We report the case of a patient who developed a checkrein deformity of the hallux and of the second toe following a direct soft tissue trauma to his right leg, with no associated fracture. This dynamic deformity caused the patient significant trouble walking and prevented him from playing any sport as in the stance phase of gait the toes were forced into maximum plantar flexion and ended up trapped under the foot. An MRI study did not show any bone injury or tendinous tethering. So the cause could be a subclinical compartment syndrome. Treatment consisted of a z-plasty and application of a pulvertaft suture to the flexor hallucis longus. Following a short rehabilitation program (2 weeks), the patient made a full recovery. Acquired clawing of the toes may be due to contracture of the muscles of the deep posterior compartment of the leg after a subclinic compartment syndrome. This produces a “fixed-length phenomenon” of the long toe flexors, also described as a “checkrein” deformity . The defThe interest of this study lies in the fact that in our case no fracture was sustained that could account for the deformity, which suggests a subclinical compartment syndrome as an etiology.This report describes the case of a patient who developed a checkrein deformity of the hallux and of the second digit of the right foot following a direct soft tissue trauma to his right leg, with no associated fracture. Treatment consisted of a z-plasty and application of a pulvertaft suture to the flexor hallucis longus, one double-loop suture at each tendon free-end, and four mattress sutures evenly spaced between. The outcome was satisfactory.The patient was a 37-year-old male who suffered a car-motorcycle accident that resulted in his right leg being trapped between the bumper of the car and the side of the motorcycle. He was initially diagnosed with a contusion injury on the anteromedial aspect of the right leg. The wound was carefully cleansed and debrided. A year later, the patient presented with a progressive plantar flexion deformity in the first and second toes of the right foot which manifested itself when the ankle was dorsiflexed .This dynamic deformity caused the patient significant trouble walking and prevented him from playing any sport as in the stance phase of gait the toes were forced into maximum plantar flexion and ended up trapped under the foot. Nevertheless, when the ankle was plantar-flexed, active toe flexion and extension was complete. An MRI study did not show any bony or musculotendinous injury . The finThe patient was scheduled for surgery. The procedure was performed through a medial retromalleolar approach. After identifying the FHL, it was subjected to a z-tenotomy. A marking stitch was applied to the resulting tendon ends making sure that the great toe remained in neutral flexion/extension while the ankle was also in neutral flexion/extension .Subsequently, a pulvertaft suture was applied . No surgAfter surgery, the patient was immobilized in a splint for 3 weeks, with the ankle in neutral position. Following a short rehabilitation program (2 weeks), the patient made a full recovery, with full restoration of active flexion/extension of the great and lesser toes when the ankle was in neutral dorsiflexion .Checkrein deformity is a dynamic flexion deformity of the flexor hallucis longus tendon, so that when the ankle is passively dorsiflexed, the metacarpophalangeal and the interphalangeal joint becomes more prominent.It has been described following tibial fracture, calcaneus fracture, talus, and closed Salter-Harris Type II ankle fracture –5. Lee eFeeney et al.  refer thIn our case, there was no entrapment of the tendon but shortening, so we thought that it could be an injury caused by a subclinical compartment syndrome.The fact that the flexor hallucis longus tendon arises from the lower two-thirds of the posterior aspect of the fibula and the interosseous membrane dictates the kind of surgical technique required to address checkrein deformity. Lee et al.  describeSanhudo and Lompa  and SinnHowever, according to Feeney et al.  and Lee In our case, the lengthening procedure was performed at the proximal level, and, although the FDL was spared, the deformity of the second digit was corrected. The reason behind an isolated release of the FHL was that the connections between the FHL and the FDL are located at the level of the distal rearfoot. In our case, there were no tendon adhesions to the tibia, only tendon shortening. At any event, early toe mobilization following injury is crucial, particularly in the presence of a fracture.Checkrein deformity is a relatively uncommon condition, which may arise as a result not only of a fracture but also of soft tissue trauma.An isolated lengthening tenotomy of the FHL at the retromalleolar level is sufficient to correct the associated second-digit deformity.


Weld bead geometry features (WBGFs) such as the bead width, height, area, and center of gravity are the common factors for weighing welding quality control. The effective modeling of these WBGFs contributes to implementing timely decision making of welding process parameters to improve welding quality and enhance automatic levels. In this work, a dynamic modeling method of WBGFs is presented based on machine vision and learning in multipass gas metal arc welding (GMAW) with typical joints. A laser vision sensing system is used to detect weld seam profiles (WSPs) during the GMAW process. A novel WSP extraction method is proposed using scale-invariant feature transform and machine learning. The feature points of the extracted WSP, namely the boundary points of the weld beads, are identified with slope mutation detection and number supervision. In order to stabilize the modeling process, a fault detection and diagnosis method is implemented with cubic exponential smoothing, and the diagnostic accuracy is within 1.50 pixels. A linear interpolation method is presented to implement sub pixel discrimination of the weld bead before modeling WBGFs. With the effective feature points and the extracted WSP, a scheme of modeling the area, center of gravity, and all-position width and height of the weld bead is presented. Experimental results show that the proposed method in this work adapts to the variable features of the weld beads in thick plate GMAW with T-joints and butt/lap joints. This work can provide more evidence to control the weld formation in a thick plate GMAW in real time. The gas metal arc welding (GMAW) process has become a popular method for joining the thick plates of steel structures and been used in the shipbuilding industry, wire arc additive manufacturing , pipe maThe modeling process of WBGFs is implemented in an offline and online fashion. In some studies, the prediction or optimization of WBGFs is the main objective using regression models between WBGFs and welding process parameters. In this case, offline modeling methods are presented via the metallurgical microscope , the calThe vision sensing technology is one of the main online measurement methods of WBGFs and the most common method for welding position detection ,23. ThisWith the development of machine vision and learning, laser vision sensing has been used in the welding industry because it can detect deep grooves with thick steel plates. This ability can measure more WBGFs. In these studies, the sensor emits a laser light with the specific wavelength onto the groove, and a charge coupled device (CCD) camera captures the image. If the sensor works statically, the two-dimensional measurement of WBGFs can be realized . A threeIt is noteworthy that the current studies mainly concentrate on modeling WBGFs for a single weld bead; most of them are implemented for lap joints, and WBGFs usually are the bead width and height. More WBGFs such as the area and the center of gravity are also useful evidence for weld formation control. In addition, the real-time modeling process of these WBGFs in thick plate GMAW is still a challenge, which demands higher adaptability of information acquisition, stable determination of the weld bead, and variable WBGFs. Thus, more effective weld seam profile (WSP) extraction and feature point identification methods, stable seam tracking technologies, and the effective modeling scheme of the WBGFs should be investigated. In addition, the WBGFs at different formation positions provide more direct evidence to optimize the welding process parameters  than only their maxima. WSP extraction is the prerequisite during the active-sensing-based WBGF modeling process. Various WSP extraction methods have been reported in the literature ,33. HoweIn this work, a laser vision sensor is used to detect the deep grooves with T-joints and butt joints during the multipass GMAW process. A more effective WSP extraction method is presented based on scale-invariant feature transform (SIFT) and machine learning algorithms to strengthen the robustness comparing with the previous ones ,37. ThisThis work consists of WSP extraction, feature point identification of the WSP, fault detection and diagnosis, modeling WBGFs, experimental results, discussion, and conclusions.In order to monitor the WBGFs, especially the center of the gravity of the weld bead, simultaneous imaging of the welding torch and the WSP is investigated through a special combination of dimmer glass and the filter in this work. Experimental results show that when the central wavelength of the filter is about 660 nm, the half bandwidth is 20 nm, and the transmittance of the dimmer glass is about 0.02%, the welding torch can be marked with the welding wire. WSPs changes with the weld beads during the multipass GMAW process. However, the image (reference image) that is captured before arc starting has a similar laser stripe  with the one captured after the welding process begins (raw image). Thus, the WSP extraction method is proposed based on SIFT and machine learning as shown in This method includes two image processing paths. The first one is using the Gabor filter and local thresholds to highlight the WSP from the background and simplify the background data, respectively. The second one is locating the WSP from the interference data based on SIFT and the nearest neighbor clustering algorithm.The first image processing path is as follows. The region of interest (ROI) is normally used to reduce the influence of the interference data on WSP extraction and the computational burden . There iG represents the Gabor filtering result. The weight 0.5 accounts for the relatively low intensity of the groove region  th to (i + 2) th rows when the columns are limited from j − 2 to j + 2.The local thresholds are defined asSIFT is a method for extracting distinctive invariant features from two target images. It can be used to perform reliable matching between different views of an object or scene. This function is invariant to image scale and rotation . In thisIn the second processing path, the orientation feature map is first binarized via local thresholds. Then, the nearest neighbor clustering is used to mark the segments of the WSP. Third, the cluster that is the nearest to the matching points is considered as a segment of the WSP. The WSP extraction process is illustrated in The feature points in this work mean the boundary points of the weld bead and the groove. They are usually identified with least squares fitting , Hough tith slope, and The extracted WSP is first thinned  before calculating the slopes. Then, the linear WSP is interpolated with the least square method. The slopes easily fluctuate because of the remaining interference data points and the distorted data points of the WSP. The slope calculation is defined asP) of the feature points has been supervised (designated), the first P monotone intervals of es in MI . The lenThe effectiveness of the feature point identification method proposed here is validated using some continuous sampling images shown in y-/x-direction in images (suppose that they are independent). The coordinate of the target feature point is predicted/optimized asT is the sampling period, and the coefficients A, B and C are defined asThe cubic exponential smoothing method is typically used to stabilize time serial variables through predicting the real state for the next several sampling periods. This method is effective when the time serial variable fluctuates periodically in a linear trend. The identified feature points have this characteristic when the welding torch moves from the start to the end welding positions. Thus, they can be predicted with the cubic exponential smoothing method when the ineffective feature point identification result is diagnosed. The state of the target feature point is iterated asAlgorithm 1 Feature point optimization process using cubic exponential smoothing.Input Output • Feature point ineffectiveness judgement: *        * Calculate            * Calculate      •   The trigger of starting the smoothing process is that the designated tracking position deviates from its last position by 3 pixels. One-step prediction is used in this work. The typical prediction process is given in Algorithm 1.The designated tracking position in the images within the red rectangle in Experimental results show that the cubic exponential smoothing method enhances the accuracy of feature point identification. It can be used to restrain the abnormal fluctuation of the tracking position in visual-sensing-based automated GMAW. This process stabilizes seam tracking as well as the subsequent WBGF modeling.In order to improve modeling accuracy, linear interpolation is applied to sub pixel discrimination of the weld bead before modeling asThe reference profile  is from APWBW is then modeled with 12, the APWBH is modeled with 13, and the area is acquired with 14.ectively . Thus, tIn addition, the zero and first moment of the binary image are used to calculate the coordinate of the center of the gravity of the weld bead asAPWBW modeling is conducted from top to bottom. APWBH modeling is carried out from left to right. The feature point optimization process based on the cubic exponential smoothing method enhances the modeling accuracy  is the most sensitive to the modeling results. It is necessary to develop an adaptive mechanism to approach the appropriate setting, which is the first aspect to be improved in the future study. Polynomial fitting is currently the popular method to model the objects. However, there is an over-fitting phenomenon. Although this work uses piecewise fitting to respectively represent the upper and lower boundaries of the weld bead, over-fitting still happens. For example, another mechanism monitoring the maximum error between the last several data points of the actual boundary and the fitting result should be built to optimize the order of the polynomial function. The relationship between the visual features of the weld bead and welding process parameters will be investigated in the next study.(1)The proposed feature point identification method combined with the weld seam profile extraction method adapts to the various weld seam profiles. It provides the valuable reference to visual information acquisition for visual-sensing-based automated welding.(2)The proposed fault detection and diagnosis of feature point identification based on the cubic exponential smoothing method shows that this optimization process enhances the identification accuracy to 1.50 pixels. This method shows its potential application value for improving tracking accuracy and welding quality.(3)The proposed modeling method in this work can obtain the area, center of gravity, and all-position width and height of the weld bead in real time in gas metal arc welding with typical joints. This modeling method provides more effective evidence to control the weld formation and planning, particularly during the multipass arc welding process.This work implemented real-time modeling on the area, center of gravity, and all-position width and height of the weld bead in thick plate gas metal arc welding with T-joints and butt joints based on visual sensing. Some conclusions about visual information acquisition, fault detection and diagnosis, and the weld bead geometry feature modeling method are given as follows.


In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended. Difficulties in the enrolment of the overall trial sample size, as indicated at the design stage, could be caused by several factors . Effects can be different depending on the population’s characteristics and the intervention under evaluation .Prior research evaluating the reasons for termination across a broad range of trials reported that insufficient enrolment is the most common reason, with a frequency ranging from 33.7% to 57%, depending on the definition used ,3. The sFrom a statistical point of view, low accrual results in a reduced sample size, compromising the ability to accurately answer the primary research question due to a reduction in the likelihood of detecting a treatment effect . The sciFor these reasons, alternative and innovative approaches to pediatric clinical trial design have been a recent topic of debate in the scientific community ,14. AlteIt is noteworthy that data from trials terminated prematurely for poor accrual can provide useful information for reducing the uncertainty about the treatment effect in a Bayesian framework . In recent years, Bayesian methods have increasingly been used in the design, monitoring, and analysis of clinical trials due to their flexibility ,20. ConsIn clinical trials that are candidates for early termination due to poor accrual reasons, a Bayesian approach may be useful for incorporating the available knowledge on the investigated treatment effect, reported in the literature or elicited by experts’ opinions . In addiIn pediatric trials, for example, the awareness that a treatment is effective in adults increases the probability of its efficacy in children. This knowledge may be quantitatively translated into a prior probability distribution ,14.However, when there is a small sample size, the final inference may be severely conditioned by a misleading prior definition . In thisHobbs modified the conventional approach, accounting for commensurability of the information in the historical and current data to determine how much the historical information is used in the inference . Other pIn general, the power prior approach is widely used for the design and analysis of clinical trial data. The method is useful for handling problems related to a lack of exchangeability between the historical and current data, and the risk that prior information overwhelms the clinical trial data information .The optimal amount of discounting factors for an informative prior remains to be discussed .This study investigated the effects of the prior choices on the final inference, especially for studies conducted with limited sample sizes, such as pediatric trials. A pediatric trial candidate for early termination due to underrecruitment, the RESCUE trial, served as a motivating example for the simulation study proposed.A set of possible trial outcomes were simulated. The simulation plan was designed to evaluate the effects of the prior choices on the trial results by evaluating different scenarios depending on the number of patients involved in the study and the magnitude of the true treatment effect.The RESCUE trial was a randomized controlled double-blind trial. The purpose of the study was to evaluate the effect of adjunctive oral steroids in preventing renal scarring in young children and infants with febrile urinary tract infections. The primary outcome was the renal scar absolute risk reduction (ARR) between the treatment arms. The study was designed expecting an ARR of 0.20 to determine a renal scar reduction from 40% to 20%.After two years, only 17 recruited patients completed the follow-up for the study outcome (6 in treatment and 11 in control) due to procedural problems and poor compliance with the study therapy and final diagnosis ,17,18. The possible trial outcomes were simulated by assuming several scenarios combining different sample sizes and true ARRs. The simulated data were analyzed via the Bayesian approach using a beta prior distribution whose parameters were derived from trials conducted in research settings similar to the RESCUE trial. The beta-binomial model was considered because it is the most widely used approach among the Bayesian methods to summarize event rates in clinical trials . This paInformative, low-informative, and uninformative priors were selected for the analyses according to the discounting levels placed on the prior parameters.The classical, non-Bayesian approach was considered a benchmark. Data generation hypotheses.Analysis of simulated data.Presentation of the results of simulations.This simulation study is defined by:A flowchart synthesizing the simulation plan is reported in The simulation plan consisted of 50 scenarios. Each scenario represents a single combination of the treatment effect (ARR) and the sample size used to generate the data. Fifty scenarios were considered, since they combined ten different sample sizes (ranging from 15 to 240) within five assumed ARRs . The ARRFor each scenario, the trial data were randomly generated 5000 times. The data were drawn from a binomial random variable, assuming a true event rate in the control arm of g et al.  and Shaig et al.  for the The treatment arm data were generated using a binomial random variable hypothesizing an ARR, one for each experiment, in compliance with the simulation plan provided in The 5000 randomly generated data points were analyzed via the Bayesian method by considering: (1) the informative prior, (2) the low-informative prior, and (3) the uninformative prior. A frequentist analysis was performed for comparison purposes. The data were simulated 5000 times by a binomial random variable in a frequentist approach. For each of the repeated simulations, the ARR was calculated and the binomial confidence interval was estimated.A mixture of beta priors was considered for the outcome evaluation, using data provided by the literature ,39. The The functional form of the distribution is characterized by the shape eta , where Πnt (n−x) .Huang et al.  reportedShaikh et al.  reportedFor the treatment arm, the beta mixture is defined as:If we denote the beta shape If we assume an equal weight value The mixture variance is given by:Equal weight was assumed for the components of the mixture, therefore, For the treatment arm, the mixture is defined as:The information was combined in a mixture of beta priors:Different levels of penalization (discounting) were provided for the historical information using a power prior approach  to perfoThe If If Analyses of the simulated trials were conducted using three different priors:Power prior without discounting , an ESS of 55 and 98 was achieved for treatment and control arm. However, discounting the beta parameters for = 0.5 (low-informative prior), the ESS is equal to 24 and 48. The ESS was computed on the mixture of beta distribution by using the Morita approach to quantify the prior influence on the final inference using the RBesT package in R  . For theThe prior distributions are presented in A beta-binomial model was employed to analyze the difference in event rates between arms . The posA first resampling of the proportion of scarring A second resampling of The posterior distribution for the parameter related to the difference in proportions was obtained by calculating esampled .The resampling procedures were performed using an MCMC estimation algorithm, as indicated in the literature , using 3An example of the inference results is reported in the The computations were performed using OpenBUGS   and R veThe Geweke method  was consThe proportion of the 5000 simulated trials for which the credibility intervals (CIs), or confidence intervals, for a frequentist analysis do not contain an ARR equal to 0. The proportion of intervals not containing the 0 and containing the data generator ARR was also calculated.The mean length across 5000 simulated trials of the CI.The mean of the posterior median estimate across 5000 simulated trials or the mean of the point-estimated ARR across 5000 simulated trials for the frequentist analysis.The mean absolute percentage error (MAPE):Four sets of 200 results summarizing 50 scenarios in combination with four methods of analysis were defined as:The proportion of 5000 simulated trials ensuring that the 95% CI does not contain an ARR equal to zero is greater than 90% for all of the informative scenarios, even if the sample size is smaller than 50, except for the 0.07 true ARR. For the 0.07 ARR, this proportion declines as the data used to estimate the likelihood increases , Panel ASimilar behavior is observed among the uninformative Bayesian , Panel CThe 95% CI length decreases as the sample size increases for all of the Bayesian parametrizations and the frequentist estimates . The infThe posterior median ARR estimates are influenced by the prior choices, especially for the informative prior. The estimated ARRs are similar to each other for smaller sample sizes across the true treatment effect, while the posterior median ARR estimates converge to the true ARR for larger sample sizes , Panel AThe MAPE estimate decreases as the sample size increases for all the prior parametrizations . A lowerAlso, the MAPE seems to be constant for a higher true ARR in informative , Panel AThe hypothesis of the stationarity of the chain was not rejected according to Geweke’s statistic for all of the analyses conducted on the simulated data and for all of the prior parametrizations. The Z-scores within iterations was also visually inspected. An example within simulations (ARR = 0.07 and sample size = 65) is reported in the An example of a possible inference result is shown in the sumption .Regulatory agencies advocate an increase in pediatric research, which is motivated by the need for more information on treatment labeling to guide pediatricians and to offer more suitable and safe treatments for children . HoweverBayesian data analysis may overcome challenges in the conduction of trials similar to the RESCUE study, allowing investigators to combine information provided by current trial data with evidence provided by the literature, as recommended by regulatory agencies to deal with small sample sizes .The present findings show that Bayesian inference can detect a small treatment effect for small sample sizes (lower than 50), even if the prior is fully uninformative compared to a maximum likelihood approach. This result confirms the potential benefits of using a Bayesian method on small sample sizes. However, the literature suggests paying attention to the use of uninformative prior distributions for small clinical trials, because there is the possibility of including in the final inference extreme treatment effects that are potentially unexpected from a clinical point of view. For this reason, it is suggested to use evidence from previous trials to inform these prior distributions .For this reason, a key issue in Bayesian analysis is the choice of prior. This simulation study demonstrated that, especially for small studies, the trial results could be influenced by the prior choices and weakly influenced by the data when using fully informative priors. In particular, a prior distribution incorporating favorable treatment effect information on small sample sizes is likely to conditionate the inference in favor of the treatment, even if, in truth, the effect is null or minimal. All of this implies that the prior in these contexts should be defined by using validated empirical evidence . ConversThe situation is different if a discounting factor is placed on the prior parameters. Looking at the estimated values of ESS, the historical information retained in the prior in the low-informative scenario is halved, compared to the informative parameterization. This implies that the inference is more data-oriented, assuming a discounting of 0.5. The probability of confirming the trial results is demonstrated to be more data-dependent and, for sample sizes less than 50, is higher than 80% only for ARRs higher than 0.17.As the power prior parameter increases, the prior becomes more informative, and the estimated precision (length of CI) increases. Looking at the differences between observed and estimated ARR, the inferential results, comparing the various parameterizations of the prior discounting factor, tend to converge toward the same conclusions in the direction of the generating data effect size starting from a sample size of 150 subjects. All this implies that, for studies conducted on a considerable number of patients, it is possible to tune the prior toward a more informative solution  in treatment effect estimation. Historical information contributes sequentially to the reduction of this uncertainty . The uncThe present results show that Bayesian methods can outperform frequentist methods with small samples by providing increased efficiency and an increased ability to determine non-null effects. However, the appropriate prior distribution choice, especially on small datasets, plays a fundamental role. Researchers might need to consult experts, meta-analyses, or review studies in the area of interest to obtain informative, accurate priors that can meaningfully contribute to posterior distributions. Furthermore, a sensitivity analysis on priors  is highly recommended for pediatric trials , which iThis study was conducted considering only the conjugate prior beta setting. It may be interesting to explore the impact of inference in the posterior case obtained in a nonclosed form. For example, instead of directly placing a parameter derived on the beta prior, it may be advisable to consider expert elicitation about treatment effects to define the specific prior distribution. Moreover, future research development is needed to investigate the effect of an eventual prior-data conflict on the trial results according to different study size.Bayesian inference is a flexible tool compared to frequentist inference, especially for trials conducted in a poor accrual setting. A full informative Bayesian inference, conducted on small samples, can generate data-insensitive results. On the other hand, the use of an uninformative prior distribution may include, in the final inference, clinically unproven extreme treatment effect hypotheses. A power prior approach on sample sizes smaller than 50 patients seems to be a good compromise between these two methods. However, the choice of parameters and discounting factors should be negotiated with expert pediatricians and should be guided by an appropriate consultation of the scientific literature. In agreement with the FDA recommendations, a sensitivity analysis of priors is highly recommended.


In previous studies, estimation of the cure point has been handled in an ad hoc fashion, often without considerations about margins of clinical relevance.The mortality risk among cancer patients measured from the time of diagnosis is often elevated in comparison to the general population. However, for some cancer types, the patient mortality risk will over time reach the same level as the general population mortality risk. The time point at which the mortality risk reaches the same level as the general population is called the We review existing methods for estimating the cure point and discuss new clinically relevant measures for quantifying the mortality difference between cancer patients and the general population, which can be used for cure point estimation. The performance of the methods is assessed in a simulation study and the methods are illustrated on survival data from Danish colon cancer patients.The simulations revealed that the bias of the estimated cure point depends on the measure chosen for quantifying the excess mortality, the chosen margin of clinical relevance, and the applied estimation procedure. These choices are interdependent as the choice of mortality measure depends both on the ability to define a margin of clinical relevance and the ability to accurately compute the mortality measure. The analysis of cancer survival data demonstrates the importance of considering the confidence interval of the estimated cure point, as these may be wide in some scenarios limiting the applicability of the estimated cure point.Although cure points are appealing in a clinical context and has widespread applicability, estimation remains a difficult task. The estimation relies on a number of choices, each associated with pitfalls that the practitioner should be aware of. Data were simulated according to three relative survival scenarios using a net survival approach, where the time to disease-related death, ependent . The surS∗, was derived using a Danish life table from the Human mortality database [R(t), was simulated from the mixture cure model in . We selected relatively low cure proportions in the simulations to obtain a large number of events, which is generally advantageous for model fitting. In scenario 1 and 3, the relative survival reaches a plateau early in the follow-up, while the plateau is reached at the end of follow-up in scenario 2. To mimic registry data, the censoring distribution was chosen to be uniform. The simulations were repeated 500 times using a sample size of 2,000.The general population survival function, database . For simmodel in  using a ε, and comparison measure, G(t), the true cure point, tε, was obtained by inserting the true mixture cure model into G(t) and solving (G(t) and solving . This implies that even small differences between the estimated and true comparison measure can yield relatively large biases in the estimated cure point whenever small values of the MOCR are considered see Fig. . This prThe true Weibull model yields the most accurate cure point estimates, but provides wide confidence intervals especially in scenario 2, where the relative survival only flattens out by the end of follow-up. More biased cure points were obtained by the FMC and ARS models. Particularly in scenario 2, these models produced cure points biases exceeding half a year. Notably, the FMC model produced wider confidence intervals compared to the ARS models. This can be explained by the identifiability issues associated with the FMC model and the restriction of the ARS model ensuring a constant relative survival beyond its last knot. The former may also explain why a confidence interval could not be computed in 1/500 simulations.Because the NRS is not a cure model, it could not be used to estimate the cure point based on the conditional probability of cure. The cure point bias of the NRS model based on the two remaining comparison measures was slightly higher compared to the true model in scenario 1 and 3. However, in scenario 2, the bias was extremely large. This is likely due to the extrapolation needed to compute these two comparison measures. The data were simulated from a Weibull mixture cure model, which only becomes flat by the end of the follow-up. When data points are drawn, which introduces random variation, the NRS model may fail to capture that the true relative survival contains a plateau, which yields biased extrapolations.To conclude, the NRS model seems to provide too biased results, while the true Weibull model will often be too simple in practice. The use of either the ARS or FMC model is likely the best choice for cure point estimation, but large confidence intervals may occur with the FMC model.To exemplify the application of the considered approach for cure point estimation, we analyzed patient data from colon cancer patients retrieved from the Danish Colorectal Cancer Group Database . The regIn total, 42,380 colon cancer patients were included in the study and the 5-year Kaplan-Meier estimate was 49% . The fitted FMC model is shown in Figure B3 together with a non-parametric relative survival estimate calculated by the Ederer I method . EstimatFigure The patients were further stratified according to age group , gender, and clinical stage  and the FMC model was fitted to each subgroup separately. The stratified cure point estimates computed with the conditional probability of cancer-related death  as comparison measure and three MOCRs  are shown in Fig. Cure points enable communication of prognostic information to cancer patients and are particularly useful for patients attending routine follow-up. Also health care planners may find cure points useful, e.g., for deciding the duration of post-treatment follow-up programs. If an early cure point is detected, the duration may be adjusted accordingly, which potentially lowers the cost of the follow-up program and avoids unnecessary patient anxiety related to routine follow-up. However, the detection of long term toxicities is typically also a goal of routine follow-up programs and should be considered alongside the mortality risk. The search for surrogate endpoints to be used in clinical trials in order to increase the pace at which these are executed has been the focus of recent cancer studies , 34. CurIn this article, we have reviewed approaches to estimate cure points from cancer survival data, discussed their usability, and introduced two new useful measures for cure point estimation, namely the conditional probability of cancer-related death and the loss of lifetime function. In recent studies of lymphoma, Maurer et al. and Hapgood et al. used sequential testing of standardized mortality ratios to evaluate the survival improvement of the patients , 7. HoweIn the simulations, the bias of the cure point was computed for three different cure models, namely the FMC, ARS, and the Weibull mixture cure model. The ARS incorporates a strict cure point by assuming a constant relative survival beyond the last knot of the splines . HoweverThe main issue associated with the loss of lifetime function and the conditional probability of cancer-related death is the need for extrapolation for which the accuracy cannot generally be quantified. This, combined with the stochastic nature of time to event data, may lead to biased conclusions. As no single extrapolation method is universally superior , it is rDeciding on a MOCR is an essential part of the present methodology. This decision is similar to the choice of a non-inferiority margin as done in non-inferiority studies . The choIn addition, the sensitivity of the cure point towards the choice of MOCR is a major issue. When the applied comparison measure is steep, the cure point is fairly robust against small changes in the chosen MOCR, whereas the cure point becomes very sensitive to small changes in the MOCR whenever the comparison measure is flat. Additionally, the variance of the cure point estimator is inversely proportional to the derivative of the comparison measure evaluated at the estimated cure point. This may lead to very large confidence intervals if the slope of the comparison measure is small at the estimated cure point, making the cure point estimate difficult to apply in practice. Thus, computing cure points is a challenging task and we suggest that extensive sensitivity analyses are conducted if cure point estimates are of interest.It is worthwhile to pay attention to the interpretation of the cure point. If the patient mortality risk reaches the same level as the general population mortality risk, it may be tempting to conclude that the patients have the expected survival they would have experienced had they never had cancer. While this is likely of interest to the patients, this conclusion cannot be drawn from these analyses as the general population is typically only matched on variables such as age, gender, and calendar year. If a study includes patients treated with a specific therapy, which is typically not given to frail patients, or merely includes patients with high socioeconomic status, the resulting patient population may be in slightly better shape than the general population. Thus, for patients achieving statistical cure, a significant excess mortality risk may still exist if compared to non-diseased individuals with similar physical capabilities or similar socioeconomic status. Information on physical condition and socioeconomic status is typically not included in publicly available life tables and thus cannot readily be taken into account.Because of the varying survival trajectory of cancers, cure points are not useful for all diseases, e.g., cancers where statistical cure is not achieved such as bladder cancer . To accoCure point estimation is challenging and requires careful selection of an interpretable measure which quantifies the mortality difference between the patient and general population, a MOCR, and an estimation procedure that can accurately estimate the dynamic mortality difference. Additionally, the cure point may be sensitive to small changes in the MOCR while large confidence intervals may be obtained dependent on the behavior of the comparison measure. Therefore, if cure points are of interest, sensitivity analysis are encouraged and the limitations of the applied approach should be clearly stated.Additional file 1 Supplementary material.


Literature is scarce on acute ischemia after intra-arterial injection of crushed tablets and no effective medical treatment against the progression of lesions is reported. The only factor able to modify the outcome is the delay between injection and management by a specialized vascular team. Moreover the risk of necrosis seems higher after benzodiazepine intra-arterial injection than with other drugs. We tried to find out mechanistic explanations. We report on the case of a 31-year-old drug addict woman who self-injected into her left brachial artery crushed tablets of zolpidem. She developed an acute ischemia of the left hand, with necrosis of the intermediate and distal phalanges of fingers II, III, and IV. Angiogram of the left upper arm confirmed the distal arterial occlusions with no run-off after the palmar arch in the necrotic fingers. Once she was admitted into our vascular unit, intravenous vasodilator therapy by iloprost, heparin and local protective care were rapidly introduced. After delineation between living and necrotic tissues, she required distal amputations of the affected fingers. The clinical severity of arterial injections of benzodiazepine tablets is linked to the association of several pathophysiological mechanisms. Rather than related benzodiazepine pharmacologic effects with tissue ischemia, by the inhibition of phosphodiesterase, a vasodilator intermediate, or through the peripheral benzodiazepine-type receptor, the predominant mechanism is more likely in relation with microcrystalline cellulose, one component of zolpidem tablets, known as potential embolic agents. They are insoluble and resistant to degradation in water. These properties are probably prominent in the case we described here. Through this case report we want to drag attention of physicians in charge of a patient with acute ischemia after crushed tablet accidental intra-arterial injection, not only to look at the drug injected but also the other components of the tablet and especially to microcrystalline cellulose. Many reports and reviews have been published on intravenous injection of crushed tablets and their local and systemic pathologic effects especially pulmonary ones . The litMoreover no effective medical treatment against the progression of lesions is reported. The only factor able to modify the outcome is the delay between injection and management by a specialized vascular team . MoreoveA 31-year-old female patient was admitted to the vascular medicine department of the Hôpital Européen Georges-Pompidou for an acute ischemia of the left hand. She presented with necrosis of the intermediate and distal phalanges of the second, third, fourth and fifth fingers of her left hand.Three days earlier, the patient turned up at the primary care center close to her home for a sudden and constant pain in her left hand. She was discharged within 2 h with painkillers. After 72 h, cyanosis and coldness of the intermediate and distal phalanges of the fingers of the left hand appeared. She then decided to consult at the Hôpital Européen Georges-Pompidou emergency department.Left hand examination revealed the following injuries: cyanosis and coldness of the intermediate and distal phalanges of the fifth finger, necrosis of the distal phalanges of the second, third and fourth fingers, phlyctens of the intermediate phalanges of the second, third and fourth fingers and the proximal phalanges of the fourth finger ,B. The hIn her medical history, she had no cardiovascular history but active smoking, as her only cardiovascular risk factor. Spontaneously, she mentioned being a drug addict, but weaned off ectasy, codein, and cannabis, and currently in a weaning off program for heroin. She had been prescribed tapered doses of buprenorphine but still used to take bromazepam and zolpidem off the counter.The continuous Doppler confirmed the presence of flow in the left radial, ulnar and palmar arteries. A digital subtraction angiography was rapidly performed which demonstrated no run-off in the digital arteries of the second, third and fourth left fingers . No throThe patient was transferred to the vascular medicine department for medical therapy and further investigations. As the patient denied any recent intravenous drug injection, a complete screening for acute limb ischemia etiology was performed.Human Immunodeficient Virus and Hepatitis B Virus serology were negative, whereas Hepatitis C Virus serology came back positive. Several analyses to find a cryoglobulinemia returned negative.Arterial thrombophilia has been investigated in vain, with normal prothrombin time and activated partial thromboplastin time, absence of anti-cardiolipin antibodies, anti-beta2GPI antibodies, lupus anticoagulant, antinuclear antibodies, anti-double stranded DNA antibodies, anti polymorphonuclear cytoplasm antibodies. There was no antithrombin or protein S or protein C deficiency and no hyperhomocysteinemia. There was no argument for a myeloproliferative syndrome in the absence of a clinical call point , the blood count was unspecialized and JAK2 mutation V617F was absent.trans-thoracic echocardiography did not find any thrombus or any argument for an infectious endocarditis. The 24 h Holter-ECG was normal. The computed tomography performed at the emergency room did not show any atheroma on the aortic arch or atheromatous plaque on the left upper limb arteries.We also looked for an embolic arterial cause. The Once she was arrived in our department, we started a daily treatment with iloprost, an arterial vasodilator, as well as pain medication, and local care with a protective dressing. Treatment with iloprost allowed delineation of necrosis at the intermediate and distal phalanges of the three fingers involved, and cyanosis of the fifth finger and the proximal phalanx of the fourth finger resolved. Management by an orthopedic surgical team specialized in the upper limb and hand was required for the amputation of the intermediate and distal phalanges of the three fingers involved .Finally, the patient acknowledged she had injected herself, 3 days before her admission, just below the fold of her left elbow with one and a half crushed tablet of zolpidem 10 mg mixed with contact lens cleaning fluid. The patient intended to do an intravenous injection, but it turned out to be an accidental brachial intra-arterial injection. The 3 days delay corresponded to the beginning of the pain and to her first consultation at the local medical center.Antithrombotic therapy by anticoagulants was changed for antiplatelet therapy and the patient was then transferred to a vascular rehabilitation unit for complete healing of the amputation wounds. Intra-arterial injection of crushed tablets of a benzodiazepine-like imidazopyridine derivative is a rare but particularly serious and devastating phenomenon, as evidenced by our observation, which describes a 31-year-old drug-addict patient who misused zolpidem by injecting crushed zolpidem tablets in her brachial artery. With regard to care, we chose to carry out an arterial vasodilator therapy using iloprost in order to limit the digital necrosis as much as possible. In addition to the examination of the patient regarding the misuse of zolpidem, we nevertheless carried out an extensive check-up looking at curable causes and better prognosis etiologies with arterial occlusion or embolization. Very few cases have been described so far , other c publisheIn terms of therapeutic management, 53% of the patients in  review rWe tried to find out some mechanistic explanations to the increased risk of necrosis after benzodiazepine intra-arterial injection and whether these hypotheses could be shared with zolpidem, a benzodiazepine-like imidazopyridine derivative. The higher ischemic risk of this related benzodiazepines may be predominantly due to their insolubility, rather than their pharmacologic effects on the arterial wall. We found out that all the manufacturers incorporate microcrystalline cellulose into the core of zolpidem tablets. A review of the literature let out several cases with severe ischemia after intra-arterial injections with drugs having microcrystalline cellulose among their components: methadone  meprobamLastly, we checked out scientific works focusing on two benzodiazepine-related mechanisms: first, on their inhibition properties on phosphodiesterase, a vasodilator intermediate, and second on the peripheral benzodiazepine-type receptor (PBR), which could take part in response to ischemia. Most of works on the inhibition of the phosphodiesterase have been done in cardiology and with diazepam and its positive inotropic effect . But no Thus, we believe that the ischemic process observed in our patient is more likely in relation with the microcrystalline cellulose component and its insolubility rather than with zolpidem and its pharmacology effects.As a conclusion, in patients looking for venous access, injections of pure substances or crushed tablets end most often unintentionally in an artery. Every physician should be aware of the danger of accidental intra-arterial injections, pay attention to any sign of drug abuse in case of acute upper extremity ischemia or pain, in order to quickly start antithrombotic therapy with vasodilators. Medical management is still debated and not consensual. However, anticoagulation does not seem to be mandatory over antiplatelet therapy. Main predictive factors of amputation are the delay to presentation after the intra-arterial injection, and the type and form of substance injected. One should not forget to take into account the excipients associated with the drug injected, in particular in case of crushed tablets. These factors may influence the functional prognosis of the hand in case of microcrystalline cellulose injection.• There is a critical threshold of 14 h in the management of acute digital ischemia to limit the risk of amputation.• The use of anticoagulants does not seem to decrease the amputation rate, which would be more related to the type of substance injected.• Microcrystalline cellulose, one of the excipients of zolpidem, are most likely the cause of acute ischemia with this drug after intra-arterial injection.• Physician should pay attention to any sign of drug abuse in case of acute upper extremity ischemia or pain, and collect the time from injection, the type of drug, and the different components of the tablet crushed, especially microcrystalline cellulose.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual for the publication of any potentially identifiable images or data included in this article.GD, AG, and TM conceived and wrote the research project, and wrote the manuscript. GG, LK, MG, AL-LL, and EM performed literature review, edited and proofread the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system. In recent years, operations undertaken by unmanned underwater vehicles (UUVs) in the offshore energy sector are changing rapidly. This is driven by both offshore oil & gas (O&G) and the offshore wind sector where production platforms are pushed further off the coast, into areas of higher energy potential. However, considering significant expenditures related to the cost of the surface support vessel and crew, and with the production platforms in remote locations, the cost related to inspection, maintenance, and repair (IMR) tasks inevitably rise. Rising costs have resulted in the development and use of permanently deployed resident vehicle systems. Although the concept of permanently deployed vehicles exists in the literature for many years , only reHowever, within the O&G, and especially the offshore wind production field, multiple assets can be spread across more than 100 kmes (AUV) ,7. Howeves (AUV) . These res (AUV) ,10. Althes (AUV) , signifiResident ROVs operating from shore fundamentally demand a high bandwidth, low latency communications link that is often unavailable, thus high levels of automation are needed. This is especially important for time-critical tasks since manual operation from the shore due to the mentioned communication problems is not viable. One of the essential time-critical tasks in resident vehicle operation is the docking of the vehicle at the end of the mission. Autonomous docking of UUVs is a well-researched area, with main focus on docking to a static docking station, both for ROV docking to a tether management system (TMS) ,13 and AAlthough docking of UUVs to a moving docking station is reported, the research is mainly focused on an AUV docking and on compensation of disturbances in the horizontal plane , while assuming minimal docking station heave oscillations. Recovering of an AUV by another AUV in shallow water is presented in . The sysOne of the major limitations of the autonomous ROV docking to a suspended TMS is the TMS heave motion, which can exceed amplitudes of 3 m. Those limits were recognized and reported during previous trials, which included, a first autonomous docking of a work-class ROV to a suspended TMS . FindingThis research paper presents development and evaluation of the method for suspended TMS heave motion prediction, based on an adaptive neuro-fuzzy inference system (ANFIS). In addition, the paper discusses the mapping of surface vehicle motions to the suspended docking station coordinate frame. The prediction of TMS heave motion has the potential benefits of allowing autonomous docking in higher sea states, extending the ROV operational weather windows, and reducing the misalignment between the ROV and TMS during the docking process, thus reducing the impact on the ROV system and extending the ROV operational life. Furthermore, the method has a dual benefit of being applicable to autonomous docking or as an aiding tool for the pilot. The ANFIS performance is evaluated on a real-world dataset recorded using a work-class ROV with corresponding cage type TMS, deployed during offshore trials in the North Atlantic Ocean.The significant restricting factor in ROV operations and associated operational weather windows relates to the launch and recovery of the vehicle. This includes both launch and recovery of the ROV from the vessel to the sea, and launch and recovery of the ROV from the TMS while underwater. In this paper problems associated with the latter are discussed. The main systems involved in the ROV deployment trials are a surface vessel, a launch and recovery system (LARS), a tether management system (TMS) and the ROV itself. The overview of the system used during the trials is shown in There are various definitions of a tether management system, and although the TMS is in essence only a subsea tether-handling mechanism, by common convention and according to The ROV Manual  it is “tThere are three main functions of the tether management system: (1) to manage, usually neutrally buoyant soft tether, which connects the TMS and the ROV, and provide power and communications to the ROV; (2) to protect the vehicle against damage during ROV deployment and recovery phase; and (3) to act as a clump weight to absorb the cross-section drag that would be otherwise introduced to a tether connecting ship and the ROV. Therefore, the ROV is completely relieved of the tether drag from the surface to the working depth, which is important due to the limited power on-board the ROV available for vehicle drag compensation caused by sea currents. To act as a clump weight, the TMS has to be negatively buoyant; thus, as a consequence, the ship’s motion is directly translated to the TMS through a steel reinforced umbilical connecting the TMS with the ship. The TMS used during the trials is a cage type, side entry TMS and weights approximately 2.2 tons.The LARS system is used as the overboarding equipment, and its primary role is to move ROV from the deck and deploy it safely. The most typically used LARS is the A-frame type, such as the one used during the trials. The system weights around 12 tons and contains 2.2 km of umbilical. On one side the LARS is connected to the control cabin and ship’s power supply, and on the other side to the TMS through the umbilical. The umbilical used for TMS deployment is steel reinforced for lifting to/from the water, and it provides the power and communication link between the ROV and the control cabin. Vessels that are specially designed for ROV operations may include the moonpool or Cage & Rail LARS system  for deplThe TMS with the ROV is usually deployed from a surface vessel or floating platform which is exposed to various disturbances such as waves, currents, wind, tides, and others. As the surface vessel for TMS deployment is the main source of the TMS motion, it is necessary to understand all the disturbances introduced to the surface vessel and how they map to the TMS. As the disturbances act on all six degrees of freedom (DOF) of the surface vessel, and considering the TMS is connected with the vessel through the non-elastic umbilical, those motions couple to the TMS directly. Therefore, the primary goal during the TMS deployment is to minimize the impact of those disturbances on a surface vessel.Given the ship heave, roll, and pitch, a total TMS heave displacement oor LARS ,23 systeThe docking station is relatively stable on the roll and pitch axis since the TMS center of gravity is below the point where TMS is attached to the umbilical, thus positive longitudinal and lateral stability is achieved. Sea current generally rotates the TMS around the yaw axis, until TMS reaches the orientation that creates the least amount of drag. However, TMS yaw is easily controllable with two or more thrusters attached to the TMS. There are two sources of TMS surge and sway: (1) surface vessel surge and sway which depends on DP capability, and (2) the TMS surge and sway caused by displacement of suspension point t seconds in the future, based on previous In summary, the suspended TMS heave displacement Docking of a ROV system is one of the most critical tasks dictated by operation weather windows. It introduces a high risk of ROV damage, and it can be a highly stressful operation for ROV pilot in challenging sea conditions. The docking maneuver can be divided into three stages: (1) the preparation stage, (2) the ROV approach and TMS heave estimation stage, and (3) contact stage. Manual ROV docking into a cage type TMS starts with the ROV stern facing the entrance of the TMS.In the first stage, the ROV heading, depth and the lateral position should be aligned with the TMS. In general, the TMS mechanically allows for certain vertical and horizontal misalignment due to the funnel-shaped entrance. Therefore, for relatively small TMS heave amplitudes, approximately However, Although the TMS heave amplitude and frequency are not fixed, once the ROV is in the approximately correct area, the ROV depth can be fine adjusted quickly. To allow for large ROV inertia, the docking maneuver is typically started before the TMS reaches the optimal position for docking. Therefore, the pilot must predict the TMS position based on experience, and current and previous observations, and undertake a decision in a fraction of second while controlling the ROV.The third stage includes the contact between the ROV and the TMS, and finishes with the ROV docked. As, in general, it is not possible to compensate for all the motion and align ROV perfectly with the TMS, the docking still includes rough or bumpy contact; however, to a much reduced extent.This section describes the implementation of the adaptive neuro-fuzzy inference system (ANFIS) for TMS motion prediction. ANFIS is an adaptive neural network which is equivalent to a fuzzy inference system (FIS) first time introduced in . With ANm inputs (X) is assigned with n fuzzy membership functions described with linguistic labels (A), constituting r rules (R). Each node in the first layer is adaptive and specifies the degree to which a given input satisfies the fuzzy membership function related with that node. The first layer is called the “fuzzification” layer, while parameters in this layer are called premise parameters. In the second layer a firing strength for each rule is determined. Every node in this layer is fixed and labeled consequent parameters. The node in the last layer performs summation of all incoming signals.Various authors reported use of ANFIS for modeling nonlinear functions such as motion prediction of moving targets ,26,27, pt, ANFIS is used to predict the future value of the D points of the time series spaced The tuning of the network is done using an existing dataset consisting of input–output pairs, while the network tries to model the function which relates input to output. By using past values of the heave displacement The ANFIS training and evaluation has been done on a prerecorded dataset. The data used for ANFIS training and evaluation was recorded during the offshore trials that took place in the North Atlantic Ocean during January 2019. The TMS depth was recorded using a depth sensor attached to the TMS frame. The sensor used during the trials was UV-SVP by Valeport. It is a conventional commercial unit that offers pressure, sound velocity and temperature measurements in one housing. Technical specification of the unit is given in D, how far in future TMS position is to be predicted P, spacing between the points For the given task, different process values are involved in constructing an efficient and reliable ANFIS network. This variables include size of the input–output dataset pairs (training dataset length), number of membership functions per input Evaluate ANFIS performance for TMS position prediction.Analyze the network training time, and consider real-time ANFIS training.Investigate the influence of the depth sensor sample rate on ANFIS performance.Although there are guidelines about the ANFIS training process ,30, as wTherefore, in the next section, an overview of the best network configuration is given at the start, followed by an ANFIS overall performance evaluation.The scope of the paper is to investigate and evaluate the usage of an ANFIS network for the prediction of TMS heave motion. Prior to the evaluation, an optimal network configuration and training parameters should be determined. The optimal ANFIS for the given problem achieves minimum error Dataset length in range 50 to 600 s.Number of membership functions D in range 1 to 12.Number of previous measurements Spacing between previous measurements Number of training epochs P in range 0.5 to 5 s.Prediction time To find optimal ANFIS training parameters the experiments included varying the following parameters.Although extensive experiments have been conducted to determine the best set of parameters, only main experiments related to the ANFIS parameters estimation have been presented, considering the main scope of the paper.The effect of each parameter on ANFIS performance and training duration is shown in D and the number of epochs, The relationship between the number of training points From extensive experiments, the optimal ANFIS input parameters for the given task are found as The amount of data used for online training should be taken into consideration as well since a larger training dataset increases the ANFIS training duration. The effect of training dataset length on ANFIS training cycle duration, training After network parameters . The method is used to extend the ROV operational weather windows, reduce operational expenses, and reduce ROV damage due to the harsh docking. The docking of underpowered work-class ROVs to a heaving TMS relies entirely on the ROV pilot experience in estimating TMS heave motion, which is not available for autonomous and resident underwater vehicles. With large ROV inertia and drag forces acting against it, the ROV is not agile enough to match TMS heave motion, thus the docking Figure starts before the ROV and the TMS align. The method presents an addition to the suite of technologies required for dynamic autonomous work-class ROV docking and is beyond the current state of the art in work-class ROV technology. In addition, the method has the potential for retrofitting to the existing ROV fleet, to be used as a ROV pilot aiding tool, and it does not require additional hardware.The method has been tested in the field on real-world data recorded during the offshore trials in North Atlantic Ocean, during January 2019. The trials during research cruise CE-19001  includedThe future work includes detailed time response ROV analysis, modeling the ROV docking strategy decision process, and implementation with the ROV and OceanRings ,36 suite


Recovering the historical patterns of selection acting on a protein coding sequence is a major goal of evolutionary biology. Mutation-selection models address this problem by explicitly modelling fixation rates as a function of site-specific amino acid fitness values.However, they are restricted in their utility for investigating directional evolution because they require prior knowledge of the locations of fitness changes in the lineages of a phylogeny.We apply a modified mutation-selection methodology that relaxes assumptions of equlibrium and time-reversibility. Our implementation allows us to identify branches where adaptive or compensatory shifts in the fitness landscape have taken place, signalled by a change in amino acid fitness profiles. Through simulation and analysis of an empirical data set of We demonstrate successful detection of selective shifts and identification of the affected branch on partitions of 300 codons or more. We successfully reconstruct fitness parameters and initial codon frequencies in simulated data and demonstrate that failing to account for non-equilibrium evolution can increase the error in fitness profile estimation. We also demonstrate reconstruction of plausible shifts in amino acid fitnesses in the bacterial  A growing number of genomes across the tree of life have now been sequenced, providing the genotypic underpinnings of a diverse array of species phenotypes e.g. –3). With. With3])For protein-coding DNA, the effects of selection are most frequently studied through characterising the rate of non-synonymous substitutions (dN) in comparison to the background synonymous mutation rate (dS) , 9. A dNvolution .Sophisticated methods have been developed for inferring episodes of elevated dN/dS across both sequence positions and lineages within the phylogeny. These include branch-and-site models of codon evolution, in which a proportion of sites may experience elevated dN/dS across pre-specified foreground branches against a background of neutral or negative selection –13. ThesCodon models estimating nonsynonymous/synonymous rate parameters are the most widely used methods for detecting positive selection, but suffer from several limitations. As inter-specific Markov models, they are divorced from the underlying substitution process . Codon mIn view of the shortcomings of dN/dS, recent years have seen a resurgence of interest in mechanistic models for analysis of the dynamics of protein evolution. Chief among these are mutation-selection models , 26. DatDespite the promise of these methods, a number of issues remain that prevent their widespread adoption in evolutionary reconstruction and hypothesis testing. The models make a number of strong assumptions; for example, mutation is assumed to be weak, with a population-scaled mutation rate much less than one . Thus ona to codon b.One assumption of the original models is that the evolutionary process is assumed to be at equilibrium throughout the tree. This stems from two aspects of the model. Firstly, the fitness landscape of amino acids at the root was assumed to be the same as that at the tips of the tree, meaning that no directional process is possible, as this would require a shift in site-specific equilibrium. Secondly, the form in which the fixation probabilities are given assumes detailed balance in the process of evolution, i.e.Detailed balance was assumed explicitly in the original formulation , and is ument}<1 . This reument}<1 . Howeverument}<1 . Reversiument}<1 , 39.The combination of these two assumptions means that mutation-selection models as presently formulated are not suitable for modelling an evolutionary process that may include adaptive episodes, shifting balance, or non-purifying selection. The assumption of stationarity has been relaxed in numerous studies by testing for differences in amino acid fitnesses among viral hosts , 40, 41,Here, we make progress towards the goal of reconstructing detailed selective histories by relaxing both of the assumptions that restrict mutation-selection models to equilibrium conditions. We seek to detect a change in an amino acid fitness profile over a homogeneous set of sites at an arbitrary node in the tree, without prior hypotheses as to its position and to determine the position of this selective shift. Furthermore, we demonstrate simultaneous reconstruction of amino-acid fitness parameters and differing codon frequencies at the root using the non-reversible model. We apply the results to a data set of a and b the Darwinian fitnesses of the background and mutant amino acids, s is the selection coefficient, and We peformed analyses using a maximum-likelihood approach within the Bio++ framework . BrieflyAfter determining maximum-likelihood amino acid fitnesses, AICc values are calculated with the additional model on each tested clade. If the best of these shows a reduction in AICc value, we infer a selective shift on that branch. The process repeats until no improvement can be found or a user-specified maximum shift limit is reached. The output consists of the number and branch position of shifts in amino acid fitness and amino acid fitness parameters for each inferred shift.We designed two series of simulations to test the identifiability of selective shift locations and model parameters under non-equilibrium mutation-selection models. In all cases, we simulated data under a model similar to that used for inference, with a single set of amino acid fitnesses across all sites but which could vary at the root or among lineages. While testing methods on data generated using methods more complex than the inference model can be valuable for establishing robustness and identifying inference problems such as parameters that take on phenomenological load from unmodeled parts of the process , in the In the first series (ASHIFT), we simulated sequences with either no selective shifts or one shift at a random position in the tree. Codon frequencies for the root were set equal to the equilibrium codon frequencies given by the model preceding the shift, while a new fitness profile was generated for the model following the shift. We then tested our ability to recover the position of the shift and the amino acid fitness profiles preceding and following the event. Since this method requires a set of amino acids with a similar selective history and grows in complexity with the size of the associated protein family, we also tested the effect of alignment length and number of taxa in the underlying phylogeny. To do this, we simulated sequences with 300, 600 and 900 codons, trees with 10 and 20 taxa, and 0 or 1 selective shifts, for a total of twelve treatment blocks. Each treatment block consisted of 20 replicate simulations.In the second simulation series (RFREQ), we tested the ability of the model to coestimate codon frequencies at the root and new amino acid fitness parameters in the substitution model over the rest of the tree. This series also tested the impact of failure to account for non-equilibrium evolution. We simulated sequences under a model in which codon frequencies were generated independently at the root. The initial sequence drawn from these frequencies then evolved through the tree under a new mutation-selection model with an amino acid fitness profile unrelated to the initial frequencies.We compared inferences of amino acid fitness profiles under three models: (1) a non-reversible equilibrium model with the codon frequencies at the root equalling the equilibrium frequencies of the model; (2) an equilibrium model using the standard reversible approximation to the probabiity of fixation; and (3) a non-equilibrium model that included separate parameters for the root frequencies. The power to infer parameters at the root and tips of the tree depends on the rate at which the protein family grows , as wellWe present results of inferring the number and position of sequence-wide shifts in amino acid fitnesses on simulated codon alignments (ASHIFT). Alignments were simulated with zero Fig.  or one  fitness When one shift was present, its position was correctly identified at least 14 times out of 20 for all numbers of tips and codons. The method returned a maximum of 6 false negative results (for the 10 tip and 300 codon simulations) and a minimum of 2 false negatives out of 20 for the two 900 codon alignments. At most 1 in 20 inferences returned an incorrect branch as the position of the only shift. A false positive result, in which a second shift was inferred when only one was present, occured in only 1 out of 20 simulations for the the 900-codon simulations and in 4 in 20 simulations for the 20-tip, 300-codon series. In one instance in the 20-tip, 900-codon series, the analysis failed to show signs of convergence within a practical time frame and was terminated.Since our simulation procedure distributed selective shifts over different distances from the root, we examined the relationship between the time depth of the true selective shift and the incidence of false positives and negatives Fig. . The uniThe inference method was able to recover amino acid fitness parameters in data sets simulated with a single selective shift when the shift was located correctly Fig. . ParametThe method was used to detect the most likely position for up to three major functional changes in Precambrian volution . Resurreeny Fig.  and for Vibrio and Moraxella, adjacent to the common ancestor of the Enterobacteria For the diversified phylogeny Fig. , the metal study . Two othMoraxella, adjacent to the shift identified within the the Proteobacteria in the diversified phylogeny. The second shift was located at the base of the tree on the branch leading to Francisella.For the Proteobacteria-only phylogeny Fig. , only twFor all analyses, we also visualised the change in relative fitnesses between the root and each inferred shift. The fitnesses were calculated relative to the median-fitness amino acid for each shift Fig. .Fig. 7EsThe performance of parameter estimation in the RFREQ simulation series varied strongly over the different treatment conditions Fig. . The strAmino acid fitness parameters were accurately recovered with median correlations above 0.75 for birth rates of 2.0 or lower, but decreased thereafter to a median correlation of near 0.5 for each treatment block. The range of performance was broad; the worst cases in the 5.0 and 10.0 birth rate treatment blocks failed to recover parameters altogether, while the best cases retained correlation coefficients over 0.75. The intermediate range, for birth rates of 1.0-2.0, allowed simultaneous recovery of both frequency and fitness parameters, albeit with less accuracy than at either extreme. We observed no clear difference between tree balance categories.The impact of accounting for differing root frequencies also varies with the lineage birth rate Fig. . At a liFinally, in order to test the identifiability of the non-reversible fixation probability, we inferred fitness parameters simulated under the non-reversible model using the standard reversible fixation probability . We founWe explored the feasibility of recovering the existence and location of selective shifts in amino acid fitness profiles without prior knowledge of possible change points. Recovery of fitness shifts was somewhat conservative, with numerous false negatives especially for shorter sequences, while incorrectly recovered shift positions were rare. However, parameter recovery after shifts is robust across all sequence lengths tested in this study, meaning that analyses of this kind are in principle applicable to realistic peptide sequence lengths of 300 amino acids and potentially fewer.Our method as currently implemented retains several important limitations. Most notably, the numerical difficulties caused by calculation of the non-reversible fixation probability without logarithmic approximations mean that the method is currently restricted to small fitness differences in order to avoid optimisation failures. The largest unscaled selection coefficient our implementation currently supports reliably is der here , and henIn our analyses, the non-reversible fixation probability produced results that were numerically indistinguishable from the reversible fixation probability on our trees. This appears to be related to the fact that selection against an amino acid quickly leads to a near-zero rate of evolution at equilibrium dominated by transient mutations, and differences between models will only be apparent in a narrow range when selection is extremely weak. Similar approximations with log-transformed selection coefficients have also been shown to produce fixation probabilities very similar to the canonical formula at near neutrality . The forOur method is also quite computationally demanding due to the need to repeatedly optimise high-parameter models. Application to trees much larger than the 20-taxon trees we explored would require parallel resources to be practical, and any attempt to implement the model for multiple partitions or in conjunction with mixtures of site-specific fitness profiles is likely to require extensive development. However, there exist numerous gene families with fewer than 20 known members  and theA further caveat is that shifts in site-specific profiles can be caused by compensatory changes  and mutaWe also demonstrated the ability to co-estimate codon frequencies at the root and independently-generated amino acid frequencies on the tree (the RFREQ series), albeit with more difficulty than in the ASHIFT inferences. The high dependence on lineage birth rates and the opposing trajectories of root frequencies and substitution model parameters mirror the relationship expected for ancestral trait reconstruction in comparative phylogenetics . Of noteEven so, our findings indicate that incorporating root frequencies may slow this decline; at higher speciation rates, errors in fitness parameter estimation are decreased when root frequencies are accounted for. The extent to which amino acid propensities, and hence codon frequencies, are likely to differ between the ancestor of the gene family and its descendants are little known. However, situations like this are to be expected if a significant proportion of gene families have their origin in neofunctionalization events following gene duplication, or indeed any other events causing changes in gene function that are linked to larger changes in protein structure and the underlying contact map , 53.Altogether, there is the potential to build exploratory non-equilibrium mutation selection models. The approach we have taken here may be seen as complementary to to novel approaches such as  or 54],,54], whiWe successfully applied the method to a small empirical data set of bacterial guration . While wFor the full diversified phylogeny, the method also recovers two other shifts at the base of Firmicutes and Spirochaetes. While we cannot rule out false positives, these shift positions could be related to broad differences in genome composition among bacterial phyla. For example, these two phyla are known to have more GC-poor genomes in comparison to the more GC-rich Proteobacteria and Actinobacteria , and thirrelated . NeverthFrancisella. It is possible that this would have been detected in the larger phylogeny if more shifts had been allowed, but nevertheless it raises the prospect that the method is sensitive to sampling scale. Broader samples may be superior where practicable.As a caveat to our empirical analysis, we note that the diversified and Proteobacteria-only phylogenies disagree on the placement of a shift at the base of For mutation-selection models, with their more mechanistic parameterizations, to become practical methods for characterizing selective pressures in protein families in comparative genomic analysis, implementations must be developed that remove their remaining restrictions. While methods exist to do this given explicit a priori knowledge of processes such as viral host shifts, there are many applications which may benefit from a more exploratory method. Here, we take a step forward in the development of such models and establish their empirical identifiability and statistical performance.The current work indicates directions for expanding our ability to detect adaptive and compensatory events in evolution. As mentioned above, immediate requirements for usability are a more effective likelihood penalty for selective shifts and a formulation of the fixation probability that is more computationally robust. Beyond these, an immediate goal is the improvement of computational performance through the powerful data-augmentation methods and massive parellization available in implementations such as Phylobayes-MPI , swMutSeTo infer selective shifts on a phylogeny, we employ a non-reversible mutation-selection model in which amino acid fitnesses are permitted to vary among branches of a phylogeny. At present, the model assumes one fitness profile for all sites analysed.a and b are given bys is the selection coefficient, and the population size Each mutation-selection model is characterised by 19 parameters pulation . The ratvolution , with trThe transition probabilities above are those given by the original diffusion approximation to the probability of ultimate fixation under a Wright-Fisher process. They differ from those used more generally in mutation-selection models by the fact that the fitness values  process . This shIn the equilibrium case, a single mutation-selection model is applied to the entire tree, and the initial codon frequencies are assumed to be identical to the equilibrium codon frequencies under the model. Our implementation allows two extensions to this case. Firstly, a separate set of 61 codon frequencies may be applied to the root of the tree (60 free parameters with the last constrained to sum to 1). While these could be viewed as being the equilibrium frequencies of a preceding mutation-selection model at the root, the formulation in terms of codon frequencies means that this is not required. These frequencies are unrelated to the fitness parameters incorporated in the substitution process.Secondly, it is possible to allow the fitness profile to change at the base of one or more branches in the tree, simulating an adaptive or compensatory shift. This introduces a new mutation-selection model parameterized by 19 new free fitness parameters. This model applies to the substitution process on the branch on which it occurs as well as all descending branches, unless another selective shift intervenes. In the present implementation, the location of these shifts in the tree need not be pre-specified. Appropriately invoked, the method will attempt to determine whether one or more shifts is present and to locate the branch on which they occur. The method is implemented within the Bio++ framework, which contains ready-made classes and maximum-likelihood parameter optimisation algorithms for mutation-selection models and non-time-homogeneous phylogenetics . Bio++ pThe procedure requires a pre-specified phylogeny and branch lengths, which may represent divergence times or another salient quantity, alongside a codon multiple sequence alignment. The process begins by assuming a single amino acid fitness profile and associated 19 fitness parameters across the tree. This profile, along with a tree-wide transition/transversion parameter In successive iterations, a selective shift is applied to each branch. A provisional set of amino acid fitnesses is then estimated by maximum likelihood for that branch and its descendants, with all other aspects of the model remaining fixed. The search procedure does not permit the case where selective shifts occur on both basal branches of the tree; the evolutionary process present at the root is assumed to persist on at least one branch.k change points and Following optimization, the corrected Akaike Information Criterion value (AICc) is calculated for each tested branch. The branch showing the greatest improvement following this procedure is selected as the most likely location for a selective shift. All frequency parameters are subsequently re-optimized. AICc values are then recalculated and compared for the previous model , we estimated shift numbers and positions using the penalized-likelihood procedure decribed above. We set the maximum number of shifts to two in order to capture false positives. This inference procedure was applied identically to simulations with zero or one selective shift. For the non-equilibrium model in the second simulation study (RFREQ), we co-estimated codon frequencies at the root and amino acid propensities across the tree, with the maximum number of selective shifts fixed to zero. For equilibrium inferences in both ASHIFT and RFREQ, we did not allow independent root frequencies and fixed the number of selective shifts to zero.To validate our methodology on real data sets, we obtained an amino-acid alignment and phylogeny of prokaryotic ctamases . AncestrTo explore the robustness of the method we analysed dge Base  and downdge Base . We aligdge Base . The phyWe analysed this method with a similar procedure to that used in the ASHIFT simulations. For the empirical data, we allowed the procedure to infer up to three global shifts in amino acid fitnesses. We assume that amino acid frequencies at the root were the same as those on the initial branches.