic NURBSs, one can add constraints such as the maximum speed of the robotic arm to make the path smoother and more stable.Compared with RRT*Smart and RRT*, the solutions obtained using RRT*Smart-AD enable robotic arms to track trajectories better, as the trajectories are straight and contain fewer waypoints. Additionally, the speed of generating efficient paths is faster. By treating the moving robotic arm as a dynamic obstacle to updating the effective path, RRT*Smart-AD is more suitable for the coordination of dual robotic arms. Therefore, it provides a more effective motion planning solution compared with RRT*Smart and RRT*. We demonstrate the effectiveness of our algorithm on a simulated dual robotic arm motion planning problem.As shown in Collision detection is a crucial step in path planning within the simulation platform for robotic arms. Traditional approaches often employ bounding box methods  to simplTo enhance the accuracy of collision detection, we utilized the open-source collision detection library called the Flexible Collision Library (FCL) . The proIn the RRT*Smart-AD algorithm, random sampling points are generated in the joint space and transformed into corresponding pose configurations using forward kinematics. By updating the joint poses, collisions between the robot arm and itself, as well as obstacles, can be detected.The RRT algorithm is a path-planning algorithm that rapidly explores the solution space and finds feasible paths through random sampling and tree structure expansion. Initially, the algorithm initializes the starting point as the root node and creates an empty tree. Then, in each iteration, the algorithm generates a random point as the target and finds the nearest node to the target in the tree. It extends a certain distance from the nearest node toward the target, creates a new node, and performs collision detection. If the new node does not collide with obstacles, it is added to the tree and connected to the nearest node. If a new node reaches the target, a path connecting the starting point and the target is found. In this way, the RRT algorithm efficiently explores the solution space and finds feasible paths.RRT* is an improved version of the RRT algorithm that builds upon the original RRT to further enhance the path quality and convergence. RRT* improves the path quality by utilizing an optimal distance metric and selecting optimal connections. It generates excellent path solutions by optimizing and pruning nodes in the tree. By combining node optimization and pruning, RRT* can generate paths that are closer to the optimal solution. In comparison with the traditional RRT, RRT* allows for node rewiring to optimize the path. This means that RRT* can select shorter paths between nodes that do not simply extend from a single node, but rather extend from the optimal point by searching among all the nodes. RRT* offers improvements over RRT in terms of the path quality, convergence, and ability to approximate optimal paths. By optimizing and pruning nodes, employing rewiring strategies, and considering nodes from the entire tree, RRT* can generate paths that are closer to the optimal solution.RRT*Smart is an extension of RRT* that operates similarly to RRT* but with the additional feature of removing redundant nodes from the initial path once it is found. RRT*Smart has demonstrated better path convergence compared with RRT*. Furthermore, it identifies intelligent beacon samples for path improvement. This sampling differs from random sampling, as it is biased toward optimizing the beacon nodes along the path. It utilizes nearby obstacle beacon nodes to set the distance for intelligent exploration around the selected beacon. Once RRT*Smart finds a shorter path, it performs the path optimization process again to generate new beacon nodes. The main functions for describing the representation of robotic arm path planning are as follows:Sample: If an initial valid path is not found, this function generates random joint angle positions Nearest: This function returns the node Steer: This function takes inputs CollisionDetect: This function is used to detect collisions in the branches of the tree, and it returns true if the path Near: This function returns all the nodes in the tree that satisfy the condition of being within a defined neighborhood distance InsertNode: This function adds the node PathOptimization: Input ExtractPath: This function extracts the optimal path from the collection of trees. A pseudo-code describing RRT*Smart is shown in Algorithm 1.Algorithm 1 The RRT*Smart-AD algorithm can be divided into three parts. First, it involves initial cooperative obstacle avoidance path planning. Second, it detects whether the path in the dynamic environment becomes invalid and re-plans an effective path. Third, it utilizes quintic NURBS trajectory optimization. In the first part, the RRT*Smart algorithm is enhanced by incorporating dynamic A* function sampling and pruning the path based on a maximum node limit. This method improves the efficiency of finding the initial path and reduces the algorithm’s runtime. The second and third parts determine the validity of the path in the dynamic region. They generate new effective paths through reconnecting and regrowing, enhancing adaptability to dynamic environments. Lastly, the RRT*Smart-AD utilizes PSO to optimize the discrete point path generated by the RRT*Smart-AD algorithm, resulting in a motion trajectory for the robotic arm.For most variations in the RRT* algorithm, it is not suitable to sample the goal point with a certain probability. This is because after generating the initial path , the closest point in the tree to the sampled point will always be the goal point itself (with a distance of 0). The A* algorithm’s heuristic function can enhance the convergence efficiency of the path. To address this issue and improve sampling thoroughness, the RRT*Smart-AD algorithm combines a dynamic A* evaluation function sampling method.The algorithm generates a sequence of random sampling points and sorts all nodes based on the cost function from smallest to largest. The optimal point in this sequence becomes the starting point for exploration. If collisions prevent expansion, a suboptimal point is selected if the optimal point fails to expand. To prevent falling into local optima, node sparsity  is emploIn these equations, The path search process of the RRT*Smart-AD algorithm can be divided into global path search and local optimization search. Before finding a valid path, it is necessary to find a relatively optimal valid path. In the fusion of the A* evaluation function sampling method, stronger guidance is required, which means a larger value of the dynamic forward coefficient To better demonstrate the efficiency of the sampling methods proposed in this paper, we conducted experimental comparisons of three sampling methods in a two-dimensional setting, as shown in From The RRT*FN algorithm  achievesThis subsection describes methods for determining the detection of path invalidation and for quickly generating a new valid path after the original path has failed.In dual-arm systems, dynamic path-planning methods can coordinate the motion of each robot arm to ensure their collaboration and cooperative work. Among the existing methods for detecting path failure in dynamic path planning algorithms, the RRT*FND algorithm detects whether the entire path is valid and generates new paths in real time. However, real-time collision detection along the entire path can greatly impact the efficiency of reaching the target point, as failure in the latter part of the path does not necessarily affect tracking the unaffected path in the front. Additionally, in scenarios with long paths and numerous obstacles, regrowth is required for almost every tracking step of the path. To address this, a dynamic region path recovery and regrowth method is proposed. It determines whether path recovery and regrowth are necessary within the tracked point’s region. The initially planned path primarily serves as a guiding path and a means to determine whether a valid path can reach the target point. The flowchart for dynamic region path recovery and regrowth within the region is shown in Assume the optimized motion path of the robotic arm for a single joint is The idea of determining path failure in a dynamic region is to avoid tracking the entire path and instead focus on a portion of the path defined by the initial joint path distance. The joint path distance refers to the sum of the angle changes of the robot arm’s joints from the starting point to the endpoint. This method sacrifices the predictive distance to reduce the computational complexity and improve the path execution efficiency. Experimental results showed that this method has better performance in dynamic path planning. As shown in In First, the failure point node of the path is identified to determine whether it meets the conditions for path repair: the number of path repairs and the distance to be repaired are calculated. The path repair condition is met when Step 1: The blue path starts from the blue square on the left and ends at the green square on the right. When a path disruption occurs within the tracking point area due to an obstacle (yellow circle), the disrupted path is shown in red. The invalid nodes that intersect with the obstacle are disconnected from the other valid nodes, and these invalid nodes are discarded.Step 2: Two failure points in the invalid path are selected, the distance between them is calculated, and a path repair is performed, resulting in the green path segment.Step 3: The process continues by searching for path breakpoints and repeating step 2 until the path is repaired. This repair process may introduce redundant nodes. To eliminate these redundant nodes, a greedy strategy is applied, resulting in the black path. The greedy strategy is applied from the start to the endpoint.In the RRT variation algorithm, Extend-RRT  proposesWhen there are frequent path repair attempts and the repair distance is long, the probability of obtaining an optimal or suboptimal path using path repair methods becomes small. In such cases, the valid nodes in the original tree are retained, and regrowth is performed on the unaffected nodes. The invalid nodes that intersect with obstacles in the disrupted path are disconnected from the other valid nodes and discarded. The failed path still serves as a guide by incorporating the unaffected points from the initially failed path into the target bias for path guidance. The regrowth principle is demonstrated in a two-dimensional visual representation in In When the dual robotic arms collaborate and a collision is predicted after the robot arms have moved to a trajectory point, it is necessary to disconnect the tracked points and their connected child nodes, as well as the invalid nodes that collide with obstacles (such as the moving robotic arm 2). As shown in To ensure that the generated path meets the practical requirements of a robotic arm, a combined approach of the PSO algorithm and quintic NURBS optimization for robotic arm trajectory is proposed. The discrete point path obtained from the RRT*SMART-AD algorithm is smoothed. The method combines quintic NURBS curves and the particle swarm optimization (PSO) algorithm to generate functions for the parameters of the joints, considering the constraints of the robotic arm. This approach aims to obtain a high-order continuous motion trajectory. Additionally, while optimizing the path for smoothness, the algorithm also minimizes the operational distance of the robotic arm.In robotic arm path planning, the quintic B-spline curve has higher-order continuity, i.e., the curvature changes between the nodes on the curve are continuously smooth. The smoothness and continuity of the robotic arm during its motion are very important. Robotic arm path planning usually needs to consider the joint constraints and motion limitations of the robotic arm, and the use of the quintic B-spline curve with higher degrees of freedom can better meet these constraints and limitations. The shape of the quintic B-spline curve can be flexibly controlled by adjusting the positions and weights of the control points. In the robotic arm path planning, the parameters of the control points can be adjusted to achieve the fine-tuning and optimization of the path so that the robotic arm can reach the target position more accurately and smoothly without collision during the movement.In the path planning of a robotic arm, B-spline curves exhibit high-order continuity, meaning that the curvature changes between each node on the curve are continuously smooth. The smoothness and continuity of the robotic arm’s motion are crucial . FurtherThe basis functions in NURBSs  are descIn these equations, The PSO algorithm is a multi-parameter optimization technique based on swarm intelligence. It draws inspiration from artificial life and evolutionary computation theories. The PSO algorithm simulates the migration and learning process of particles in the search space to find the optimal combination of parameters. During the iteration process of PSO, the particle swarm gradually moves closer to the target area, searching for the optimal solution through information exchange and collaborative behavior. Each particle optimizes its search by tracking the best solution it has found so far and the best solution found by the entire swarm. The PSO algorithm is simple to implement and does not rely on the gradient information of the objective function, making it suitable for various types of optimization problems. The optimization approach is as follows:(1) Introducing constraint conditions: In practical applications, a robotic arm needs to satisfy a series of constraint conditions, such as collision avoidance, maximum joint acceleration limits, and maximum velocity limits. By incorporating these constraint conditions into the optimization of quintic NURBSs, the resulting trajectory can better adhere to real-world application requirements.(2) Number of control points: In quintic NURBSs, the more control points used, the more accurately the curve can match the target shape. However, this can also lead to overfitting and increased complexity of the curve. Therefore, a balance needs to be struck in selecting the number of control points.(3) Adjustment of weight values: In quintic NURBSs, each control point contributes to the shape of the curve, and the extent of this influence is determined by the weight value assigned to that control point. Optimizing the weight values ensures a more reasonable and balanced contribution of control points toward the desired trajectory.To determine the appropriate number of control points and weight values that satisfy the external constraints of the robotic arm and obtain an optimal robotic arm trajectory with a minimized distance, the PSO is employed as a multi-objective parameter optimization algorithm. It utilizes the PSO in conjunction with the RRT*Smart-AD algorithm to generate efficient paths (discrete points) and search for the optimal solutions of the two sets of values for the quintic NURBSs, thereby obtaining a distance-minimized path that satisfies the motion constraints of the robotic arm.Due to the limited constraints of maximum velocity and acceleration in the robotic arm used in this study, only the first-derivative velocity curve and second-derivative acceleration curve of the quintic NURBSs were obtained via differentiation. The set of first-derivative velocity values is denoted as The maximum value The quintic NURBSs curve is employed as a representation of the motion trajectory. It generates high-order continuous curves that comply with the constraints of the robotic arm, making it suitable for arm planning. The PSO algorithm is utilized to optimize the parameter search process, with the aim of finding the optimal combination of parameters and achieving desirable motion effects. By combining these two methods, an effective and continuous fifth-order motion trajectory is obtained, enabling smoother and more stable arm movements.The process of RRT*Smart-AD is outlined in Algorithm 2.Algorithm 2 Path planning algorithms for robotic arms involve sampling in a six-dimensional space of one-dimensional joints. In contrast, two-dimensional simulations involve sampling in two one-dimensional spaces, offering more intuitive results. To fully demonstrate the superiority of the RRT*Smart-AD algorithm, we designed static and dynamic two-dimensional simulations for the robotic arm. These experiments were compared against other excellent improved RRT*-based algorithms.To facilitate analysis and ensure practicality, we controlled other unrelated variables to remain consistent while focusing on the algorithm itself. To mitigate the impact of randomness in the RRT sampling algorithms, we performed 50 experiments and compared and analyzed the results.The computer setup consisted of a 64-bit Windows 10 Professional operating system, an AMD Ryzen 5 2600X six-core processor, a Radeon RX580 graphics card, and a Python programming environment.Setting a complex two-dimensional map (800 × 520) for validating the RRT*Smart-AD algorithm: To verify the performance of the RRT*Smart-AD algorithm and other algorithms in a static obstacle environment, we constructed a complex two-dimensional map with dimensions of 800 × 520. The map is located in the first quadrant, with the start point at  and the goal point at . All algorithms shared the same parameters, with a step size of 8 and a goal bias of 0.2. The path planning results of the Bais RRT*, RRT*FND, RRT*Smart, informed-RRT*, FMT*, and RRT*Smart-AD algorithms are shown in From the comparison in The time recorded in From From To demonstrate the efficiency of the RRT*Smart-AD algorithm, static obstacle avoidance experiments were conducted using robotic arm 2 as a stationary obstacle. The robotic arm’s joint configuration space was considered to be a six-dimensional vector, denoted as For comparison, the RRT*Smart-AD algorithm was compared against the goal-biased RRT* and RRT*Smart algorithm. The initial joint poses and target joint poses for robotic arm 1 were as follows:For all the compared algorithms, the parameters remained consistent. The step size was set to 3°, and the goal bias was set to 0.18. The population size for the PSO optimization was the number of initial path discrete points, and the number of iterations was 30. The joint poses for the stationary robotic arm were as follows:In the planning path process results of the robotic arm shown in In To demonstrate the reliability of this method, a dual-robot collaborative experiment was designed. The initial path was generated using the RRT*Smart-AD algorithm. During the tracking process, a dynamic region-based collision detection method was employed. Specifically, as each robot arm moved to an interpolated point, the model was used to predict and assess whether there would be a collision between the interpolated points of the two robot arms within a certain future time range. If a collision was predicted, the RRT*Smart-AD algorithm was used to repair or regrow the path during the robot arm operation to generate a valid path.The initial joint poses and target joint poses of the main robot arm 1 were as follows:The parameters of all the compared algorithms were kept the same, with a step size of 3° and an objective bias of 0.2, and the PSO algorithm performed the optimization with a population size of the number of discrete path nodes, with a number of iterations of 30, and from the joint position of the robotic arm 2 as follows:During the algorithm planning process, as shown in The experimental data in The proposed RRT*Smart-AD motion planner aims to address the cooperative dynamic motion planning problem of dual manipulator arms. This method was designed for six-DOF dual-arms cooperation in complex scenarios, to obtain cooperative motion trajectories that adhere to joint ranges, differential constraints, and physical collision constraints. The effectiveness of the algorithm was validated through simulation comparisons and real robot experiments. These experiments demonstrated that the robotic arm could effectively navigate static global obstacles and execute dynamic dual-robot cooperative trajectory planning, showcasing a motion process characterized by exceptional smoothness, intelligence, and flexibility.Previous research failed to address the cooperative motion planning of dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. The proposed RRT*Smart-AD motion planner addresses these issues. In the motion planner, to enhance efficiency, a dynamic A* cost function sampling method is introduced in combination with an intelligent beacon sampling method. Compared with traditional methods, like random sampling and goal-biased sampling in RRT, this approach significantly accelerates the discovery of effective paths, thereby improving the exploration speed and efficiency. Additionally, dynamic regional path repair and regrowth strategies are proposed to enhance adaptability in dynamic scenarios. These strategies prevent the discarding of original path nodes and enable the rapid generation of new paths within dynamic environments. PSO optimizes the motion trajectory by optimizing the parameters of the quintuple NURBSs for path-smoothing interpolation to avoid collision problems. The use of higher-order spline functions ensures the continuity of acceleration and bumps during the smoothing process.However, there are certain limitations to this approach. First, in scenarios involving dual manipulators working together, the manipulators are categorized as primary and secondary. Although the obtained results generate optimal trajectories for the primary manipulator, the motion trajectory of the secondary arm is catered to the motion trajectory of the primary arm by sacrificing the optimality of the secondary arm. In future research, it would be worthwhile to explore strategies for achieving equal optimization of both manipulators, such as equipartition and mutual avoidance in dual manipulator collaboration. Second, the use of maximum curvature could be further investigated in a wider range of trajectory optimization applications to ensure the generality of PSO in conjunction with the quintic NURBSs optimization method.


Natural sounds contain rich patterns of amplitude modulation (AM), which is one of the essential sound dimensions for auditory perception. The sensitivity of human hearing to AM measured by psychophysics takes diverse forms depending on the experimental conditions. Here, we address with a single framework the questions of why such patterns of AM sensitivity have emerged in the human auditory system and how they are realized by our neural mechanisms. Assuming that optimization for natural sound recognition has taken place during human evolution and development, we examined its effect on the formation of AM sensitivity by optimizing a computational model, specifically, a multilayer neural network, for natural sound  recognition and simulating psychophysical experiments in which the AM sensitivity of the model was assessed. Relatively higher layers in the model optimized to sounds with natural AM statistics exhibited AM sensitivity similar to that of humans, although the model was not designed to reproduce human-like AM sensitivity. Moreover, simulated neurophysiological experiments on the model revealed a correspondence between the model layers and the auditory brain regions. The layers in which human-like psychophysical AM sensitivity emerged exhibited substantial neurophysiological similarity with the auditory midbrain and higher regions. These results suggest that human behavioral AM sensitivity has emerged as a result of optimization for natural sound recognition in the course of our evolution and/or development and that it is based on a stimulus representation encoded in the neural firing rates in the auditory midbrain and higher regions.SIGNIFICANCE STATEMENT This study provides a computational paradigm to bridge the gap between the behavioral properties of human sensory systems as measured in psychophysics and neural representations as measured in nonhuman neurophysiology. This was accomplished by combining the knowledge and techniques in psychophysics, neurophysiology, and machine learning. As a specific target modality, we focused on the auditory sensitivity to sound AM. We built an artificial neural network model that performs natural sound recognition and simulated psychophysical and neurophysiological experiments in the model. Quantitative comparison of a machine learning model with human and nonhuman data made it possible to integrate the knowledge of behavioral AM sensitivity and neural AM tunings from the perspective of optimization to natural sound recognition. Amplitude modulation (AM) is a critical sound feature for hearing . Not onlc). It shows apparent interactions with the carrier bandwidth. The detection thresholds are higher (less sensitive) at an AM rate equal to the carrier bandwidth. These patterns have been interpreted in terms of frequency masking in the modulation domain. Stimulus parameters other than the carrier bandwidth  may be additional factors determining the TMTF form.The properties of AM sensitivity have been investigated mainly through two separate approaches, psychophysics and neurophysiology. On the one hand, psychophysical studies have identified a wide variety of sensitivity curves in the form of the temporal modulation transfer function TMTF; , b. The Fig. 1On the other hand, neurophysiological studies have found that many neurons throughout the mammalian auditory nervous system (ANS) show tuning to AM . Their sInspired by the psychophysical and neurophysiological findings, a) as a way of simulating the optimization that is presumably happening in the auditory system during its evolution and development. We assumed that better recognition of natural sounds yields better evolutionary fitness and hypothesized that natural sound recognition plays a major role in shaping human AM sensitivity. We used everyday sounds (https://doi.org/10.35111/17gk-bn40) as examples of natural sounds. Then, we simulated psychophysical experiments on the NN to see whether human-like AM sensitivity emerges in some of its layers . This kind of two-step optimization and analysis procedure has explained a number of auditory properties . By taking advantage of a method established in our previous study that maps the AM representation between an NN and the ANS based on single-unit activity  as a “nonoptimized model” and a model after optimization as an “optimized model.” The sound data were divided into training and validation sets. We used the early stopping strategy. This means that the parameter update was conducted with part of the training set until recognition accuracy stopped improving for the other part of the training set.The trainable parameters of the model were the connection weights and biases in the convolutional layers. Initially, the connection weights were random, and the biases were zero. These parameters were optimized for sound recognition with a standard backpropagation method using the Adam optimizer with a learning rate of 10The model was very similar to the one in our previous study in that it consisted of a stack of a dilated temporal convolution followed by an ELU activation function and that it was optimized to categorize everyday sounds or speech sounds based on the softmax cross entropy . On the https://doi.org/10.35111/17gk-bn40). Both datasets are commonly used for sound recognition and are relatively small  but also sounds from a single event . Such sounds are often called everyday sounds .TIMIT defines training and test sets. The test set includes sentences spoken by the core-test speakers and non-core-test speakers. For validation, we used sentences spoken by the core-test speakers. For training, we used the training set for the parameter update and the sentences spoken by the non-core-test speakers for early stopping. We excluded sentences included in both the training and test sets. This process ensured that there was no duplication of sentences or speakers in the training and validation sets. We merged 61 categories contained in the dataset into 39 categories as proposed by the previous study . BecauseBefore being fed to the model, the sound signals were high-pass filtered at 20 Hz, and the 10 ms raised-cosine ramps were applied to the onset and the offset. During training, the sound amplitude was slightly varied clip by clip. During the evaluation, it was fixed to the mean value of that for training.For the architecture search, we tested architectures that varied in the number of layers, number of units per layer, and convolutional filter size and dilation width. The number of layers was 7, 9, 11, or 13. For each number of layers, we sampled 20 models by varying the number of units per layer, convolutional filter size, and convolutional dilation width. The convolutional filter width and the dilation width were randomly sampled for each layer with the constraint on the input time window being 0.2 s. The number of units per layer was either 32, 64, 128, 256, or 512. To avoid an expensive computation of training all models over numerous iterations , we condTo measure the AM detection threshold in the model, we simulated AM detection experiments in human psychophysics. To compare our results fairly with those produced by humans, the simulations duplicated the procedure of the human experiments as precisely as possible. One exception was that in human studies a detection threshold is estimated with a staircase method, whereas we computed the AM detection accuracy for each modulation depth independently.We simulated a two- or three-interval forced-choice  task. In each trial, two or three stimuli were presented to the model, one of them being modulated, the others not. The task was to correctly identify the modulated interval. We conducted this task by assuming an AM detection process based on the model activities. We conducted 128 trials for each AM depth, from which the proportion of correct trials was calculated.The proportion of correct trials plotted against the AM depth yields a psychometric curve. It was fitted with an asymmetric sigmoid function . The detxIFC (x = 2 or 3) task was conducted by estimating the modulated interval from model activities. Specifically, we assumed AM detection based on time-averaged unit activities. For each stimulus interval, unit activities in the model were averaged over time. From the time-averaged activities in a single layer, a logistic regression was trained to estimate whether the stimulus was modulated. The proportion of correct trials was computed in a 4-fold cross validation of a total of 128 trials. In each of the 32 held-out trials, the probability of the stimulus being modulated was calculated for each stimulus interval, and the interval with the maximum probability was considered as the response in that trial. If that interval was actually the modulated interval, the trial was considered correct. L2 regularization was applied to logistic regression. The regularization coefficient was optimized in another 4-fold cross validation within the training set.An We tested six stimulus parameters from three independent human studies. All of them were sinusoidally amplitude modulated narrowband or broadband white noise. The stimulus parameters and generation procedure were as close as possible to those in the human studies, except for amplitude scaling; in the human studies, it was based on sound pressure levels, whereas in this study, it was based on the root mean square (RMS). The stimulus RMS was adjusted to the average RMS of the training set. In the short broadband condition, the stimulus amplitude was scaled before applying modulation . In the In the narrowband carrier conditions, Gaussian noise was bandpass filtered with a digital Fourier transform. In the 314 Hz carrier bandwidth condition, bandpass filtering was applied after modulation . In the m in dB and rate f is defined as follows:t is time, and C(t) is a carrier signal. The AM starting phase was fixed to zero in the 3 Hz, 31 Hz, and 314 Hz bandwidth conditions and in the long broadband condition  and the AM rates in each condition (size = 8), and y is a detection threshold.Likewise, we averaged TMTFs of the four selected models. Then, we compared the averaged human TMTFs and averaged model TMTFs in terms of their relative patterns and absolute values. Similarity of their relative patterns was quantified by the pattern similarity index, that is, the correlation coefficient of the human and model TMTFs. Similarity of the absolute values was quantified by the discrepancy index, that is, the RMS deviation as follows:The net difference between the human and model TMTFs was defined as the average signed difference between them as follows:Positive/negative values of net difference mean larger/smaller thresholds in the model than in humans on average. To take all stimulus conditions into account, TMTFs in all conditions were pooled when calculating those indices.p values were Bonferroni corrected for the number of layers.Correlations between the recognition accuracy and the pattern similarity and the discrepancy were assessed with Pearson correlation coefficients. The To evaluate the importance of amplitude envelope (Env) and temporal fine structure (TFS), we made degraded versions of the training data by disrupting either the Env or TFS components of the sound.sEnv and sTFS are the Env and TFS components of a signal s, x and wn are the original sound and a white noise with the same RMS as x, t is time, i is the imaginary unit, and real converts a complex signal to its real part. The Env and TFS components are defined as the magnitude and phase of the Hilbert-transformed complex analytic signal.Single-band Env signals were made by combining the Env component of a sound and a TFS component of white noise as in the following:sFlatEnv is a flattened Env of a signal s, which takes a constant RMS value of the Env component.Single-band TFS signals were made by flattening the Env component of a sound as follows:fs is the fth sub-band of the frequency-decomposed signal s. Other than the difference in the training data, the procedures of the optimization and analysis were completely the same as the models trained on the original sounds.When making multiband Env and TFS signals, we first decomposed the sound into sub-bands with a linear bandpass filter bank. The filter center frequencies ranged from 20 Hz to the Nyquist frequency and were spaced every one equivalent rectangular bandwidth . BecausexIFC task, the correlation was calculated between the unit activities in a layer in response to the stimulus and the template. The interval with the largest correlation was taken to be the response to the trial. The correlation was defined by the sum of products as in the previous study . The optimization objective was to correctly estimate the category of an everyday sound or the phoneme categories in a speech sound. We built and analyzed a model for each sound type. Because the results were generally consistent across different sound types, below we report the results for everyday sounds before those for speech sounds.The model was optimized to correctly classify natural sounds. We used two types of sounds, everyday sounds  and speeThe recognition performance of an NN generally depends on its architecture . In thisAfter optimization, we evaluated the recognition performance for sounds not used in the model construction. The recognition accuracy was 0.477. This value is well above the chance level (0.02) but lower than that of state-of-the-art machine learning studies . AlthougTo investigate the relationship between sound recognition and AM sensitivity, we measured the TMTFs in each of the four best models by simulating psychophysical AM detection experiments. To fairly compare the TMTF of the model with those of humans, we replicated the procedures of human psychophysical experiments as precisely as possible. We simulated six psychophysical experiments from three independent human studies , b. In aThe stimuli were sinusoidally amplitude-modulated broadband or narrowband Gaussian noise. They differ in their stimulus parameters . BecausexIFC task, we presented a stimulus to the model and averaged the activity of each unit over the stimulus duration . The threshold was defined as the AM depth at which the proportion of correct trials was 70.7% on the fitted curve.In the present study, to conduct an duration . Then, fAM depth c. The thThe forms of the TMTFs of the model (detection thresholds as a function of AM rate) varied depending on the stimulus condition and model layer  and absolute values . We examined the relationship between the recognition accuracy and TMTF similarity indices .An additional analysis revealed a close link between the sound recognition performance of the model and the TMTF similarities. During the NN architecture search, we trained 20 models with 13 layers with different architectures.  The 20 models exhibited recognition accuracies ranging from 0.043 to 0.492 and produced TMTFs with a varying degree of similarity to those of humans a,b. We ec). The positive and negative correlations, respectively for pattern similarity and discrepancy indices, mean that the models that performed sound recognition better exhibited AM sensitivity more similar to that of humans. This result further supports the idea that there is a strong relationship between optimization for natural sound recognition and emergent AM sensitivity.The TMTF similarity indices correlated with the recognition accuracy in the higher layers c. The poWhat components of the optimization for natural sound recognition are essential for acquiring human-like AM sensitivity? We hypothesized that natural AM patterns in the sound are the critical feature. To test this hypothesis, we conducted control experiments in the models optimized for the manipulated sound signals. Only the training signals were manipulated. No modifications were made to the stimuli for measuring AM sensitivity.The manipulation involved dividing a sound signal into an amplitude envelope and TFS and disrupting either of them while preserving the other for the entire signal or each sub-band. This is a common strategy for manipulating the AM structure of sound in auditory science . SpecifiWe optimized the NN models to recognize the manipulated sounds. Hereafter, we call the optimized models for the above signals the single-band Env model, multiband Env model, single-band TFS model, and multiband TFS model. We refer to the model trained for intact sounds  as the original model.a, blue lines). The pattern similarity index for the single-band Env model was at a comparable level to the original model throughout its layers . However, the discrepancy index of the single-band Env model deviated from the original one in the layers above the eighth, exhibiting higher values . These results indicate that the model had TMTFs whose patterns were similar to those of humans, while its sensitivity to AM was higher than that of humans . This difference in AM sensitivity was quantified as the net difference, the average signed difference between the model TMTFs and the human TMTFs . The net difference was largely negative in the single-band Env model, indicating that its thresholds were on average lower than that of humans. These results suggest that optimizing to sounds that only retain natural single-band AM patterns made the model more sensitive than humans to AM. This is probably because the model was biased toward exploiting the AM that was the only available feature for recognition.Differences in the TMTFs across the models were more apparent for higher layers. TMTFs of the single-band ENV model appeared to be closest in general shape to the human TMTFs . A template was defined as the average unit activities in response to fully modulated stimuli minus the average activities in response to nonmodulated stimuli. The average was taken over multiple carrier instances. Then, in each trial of the xIFC task, the response of the model was defined as the stimulus interval with the largest correlation between the activities of the model and the template. In accordance with the previous study . The similarity to human TMTFs was the highest at the topmost layers , but it was still lower than the similarity of the TMTFs of the time-average-based detector . The results were generally consistent with those of the models optimized for everyday sounds reported above. This indicates that human-like TMTFs robustly emerged in the models that were independently optimized to two different types of sound.We conducted the same analysis on the models optimized for phoneme classification of speech sounds , whereas neither the TMTFs of the nonoptimized model nor those calculated with the template correlation were similar. According to the neurophysiological analysis, the brain region most similar to the eighth and ninth layers was the IC . The MGB and AC also exhibited high neurophysiological similarity.The phoneme classification accuracy was 0.747, which is high above the chance level, 0.026. The TMTFs of the layers around the eighth and ninth layers in the optimized model were similar to those of humans , and there was no significant correlation with recognition accuracy (b). This lack of correlation may be explained by the dynamic range of the recognition accuracy in the models for speech sounds: Their recognition accuracies ranged from 0.499 to 0.771, whereas, for the models optimized to everyday sounds, the range was from 0.043 to 0.492. All models trained on speech sounds showed recognition accuracies well above chance level, whereas some models trained on everyday sounds exhibited very low recognition accuracy almost as low as chance level. Probably, the correlations between the recognition accuracy and the similarity to human TMTFs are nonlinear. Models that failed to perform sound recognition exhibited AM sensitivity that was not similar to humans, but models with good sound recognition performance exhibited more or less human-like AM sensitivity regardless of the small variation in their performance. Probably, once the performance of sound recognition surpasses a certain level, the similarity of AM sensitivity to humans does not change largely on any further increase in recognition accuracy.On the other hand, we did not observe high correlations between similarity to human TMTFs and recognition accuracy. The pattern similarity indices and the discrepancy indices did not exhibit appreciable variations across the 20 models with different architectures a, and thaccuracy b. This la). The multiband Env model had the highest recognition accuracy, followed in order by the single-band TFS model, single-band Env model, and multiband TFS model. This order is consistent with human performance as shown in a previous study . The TMTFs of the single-band Env model exhibited high pattern similarity to those of humans, and their net difference shows their thresholds were lower than those of humans. The TMTFs of the multiband Env model were similar to those of humans in terms of both their pattern and absolute value. The TMTFs in the TFS models were not similar to those of humans.The TMTFs of the models optimized to degraded speech sounds were qualitatively consistent with those of the models optimized to degraded everyday sounds b. The TMThe present study demonstrated that a model optimized to recognition of sounds with natural AM statistics exhibited human-like AM sensitivity. In building the model, we did not make any attempt to design model architectures or adjust parameters for achieving similarity to humans, nor did we use any knowledge about human AM sensitivity. The results therefore suggest that the nature of AM sensitivity in humans might also be a consequence of optimizing to natural sound recognition in the course of evolution and/or development in the natural environment and that it would not have emerged if the input sounds would have had different AM characteristics.By simulating previous human experiments as precisely as possible, we could quantitatively compare the TMTFs of our model and of humans. We explained TMTFs in six experiments conducted in three independent human studies with a single unified framework. Our findings strengthen the existing knowledge that general AM sensitivity is closely linked to sound recognition ability .We built two types of models using either everyday sounds or speech sounds and analyzed each one independently. The results on the two datasets were qualitatively similar, although some relationships were stronger for everyday sounds. They suggested that human-like AM sensitivity is related to both sound types. This conclusion is consistent with the previous studies on cochlear frequency tuning and neural AM tuning, where qualitatively similar tunings were obtained from optimization to everyday sounds and speech sounds . There mThe MFB is a conceptual realization of midbrain neurons that are tuned to the modulation rate. It was formalized to explain various psychoacoustic phenomena, for example, frequency selectivity in the modulation domain ,b. Our NCompared with previous computational studies involving the MFB , the TMTOur previous study showed that middle layers in an optimized NN (as the nuclei in the ANS) exhibited units with tuning to various AM rates, some of which probably work as modulation-domain filters . This alAlthough we did not implement hardwired cochlear filters, our previous study suggested that lower layers in the optimized NN conducted some kind of initial frequency analysis .In the MFB models, additive noise was applied to the internal representation of the model from which AM detection was simulated. In contrast, our model is deterministic, meaning that the unit responses are the same for the same stimulus, except for the nondeterministic behaviors of atomic operations in a graphics processing unit. In our AM detection experiments, the only source of stochasticity was the noise carrier and the AM starting phase in the conditions with a random starting phase, both of which were sampled independently stimulus by stimulus . PsychopIn the optimized model, TMTFs in the middle to higher layers were most similar to humans' TMTFs. Here, we discuss the implications of this result in terms of signal processing and anatomic brain regions. Generally, an optimized NN behaves as an effective signal processor and feature extractor. While processing an input signal, each of its cascading layers computes its representation by integrating and nonlinearly transforming the one below. Its lower layers compute relatively simple and temporally and/or spatially local features. As the processing stage progresses, the extracted features gradually become more complex and global . Therefoc). This result suggests that human-like TMTFs could be observed when conducting an AM detection task from the stimulus representation in these brain regions. A human brain might also use stimulus representations in these regions when conducting AM detection tasks, but our results alone cannot distinguish whether such a computation is running within those brain regions or somewhere outside, possibly regions associated with higher-order cognitive functions. Another unanswered question is which  of these regions is actually the source of the neural representation used by the AM detector (if it exists) implemented in the human brain. Nevertheless, the present results, at least, suggest that the stimulus representation necessary for AM detection emerges as early as in the IC and is kept until the signal reaches the AC.Interestingly, a similar tendency can also be seen in the ANS. Peripheral regions in the auditory pathway are generally sensitive to fast temporal changes in a sound and relatively linear features, whereas central regions are sensitive to slower changes and more nonlinear features . In the 14We obtained human-like TMTFs by assuming an AM detection process based on time-averaged unit activities. Time-averaged unit activities can be interpreted as the average neuronal firing rate . TogetheThe other AM coding strategy in the ANS is temporal coding . We alsob). Detailed inspections of the individual stimulus parameters, however, indicate that changes in the form of TMTFs along the layers appeared to vary with the stimulus parameters. The change seems most prominent in the broadband carrier conditions, and least in the condition with the 314 Hz carrier bandwidth, showing almost constant TMTF forms across layers (14a). In this study, we could not see a consistent relationship between the TMTF difference across layers and the stimulus parameters. There is a possibility that humans perform AM detection with different strategies in different experimental conditions. These results highlight the importance of testing multiple stimulus parameters when investigating AM sensitivity.The TMTFs in the lower layers were less similar to those in humans, and as the layer number went higher, the similarity also became higher , 14b. De14s layers , 14a. InFrom a machine learning point of view, this study can be viewed as an attempt to understand an NN with a combination of psychophysical and neurophysiological methods. A number of methods have been proposed for analyzing the behavior and stimulus representations of an NN . It woul


It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user’s configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at  Antibiotic resistance poses a significant risk to human health. Antibiotic resistance genes (ARGs) encode resistance to antibiotics and can be carried in the bacterial chromosome or on mobile genetic elements (MGEs). ARGs are of greatest concern to human health, especially when they are found in known or emerging pathogens . The neeContemporary environmental metagenomic data sets typically consist of a number of short read sequence files, typically generated by Illumina sequencing producing files ranging in size up to 100 Gbp (Gigabase pairs) , each reMany metagenomic analysis pipelines exist with much variation. However, the goal of a typical pipeline is to identify microbial taxa and genes of interest in a subset of samples, and to estimate their abundances. Further analysis of the annotation is often left to specific tools selected by the researcher. A pipeline may assemble the reads into contigs to allow identification of complete or nearly complete genes and to improve resolution for annotation. A classic metagenomics pipeline is the MG-RAST server, which is designed to process numerous samples on high-performance computing clusters . A numbeA common limitation among the pipelines is a lack of integrated tools for additional analysis and visualization beyond basic annotation. Moreover, these pipelines do not provide flexible input, which results in a disincentive to data sharing and greatly detracts from the overall utility of the data. Metadata, which is the data describing properties  of the sample, is nowadays commonly provided along with the sample sequences. Lack of extensive provision and sharing of metadata diminishes the ability to perform analyses that harness the power of metadata to support predictive modeling of environmental metagenomes. This deficiency in metadata sharing also detracts from encouraging reporting of comparable data, which is a critical need for the broader goal of large-scale environmental ARG monitoring. While researchers might recognize the importance of the extensive metadata that they collect for each sample, the actual types of metadata captured can vary greatly across research projects . As one Another notable framework, not limited to metagenomics analysis, is Galaxy . Galaxy Towards addressing the aforementioned issues, we present ARGem pipeline. This locally deployable pipeline supports ARG annotation as well as the capture of a flexible set of metadata, which will encourage comprehensive data sharing and be ultimately accessible to support more sophisticated future analysis after annotation is complete. To achieve this purpose, users are provided with a simple spreadsheet with required and recommended metadata fields and standardized units. Users complete the spreadsheet and submit it as input to create an ARGem project, in which the data are stored in a relational database that can be further cross-analyzed.Assembly and Annotation, we have extensively examined the bioinformatics components of the ARGem pipeline. In particular, we prioritized comprehensive databases for ARGs and MGEs annotation. One comparable pipeline is our own MetaStorm server  is used by the back end to define tasks and chain them together to construct a workflow for the pipeline, as well as managing the scheduling of tasks, handling errors, and visualizing the status of the pipeline.Luigi manages multiple tasks in the workflow by assigning them to different classes and drivers. Each class is designed to execute a particular task, such as short reads annotation or co-occurrence network analysis. Once the Luigi task classes are defined, they are aligned with each other in a workflow by indicating the dependencies between pairs of modules. Tasks without direct or indirect dependency on each other can be run in parallel, depending on how much resources the scheduler allocate for them. *.fasta or *.fastq format. For the uploaded SRA numbers via the input spreadsheet, ARGem checks whether the SRA project numbers are accessible a suitable format through a pre-download. Invalid SRA numbers or those associated with incorrect formats are logged to a designated log file. Upon completion or interruption of the pipeline, these SRA numbers are then reported to the user in an email notification.ARGem provides automatic raw sequence data retrieval from the public NCBI database  through *.sra format and then converted into *.fastq format. For paired end samples, the file format conversion process is split to convert each SRA sample into a paired files for assembly.Once the accession and format verification is complete, ARGem begins the SRA sample retrieval process where the raw sequence files are downloaded individually for each SRA number. The retrieval of each sample is initialized with a query to the accession-size of the SRA project numbers through the SRA Toolkit to ensure that the size of a single sample is lower than the hard limit, which is set by default to be 70 gigabytes. In the case where the SRA sample is above the size limit, an error can be raised and logged accordingly by ARGem pipeline. Once the size verification is complete, the SRA sample is prefetched via the SRA Toolkit in *.fastq sequence data files to validate their data integrity. Upon completion, annotation and assembly tasks for the valid samples will be scheduled according to the Luigi workflow.After all the raw sequence files are retrieved from NCBI website , ARGem wThe pipeline design of ARGem offers easy and flexible updates and interchanges for databases. Once a new ARG or MGE database is converted into a fasta file and a proper format for the annotation tool, ARGem redirects assembly and annotation tasks into the new databases. Default ARG and MGE databases were selected based on how widely they are used for metagenomic analysis, with a preference for databases that are frequently updated.To annotate the raw sequences and assembly results into ARGs, ARGem integrates the current Comprehensive Antibiotic Resistance Database (CARD)  as the dARGem utilizes three databases for MGEs: Mobile-OG , NanoARGThe sequence data used in this study are available from the NCBI database  and retrTo select a suitable assembler for our short read metagenomic data, we carefully evaluated the performance of a set of assemblers on our server and on targeted data sets. The pre-selected set of assemblers was chosen based on evaluations in previous studies . Table 1Results. The first two samples were produced by our group, and the latter two samples were published in previous work , one final treated biosolids sample (water sample 2), and two raw sewage and treated wastewater samples (water sample 3 and 4) for the results depicted in ous work . For theous work , IDBA-UDous work  and Megaous work . While tDIAMOND ; 2021 waCo-occurrence analysis is a widely applied technique in bioinformatics, and can infer important relationships among genes, such as their taxonomic host, their tendency to be co-expressed, and their ability to be co-mobilized via HGT . SequencFor co-occurrence analysis of ARGs and MGEs, the ARGem pipeline combines an ARG database and an MGE database to count the number of co-occurrence of contigs for each pair of one ARG and one MGE.x1, x2), we calculate the mean values , the standard deviation , and the correlation ρ. To accomplish this, we need at least three complete gene pairs. Let N be the total number of experiments, and let f(⋅) represent the probability density function (pdf) of the underlying bivariate normal distribution. F(⋅) represents the combination of the pdf and the cumulative distribution function (cdf) of the normal distribution. The likelihood function L is defined as follows:c1 and c2 are the detection cut-offs for x1 and x2, and δi1 and δi2 are indicator variables indicating whether or not data is available for each xij.For correlation analysis, ARGem first imputes the missing values with zeros for the abundance data and then renormalizes it to be relative abundance data. This method is adapted from . We assuf(⋅) when data are available for both pairs and then the second term factorizes into the pdf of x1 and the cdf of x2 at the cutoff term in a normal distribution that is shifted up by the distance of the current x1 observation from its mean multiplied by the correlation coefficient and scaled by the ratio of variances using F(⋅). If the correlation between the genes is strong, we expect the cdf of x2 at the cutoff to be directly related to the distance of x1 from its mean and vice versa. Then we calculate the joint cdf of the bivariate normal distribution at both cutoffs. The joint cdf term grows as the values of the cut-offs rise relative to their corresponding means. As this term increases, it tends to overshadow information from other terms.In the above equation, we first calculate the regular likelihood term Our approach involves maximizing the likelihood of observing a given expression pair while adjusting for a known cut-off threshold. In addition, we also capitalized on the data structure by introducing correlation bounds. To obtain sharper correlation estimates, we further utilize the partial correlation definition inequality to update our correlation estimates based on the correlation between other pairs. In this way, the proportional value of relative abundance can directly reflect the degree of correlation of the potential related gene pairs and we are able to produce correlation estimates even with severe missing data issues.In the next step, our user can apply the desired threshold within the range  on the correlation matrix to filter out the relevant gene pairs for further analysis or visualization.Gene Co-occurrence and Correlation Analysis. Co-occurrence graphs map ARGs and MGEs based on a number of co-occurrence pairs annotated on the same contigs assembled from raw sequences. For example, in three contigs C1, C2, and C3 in one sample, all contain the ARG-MGE pair ARG A1 and MGE M1, the occurrence of  is 3. The width of the edge between A1 and M1 will reflect the co-occurrence, in this case, which is 3. In correlation graphs, the width of the edges is based on the correlation score between two genes Gene Co-occurrence and Correlation Analysis and indicates the relative strength of the relationship in Assembly and Annotation. The size of each node is determined based on the sum of abundance in the metagenomic library. Co-occurrence networks, on the other hand, are an analysis of ARGs and MGE annotated on assembled contigs . Each edge that connects an ARG node and an MGE node represents the count of the given combination, where the width of the edges indicates the frequency that the combination is encountered , the DNA extraction method, the DNA sequencing platform, DNA sequencing output , and the SRA accession number for each sample. The required columns are provided along with conditional columns depending on the type of aquatic environment matrix selected from the drop down menu.An ARGem Excel spreadsheet was designed through collaboration with environmental scientists to identify required Through an SRA number column, each sample is associated with a unique SRA number  in the iTypically, the user selects one template from the ARGem library of spreadsheet templates that best represents the environment under study, customizes the template for their project, and enters the metadata into the spreadsheet with one row for each metagenomic sample.There are in total six templates in the library, including five templates for different environment sample types and one user custom template. Through collaboration with environmental scientists, we designed specific templates for water, soil, treated or raw manure, pre- and post-harvest crop production system, and air samples.In this step, sequence data are retrieved based on the input SRA numbers provided for each sample in the metadata spreadsheet. These data subsequently serve as raw sequence files for the samples required for subsequent analysis.In this step, the pipeline assembles the retrieved sequence files using the integrated assembly tool, namely, MEGAHIT . This asThis step performs annotation on both the assembled contigs (long-contig annotation) and retrieved short reads (short reads matching) using the integrated annotation tools  of the pipeline. When the pipeline does not execute successfully, the notification will include specific information about the detected errors to help guide the user in addressing the problem.The ARGem pipeline was tested using publicly-available data extracted from the NCBI database . ResultsThe pipeline generated tables that summarize results for three analyses: 1) short read matching to profile ARGs and estimate their relative abundances, 2) assembly of contigs from short reads, and 3) annotation of ARGs and MGEs in assembled contigs. Short read matching results for these fifteen samples yielded 380 annotated ARGs found in at least one sample out of the fifteen, with 16S rRNA, TPM and FPKM normalization reported in three separate files. Contig assembly generated assembled contigs for all fifteen samples. The ARG and MGE annotation based on assembled contigs generated one table of annotated ARGs and one table for annotated MGEs, for each sample. A table was also generated to report ARGs and MGEs that were found to co-occur in the samples.The visualizations shown here are examples of the analysis that can be performed based on ARGem outputs, but do not have to be limited to the tools and methods described above. Overall, the result tables generated by the ARGem pipeline are capable of further analysis and can be processed by different analysis and visualization tools.Antibiotic resistance is a significant public health concern that cannot be ignored . MetagenHere we integrated several essential aspects of metagenomic data processing into the ARGem pipeline, including short read matching, contig assembly, and annotation of ARGs and MGEs on assembled contigs. These steps are aligned and automated to provide an all-inclusive pipeline to support global ARG monitoring. The ARGem pipeline allows flexible metadata table inputs, including user-customizable metadata attributes, to be applied to data from different environmental sources and allows possible customized usage by users of this pipeline. A supporting SQL database structure has been developed to manage the flexible input and released along with the pipeline. In the ARG and MGE annotation step, this pipeline provides several different MGE databases for users to choose from. In the short read matching step, the normalization results of three different methods  are provided to suit different research purposes. The data generated from this pipeline are capable of being further analyzed and visualized using various tools. Among those, two analysis tools, namely, the correlation analysis and co-occurrence network analysis tools, are included in the release of the pipeline.Our intention is to offer the community an available, flexible and convenient pipeline designed specifically for metagenomics data to accommodate tincreasing needs in related fields, primarily focusing on the threats of ARGs posed to the agriculture chain and human health. The ARGem pipeline is constructed based on the discussion, suggestion, and testing by actual users who have conducted metagenomics studies and performed agriculture practices in related fields. By implementing flexible metadata input and relational database storage, user customizable reference databases, and an extendable analysis module, the ARGem pipeline intends to introduce flexibility and variety for data input and subsequent analysis, as well as automate the handling of such data. With the release of this pipeline, it is our intention for researchers to have a convenient pipeline to deploy and run on lab scale resources.In this study, we present the ARGem pipeline as a tool for investigating features relevant to antibiotic resistance in environmental metagenomic data sets. As a significant impact on human health, antibiotic resistance has gained increasing attention from researchers and policymakers. As metagnenomics studies being an effective means of comprehensively monitoring ARGs and pathogens in healthy environments, we aim for the ARGem pipeline to contribute to this purpose as an integrated, flexible, and deployable tool.We describe in this paper the overall workflow and mechanics of each step within the ARGem pipeline, including the methods and tools integrated into the pipeline. We demonstrate its applicability and flexibility through the analysis of metagenomic samples collected from aquatic environments. The ARGem pipeline is developed to be deployable on lab-scale resources, distinguished from other large, general and online pipelines.Our intention is to make this pipeline readily accessible to a broad range of users, including governmental and academic researchers and policymakers, for tracking key drivers of antibiotic resistance in various environments using metagenomic data. The ARGem pipeline is available in the public domain for free use. In the future, more sequence process and analysis steps can be incorporated into the ARGem pipeline to accommodate the rapid pace of development in this field, which will be facilitated by the adaptable nature of ARGem.


Stroke is a leading cause of disability, impairing the ability to generate propulsive forces and causing significant lateral gait asymmetry. We aim to improve stroke survivors’ gaits by promoting weight-bearing during affected limb stance. External forces can encourage this; e.g., vertical forces can augment the gravitational force requiring higher ground reaction forces, or lateral forces can shift the center of mass over the stance foot, altering the lateral placement of the center of pressure. With our novel design of a mobile Tethered Pelvic Assist Device (mTPAD) paired with the DeepSole system to predict the user’s gait cycle percentage, we demonstrate how to apply three-dimensional forces on the pelvis without lower limb constraints. This work is the first result in the literature that shows that with an applied lateral force during affected limb stance, the center of pressure trajectory’s lateral symmetry is significantly closer to a 0% symmetry ( Stroke is the second leading cause of disability and death in adults, resulting from damage to brain cells due to a temporary blockage of blood flow to the brain or bleeding in surrounding brain tissue . In 2003Multiple deficits at the level of the skeletal musculature have been reported post-stroke, including muscle weakness, increased muscle tone and spasticity, reduced muscle coordination, and increased co-activation of antagonist muscles . In addiThese alterations to the internal forces of the skeletal musculature post-stroke also affect the loading of each leg and the force interaction between the feet and the ground during gait. The ground reaction force (GRF) is the resultant vector combining all forces acting between the foot and the ground . Stroke The application point of the GRF is called the center of pressure (COP) and can be an essential tool for evaluating gait pathologies . During Therefore, the loading of the paretic limb and the lateral symmetry of the SS COP trajectories are essential aspects of the overground gait of stroke survivors that are correlated with walking ability and independence. The ability to rehabilitate the paretic hemibody may recover or improve coordination and mobility deficits. Gait therapy is an integral part of rehabilitation for these individuals, and methods range from functional electrical stimulation (FES) to pharmacological interventions to traditional or robotic gait training . TreadmiThe literature on post-stroke robotic gait training is mixed ,33,34,35While there are few existing studies investigating the effects of robotic gait training on the GRFs of stroke survivors, the impact of specific applied external forces on different characteristics of the GRF is not well understood. Furthermore, previous robotic interventions do not tailor forces to the individual’s gait and needs. Therefore, a gap exists in the current literature on an overground robotic gait trainer that can alter the user’s GRFs while tailoring the applied forces to the user’s gait in real time. The mobile Tethered Pelvic Assist Device (mTPAD), which can apply three-dimensional external forces to the pelvis , could cThe mTPAD applies pelvic external forces synchronized to the user’s gait in this work , specifiThis work uses the mTPAD to explore the effects of different forces applied to the pelvis during overground walking. The mTPAD is a cable-driven, parallel robotic platform with seven degrees of freedom (DOFs), as shown in T is the vector of cable tensions  model. The Encoder–Decorder RNN model (ERM) uses an Encoder module, an RNN module, a dense module, a Decoder module, and a fully connected layer with a single neuron. For each shoe, the ERM’s input was the nine raw data channels, and the output was a single value from 0 to 1, representing the gait cycle percentage prediction. Time-synchronized gait data from a Zeno Walkway were collected and used as the ground truth to train the ERM. The ground truth gait cycle percentage was calculated from each foot’s heel strike to the next ipsilateral heel strike, as detected by the walkway. Prado de la Mora et al. detail each ERM module and accuracy of the gait cycle percentage prediction here .k denotes the percentage of body weight applied, m is the participant’s body mass in kilograms, g is the gravity constant, and The predicted gait cycle percentage corresponding to the participant’s affected hemibody is input to the mTPAD force controller. We aim to apply force to the participant’s pelvis during the affected limb stance but not the contralateral stance. To determine the profile for the applied pelvic force, a sawtooth function is considered the function for the gait cycle percentage, assuming a constant gait velocity. Taking the sine of the sawtooth function, each gait cycle would encapsulate one period with a ramp-up and a ramp-down around the peak values. Using the sine-transformation of the gait cycle prediction, a smooth forcing function with a ramp-up to and ramp-down from the goal applied force magnitude can be used instead of a force step function. This is beneficial, as it can avoid sharp perturbations felt by the individual. However, the force will not be applied during the non-affected limb stance, so we only consider the positive half-cycle of the sine wave. Therefore, mapping from the gait cycle percentage to the applied pelvic force for this work will follow Equation (3) and is shown in x-axis and the anteroposterior (AP) displacement on the y-axis. The COP trajectory is plotted with the two feet superimposed next to each other to visualize the symmetry of the COP throughout the gait cycle with respect to the location of each foot.We will investigate how the point where the GRF acts on the foot, i.e., the COP trajectory, changes when frontal plane forces are applied to the pelvis. A cyclogram is the locus of the center of pressure during a gait cycle . Figure For one gait cycle that starts at the right heel strike, the center of pressure makes one complete butterfly-like cycle. The gait cycle begins with the right foot’s initial contact, which is shown in the upper left of The point at which the double stance diagonal COP segments cross is the COP intersection point (CISP). This point can be used as a measure of symmetry. If the COP trajectory along one foot is shorter, the COM forward displacement is less. This asymmetry in gait would alter the CISP point, laterally shifting it toward the affected side. For instance, the top portion of An overground walking dataset of different applied forces on the pelvis was collected to determine the mTPAD’s ability to target stroke survivors’ overground gait asymmetry. The protocol and data collected were designed to investigate: (i) the feasibility of using the mTPAD as a robotic gait assessment and training tool for stroke survivors, (ii) the effects of a lateral force applied at the pelvis during the affected side stance, and (iii) the effects of a downwards force applied at the pelvis during the affected side stance. By understanding how these forces alter the underfoot pressure and trajectory of the COP, we can evaluate the mTPAD’s potential as a gait-training tool for stroke survivors.Five stroke survivors participated in the following experiment  were included for the training and analysis per condition: Baseline, After completing the Baseline, gait mat data were processed with the accompanying PKMAS software. The left and right footsteps were labeled, and the timestamps for the right heel strikes were calculated. These data and the time-synchronized raw DeepSole IMU and pressure data were added to the training dataset as illustrated in Once we collected data, the gait cycle percentage and frontal plane force applications were characterized. We evaluated the changes in the COP trajectories and foot pressures. We segmented and averaged all cyclic data to obtain a representative gait cycle. We calculated the gait parameters, foot pressures, COP, and cyclogram data using the ProtoKinetic software, PKMAS . Left anThe continuous COP with respect to the mat was calculated using the PKMAS mat software. The cyclogram per stride, as segmented by the affected heel strike, was calculated per stride per trial per participant. The Euclidean distance between the first and last points of the COP single stance trajectory was calculated per foot and defined as the single stance COP distance. The ML and AP positions of the CISP per stride were also computed using the PKMAS mat software. An example of these cyclogram measures is shown in The foot pressures of the affected and non-affected limbs are studied to determine changes in weight-bearing. Total foot pressure values for the affected and non-affected limbs in relative pressure are sampled at 120 Hz, output from PKMAS, and processed using a custom Python script. The foot pressures for the affected and non-affected sides are time normalized using the affected side’s heel strikes and interpolated to 100 points. An example of the average affected and non-affected foot pressures per stride for one participant is shown in t-tests were used when not significantly different from a normal distribution. All tests were run using Python Statsmodels [The Baseline data are compared to each mTPAD force condition data to evaluate the effects of timed frontal plane forces on the center of pressure trajectory and foot pressures. Before selecting each variable’s statistical test, the variable’s data distribution and normality were evaluated by a one-sample Kolmogorov–Smirnov normality test. When significantly different from a normal distribution, a Wilcoxon signed-rank test was used. Paired tsmodels  and ScipThe mTPAD has the unique ability to apply three-dimensional external forces on the pelvis synchronized with the user’s gait. To determine the mTPAD’s ability to target specific gait needs, we investigated these measures to determine if downward or lateral forces applied by the mTPAD encouraged increased weight-bearing during the affected limb stance as hypothesized.Applied forces to the pelvis were regulated based on a BW% and thresholding scheme. A maximum tolerable force BW% was determined per participant for each force condition. Force application started at 2% body weight and incremented by 2% until either 10% was reached or the participant could not tolerate the force, as verbally expressed. The group forces (avg ± std) are t-tests were used to investigate the changes from the Baseline. ML CISP % is shifted significantly closer to the unaffected side when a lateral force is applied to the pelvis by the mTPAD (The spatial characteristics of the cyclogram are shown in t-tests were used to investigate the changes from the Baseline due to each force condition. When no external force is applied to the pelvis by the mTPAD, the mean maximum pressure value per stride is (To determine changes in weight-bearing during applied pelvic forces, the maximum pressure for both the affected and non-affected limbs is evaluated. The group results and results per participant for maximum relative pressures are shown in Stroke survivors, or individuals with any gait pathology that has altered ground reaction forces, benefit from gait therapy that aims to improve weight bearing and symmetry through the gait cycle. By encouraging weight bearing during overground gait, we may alter the GRF by changing its magnitude or placement under the feet. Improving gait quality and even independence in daily life activities may be possible through interventions that induce these changes. Stroke survivors and others may benefit from a training paradigm targeting specific gait deficits during overground walking, which is a unique ability demonstrated by the mTPAD.Here, the mTPAD was used to apply a frontal plane pelvic force during the affected side stance for a group of stroke survivors. The ability of the mTPAD to apply custom forces timed with the user’s gait cycle percentage gives the mTPAD the unique ability to tailor these frontal plane forces to the user’s gait, even when the user’s gait is asymmetric.When a force is applied laterally to the pelvis during the affected stance, the lateral symmetry of the COP trajectory changes. A maximum of 10% BW was used, with When a downward force is applied to the pelvis during the affected stance, the underfoot pressure maximum increases for the affected side. In this force condition, To better understand these changes to the characteristics of the GRF when two forces are applied at the pelvis, let us consider a simple quasi-static inverted pendulum model, as shown in Therefore, the changes to the underfoot pressure and ML CISP made by the mTPAD’s applied forces can be motivated by the theoretical modifications to the GRF components. During an applied lateral force, the lateral shift of the COP is reflected in the significantly increased symmetry of the COP trajectory without significantly changing the maximum pressure value. For the downward force, the maximum pressure significantly increases for the affected side, with no significant change to the unaffected side’s maximum pressure or lateral symmetry of the COP trajectory. Thus, the forces applied to the pelvis at the mTPAD can alter the GRF’s characteristics, making it a promising tool for overground gait training. This robotic platform allows us to motivate different force applications to target specific loading responses to tailor the prescribed overground force intervention to each individual’s gait deficits and needs.While this work highlights the mTPAD’s ability to alter specific components of the GRF during a group of stroke survivors’ overground gait, this work is not without its limitations. The interaction between the mTPAD and the participant’s arms was not scientifically measured and could act as a distribution path for the applied force to the ground . HoweverThis work demonstrates the efficacy of using the mTPAD to alter GRF characteristics of stroke survivors—a critical part of increasing the gait ability and quality of life for these individuals. We applied two frontal plane forces that encouraged weight bearing and lateral symmetry during overground affected limb stances. We evaluated these forces experimentally with a group of stroke survivors. The lateral force reduced the ML CISP, increasing overground gait symmetry. The downward force increased the maximum relative pressure under the affected limb. These applied lateral and downward forces show promising benefits that could improve the ability to bear weight on the affected limb through a multi-session training regimen. This work opens the door to overground robotic gait training protocols to enhance lateral symmetry and weight bearing for individuals with these deficits. The potential of the mTPAD as a gait-training tool can now be evaluated and even compared against traditional gait therapy.Stroke survivors, or individuals with any gait pathology that has altered ground reaction forces, benefit from gait therapy that aims to improve weight bearing and symmetry through the gait cycle. By altering the GRF, either by changing its magnitude or placement under the feet, it may be possible to improve gait quality and even independence in daily life activities. In addition, these individuals may benefit from a training paradigm that can target the COPs during overground walking, which is a unique and proven ability of the mTPAD.