For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions. Gait analysis is an important element for the understanding of equestrian sport and can be performed by examining gait characteristics and body segment positions of the horse while moving. Gaits can be distinguished by their foot-fall pattern in addition to knowledge about the duration of the support phase compared to the whole stride duration of one leg . For gaiIn general, the force plate is considered the gold standard for kinetic gait analysis. With this method, hoof impacts can be registered from the vertical force signal by applying a threshold which is subjective. Furthermore, data collection is time consuming  and thisPrevious studies investigated the accuracy and precision of IMUs compared with the force plate , 9 and ODuring this study, two algorithms for automatic hoof- events detection based on the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface were developed. For gait classification, the needed accuracy and precision for hoof-event detection are not yet investigated. However, estimations of stance and swing durations in addition to knowledge about the timing of lateral and diagonal hoof placement are essential. We therefore aim for accuracies and precisions similar or better compared with the study of Braganςa et al. (2017) .At the start of this study, force plate and IMU data were visually examined. The IMU data showed distinctive peaks coinciding with the hoof-on and hoof-off times measured with the force plates, as previous described by Olsen et al.  and depiEquus ferus caballus; for further details see For the current study, we used data that was collected for a previous study . Measureg acceleration with a range of ±16 g and high-g acceleration with a range of ±400 g, and angular velocity, with a range of ±2000°/s, and sampling frequency of 200 Hz. Two IMUs were attached to the lateral wall of the right front (RF) and hind (RH) hooves with double sided and normal tape as can be seen in All horses were equipped with ProMove-mini wireless IMUs  offset drift was removed from the acceleration signal, and 2) the root of the sum of squares, Euclidean norm, was calculated of the tri-axial acceleration and angular velocity signals resulting in a one-directional acceleration and angular velocity signal. The Euclidean norm was used to reduce the calculation time in contrast to calculating a horse specific rotation matrix  and to cThe preprocessed signal is shown in Next, we determined the hoof-on and hoof-off from the acceleration and angular velocity signal separately by developing two algorithms.The algorithm for the angular velocity signal assessed every swing phase separately. Peaks were detected, indicated by the dots in The algorithm for the acceleration signal assessed the signal in a similar manner as described above. However, only the peaks detected in the first half of the swing phase were assessed for hoof-off detection and the peaks detected in the second half of the swing phase were assessed for hoof-on. The first and second half of the swing phase are indicated by the dotted boxes in With the determined hoof-on and hoof-off time points the following stride parameters were determined:Stance duration–time between hoof-on and hoof-off of the same hoofHoof-on time difference–time difference between the hoof-on detection of both algorithms were assessed with the force plate hoof-on detection separately for a given hoofHoof-off time difference–time difference between the hoof-off detection of both algorithms were assessed with the force plate hoof-off detection separately for a given hoofThe normality of the stride parameters was visually checked by examining the QQ plot and histogram in R . Thereafter, the distribution of the hoof-on and hoof-off time differences was evaluated to interpret the results and the performance of both algorithms was evaluated by Bland Altman and linear mixed model analysis.The agreement between the acceleration algorithm and the force plate and the angular velocity algorithm and the force plate was evaluated for the stance duration. This evaluation compared two different methods to measure the stance duration and therefore a Bland Altman analysis was performed with the “BlandAltmanLeh” package .The results of this analysis showed the mean difference in stance duration between the algorithms and the force plate and the standard deviation (SD) of these differences. These results were deemed better if closer to zero since this indicates a small and consistent difference between the algorithms and force plate, i.e. a good accuracy and precision. A positive mean indicates a shorter stance duration measured with the force plate and a negative mean indicates a longer stance duration measured with the force plate compared with the algorithms. In addition, the upper and lower confidence interval limits were used to calculate the width of the confidence interval. The width of the confidence interval was preferred to be small which means that the differences between the algorithms and the force plate were consistent.ijkl is the predicted value of the ijkl-th record, μ is the overall mean, hoofi is the effect of hoof (i can be RF or RH hoof), gaitj is the effect of gait , trialk is the effect of trial , (hoof x gait)ij is the effect of the interaction between hoofi and gaitj and ɛijkl is the residual error term associated with the ijkl-th record. A random intercept for every horse was included in the model.A linear mixed model analysis was performed to estimate the effect of horse, hoof, gait and trial on the performance of the algorithms for all stride parameters. This analysis was performed with the “lme4” package . The indi and gaitj  were calculated for every combination of hoof package ). In add package ).The performance of these algorithms was evaluated based on the predicted values and width of the confidence intervals. For the stance durations, the predicted values of both algorithms were deemed better if closer to the predicted value of the force plate and the width of the confidence interval was preferred to be small, which indicates a good precision. For the hoof-on and hoof-off time differences between the algorithms and the force plate, the predicted value was deemed better if closer to zero since this indicates a small difference between the algorithms and force plate, i.e. a good accuracy. A positive predicted value indicates a delayed detection by the algorithms and a negative predicted value indicates a too early detection by the algorithms compared with the force plate measurement. The width of the 95% confidence interval was preferred to be small, which means that the differences between the algorithms and the force plate were consistent, i.e. a good precision. Schematic representations of these predicted values were used to evaluate the accuracy and precision of the algorithms.A total of 147 trials were analyzed: 75 trials of the right front (RF) hoof  and 72 trials of the right hind (RH) hoof . In The distributions of the time differences for hoof-on detection are illustrated in The distributions of the time differences for hoof-off detection are illustrated in The results in The residuals of all selected linear mixed models were normally distributed and did not show homoscedasticity.The results presented in The results in In These results indicate that the agreement with the force plate was, in general, better for the angular velocity algorithm with an accuracy between 2.39 and 12.22 ms depending on the gait and hoof and a precision of around 13.83 ms for the hoof-on detection.The predicted values of the time differences between the acceleration algorithm and the force plate (model 3) were best explained with an empty model with no random effect. For the time differences between the angular velocity algorithm and the force plate (model 4), an empty model with random effect for horse was needed to explain the data best.The results in These results indicate that the agreement with the force plate was better for the acceleration algorithm with an accuracy of 3.20 ms and precision of 6.39 ms for hoof-off detection.For all three models, the predicted values for the stance duration were best explained when hoof, gait and interaction term were included as fixed effect and horse as random effect in the model. The results in Two algorithms are presented to automatically detect hoof-events from the acceleration and angular velocity signals measured with hoof-mounted IMUs in horses walking and trotting on hard ground. Results of internal validation with the force plate showed that, for the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 and precision of 6.39 ms, independent of gait and hoof.From the results we can conclude that hoof-on is better detected by the angular velocity algorithm which might be explained by the fact that the hoof will slide forward after vertical impact on a hard surface. The forward slide results in a silent angular velocity signal while the acceleration is not silent. Also, a difference in accuracy between the RF hoof (2.39 ms) and RH hoof (12.22 ms) was found which can be explained by the fact that horses place their front and hind hooves differently on the ground. In previous studies, also different landing and braking characteristics are found for hind, front, leading and trailing limbs –23. FurtIn a previous study by Braganςa et al. (2017), accuracy and precision for hoof-on were slightly better for the RH hoof and similar for the RF hoof. For hoof-off detection, the accuracy and precision found in this study were better . It was In another study, algorithms were developed to detect gait events from OMC data. Validation with the force plate showed an accuracy between -13.6 and 21.5 ms and a precision between 5.8 and 32.9 ms, depending on limb and gait , which iIn previous studies, reservations about the use of the force plate as gold standard for lameness detection are described , 29. TheThe OMC system used guarantees a relative precision of 1.9 mm  measurinThe needed accuracy and precision for gait classification are not yet determined. Stance duration are measured in different gaits at different speeds and the shortest stance duration reported was 103 ms in pace . TherefoTwo algorithms are presented to automatically detect hoof-on and hoof-off from acceleration and angular velocity data measured with hoof-mounted IMUs in walk and trot on a hard surface. Internal validation against the force plate was performed. The results showed that for the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms seem promising for gait classification, although a more extensive validation process should be performed. Also, the applicability of these algorithms should be investigated under different circumstances, such as different ground surfaces, gaits, speed and different hoof trimming conditions.S1 AppendixDocument with additional information about the population, data collection and synchronization of all the measurement systems.(DOCX)Click here for additional data file.S2 AppendixExcel file with hoof-on, hoof-off and break-over data.(XLSX)Click here for additional data file.S1 TableTable with stance durations as detected with the algorithms for acceleration, angular velocity and force plate.(DOCX)Click here for additional data file.S1 FigPreprocessed signals of the vertical force (A), the acceleration (B) and angular velocity (C) signals of the IMU for one hoof from one measurement in trot. The hoof-on events are depicted with upward-pointing triangle markers and hoof-off events are depicted with downward-pointing triangle markers.(TIF)Click here for additional data file. 25 Mar 2020PONE-D-19-27743Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensorsPLOS ONEDear Drs. Tijssen,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.We would appreciate receiving your revised manuscript by May 09 2020 11:59PM. 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The reviewers were generally complementary of the manuscript and the comments are really of an editorial nature and should provide greater clarity for readers not embedded in the field of equine biomechanics. I doubt it will take long to make the necessary emendations. i will have a look on the editor manager software and see if i can extend the due date for the revision of the companion manuscript to this so you can ensure complementary changes etc.Journal Requirements:When submitting your revision, we need you to address these additional requirements:http://www.plosone.org/attachments/PLOSOne_formatting_sample_main_body.pdf and http://www.plosone.org/attachments/PLOSOne_formatting_sample_title_authors_affiliations.pdf1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 2. 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Please note that we cannot proceed with consideration of your article until this information has been declared.Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” . The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The sampling rate of the IMU used is only 200 samples per second, so time resolution is already greater than 5 ms so insufficient, especially for faster gaits.Using the force plate as a measurement standard does not have a strong justification.The force plate measures force while the IMU measures motion. Therefore, it is not clear what the time relationship is between the resolved acceleration vector and the force plate loading during hoof impact and hoof rollover.No mention is made of the response time lag of the force plate signal.If the aim is to compare timing events measured with an IMU between limbs, or on the same limb under different conditions, it would appear to be circuitous to use a force measurement as a reference.Miniature high sampling rate IMU systems and recorders have variable integrated circuit clock speeds. It is important when trying to achieve timing accuracies to less than 5 ms that the recording systems are all calibrated. It is not clear how the force plate and IMU time records were synchronised or aligned.Until the required level of timing accuracy from the IMU can be demonstrated for a single horse there would appear to be little value in evaluating the system with a larger number of horses.Reviewer #2: The study presented a method to detect timings of hoof-on and off in horses using IMU sensors attached to the hooves. I have the following concerns regarding the presentation of the results and discussions associated with them. These are in no particular order.1) The authors claimed that the angular velocity algorithm was more useful for accurate detection of the hoof-on timing whereas the acceleration algorithm was better for the detection of the hoof-off timing. I was quite surprised with this result because I expected that the acceleration signal was more useful for the hoof-on detection because the sudden change in the velocity (i.e. large magnitude of acceleration) of the hoof occurs due to the collision, but less useful for the hoof-off detection because the hoof just leaves the ground and no collision takes place. The authors tried to explain the reasons behind by explaining the behaviors of the hoofs in the hoof-on and off events in the Discussion section, but no actual data about the movement of the hooves were provided. I think it is necessary to provide the actual movement data of the hooves during locomotion measured using the motion capture system to warrant the provided explanations.2) Figure 1 presented a schematic representation of the movements of the hoof, but not actual data measured by the IMU, force plate and motion capture system were provided. Why not provide actual data? Actually measured acceleration and angular velocity profiles from the IMUs, displacement profiles from the motion capture system and ground reaction force profiles from the force plate of both fore and hindlimb hooves from at least one representative trial should be provided to provide readers a better picture of what’s really happening to the hooves during locomotion.3) Means and standard deviations of some basic spatiotemporal parameters of analyzed walking and trotting trials such as stride time, stride length, and stance time should be provided. Such information is necessary to determine the necessary sampling frequency and needed accuracy of the system.4) Time scale should be provided in figures 1 and 3.5) When using an IMU for motion analyses, what is always a problem is a drift. How did the authors cope with this problem in this study?6) Why did the authors conduct both Bland Altman and linear mixed model analysis? I think the former is just sufficient for the evaluation of the algorithm because it is more direct and the two analyses essentially draw the same conclusion here.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  1 May 2020Reviewer #1:1. The sampling rate of the IMU used is only 200 samples per second, so time resolution is already greater than 5 ms so insufficient, especially for faster gaits.Indeed, the sampling rate of the IMUs was set to 200 Hz in this study. In other studies, IMUs were set to a sampling frequency of 500 Hz to be able to measure faster gaits like pace and tölt as described in the abstracts of Gunnarsson et al. 2018 and Serra Braganca et al. 2018 . The sampling frequency of the IMUs can be further increased to 1000 Hz if needed.2. Using the force plate as a measurement standard does not have a strong justification. The force plate measures force while the IMU measures motion. Therefore, it is not clear what the time relationship is between the resolved acceleration vector and the force plate loading during hoof impact and hoof rollover.Thank you for your comment. We agree that the force plate and IMUs measure another quantity which makes it difficult to compare the output of both systems directly. In this paper, we compare the timing of hoof-on and hoof-off. We detect these timings based on algorithms adapted to the specific signal and signal behavior, so whether the signal predicts vertical force, acceleration or angular velocity is not of importance to us. For this paper, we chose to use the force plate as measurement standard because this system is used in most research facilities and is still considered the gold standard for kinetic measurements . For the break-over, the rolling motion of the hoof, the quantity of the measurement system becomes more important. Therefore, we used the OMC system as additional reference measurement for break-over detection onset as described in companion paper (5).3. No mention is made of the response time lag of the force plate signal. Miniature high sampling rate IMU systems and recorders have variable integrated circuit clock speeds. It is important when trying to achieve timing accuracies to less than 5 ms that the recording systems are all calibrated. It is not clear how the force plate and IMU time records were synchronized or aligned.We agree that the time synchronization between the different measurement systems was not clearly described. We have added a section about time synchronization in the supplementary file S1 and elaborated a little bit more on this in main method section in line 115-118. The response time lag of the force plate was 5.5 ms ) and is removed during preprocessing of the data.4. If the aim is to compare timing events measured with an IMU between limbs, or on the same limb under different conditions, it would appear to be circuitous to use a force measurement as a reference.Thank you for your feedback. The aim of this article was not to compare timing events under different conditions or on the same limb under different conditions. The aim of this paper was to develop an algorithms to automatically detect the hoof-on and -off events from the acceleration and angular velocity signal measured with IMUs. To validate these algorithms, hoof-on and hoof-off timings measured with the force plate were used since this is the gold standard for kinetic studies and is widely used in the clinics.5. Until the required level of timing accuracy from the IMU can be demonstrated for a single horse there would appear to be little value in evaluating the system with a larger number of horses.Thank you for your comment. We agree that the required level of accuracy is not clear yet. The level of accuracy needed depends on its purpose, e.g. for gait classification less accuracy is needed compared to lameness detection. Furthermore, also the type of surface will be of importance as described in the discussion. In this study, we made a start by developing an algorithm for automated hoof-on and hoof-off detection. We used data of two legs per horse (one front and one hind) and we analyzed consecutive steps to increase the number of measurements although the number of horses was small. The next step will be to determine the needed accuracy and precision for different purposes and different surface types, and to perform another study with a larger number of horses to determine the required level of accuracy.Reviewer #2:The study presented a method to detect timings of hoof-on and off in horses using IMU sensors attached to the hooves. I have the following concerns regarding the presentation of the results and discussions associated with them. These are in no particular order.1) The authors claimed that the angular velocity algorithm was more useful for accurate detection of the hoof-on timing whereas the acceleration algorithm was better for the detection of the hoof-off timing. I was quite surprised with this result because I expected that the acceleration signal was more useful for the hoof-on detection because the sudden change in the velocity (i.e. large magnitude of acceleration) of the hoof occurs due to the collision, but less useful for the hoof-off detection because the hoof just leaves the ground and no collision takes place. The authors tried to explain the reasons behind by explaining the behaviors of the hoofs in the hoof-on and off events in the Discussion section, but no actual data about the movement of the hooves were provided. I think it is necessary to provide the actual movement data of the hooves during locomotion measured using the motion capture system to warrant the provided explanations.Thank you for your comment. We were also quite surprised, at first, with the finding that the angular velocity was more accurate for the hoof-on detection and the acceleration for the hoof-off detection. After some consideration of the sliding and braking characteristics of the hoof, described by Hernlund et al. 2010 (7), we really think that the explanation as given in the discussion section, is valid for this finding. We understand that actual movement data of the hoof would be helpful to visualize the movement of the hoof and might help to understand this explanation. However, adding position and angular data of the hoof requires adding the OMC data to this paper. After careful consideration, we think that this is beyond the scope of this paper for multiple reasons. Firstly, the OMC data show more complicated data regarding the kinematics of the hoof, i.e. position of the markers and angles of the markers relative to other markers. Adding this information would make the paper more technical and harder to read. Secondly, this type of data is relevant when studying angles or relative position and we therefore included the OMC data in the companion paper (5) where we described the break-over onset detection. Furthermore, adding OMC data would also introduce the discussion of how to define the hoof-on moment. Namely, hoof-on can be determined as the toe-on or heel-on time point and we did prefer to stay away from this discussion. However, we understand the need to provide readers with actual data and therefore we have added a figure with data from the force plate, OMC system and IMU to the supplementary file.2) Figure 1 presented a schematic representation of the movements of the hoof, but not actual data measured by the IMU, force plate and motion capture system were provided. Why not provide actual data? Actually measured acceleration and angular velocity profiles from the IMUs, displacement profiles from the motion capture system and ground reaction force profiles from the force plate of both fore and hindlimb hooves from at least one representative trial should be provided to provide readers a better picture of what’s really happening to the hooves during locomotion.3) Time scale should be provided in figures 1 and 3.We understand your concern and want to explain that figure 1 and 3 are generic illustrations of the IMU and force plate signals. These illustrations are based on actual data that is manipulated to illustrate the reader for which features/characteristics the algorithm is looking in the signal. We think that adding a time scale does not provide extra information to these illustrations since the steps of a horse are repetitive behavior that is not bound to one specific time interval. Actual data of one measurement is added to the supplementary file S1. Furthermore, we have changed the captions of figure 1 and 3 (line 86 and 163) to avoid misinterpretation.4) Means and standard deviations of some basic spatiotemporal parameters of analyzed walking and trotting trials such as stride time, stride length, and stance time should be provided. Such information is necessary to determine the necessary sampling frequency and needed accuracy of the system.Good suggestion, we have added a table with the mean and standard deviation values of the stance durations detected by the acceleration, angular velocity and force plate to the supplementary file S1. During this study, stride lengths were not analyzed, although this is possible the method to do this not yet validated and beyond the scope of this current study.5) When using an IMU for motion analyses, what is always a problem is a drift. How did the authors cope with this problem in this study?Indeed, you always have to be careful about drift. In this paper, we work with the acceleration and angular velocity data and not the integrated data to avoid drift. Furthermore, we work with one-directional data by calculating the Euclidean norm to avoid orientational issues as well.6) Why did the authors conduct both Bland Altman and linear mixed model analysis? I think the former is just sufficient for the evaluation of the algorithm because it is more direct, and the two analyses essentially draw the same conclusion here.Thank you for your question. Indeed, a Bland Altman test is more direct and easier to understand analysis method compared with a linear mixed model analysis. The outcome of the Bland Altman analysis shows the agreement of both algorithms with the force plate. However, with a linear mixed model analysis we were able to statistically test the effects of the independent variables, in this paper hoof, gait and number of analyzed trails. This test provides extra information about how the differences between the algorithms and the force plate arise. For this paper, we deemed this extra information important to able to give the reader an indication of how well the algorithms perform in different situations, i.e. different hooves and gaits. References:1. Braganca FM, Bosch S, Voskamp JP, Marin-Perianu M, Van der Zwaag BJ, Vernooij JCM, et al. Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot. Equine Vet J. 2017;49(4):545-51.2. Gunnarsson V, Tijssen M, Björnsdóttir S, Voskamp JP, Van Weeren PR, Back W, et al. Objective evaluation of stride parameters in the five-gaited Icelandic horse. 10th International Conference on Equine Exercise Physiology; 12 - 16 November 2018; Lorne, Australia2018. p. S52.3. Serra Braganca FM, Tijssen M, Gunnarsson V, Björnsdóttir S, Persson-Sjodin E, Van Weeren PR, et al. The use of an artificial neural network to classify gait in Icelandic horses. 10th International Conference on Equine Exercise Physiology; 12 - 16 November 2018; Lorne, Australia2018. p. S41.4. Starke SD, Clayton HM. A universal approach to determine footfall timings from kinematics of a single foot marker in hoofed animals. PeerJ. 2015;3:e783.5. Tijssen M. Break-over detection using hoof-mounted inertial measurements units. Submitted to Journal of Experimental Biology. 2019b, companion paper.6. Qualisys AB. Qualisys Track Manager - User Manual. Gothenborg, Sweden2011.7. Hernlund E, Egenvall A, Roepstorff L. Kinematic characteristics of hoof landing in jumping horses at elite level. Equine Vet J Suppl. 2010(38):462-7.AttachmentResponse to Reviewers A.docxSubmitted filename: Click here for additional data file. 4 May 2020Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensorsPONE-D-19-27743R1Dear Dr. Tijssen,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. 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I am happy to say it may now proceed to publication.Reviewers' comments: 21 May 2020PONE-D-19-27743R1 Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensorsDear Dr. Tijssen:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals. Kinase signaling, the reversible enzymatic addition of a phosphate group to a substrate, is an essential part of cellular activity. Because its dysregulation contributes to many diseases, numerous clinical trials have been performed with kinase inhibitors resulting in over 50 FDA-approved small molecules and targeted antibodies , 2. TherWhile kinase signaling has been studied for over 100 years using a variety of experimental methods, the recent generation of mass spectrometry-based phosphoproteomic profiling allows for an unprecedented global exploration of phosphorylation. Phosphoproteomics data analysis involves two major steps. The first step includes the identification, phosphosite localization, and quantification of phosphopeptides. The second step aims to translate phosphopeptide identification and quantification results into novel biological and clinical insights. Although analyses in the first step are typically performed by the proteomics cores using standardized computational tools, those in the second step require and can benefit from active involvement of biologists and clinicians.A vast array of resources and tools are available to facilitate the interpretation of phosphopeptide identification and quantification results. However, each of these tools exists as a silo without connection to tools with complementary functions. In addition, many tools have overlapping functions but differ in underlying knowledge bases, algorithms, input and output format of data, accessibility, advantages, limitations, and maintenance. Although newly developed tools are usually compared to similar, previously published tools, comparisons often do not include real-world, biological use-cases. For example, inference of kinase activity based on the observed phosphorylation of its substrates is a powerful application of phosphoproteomics profiling, and multiple methods have been developed to address this need , 4. HoweBiological and clinical scientists are in the best position to extract biologically and clinically relevant findings from phosphoproteomics data, however, they are rarely consulted for tool design input or requested to test the final product. Furthermore, there is no comprehensive list of tools to aid those using phosphoproteomic data in their research. Therefore, this article aims to provide a comprehensive collection of resources that can be used to gain insights from phosphoproteomic data, including knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. We perform some benchmarking comparisons to determine the best tool available and assess usability of the tools from the standpoint of typical biologists or clinicians.https://omictools.com) is a manually curated collection of bioinformatics tools [The OMICtools resource . Notably, no kinase resource collects data on non-protein kinases. KinBase, which was developed by Gerard Manning, contains 538 protein kinases and is considered the primary source of human protein kinases and their classification . Many otKinomer and KinG are general kinase sequence databases that provide very little other information , 9. KinMUse of the remaining general resources depends on which data one wants to access. KinaseNET, ProKinO, and iEKPD contain the most comprehensive data on protein kinases, but KinaseNET and ProKinO are only available as online resources , 13. TheFor disease studies, MOKCa and Kin-Driver specifically have data on protein kinase mutations , 15. MOKBecause phosphatases are less well studied than kinases, there are fewer resources dedicated to their collection. EKPD and iEKPD provide the same information for phosphatases as they do for kinases. HuPho, however, was the first comprehensive collection of phosphatases and the database includes pathway and substrate data, as well as siRNA phenotype data and links to orthologs in other species . DEPOD uBesides information about specific kinases and phosphatases, data on phosphorylation sites are important for studying the signaling process. Phosphorylation site databases collect information on the location of phosphorylated residues in proteins from experimental data. These experiments can be low-throughput or high-throughput. High-throughput phosphorylation site identifications are assigned by probability unlike the more stringent experimental validation in low-throughput experiments, but some databases combine sites from both types of experiments without identifying the source experiment type.In addition to phosphorylation site information, 16 of the 27 (60%) resources collect interactions between kinases or phosphatases and their substrates  is known to phosphorylate CDC25B [PDPK1, 3-phosphoinositide-dependent protein kinase 1, produces a protein known to the biological community as PDK1. However, there is an additional kinase, pyruvate dehydrogenase kinase, that is produced by the gene PDK1. Databases that try to integrate sites frequently attribute the substrates of PDPK1 to PDK1. Finally, integrating databases propagate errors from the original databases. For example, HPRD contains an entry for PTPN11 phosphorylating PTK2B although PTPN11 is a known phosphatase and not a kinase. The original manuscript connected to this entry confirmed that PTPN11 is a phosphatase and that it just binds to PTK2B at that particular site [Based on our examination, PhosphoSitePlus is the preferred resource for experimentally-identified phosphorylation sites and kinases for phosphorylation sites. PhosphoSitePlus is frequently updated, well-curated, and distinguishes between low and high-throughput identified sites. The downstream integrating databases suffer from ID mapping errors. For example, in PhosphoAtlas there is an entry for PEG  phosphorylating CDC25B. PEG is not a known kinase, but pEg3 kinase , while 70% of the phosphatases have fewer than 10 substrates  are shown in Fig. The known or predicted kinases for phosphorylation sites can be used to infer kinase activity from global phosphoproteomic data. Tools and methods have been developed to predict kinase activity, but there has been little effort spent towards comparing these tools or determining the most biologically-relevant set of parameters. The available tools  each use a different algorithm to infer activity Table . The PHOWe used a phosphoproteomic dataset from a cell line experiment with 20 kinase inhibitors  to test Comparison of these tools is challenging because they use different input and underlying databases. KEA2 requires a set of sites in the format of HGNC symbol and phosphorylated amino acid residue position separated by an underscore. It contains sets for 250 different kinases. KSEA App requires a strictly formatted comma-delimited file with the HGNC symbol, phosphorylated position, and non-log-transformed fold change. Users can choose between known sets from the July 2016 release of PhosphoSitePlus or the known + predicted site sets from PhosphoSitePlus and NetworKIN. PHOXTRACK requires a two-column file with a thirteenmer peptide and log-transformed fold change. It can use substrate sets from the four main databases or a user-supplied database. Finally, IKAP required tabular data entered into MATLAB, manual modification of MATLAB code to change parameters, and allowed a user to upload their own set of substrates. Because one thirteenmer might match multiple proteins and phosphorylated positions, the actual substrate list presented to each tool may differ slightly.To determine how well each tool covered the known targets of kinases, we counted the number of significantly downregulated known kinases of each inhibitor and the significantly downregulated kinases of each inhibitor that were not known targets of that inhibitor. The KSEA App made the most true positive predictions across all experiments, while IKAP made the fewest true positive predictions Fig. a. PHOXTRBesides upstream kinase activity, phosphoproteomics data could additionally be used to explore altered downstream pathways. While standard tools and methods such as GSEA are typically used for this analysis, all are limited to using overall gene-level phosphorylation . UnfortuBesides activity prediction, phosphoproteomic data can be used for other analyses. SELPHI is a good tool to first explore the data as it allows biologists to quickly and easily analyze phosphoproteomic data with clustering analyses, kinase-substrate correlation, and pathway enrichment . PhosFoxAnalysis and interpretation of phosphoproteomic data can be enhanced with other multi-omics data types. For example, sequence variants can affect kinase function or presence of a phosphorylation site. The databases PhosSNP  and ActiAfter discovering altered kinases from phosphoproteomic data to use as therapeutic targets, identifying inhibitors is essential. Most available resources connect known drugs to their known kinase targets Table . DrugKiNThe final set of bioinformatics tools, summarized in Table The available databases and tools for studying kinase signaling cover diverse functions and include information on enzymes and their substrates, inhibitors, activity, and mutations. Together these knowledge bases, prediction tools, and analysis tools comprise the current best standard for studying kinase signaling and many can be used without extensive computational knowledge. Overall, these tools allow a researcher to discover vast amounts of information from their phosphoproteomic data and some tools can even perform entire sets of analyses with a single button click .Despite the work that has been done, there is room for advancement to fully utilize phosphoproteomic data for use in the clinic. First, the majority of tools focus almost exclusively on the study of protein kinases. However, phosphatases are critical components of the kinase signaling cascade and are frequently dysregulated in cancer. Understanding the role of the interplay between kinases and phosphatases on the net phosphorylation seen in global phosphoproteomic data is essential to identifying abnormal cell signaling in disease. Furthermore, while the current tools and research are aimed at studying dysregulated protein phosphorylation, non-protein phosphorylation is also often altered in disease. For example, hexokinases, which phosphorylate glucose, drive glucose metabolism and contribute to tumor initiation in mouse models of lung and breast cancers . The devWhile the current tools provide critical functions, their error rate and accuracy could be improved. Errors are frequently propagated or amplified when tools collect data from a variety of resources. However, the impact of these errors on downstream analyses and biological inferences remains to be determined.For all tools, usability can be an issue, both for bioinformaticians and biologists with no computational experience. Tools are frequently platform-dependent, do not allow downloadable results, and are not well annotated. Furthermore, tools are difficult to compare or to use more than one during analysis. The input and output formats are not standardized and use a variety of protein naming conventions.The largest challenge was deciphering input limitations and understanding results. For example, submitting a sequence with a large number of phosphorylatable residues to GPS caused the software to stall without an error message and no documentation mentioned a size limit. Musite did not provide results for a sequence or two each run without explanation. Furthermore, downloadable result files for many tools had no column headers so the column contents were unknown. For example, the downloadable file from musite has no column titles, so you have to check the table on the website to understand the results. Additionally, scores are usually presented without explanation. Only careful reading of the manuscript or the manual elucidates what value signifies a “good” response. For example, in Scansite, the score 0 is the best, with scores closest to 0 indicating the best match. But in PhosphoPICK, the score indicates the probability of being phosphorylated by a kinase at that site so a score closer to 1 is better. Experts in machine learning might understand the score without explanation, but naïve users likely will not.One way to fix this challenge is to have a detailed, easy-to-find manual. The manual should include ways to run the tool, the underlying mechanism of the method, and detailed description of the results. The description of the results should also be available where results are visualized. Furthermore, sample input is helpful for a new user to test the tool and determine whether the results will be useful for their experiment before preparing their own data files.There are many tools and resources that can be used to study kinase signaling and these tools will become even more essential with the continued production of phosphoproteomic data. It is essential for the biological community to research under-studied enzymes and to validate specific substrates of kinases and phosphatases. Furthermore, bioinformaticians should consider creating tools that utilize information from both sides of the enzymatic phosphorylation reaction. Finally, resources should be carefully planned, easy to use, and well maintained and the community should work to standardize the use of enzyme IDs and phosphorylation site location.Additional file 1. List of URLs for all resources.


Ceratotherium simum) and black (Diceros bicornis) rhinoceros occur on private land, in the latter case under a custodianship program of the Namibian Ministry of Environment and Tourism (MET). Black rhinoceros custodian landowners are responsible for the protection of the rhinoceroses on their land and are required to report regularly to the MET. Monitoring imposes a financial burden on custodians yet many of the techniques used involve expensive monitoring techniques that include the need for aerial support and/or animal instrumentation. During May and June 2018, WildTrack undertook a pilot study to census black and white rhinoceros on three private custodianship properties in Namibia. We tested three footprint identification methods for obtaining estimates of rhinoceros populations in an effort to provide less costly alternative monitoring options to rhinoceros custodians. The first was a full monitoring protocol with two components: (a) tracking each individual animal and matching them to their footprints, (b) identifying those individuals from the heel lines on the prints. The second method used simple visual heel line identification ex-situ, and the third method used just an objective footprint identification technique. These methods offer different options of fieldwork labour and cost and were designed to offer monitoring options to custodians that provided information about rhinoceros movement and location, with minimal disturbance to the rhinoceros, and best matched their human and economic resources. In this study, we describe the three methods and report the results of the pilot study to compare and evaluate their utility for rhinoceros monitoring. The first method successfully matched each trail photographed to a known rhinoceros at each site. When the other two methods disagreed with the first, they did so by failing to match single trails to a known rhinoceros, thereby creating fictitious identities consisting of a single trail. This failure occurred twice in one application, but otherwise at most once. We expect this failure can be eliminated through more stringent criteria for collecting photographs of footprints. We also briefly compare the use of footprint monitoring with other commonly used monitoring techniques. On this basis, landowners hosting rhinoceros can evaluate which method best suits their needs and resources.Routinely censusing rhinoceros’ populations is central to their conservation and protection from illegal killing. In Namibia, both white ( Diceros bicornis) as critically endangered, with a global population estimate in 2016 at between 5,053–5,467 black rhinos  (k rhinos .Diceros bicornis bicornis (The majority (96.1%) of the population is now confined to Namibia, South Africa, Zimbabwe, and Kenya . Namibiabicornis . Due to bicornis . After 2Monitoring by custodians is mandated by the MET for two main purposes: to obtain reliable population demographic data to assess breeding success and population growth rates at each site; and to be able to rapidly act on any poaching.Routine monitoring to obtain reliable data on numbers and distribution of rhinos is central to their population recovery and protection from poachers. Black rhino monitoring methods have ranged from classic field observations , to ear The authors’ interest in identifying rhinos from footprints came from their finding that female black rhinos that were immobilized for repeated instrument fitting and (one-off) ear-notching exhibited significantly longer inter-calving intervals . After wCeratotherium simum simum) of white rhino was considered secure. Nearly 9000 African rhinos were poached in the decade ending 2018 3 non-profit registered in North Carolina, has been working with the Namibian Ministry of Environment and Tourism (MET) since 1997 to monitor both black and white rhino in National Parks and on private conservancies. On the basis of our prior experience censusing and monitoring rhino populations in Zimbabwe and Namibia, we developed three methods for censusing and monitoring rhino that exploit rhino footprints as the primary data. One of these methods is WildTrack’s FIT, which our prior studies have focused on testing in the field . This stThe authors obtained verbal and written permission to conduct this study from the landowners and the Ministry of Environment and Tourism of Namibia.Census: A complete count, or estimate, of individuals in a populationMonitor: To follow the demographics and distribution of a population over time, by repeated censuses or other assessments conducted at regular intervalsFootprint: An impression in the substrate made by the foot.Trail: An unbroken series of footprints made by one individual animal.This pilot study was conducted at three custodianship properties in Namibia in May-June 2018; sites A, B and C. Custodian sites were chosen by the MET as those that performed regular monitoring and were presumed to have reliable estimates of rhino numbers. Sites A and B were predominantly dry bushveld and Site C was mixed grassland and dry bushveld. Site locations are not disclosed to maintain rhino security. Each site hosted a small black rhino population and C also hosted white rhino. Custodian estimates of rhino populations were obtained prior to the survey and are reported in Sites A and C were commercial hunting farms, A on mostly flat sandy terrain while C had flat sandy, largely grassed terrain transitioning to rocky undulating bushveld terrain. Site B was a recreational tourism site on flat sandy terrain. At each site, rhinos were monitored regularly through anti-poaching and infrastructure repair patrols, and through observations with clients. At all three sites, roads, game tracks, and bare areas around waterpoints provided generally good substrate for tracking. There was no rain during the study period which took place in the middle of the dry season.Footprints were collected by photography from a thorough search of roads, tracks, and waterholes as the common component of all three methods.Guided by the custodian trackers, the aim of FIDP was to match opportunistic visuals to fresh spoor that could be photographed and to track fresh spoor, while being photographed, to a visual of the rhino. Footprints photographed with heel pattern but not matched to a visual could be compared to those footprints already matched to a visual. Absence of a match directed subsequent field searches until a visual with matching heel pattern was found. As rhino heel lines are unique to individuals, a simple visual comparison of these lines would provide custodians with the option of ongoing monitoring of individuals from footprints. The footprint images linked to individuals were included in the total number of footprints collected for use in Method 2 (Identification by heel pattern only), and 3 . See below under Field Protocol for more detail on ‘heel line pattern’, photography protocol, and FIT.This method involved a census to provide a population estimate just using the heel lines visible in all footprint images collected that were sufficiently clear. Without using any reference to known individuals, the heel lines were matched visually from the footprint images alone and processed off-site by co-author SA, who was not involved in the field work. This method is potentially quicker than Method 1, but since rhinos were not tracked to a visual, it would not match footprints to individuals. Heel lines were disregarded, as was any known information about the individual from whom the footprints had come. Only footprints with clear outlines of the three toes were used for this method. FIT analyses all footprints of suitable quality to estimate how many animals were represented by those footprints. As with method 2, this method does not match footprints to individuals, as is necessary for ongoing monitoring.While the chance of encountering a wild rhino is low, footprints can be much more accessible. Our experience is that footprints will last at least 24 hrs unless rain or high winds interfere. Other variables, including substrate type and other animal activity can also impact on the longevity of footprints. One of the benefits of using ‘fresh’ (<24 h old) footprints is that they inform on a rhino’s recent location and behaviour. Such footprints can be identified with experience by their clean, sharp outlines. Upon first observation, each footprint to be imaged was circled by a line in the substrate to prevent repeated sampling. The FIT protocol for rhinos requires images of multiple footprints in an unbroken trail . The images are of left hind (LH) footprints that clearly show the outline of the foot and its three toes . FootpriThe footprint identification technology (FIT) utilizes the traditional ecological knowledge of local trackers to find, follow and photograph animal footprints, and is based on the fact that not only do individual animals have unique foot structure, but also that each footprint itself is unique, it’s structure being dependent on substrate quality, animal gait, pace, weather conditions etc. Footprint images are collected according to a specific protocol , then maBased on prior experience, at each custodian site our objective was to collect a target of at least 30 footprints from each individual rhino, to allow for variation of substrate, walking gait, pace and other factors that might vary between individual footprints. Trails made by running rhino were not used.The cracks in the pad of rhino feet, unique to each animal, can leave raised impressions in the footprint. Where the substrate texture is soft enough to deform to a clear pattern of the sole of the foot (footprint), but firm enough that this imprint is retained after the animal lifts the foot, these patterns are sufficiently distinct amongst individuals to serve for individual recognition, at least for a single field season and for not too large a population  and group associations, which were stable for our short field season. In one case, however, a trio of subadults, two female and one male, although this group could be reliably identified visually, the two females within it could not be visually distinguished reliably. Records of white rhino releases, births, and deaths indicated that these 17 white rhinos constituted the white rhino population. The only possible exception would have been unobserved births resulting in individuals that remained unknown to the staff or never recognized as distinct from the other 17. Given that the potential mothers were well known, this possibility seemed remote.With these procedures we kept records of footprint counts, ‘trail identities’ to be visually identified, and known rhino still to be associated with a ‘trail identity’. The ‘trail identities’ were only employed as part of the methodology of method 1 and were considered unknown for methods 2 and 3. For methods 2 and 3, trails collected separately in the field, whether subsequently matched by pattern or not by method 1, were treated as distinct prior to analysis in methods 2 and 3.The field team consisted of the team leader, co-author PRL, two field assistants, and a tracker provided by the custodian, in most cases charged with monitoring the custodian’s rhino population. Each day was split into a morning (from just after sunrise to about midday) and afternoon (from about 3pm until sunset) field session.The time allotted for field work at each site was pre-determined by arrangement with each custodian, a compromise between our estimate of the minimum effort necessary and the imposition on the custodian. The evaluation of the three techniques was thus conducted under a realistic constraint on effort.With the knowledge that all sites were fenced, and that all rhino had to visit water points at least every 2 days, water points were the primary target for footprint searches. Most water points at sites A and C were small artificial water points with only a relatively small area of bare substrate around them, which was usually trampled with spoor of ungulates of various species. Thus, water points were primarily useful for signs that rhino had visited rather than for collecting trails themselves. Whenever such signs were detected, we attempted to locate a trail where a rhino had approached or departed from the water point and then follow this trail in the pursuit of useful photographs of spoor and a possible visual. These attempts were most successful when rhinos used the roads leading to and from the water points, as tall grass was still plentiful in June off the roads. Otherwise, the roads themselves provided search patterns for trails. Site B possessed mostly larger natural water points with extensive bare areas surrounding them so that rhino spoor was not typically trampled by other ungulates. As a result, it was easier to identify trails of rhino approaching and departing from water points. Although trails on roads were still of course useful, routinely checking the water holes was an efficient protocol at site B for locating rhinos.For white rhinos at site C, visuals could often be made of rhino grazing in the early morning in the open grasslands, and we attempted to locate the associated trails when the rhinos moved off and to track the rhinos to their resting sites, photographing acceptable footprints in the process.We typically set out to tour water holes and roads in a section of the site each morning and could cover the site in two or three days if uninterrupted. Of course, sightings of trails and or rhinos did interrupt such routine inspections and, if promising, became the priority of the session. Afternoons either resumed where the morning left off or started a new routine tour. As time passed, we also used an adaptive protocol in focusing more attention in areas where greater rhino activity was detected or focused on locating more elusive individuals.At site C, we focused first on the white rhino, beginning on 29 May 2018. By 9 June we had achieved visual identities on each of the 17 white rhinos known to the staff, sexed the two most recent calves (previously unsexed), and unambiguously photographed and matched footprints to each of these individuals. Every ‘trail identity’ established in the field was matched to a known rhino.The black rhino at site C had been introduced recently as a group of six. We collected footprint images for black rhino at site C from June 1 through 10, focusing most of our attention on the black rhino from June 5 through 10. We obtained visuals on four of the six, identified the footprints of one other seen by a professional hunter (identified by ear notches), and had unambiguously photographed trails from these five black rhinos plus another trail we did not match to these five. These black rhinos were walking extensively along roads, still apparently exploring their new landscape. This fact allowed us to capture many images from single trails for some of these black rhinos.Although the black rhinos were long established at site A, they did not confine their movements to small ranges, though there were parts of site A that rhinos apparently did not utilize. Between 21 and 29 June, we obtained visuals on all four known adult black rhino at site A and obtained the first visual of a recently born calf for which only spoor had previously been reported, thereby identifying its mother. We also obtained unambiguous spoor photographs for each adult rhino and all ‘trail identities’ were matched to known rhino.Between 13 and 19 June we obtained visuals on each of the eight black rhinos known to staff at site B, obtained unambiguous footprint images for each of these individuals, and matched each ‘trail identity’ to a known rhino.More than 1,500 footprints were collected from the three sites, over a five-week period. 608 photographs of white rhino footprints and 331 black rhino footprints were collected at site C over 17 days. 402 photographs of footprints of black rhino were collected at site B over 7 days and 300 at site A over 7 days. Over the entire period of five weeks, 45 separate visuals  were achieved. 23 visuals resulted from tracking footprints; the remainder began as a visual from a vehicle. Footprints were analysed as described above, and grouped by individual identity, according to the three methods. The individual groupings for the animals at each site are presented in At all sites, Method One confirmed the Custodian estimates. At site A, methods Two (HP) and Three (FIT) each estimated an additional individual. At site B, FIT estimated the same number of rhinos as FIDP, but HP estimated one additional rhino. At site C, all three methods agreed for black rhino. For white rhino, HP gave the same estimate as FIDP while FIT estimated 18 individuals; however, with the benefit of FIDP, it is known that no trail for one of the known white rhinos proved suitable for FIT analysis so in fact FIT recognized 16 of the known white rhinos but also created two additional identities.The results of this study demonstrate some of the benefits of using footprint identification to monitoring rhinos and offer a comparison of three different footprint-based approaches.The most intensive method, FIDP, provided results that agreed with the custodian estimates and is also most informative about a population of rhinos. As the only method of the three that matches footprint images to visual confirmation of an individual, and thus provides the custodian with a reference set of images matching each individual to its footprints, it also allows custodians to continue to monitor individuals without prior knowledge of the population. On the other hand, the data required for methods 2 and 3 can be collected as part of the custodianship’s routine fieldwork, requiring only that this be extended to include photographing of footprints, an activity which in itself improves knowledge of the whereabouts and movements of rhino, and thereby the effectiveness of the routine monitoring. It would also draw attention to individuals whose footprints were not being observed once a library of footprints of all individuals is established.That every trail photographed for the FIDP method was ultimately matched to a rhino known by a visual identity not only confirmed the Custodian estimates but provides evidence for its utility as a complete census. Accepting the resulting censuses provided by FIDP, method 2 (HP) erred at sites A and B by inferring one extra identity at each. Each of these identities consisted of a single trail not matched by HP to any other trail in the site’s database. For site A, that trail was photographed under a midday sun, and despite shading the footprint and using a reflector to attempt to draw out contrast, the photographs show little detail of the heel pattern, and less than was observed in the field. This trail was together with the trail of another rhino. We had seen and photographed two trails together two days previously and were able, with the detail observable in the field, to match these two trails to the previous ones; in particular, the trail constituting the additional identity in method 2 was recognized by the field workers as belonging to the bull at site A. At site B, the additional identity under method 2 also consisted of a single trail. Through method 1, it was known to the field workers that this trail was collected as the trail of the calf in a cow-calf pair. Each trail manifests variation in the heel pattern impression and highlights the difficulty that can be encountered with relying on heel patterns alone.Method 3, FIT, agreed with method 1 at site B and for black rhino at site C. At site A, FIT, assigned an extra identity, which consisted of a single trail known to belong to Cow8 through tracking. For white rhino at site C, no trail for an adult female with calf designated cow4 proved acceptable for FIT analysis so this identity does not occur for FIT, but FIT did recognize the other 16 white rhino. In addition, two trails whose identities were known by FIDP were not matched to any other trail in the site C database by FIT and thus each constituted a separate identity.Thus, each fictitious identity produced by methods 2 and 3 as judged by method 1 consisted of a single trail only, for which method 1 provided additional information through tracking and visuals to identify. In this pilot study we sometimes photographed trails that were less than ideal for either methods 2 or 3 because with supplementary field information they were useful in method 1. The fact that methods 2 and 3 never created a fictitious identity consisting of more than one trail suggests that the trails yielding the fictitious identities can be considered outliers for either methods 2 or 3 and that the precision of these methods can be improved by stricter adherence to the protocols for collecting photographs for these methods. At the same time, identities consisting of a single trail of less than ideal quality can be highlighted as suspect and deserving follow-up investigation. We also note that the incidence of this error did not increase with population size; specifically, the per capita rate decreased with population size.Custodians are required to obtain regular sightings of their rhinos and report to the Ministry of Environment and Tourism. If an individual is not seen within the allocated time, an intensive search is required. To fulfill these demands, they must make a strategic choice of monitoring strategy depending on their resources including staff availability and expertise, and the budget available for monitoring. Terrain suitability and climate will also dictate the method(s) chosen. We recommend that a full ID protocol (method 1) should be undertaken at each custodianship annually if possible. In between times, and at least monthly, custodians can either use their own team or host a team trained in the protocol to do either a quick onsite survey (method 2) or take images using the FIT protocol to submit to an off-site survey (method 3). Any or all of these approaches can and should be combined and/or augmented using camera-traps and regular traditional patrols on foot or waterhole counts to ensure that all rhinos are seen regularly (see below). Clearly it is in the interests of the custodians to be self-sufficient in rhino monitoring, and the WildTrack non-profit program aims to provide training and a JMP software license without cost to all custodians who are interested in using FIT to monitor their rhinos. The training can be for data collection, or for collection and analysis, depending on custodian staff resources.During the fieldwork, other demographic observations were made. All custodians shared some natural uncertainty about the sex of new calves and the identity of their dam. The ground survey teams were able to locate all the new calves, sex some of them from direct observation and identify their mothers. At site A, trackers occasionally attributed a rhino identity to a trail based on its location, rather than careful inspection of the pad impression. We have encountered this behavior in previous field work. New light has been shed on black-rhino recursive movement patterns  that couFootprints, in the form of the three methods tested in this study, not only provide a monitoring tool but also current information such as ranging and social interactions. When the team arrived at site B, staff reported seeing footprints on their walks and were unsure of the identity of the animal that made them. Tracking and FIT identified these as from the younger bull at this location. Method 1 will provide the most information of this sort and consequently, the most accurate representation of the population.Although a detailed analysis of costs and other benefits is outside the remit of this paper, it is worth considering, from the perspective of a custodian, how footprint identification compares with other available techniques. There are several advantages to the use of footprints compared with other current alternatives. In terms of a cost-benefit ratio, footprint identification ranks relatively highly because it requires only minimal equipment  and can engage scouts who are already on site for anti-poaching/tourist work. However, it is only effective where the substrate will hold clear footprints, where expert trackers are available, and where the weather does not compromise the integrity of the footprints. Also, our experience indicates that as an animal ages, the heel patterns and footprint morphometrics may change. In our experience of work at the same site over several years  this doeWhere trackers are available, the use of a footprint-based method can also act to leverage their traditional ecological knowledge (TEK) and, if trainees are engaged, revive these skills for the benefit of wildlife conservation. Trackers are often employed by hunting or photographic tourism sites specifically to locate an animal, or for anti-poaching duties. However, where they are employed as part of an ongoing rhino monitoring strategy their TEK about rhino ecology and behavior can be integrated into the process, contributing to local capacity in wildlife custodianship and helping develop sustainable monitoring in the absence of external experts. Footprint monitoring techniques can thus be particularly cost-effective in leveraging scout TEK for monitoring at the same time as guiding and anti-poaching.Footprint surveys, consisting of a tracker and photographer, can provide a reliable census figure with minimal equipment  in about a week for a typical custodianship. They also provide a means of matching each individual rhino on a property to its unique footprints, so that if patrols cover the ground regularly it can provide a cost-effective ongoing monitoring approach. If deployed effectively, and ideally in combination with other ground sensors , it is also a robust anti-poaching tool. With effective monitoring rhino populations will increase and animals disperse. Footprint monitoring need not be compromised by large population sizes, however, if geographic location, social groups and known movement patterns are included as prior knowledge to assist in the identification of more animals.Aerial counts are more expensive but have the advantage of rapid deployment. However, rhinos are not always easy to spot, particularly with vegetation cover. The advent of UAV monitoring  will cerTo summarize, every monitoring technique has pros and cons. We would argue that the best results are likely to be obtained by identifying the available resources at a site, particularly the availability of skilled trackers, and then deploying the combination of techniques that will provide the data required in the time available. The selection of which techniques to use can be guided by 10.7717/peerj.9670/supp-1Supplemental Information 1The grouping of trails produced by each of the three techniques for each of the three sites and two species.Click here for additional data file.10.7717/peerj.9670/supp-2Supplemental Information 2The grouping of trails produced by each of the three techniques for each of the three sites and two species.Click here for additional data file.10.7717/peerj.9670/supp-3Supplemental Information 3The grouping of trails produced by each of the three techniques for each of the three sites and two species.Click here for additional data file.10.7717/peerj.9670/supp-4Supplemental Information 4The grouping of trails produced by each of the three techniques for each of the three sites and two species.Click here for additional data file.10.7717/peerj.9670/supp-5Supplemental Information 5The grouping of trails produced by each of the three techniques for each of the three sites and two species.Click here for additional data file.


A combination of DFT calculations, structural characterization, and kinetic (isotopic) analysis reveals that Pt and WO3 act as the active sites for ammonia borane and H2O activation, respectively. A trade-off between the promoting effect of WO3 and the negative effect of decreased Pt binding energy contributes to a volcano-shaped activity, and Pt/CNT-5W delivers a 4-fold increased activity of 710.1 molH2·molPt−1·min−1. Moreover, WO3 is suggested to simultaneously act as the sacrificial site that can divert B-containing by-products away from Pt sites against deactivation, yielding an increase from 24% to 68% of the initial activity after five cycles. The strategy demonstrated here could shed a new light on the design and manipulation of dual-active-site catalysts.Development of synergistic heterogeneous catalysts with active sites working cooperatively has been a pursuit of chemists. Herein, we report for the first time the fabrication and manipulation of Pt-WO  •3 dual active sites boosts H2 generationMechanism-guided design of Pt-WO•3 loading and Pt B.E. yields a volcano-shaped activityA trade-off between WO•3 acts as the sacrificial site to divert by-products against deactivationWO•Multifunctional Pt-MO/C catalysts achieve enhanced activity and durability Inorganic Chemistry; Catalysis; Nanomaterials Noble metal catalysts are the workhorses for energy and environment applications, which enable the conversion of feedstock molecules to desired products . Neverth3BH3, AB) has been regarded as a promising hydrogen storage material ∗+H∗ as the rate-determining step (RDS). Moreover, DFT calculations reveal that pure Pt metal surface binds H2O too weakly to dissociate H2O, whereas some transition metal oxides exhibit great potentials for facile H2O dissociation  investigations, and DFT calculations was employed to reveal the structure-performance relationship, and a dual-active-site mechanism was proposed to contribute to the simultaneously enhanced activity and durability. This provides a feasible avenue to design and develop dual-active-site catalysts by combining theoretical and experimental studies in this research area.Herein, we report a strategy to design and fabricate dual-active-site catalysts to boost hydrogen production from ammonia borane hydrolysis. DFT calculations were first carried out to assist the fabrication of Pt-WO2O. Exemplified with WO3, the adsorption and activation of ammonia broane and H2O on the representative WO3(100) and Pt(111) surfaces, as the thermodynamically stable and most exposed facets (3(100) surfaces are listed in 3(100) surface. This indicates that the Pt site facilitates the activation of ammonia borane with respect to the WO3 site. In contrary, the WO3 site  shows much lower activation barrier for H2O dissociation than the Pt site  as shown in 3(100) surface as shown in 3 dual sites for acting as a synergistic catalyst can promote ammonia borane hydrolysis.As discussed above, fabricating Pt-metal oxide might be an effective strategy to obtain synergistic catalyst, with dual active sites working cooperatively for the activation of ammonia broane and Hd facets , were co3 is unfavorable for Pt immobilization  at 90°C for 10 h, followed by filtering, washing, drying, and calcination under Ar at 450°C for 2 h. The as-obtained samples were denoted as CNT-γW, in which γ represents the concentration of ammonium tungstate aqueous solution. X-ray diffraction (XRD) spectra in 3, the CNT-γW samples exhibit some distinct characteristic diffraction peaks of WO3 (JCPDS No. 43-1035), in addition to the two characteristic diffraction peaks of graphite (JCPDS No. 65-6212) from the CNT. Notably, these diffraction peaks become intensive and sharp with the concentration of ammonium tungstate solution, indicating the increased amount and size of WO3 particles over the CNT-γW.Considering that the low specific surface area of WOlization , carbon lization  was empl3 over the CNT-γW. As shown in 3 over the CNT-1W, CNT-5W, and CNT-10W are determined as around 7.0, 24.1, and 47.2 wt%, respectively. It can also be seen that the onset of carbon support decomposition shifts to low temperature with the loading of WO3. This is most likely because the presence of WO3 reduces the thermal stability of CNT in air by catalyzing the low-temperature oxidation of CNT, which has been also observed in previous studies  was further carried out to determine the loadings of WO studies .3 affects the surface defects of CNT, for which the intensity ratio of D1 band at ∼1340 cm−1 to G band at ∼1570 cm−1 (ID1/IG) is used to quantify the surface defects  measurement was first conducted to explore the interactions among Pt, WO3, and CNT. As shown in 3. Notably, the increasing WO3 loading leads to the upshift of WO3 reduction peak, which eventually overlaps with the methanation peak, possibly due to the formation of larger WO3 particles in 3 loading, resulting from the interaction of Pt with WO3.The above fabricated-tungsten-incorporated CNT-γW samples as well as the pristine CNT as a reference were impregnated with H3 clusters, with some small spots, which could be Pt and WO3 nanoparticles. Considering that the contrast variations in HAADF-STEM characterization are proportional to the square of the atomic number  mapping of the selected areas in 3 patches as well as a few dispersed particles, consistent with the HAADF-STEM results. Interestingly, as shown in 3 patches instead of on CNT. Considering that the electron microscopic characterization could only reflect the local information of the sample, another two areas as depicted in 3 patches rather than with CNT, and the corresponding Pt particle sizes still remain in the range of 1–2 nm, which is comparable to that for the pristine CNT. Hence, all the above results indicate that the Pt particles prefer to interact with the WO3 patches, which is consistent with the decreased reduction temperature as shown in As shown in 7/2 and Pt 4f5/2, which can be deconvoluted into three pairs of doublets, i.e., Pt0, Pt2+, and Pt4+. 0, which has been identified as the main active species for this reaction  can be calculated based on the slope of linear part for each plot in initial values of Pt/CNT, Pt/CNT-1W, Pt/CNT-5W, and Pt/CNT-10W catalysts are determined as 165.2, 439.2, 710.1, and 557.5 molH2·molPt−1·min−1, respectively. By combining previous results that the high Pt B.E. is favorable for ammonia borane hydrolysis  than the Pt/CNT catalyst, and the trend of Ea is consistent with that of Rinitial mentioned above. This suggests that the fabrication of Pt-WO3 dual active sites contributes to the enhanced kinetics. Furthermore, kinetic isotope experiments by replacing H2O with D2O as the reactant were conducted to probe the kinetic isotope effects and further the reaction mechanism , which are higher than that of the Pt/CNT catalyst (1.9). The change in the kH/kD is most likely ascribed to the change in the active sites, i.e., the single Pt site for the reaction over the Pt/CNT catalyst, but the dual Pt and WO3 sites for the reaction over the Pt/CNT-γW catalysts.To gain more insights into the reaction over such Pt-WOechanism , and the3 remarkably promotes the catalytic durability of Pt/CNT, which increases with the WO3 loading. The pH values of the reaction solutions were firstly measured in the range of 9.1–9.8 for all the catalysts during the reaction. This could be ascribed to the acid-base equilibrium of B(OH)4-⇋B(OH)3+OH− for the B-containing byproducts in the reaction solution 3–B(OH)4- is 9.2  in comparison with 24% for the Pt/CNT catalyst, which mainly arises from the most sacrificial sites of WO3 for the adsorption of B-containing by-products.As mentioned above, the adsorption of B-containing by-products on the Pt surfaces is another main factor leading to the catalyst deactivation . The XPS3 site exhibits much lower activation barrier for H2O dissociation than on the Pt site. On a single Pt active site, ammonia borane hydrolysis over the Pt/CNT catalyst has been demonstrated to proceed by the NH3BH2∗-assisted cleavage of O-H bond within H2O  analysis shows significantly decreased activation energy of the reaction, but an increase in kH/kD ratio from 1.9 to 2.6, which contribute to the enhanced kinetics for the reaction over the Pt/CNT-γW catalysts.As described above, DFT calculations show that the activation of ammonia borane reactant proceeds by its dissociative adsorption on the Pt site, and the WOthin H2O . Interes3 by incorporating it onto the CNT with larger specific surface area, close ends, and mesoporous structure, which is a promising candidate to immobilize Pt for favorably constructing Pt-WO3 dual active sites and enhancing hydrogen generation activity. If directly using commercial WO3  as the catalyst or catalyst support, the catalyst testing results in 3 is almost inactive for the reaction, and the Pt/WO3 catalyst gives much lower hydrogen generation rate than the Pt/CNT-γW catalysts, mainly due to the larger Pt particle size and undesirable Pt B.E in 3 immobilized on the CNT is suggested as an effective way to obtain more Pt-WO3 dual active sites for the promoted reaction.The strategy demonstrated above has been successfully developed to fabricate high specific surface area of WO3 not only leads to the Pt/CNT-γW catalysts with lower Pt B.E. than the Pt/CNT catalyst, unfavorable for the reaction, but also fabricates highly active Pt-WO3 dual active sites, favorable for the reaction. Such trade-off between the promotion effects of WO3 and the negative electronic effects of Pt B.E. can pave an explanation for the observed volcano-shaped activity in In our previous studies , it has 3 also demonstrates remarkable enhancements in the durability. This is attributed to the preferential adsorption of B-containing by-products over the WO3 sites. As the reaction proceeds, the produced B-containing by-products would transfer and accumulate on the WO3 site instead of the Pt site. Considering that WO3 is in large excess, the accumulation of B-containing by-products has much less influence on the activity. Therefore, the WO3 site is also suggested as the sacrificial site, diverting B-containing by-products away from the Pt site during the reaction. With the increase of WO3 loading, more and more Pt sites in contact with WO3 sites are sterically protected from deactivation.In addition, the incorporation of WO3 dual sites for the enhanced activity and durability, 3 dual active sites, in which the Pt site and WO3 site contribute to the activation of ammonia borane and H2O, respectively. On the other hand, the WO3 site also acts as the sacrificial site to preferentially adsorb B-containing by-products, which can suppress the adsorption of B-containing by-products on the Pt surfaces. Such cooperativity between Pt and WO3 sites creates a unique synergy and demonstrates robust hydrogen generation from ammonia borane hydrolysis.Based on the discussion of the mechanism of Pt-WO3 dual sites enhance not only the hydrogen generation activity and kinetics due to the Pt-WO3 dual active sites but also the durability due to the sacrificial site of WO3 for the preferential adsorption of B-containing byproducts during the reaction. Moreover, it can be seen in 2, Fe3O4 and TiO2) endow the catalysts with a significantly enhanced durability. All of these discussions could shed a new light on the rational design and manipulation of highly active and durable Pt-based catalysts for the reaction, e.g., fabricating Pt-MO/C multifunctional catalysts with appropriate transition metal and carbon toward significantly enhanced hydrogen generation activity and durability.In addition, 3/CNT catalysts with simultaneously enhanced hydrogen generation activity and durability. A combination of DFT calculations and multiple characterizations with kinetic (isotopic) analysis demonstrates that the Pt-WO3 acts as the dual active sites for the activation of ammonia borane and H2O. By incorporating the WO3 onto CNT to immobilize Pt particles, the resultant Pt/CNT-γW catalysts give rise to more Pt-WO3 dual active sites with the desirable Pt B.E. than the commercial WO3-supported Pt catalyst. By increasing WO3 loading, a trade-off between the promotion effect of WO3 and the negative electronic effect of decreased Pt binding energy contributes the volcano-shaped activity, in which the Pt/CNT-5W delivers the highest catalytic activity of 710.1 molH2·molPt−1·min−1, more than four folds higher than that of pristine Pt/CNT. On the other hand, the WO3 site acts as the sacrificial site and diverts B-containing by-products away from the Pt site, thus inhibiting the catalyst deactivation during the reaction and yielding a significant increase from 24% to 68% of the initial catalytic activity after five cycles. This report not only shows a high potential to achieve robust hydrogen generation from ammonia borane hydrolysis but also guides the rational design and manipulation of dual-site catalysts with the multifunctional properties by combining theoretical and experimental studies.In summary, we report for the first time the fabrication and manipulation of multifunctional Pt-WOCurrently, it is very difficult for this research to precisely characterize the microstructures of the fabricated highly active and durable Pt/CNT-γW catalysts at an atomic level. This would be facilitated by advanced in-situ/ex-situ catalyst characterization, such as Cs-corrected TEM and XAS. Then, more desirable theoretical models are needed for in-depth understanding of the reaction mechanism and kinetics to reveal the underlying dual-active-site mechanism.All methods can be found in the accompanying