Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts. The notion of the brain as a predictive machine pervades contemporary neuroscientific concepts The update mechanisms of predictions are described in the predictive coding account of perception Neuroscientific research on learning has discussed the interplay and competition of two learning systems The hippocampal memory system is understood to be an associative mismatch detector The striatum and its underlying dopaminergic projections have famously been associated with prediction errors in the context of reward-related learning new originals, hereafter). The according activity change basically pertains to the adaptation of novelty responses, signified by an attenuation of the BOLD response. The second type of learning we investigated was the adaptation of predictions when the expectations of a model were violated by a divergent version of the action , which was thereafter repeated. Lastly, we investigated the response to constant violation of a model by unpredictable versions of the according action movie . This last manipulation did not allow predicting the content of a model, corresponding to a type of learning that is signified by a lot of uncertainty.The current study aimed to dissociate the contributions of the hippocampal and striatal systems to different types of learning that are marked to different degrees by novelty and prediction errors. We presented subjects with videos of everyday actions that were either entirely new to the participant, were related to a known movie but then repeatedly shown in a different version, or were related to a known movie and but then repeatedly shown in different versions . The firWe hypothesized that the hippocampal memory system should be activated to a larger extend by associative novelty than novelty per se and thus show more activity towards the unpredictable movies and divergent movies than the novel movies. Moreover, in line with the results reported by Strange and colleagues With regard to the striatal responses during learning, we focused on a subdivision of the caudate nucleus that was previously associated with perceptual prediction errors 19 right-handed, healthy participants  took part in the study. The participants were right handed as assessed with the Edinburgh Handedness Inventory divergents). Some movie scripts existed in 6 different versions (unpredictables). All of these scripts had an identical beginning, but started to diverge at some individual point, whereafter no commonality existed . The movies were shot from the third-person perspective, not showing the actor's face. They contained every-day actions taking place at a table. Most movie scripts, e.g. making a sandwich, existed in 2 versions . The participants watched 27 scripts, a third of which was displayed three times, another third six times and the last third nine times, in a randomized fashion over the course of the 28 minutes lasting session. As mentioned above, the participants watched one version of each script; but each repetition was another shot of the same script . Questions concerning whether a specific action had occurred in the immediately preceding script (e.g. “grasping an apple?") were posed on average after every fifth script  to ensure ongoing attention to the stimulus material. Participants received visual feedback for 400 ms on whether they had answered correctly, incorrectly, or too late. After pre-exposition, participants were transferred directly to the fMRI chamber.originals) as in the pre-exposition. Another nine of the pre-experimentally shown scripts were presented nine times in the fMRI session in different, but always the same different version (divergents). Another nine scripts appeared in five different versions during the fMRI. Each of these different versions was displayed only once (unpredictables). One third of all movies  had previously been displayed three times each, another third six times each, and one third nine times each. The design moreover encompassed three scripts that were repeated nine times during the fMRI session and completely new to the participants at first exposure . Finally, there were six single movies that were displayed only once and had not been pre-exposed previously  (unpredictables or divergents existed or whether the current movie belonged to either category). Apart from the question trials, the design also encompassed null-events. Null-events consisted of the display of the grey background screen for 10 seconds. Immediately after the fMRI session, participants filled in a questionnaire encompassing a free-recall task for the movie scripts.36 different scripts appeared in the fMRI session. Each script was repeated over the experiment. Nine scripts that had previously been displayed during the pre-exposition were now displayed nine times each in the same version (reafter) ; Table 1The functional imaging session took place in a 3T Siemens Magnetom Trio scanner . In a separate session, prior to the functional MRI, high-resolution 3D T-1 weighted whole-brain MDEFT sequences were recorded for every participant .The functional session engaged a single-shot gradient echo-planar imaging (EPI) sequence sensitive to blood oxygen level dependent (BOLD) contrast  parallel to the bicommisural plane, echo time 30 ms, flip angle 90°; repetition time 2000 ms; serial recording). Following the functional session immediately, a set of T1-weighted 2D-FLASH images was acquired for each participant .new originals compared to the other analyses. The filter in the contrasts investigating only unpredictables and divergents was set at 1/85 Hz. The (parametric) contrasts containing new originals were high-pass filtered at 1/90 Hz. The matching parameters  of the T1-weighted 2D-FLASH data onto the individual 3D MDEFT reference set were used to calculate the transformation matrices for linear registration. These matrices were subsequently normalized to the standardized Talairach brain size . Further processing was conducted with the LIPSIA software package The statistical evaluation was based on a least-square estimation using the general linear model (GLM) for serially auto-correlated observations divergents and unpredictables) differed from its original pre-experimental exposition version. The originals and new originals were modeled after the point in the movie that would have been the breach, if they had been displayed in their complementary version. This pseudo post-breach modeling was employed for the originals and new originals, as all scripts were counterbalanced in their assignment to conditions across participants. Thus some participants could have encountered in the function of divergent what to others was the original, or even new original. We did this to ensure that the measured effects did not stem from the identity of scripts or comparative length, but solely their assigned condition in the experiment. The breach had previously been visually timed to the moment when movement trajectories revealed that either the manipulation or the reached-for-object was different from that in the originals. The length of the modeled events corresponded to the length of the script from the breach to the end of the script .The design matrices were generated by hemodynamic modeling using a δ-function and its first derivate. The onset vectors in the design matrices were modeled in a time-locked event-related fashion and set to the point in time (hereupon ‘breach’) when the movie  parametrically. To that end, we generated three separate design matrices, each containing three event types, two times the movie type of interest and null-events. For example, the design matrix for unpredictables contained as a first event type all unpredictables with an amplitude vector of one. As a second event type, it contained all unpredictables with an amplitude vector corresponding to the specific script's iteration in the fMRI session. . The last event type in the design matrix were null-events, assigned an amplitude of one. The same set up applies to the design matrices for the linear parametric attenuation modeling of divergents and new originals. In a second step, we contrasted the unpredictables with the divergents and the divergents with the new originals to investigate the relative and persistent involvement of the hippocampus proper and the striatum, i.e. caudate nucleus, in the processing of the different movie types. Thus, the fourth design matrix contained as the first event-type all unpredictables, each with a vector amplitude of one, as the second event-type all divergents, with a vector amplitude of one and lastly as a third event-type all null-events with a vector amplitude of one. The fifth design matrix contained the event-types divergents, new originals and null-events, all modeled with a vector amplitude of one. The sixth analysis contrasted 12 randomly chosen unpredictables (each with an amplitude vector of one) with the first presentation of the new originals and singletons (with the same amplitude) and also contained null-events.The fMRI data analysis proceeded in two steps. In a first step, we modeled each condition of interest  was calculated as:i)") of a specific movie version. It indicates the amount of information that is conveyed by the observation Following previous approaches i") is thus the negative probability (“-p(xi)") of the observation of a movie script multiplied with its logarithmic probability (“ln p(xi)"), summed for all versions (“i-k") that are known to have occurred.  for significance testing was set at p = .05. This means that 1900 permutations of the assignment between subjects and conditions must result in a lower t than the original experimental assignment wherein the control condition is used as control condition and the experimental condition used as experimental condition for all 16 subjects.It can thus be determined, whether the analysis that agrees with the experimental setup in all participants reaches a higher (unpredictables), but apply different models, we subdued the fMRI data to additional analyses. These additional analyses made use of a unified design matrix and orthogonalised the modeled effects of entropy, surprise, and the linear parameter. To that end, we generated one design-matrix as follows: The unpredictables appeared in 4 event types. In the baseline entry, all amplitudes were set to one. This was the first event type. For the second event type, the amplitude vector was set corresponding to the item-specific entropy. The third entry carried an amplitude vector corresponding to surprise orthogonalised to entropy. The fourth entry carried an amplitude vector according to the parameter signifiying linear decrease, in a first step orthogonalised to entropy and in a second step orthogonalised to surprise. The last entry of this design matrix were null-events. The second-level permutation-based analysis was the same as for the main contrasts (cf. 2.5.3).In the analyses as laid out above, different design matrices were used for each contrast, yielding maximum power to the specific analysis. However since a number of analyses pertain to the same events divergents, unpredictables, new originals, originals and singletons. Furthermore, it was counted how often each of the recalled movies had been seen during the experiment . The number of expositions was aggregated for each version of the movies, i.e. divergent movies have been exposed 3+9, 6+9, or 9+9 times , whereas all new originals had been exposed 9 times . To calculate the average number of exposition of the recalled movies of one condition, the numbers of expositions of the recalled movies were summed and then divided by the number of the recalled movies. The inferential analysis was performed in three steps:After the fMRI session, participants were asked to recall as many actions as they could remember. To test if the different actions were differently well remembered depending on their condition, these free recall rates were analyzed. Therefore, it was counted how many movies of each condition were recalled by each subject, which resulted in the absolute number of recalled condition .At first, the influence of the exposition frequency was partialed out by running a multiple regression with the sum of the recalled actions (per condition) as dependent and the number of pre-expositions as independent variables. The standardized residuals of this analysis, i.e., the information that was not explained by exposition frequency, served as dependent variable in the analysis of the condition effect. To that end, a repeated-measures ANOVA was calculated with the factor unpredictables condition was therefore not necessarily harder than naming a script from the originals, new originals or divergents condition.It must be borne in mind that all unpredictable versions of one movie shared common actions in the common beginning of the script. Moreover, the objects in different versions were sometimes the same as in other versions, while the manipulation of the object differed. For instance, all 6 different versions of one particular movie (the pre-exposed version as well as the five unpredictable versions during the fMRI) contained a piggy bank. Naming a script from the new originals), whether it was displayed in a divergent version during the fMRI compared to the preexposition (divergents), or whether it displayed an altered version on each iteration during the fMRI (unpredictables). The correlation between the sum of recalled actions per condition and the exposition frequency was significant . The repeated measures ANOVA on the standardized residuals of the number of recalled actions and the factor condition yielded significance  = 3.505, p = .024; Greenhouse Geisser corrected for non-sphericity). On average, the number of recalled actions in the condition divergents was higher than for actions in the conditions new originals and unpredictables . This difference was also visible in the standardized residuals, which served as dependent variable in the ANOVA .The behavioral analysis assessed how many actions were recalled depending on the contingencies of the corresponding action movie during the experiment, i.e., whether it was completely new when encountered during the fMRI session . The substantia nigra showed attenuation of activity in the same parametric contrast . There was no significant attenuation of activity in the caudate nucleus ROI.There was a significant attenuation of activity with repeated exposures of the new originals vs. divergents. Thus, it was significantly less activated by the processing of divergents compared to new originals .The substantia nigra was the only structure that showed a main effect for the processing of divergents .The hippocampal ROI revealed a significant attenuation of activity with the repeated exposure of new originals and singletons . In the caudate ROI there was more activity for the processing of unpredictables at all stages than for the processing of divergents . Likewise, the habenula  and the substantia nigra  were activated more by unpredictables than by divergents. There was no attenuation with the repeated exposure of unpredictables in any ROI at p<0.05.Processing of the unpredictables activated the hippocampal ROI significantly more than processing of condition  on attenuation effects in the hippocampal ROI. The repeated measures ANOVA yielded a significant main effect of condition. This effect was due to significant differences between new originals and unpredictables. Since the dependent variable reflected the slope of the attenuation, these results indicate an interaction between the course of the attenuation and the condition in the hippocampus.A repeated measures ANOVA testing for main effects of unpredictable . Activity in the caudate ROI for the modeling of surprise (as defined by information theory) during the observation of unpredictables approached significance at p = .054 The BOLD changes in the hippocampal ROI varied to a statistically significant degree with the Shannon entropy of each observation of an unpredictables . Modeling surprise orthogonal to entropy yielded significant activity in the caudate ROI during the observations of unpredictables  exceeding the probability of the marginal effect in the former analysis. No other ROI yielded significant activity corresponding to entropy during the observation of unpredictables. No other ROI yielded significant activity according to surprise orthogonalised to entropy during the observation of unpredictables. No ROI yielded significant activity for the modeling of a linear decrease orthogonalised to entropy and surprise during the observation of unpredictables.Modeling entropy yielded replication of the former significant result for the hippocampus ROI during the observation of Hippocampus, caudate nucleus and midbrain dopaminergic system are supposed to contribute to learning and all of these systems have been associated with learning from prediction errors. But each structure's specific contribution to learning in the absence or from the presence of prediction errors deserves further investigation. Here we used fMRI to tap these structures' roles in learning of observed action episodes. The hippocampal ROI showed a decrease in activity as hypothesized during the acquisition of a new model and the adaptation of an internal model to a changed script. Thus, both adaptation processes are signified by hippocampal decrease.unpredictables that reflects the Shannon entropy, or average surprise, elicited by the prediction errors inherent in this condition  elicit less activity than unpredictables that relate to a previous association can also be recast in terms of predictive coding. As described previously, predictive coding rests on Bayesian inference. That is, the first of frequently paired items starts to predict the second item with a high conditional probability. If this pairing is consistent, the brain experiences little entropy and will therefore not expect any deviations. A violation of this prediction results in a higher activation than the encounter of an action movie that is not encompassed in a recently acquired internal model, as in the case of the first new originals and singletons. If no solid internal model exists so far, the input will be filtered only to the degree that is proposed by known action semantics. In comparison to the episodic internal model trained for the unpredictables, the internal model for the new originals does not ascribe a solid probability to specific episodically acquired predictions. Thus, the mismatch signal is smaller due to more lenient semantic predictions.The associative mismatch account of hippocampal function unpredictables would have mirrored the responses towards the divergents.The current results suggest that the hippocampal activity reflects Shannon entropy of the unpredictable courses cf. . Shannondivergents surpassed that for unpredictables, suggesting a less successful encoding of the unpredictables. This finding may be not surprising, given the fact that unpredictables did not possess the reliability to enable future valid predictions. We thus find tentative evidence that while stimulus sequences exposing high Shannon entropy are encoded to a certain degree, the encoding is not as successful as that for low-entropy or stable sequences. In similar vein, an interesting study by Davis and colleagues Having said that, it is interesting that the free recall rates for Based on the results of the present study, we propose that the hippocampus adapts its models of sequential sensory input as implied by the associative mismatch account unpredictables than to divergents. Each unpredictable contained a breach of expectation on the content level, that is the sequence of actions. But only the first divergent contained a breach of expectation on the content level while each subsequent divergent version of the same movie repeated the same divergence compared to the original script. On a higher level of description, each breach of expectation of the unpredictables that occurred after the second iteration was fully predictable as such, . Caudate nucleus activity was therefore driven by prediction errors on the content level, indicating a lack of meta-learning. We could not establish a linear decrease in caudate activity that would further argue in favor of a prediction error account, as it would signify a decrease in activity as predictions become more reliable. However, this null-finding could also be due to a non-linear decrease over the iterations of divergents. Caudate signaling of prediction errors is noteworthy in itself, as only few fMRI studies have discussed the striatal involvement in not reward related prediction errors temporal difference model that is usually associated with reward related learning The caudate nucleus showed a higher response to


Obesity is a global epidemic with major healthcare implications and costs. Mobile technologies are potential interventions to promote weight loss. An early systematic review of this rapidly growing area of research was conducted. Electronic databases were searched for articles published between January 1998 and October 2011. Data sources included Medline, Embase and the Cochrane Central Register of Controlled Trials. Ongoing research was searched for using clinical trials databases and registers. Out of 174 articles retrieved, 21 met the inclusion criteria of randomized controlled trials (RCTs) on mobile technology interventions facilitating weight loss in overweight and obese adults with any other comparator. A narrative synthesis was undertaken. Seven articles were included and appraised using the Cochrane risk of bias tool: four presented a low risk of bias and three presented a high risk of bias. There is consistent strong evidence across the included multiple high-quality RCTs that weight loss occurs in the short-term because of mobile technology interventions, with moderate evidence for the medium-term. Recommendations for improving the reporting and quality of future trials are made including reporting weight loss in percent to meet clinical standards, and including features such as long-term follow-up, cost-effectiveness and patient acceptability. Populations who are overweight or obese have a higher likelihood of serious health complications including type 2 diabetes, stroke, cardiovascular diseases, cancer and osteoarthritis Currently, commissioned weight loss services and interventions in Europe and the USA often start by considering the use of an appropriate combination of diet, exercise programme and counselling for the individual – drawing on behaviour change theories A systematic review focusing on self-monitoring activities in weight loss reported on the use of digital technology in five out of 22 studies, the five included use of the Internet, personal digital assistants  and electronic digital scales et al. While these reviews may include data directly relevant to weight loss interventions, their main goal was to draw inferences from results across health conditions and other outcomes, therefore providing a more limited focus on weight loss. Wide-ranging technology-based weight loss interventions (e.g. cell phones/Internet) have been reviewed Use of digital mobile technologies in healthcare while increasingly common is still a relatively new modality, and relatively few RCTs have been published exploring their effectiveness. There is therefore a need for a review of interventions employing mobile digital technologies. Such reviews need to consider the strength of the evidence presented by published studies when informing their design and the evidence already published (if any) for the intervention they plan to investigate. Well-designed RCTs, especially double-blinded ones, are widely regarded as the best In the light of the published reviews and discussion mentioned earlier, the aim of this systematic review was not only to study in more detail whether mobile technology is an appropriate medium for facilitating weight loss in overweight and obese adults, but also to identify, compare and contrast the features of the studies and interventions to inform future RCT study design.The systematic review was conducted using an agreed, but unpublished, predetermined protocol and in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement A systematic search was undertaken to identify RCTs in the area of mobile technology and obese adults. Data sources were Medline, Embase, Science Citation Index, Social Science Citation Index, Cumulative Index to Nursing and Allied Health Literature, the Cochrane Central Register of Controlled Trials, Meta register of controlled trials and Clinical Trials.gov. Keywords and subject heading terms for mobile technology were combined with terms relating to obesity or weight management. The search was limited to RCTs for the period between 1998 and October 2011. There were no RCT search results on Medline before 1998 and given the resources available we decided to set this date limit on the search. The Medline search strategy is shown in The inclusion/exclusion criteria are shown in Portable  digital technology to run software applications that is carried  or worn (on the body or embedded in clothing) whose interventions can be individually tailored and delivered at any time it is worn or carried in everyday life .Thus, technologies of interest are palmtop computers, mobile phones, PDAs and other handheld, worn or pocket computers.Two reviewers (RB and CL) independently screened the titles and abstracts of articles retrieved from the database search. Relevant articles were selected according to the inclusion/exclusion criteria, and then two reviewers (RB and CL) agreed on the selected articles as to whether they should be included. Disagreements were resolved by consensus with a third reviewer (PC). The full texts were obtained for in-depth review and data extraction.Two independent reviewers used a standardized form to extract data (shown in The risk of bias of the included studies was undertaken independently by two reviewers (RB and CL) using the Cochrane risk of bias tool Dropout > 50%.Statistically and clinically significant differences between groups at baseline indicating unsuccessful randomization.This approach has previously been validated Qualitative analysis and comparisons of the articles were conducted (RB and PC). Because of the heterogeneity of the studies retrieved, with regards to the mobile technology systems offered and associated features of interventions, a qualitative synthesis tool using a rating system for levels of evidence The review stages are summarized in The full text of 21 potentially relevant articles were retrieved and reviewed. Out of these, 14 were excluded for not meeting the criteria relating to study type, participants, interventions and language. A list of the excluded studies can be found in Supporting Information All the interventions comprised the participants being educated about weight loss via diet and exercise, and carrying with them in their waking hours a mobile device  that was a motivator in behaviour change. In some the mobile device was a medium through which the participant received a motivational message. In others, it was used to record what had been eaten, in yet others, it recorded the amount of physical activity, some did both. One technology did all these things by reporting the current daily energy balance. More detail can be found in The results of the Risk of Bias Assessment are shown in A summary of the characteristics of the included studies and main results is provided in However, in other ways, the studies are different. Five of the RCTs recruited otherwise clinically healthy overweight or obese adults in the USA There were financially relevant considerations for participants across the studies, which spanned charging to rewarding participants including doing neither . The comThe five recommended key components for effective technology-based weight loss interventions are self-monitoring, counsellor feedback and communication, social support, use of a structured programme, and use of an individually tailored programme Weight loss and BMI change results can be observed in P = 0.02], 14.0 lb for the deposit contract group [P = 0.006] vs. 3.9 lb for the control group). Post-intervention at 7 months, there was still weight loss in the intervention groups, but the difference to the control group was no longer statistically significant.In one RCT focusing on weight loss following financial incentives and text pager messages P = 0.006). All the participants were free to engage in whatever weight loss activities they wished, and the results are reported to be upheld when these are taken into account.For a mobile phone-operated weight loss programme vs. no intervention P = 0.06). However, the study reports statistically significant lower-percentage body fat in the short-term (−2.18% vs. −0.17% in 9 weeks). It can be seen in 2) when compared with the other studies because many participants had normal BMIs and the primary goal was to observe increased activity not weight loss.The Internet- and mobile phone-based physical activity programme study P = 0.000).In a weight loss RCT with obese patients with type 2 diabetes using telemedical equipment vs. no access to the technology and service P = 0.04). The study did not report percentage reduction in weight or BMI change.For the weight loss intervention study using financial incentives and mobile phones P = 0.033). The higher percentage is equivalent to an average of 1.25 kg per month for a BMI of around 30. While not clearly stated there is an inference that participants during the study only employed the weight loss interventions they were provided with by the study. However, we note that the physical activity aspect of the intervention is not well described, there is no explanation of what participants in the various groups were told about the role of exercise in their weight loss programme, which may mean they were free to engage or not in this as a co-intervention.For the telemonitoring intervention with weighing scale and activity monitoring by accelerometer vs. the same training and information without the use of the technology and its services P = 0.02). The usual care comprised the same baseline assessment followed by monthly information or tips about achieving weight loss through managing diet and exercise. For the experimental group, the Internet was used just to choose preferences for tailored messages, otherwise, they used mobile phones to receive and send messages during the self-monitoring. Because the control group is described as ‘usual care’ and the experimental group as usual care plus the study intervention, it is not clear what freedoms the participants had to employ weight loss techniques.An RCT involving mobile phone feedback message intervention vs. usual care In light of the results mentioned earlier on weight loss across the studies, if the studies are considered to be sufficiently clinically similar, then the level of synthesized evidence according to the Van Tulder evaluation This systematic review summarizes the results of the most robust and well-conducted RCTs available at the time of writing for mobile technology interventions for overweight and obesity From the available evidence, one study It is worth pointing out that none of the studies (apart from 33 that followed-up after 36 weeks) conducted a post-intervention follow-up to determine sustained long-term benefits or behaviour change, and none of the studies show long-term weight loss. Six of the interventions are of 6 months duration or significantly less, and just one study In one study The results of this review are that three out of four individual low risk of bias studies each support the use of mobile interventions and, if considered sufficiently similar, according to the measure of quality Despite the stated strong evidence The generalizability of some of the included studies may be limited because of the small sample size The risk of bias assessment  highligha priori, participants cannot be blinded to their own behaviour change intervention that they actively manage themselves, it is possible to blind the participants and others, including outcome assessors, to which arm of the trial they are in and often confounding details of the aims of the study Apart from the care giver blinding in one study Cost-effectiveness of the interventions was not addressed by any of the studies included in the review and should be included in future research given the high cost associated with overweight and obesity It is well accepted and published that incentives such as the payment of participants can be a successful intervention for weight loss To ensure transfer of any new effective intervention programmes from research study to implemented care, it is essential to recognize this requires changing the ways existing services work. Central to achieving effective transfer is consideration of and with the individuals involved Because of the high risk of bias of three of the studies It appears the included studies did not use a systematic mixed methods approach The strengths of this systematic review relate to the methodology employed Limitations include the fact that terms could have been missed in the search strategy if they were not mentioned in the title/abstract. Finally, our definition of technology was mobile and therefore excluded technology like the Internet, but these technologies have been reviewed elsewhere It is difficult to classify, compare and contrast the features of the studies and interventions because of the heterogeneous nature of the studies reviewed  and 5, aFuture trials should have the following features to ensure high-quality design. Research should: involve stakeholders If the trials included are considered sufficiently clinically similar, as might be inferred from the meta-analysis by Liang


A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study. One objective of genetic regulatory modeling is to design intervention strategies that affect the evolution of the gene activity profile of the network. Such strategies can be useful in identifying potential drug targets and treatment methods to alter network evolution in some desirable manner. The states of the network can be partitioned into two sets, desirable and undesirable, which correspond to functional cellular states, such as proliferation and apoptosis . In biolpoptosis 4. In culll death . The p grvention 7.As this and many other examples suggest, it is prudent to use external variables to beneficially alter the evolution of gene regulatory networks. The design of intervention strategies that reduce the likelihood of states favorable to metastasis in cancerous cells has been recently studied by the systems biology community 8. In parUnder appropriate assumptions, a Markov chain models the dynamical behavior of a PBN 13. An opCommon to these approaches is a utility function that is to be maximized in order to reduce the aggregated probability of disease states. In reality, treatment options, for example, chemotherapy, cause collateral damages. For instance, consider a second example borrowed from . A treatUsing constrained intervention methods, we seek an effective regulatory treatment that reduces the likelihood of visiting undesirable gene-activity profiles, that is, state, in the long run while providing an upper bound on the expected number of interventions a patient can receive. Instead of introducing a single utility function whose maximization reduces the likelihood of entering undesirable states, we consider a situation where one type of utility is maximized while keeping the other cost function below a given threshold. Posed this way, the intervention problem can be viewed as a constrained Markov decision process.In our framework, a gene regulatory network is modeled as a dynamical system in which decisions regarding treatment are taken sequentially. We wish to design an intervention strategy that selects treatments (actions) as a function of time and available information. For a given intervention strategy, the choice of treatments at different decision epochs may depend on the whole observed history. The choice of an intervention strategy will determine the evolution of the state of an intervened biological system in some probabilistic sense. The trajectories of the states together with the choice of treatments determine the expected utility in conjunction with the expected cost that we encounter. Hence, the proposed method enables us to design therapeutic intervention strategies by defining problem dependent constraints. Although various forms of constraints are plausible, hereafter, we focus on the expected number of treatments.We provide the necessary background and formulate the problem of unconstrained intervention in a PBN as a Markov decision process in Section 2. The constrained intervention method is formulated in Section 3. As a numerical study, in Section 4, we consider a network obtained from the mammalian cell cycle with mutated phenotype. We design a constrained intervention strategy to hinder cell growth in the absence of growth factors, while keeping the expected number of interventions bounded. We investigate how the constrained intervention strategy performs in comparison to the unconstrained policy.A probabilistic Boolean network (PBN) consists of a sequence Two quantization levels have thus far been used in practice. If The gene-activity profile (GAP) is an In the presence of external controls, we suppose that the PBN has Brun et al. showed that the dynamic behavior of a PBN can be modeled by a Markov chain . In thiswhere state for all ribution .The problem of optimal intervention for PBNs is formulated as an unconstrained Markov decision process . A rewarWe consider the discounted formulation of the expected total reward. The discounting factor, long-run . In the A policy Frequently, the discounted reward is defined without the normalizing constant The vector It is known that an optimal intervention strategy exists for the unconstrained discounted intervention problems, and it is given by the fixed-point solution of the Bellman optimality equation:Moreover, an optimal policy determined by the Bellman optimality equation is deterministic, and independent of the initial state . StandarCancer therapy may include the use of chemotherapy, radiation therapy, targeted gene therapy, and so forth. All of these treatment options are directed at killing or eradicating cancerous cells. Unfortunately, cancer treatments may also damage healthy cells. This results in complications and harmful side effects. It is therefore desirable to maintain the side effects of a treatment to a minimum. This goal can be achieved by enforcing an upper bound on the expected number of treatments a patient may receive during therapy. A deterministic intervention policy devised by solving the unconstrained optimization (5) reduces the chance of visiting undesirable states; however, this intervention policy does not provide a way to constrain the frequency of applying treatments within a prescribed intervention policy. To address this shortcoming, we impose an appropriate constraint on the optimization problem (5) by introducing constrained intervention in PBNs.For the same reasons articulated in Section 2, we consider a discounted formulation to define both the objective reward function and constraint cost function. To restrict the frequency of applying intervention, we associate a cost-per-stage Having the constrained cost function defined this way and the objective reward function as in (4), we can state the constrained intervention problem in a PBN aswhere We wish to find an optimal intervention policy Given an arbitrary policy We further define the normalized discounted total expected time spent in the state-action pair for all occupation measure[denotes a probability measure over the set of state-action pairs n measure. The occThe normalized discounted reward function (4) can be expressed as the expectation of the average immediate reward The normalized discounted reward function in (12) can be equivalently expressed asUsing definition (10) and probability measure (11), we can express the latest form of the normalized discounted reward (13) as the expectation of the average immediate reward with respect to the occupation measure:Similarly, we can express the normalized discounted objective cost corresponding to policy Using (14) and (15), we can rewrite the constrained optimization problem (8) asIt is evident that the constraint in (16) prevents the discounted expected number of interventions in the long run from exceeding the upper-bound In other words, using the definition of cost-per-stage in (17), the left side of the inequality constraint in (16) corresponds to the total discounted expected number of times that state-action pairs with active treatment, Several solutions for the constrained optimization problem of (8) are presented in . We nextdenotes the set of all the occupation measures anddenotes the set of occupation measures generated by stationary policies only, then where It has been shown that policies . MoreoveThis linear program is called the primal problem.In , it is sWe should point out that the optimal policy devised by (22) is not necessarily a deterministic policy, in contrast to a policy that maximizes reward function (4) without limitations.Depending on the utilized numerical method, the computational complexity of finding a solution for the linear program in (21) varies. It is known that the complexity of the interior-point method increases polynomially with the number of states in ot large . MoreoveIn this section, we construct a PBN that is a probabilistic version of the Boolean model for the mammalian cell cycle regulation proposed in . This PBDuring the late 1970s and early 1980s, yeast geneticists identified the cell-cycle genes encoding for new classes of molecules, including the cyclins  and their cyclin dependent kinases (cdks) partners . Our modThe key genes in this model are CycD, retinoblastoma (Rb), and p27. Rb is a tumor-suppressor gene. This gene is expressed in the absence of the cyclins, which inhibit Rb by phosphorylation. Whenever p27 is present, Rb can be expressed even in the presence of CycE or CycA. Gene p27 is active in the absence of the cyclins. Whenever p27 is present, it blocks the action of CycE or CycA. Hence, it stops the cell cycle.The preceding explanation represents the wild-type cell-cycle model. Following one of the proposed mutations in , we assuThe Boolean functions in Table According to Table Preventing the states with simultaneously down-regulated CycD and Rb as our objective, we apply the constrained intervention method described in Section 3 to the constructed PBN with various bounds on the frequency of applying control in a policy. We only consider a single control, We assume that the reward of the states with down-regulated Rb and CycD is lower than those for the states in which these two genes are not simultaneously down-regulated. We also consider the cost of applying a control action, which reduces the reward of each state. We postulate the following rate-of-reward function:We select an arbitrary rate of reward; however, the reward and control cost are selected so that applying the control to prevent the undesirable states is preferable in comparison to not applying control and remaining in an undesirable state. In practice, the reward values have to capture the benefits and costs of the intervention and the relative preference of the states. They have to be set in conjunction with physicians according to their clinical judgement. Although this is not feasible within the domain of current medical practice, we do believe that such an approach will become increasingly mainstream once engineering approaches are demonstrated to yield significant benefits in translational medicine. Assuming the preceding rate-of-reward function, we can compute control policies for the PBN associated to the cell-cycle network according to various constraints.Figure We define If we assume that we can alter the expression level of any gene in the network as a therapeutic method, then it is natural to ask which gene should be used to alter the behavior of the model. To this end, we find a constrained intervention policy for each gene in the network using the intervention method explained in Section 3, while limiting the expected number of times a control can be applied. First, we assume that the PBN's initial state is the undesirable gene-activity profile with the highest probability in the steady-state distribution of gene-activity profiles prior to intervention. Table Among all the genes, Rb offers the best performance when control can be applied without any constraint, based strictly on maximization of the reward function, Figure According to Table Comparing Figures The results of Table Furthermore, if we do not assume that the PBN's initial state is the undesirable gene-activity profile with the highest probability in the steady-state distribution of gene-activity profiles prior to intervention but instead initialize the PBN from an arbitrary undesirable gene-activity profile, we observe that the policies are robust to the initial state unless the constraint is too tight. For We have formulated the constrained intervention method in probabilistic Boolean networks and demonstrated that one can reduce the likelihood of a subset of undesirable states while bounding the expected number of interventions in a therapeutic strategy using the proposed method. We have considered a mutated mammalian cell-cycle network in which the cell growth does not stop in the absence of growth factors. We have then utilized the proposed intervention method to design constrained intervention policies to influence the dynamics of the PBN constructed for the mutated mammalian cell cycle. The goal of intervention is to reduce the chance of undesirable cell proliferation in the long run, while maintaining a bound on the expected number of interventions. The presented numerical studies strongly suggest that constrained intervention can effectively alter the dynamics of the cell-cycle model. Various control genes can be considered given different constraints. The most effective control gene may vary depending on the restrictions imposed on the intervention policies.


Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases  using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required. Electroporation is the phenomenon that occurs when a biological cell is exposed to an adequately high electric field, which results in the cell membrane becoming transiently permeabilized Electrochemotherapy is performed by high-voltage electric pulses delivery using applicators (i.e. electrodes) that are in contact with (or located near the) target tissue. Electrochemotherapy has already been introduced into clinical use for treating skin melanoma using plate or needle electrodes with a fixed geometry; the use of such electrodes imposes following predefined standard operating procedures for a successful treatment non-thermal irreversible electroporation (N-TIRE) and is used for tissue ablation performed using an externally applied electric field with electric field strengths and higher number of pulses than the values used for electrochemotherapy Another important electroporation-based application is termed To prepare a robust treatment plan for electroporation-based treatments, an anatomical model that is built from medical images (Magnetic Resonance Imaging – MRI) needs to be constructed first In order to establish the concept of electroporation-based treatment planning, we follow radiotherapy treatment planning as the basis In this study, we evaluated three different liver segmentation algorithms that can be used for electroporation-based treatment planning: region growing, adaptive threshold and active contours. Region growing was selected for evaluation because despite its simplicity (i.e. segments are included solely based on their intensities) this algorithm is robust and can provide good results if its basic drawbacks (e.g. oversegmentation due to leakage) are eliminated Image Processing Toolbox and Parallel Processing Toolbox. The procedure for loading images loads all DICOM  SeriesNumber parameter in order to determine the number of different acquisition series present in the folder. Then, an image from every series is presented to the end-user (e.g. the attending physician); an image from every acquisition series is displayed and labeled using the original label that was stored at acquisition time and is read from the DICOM header as the string stored in the SeriesDescription parameter. Finally, the end-user determines which acquisition series will be used for planning of the electroporation-based treatment.The segmentation procedure begins with importing the patient's images into the treatment planning software. The latter is a MATLAB application, developed in MATLAB R2012a  using the SliceLocation parameter) using a common bubble-sorting algorithm. If all obtained Z-indexes after bubble-sorting are not evenly distributed, empty slices are inserted where the slices are detected as missing. However, since missing slices may indicate corruption of the patient's images collection, the software does not try to interpolate the missing slices but notifies the end-user instead.After that, all the images from the selected acquisition series are loaded and sorted according to their spatial location  parameters WindowCenter (WC) and WindowWidth (WW) which are most important. Namely, WC and WW determine how source image data need to be interpreted when displayed; therefore, an initial sigmoid transformation using parameters WC and WW needs to be performed first for the image data to be displayed correctly. The latter is done within the preprocessing procedure.Besides the image data, essential DICOM metadata is loaded; the metadata structure appended to the slices includes these parameters: layers, starting with the source slices (i.e. slices stored as DICOM data), which enables the developers of the algorithms to have a clear overview of the whole preprocessing procedure.For segmentation algorithms to perform without problems, the imported slices first need to be preprocessed. Preprocessing is a procedure which is executed on each slice separately; therefore, the procedure is non-recursive and can be run in parallel using multiple processors or processor cores. Since the preprocessing procedure comprises of several steps, the steps are marked for debugging and algorithm evaluation purposes by storing partial results  into separate source) slices is defined by the Volume-of-Interest (VOI) parameters  that are stored as DICOM metadata. Since WC and WW differ from slice to slice, each slice first needs to be transformed from the imported data values (i.e. source layer) to the normal values  which are defined by the VOI parameters. The transformation can be performed using a sigmoid function as defined by the DICOM standard Interpretation of imported . When the WW parameter is large (e.g. WW > output_range), the sigmoid function changes into an approximation of a linear function  depending on the WC and WW parameter values.As seen in function  changes function  and can function  and can source slices into original slices using the transformation from Eq.1 and parameter values  from the DICOM metadata, the slices are then de-biased. De-biasing is a procedure that removes intensity inhomogeneity 16WC = 20000, WW = 100, output_range = 2) which ensures the intensity distribution of the slices is redistributed in the whole 16-bit range regardless of the source slices' range, and an adequate contrast which is dependent on the WW parameter. The fixed VOI values were selected based on our experience using real case data, and assure that the liver segment will have an appropriate intensity value range for the segmentation to be successful. After these procedures are applied, the resulting slices are stored as preprocessed slices (i.e. on a separate layer) and are ready for segmentation.After transforming the referential slice as the slice in the patient's medical images collection with a high probability to include a large liver segment. We define REFZ as the index  of the referential slice using MAXZ is the number of all the slices in the patient's medical images collection and the constant 0.65 was found empirically on real case data. Using referential slice with a liver segment that is morphologically similar to the liver segment shown in First, we define the referential slice serves as the beginning point of segmentation, i.e. the slice where segmentation is initiated, and its identification is independent of the chosen segmentation algorithm. Hence, there is a high probability that the referential slice will include a liver segment as the one shown in referential slice is used as the slice that is presented to the end-user (e.g. attending physician) in order to place the initial seed on the slice; for adaptive threshold , the referential slice is used for comparing the dynamically thresholded slice to the presets that include similar liver segments, and marking the threshold value with the highest similarity to any of the presets as the initial liver segment.The identified referential slice is presented to the end-user who is required to click on the liver segment where there are no internal liver structures such as vessels or tumor nodules. The pixel clicked then serves as the initial seed voxel.We first implemented an intensity-based segmentation algorithm known as the region growing algorithm. The latter determines whether voxels are part of the target region or not by comparing their intensities to the intensity of the initial seed. The initial seed is a voxel manually selected by the end-user (e.g. the attending physician) at the beginning of the procedure; in our case, the current voxel in the queue by comparing the intensity of every current voxel's neighbor to the intensity of the current voxel, as shown in current voxel (i.e. 26-connected neighbors).The region growing segmentation algorithm works in three dimensions and evaluates the voxels that are scheduled into the queue. At the beginning, a single voxel is added to the queue, namely the initial seed. The algorithm examines the I denotes the voxel's intensity and a bit is its unit). Therefore, any of the evaluated neighbor voxels that have the intensity in the defined range are marked as part of the target region by being added to the queue. After all the neighbors of the current voxel are evaluated, the algorithm evaluates the next voxel in the queue; the next voxel becomes the current voxel and its neighbors are evaluated. The procedure is repeated until there are no voxels left in the queue. Finally, all the voxels that are stored in the queue represent the target region which in our case is the liver. Since intensity of the target region varies in all three dimensions due to inhomogeneity, it is imperative to allow some intensity deviation when evaluating if the neighboring voxels belong to the target region. The allowed intensity deviation is defined using a threshold deviation value . The intensity threshold value is swept over the whole intensity range, and the similarity comparison is done using normalized cross-correlation which performs segment area comparison (i.e. surface overlap). Similarity to the previous slice is chosen as the criterion for segment determination of adaptive threshold algorithm because the difference in liver shape and size between two neighboring slices is expected to be minimal; therefore, choosing similarity with the previous properly segmented slice as the criterion ensures that the current slice will also be segmented properly. Hence, the error of such a procedure is cumulative and shall the segmentation fail on one slice, all the following slices will be improperly segmented as well. The procedure is shown in referential slice and repeated on the following slices until the end of the slices collection; then, the procedure is restarted from the referential slice to the beginning of the slices collection. If the highest similarity is lower than a certain similarity threshold, the algorithms determines the segment has ended and empties the current and all the following  slices; in our case, the similarity threshold was set to 0.70 based on our experience on real case data. Moreover, since the referential slice has no prior slices it could be compared to, a set of six presets that include various possible liver segments is used instead, and the maximum similarity to any of the presets indicates the final intensity threshold value for the referential slice. Three out of six example presets that are used for thresholding the referential slice are shown in The comparison results (i.e. similarity) which are obtained using normalized cross-correlation are stored during sweep for each intensity threshold value. After the intensity threshold value sweep is done, the intensity threshold value with the highest similarity  is selected, and the current slice is, finally, transformed using the selected intensity threshold value. The procedure is started from the snakes segmentation algorithm. This algorithm is based on placing a deformable (i.e. active) contour, which is a closed curve made of points, on the same location as certain voxels . Then, for each point of the active contour (located at the current voxel), the energy of the current and all its neighboring voxels is calculated based on four energy contributions: elasticity of the contour's point in the current voxel, curvature of the contour's point in the current voxel, magnitude of intensity-based energy in the neighboring voxels, and direction of the intensity-based energy in the current voxel. Each point of the active contour is, then, moved to the voxel with the lowest energy. The procedure is repeated until the active contour reaches the desired location (e.g. after a defined number of iterations).The third algorithm for performing liver segmentation that we evaluated for electroporation-based treatment planning is the active contours segmentation, sometimes referred to as the referential slice. The active contour is attracted by the edges in the image referential slice and transforming the edge of the segment identified on the referential slice into a closed curve with points sorted according to their location on the segment's circumference. Moreover, the number of all the points in the active contour is reduced by decimation; in our case, the number of circumference pixels between two active contour points is limited to 8. Next, the image energy is calculated as the Gradient Vector Flow (GVF) of the image; a publicly-available GVF calculation algorithm The active contours segmentation algorithm is initiated by placing an initial active contour on top of the curve elasticity energy contribution is calculated based on curve curvature energy contribution is calculated based on GVF magnitude energy contribution is calculated as the magnitude of the GVF vector in the evaluated voxel, while the GVF direction energy contribution is defined as representing low energy (0) in the neighboring voxel that is located in the direction the GVF vector of the evaluated voxel is pointing to, while all the other voxels have a high energy (1). In E denotes the energy contribution, while CURR, PPREVP and NEXTP denote the current, the previous and the next points of the active contour, respectively.The energy contributions to the total voxel energy are, based on our experience with optimizing algorithms using real case data, balanced using the coefficients 1, 3, 9, 3 for curve elasticity, curve curvature, GVF magnitude, GVF direction, respectively. All the energy contributions are normalized to reside within the range  in order for the energy coefficients to be properly balanced. In our segmentation algorithm, the curve elasticity and curve curvature contributions are recalculated after every iteration, and the total energy is recalculated as well. The active contour movement is stopped after a predefined number of iterations .After the total energy on and around each active contour point is calculated, and balanced using energy contribution coefficients, the active contour points iteratively move toward the voxel with the lowest total energy. Since the energy depends on the active contour points' locations, the referential slice and is repeated on the next slices until the end of the slices collection; then, the procedure is restarted from the referential slice to the beginning of the slices collection; therefore, the active contour segmentation algorithm may be split into two processing threads.When the active contour movement is stopped on one slice, the procedure is repeated on another slice. The procedure is started from the current slice is the same as the final active contour on the previous slice. Because the difference in liver shape and size between two neighboring slices is expected to be minimal on the slices where the organ that is subject to segmentation is present, the previous slice's final active contour is a good initial contour for the current slice. Moreover, due to the expected minimal active contour movement, less iterations are required when calculating the image's GVF since edges will attract active contours in their vicinity even when the GVF is calculated in less iterations; therefore, the processing time of the active contour segmentation algorithm is reduced .In order to perform segmentation using the active contours algorithm in three dimensions, the initial contour on the After segmentation, a postprocessing algorithm needs to be executed to eliminate possible anomalies that may occur during segmentation (e.g. segment leakage). Postprocessing eliminates redundant segments that cannot be part of the final results; elimination is based on comparing neighboring slices in the direction of the third dimension (i.e. component Z) using normalized cross-correlation.referential slice; namely, the probability that a slice includes only one identified segment is the highest on the referential slice, since segmentation was initiated on this slice regardless of the chosen segmentation algorithm: for region growing, the seed was placed on this slice; for adaptive threshold, the comparison with presets was made on this slice and also, the active contour was initiated using the adaptive threshold segmentation algorithm on the referential slice as well. Shall the morphological operations during segmentation split the segment on the referential slice into multiple segments, the first step of postprocessing eliminates such redundant segments by only keeping the largest segment on the referential slice.The postprocessing algorithm is initiated on the referential slice, i.e. CURR = ZREFZ, is used as the basis for performing normalized cross-correlation with the next, i.e. CURR+1Z or the previous, i.e. CURR−1Z slice, respectively. The template for the normalized cross-correlation is generated by intersecting each segment on the current slice, i.e. CURRZ, with the finally postprocessed previous, i.e. CURR-1Z or next, i.e. CURR+1Z slice, respectively (depending on the postprocessing Z-direction). Then, each segment on the current slice is compared to its corresponding template generated from its neighboring slice using normalized cross-correlation; if the result of the comparison exceeds a predefined threshold, the segment is kept on the slice, else it is discarded. In our case, we set the comparison threshold to 0.65 which was found empirically on real case data. The postprocessing procedure may be split into two processing threads, since it is symmetrically executed from REF+1Z = Z to MAXZ = Z, and from REF-1Z = Z to Z = 1 (i.e. the postprocessing Z-direction).Then, the postprocessed disk structuring element of 3×3 pixel size.Moreover, if we compared the neighboring slices only by using the whole next or previous slice as the template for comparison to the current slice (i.e. without comparing separate segments on a slice), we would be unable to extract these separate segments and determine whether they derive from the target tissue (e.g. liver) or not; therefore, such comparison enables us to keep multiple segments on a slice with the possibility of eliminating segments that are not part of the target tissue. Besides, in order to allow separation of single segments that are in fact multiple segments connected by a thin bond (possibly due to leakage), all the slices are eroded before and dilated after the postprocessing procedure using a Since segmentation algorithms are required to produce not only meaningful but also accurate results, validation of the algorithms is an imperative. In our case, validation was performed as a two-step procedure. In the first step, the algorithms were optimized using radiologist dataset as a training set, and then in the second step, the algorithms were validated after being optimized using another radiologist dataset.In order to perform segmentation algorithms' optimization, seven sets of patient's liver manually segmented by a radiologist were used as a training set. The patient images that were used for development and optimization of segmentation algorithms belong to patients from the clinical study “Treatment of Liver Metastases with Electrochemotherapy (ECTJ)” . The study was prospective, phase I/II, conducted at the Institute of Oncology Ljubljana, Ljubljana, Slovenia. Regulatory approvals from the Institutional board, as well as from the National Medical Ethics Committee were obtained. Written consents of the patients were obtained. Additional 4 patients were included for final validation of segmentation algorithms: these patients were under standard treatment, according to Declaration of Helsinki, and all patient data were anonymized before processing. For each segmentation algorithm, changeable parameters that significantly influence the functioning of the algorithms were defined, and their possible value ranges were defined based on our previous experience using real-case data. These changeable parameters were then subject to variation within optimization iterations; every of the seven cases that were already manually segmented by the radiologist was re-segmented using our three segmentation algorithms  in the optimization process. During this optimization process, variation of the defined changeable parameters was performed in order to each time automatically obtain a liver object that is most similar to the one segmented by the radiologist. After acquiring the data obtained from the radiologist and arranging them into the form that was applicable to optimization and validation , value ranges of the changeable parameters were defined. For region growing, the parameters subject to variation during optimization were the size of the noise-elimination filter mask during preprocessing, and the threshold deviation value which determines the range of the intensities that are acceptable for inclusion into the target region during segmentation . For adaptive threshold, the parameters subject to variation during optimization were the size of the noise-elimination filter mask during preprocessing, and the initial coefficient that determines the targeted size of the referential segment  in the beginning of segmentation. For active contours, all four energy-contribution coefficients were varied during optimization.C in G in Optimization was run on a workstation with Intel Core-i7 965 Extreme Edition processor  with 12 GB of DDR3 memory (frequency 1.333 MHz), two 300 GB hard disk drives (velocity 10.000 rpm) running in stripe mode, and operating system Windows 7 Enterprise (64-bit). The segmented three-dimensional liver object that was produced in each iteration of optimization was compared to the corresponding model that was manually segmented by the radiologist; comparison was done on a slice-by-slice basis using normalized cross-correlation. Similarity  was stored together with the current values of the changeable parameters, and finally the iteration with the maximum similarity , was chosen as the optimum  mean slice similarities SG in  from 40.G in Our implementation of active contours algorithm provides globally optimized  mean slice similarities SG in  from 54.V in G in The results of validation of the four additional cases manually segmented by the radiologist are shown in V in As presented in referential slice, with an additional task of selecting a pixel with a median intensity of the segment on the referential slice which should ensure avoidance to selecting vessels or nodules as the target region.The aim of this paper was to present three possible automatic liver segmentation algorithms and perform their evaluation by optimizing their functioning for an accurate liver segmentation; the optimization was based on seven-patients dataset segmented by the radiologist and used as a training set, while validation was performed using additional radiologist dataset consisting of four additional patients. As presented in the Results section, developing an automatic segmentation algorithm that generates accurate three-dimensional liver models is demanding, especially because of the variability of input data . Nevertheless, because the region growing requires the end-user to place an initial seed (i.e. to click on the liver on one image), this segmentation algorithm cannot be classified as a fully-automatic algorithm but rather semi-automatic; however, an algorithm for automatically placing the initial seed could upgrade the region growing algorithm to a fully-automatic algorithm. The initial seed-placing algorithm can be developed based on the adaptive threshold segmentation algorithm (described in 2.1.5.) on the The liver includes or may include various inner structures  that do not have a predictable intensity range or texture; these structures may directly interfere with segmentation algorithms by possibly influencing the generation of segments. Also, certain organs near the liver  have similar intensity ranges to the liver Since similarity comparisons using normalized cross-correlation were performed on each slice of a case separately, final results of each case are presented using two representations: the mean similarity of all the slices, and the median similarity of all the slices in a case. Two representations of the data were presented since there are many individual slices with a similarity of 0%, which means whether the radiologist marked a segment on that slice and the segmentation algorithm missegmented it, or vice-versa . Hence, such slices significantly contribute to the final results of the comparison regardless of the size of the segment that caused the 0% similarity on the slice . Since such slices significantly impact the quality of the final results, the mean similarity of all the slices in a case does not reflect success of the segmentation algorithm enough. Therefore, the median similarity of the slices in a case was presented as well.C in Optimization results presented in Results of the final validation that are presented in Tumor detection from the patient's medical images is currently still in development and will be implemented as a semi-automatic procedure: identification of structures within the segmented liver (including its edge area) will be done automatically, but finally the end-user (i.e. the attending physician) will be required to manually determine which identified structures are tumors and are, therefore, subject to electroporation-based treatment. Therefore, the whole liver needs to be properly segmented, since tumors are detected as structures within or on the edge of the liver, and could be missed if some slices at the top or at the bottom of the liver are not segmented. Currently, only Magnetic Resonance Imaging (MRI) images are segmented for electroporation-based treatment planning, as MRI is preferred in clinical practice for the liver. Namely, MRI is a non-invasive procedure (i.e. there is no ionizing radiation present during MRI) and using a contrast medium it can provide satisfactory images of the liver for electroporation-based treatment planning. Hence, our algorithms are currently written and optimized only for MRI images; the preprocessing procedures are prepared for expected intensity ranges and Volume-of-Interest (VOI) values that derive from MRI sources, and could therefore not perform segmentation of e.g. Computed Tomography (CT) images. However, since the modality of obtained images can be easily determined from the header of the DICOM files, and because only minor modifications of the preprocessing algorithms would be required, a modification allowing non-MRI image segmentation would not be demanding to implement.Further steps of our research in the field of electroporation-based treatment planning include improved algorithms for tumor detection and, also, automatic vessel segmentation. Namely, segmentation of tumors and vessels needs to be also developed since algorithms for liver segmentation  cannot be used for this purpose without thorough modifications of the algorithms. We expect to develop vessel segmentation algorithms as a combination of intensity- and morphology-based methods with the aim of extracting line-like structures from the liver.We plan to develop electroporation-based treatment planning as a web application that will be remotely accessible from a web browser and will allow generating treatment plans by allowing the attending physician to upload the DICOM images of the patient and, after calculations and manual validation of the results, obtaining a directly applicable treatment plan. Hence, besides the automatic liver segmentation, the treatment planning software will also need to include electrode insertion and calculation of the electric field distribution with electrode position optimization in order for the treatment plan to comprise all the required information. The treatment planning procedure will be simplified in order to minimize the input of the clinician: because the segmentation of the tissue is automatic, the clinician will only need to validate the segmentation results  and, finally, determine the entry direction of the electrodes. Namely, the electrodes will be automatically inserted towards the gravitational center of the tumor (or tumors), and the required number of electrodes will be proposed by the software based on the shape and size of the tumor (or tumors). Therefore, the clinician will only need to rotate the electrode array towards the excepted intraoperative entry direction, which will simplify the procedure.Finally, since automatic liver segmentation can be implemented for other applications


Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models. With the development of high-throughput sequencing technology, the rate of addition of new sequences to the databases increases continuously. However, such a collection of sequences does not by itself increase the scientist's understanding of the biology of organisms. Comparing a new sequence with the sequences of known functions is an effective way of assigning function to the new genes/proteins and understanding the biology of that organism from which the new sequence comes.Owing to the importance of sequence comparison, numerous researches have been taken in past and obtained some effective tools for similarity search Up to now, many efficient alignment-free methods have been proposed, but they are still in the early development compared with alignment-based measure ACGAATAATAAATAAGGCAATAAC, there are four occurrences of AATAA . But the occurrence of AATAA starting at the position 4 is different from the one starting at the position 19, because the form is composed of three overlapping occurrences of AATAA whereas the second one is composed of a unique occurrence. Because the overlapping structure of the words usually form conservative patterns in biological sequences that are strongly associated with genes First, little attention has been paid to the overlapping structures of the words in biological sequences Second, background information of the words has not been fully utilized in existing biological sequence comparison This paper proposed an efficient statistical method for sequence comparison. It takes into consideration the overlapping occurrences of the words and has the ability to adjust the background information of the words in biological sequences. The contents can be summarized as follows:An efficient word-based statistical measure based on the statistical model proposed by Schbath Extensive experiments were taken to evaluate the performance of proposed model in discrimination between (a) functionally related regulatory sequences and unrelated sequences, intron and exon, and (b) different HEV genotypes. A comparison of proposed method with existing alignment-based and alignment-free models was also taken to assess its superiority.A biological sequence can be described as a succession of symbols, and a DNA and protein sequences have been realized to be a mixture of local regions that consist of compositional characteristics and pseudo-periodic sequence patterns. To utilize the background information of these local regions, we choose Markov model as a background model. It takes into consideration this ‘periodical’ behavior of the bio-signal by making use of transition probability matrix Because Occurrences of the same word may overlap, and these overlapped words usually form a conservative pattern that is strongly associated with conservative motif By incorporating the overlapping structures and the background information of the words in the existing statistical model, a novel word-based statistical model is proposed and denoted in a compact formThere are several distinctive features of this model. First, it emphasizes the structures of the words and indicates differences in terms of their contribution to the conservative patterns. Second, the influence of two overlapping occurrences of the word Since the model parameters are priori unknown, they have to be estimated based on the observed sequences. The accuracy of this estimation is an important issue to be considered, and the existing perturbation theory for Markov chains and hidden Markov models can allow us to assess the uncertainty in the Markov chain behavior given the uncertainty With the assumption of the uniform distribution (U), Lu For the probability distributions A statistical measure between two proposed statistical models was proposed here based on the cross entropy Receiver Operating Curve analysis. Receiver operating curve (ROC) analysis has been widely used in signal detection and classification ROC curve is a graphical plot of sensitivity versus (1-specificity) for different threshold values. The area under a ROC curve (AUC) is an important value used to quantify the quality of a classification because it is a threshold independent performance measure and is closely related to the Wilcoxon signed-rank test F-measure. F-measure is a measure of a test's accuracy and often used in the field of information retrieval for measuring search, document classification, and query classification performance Regulatory sequence comparison plays an important role in the The proposed Experimental program is designed according to following settings: (1) A set of CRMs, known to regulate expression in the same tissue, is taken as the ‘positive’ set for each sequence in this set is the really For comparison purpose, widely-used alignment tools were tested. These alignment tools include Needleman-Wunsch  and Smith-Waterman  raw scores, with no correction for statistical significance, using linear gap penalties or affine gap penalties, with a gap penalty of 2. We also implemented four word-based measures: Euclidean distance , and they are randomly divided into four sets separately. The set of the exons is taken as the ‘positive’ set, and the set of the introns, is taken as the ‘negative’ set.The secondary test of the proposed model is to discriminate the human exons and introns. These data sets were organized as follows: 1200 human exons and 1200 human introns are extracted from the human exon and intron data , which have been clustered into four genotypes Hepatitis E virus (HEV) is a major cause of enterically transmitted acute hepatitis in developing countries. HEV was classified recently as the sole member of the genus Hepevirus in the family Hepeviridae. Its genome consists of a single-stranded, positive-sense RNA of approximately 7.2 kb, with three partially overlapping open reading frames . Although only one serotype has been identified to-date, HEV displays considerable genetic diversity. Based on the extensive full-length genomic variability noted among different strains, HEV has been classified into four major genotypes This experiment aims at assessing how well the proposed model performs on identifying HEV genotype. In relation to the clustering literature In addition to the proposed method, four other typical methods were used for comparison. The used alignment-based method is Clustal W rather than Needleman-Wunsch  or Smith-Waterman  raw scores, because the length of genome of the HEV is approximately 7.2 kb that is difficult to handle by dynamic algorithm. The measures For a better understanding of the proposed method, an evaluation of the word overlapping structures in biological sequences was performed. A measure, We observe that the measure Another feature of the proposed measure The In all cases, the classification of the proposed measure This paper proposed an efficient statistical method for biological sequence comparison, which integrates both the overlapping structures and background information of the words in biological sequences. It compares biological sequence by taking advantage of the tendency of the The proposed method originates from the existing methods but different from them in several key aspects. Blaisdell, Wu et al. and Stuart et al. One major limitation of the proposed method is that different Table S1AUCs obtained from all the models for detection of functionally related regulatory sequences.(DOC)Click here for additional data file.Table S2AUCs obtained from all the models for classification of human exons and introns.(DOC)Click here for additional data file.Table S3Abbreviation for the strains, accession number, nucleotide length, genotype, and country for each of the 48 complete HEV genomes.(DOC)Click here for additional data file.Table S4F-measures obtained from all the models for classification of HEV genotypes.(PDF)Click here for additional data file.