Falls account for approximately 50% of infant injury hospitalizations, and caretaker behavior is central to preventing infant falls. Behavior theory–informed interventions for injury prevention have been suggested, but to date, few have been reported. The potential of using smartphones for injury prevention intervention delivery is also underexploited.This study aims to develop a behavior theory– and evidence-based as well as user-centered digital intervention as a mobile app for parents to prevent infant falls following agile development practices.Infant falls while feeding was selected as the fall mechanism to demonstrate the approach being taken to develop this intervention. In phase 1, the Behaviour Change Wheel was used as a theoretical framework supported by a literature review to define intervention components that were then implemented as a mobile app. In phase 2, after the person-based approach, user testing through think-aloud interviews and comprehension assessments were used to refine the content and implementation of the intervention.The target behaviors identified in phase 1 were adequate rest for the newborn’s mother and safe feeding practices defined as prepare, position, and place. From behavioral determinants and the Behaviour Change Wheel, the behavior change functions selected to achieve these target behaviors were psychological capability, social opportunity, and reflective motivation. The selected behavior change techniques aligned with these functions were providing information on health consequences, using a credible source, instruction on performing each behavior, and social support. The defined intervention was implemented in a draft Android app. In phase 2, 4 rounds of user testing were required to achieve the predefined target comprehension level. The results from the think-aloud interviews were used to refine the intervention content and app features. Overall, the results from phase 2 revealed that users found the information provided to be helpful. Features such as self-tracking and inclusion of the social and environmental aspects of falls prevention were liked by the participants. Important feedback for the successful implementation of the digital intervention was also obtained from the user testing.To our knowledge, this is the first study to apply the Behaviour Change Wheel to develop a digital intervention for child injury prevention. This study provides a detailed example of evidence-based development of a behavior theory–informed mobile intervention for injury prevention refined using the person-based approach. Falls account for almost half of all injury-related hospitalizations in infants aged <1 year ,3, and tAs fall mechanisms change with the age of the infant , any typSmartphones are an ideal delivery channel for child injury prevention interventions, with new parents increasingly using technology to access health information, especially in countries with high smartphone use . SmartphAs usability is critical to the success of DBCIs , user teThe aim of this study is to develop an intervention using the Behaviour Change Wheel, supported by empirical data and expert feedback, to systematically identify behavior change techniques and implement them digitally (phase 1) and to optimize the digital intervention modules through user feedback and assessment of comprehension of information (phase 2). In previous work, we have identified key fall mechanism priorities ,21, and problem behaviors. The target behaviors were then used in a behavior analysis to identify intervention functions and behavior change techniques following the Behaviour Change Wheel [The aim of phase 1 is to identify problem and target behaviors to inform and then develop the intervention content for the DBCI. The Behaviour Change Wheel framework , a literge Wheel  process.ge Wheel .The intervention content was then drafted and reviewed by a team of health care professionals, including injury experts, a pediatric surgeon, and content area specialists. They included breastfeeding specialists and midwives. The final draft content was then included in a purpose-built digital intervention module in the form of a mini-app. App feature selection was informed by previous studies reporting common characteristics of health apps to change and manage behaviors . NC condPhase 2 objectives are to ensure usability of the intervention, including comprehension of the intervention content. This was achieved by exposing potential users to the draft intervention content through the mini-app. Ethical approval was granted by the human research ethics committee of South Eastern Sydney Local Health District (2019/ETH00298). Participation involved an initial demographics and falls perception questionnaire, followed by a think-aloud interview as well as a comprehension assessment.Participants were recruited in sequential rounds of 5 from a single tertiary maternity hospital antenatal ward and day-stay unit. Adult expectant parents were identified as the key user group because the intervention targets fall prevention in infants from birth to 12 months of age and the intention is to ultimately deliver the intervention to this group of the population. To be included, the expectant parents had to be conversant in English and could be first-time or experienced parents. This recruitment method prioritized mothers over fathers; however, this was deemed acceptable for the purposes of this study because mothers are commonly the primary caretakers of infants . Writtenthink-aloud interview  or acceptability and feasibility. If the changes were uncontroversial and feasible to apply, they were implemented immediately. In other cases, more data were collected from another round of testing to seek more opinions before implementing the change. Finally, modifications requiring further tailoring and major changes to the app were discussed with the broad research team and if agreed upon were noted for later implementation in the final integrated app. For analysis of the comprehension questionnaire, comprehension scores were calculated for each user per round of testing, and percentages were tabulated.After further review of the emerging themes listed in Getting sufficient rest with the newborn Preparing before the feedSafe positioning during the feedSafe placement of the infant after the feedLearn section included information articles with an interlinked Action section that provided a self-monitoring mechanism, including one-time and multitime actions. Multitime actions were intended to support behaviors that require repetition. The Engage section included a group chat where users could get social support. This feature was also intended to enhance user engagement with the app. In addition, there was an onboarding section to introduce the app to the users. Users were informed about the option of setting up reminders for the Actions without fully implementing this feature in the mini-app for testing. The Learn, Action, and Engage categories were devised to allow appropriate implementation of selected behavior change techniques and align with approaches commonly used in other digital behavior change apps.To implement the planned intervention strategy, a minimum viable product mini-app was developed for use on the Android platform. The mini-app had 3 main sections. The A total of 23 women were recruited for the user-testing exercise; 13% (3/23) withdrew because of time constraints. Of the 20 participants, 15 (75%) were aged 26-35 years, 14 (70%) were nulliparous (70%), 10 (50%) were Australian-born, 12 (60%) were in de facto relationships, 13 (65%) were employed full time, and 15 (75%) were living in apartment buildings. Of the 20 participants, 16 (80%) had attained either a university or Technical and Further Education graduate degree or a postgraduate degree and 9 (45%) had high household income  falls from baby products, and (3) falls related to risky home environments ; the module used as the case study in this paper was arbitrarily chosen. The decision to present just 1 module as a case study was made to ensure that the full detail of the systematic intervention development method could be presented.This paper describes a behavior theory and user-centered approach to developing a DBCI, an intervention to target the problem of infant falls. In this paper, we have outlined the entire development and user-testing process undertaken to construct an intervention module targeting falls that occur while the infant is feeding. The same process is being applied to 3 more modules targeting the remaining common fall mechanisms: (1) The systematic exploration of the problem from a behavior perspective and the identification of intervention content to specifically target behavior using the strong theoretical base of the Behaviour Change Wheel  is a strIn our behavior theory–driven approach using the COM-B Model and Behaviour Change Wheel we used a literature review and qualitative analysis of infant fall events from a web-based parenting forum  to identseek and find information [Another strength is the inclusion of a comprehension assessment in the user-testing component. This is not a common feature of person-centered approaches to behavior change and DBCIs; yet, in other areas of health communication, ensuring comprehension is recognized as critical . This alormation . We inteIn addition to describing the intervention development process, this paper also demonstrates the benefit of the user-testing process in behavior change app development. Several important insights from user perspectives have been identified that may be important for encouraging the use of the app in parents of infants, and we will attempt to incorporate these strategies in the final integrated app. Of particular interest is the feedback centered around integrating the injury prevention intervention into an app with broader scope and incorporating noninjury prevention advice to mothers and caregivers of infants. Although there is emerging interest in the integration of injury prevention with more general pediatric health care ,45, to oThe intervention development process we have described increases the likelihood that the intervention will be effective in promoting desired parental behaviors for preventing infant falls. The process should also increase acceptability and usability of the end product among the target audience. However, the work to date does not yet demonstrate this. Once the intervention modules have been integrated into the app, there will be a need to robustly establish the effectiveness of the intervention. This is particularly important because despite reports of the promise of mobile behavior change interventions for reducing childhood injury ,47, therMore broadly, our user-centered approach to intervention development and intention to robustly evaluate the effectiveness of the intervention responds to research needs in the digital health care space generally ,14. The A limitation observed in the user-testing phase of the study is the homogeneity of the mothers recruited. All were relatively highly educated and from high-income sectors of the community. This is problematic, given that the target audience for this intervention includes the complete demographic range of parents of infants, particularly because it is recognized that there is an increased risk of injury among children from the lower socioeconomic sectors of communities . PreviouThe work presented in this paper provides a detailed description of a behavior theory–driven and person-centered approach to designing, developing, and optimizing a DBCI targeting a significant childhood injury problem. The process described and the intervention being developed address important gaps in the literature regarding the development of digital child injury prevention interventions. Ultimately, this work represents the first stage in the development of a unique intervention targeting the widespread problem of falls in children aged <1 year. This will be the first intervention of its kind, and as demonstrated in this paper, it is being developed in a unique, systematic, and robust manner.


Since the first reported case of the coronavirus disease 2019 (COVID-19) in Wuhan China, the virus has spread to every continent, including sub-Saharan Africa. There exist no cure or vaccine for COVID-19. Classic public health approaches such as hygiene and sanitation, and social distancing are the recommended measures to contain the spread of the causative virus. While it is possible to combine strict lockdown measures in some western countries, this is not practical in almost every country in sub-Saharan Africa. In Cameroon, those without symptoms are encouraged to respect measures of hygiene and sanitation, physical distancing, and to wear a mask in public places. Those who develop symptoms are isolated in accredited COVID-19 management centres until they recover. However, the latter strategy is ineffective in containing the local spread of the virus because testing is not robust. Intuitively, the control of the virus in Cameroon depends largely on how engaged the public is in fighting against the virus. Social media can complement the use of community health workers for community or public engagement. In this viewpoint, we discuss how to optimize public engagement, to combat misinformation and to develop a culture for preparedness amidst the COVID-19 pandemic when time and resources are of the essence. The world has experienced a series of pandemics with the 1918 influenza pandemic being the most severe in recent history. Globally, about 500 million people were infected with the H1N1 virus, which resulted in 50 million deaths . This chToday, the world is experiencing the Coronavirus disease 2019 (COVID-19) pandemic which is an infectious respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 1 June 2020, about six million cases had been confirmed with over 300,000 deaths . A totalGlobally, over 2.9 billion individuals use social media . A surveSocial media provides an invaluable opportunity to enhance rapid dissemination of health information necessary to curb the COVID-19 pandemic, and to socially connect people while intensifying physical distancing measures. For example, the United Nations Child Fund is using various social media platforms such as Twitter, Facebook, YouTube, and Instagram to disseminate information on COVID-19 and collect feedback from the public . This isAccording to Cameroon´s situation report of 1 June 2020 on COVID-19, strategies taken to sensitise and engage the public involve the use of community health workers and volunteers to communicate the risk of transmission of the virus and methods of prevention in travel agencies, markets, and other social places . Other sWe recommend the use of limited social media accounts at central and regional levels to disseminate COVID-19-related information and to collect feedback from the public. These social media accounts, at least for the ministry of health and some regional delegations of health, do exist but their usage could be optimised. Having central and regional accounts will help track the public engagement. Bloggers or volunteers with the required experience could be employed and trained to ensure the daily running of these accounts. It is vital to have a steady source of reliable information which the public can always refer to for guidance. It is important to identify the best media outlet to disseminate information depending on the content needing to be shared. Based on our observation, WhatsApp, Twitter, and Facebook are currently the most active social media platforms in Cameroon. These platforms provide numerous opportunities to engage journalists, trusted social media influencers, and the wider public.#covid19cmr and #covid19cameroon are the most frequently used on Facebook and Twitter. In addition, Twitter and Facebook Live could be used to update the public on the government´s strategies to curtail the pandemic, and address queries from the public. The applications have options for followers to react to and share a post within their networks.There are features on certain social media applications which can be utilised to optimise their efficiency. It is possible to tag other Twitter and Facebook users to maximise the target audience reached with each post. Hashtag is an important feature on Facebook and Twitter that could help followers retrieve previous posts on related topics. Currently, the hashtags To optimise the coverage of a post, it is important to encourage sharing. We recommend working with credible local organisations or influential individual accounts to share COVID-19 content aimed at engaging the public. Religious leaders and institutions form an influential group in Cameroon and connect with their faithful through various social media platforms. About 90% of the Cameroonian population identify as either Christians or Muslims . Most reThe socioeconomic impact of COVID-19 on the lives of Cameroonians cannot be overemphasised. It is important for official government social media pages to engage in a dialogue with the public to receive feedback on how the pandemic is affecting their lives, and the difficulties they face in adhering to the measures put in place by the government to limit transmission of the virus. This will help the government identify gaps in its policy to tackle the pandemic and to strengthen her response accordingly. Also, social media ensures the public has a voice to demand accountability from the government in the management of COVID-19 and promote the dissemination of reliable information.Misinformation involves the spread of information that is not backed by facts. Disinformation is an intentional distortion of information to misinform. Since the first recorded case of the COVID-19 pandemic, there has been an escalation of misinformation globally that has been primarily incited by social media; a phenomenon which the World Health Organisation (WHO) refers to as an infodemic , 15. LacEfforts could be coordinated to identify, understand, and disrupt sources of misinformation. Specific hashtags of the COVID-19 pandemic in Cameroon could be browsed through to identify misinformation and address it through social campaigns. For example, a local non-government Health Education and Research Organisation (HERO) Cameroon launched a social media campaign to combat COVID-19-related misinformation . CombatiInformation targeted towards the public needs to be succinct and reader-friendly. At the same time, information shared with the public has to be complete, practical, and if necessary, repetitive to prevent misinterpretation. Cameroon being a bilingual country means information should be communicated in English and French. Therefore, it is imperative that information is carefully translated from one language to another with the help of experts to prevent misinformation. The use of infographics or video communications are efficient ways to improve comprehension of the information being transmitted . MoreoveThe newness of COVID-19 has meant that it has become associated with social stigma, people fear the unknown and this fear can translate to fear of those around them. The stigma is worsened by the propagation of misinformation. Social stigmatisation of COVID-19 has significant implications in curbing the burden of the disease especially in developing countries like Cameroon where stringent social distancing measures like lockdowns are impractical. Stigmatisation can drive infected persons into denial because of fear of being discriminated against; meaning those infected with COVID-19 continue their daily activities, potentially contaminating others in the process. Stigmatisation can cause late presentation to care and discourage adherence to good prevention practices like wearing masks in public places. Destigmatising COVID-19 is crucial for the success of public health measures to contain the disease. Social media could be used to improve the public´s knowledge on COVID-19, break the myths about COVID-19, show empathy towards persons diagnosed with COVID-19 and their families, and support health care workers with higher risk of infection.st June 2022, 264 (3.9%) were healthcare workers [As the health system of Cameroon becomes overwhelmed with the evolution of the pandemic, preventing infection of the already limited number of healthcare personnel should be an utmost priority. Infection of healthcare workers has significant repercussions on the COVID-19 response. On the one hand, infection of healthcare workers means a reduction in the capacity of the healthcare team leading to overburden and poor performance of the health system. On the other hand, healthcare workers are at risk of transmitting the virus to high-risk patients, leading to COVID-19 related hospital fatalities. Healthcare workers, if not protected would play an important role in the community transmission of SARS-CoV-2 . Of the The website of the Ministry of Public Health of Cameroon dedicated for the management of COVID-19 contains a link to an online screening tool for patients who suspect they are experiencing symptoms of COVID-19 . This toSocial media use is less likely to be an effective strategy to manage the COVID-19 pandemic in rural areas where there is limited access to electricity, internet connection, or smartphones. Social mobilisation using community health workers and volunteers is crucial to control the pandemic in rural areas and institutionalised groups such as prisoners. The ability for government agencies to engage the public through social media depends largely on how much the public can trust the government. Rebuilding the public´s trust in times like this demands strong political will to remain transparent, accountable, and committed.Social media is very popular in Cameroon, and despite it being used to disseminate misinformation regarding the COVID-19 pandemic, it can be a useful tool to fight the pandemic in Cameroon. While this looks promising, we do not underestimate the fact that there are inequalities in the access to internet services in Cameroon and a high cost associated with an even suboptimal internet supply.


In doing so, tissue tectonics provides a mechanism by which the cell specification events intrinsic to cells can be modulated by the temporal exposure to extracellular signals. This exposure is in turn regulated by higher-order properties of the embryo, such as their physical properties, rates of growth and the combination of dynamic cell behaviours, impacting tissue morphogenesis. Tissue tectonics creates a downward flow of information from higher to lower levels of biological organization, providing an instance of downward causation in development.Development encompasses processes that occur at multiple length scales, including gene-regulatory interactions, cell movements and reorganization, cell signalling and growth. It is essential that the timing of events in all of these different processes is coordinated to generate well-patterned tissues and organs. However, how the timing of intrinsic cell state changes is coordinated with events occurring at the multi-tissue and whole-organism level is unknown. Here, we argue that an important mechanism that accounts for the integration of timing across levels of organization is provided by  Developmental biology provides a strong context to study biological time, as it allows for the study of developmental timing at many different levels of biological organization—opening the possibility for the identification of mechanisms that coordinate these different length scales. Developmental timing can be thought of in terms of the absolute timing of a given event, the ordering of events relative to one another, the directionality of developmental processes, and the more general tempo (speed) at which development proceeds –3.absolute timing of a given event in development is controlled and propose a mechanism by which timing may be coordinated across different levels of organization in the embryo. Our aim here is to take a multi-scalar perspective on the analysis of developmental timing, emphasizing how timing at one level of a biological system  can be modulated by events happening at the cell population, tissue and multi-tissue levels. In doing so, we will highlight how timing is a highly distributed phenomenon in development that can be discussed in terms of emergent properties—how processes can be observed to occur at a higher organizational level from dynamic processes occurring at lower levels. We will also highlight how the reverse can be true, such that the dynamics of lower-level processes are regulated by alterations at higher levels, through downward causation. While this viewpoint abdicates the search for a single causative source of a developmental timer, we believe this holistic view to be essential for the development of meaningful explanations for core developmental principles such as self-organization, pattern regulation and developmental robustness to heterochronic shifts in evolution.We will focus specifically in this review on how the . 1.1Intrinsic timers function within the cell, while extrinsic mechanisms implicate the importance of the external cellular environment in providing inputs to the timer . If the timer is able to progress outside of the embryo, this suggests that its underlying mechanisms act cell-intrinsically. Note that these experiments are very similar to those used to investigate cell specification and determination. A cell may be defined as specified to form a particular structure if, when isolated from the embryo and placed in a neutral environment, it will still form that structure in the absence of any external inputs . A cell . 1.2The specification of distinct cell types during development is inherently linked to the timing at which cells receive either the inhibition or activation of extracellular signals. One example of this is in the patterning of the early ectoderm into epidermis, neural plate border or neural cell states, and the subsequent patterning of these embryonic territories. Neural specification requires a continued modulation of FGF, Wnt and BMP pathway activity from pre-gastrulation stages onwards –8, and ntissue tectonics. This term has been introduced elsewhere in relation to the tension and stress forces acting within tissues to drive morphogenesis [The timing at which cells receive external signals to modulate intrinsic timers of cell state transitions is in turn determined by when the two cell populations  become opposed to one another in the embryo, or become shifted relative to the position of cells releasing secreted modulators of the signalling pathway activity . We will then use two case studies to show how these intrinsic timers can be modulated at specific windows in development through the movement of tissues during embryogenesis. We will then briefly discuss how alterations in the timing of extrinsic signal exposure can act as a mechanism for evolutionary change in reference to a fundamental concept in evolutionary developmental biology: heterochrony. Together, these examples implicate the importance of signalling events between tissues as points of control in developmental timing, supporting our central thesis that tissue tectonics is important in coordinating developmental timing across levels of organization. Finally, we will review some recent work that demonstrates the importance of tissue tectonics in coordinating multi-tissue morphogenetic events with the timing of cell fate decisions and the emergence of spatial patterning in development.. 2. 2.1Xenopus animal cap explants, there is a clear delineation in time in the transition between a competent and a non-competent state for mesoderm induction: the competence of animal cap tissue to respond to contact with vegetal tissue is lost at the early gastrula stage [As a starting point to consider how developmental timing is regulated during embryonic development, we will first consider examples from the literature where the intrinsic capability of individual cells has been demonstrated experimentally. The concept of cell-intrinsic timers is inherently linked to the concept of competence in development: how cells change in their ability to respond to a given inductive cue over developmental time. Induction may be defined as the process in which an inducing tissue releases a signal that results in a change in the direction of differentiation of a responding tissue . In Xenola stage . To invela stage . These rla stage . By placla stage . TogetheDrosophila nervous system. Here, neuroblasts give rise to the precursors of the nervous system, ganglion mother cells (GMCs). GMCs are produced by asymmetric divisions of a neuroblast that produce a daughter neuroblast and a daughter GMC. Through the sequential expression by the neuroblast of ‘temporal identity genes' , each of the sequentially generated GMCs has a specific identity [in vitro showed that gene expression transitions occur in isolated cells outside of the embryonic environment, suggesting that this gene expression timer is controlled by cell-intrinsic mechanisms [hb and Kr genes have little effect on later gene expression of the other temporal identity genes. More recently, mathematical modelling work has provided insights into the mechanistic basis for the Drosophila neuroblast temporal identity timer. Experimental data are consistent with a repressor-decay timer, where the decay of a previous timer component (e.g. hb) times the onset of expression of a later component (e.g. pdm1) through the relief of repressive interactions [An additional example of single-cell-intrinsically timed developmental events comes from the identity . How arechanisms . In G2-achanisms . This isractions .in vitro with recombinant FGF, oscillations in Her1 expression are still observed [An additional example of an intrinsic timer shown to function in individual cells is given by the oscillatory component of the segmentation clock. During the production of the embryonic anteroposterior (head to tail) body axis, paraxial mesoderm on either side of the midline is sequentially segmented into blocks termed somites (somitogenesis). The primary model for somitogenesis is the clock and wavefront model, in which an anteroposterior gradient of FGF and Wnt signalling (the wavefront) is combined with a cell-intrinsic oscillator based on Notch signalling to segment blocks of tissue ,24. A seobserved . Mathemaobserved . Thus, iobserved ). This c. 2.1.1An additional example of cell-intrinsic timing mechanisms comes from a key set of studies that have recently investigated the developmental basis for species-specific differences in developmental timing. The pace (tempo) and the duration of embryonic development are highly variable among different animal species. For example, even among mammals, variation in the gestation period is vast. Mouse embryonic development typically spans 20–30 days (dependent on the species), while human development takes nine months. Many of the developmental processes encompassed within embryogenesis differ in their pace between species, and so an active field of research focuses on asking how different tempos of development are achieved in different organisms. A study that differentiated mouse EpiS cells and human ES cells to neural fates in culture demonstrated that species-specific developmental timing is maintained in culture, with neural differentiation markers expressed with accelerated timing in the mouse cells .HES7 promoter in order to image oscillations in gene expression over time, finding that the period of oscillations differs between human and mouse cells (in culture)—the period of oscillations is 2–3 times longer in human cells than in mouse cells. This cell culture assay allowed the authors to ask how different species' cells, cultured in identical conditions, exhibit different oscillatory periods of gene expression. Experiments that swapped the HES7 loci between cells of each species  demonstrated that the difference in period is not a result of sequence differences at this locus. Measurement of biochemical reaction parameters revealed differences in the rate of degradation of HES7 protein in human cells relative to mouse cells, regardless of whether this protein was encoded by the mouse or human gene sequence. Further, the delay in transcription and translation of HES7 was greater in human cells than mouse cells. Using mathematical modelling, the authors showed that altering biochemical reaction parameters in line with these observations is sufficient to account for the different periods of oscillation in human and mouse cells [A recent study used mouse and human cells in culture to investigate the basis for species-specific differences in the tempo of the segmentation clock . Matsudase cells . These rIsl1 occurs after 2–3 days of culture in mouse cells but not until approximately 6 days in human cells [Olig2 gene into mouse cells reveals that the timing of expression of this gene is determined by the cellular context: the human Olig2 gene in mouse cells is expressed with the same timing as the mouse Olig2 gene in mouse cells. Given that the observed differences in the timing of motor neuron differentiation do not result from sequence differences in the known gene-regulatory network components, the authors assayed kinetic parameters of gene expression in order to look for differences between mouse and human cells. They found that, while mRNA half-life was similar in the two cell types, protein half-life was significantly shorter in mouse progenitors than in human progenitors. The authors argue that this is a general pattern, because the introduction of an exogenous reporter protein showed that this protein also has a species-specific half-life dependent on its cellular context [Similar experiments that differentiated mouse and human spinal cord progenitors to form motor neurons in culture revealed global differences in protein stability between mouse and human cells . Motor nan cells . These d context .These studies investigating the basis for species-specific developmental tempos are intriguing. A species-specific complement of transcription rate, translation rate, mRNA and protein stability may determine the tempo of embryonic development. Interestingly, a study that compared eight different rodent species showed that protein degradation rates are negatively correlated with species lifespan . ImportaThe numerous examples outlined above provide good evidence for the existence of cell-intrinsic timers, many of which are able to operate in isolated single cells. Furthermore, they provide examples of the utility of experimental assays in investigating the intrinsic or extrinsic control of developmental timers. Together with methods for the improved imaging and analysis of real-time changes in single-cell gene expression, experimental assays such as those described above are key in enabling questions to be asked relating to the degree to which single-cell-intrinsic timers can be coordinated across groups of cells in a given tissue. While we have outlined the extensive evidence for cell-intrinsic timers, a full understanding of timing in development must consider how these can be tuned by extrinsic mechanisms, as cells do not exist in isolation in the embryo.We will now turn to two specific case studies that demonstrate the ways in which intrinsic timing mechanisms can be modulated by extrinsic signals—moving our discussions from the single-cell level, to the population and multi-tissue levels of organization.. 2.2Sonic Hedgehog (Shh) for a defined duration between Hamburger–Hamilton stages HH20–27 (approximately embryonic days 3.5–5.5) [Shh 32 h post-graft. At this time point, the host polarizing region in the contralateral wing has downregulated Shh expression and transcripts are not detectable by in situ hybridization [Shh transcription in this tissue are not dependent upon extrinsic signalling. Population-level analyses of cell-cycle parameters showed that there are distinct stereotyped changes to the progression of the cell-cycle between HH20–30, including a marked increase in the proportion of cells in G1 [Shh is expressed [Shh expression in the polarizing region, with cell-cycle progression as an important input.Heterochronic grafting has provided good evidence for a number of population-level intrinsic timing mechanisms in limb development. Here, cells of the polarizing region  express 3.5–5.5) . Sonic H3.5–5.5) ,38. If tdization . This rels in G1 . Inhibitxpressed . TogetheTGFβ pathway, as well as functional BMP signalling, was maintained in grafted tissue with a transcriptional profile similar to HH29 ungrafted tissue [A second cell population intrinsic timer acting in avian limb development controls the termination of limb bud outgrowth . The limd tissue . The autHoxa13, in HH20 to HH24 heterochronic grafts. Donor tissue in this context expresses Hoxa13 with an expression pattern distinct from surrounding tissue, suggesting its fate is not reset upon grafting to this new context. This timer mechanism is associated with an intrinsically timed change in cell adhesion properties. Interestingly, in this example, the intrinsic timer is understood to function from HH20 onwards. Prior to this point, flank-derived retinoic acid (RA) causes cells to take stylopod  fates. As the limb bud grows and progenitor cells are displaced from the source of RA, they are released from the influence of this extrinsic timer and there is a switch to intrinsic control of cell fate. In this way, an intrinsic timer can be modulated by an RA-based extrinsic timer. This is an example of the tissue tectonics concept that we propose here—in this case, physical tissue movements of the limb progenitors away from the flank allow extrinsic modulation of an intrinsic timer mechanism. Thus, a link between cell-intrinsic timing and extrinsic cues is provided by tissue tectonics—the relative displacement of signalling and responding tissues.An important example of an intrinsically controlled developmental timer is found in the specification of avian limb proximodistal values. The proximodistal axis of the limb comprises three segments: the stylopod, zeugopod and autopod . The contribution of progenitor cells to more distal segments of the wing (zeugopod and autopod) is controlled by an intrinsic timer mechanism . Evidenctissue tectonics. Here, the functioning of the intrinsic timer is carefully modulated through physical tissue movements, altering the exposure of cells to an extrinsic signal.Taken together, these examples from avian limb development clearly demonstrate the utility of experimental embryological techniques in investigating the contribution of cell-intrinsic and cell-extrinsic timers to the regulation of developmental events. Importantly, studies of proximodistal patterning in this system provide an example of the interplay between intrinsic and extrinsic timers through We will now turn to another developmental system where there is good evidence for intrinsic timers that are also likely modulated by extrinsic signals: mammalian neurogenesis.. 2.3Drosophila neuroblasts described above, revealing an important role in diverse species for cell-intrinsic timers in the generation of the nervous system.The mammalian brain is complex and comprises a large repertoire of diverse cell types. In many cases, neural progenitor cells undergo temporal changes in the types of daughter cells that they produce, allowing for the production of a diverse set of daughter cells over time. For example, progenitor cells of the mouse cortex may be maintained in a culture system, where they express molecular markers with similar timing to that observed in the embryo . As diffThe timing of differentiation of cells that make up the nervous system may also be determined through cell-intrinsic mechanisms. Experiments that removed rat optic nerve cells from the embryonic environment and cultured them in platelet-derived growth factor (PDGF) demonstrated that oligodendrocyte precursor cells (OPCs) divide and differentiate with timing that closely replicates that observed within the embryo . If singGradual changes in transcription over time have been implicated in another study focusing on mammalian cortex development. Here, apical progenitor cells (APCs), which give rise to cells of the dorsal cortex, were subjected to transcriptional analysis at several time points in development . Throughin vitro. In normal development, these intrinsic timers are likely modulated by extrinsic signals. For example, what determines when the intrinsic timer ‘starts’ its sequence of events? Given the regulative nature of mammalian development, it is highly improbable that an intrinsic timer functions from the beginning of development.The numerous studies outlined above provide strong evidence for the importance of intrinsic timing mechanisms within cells, both in modulating the expression of cell fate markers in developing neural tissues and in the timing of cellular differentiation. As we previously discussed in the context of avian limb development, cells do not exist in isolation in the embryo. The studies described here culture progenitor cells Heterochronic grafting has provided evidence for population-level extrinsically controlled timer mechanisms in neural development. Progenitor cells of the mammalian neocortex produce different daughter cell fates sequentially during development. Radioactive labelling of progenitor cells in ferrets has shown that the deepest layers of the cerebral cortex are generated before the more superficial ones –52. In eStudies in mammalian neurogenesis provide evidence for the importance of balancing intrinsic and extrinsic timing inputs in development. A number of studies have shown the existence of intrinsic timers in this system, implicated in the control of transitions between generating different neural cell types as well as the timing of differentiation of individual progenitor cells. In this example, it remains to be seen what the extrinsic cues that feed into cell-intrinsic systems are, and how they are modulated in time and space by the movement of tissues relative to one another.We will now turn to a consideration of evolutionary changes to the timing of signalling events at the multi-tissue level—here, changes to tissue tectonics result in altered signalling timing (heterochrony) and large-scale morphological changes.. 3Heterochrony, defined as a shift in the developmental timing of events, has long been recognized as an important concept in the field of evolutionary developmental biology (evo-devo). Both De Beer and Gould published influential texts arguing that changes in developmental timing of events relative to an ancestral form are an important force in the evolution of morphology ,55. DramDromaius novaehollandiae. The emu is one of many species of flightless bird within the ratites  and has very small wings that have a single digit. The timing of forelimb development in the emu is significantly delayed relative to that of other birds and other amniotes. The limb bud does not grow out from the flank of the emu embryo until HH20—while in the vast majority of birds outgrowth begins at HH17 [Fgf8 at HH18 [Fgf10 is expressed by the emu LPM in the forelimb region, the authors suggest that the quantitative level of expression of this signalling ligand is insufficient to induce the expression of Fgf8 in the ectoderm (a). In support of this hypothesis, the overexpression of Fgf10 in the emu LPM results in Fgf8 induction in the overlying ectoderm and precocious limb bud outgrowth [Fgf10 expression in the chicken and emu embryo was also identified.An example where developmental heterochrony has been implicated in morphological novelty is given by the emu,  at HH17 . A recen at HH17 . Prior t at HH17 . However at HH17 . This di at HH17 . FGF8 si at HH17 . However at HH18 . Graftin at HH18 . Though ectoderm a. In suputgrowth . An enhaTogether, these results reveal that subtle changes to the timing of expression of a signalling ligand in development are able to alter the timing of development of the emu forelimb, contributing to changes to the gross morphology of this structure. This example is consistent with a role for tissue tectonics in the control of developmental timing, as it implicates the importance of the signalling event in control of downstream events. In this case, the signalling and responding tissues remain apposed but the loss of the signalling ligand expression abolishes the signalling event.Stenella frontalis) embryos over the period of limb development revealed that hyperphalangy is localized to digits II and III of the dolphin forelimb [b). Thus, localized persistence of the AER over digits II and III may promote the formation of additional phalanges in these digits.An additional example of heterochrony in morphological evolution is provided by the dolphin flipper. Dolphins are aquatic mammals possessing many adaptations for life in water, including the modification of the forelimb to form a flipper. The flippers act during swimming as rudders, and in many species the digits exhibit hyperphalangy: relative to the ancestral state, they possess numerous finger bones  . Carefulforelimb . This chforelimb  , buthosphere . The ratTo fully illustrate how alterations in tissue tectonics can impact the spatial and temporal regulation of patterning in development, we will briefly review three examples of ways in which tissue movement and shape changes act as extrinsic signals that feed into cell-intrinsic signal transduction networks.. 4.2.1During gastrulation, multiple tissue interactions act together to both specify and pattern the three principal germ layers along three principal axes of orientation: anterior–posterior, dorsal–ventral and left–right. In addition to establishing these essential coordinate systems of the body plan, a series of cell movements act in a well-orchestrated manner to progressively separate layers of tissues, and to begin the process of embryo elongation along the anterior–posterior axis. A particularly well-studied aspect of patterning during this process is the initial specification of neural tissue with the ectoderm, and its subsequent patterning along the anterior–posterior axis. While signals from the early gastrula stage organizer are important for ‘activating’ the initial anterior character of neural tissue, subsequent ‘transforming signals’ then act to convert this character to more posterior neural tissue as gastrulation proceeds (for a recent review see ). DuringWe propose that tissue tectonics is a key aspect of the temporal regulation of signal exposure, and it is essential for ensuring appropriate coordination between the morphogenetic and patterning aspects of gastrulation. One aspect of this coordination is well studied and requires information to flow from gene-regulatory network activity through to the control of cell movements, tissue morphogenesis and embryo elongation. Critically, however, it also requires information flow in the opposite direction, i.e. cells must be able to determine the state of embryo elongation and tissue morphogenesis to coordinate these processes with cell specification and patterning. A recent study has approached this question using explants of zebrafish embryonic cells that were cultured away from the yolk and yolk syncytial layer . Such ag. 4.2.2dickkopf1b (dkk1b) is expressed in two distinct populations at 50% epiboly in the river-dwelling morph, but in one continuous domain at the same stage in the cavefish morph. This difference in expression domain is associated with advanced internalization of these cells  in the cavefish morph relative to the river-dwelling morph. Notably, the expression of dkk1b is also downregulated earlier in the cavefish morph than the river-dwelling morph. As a consequence of shifted timing of AP axis formation (heterochrony) in the cave morph, the eyefield that forms within the overlying neurectoderm is reduced in size. Through functional experiments that mimicked the impact of advanced Wnt signalling activation in the eyefield , the authors showed that increasing Wnt signalling in early surface-dwelling embryos results in a reduced eyefield and later a misshapen retina. Together, these results give an example of a developmental signalling event that is altered in timing through changes to the timing of apposition of tissues . In this example, changes to the timing of these events have a marked morphological effect, accounting for the loss of eyes in the cavefish morph. This study opens a set of fascinating questions over the limits of developmental constraint and robustness in the evolution of gastrulation morphogenesis, and the causal role that tissue tectonics might play in linking these two aspects of body plan development and evolution.Alterations in the morphogenesis of gastrulation are common and require a mechanism for such alterations to impact patterning in a manner that a conserved body plan can be generated at later developmental stages. A recent study examined differences between two different morphs of the characid fish during gastrulation: a wild-type river-dwelling morph and a cave morph. While the overall body plan is broadly similar, cavefish possess a number of morphological differences relative to surface fish, including a complete loss of eyes. This opens the question of how alterations in the morphogenesis aspect of gastrulation might impact the patterning aspect of gastrulation in the adaptation of populations to new ecological environments. An examination of the expression of the homologues of various genes expressed by the organizer in the embryos of characid fish revealed substantial differences in the expression of these genes during gastrulation . For exa. 4.2.3Xenopus embryos. Neural crest cells emerge from the dorsal neural tube as a series of three migratory streams and begin to migrate collectively towards the ventral region of the embryo. Within the deep layer of the ectoderm, an initially common pre-placodal region (PPR) progressively splits up into distinct placodal thickenings via a series of cell movements [The sensory structures of the vertebrate head form from multiple different cell types that can be traced back to two distinct embryonic cell populations that lie along the neural plate border: the cranial neural crest (CNC) and sensory placodes. As they become specified, and undergo collective morphogenesis, differentiation and migration, there exist multiple points of interaction between these two populations that are important for forming appropriately formed sensory structures . One parovements . Within ovements ,81. Impo. 4.3Historically, developmental biologists have focused on the emergent properties of development: how processes at a lower level of a complex system (i.e. at the level of a cell and the mechanisms driving its cell state transitions) can impact observable features at higher levels (i.e. the patterned expression of genes across a field of cells when observed at a fixed time point). However, one of the salient properties of developmental systems is their ability to regulate pattern upon the loss or experimental removal of certain parts of the embryo. Some striking examples include, but are in no way limited to, the ability of halved sea urchin embryos to each give rise to a fully formed individual, the ability of the chicken embryo to develop normally after surgical removal of a large portion of the primitive streak, and the formation of monozygotic twins ,83. ThisRegulative development requires a mechanism that enables the sensing of changes to the properties of a system at higher levels , and to convey changes in the state of the system at lower levels  . To achiProbing the mechanisms of tissue tectonics in development will require a new experimental framework that requires a return to the approaches of experimental embryology, but with the advantages of new technologies in multi-scalar imaging and targeted tissue manipulations. The era of developmental genetics has provided a ground-up perspective of gene function in development, focusing on the targeted manipulation of gene function to then assess the phenotypic consequences at higher-order levels of the system. In parallel to this, we propose top-down manipulations that perturb morphogenetic events at the multi-tissue level and observe consequences at the sub-cellular, genomic and gene-regulatory levels. Thus, a typical experimental paradigm for probing tissue tectonics would be as follows. First, to gain a quantitative understanding of the morphometric changes to the tissues and cell populations under study. Second, to achieve a spatially and temporally targeted manipulation of these processes, which may be through the use of tissue ablation or through the use of optogenetics to manipulate key regulators of tissue shape change. Third, to observe the impact of these manipulations on the relative positioning of signalling and responding tissues and how this alters the dynamics of cell fate specification. Finally, to investigate the extracellular signalling events that are responsible for providing an extrinsic cue—this may be through the regulation of temporal exposure to signals and signal inhibitors  in distinguishing between these modes of developmental timer control.Through changes in morphology and structure throughout ontogeny, tissues are brought into and out of close apposition, allowing for the controlled timing of developmental signalling events. Signalling events provide an important level of control for developmental timing, being converged on by both low-level events, including gene expression, as well as dramatic changes in the morphology of the embryo. We propose that tissue tectonics is a key mechanism that integrates timing information across scales of organization within the embryo during development. As we have seen, changes to the timing of a signalling event can have a dramatic effect on morphology; for example, in the development of the emu wing, in the development of the cavefish eyefield and in the development of the dolphin flipper. It is conceivable that the diversity of gastrulation stage embryonic forms in vertebrates is associated with changes to the timing of developmental events, as tissue tectonics will be markedly different .In summary, developmental timing is a highly distributed phenomenon in developmental biology that is coordinated over diverse levels of organization. A huge number of timer mechanisms, some intrinsic and others extrinsic, work together to reproducibly time developmental events in the embryos of a given species. We have shown the importance of inter-cell signalling events as a point of control and coordination across these levels of organization and have argued for the importance of tissue tectonics  in timing events.


Recent research in computer vision has shown that original images used for training of deep learning models can be reconstructed using so-called inversion attacks. However, the feasibility of this attack type has not been investigated for complex 3D medical images. Thus, the aim of this study was to examine the vulnerability of deep learning techniques used in medical imaging to model inversion attacks and investigate multiple quantitative metrics to evaluate the quality of the reconstructed images. For the development and evaluation of model inversion attacks, the public LPBA40 database consisting of 40 brain MRI scans with corresponding segmentations of the gyri and deep grey matter brain structures were used to train two popular deep convolutional neural networks, namely a U-Net and SegNet, and corresponding inversion decoders. Matthews correlation coefficient, the structural similarity index measure (SSIM), and the magnitude of the deformation field resulting from non-linear registration of the original and reconstructed images were used to evaluate the reconstruction accuracy. A comparison of the similarity metrics revealed that the SSIM is best suited to evaluate the reconstruction accuray, followed closely by the magnitude of the deformation field. The quantitative evaluation of the reconstructed images revealed SSIM scores of  The number of deep learning techniques employed to solve segmentation and classification tasks in medical imaging is increasing by the day ,2. Deep One of the most important aspects when using medical images to train deep neural networks is patient data confidentiality. Within this context, patient data does not only include the imaging data itself , but may also include other relevant attributes used for training of deep neural networks, such as age, sex, medical condition , and life style factors . Within this context, even from ‘a blurry version of the original scan’, patient-identifying information may be inferred based on, for example, specific pathologies  or using advanced facial recognition methods . This isUntil recently, patient data confidentiality was thought to be protected in trained deep learning models, which were assumed to be black boxes and learn from a plethora of information. Recent research in computer vision ,8 has shThe primary aim of this work is to examine the vulnerability of two deep learning segmentation networks commonly used in medical imaging to model inversion attacks. The two network architectures chosen for this purpose are the SegNet  and the First, we investigated the feasibility of model inversion attacks on two well established segmentation models, namely a U-net and a SegNet, trained on 3D medical images.Second, we implemented and compared multiple similarity metrics, which can be used to evaluate the quality of the reconstructed medical images. This is especially important for future research endeavors in this domain as metrics in the computer vision domain might not be applicable or might not be ideal when applied to 3D grayscale images from the medical domain.The main contributions of this work are two-fold:Addition of the SegNet architecture.Use of a larger database of 3D brain MR images with manually defined labels for multiple brain structures (LPBA40 ).Segmentation of brain structures as the task being analyzed instead of ischaemic stroke lesion segmentation.Addition of multiple evaluation metrics to measure the reconstruction quality and possibility to identify single subjects based on the reconstructed images.This work is based on our recent conference paper  and exteIn general, there are multiple types of deep learning model attacks that attempt to reconstruct the original data used for the training of deep neural networks. The first type is the membership inference type attack. This type of attack ,14 aims Generally, model inversion attacks take advantage of two important properties of deep learning techniques. First, many deep learning techniques learn an abstract representation of the images, which are then processed across multiple stages into the output . Second, compared to the large number of parameters that are optimized during model training, the models are typically trained on rather small and often imbalanced training sets.This second problem is more severe in medical imaging compared to the computer vision problems mentioned above since the training is typically performed using small medical imaging datasets on the order of a few tens or hundreds of images, which increases the risk of overfitting. Both of these properties are utilized during an inversion attack of the deep learning models. Based on the amount of information available to the attacker, attacks are categorized into white box attacks and black box attacks. In white box attacks, the deep learning network parameters are known to the attacker, which is not the case in black box attacks. Given the type and information available of many publicly available deep learning models in medical imaging, the more simple white box attacks pose a significant threat to patient privacy.While there has been some research on the reconstruction of images used for training of deep learning models in computer vision, the number of studies that focus on model inversion attacks for medical images is rather limited.z doubles at every contraction, and there are a total of n layers. In the expanding path, there is a corresponding up-convolution at every step. A concatenation of the correspondingly cropped feature maps from the contracting path is achieved using the skip connections between the corresponding levels. Finally, there are again convolutions with The U-Net model is a popular convolutional neural network. The architecture consists of a contracting path to capture the context in the images and a symmetric expanding path to enable precise localization. The schematic illustration of a U-Net is shown in z filters that double at each contraction step with a total of n steps. Each encoder has a corresponding decoder in the decoder path, which performs the inverse operation. The final decoder output is typically fed to a multiclass soft-max classifier to produce class probabilities for each pixel independently.The SegNet architecture also follows an encoder-decoder network architecture principle shown in , with thThe attacker can access the latent space representation of arbitrary input images.The attacker knows the encoder’s architecture that is used to generate the latent space information of the images.The attacker can train a separate decoder to reconstruct images based on the latent space representations.The inversion attack scenario used in this work was first described for computer vision applications ,8 and isBriefly described, model inversion attack techniques train a separate inversion decoder that is able to reconstruct images based on their latent space representation . As the Z to the output space Y, in this case, a segmentation image, using the task-specific decoder For this study, we assume a trained encoder-decoder segmentation network to be given. For a 3D input image volume Z obtained by the encoder path X and its reconstructed version Model inversion now aims to create a separate inversion decoder that is optimized by minimizing the difference between an original image and its reconstruction from the latent space representation as shown in To simplify our analysis and to be able to focus on the reconstruction aspect of a model inversion attack, we assume the latent space representations of all images from the original training database The inversion attack scenario described above was developed and evaluated using the LPBA40 database, which is comprised of 40 brain MRI datasets . All imassue see . TherefoFor preprocessing, all 3D images of size rrection .The encoder path comprises of Four-fold cross validation was used to train the original networks and corresponding inversion decoders. More precisely, in each iteration, 10 images were used as the private database for training the segmentation CNN models (U-Net and SegNet). The remaining 30 images were used to train the inversion decoder aiming to reconstruct the 10 images originally used for training, which were compared qualitatively and quantitatively to the private original images used for training.In our experiments, we used the full brain MRI scans, as well as skull-stripped versions. Proportionally, the volume of the brain segmented in the non-skull-stripped images is smaller than the volume of the image segmented in skull-stripped images. Thus, this experiment enables an analysis if the change in proportion of the image segmented has an effect on the reconstruction quality.Four separate evaluation metrics were implemented in this work to measure the reconstruction success in our inversion attack scenario: The first metric is the frequently used Matthews correlation coefficient (MCC)  between Exemplary qualitative results for U-Net and SegNet-based reconstructions for the skull-stripped and original images are shown in MCC- and SSIM-based quantitative results of all experiments are summarized in So far, we have shown that SSIM and MCC values are both useful indicators of the visual quality of the reconstruction. However, in order to really determine reconstruction success based on these measures, it is necessary to establish baseline values and investigate if SSIM and MCC values comparing a reconstructed image and its original are higher than when comparing the reconstructed image to images of other subjects. Therefore, we performed an additional experiment in which we computed the SSIM and MCC values of each reconstructed (skull-stripped) image with all other images in the test dataset see . As can Next, we ranked the different SSIM and MCC results to investigate if the similarity measurements comparing a reconstructed (skull-stripped) image with its true original image are always higher than the values achieved for other images. The corresponding results for this experiment are summarized in Apart from the two main intensity-based similarity metrics described above, we also computed the norm of all displacement vectors of the deformation field after registration of the original image and the corresponding reconstruction to assess the success of the inversion attack. Therefore, we affinely registered each reconstructed image to the true original image and all other original images of the database using ANTs  and mutuFor the final experiment, the reconstructed images (skull stripped) were segmented using the originally trained U-net and SegNet to determine how the segmentations compare against those generated based on the true original images. In both cases, the Dice similarity metric was computed by comparison of the CNN segmentation to the manual ground truth segmentation. This experimental setup led to a mean Dice value of The aims of this paper were twofold: (1) We wanted to examine if popular segmentation CNN architectures in medical imaging (U-Net and SegNet) can be attacked via model inversion attacks. (2) We aimed at examining the ability of different similarity measures to quantitatively assess the success of the attack by comparing the reconstructed image with its original.Based on the visual results exemplified in The results of this study also suggest that the proportion of the volume that is actually segmented by the deep learning technique is relevant with respect to the reconstruction quality. More precisely, in case of the skull-stripped images, where the proportion of relevant image voxels that have been segmented is larger, the reconstruction is considerably better, which holds true for the U-Net and for the SegNet. This shows that CNN models trained to segment larger regions of medical images are easier to attack than CNN models trained to segment small regions, such as brain lesions.The most commonly used metric in the computer vision ,8 and inOne important limitation of this study is that the training and the test sets were part of the same overall database. Thus, it remains to be seen whether the same levels of consistency of reconstruction can be obtained with databases obtained under different acquisition conditions. To model this potential problem in our experimental setup, we also trained a separate encoder when training the network used for inversion attack in this work and then replaced the latent space. Another important limitation is that the CNN models were only used based on brain MRI datasets of healthy subjects. However, we believe that our study is an important first step in analyzing privacy problems related to deep learning models in medical image analysis in general and that it shows that training data leakage is a serious problem. Furthermore, we are confident that the results of this study also hold true for other imaging modalities or body parts. This should be verified in future studies where it would also be interesting to add clinical metadata  to the analysis. Being able to not only reconstruct imaging data, but also clinical metadata, would pose an even more severe privacy breach.The results of this paper show that it is possible to reconstruct a brain MRI from a trained U-Net and SegNet, albeit with some degradation due to blurring, provided that the U-Net and the SegNet have been trained to segment large parts of the original volume. Despite the blurring, the reconstructed images can be correctly matched to the original images in the majority of the cases. Finally, examining the various scores for measuring the quality of the reconstruction suggests that the SSIM score reflects the reconstruction success best and should be used in future studies in this domain.


Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor. Frailty is a common geriatric syndrome of physiological decline, with associated weakness, slowness, low activity, etc., which can be described as a clinical state of increased vulnerability to unfavorable health outcomes ,2. It caFrailty can be identified based on evaluation of the impairment in daily physical activities  and numeMachine learning (ML) modeling was recently applied in numerous studies to enhance the analysis of human motion  which caProviding a real-time ML classification tool to consider muscle strength, aerobic endurance, and mobility, which can be represented as the postural transitions and execution of different motor tasks, could be beneficial for early identification of the risk of frailty. This methodology can be introduced as a robust assessment tool to enhance the accessibility of the frailty classification. Hence, the main contribution of this study is to assess the feasibility of frailty prediction using ML methodology based on the comprehensive dataset composed of features extracted from the Kinect sensor.Seven hundred and eighty-seven community elderly  participated in this study. Individuals were excluded from this study if they had (1) severe cardiopulmonary disease who could not maintain activity for more than 10 min, (2) severe visual or hearing impairment to have trouble getting information on the screen, and (3) disability to manipulate with both hands. Signed informed consents were acquired from all participants prior to their enrolment in this clinical protocol, which was approved by the institutional medical research ethics committee. Adequate explanations about the experimental test procedure were given to all participants prior to commencement of the experiments.WL, WK, PE, SL, and LA represents the weight loss, weakness, poor endurance, slowness, and low physical activity, respectively. All of these characteristic terms are binary metrics which were defined based on the Fried’s guideline  equipped with laboratory-developed software was used to capture the movement of joint centers  when perA detailed methodology was proposed to classify the frailty level based on the Kinect data, which is presented in Number of subjects with different frailty level  or gender classes  was quite diverse. This could lead to a bias in the classifier prediction toward the more frequent labels. In order to avoid such unrealistic bias, a random subject selector was applied to identify the same count of subjects for each of different classes. Additionally, our examination indicated that none of classifiers implemented in the present study were successful in accurate prediction of the frailty level ‘2′, which might refer to the small number of subjects with this label (20 subjects) compared to the number of individuals with the frailty levels ‘0′ and ‘1′ . Consequently, we focus on the levels ‘0′ and ‘1′ for the present study and postpone prediction of the level ‘2′ for future works by gathering more experimental data.Based on inherent sensors errors, the temporal evolution of joints’ 3D location was noisy and contained many local maximums and minimums. The presence of such fluctuations in the time series could complicate the machine learning model and make it more potential to overfitting. Additionally, the performance of cycle identification stage (as described in the following) deteriorates for a noisy time series. Consequently, it was necessary to smooth the data prior to further analysis. This was performed by applying a Gaussian moving average filter to the original Kinect data . The appBefore extracting the features from the original data set, it was essential to identify and extract a portion of the time series that was consistent for various subjects. Accordingly, instead of using the entire time series for each exercise, it was quite crucial to identify one or a few cycles of it and do the subsequent process on that basis. For each exercise, a complete cycle was detected by monitoring the cyclic pattern of an appropriate distance metric. The cycle identification approach introduced in The original Kinect data involves three-dimensional body joints coordinates that are not suitable for direct implementation as the input features of machine learning models. Regarding gait-based classifiers, various geometrical and kinematic features have been extracted in the literature. The proposed methodology in the present study was based on the following features extracted from the original acquired Kinect data:The skeletal angle in spherical coordinate (SA): Two spherical coordinate angles (ϕ and θ) could be calculated for each pair of body joints with given Cartesian coordinates, namely Euclidean distance between joints (ED): The distance between each pair of body joints can be calculated instantaneously using Equation (4) [tion (4) . This feJoint relative cosine dissimilarity (JCD): This feature represented the cosine distance between two arbitrary body joints in three-dimensional vector space. It was capable to reflect the directional motion of every body joint with respect to the others [e others . R1 and Joint relative triangular area (JTA): Relative position of three body joints could be considered concurrently by definition of joint relative triangular area. It calculated the area of the triangle constructed by the spine joint (as the most stable body joint) and two other arbitrary joints : After detection of a complete cycle, the segmented time series with different lengths for various subjects/exercises were resampled to a lower frame rate. Downsampling was performed by calculating the mean of data over each customized interval (bin) of the time series. Considering the number of bins to be Histogram-based aggregation: The histogram of each single- or multiple-cycle pattern in a predefined range was another way to downsample the original feature vector. The probability of data at each bin for a histogram with After resampling each feature vector of a subject/exercise, all vectors should be concatenated into a unified feature vector for future stages. When all four feature types, namely SA, ED, JCD and JTA were implemented, the length of the concatenated feature vector was:Based on diversity of the scale of various feature types, it was crucial to implement some pre-processing on the input data to enhance consistency of various features scale and avoid any unfavorable effect of outliers in the training procedure. Standard normalization was implemented on each concatenated feature vector to transform it to a distribution with zero mean and unity variance.Curse of dimensionality could be an important challenge in the Kinect-based classification, because performance of any classifier could be significantly deteriorated in high-dimensional feature space . Existen studies ,51,52,53k-nearest neighbors (KNN): The frailty class in this instance-based model was determined from the majority vote among all neighbors identified based on the distance metric calculated in the feature space [re space . SimplicSupport vector classifier (SVC): Using the so-called kernel trick approach to capture non-linear characteristics of the feature space, SVC provides a robust model that is suitable for classification of complex data sets with small to medium size [ium size . This isium size . SVC is ium size . The litium size .Multi-layer perceptron (MLP): Implementation of numerous layers in conjunction with applying non-linear activation functions in MLP introduce it as a powerful tool in classification of non-linear problems. Flexibility of MLP in the learning process regardless of network structure, particularly in higher dimensions of feature space, and its reasonable classification accuracy are the other supportive reasons for selection of this classifier. Based on the literature in human gait patterns recognition, the MLP classifier has resulted in satisfactory predictions by comparable or even higher accuracies with respect to the SVC [ the SVC ,59,60.Ensemble classifiers: Aggregation of predictions performed by a group of classifiers could often enhance the accuracy compared to each individual classifier performance. Bagging and voting classifiers were two approaches among such ensemble learning methods, utilized in the present study.Bagging Classifier (BC) based on decision tree: The bagging classifier works based on this principle that a group of classifiers can be combined to form a more reliable classifier [assifier . Individassifier . Implemeassifier .Voting classifier (VC): This ensemble approach aggregated the predictions made by multiple different classifiers (four previously mentioned classifiers) based on the predicted class probabilities to provide a more accurate classification. For instance, implementation of VC to predict freezing of gait in Parkinsonian patients using movement data from wearable sensors resulted in comparable accuracies to the SVM-based classifiers (as the most accurate estimators in that study) [t study) .Frailty level classification based on machine learning approach has not been reported in the literature. We selected five classifiers based on advantages reported in the relevant literature regarding classification of Kinect time series data for various applications. They are summarized as follows:It was crucial to achieve the optimal values of the models hyper-parameters in a way to avoid overfitting and leads to suitable generalization performance for the model. In this regards, the random search approach was proved empirically and theoretically to be more efficient for hyper-parameter optimization than the grid search scheme  and consA k-fold cross validation procedure was employed to evaluate the model performance and in order to avoid occurrence of overfitting. After splitting the training data set  in order to obtain the model performance in correct prediction of the frailty level . The percentage of correct predictions made by the classifier compared to those obtained from the Fried’s frailty score (as the target labels) was considered as the model accuracy in either of the training, validation and generalization stages.The first was the data collected by Andersson and Araujo , known aThe second one was the dataset of the present study, in which the gender of subjects was considered as the classification target class. The gait analysis data of at most 140 individuals out of 787 subjects was used in the gender identification procedure.In order to validate the implemented methodology for frailty level classification, no existing results were available in the literature. Since we had no impression on the expectable accuracy for the present novel classification, in order to evaluate performance of our proposed methodology in comparison with the literature, we performed another classification to predict a different target label (the subject gender), but based on a similar input data to one of our exercises, namely the Kinect gait data. Although these two classification problems are not identical, their general structure is very similar. They both include extraction of relevant features from the original Kinect time series data, selection of appropriate attributes, feature scaling, training of classifiers, tuning of hyper-parameters and model generalizations. The main differences are the target labels and their correlations to the original Kinect data that could be handled in the training process of each classifier. The accuracy of classifiers in gender identification was evaluated based on two Kinect-based gait datasets:Binary classification of the gait data based on 140 subjects of the mentioned datasets is presented in Utilizing four feature vectors, namely the limb angle, Euclidean distance between joints, joint relative cosine dissimilarity and joint relative triangular area, the accuracy of various classifiers in prediction of frailty level is presented in Another point about The effect of the number of features utilized in the learning process on the prediction accuracy of the five estimators is evaluated in Regarding accuracy of different classifiers, A predictive model that can use simple physical tests would be beneficial for clinics to classify those individuals who are at risk of frailty in order to deliver the preventive interventions. The present study investigated the feasibility of the elderly frailty prediction based on the extracted data from functional assessment tests using the Kinect sensor. For this purpose, the ML methodology was utilized to provide a robust predictive model and different classification techniques were evaluated to achieve the most reliable and accurate ones. The strength of this study is the possibility to include the real-time skeletal movement data to develop the ML model, in contrast to the previous studies, which only used the administrative health database of clinical characteristics and socioeconomic factors ,36. To eThe Kinect sensor was used in this study as a portable, low-cost, and practical instrument ,65 to exAlthough prediction of the frailty level by ML classifiers was not performed in the literature, we utilized a well-known Kinect gait biometry dataset provided by Andersson and Araujo  in orderBased on unavoidable errors in the tracking process, the raw skeleton data contains noise in registered joint positions ,65. HencAmong the four executed exercises in the present study, each one has a different contribution in the frailty level. It was found that gait analysis data caused the best accuracy, marginally better than the accuracy obtained from 30 s sit-to-stand and 30 s arm curl exercises. It is worthy to discuss that selected physical tests evaluates different abilities. The main purpose of the 30 s STS test relies on the strength and endurance of the lower extremity which is more relevant to the frailty expression ,39,40,41Some limitations of this study should be deliberated. The first limitation of the current study is frailty level imbalance of the subjects. Among 787 participants in our experiments, 323 subjects were recognized as ‘healthy’, 444 cases were assigned as ‘pre-frail’ and only 20 subjects were identified by ‘frail’ condition. Although a procedure was implemented to balance the dataset labels, existence of very few samples with the ‘frail’ label caused us to ignore the data corresponding to this label in the present study and postpone its prediction to our future research after gathering more data dealing with the ‘frail’ subjects. Another unavoidable limitation of this technique is due to this fact that the resolution of the captured data by the Kinect sensor decreases with distance . TherefoWe developed a ML classifier model to identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using Kinect sensor. Our achieved results reveal that SVM and MLP classifiers can reliably and accurately distinguish the healthy subjects from the pre-frail ones which have potential to be recommended as consistent substitutions to the classic identification techniques. By applying the methodology used here, we can be hopeful that this technique can be used for remote patient monitoring and managing the rehabilitation exercises using virtual reality approaches. Further study is warranted to extend the clinical examinations of this methodology in larger populations.


Prostate cancer disease is one of the common types that cause men's prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters' statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis. As per the American Cancer Society , in 2020During the last years, physicists are still working to improve imaging techniques, and they continue updating them in a multidimensional space field to aid radiologists in recognition and analysis of cancer cells. Lately, Neuman et al.  have expOne noninvasive therapy is photodynamic therapy (PDT), which is clinically used in cancer treatment and the other diseases , 9. PDT PSMA is an exclusive membrane bound glycoprotein, initially exposed in the androgen-dependent prostate adenocarcinoma of the human cell by means of a monoclonal . TherefoPrecise prostate image segmentation and volume assessment assume a fundamental part in the conclusion and therapy of prostate-related illnesses, particularly the organizing evaluation of prostate malignant growth. As of now, attractive reverberation imaging (MRI) has turned into the principle imaging strategy for helping prostate finding because of its high-goal and delicate tissue contrast .Nevertheless, the assessment of the prostate images depends on the visual investigation accomplished by the radiologist, which is very tedious, time-consuming, subjective, and complicated. Thus, in the previous decade, different prostate segmentation techniques have been proposed. For example, Shi et al.  took advA deformable segmentation technique for prostate images was proposed by Guo et al.  to combiUshinsky et al.  introducPan et al.  proposedIn this study, an optimized approach for NIR prostate image segmentation is proposed using the k-means clustering algorithm with the elbow method. The optimized approach can give better clustering of pixels by determining automatically the best number of clusters. The NIR images of prostate are acquired throughout PSMA-1-PC413, two PSMA-1-based PDT conjugates, and PSMA-1-IR700. In clinical trials, currently, the Pc413 is a second generation analogue of phthalocyanine PDT drug Pc4 , and comAn optimized approach is proposed for prostate image segmentation using the k-means clustering algorithm with elbow method. The approach is able to find the best number of clusters to k-means for better prostate images segmentation and supporting the radiologist to give accurate diagnosis regarding to the visual inspection of prostate malignant growth.The proposed approach is implemented by combining the k-means clustering algorithm with elbow method and evaluating it on two datasets by analyzing the clusters' statistics and ratio results of pixels in the segmented images.The core contributions of this research work can be given as follows:The reminder of the paper is outlined as follows. Two datasets are used to validate the applicability and effectiveness of our proposed clustering method. They are collected from two different sources for generating dataset 1 and dataset 2. The dataset 1 is acquired from our previous work . It conthttps://pathology.jhu.edu/prostatecancer/). Additionally, dataset 2 consists of four prostate histological color images obtained from pathology dataset images collected by the Pathology Laboratory of the Johns Hopkins University and available in its website link  prostate cancer images. It is built based on the traditional k-means clustering algorithm and elbow criterion technique described in the following subsections.x1, x2, x3,…, xn) is divided into K number of clusters  regarding the distance value between the data points in this dataset. Then, the goal is minimizing the sum of squared errors within cluster(SSEWC), which can be computed as follows:Ci| represents the size of cluster i, var Ci is the variance of cluster i, and μi is the mean of data points in Ci. This is similar to minimizing the data points pairwise squared deviations of the same cluster as follows:Clustering methods are algorithms used for grouping the objects or regions into clusters based on their attributes. Each cluster can be defined as a group of instances which are similar between them and dissimilar to the instances, which are in other clusters. In machine learning field, clustering-based methods belong to unsupervised learning concept. The unsupervised learning concept can train from the features of available unlabeled data. In other words, clustering can be defined as a static-classification of similar entities or objects into several different groups or more subsets. Consequently, the member objects in the same group have similar attributes, which is commonly contained in the coordinate system with shorter spatial distance. In prostate cancer image segmentation and analysis, similar pixels are grouped together to form clusters or segments according the similarity condition, which are well-defined between pixels . K-meansAlso, we deduce the difference between data points from their mean using identity as follows:μ is the mean of clusters, μi is the mean of data points in Ci, and |Ci| is the size of cluster i.By equivalence and because the total variance is normally constant, minimizing the sum of squared errors within cluster (SSEWC) is equal to maximizing the sum of squared error between clusters (SSEBC) or between data points of different clusters, computed as follows:k-means algorithm aims to make the distance between the clusters of the dataset as large as possible and make the distance between the data points of the same cluster as small as possible.Hence, the K centroids are created; then, each point is assigned to the nearest centroid.The centroid is recalculated. This process is repeated several times until the result of cluster allocation of data points no longer changes.The main steps of the k-means clustering algorithm can be summarized as follows:K that might lead to poor clustering results. This is why it is necessary to perform feature checks to determine the number of clusters in the dataset, and this depends on the application domain. Other disadvantages of the k-means algorithm represented in it might converge to a problem of local minimum; it has a slower convergence on large-scale of data sets; and it is more sensitive to outliers.The main advantages of k-means include a number of points: first, it belongs to unsupervised learning; no training set is required; second, the principle of k-means is very simple, and it is easier to implement; and finally, the result of k-means is better interpretable. In contrast, one disadvantage of k-means comes in the difficulty of choosing an inappropriate value of K increases, the model separation is more distinguished, and the amount of aggregation for each cluster will gradually be increased; therefore, the SSEWC will gradually and obviously become smaller.As the number of clusters K is less than the true number of clusters, increasing the value of K significantly increases the amount of aggregation for each cluster. SSEWC will be decreased, and once K will reach the true number of clusters, the amount of aggregation achieved using K will be increased. Therefore, the return will be decreased quickly, and the SSEWC will be decreased sharply. Consequently, it will be flattened out when the K value remains increasing. This means that the relationship between SSEWC and K can be represented in the elbow shape corresponding to the K value, which is the number of true clusters for the example points. For instance, in K value of 4. Hence, the best number of clusters will be 4.When The proposed method aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images by improving and optimizing the traditional k-means algorithm by finding the number of clusters through elbow criterion technique. Elbow criterion technique is a heuristic method applied to determine the number of clusters of the data points in a dataset. Elbow technique is used to obtain the optimal number of clusters for a set of data points because it is an empirical method, simple and easy to implement. By applying the k-means clustering algorithm, the elbow technique plots the explained variations through the number of clusters and picks the curve of elbow to get the number of clusters. It depends on computing the sum of squared errors within-cluster (SSEWC) given in Equation  of all dK = 4, and after this value, the curvature seems to be stable and does not have much changes. Therefore, the best number of clusters for the images of dataset 1 is 4.This section gives and discusses the results of the experiments conducted by the proposed image clustering method on the prostate dataset images described in Similarly, K = 3 and K = 4 in Figures To show the effect of number of clusters selected by elbow criterion technique, Figures Furthermore, Figures For more analyzing, Tables Figures As shown in k value for prostate image segmentation. Even though there are advantages, there are some disadvantages related to the change in illumination and contrast to the prostate images as well as the outliers presented in the acquired input images. In future work, we plan to extend the proposed approach to deal with uncertainty and changes in the input prostate images, and we will combine this proposed approach with the machine learning algorithm for prostate cancer diagnosis.Reliable segmentation of prostate images is important for radiologists to analyze and detect disease. For improving visual inspection in prostate cancer diagnosis, this paper presents an optimized approach for prostate image segmentation using the k-means clustering algorithm with elbow method. The presented approach is able to solve the limitation of current methods in determining the best number of clusters for prostate image segmentation. It can segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumor and allow for the subsequent PDT to remove remaining or not eradicable cancer cells. The experimental results on two datasets of color prostate images are obtained using the optimized image segmentation approach. The analysis of segmented images with more attraction to clusters with small regions can help to attain more perfect diagnosis. Clusters' statistics and ratio results of pixels in the segmented images show the applicability of proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis. The proposed approach has a number of advantages such as its simplicity and its ability to solve the ambiguity that arises in the k-means algorithm to select the