Because larvae that overexpress Notch exhibit uniformly oriented, uniformly innervating hair-cell siblings, wiring specificity is mediated by the Notch signaling pathway.The lateral-line neuromast of the zebrafish displays a restricted, consistent pattern of innervation that facilitates the comparison of microcircuits across individuals, developmental stages, and genotypes. We used serial blockface scanning electron microscopy to determine from multiple specimens the neuromast connectome, a comprehensive set of connections between hair cells and afferent and efferent nerve fibers. This analysis delineated a complex but consistent wiring pattern with three striking characteristics: each nerve terminal is highly specific in receiving innervation from hair cells of a single directional sensitivity; the innervation is redundant; and the terminals manifest a hierarchy of dominance. Mutation of the canonical planar-cell-polarity gene  Although the guidance of an axon to a general region of the nervous system restricts the search for an appropriate synaptic partner, there remain numerous cellular targets with which the axonal terminal could create a synapse. Environmental stimuli play a role in nervous-system development, but many neuronal connections nevertheless form normally in the absence of activity, suggesting the presence of additional mechanisms guiding the targeting of synapses. The zebrafish's lateral line represents a simple but powerful model system for exploring the mechanisms that guide the assembly of microcircuits.The assembly of the 10Most fishes and many aquatic amphibians possess mechanoreceptive systems known as lateral lines, whose responsiveness to water currents underlies rheotaxis, predator avoidance, and schooling. Each lateral line consists of one to several neuromasts, which are discrete epithelial organs that include hair cells as well as the underlying supporting cells and surrounding mantle cells. Although lateral lines may be distributed over an animal's head, body, and tail in a variety of patterns, a general feature is directional sensitivity. Each hair cell possesses on its apical surface a mechanically sensitive hair bundle that is usually responsive to water movement either in the rostrad direction—toward the animal's front end—or in the caudad direction—toward the rear end. Vectorial sensitivity results from morphological polarization of each hair bundle, which consists of an array of actin-filled stereocilia whose lengths increase progressively in the direction to which the bundle is responsive.In the larval zebrafish, directional responsiveness arises from differential innervation by two oppositely polarized subpopulations of hair cells, each attuned to the detection of water currents toward either the animal's front end or its rear end. During early development, the primary lateral line on each side of a larva comprises seven neuromasts, discrete organs each containing 8–20 hair cells that make synapses onto afferent axonal terminals and receive synaptic inputs from efferent terminals . In wildtrilobite mutant larvae, in which the vangl2 protein is inactivated, possess hair bundles with random orientations, the canonical planar-cell-polarity pathway participates in cellular patterning. Finally, through a process that requires neither mechanotransduction nor synaptic activity, each afferent neuron of the posterior lateral line receives innervation from hair cells of only one orientation  has made possible the reconstruction of complete axons and dendrites within modules of the nervous system . NeuronaTo establish a basis for comparison with mutant specimens, we first sought to determine the complete connectome of the wild-type zebrafish's neuromast. We focused on the posterior lateral-line organs of larvae two to four days post-fertilization (2–4 dpf), a period during which the animals demonstrate behaviors dependent on a functional lateral line such as rheotaxis, escape swimming, and upright orientation. We analyzed single neuromasts from each of eight zebrafish ; Video 1The majority of neuromasts included one axonal terminal that made direct contact with every hair cell without apposition to any synaptic ribbon. Together with the presence of numerous presynaptic vesicles and postsynaptic membrane cisterns, this wiring pattern identified these structures as the terminals of efferent axons . The remIn view of the relatively restricted number of afferent and efferent terminals, hair cells, and their associations, we represented the data from each neuromast as a schematic diagram showing ribbon synapses, membrane contacts, and perisynaptic associations between hair cells and axonal terminals ; Video 2To assess the degree of neuronal wiring specificity throughout development, we used preimaging of neuromasts by fluorescence confocal microscopy to divide postmitotic hair cells into three temporal classes associated with characteristic cellular activities: 0–5 hr, when sibling pairs of hair cells undergo rearrangement along the anteroposterior axis; 5–15 hr, when the hair cells extend dynamic basal projections to mediate innervation; and more than 15 hr, when the formation of ribbon synapses largely concludes and the hair cells no longer rearrange or extend basal projections . For thoBy viewing the complete set of cellular interactions at ribbon synapses during different stages of synaptic development, we recognized that specificity is mediated primarily by direct interactions between hair cells and axonal terminals. A perisynaptic compartment contained as many as nine terminals, and the average number increased during maturation. Because no other cells intervened between the nerve fibers and the synaptic regions of hair cells, it is unlikely that a guidepost cell or other third party mediated microcircuit assembly as has been reported in other systems . This paAlthough the neuromast connectome displayed a high degree of specificity, it also manifested two levels of redundancy. First, the average number of synaptic partners per hair cell increased from one in cells aged 5–15 hr to two for cells older than 15 hr . Second,The redundant innervation by hair cells was unequally distributed: one axonal terminal occupied the majority of the hair cells' presynaptic active zones and contact area. This dominance by a single terminal increased with age: the dominant terminal received 34% of the ribbon synapses and 53% of the contact area of hair cells at 5–15 hr compared to 75% of the synapses and 73% of the contact area of hair cells older than 15 hr . FurtherPrevious research has shown that lateral-line afferent neurons contact three to five consecutive neuromasts and often do so unequally, with some axonal branches ramifying extensively throughout a neuromast but other branches entering more distal or proximal neuromasts with minimal arborization . This paConsidering that the formation of ribbon synapses appeared to arise from a combination of short-range, polarity-specific cues and interneuronal competition for dominance, we wondered how the two neuronal characteristics of dominance and specificity interact. When all fibers save one have been experimentally ablated, hair cells are able to innervate neurons of the opposing polarity . NeverthTaken together, these data indicate that the microcircuitry of neuromasts is characterized by specificity, redundancy, and dominance. Polarity-specific neuronal associations begin at the earliest appearance of synaptic ribbons and increase with age along with the dominance hierarchy of axonal terminals and the redundancy of both innervated and contacted fibers. Each hair cell recruits a few polarity-specific neuronal partners while maintaining an extensive but loose network of diverse neuronal associations.trilobite mutant larvae, in which inactivation of the canonical planar-cell-polarity protein vangl2 causes hair bundles to orient randomly , the mediator of Notch signaling, and are thus regarded as Notch-overexpressing. All the hair cells in these larvae possessed anteriorly polarized hair bundles. We investigated by SBFSEM one mutant specimen from each of two animals ; Video 4trilobite specimens, the Notch-overexpressing mutants also retained the wild-type microcircuit characteristics of redundancy and dominance even though the specificity of innervation had been eliminated. The transgenic neuromasts each showed only one highly dominant terminal as well as one of moderate dominance. Because the proportion of axonal terminals innervated by hair cells was the same as in normal neuromasts, high-dominance terminals with a preference for caudad-polarized cells were evidently able to displace some polarity-appropriate but low-dominance terminals. In the setting of hair cells uniformly possessing an anteriorly polarized phenotype, terminals of the opposing subset could thus outcompete the polarity-specific subset of fibers.Like the trilobite mutants with random hair-bundle orientation, we establish that coincident hair-cell depolarization is neither necessary nor sufficient for specificity. Moreover, our data suggest that each daughter hair cell acquires a distinct biochemical identity that guides its innervation of axonal terminals.We have employed SBFSEM reconstructions to compare the development of the neuromast's microcircuit among wild-type individuals, mutants, and transgenic animals. The microcircuit is characterized by the progressive development of specificity, redundancy, and hierarchical dominance of innervation. Although previous research identified the phenomenon of polarity-specific afferent synapses in the neuromast, our approach details exquisite precision at the level of ribbon synapses as well as the degree of specificity among non-synaptic contacts and perisynaptic associations. We also find that the specificity of membrane contacts increases during hair-cell development; by analyzing In terms of redundancy we observe that the average hair cell forms ribbon synapses with two or more afferent terminals, develops membrane contacts with four afferent terminals, and is located near five or more afferent terminals within the perisynaptic compartment. We have also characterized efferent terminals, one of which occurs in each neuromast and contacts every mature hair cell. The fact that a single efferent axon innervates hair cells of both polarities suggests that activation of the efferent system nonspecifically desensitizes a neuromast, rather than adjusting its relative responsiveness to rostrad and caudad water movements. Such a system might, for example, suppress reflexes driven by the lateral line during an animal's own movements.Despite the redundancy, the multiple terminals are distinctly unequal: across a majority of ribbon synapses and for most hair cells, there are varying levels of dominance of innervation and membrane contact. Among wild-type specimens, axonal terminals of a given polarity preference associate with ribbon synapses to the exclusion of more dominant terminals of the opposite preference. In Notch-overexpression larvae in which all hair cells adopt a common polarity, however, terminals of the opposite polarity preference participate in a limited number of ribbon synapses, suggesting that there are limits to the ability of polarity-specific afferent terminals to outcompete dominant ones of the opposite polarity.Emx2 knockout fish  in an incubator maintained at 28 ˚C. The experiments involved zebrafish of the trilobite mutant line and of the transgenic lines Tg(myo6b:actin-GFP), Tg(myo6:GFF), and Tg(UAS:NICD-myc)  .Throughout the lifespan of a zebrafish, mechanosensory hair cells arise continually within the neuromast to expand the total number of sensory cells and to replace senescent ones. In order to correlate hair-cell age with the SBFSEM data, we performed time-lapse confocal fluorescence microscopy for up to 39 hr on individual neuromasts prior to fixation.The specific neuromasts examined, L1 to L5, were deposited by the first lateral-line primordium. Embryos of 1–3 dpf were anesthetized in 600 μM 3-aminobenzoic acid ethyl ester methanesulfonate in E3 medium and mounted in a 35 mm glass-bottomed dish in 1% low-melting-point agarose. Laser-scanning confocal imaging was performed with an inverted Zeiss Axio Observer Z1 with an LSM 780 system equipped with a 60X oil-immersion objective lens . Neuromasts were imaged at intervals of 45–60 min as Z-stacks acquired with 1.0 μm steps under laser excitation at 488 nm and 561 nm. Images were processed into hyperstacks and analyzed with FIJI . Each image depicted a plane of view tangential to the animal’s surface and was oriented with anterior to the left and dorsal upward.2. The specimen remained in fixative for 18 hr at 4°C before a single transverse cut was made approximately 200 μm posterior to the relevant neuromast.With the exceptions described below, SBFSEM was performed as reported previously . Followitrilobite zebrafish, and two neuromasts from two Notch-mutant zebrafish.All preparative steps remained as previously described except that the specimen was immersed in acetone following a graded series of ethanol washes and was embedded in Durcupan  following penetration of the specimen with graded mixtures of Durcupan and acetone that was enhanced by 40 s of microwave treatment at 150 W . The remainder of the protocol—curing the resin, mounting the specimen, and trimming the block face—followed the previous description . In totaSpecimens were imaged at the Electron Microscopy Resource Center of Rockefeller University with a Merlin high-vacuum scanning electron microscope  equipped with a 3View2XP camera . The ultramicrotome shaved the block face in 30 nm increments and images were acquired with a lateral resolution of 6 nm X 6 nm per pixel. Alignment was performed with Digital Micrograph software (Gatan Inc.).As described previously, data analysts were hired to contour cellular features within the SBFSEM data at their home computers. Gross features within the data sets, including hair cells and neurons, were identified by the lead scientist (ED) or project manager (SH) before being assigned for complete annotation by an analyst. The annotations were reviewed by the project manager for completeness and neatness. The results were additionally examined as a three-dimensional rendering to detect unlikely features in hair-cell somata or axonal terminals. Next, the annotations were reviewed in detail and edited by a senior data analyst or the project manager. Finally, all data were reviewed and edited by the lead scientist. Most annotation was performed accurately and precisely by the initial data analysts; editing generally involved the addition of small neurites or the resolution of complex or ambiguous features in the data.The polarity of each hair cell, as defined by the orientation of its hair bundle, was determined by the position of the kinocilium relative to the stereocilia in the SBFSEM data and was confirmed by viewing the hair cell's apex in the confocal fluorescence-microscopic data.Each presynaptic active zone was marked by a prominent synaptic ribbon. This spherical, electron-dense structure resided adjacent to the basolateral membrane of a hair cell and was surrounded by numerous synaptic vesicles. An axonal terminal was assigned as the synaptic partner of a ribbon if it was situated immediately across the synaptic gap from a presynaptic active zone. For instances in which the area opposite the ribbon was occupied equally by two terminals, each neuron was assigned half of the ribbon.The contact areas between neurons and hair cells were determined automatically with a Python script developed with the computational-geometry package Shapely . We deteNine of the 14 specimens underwent timelapse confocal fluorescence microscopy for over 18 hr. We trained a machine-learning classifier on features of the hair cells in these data sets to assign age categories to the hair cells in the five data sets with only limited imaging. We obtained age information for 88 hair cells of a total of 149, randomly assigned 70% of these cells to a training set, and used the remaining 30% as a test set. Using these sets, we trained several classifiers using Scikit-Learn , a Pythot-tests. For all figures, bars denote standard errors; p<0.05 is indicated by *, p<0.01 by **, and p<0.001 by ***.The significance of pairwise differences between groups of samples was computed by Student's two-tailed  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Eve Marder as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Florian Engert (Reviewer #2).Thank you for submitting your article "Comparative connectomics of innervation in neuromasts of the zebrafish lateral line" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.As you will see from the reviews, both reviewers are in principle enthusiastic about the work. However, both reviewers raise similar issues regarding the presentation and analyses of the data, and clarity of the writing. Given the extent of the suggested revisions, both reviews are appended in full below. In particular, please ensure that the revision clearly states how the raw EM data and analyses files will be shared with the community.eLife has no limitations on the number of figures, and also uses figure supplements. So please take advantage of this to provide an excellent representation of your data.Please remember that Reviewer #1:In this paper the authors use block face EM to analyse the microconnectivity of neuromasts in the zebrafish lateral line. The small volumes analysed and the throughput of the technique allows them to analyse microcircuits in several specimen, including mutant individuals. This is a very interesting study, but the authors need to work on the text and the presentation of the data.Drosophila and Platynereis larvae).Unfortunately, the manuscript places too much emphasis on the technique and describing the networks rather than addressing important biological questions. It is not a critique of the results, rather the way the authors wrote the paper. It is very clear for example from the closing sentence of the Abstract. It is not a conclusion of what was found, but a manifesto. Furthermore, 'comparative connectomics' is usually used to refer to inter-specific comparisons (see PMID: 23332749) and not to comparing the stereotypy of connections between individuals  that requires the integration of signals from anterior and posterior sensitive hair cells. The finding that nerve terminals are highly specific in innervating hair cells of one directional specificity is highly relevant to this behavior and should be discussed.Unfortunately, the large amount of EM data are not presented in any detail. The authors should show volume renderings of reconstructed neuromasts to present for example how hair cells of one orientation connect to a specific subset of neuronal terminals.The raw EM data should also be provided, at least for some specimen, as supplementary files .In general, more example EM images would be needed to show the different types of associations, the postsynaptic membrane cisternae and myriad presynaptic vesicles in efferent axon terminals, etc.Likewise, the time-lapse fluorescence confocal microscopy data should be provided and also integrated into some of the figures, e.g. by providing the video, and showing snapshots in the figure, and then the corresponding EM reconstruction for the imaged neuromast, demonstrating that the cells can be assigned one-to-one between the datasets.The mis-wiring results in the trilobite mutant and the Notch overexpressing fish are not discussed in any detail. The authors should come up with a summary diagram based on their results that places in cellular context Notch signaling, planar polarity, and synaptic wiring specificity.The authors should include as Figure 1 a summary diagram of a fish with the lateral line neuromasts and indicate which ones were reconstructed, what is the size of the volumes, what is the general anatomy of a neuromast, how does it develop, etc. It is very hard to follow the paper otherwise for the non-specialists.Reviewer #2:The authors use serial block face electron microscopy to delineate the connectivity of afferent and efferent neurons within the zebrafish larva neuromast circuitry. They provide a comparative connectomic approach, both across neuromasts and genotypes. The data sets generated are impressive and clearly useful for the community. Overall, this is an important, novel and timely research project. However, there is a lot of room for improvement with respect to clarity and presentation.The manuscript in its current form looks more like a sketch rather than a completed document:1) There is no clear connection between the text and the figures. In particular, what is missing is a clear statement of each particular finding in the text, how this finding is corroborated by the data, and, with the help of specific and explicit figure panels, representative examples of such data need to be shown.Furthermore it would be helpful to have cartoon-examples of main findings such as directional specificity, redundancy, dominance and hierarchy. These can then also be used to highlight the main differences in the two different genotypes.2) An introduction to the neuromast system is missing. An overview figure with the known wiring diagram from the PLLG and the efferent nuclei are minimally required. This should also include a description of the known polarity specificity. Moreover, the relationship between the hair cells hair bundles position, flow sensitivity and age can clearly be illustrated. The classification of hair cells' mechano-sensitivity based on the position of hair-cells would also benefit from several examples drawn from the data.3) The number of animals used and the number of neuromasts studied for each condition should clearly indicated.4) The figures are not labelled and poorly referred to in the text. It is nearly impossible to figure out – in many cases – which figures are being talked about in the text. The connectivity diagrams in Figures 2, 5 and 6 are very hard to understand for a reader not familiar with neuromast connectivity. A graphic legend should be added to each figure explaining the different line sizes and line types. There are generally many ways in which the clarity of all figures can be improved. Every single one feels like a sketch with the essential data shown where little effort was made to guide the reader through the process of how the data was extracted and analysed. This makes the manuscript very difficult to read.5) A 3D rendering of the reconstructions is missing, as well as illustrative "fly-throughs" across blocks of data – such videos are useful, essential and by now a standard way of displaying serial EM data.6) The raw data, the segmented data and the primary analysis files all need to be shared with the community. It is not clear whether the authors plan to do so and, if yes, how.7) A true strength of this study is the ability to quantify a neuron's "dominance" based on its occupation of presynaptic ribbons. The use dominance, however, needs to be clarified in the text because it refers to two types dominance (1) for a specific hair cell, and (2) for all hair cells of a single polarity. It is often confusing in the text to figure out which type of dominance is being discussed, and this is important in order to understand the authors' arguments on how this dominance arises .8) The authors use trilobite and Notch mutants to build clear arguments for the establishment of afferent specificity. However, since they highlight two other interesting metrics: redundancy and dominance, they should also discuss how these metrics are perturbed (or not) in the mutants in comparison to the wild-types. This comment pertains to the text overall: the utility of using "redundancy" and "dominance" to characterize the neuromast circuit should be demonstrated more clearly: What are the lessons we learn from these? Or, alternatively: What questions can we pose using these descriptors?9) The Discussion needs to be a lot more specific in highlighting what new insights or findings, if any, were extracted from the data that go beyond what's already described in the existing literature. Reviewer #1:In this paper the authors use block face EM to analyse the microconnectivity of neuromasts in the zebrafish lateral line. The small volumes analysed and the throughput of the technique allows them to analyse microcircuits in several specimen, including mutant individuals. This is a very interesting study, but the authors need to work on the text and the presentation of the data.Unfortunately, the manuscript places too much emphasis on the technique and describing the networks rather than addressing important biological questions. It is not a critique of the results, rather the way the authors wrote the paper. It is very clear for example from the closing sentence of the Abstract. It is not a conclusion of what was found, but a manifesto. Furthermore, 'comparative connectomics' is usually used to refer to inter-specific comparisons (see PMID: 23332749) and not to comparing the stereotypy of connections between individuals .The manuscript seeks to present a connectomic analysis of the microcircuit of the neuromast. To establish the basis for this novel approach, we believe that it is important to describe the technique and to delineate patterns of innervation in wild-type individuals that can be used for comparison to genetic mutants and subsequently to morphants and larvae exposed to different environment conditions or other manipulations. Having established this foundation, we apply the approach to important biological questions, namely to that of asymmetric cell division, its role in neuronal wiring, the decoupling of cellular identity and spatial position, and the relation of the canonical planar-cell-polarity pathway to other asymmetric-division pathways such as Notch. We recognize that the biological significance may not have received sufficient attention in some parts of the manuscript and we have sought to bring this to light.The title has been changed so as not to confuse readers who might associate "comparative connectomics" with interspecific analyses.It is not clear from the Introduction what motivated the study and how it helps us to interpret previous findings. Any reference to behavior and the relevance of the connectome to interpreting it are lacking from the discussion. For example, zebrafish larvae show rheotaxis (PMID: 28700578) that requires the integration of signals from anterior and posterior sensitive hair cells. The finding that nerve terminals are highly specific in innervating hair cells of one directional specificity is highly relevant to this behavior and should be discussed.We have added this information to the Introduction.Unfortunately, the large amount of EM data are not presented in any detail. The authors should show volume renderings of reconstructed neuromasts to present for example how hair cells of one orientation connect to a specific subset of neuronal terminals.We have added Figure 2 to provide examples of SBFSEM images of a representative neuromast and of the specific features scored in our analysis of the interactions between hair cells and nerve terminals. Moreover, we have included three dynamic videos depicting volume renderings of representative data.The raw EM data should also be provided, at least for some specimen, as supplementary files .In general, more example EM images would be needed to show the different types of associations, the postsynaptic membrane cisternae and myriad presynaptic vesicles in efferent axon terminals, etc.As noted above, we have added Figure 2 to provide examples of SBFSEM images of a representative neuromast and of the specific features scored in our analysis of the interactions between hair cells and nerve terminals. When the manuscript has been accepted, we shall make 18 complete neuromast data sets and annotations publicly available to navigate or download at the webKnossos site hosted by scalableminds.Likewise, the time-lapse fluorescence confocal microscopy data should be provided and also integrated into some of the figures, e.g. by providing the video, and showing snapshots in the figure, and then the corresponding EM reconstruction for the imaged neuromast, demonstrating that the cells can be assigned one-to-one between the datasets.We have created an additional video that features images from time-lapse confocal fluorescence microscopy and the correlation of hair-cell identity with SBFSEM data.The mis-wiring results in the trilobite mutant and the Notch overexpressing fish are not discussed in any detail. The authors should come up with a summary diagram based on their results that places in cellular context Notch signaling, planar polarity, and synaptic wiring specificity.Although we would like to offer a definitive response to this suggestion, there are several questions remaining to be answered that preclude a summary of the mediation of cellular innervation, planar-cell polarization, orientation of the hair bundle, and cellular rearrangement by signaling pathways. We are currently preparing another manuscript that incorporates many additional experiments bearing on these issues.The authors should include as Figure 1 a summary diagram of a fish with the lateral line neuromasts and indicate which ones were reconstructed, what is the size of the volumes, what is the general anatomy of a neuromast, how does it develop, etc. It is very hard to follow the paper otherwise for the non-specialists.We have accepted this useful suggestion and have prepared a Figure 1 with the requested information.Reviewer #2:The authors use serial block face electron microscopy to delineate the connectivity of afferent and efferent neurons within the zebrafish larva neuromast circuitry. They provide a comparative connectomic approach, both across neuromasts and genotypes. The data sets generated are impressive and clearly useful for the community. Overall, this is an important, novel and timely research project. However, there is a lot of room for improvement with respect to clarity and presentation.The manuscript in its current form looks more like a sketch rather than a completed document:1) There is no clear connection between the text and the figures. In particular, what is missing is a clear statement of each particular finding in the text, how this finding is corroborated by the data, and, with the help of specific and explicit figure panels, representative examples of such data need to be shown.Furthermore it would be helpful to have cartoon-examples of main findings such as directional specificity, redundancy, dominance and hierarchy. These can then also be used to highlight the main differences in the two different genotypes.There were submission errors that resulted in figures being out of order and otherwise incorrectly referenced in the text. We apologize to the reviewers for these mistakes. After correcting these errors, we have extensively revised the figures and text to create a closer connection between the two. We have included figures that directly display characteristics of the microcircuit as well as a descriptive figure illustrating the major features of the lateral-line neuromast.2) An introduction to the neuromast system is missing. An overview figure with the known wiring diagram from the PLLG and the efferent nuclei are minimally required. This should also include a description of the known polarity specificity. Moreover, the relationship between the hair cells hair bundles position, flow sensitivity and age can clearly be illustrated. The classification of hair cells' mechano-sensitivity based on the position of hair-cells would also benefit from several examples drawn from the data.We have added Figure 1, which includes most of the points specified by the reviewer with an emphasis on hair-cell polarity and polarity-specific innervation, the major characteristics explored in our investigation.3) The number of animals used and the number of neuromasts studied for each condition should clearly indicated.We have revised the manuscript to make clear the number of specimens and individuals both in the Results and the Materials and methods.4) The figures are not labelled and poorly referred to in the text. It is nearly impossible to figure out – in many cases – which figures are being talked about in the text. The connectivity diagrams in Figures 2, 5 and 6 are very hard to understand for a reader not familiar with neuromast connectivity. A graphic legend should be added to each figure explaining the different line sizes and line types. There are generally many ways in which the clarity of all figures can be improved. Every single one feels like a sketch with the essential data shown where little effort was made to guide the reader through the process of how the data was extracted and analysed. This makes the manuscript very difficult to read.We have added Figure 2 to show the general structure of a neuromast and its components and to provide examples of the various types of interaction between a hair cell and the associated afferent nerve terminals. At the bottom left of Figure 3 we have included a key to the cellular positions, hair-bundle orientations, afferent and efferent neuronal identifies, and types of interaction between hair cells and afferent axonal terminals.5) A 3D rendering of the reconstructions is missing, as well as illustrative "fly-throughs" across blocks of data – such videos are useful, essential and by now a standard way of displaying serial EM data.trilobite mutant, and a Notch-overexpressing transgenic larva.We have created dynamic videos depicting volume renderings of representative data for a wild-type larva, a 6) The raw data, the segmented data and the primary analysis files all need to be shared with the community. It is not clear whether the authors plan to do so and, if yes, how.We fully intend to make the neuromast data sets and annotations publicly available for examination and downloading. Unfortunately, there is as yet no standard means of sharing such data and no publicly available repository for doing so. The best solution we have found is to set up a repository using the platform developed by scalableminds, which specializes in hosting SBFSEM data sets. We have retained their services and are working with them to make the data available; we shall provide a link when the manuscript has been accepted.7) A true strength of this study is the ability to quantify a neuron's "dominance" based on its occupation of presynaptic ribbons. The use dominance, however, needs to be clarified in the text because it refers to two types dominance (1) for a specific hair cell, and (2) for all hair cells of a single polarity. It is often confusing in the text to figure out which type of dominance is being discussed, and this is important in order to understand the authors' arguments on how this dominance arises .The question of whether a neuron possesses dominance across neuromasts or only within a neuromast is interesting and deserves further study. Because only individual neuromasts were interrogated in this study, however, we are unable to drawn such conclusions. We have mentioned anecdotal data from published studies of single-fiber labeling that may relate to the matter. In revising the manuscript we have sought to clarify the issue of neuronal dominance for an individual hair cell versus dominance across multiple hair cells.8) The authors use trilobite and Notch mutants to build clear arguments for the establishment of afferent specificity. However, since they highlight two other interesting metrics: redundancy and dominance, they should also discuss how these metrics are perturbed (or not) in the mutants in comparison to the wild-types.trilobite mutants and Notch transgenics, the redundancy and dominance are not.As mentioned in the text, SBFSEM has allowed us to conclude that although specificity is affected in distinct ways in the This comment pertains to the text overall: the utility of using "redundancy" and "dominance" to characterize the neuromast circuit should be demonstrated more clearly: What are the lessons we learn from these? Or, alternatively: What questions can we pose using these descriptors?We have amended the language throughout the manuscript to better highlight the meaning and utility of “redundancy," the multiple terminals associating at each level with a hair cell, and “dominance," the hierarchical nature of the multiple associations. One could pose many questions focusing on these terms. Would a terminal exert greater dominance at a neighboring or new hair cell if its associations at another hair cell were eliminated? Might that terminal's status in the dominance hierarchy of one neuromast increase if its associations at a second neuromast were eliminated? Does a redundantly innervated terminal assume the synaptic associations of another terminal that has been destroyed, or does a third terminal supervene? Could redundant innervation lead to the differential response of fibers to mechanical deflection of the hair bundle for more finely graded characterization of stimuli? What are the molecular pathways that mediate the competition of terminals in the neuromast, and by over-activating or eliminating a factor can we render a terminal more or less dominant? We hope that our initial publication will provide a set of baseline observations against which the results of these procedures can be tested.9) The Discussion needs to be a lot more specific in highlighting what new insights or findings, if any, were extracted from the data that go beyond what's already described in the existing literature.We have expanded the Discussion to highlight the main findings of the research.


In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively. Sensor nodes (SNs) in most WSN applications are powered by a battery and the amount of energy consumed by the nodes determines the network’s lifespan. For future Internet of Things (IoT) applications, reducing energy consumption and prolonging the battery life of SNs have become compulsory. The power consumption of sensor nodes can be reduced by controlling the transmitted power of the wireless protocols. This control can be achieved through an accurate distance estimation between the nodes in the WSN. One method of reducing sensor node power consumption is by exploiting the location or distance between the nodes.One of the key challenges in wireless sensor networks (WSNs) is localization . LocalizThere are several techniques used in WSN localization. These techniques can be classified into two categories: classical localization techniques and artificial intelligence techniques that use soft computing optimization algorithms, as shown in However, in most previous localization techniques, the localization or distance accuracy is still not satisfactory. The distance estimation accuracy of track cycling is essential for paving the way for more studies related to energy saving in WSNs, where the transmitted power of the sensor nodes can be controlled according to the accurate distance measurement between the coach and the bicycle on the track. With accurate localization, the power consumption of the WSN can be improved and the battery life of the sensor nodes can be extended. This paper presents range-based localization using RSSI with two soft computing techniques  to improve the distance estimation accuracy between the mobile node  while moving on the cycle track and the coach (located at the centre of the track cycling field) in both outdoor and indoor velodromes. Three optimization algorithms PSO, GSA, and Backtracking Search Algorithm (BSA), were combined individually with ANN to improve the distance estimation accuracy. The PSO, GSA, and BSA were designed to determine the optimum number of neurons in each hidden layer and the learning rate of ANN. A high level of distance estimation accuracy was achieved that was better than in the previous works.Several studies adopting different approaches for wireless positioning on a bicycle track have been conducted. The location and speed of an athlete were monitored in  using GPIn , the autWireless bicycle sensor nodes with a Wi-Fi infrastructure have been proposed by Pias et al.  to estimShin et al.  used a ZThe proposed bicycle wireless sensor network (BWSN) topology is shown in The outdoor experiment was performed on a track cycling field . AN1 was located at the centre of the cycling field. The anchor nodes AN2 and AN3 were 27 m away from AN1 and the mobile node received the beacons from the anchor nodes to collect the RSSI data, as shown in The bicycle track area was divided into two symmetrical halves (A and B), and the measurements were only performed for the first half (A). Half B was excluded due to the resemblance between the two halves. Half A was selected because it represented the starting line (pursuit line) for cycle training and competitions. The mobile node used the RSSI values to determine the distance between itself and AN1 according to the soft computing techniques. The RSSI was measured for 18 pre-defined positions in half A. A total of 20 samples were recorded at each position, with one sample per second. Each sample contained one data packet, and each data packet contained 34 bytes. Therefore, 900 samples (300 for each anchor node) of RSSI values of the three anchor nodes were collected and used for training, testing, and validating ANN and ANFIS to determine the distance to AN1. The collected RSSI values were used for training (70%) ,46, testThe indoor experiment was performed in the sports hall (Dewan Gemilang) of the University Kebangsaan Malaysia, Bangi  to repreSoft computing techniques are employed to solve complex numerical optimization problems as well as non-linear and non-differentiable systems . There aANFIS is a technique for the evolution of self-organizing neuro-fuzzy systems . ANFIS imfs) were trained and tested. Two types of membership function, namely the triangle membership (trimf) and generalized bell membership (gbellmf), were also used in ANFIS. Different numbers and types of membership function give different minimum distance error values. For training, testing, and validating ANFIS, a large number of RSSI samples are required. 900 samples (outdoor) and 780 samples (indoor) of RSSI values were used for training, testing, and validating ANFIS to accurately determine the distance between the bicycle on the track and the coach.For each input, three, five, and seven inputs membership function  to the neurons in the first hidden layer. The input iRSSI were weighted against the strengths of particular connections ijw and summated by each neuron of the first hidden layer to pass the output of the first hidden layer to the neurons of the second hidden layer. The inputs of the second layer were weighted against the strengths of particular connections izw and summated by each neuron of the second layer to calculate the output ky in the fourth layer. The first and second hidden layers used the tansigmoidal activation functions to cover all ranges of the negative RSSI values, whereas the output layer employed the linear activation functions to cover the positive values of distance. The first and second hidden layers consisted of number of neurons in each hidden layer. The number of neurons in each hidden layer and learning rate were selected based on the optimization algorithms  because this parameter selection was not secure and subject to the trial-and-error method, which does not always provide the optimum solution. The PSO, GSA, and BSA algorithms address such a problem by determining the best number of neurons in each hidden layer and the optimum learning rate of ANN. Thus, the performance of ANN can be improved. In this case, these algorithms could be hybridized with ANN to form three different algorithms, which were known as the hybrid PSO-ANN algorithm, hybrid GSA-ANN algorithm, and hybrid BSA-ANN algorithm through which ANN was able to achieve a minimum distance error.ANN is an information processing system which has been developed as a generalized mathematical model of human biological nerves . ANN-basHeuristic algorithms are a technique which tries to find a good solution  at a sensible computational cost without the ability to undertake either optimality or feasibility, or even in several cases to clarify how close to the optimum solution . BecauseUsing the parameter settings of heuristic algorithms as shown in F is the gravitational force, R is the distance between the first particles mass (M1) and second particles mass (M2), and G is the constant value of the gravitational. Newton’s Second Law states that acceleration a is inversely proportional to mass M and directly proportional to force F as follows:The GSA algorithm was proposed in 2009 by Rashedi et al. . This alG)” depends on the real age of the universe. Equation (3) provides the decrease of the gravitational constant with age [G(t) is the gravitational constant at time t and G(t0) is the gravitational constant at the first cosmic quantum-interval of time t0 [Because of the effect of declining gravity, the real value of the “gravitational constant (with age :(3)G(t) time t0 .thi agent can be defined by:N is the number of agents, thi agent in the thd dimension and k is the space dimension. To compute the fitness function of GSA, a root mean square error (RMSE) can be used to determine the best and the worst fit for each iteration. The computations aim to minimize the problems and determine the masses of each agent as follows [e is the estimated distance error and n is the number of samples. The actual distance was obtained based on measurement, whereas the estimated distance was obtained using the hybrid GSA-ANN. The gravitational constant t was computed as follows:The positions of the agents are initialized . The position of the  follows :(5)RMSEthi agent, the velocity and acceleration calculation, and the position of the agents in the next iteration are as follows:The total force computation in different directions in the The details of the operation of the hybrid GSA-ANN based on the previous equations are shown in the flow chart in In this section the results of the soft computing techniques will be discussed in terms of distance estimation accuracy. The ANFIS results will be presented first, followed by those of the hybrid PSO-ANN, hybrid GSA-ANN, and hybrid BSA-ANN. The outcomes of the hybrid GSA-ANN will be considered for comparison with the previous works because it achieved the minimum distance error relative to the other soft computing techniques.gbellmf type is better than trimf for both outdoor and indoor. Therefore, seven gbellmfs were considered in this study for outdoor and indoor velodromes. For each ANFIS input, the membership function is shown in gbellmfs after testing and validating data for outdoor and indoor velodromes. The figure shows that there is no significant difference between the estimated and the actual distance for outdoor and indoor velodromes. The red points represent the estimated distance (FIS out), whereas the blue points and plus signs are the actual distance . For outdoor environments, most of the estimated distances matched the actual distances, as can be seen in As previously mentioned, the collected RSSI values were used for training and testing ANFIS  to establish a relationship between the input RSSI values and the output physical distances. As a result, the distance error was obtained for different numbers and types of the membership function for outdoor and indoor, as shown in Several population sizes were simulated in Matlab for the hybrid GSA-ANN, hybrid PSO-ANN, and hybrid BSA-ANN to allow each heuristic algorithm to select the optimum number of neurons in each hidden layer and learning rate of ANN. Thereby, the minimum fitness function could be achieved. Once the adopted population size was tested for each heuristic algorithm, it was found that the 20 population size gave the minimum fitness function (based on RMSE) for all heuristic algorithms for the outdoor velodrome, whereas the population size was 20 for the hybrid PSO-ANN and hybrid BSA-ANN and 50 for the hybrid GSA-ANN, which gave the minimum fitness function for the indoor velodrome. On the other hand, GSA-ANN achieved the minimum fitness function compared to the other heuristic algorithms for outdoor and indoor. GSA-ANN was able to reach the best fitness function at 20 (outdoor) and 50 (indoor) population sizes, as shown in x-axis) for the outdoor and indoor velodrome is plotted against the estimated distance . The regression coefficient (R) of determination between the actual and estimated distance is a good indicator for the investigation of the prediction performance of the hybrid GSA-ANN algorithm. From R values were 0.9999 (testing) and 0.99991 . For R values were 0.99606 (testing) and 0.99587 . The regression coefficient results suggested a close agreement between the actual and estimated distances.The hybrid GSA-ANN was used to optimize the ANN operation by selecting the optimum values of neurons in each hidden layer and learning rate. Although GSA is a famous optimization technique  it has ngbellmfs and hybrid BSA-ANN, respectively, for the indoor velodrome, whereas the MAE of hybrid GSA-ANN was improved by 21% relative to the hybrid BSA-ANN for the outdoor velodrome.It can be observed that there was a small difference between MAE and RMSE for the case of outdoor, as shown in The hybrid GSA–ANN algorithm can be compared with previous works ,77,78,79Two soft computing techniques for WSN distance estimation were presented in this paper for outdoor and indoor velodromes. These techniques aimed to determine the distance between the bicycle position while moving on the cycle track and the coach. The first method was based on ANFIS, whereas the second approach adopted heuristic algorithms such as GSA, PSO, and BSA hybridized with ANN. The ANN algorithm was further improved by combining with GSA, PSO, and BSA to select the optimum number of neurons in the adopted two hidden layers and to select the optimum learning rate of ANN. This resulted in an improvement of the distance error between the bicycle and the coach. The soft computing techniques were compared with each other to select the best algorithm that gave the minimum distance error. The results indicated that the hybrid GSA-ANN was more convenient than the other algorithms adopted in this work in terms of distance estimation accuracy. The comparison results showed that the MAE of the hybrid GSA-ANN outperformed the previous works for both outdoor and indoor environments. Therefore, GSA-ANN is suitable in both indoor and outdoor environments and can be applied to any static or mobile WSN node.The limitation of this study lies in the possibility of implementing the hybrid GSA-ANN in real-time as the implementation of ANN requires a considerable amount of memory. The huge computation memory comes at the expense of limited memory size and processor speed of the microcontrollers. In such a case, the mobile node (bicycle) requires a microcontroller with a high speed and large memory size such as an Arduino Due. However, using an Arduino Due leads to high power consumption, large size and extra weight, all of which are considered critical issues in bicycle sensor nodes. The high level of power consumption will eventually reduce the battery life of the sensor nodes. In addition, the extra size and weight on the bike increases aerodynamic resistance, which consequently reduces the bike’s speed and induces fatigue in the athlete during cycling. These considerations are critical in competitive events. It is expected that once quantum computing is in place and Moore’s Law has achieved its saturation in future, the use of high computing will be possible for small sensor applications.


The separation of enantiomers is of great importance due to their possible differences in therapeutic properties. Preferential crystallization in various configurations of coupled batch crystallizers is used as an attractive means to separate the conglomerate-forming enantiomers from racemic mixtures. However, the productivity of such batch processes can be limited by the nucleation of the counter enantiomer and consumption of the supersaturation. In this work, a recently proposed process configuration, which uses coupled mixed suspension mixed product removal (MSMPR) with liquid phase exchange, is investigated by simulation studies. A detailed study on the effect of process parameters  on the productivity and yield of the coupled MSMPR has been presented. Moreover, a comparison of various coupled crystallizer configurations is carried out. It is shown through simulation studies that the productivity of the enantiomeric separation can be significantly improved compared to the previously proposed batch modes when the continuous configuration is used. The effect of nucleation kinetic parameters on the performances of various crystallizer configurations is studied as well. A set of coupled population balance equations (PBEs) was used to describe the evolution of the crystal phase of the both enantiomers in each vessel. These equations were solved numerically using the quadrature method of moments. The insights obtained in this study will be useful in the process design of coupled crystallizer systems. S(−)-fluoxetine shows remarkable therapeutic effects in preventing migraines, while the racemic (equimolar mixture of both enantiomers) drug (the antidepressant Prozac) has no effect [S(−) and R(+) enantiomers can interconvert under physiological conditions. R(+)-thalidomide seems to act as a sedative while S(−)-thalidomide and its derivatives are reported to be teratogenic  [Enantiomers are the result of the chirality or handedness of molecules which are the non-superimposable mirror images of each other. Enantiomers are widely found in organic compounds and their separation is a major concern in the modern pharmaceutical, food, and agricultural industries. Separation of the enantiomorphs is particularly important in pharmaceutical industry, since very often one of the enantiomers exhibits the intended therapeutic activity while the other is inert. Moreover, in some cases the counter enantiomer can cause toxicity or adverse effects. For example, o effect . Thalidor fetus) . Thus, ir fetus) . This stThere are two major strategies to obtain a single pure enantiomer: 1) the ‘chiral approach’ is based on developing the asymmetric synthesis of the selected enantiomer; and (2) the ‘racemic approach’ is based on the separation of the mixture of two enantiomers . Althoug the ‘chiS enantiomer or pure R enantiomer. This is in contrast to the racemic compound-forming system where there is an additional racemic phase consisting of equal amounts of R and S enantiomers. Crystallization can be carried out in standard equipment readily available in pharmaceutical and fine chemical industries. Manufacturing methods of the enantiomorphic drug are still dominated by crystallization [Among various means, preferential crystallization is an attractive and efficient way to separate the racemic mixtures since no auxiliaries and reagents other than solvent are needed . Howeverlization . In preflization . Additiolization ,10,25. Hlization ,26. Thislization ,26.Recently, a continuous version of the CPC configuration was proposed ,28,29. TAs shown in This configuration, shown in This configuration is run in batch mode. However, the two tanks are maintained at two different temperatures as shown in In this study, the preferential crystallization of amino acid threonine dissolved in water is used as a model system. The process is modeled using widely used population balance equation (PBE) ,30 couplk in Tank j where L is the characteristic size of the crystals, q is the volumetric flow rate for the feed  can be approximated by a log-normal distribution with sufficient accuracy in terms of the characteristic lengthThe parameter k at the liquid phase in Tank j, In order to track the liquid phase concentration the mass balance has to be considered as followsThe mass of water The solubility of the DL-threonine in water for a relatively wide temperature and composition range is given by the following expressionThe empirical size-dependent growth rate expression is found to be as followsk takes place predominantly if crystals of that enantiomer are already present. It can be expressed ask. The temperature-dependent rate constant New particles are born in a supersaturated solution due to primary and secondary nucleation. The secondary nucleation of enantiomer bly well . Ψ is thAll the required parameters are listed in 2O is chosen as a model system for these studies [The well-investigated DL-threonine/H studies . The kink in the crystallizer. For the CPC and CPC-D configurations the equivalent productivity for comparison is defined asEicke et al.  have shok in the liquid phase in the feed stream and in the product stream, respectively. For the CPC and CPC-D configurations, the yield is defined as followsk in the racemate. It is to be noted that for the CPC-D configuration, The simulation results for various process configurations are presented in this section. In The effect of feed flow rates on productivity and yield is investigated first for the CPC-MSMPR configuration. The simulation results for Tank 1 are presented in ncreases ,33.The liquid phase exchange between the coupled crystallizers helps to compensate the depletion of the preferred enantiomer concentration. The effects of liquid phase exchange rate on productivity and yield are shown in Here, we investigate the effect of seed mass on the productivity and yield for the CPC-MSMPR configuration. The simulation results are presented in In Next, in In As can be seen in this figure, the productivity of CPC-D is higher than in the CPC configuration. This result is consistent with Eicke et al. . The shae et al. ). Such pIn 2O system were used in the simulation results. Here we investigate how the nucleation kinetic parameters affect the performance of the various configurations. The secondary and primary nucleation rates for this system are given by Equations 2O systemPreferential crystallization in vessels coupled via liquid phase exchange is an attractive means for chiral resolution for conglomerate-forming systems. There are various process configurations available for such crystallizations, e.g., batch coupled preferential crystallization (CPC), batch coupled preferential crystallization-dissolution (CPC-D) and coupled preferential crystallization in MSMPR crystallizers (CPC-MSMPR). In this work, first, we investigate the effect of various process parameters on the productivity and yield of the various process configurations with particular focus on the recently proposed CPC-MSMPR. This study is based on process simulation using experimentally verified kinetic parameters from the literature. We then compare the performances of these configurations in terms of productivity and yield. It is found that the productivity of CPC-MSMPR configuration increases as the feed flow rate increases up to about 80 mL minThe insights obtained in these results can be useful in designing a particular process configuration or improving the existing one by manipulating the appropriate process parameters. The presented results will also be helpful in choosing suitable process configuration. For instance, when a continuous supply of pure enantiomers is required, CPC-MSMPR configuration will be preferred. Conversely, if there is no provision for recycle and the separation yield is more important, then the CPC-D configuration will be preferred. Moreover, the analysis presented will be useful in deciding the appropriate feed flow rate, liquid phase exchange rate, and seed mass at the design stage of the process so that the performance of the process can be improved in terms productivity, purity, and yield.


Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings . Five of the recordings were from the publicly available database , which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data  for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work. Electronic fetal monitoring (EFM) is a routine monitoring modality during labor and delivery in developed countries. Currently, Doppler ultrasound and fetal electrocardiography (both invasive and non-invasive) are recognized as reliable and proven techniques for monitoring the Fetal Heart Rate (fHR)  as well as technical noise from the powerlines and their surrounding electrical devices that contaminate the recorded abdominal ECG (aECG) signals. Most of these unwanted signals can be eliminated by conventional signal processing techniques (linear filtering) because their frequency ranges are different from the spectrum of the desired (fECG) signals  seems to be very promising for the future of continuous fetal monitoring. In such a system, the ICA and PCA methods presented in this article could prove to be the most suitable for implementation. Consequently, to complement our previous work and compare the effectiveness of non-adaptive signal processing methods in suppressing the mECG signals, we carried out the current investigation focusing on the Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The results of our initial experiments applying the popular PCA and ICA methods to synthetic data are presented and reported elsewhere . In other words, reliable morphological analysis of high quality fECG signals offers the possibility of introducing the non-invasive STAN (NI-STAN) to clinical practice, which would in turn lead to a significant reduction in unnecessary C-sections due to misrepresented EFM results.The fetal heart rate undergoes dynamic adjustments as it responds to the fetal environment and other stimuli. The changes in fHR can reflect both physiological and pathological influences. Physiological changes are associated with fetal movements and also with maternal contractions during labor  using Cardiotocography (CTG) is the most frequently used tool to assess fetal well-being during labor and delivery. The fHR and uterine contractions are detected by two external transducers placed on the maternal abdomen. One of the transducers, placed above the fetal heart, uses Doppler ultrasound to detect fetal heart motion. The second transducer, placed at the fundus of the uterus, measures the frequency of the uterine contractions  results. These in turn lead to increased rates of unnecessary caesarian sections, thereby increasing labor and delivery costs Vintzileos et al. . One of In addition to CTG, fHR can be obtained from fECG signals. Recent studies show that this method is the most promising one  on the examiner  according to the information in the filtered signal. These methods are often utilized for noise suppression in a variety of applications, where the source of the signal is known and measurable, such as Channel Equalization ,  and those that have been applied to extract the NI-fECG signals include: Adaptive Neural Networks (ANN), Hasan et al. , Hybrid Non-adaptive filtering methods eliminate the undesired signals to yield the fECG signal without filter adaptation. More specifically, in some of these methods, filter weights are determined by using some initial training data and remain constant. These methods can use either a single-channel or multi-channel signal source. Techniques utilizing a multi-channel signal source include multiple and single-source methods. Single-channel signal source methods are based on for example Wavelet Transform (WT), Karvounis et al. , Datian The multi-source methods are based on Subspace Denoising Fatemi and Sameni  or BlindDe Lathauwer et al. applied the ICA method to fECG signal processing and explained that it is a rather demanding approach , Marossero et al. . By deteSameni et al. applied the ICA method to extract fECG signals, while taking into account the dimensionality of these signals and the theory of the heart dipole  fECG signals recorded by means of the FSE using the fHR values determined from the extracted as well as direct fECG signals. The FSE recordings were quality assured by clinical experts resulting in a set of reference markers precisely indicating the time of occurrence of the R-waves. Thus these transvaginal records could be considered as a “gold standard” for fHR determination and a relevant reference for validating the outcomes of our experiments. Additionally, the results were statistically evaluated by means of the Bland-Altman method. We also provide the graphical interpretation of the results to facilitate the visual evaluation of the fECG signal separation process.In addition to fHR determination, this article also focuses on testing possible morphological analysis (MA) of the fECG signals, namely non-invasive ST analysis (NI-STAN). We analyzed the estimated as well as the reference fECG signals and also carried out the evaluation of the MA as well.Non-adaptive methods have been applied to the fECG signal extraction problem . Each recording in the Polish data included four aECG signals. One recording could be tested by:(1) using 11 possible combinations of electrodes; (2) 6 possible combinations when utilizing 2 signal channels; (3) 4 possible combinations when using 3 channels; and (4) 1 combination when using all of the channels simultaneously.* signals . There was a time shift between the estimated aECG* and mECG* components which needed to be centered. Moreover, it was necessary to equalize the amplitudes of both components in order to ensure that the maternal components had the same amplitude. For this task, we developed an adaptive algorithm that was able to correct the amplitude and phase shifts  of fECG, namely non-invasive ST analysis. We analyzed the estimated as well as the reference fECG signal and evaluated results .Figure x1(t) and the second as x2(t). Each of these recorded signals was generated by weighing the signals s1(t) and s2(t) of the individual signal sources (hearts). The relationship between the signals obtained from the electrodes xi(t) and the signals generated by the individual hearts si(t) can be expressed as follows:Figure Independent Component Analysis (ICA) is a statistical analysis technique used to decompose a multivariable signal into a set of mutually independent, non-Gaussian components, assuming that the measured signals are a combination of independent source signals described mathematically by the ICA model  of the random variables, not functions of time. The aim of ICA is to return the linear unmixing matrix W in order to acquire the estimated independent components y such that:where again s is equal to the number of observed variables; this is a simplifying assumption that is not completely necessary. If the unknown mixing matrix A is square and non-singular, then W is the inverse matrix W = A−1. Otherwise, the best unmixing matrix, that separates sources as independent as possible, is given by the generalized inverse Penrose-Moore matrix:We assumed here that the number of independent components simplification of algorithms,reduction of dimensionality of the problem,reduction of number of parameters to be estimated,highlighting features of the data set not readily explained by the mean and covariance.The primary reasons for pre-processing are:There are two main pre-processing strategies in ICA, namely centering and whitening/sphering.a) Centering Centered vectors have zero mean. Centering is a very simple operation and simply refers to subtracting the mean E{x}:b) Whitening Whitened vectors have unit variance. Whitening can be performed by using the eigenvalue decomposition of the covariance matrix:V is the orthogonal matrix of eigenvectors and D is the diagonal matrix of its eigenvalues. A new whitened vector is created as follows:where x).Only one original independent component can have Gaussian distribution. If multiple Gaussian sources exist, the ICA method is not able to extract these sources (independent components) from the data  calculated The order of the original independent components cannot be determined by the ICA method.There are four basic limitations of the ICA method Lee et al. :Only onThe PCA  is provided for both reference and estimated data.Figure n. We have a set of data that consists of m input vectors. The entire ensemble is compactly represented by the n × m data matrix X = . The purpose of PCA is to find those m(m < n) components of the elements of X, which reduce the dimensionality of the input vector in a mean squared error sense. One of the approaches to this solving problem is the projection of the data along orthogonal basis vectors through eigen decomposition of the covariance estimate of the input.The other main advantage of the PCA method is that we can find patterns in the data and then we can compress the data, i.e., by reducing the number of dimensions, without much loss of information. The input data vector is represented by the column vector x vector have a zero mean value, E{x} = 0, then the transformation of vector x can be defined as:If we let Or alsoU =  is the transformation matrix with the condition:where matrix ui is equal to one. Equation (12) is also called the PCA data model. The value si is the projection of the input data x to the orthogonal base vectors ui. The vector si are the principal components.That means that the module of the base vector si  have been found, the data vector x can be reconstructed as follows:We can also do the opposite approach. After PCA's main components U matrix so that the variance of si projection is maximum. The variance can be defined as:The aim of the PCA method is to find a linear orthogonal transformation represented by the C = E{xxT} = CT is a symmetric covariance matrix of random vector x. From equation (14) it follows that the variance of the s projection is a function of the unit vector ui.where u) is the variance probe.where ψ(ui for which the variation probe ψ(u) is maximal under the condition expressed in Equation (12).The aim of the PCA method is to find the base vectors ui is based on solving equation (16):The problem about estimating the base vectors i (the numbers of the covariance matrix C) and the vectors of the covariance matrix C.The solution of the equation is to find the actual numbers λC matrix's eigenvalues are ordered from the largest value to the smallest value λ1 > λ2 > ⋯ > λn; λ1 = λmax and by the ordered vectors ui it is possible to create the matrix U = , then the equation (15) can be expressed as:The 1, ⋯ , λn. Respective column vectors ui satisfy the orthogonality condition:where Λ denotes the diagonal matrix with the eigenvalues λU is an identity matrix (15) and can be expressed as:That means that By comparing equations (14) and (19) we can assume that:ui) seek the maximum variance and the matrix's eigenvalues are identical.This means that the variance probes ψ axis, the information about 2 clusters is hidden.The PCA principle is illustrated in Figure The research material consisted of multichannel fetal electrocardiograms obtained from 12 different women at ITAM in Zabrze, Poland during established labors, between 38 and 41 weeks of gestation and saved in 12 different recordings. Five of these recordings constitute the Abdominal and Direct Fetal Electrocardiogram Database (AD FECG) available to the public on the PhysioNet website , in the Department of Obstetrics at the Medical University of Silesia. Each individual recording was comprised of signals acquired from four differential channels using electrodes placed on a maternal abdomen, and a direct electrocardiogram was registered from a fetal head as a reference signal  placed on the left leg. To reduce the skin impedance, the areas under the Ag-AgCl electrodes (3M Red Dot 2271) were abraded (3M Red Dot Trace Prep 2236). It is important to emphasize that the acquisition of abdominal fECG signals during labor presents additional challenges we had to be aware of. Firstly, during labor the strongest signal was observed when the fetus was fully developed. On the other hand, the quality of abdominal signals acquired during labor was contaminated by considerable muscular activity of the uterus. Moreover, during signal acquisition the scalp electrode very often lost contact with the fetal head, causing temporary signal loss. Some fragments of the direct fECG signals were also distorted by interferences caused by maternal movements. Consequently, we had to select only those short fragments of the recordings for which there were no signal loss periods in the direct (reference) fECG signal. The acquisition of direct fetal electrocardiogram was carried out with a typical spiral electrode, commonly used in the direct fECG channel of popular bedside fetal monitors. The R-wave peaks were then automatically determined from the direct fECG signals by means of an on-line analysis algorithm applied in the KOMPOREL system. The accurate occurrence of these peaks was then verified (off-line) by a group of cardiologists, resulting in a set of reference markers precisely indicating the location of the R-waves. These markers were then stored together with the direct and indirect fECG signals. In 12 5-min recordings, a total of 5,165 maternal QRS complexes as well as 7,863 fetal QRS complexes were detected. Assuming the maximum width of the maternal QRS complex as 100 ms and that of the fetal as 40 ms, the number of complexes without feto-maternal coincidence was 2,415 maternal and 5,142 fetal.We used quantitative measures  related to the Signal-to-Noise Ratio (SNR) to evaluate the sufficiency and diversity of the research data to clearly distinguish between useful signal components and contaminating interferences in the abdominal signals. These indices allow the evaluation of the mutual amplitude dependencies of maternal and fetal components on each other and their relation to other noise components in the aECG signals Figure .N in the abdominal signal can be presented as an average signal power outside the QRS complex locations given by:Assuming that the signal powers of P and T waves in the fetal and maternal complexes are negligible, the average power of interferences PL is the number of samples in the abdominal signal x(k) that are not associated with any QRS complex.where M is given by:Additionally, assuming that there is no correlation between the useful signal and the noise component, the average power of the maternal signal PJ is the number of detected maternal QRSs that do not overlap the fetal QRSs; SM is the width of the maternal QRS (100 ms); and FPM(i) is the timing of the i-th maternal QRS occurrence.where F is:Similarly, the average power of the fetal signal PI is the number of detected fetal QRSs that do not overlap the maternal ones, SF is the fetal QRS width (40 ms), and FPF(i) is the timing of the i-th fetal QRS occurrence.where Based on these values, the WMF index relating the maternal and fetal components can be defined as follows:and similarly, the WM and WF indices describing the mECG signal and fECG signal powers in relation to the power of the interfering components, can be respectively defined as follows:From the analysis of these indices based on the results obtained from the four abdominal signal channels, it could clearly be seen that for the research data used, the dominant signal was the mECG, whose amplitude was always more than three times higher than that of the noise component signal . Similarly, its relation to the fetal signal, as indicated by the WMF index, exceeded 7 dB on average (range from 1.8 to 11.9 dB). The WF indicator, informing the relationship between fetal QRSs and the noise component, varied very considerably. Its average value was 3.9 dB, but there were cases where the WF oscillated within almost 7 dB , and in cases when WF was close to only 0.5 dB the fetal QRS level was comparable to the amplitude of interferences.In this section, we present our evaluation results of the fECG signals extracted from aECG signals acquired by using 4 abdominal electrodes. The fECG signals acquired by means of the Fetal Scalp Electrode (FSE) were used as reference. This direct signal was used for the verification of the extracted signal by comparing the fHR values determined from each of them.In the reference and extracted fECG signals, the R waves were detected and the intervals between subsequent peaks  were determined. Fetal HR was then calculated as the number of the RR intervals in 1 min.Figure 14) show the details of the fHR waveforms shown in Figures In this study, we used two types of moving average filters (MAF). For the dataset overview depicted in Figure Figures For other recordings, however, the results were different. In the case of the real09, real10, and real11 recordings  to achieve this task: Sensitivity (Se), Positive Predictive Value (PPV), Accuracy (ACC), and F1  , and 95 % of the limits of agreement as the mean difference (2 SD), or more precisely, (1.96 SD). It is expected that the chosen limit of 95 % contains 95 % of the variances between the two tested methods: the reference and estimated fHR values in our case. Therefore, the fHR values determined from the extracted fECG signals are assumed to be accurate, such that 95% of all of them fall in the range of ± 1.96 SD  for the extraction of the fECG signal  based method;Detection of the T-waves and their amplitude calculations. It is important to emphasize that due to the very low T-wave amplitudes, the mean of 30 Heartbeats (Cardiac Cycles) was used to calculate the T/QRS ratios.In addition to fHR determination and its variability (HRV), we focused on the morphological analysis of the estimated fECG signals extracted from the abdominal records, with primary emphasis on ST segment Analysis (STAN) and T/QRS determination. These capabilities have the potential to improve the diagnosis of fetal hypoxia  and decrease the number of unnecessary surgical terminations of pregnancy (cesarean sections). Besides, as currently there are no gold standards available for STAN, we are unfortunately unable to perform the quantitative verification and evaluation of the efficacy of our results. Consequently, our initial tests in this article include only the results for the T/QRS determination without deeper statistical analysis. The ST segment is considered to be the most variable part of the fECG signal. The changes in its duration and morphology may indicate pathological states. In particular, the elevation of the ST segment and the T-wave amplitude increase occur when the cardiovascular adaptation to hypoxia is no longer sufficient. STAN automatically detects and alerts changes that are related to the risk of fetal hypoxia  the user must select one of the graphs to be analyzed. The details for samples from different recordings used for the experiments carried out in this paper are shown in Figure 26.Figure The graphs in Figure The results presented in section 6 prove that the extracted fECG signals fom aECG signals could serve as a valuable source of information and that using advanced signal processing methods, such as ICA and PCA, could enable the utility of this type of monitoring in clinical practice as an alternative to the conventional Doppler-based technique. Our statistical analysis results revealed that these non-adaptive methods did not perform very well for some of the recordings . The results of some unsatisfactory examples  are depicted in Figure We also need to emphasize that for reliable Morphological Analysis , the quality of the analyzed fECG signal is of paramount importance and must be adequately high. For this reason STAN is currently performed exclusively on the fECG signals acquired invasively. Such recordings have higher Signal-to-Noise Ratios and therefore the amplitude and shape of the essential components of the fECG signals (T-waves and QRS complexes) are not distorted by contaminating signals. Considering these facts, the only records that could be used for NI-STAN in our work were real01, real02, real05, real08, and real09.There are two main options for achieving sufficient signal quality of fECGs that is sufficient for NI-STAN: (1) by improving the quality of the input recordings (as discussed in chapter 6.4), or (2) utilizing more robust techniques, such as the aforementioned hybrid methods leveraging the combined capabilities and advantages of non-adaptive and adaptive methods. For NI-STAN, the key to success is the standardization (optimization) of the electrode placements that has to accommodate varying circumstances, especially gestation age and fetal position in the uterus.This article is focused on two popular non-adaptive signal processing methods, even though the adaptive techniques have proven to be effective tools for fECG signal extraction from aECG signals. The adaptive methods are theoretically more suitable but have their drawbacks, mainly the need for the additional thoracic electrodes and leads (which restrict the pregnant woman's movements) and thus limit the utility of such methods in clinical practice. Furthermore, the efficiency of the adaptive methods is closely dependent on the adaptive filter's settings. The optimal filter settings vary with gestational age, fetal position in the uterus, etc.  methods. With this approach we would benefit from the advantages offered by both methods. The hybrid methods would be more accurate for detailed Morphological Analysis while making use of the abdominal recordings alone. Our approach would be based on extracting the reference fECG signals from the aECG signals using the ICA and PCA methods. However, we needed to test adaptive and non-adaptive methods separately at first before reaching that goal.The evaluation of our results was based on comparing the estimated signals with the reference fECG signals recorded by means of the FSE. Five of the recordings used in our experimentation are currently available from the Abdominal and Direct Fetal Electrocardiogram Database Jezewski et al. . The othWe obtained the reference fECG signals from raw data in the Database and subsequently processed and annotated them in cooperation with a team of experts including both engineers and clinicians. The R-wave peak detection was semi-automatic. First, we used the Continuous Wavelength Transform to detect the R-waves. Subsequently, we went through all the recordings and visually inspected the detection of the RR intervals to eliminate any automatic errors, see Figure In this paper, we compared the effectiveness of two popular non-adaptive signal separation techniques, the ICA and PCA methods, to extract fECG signals from aECG signals noninvasively. The performance of these conventionally used methods was improved by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. Currently, the non-adaptive methods are successfully used for fHR determination. However, there are still great research opportunities and scope for improvements, especially in terms of further Morphological Analysis. We provided the performance evaluation results on real data based on comparing the extracted signals with the reference fECG signals recorded by means of the FSE. The novelty in our work is centered around obtaining the reference signals from raw data that were subsequently processed and annotated by a team of experts. Thus, in contrast to the other results reported in the field . The ICA and PCA methods have the potential to be utilized as early as the 20th week of pregnancy since the signals are too weak for the extraction before that period. As a fundamental limitation in the utility of the ICA and PCA methods for extracting fECG signals, the case of multiple pregnancy must be mentioned since these non-adaptive methods may have problems in extracting the components with same frequencies (fHRs of each fetus in the uterus). Similarly, problems could arise in pathological cases when the maternal and fetal heart rates increase/decrease by the same amount. Generally, the fetal beating frequency is about 2 times greater than the maternal rate. However, in case of fetal bradycardia or maternal tachycardia, these frequencies might have almost the same value.At present, a great deal of attention is being paid to single-channel methods. It is obvious that for more non-invasive STAN, multichannel recordings are needed. A Single-channel signal is likely to be of low quality due to several factors such as changes in the fetal position and others. Single-channel methods are sufficient to determine the fHR, but for deeper Morphological Analysis they are rather inadequate.We envision that a multichannel system is essential for the advanced analysis of non-invasively acquired fECG signals. In our future research, we aim to explore the combined utility of non-adaptive and adaptive signal processing methods in order to achieve better results. This approach could lead to the development of a new diagnostic method: non-invasive STAN (NI-STAN).The study protocol was approved by the Ethical Committee of the Silesian Medical University, Katowice, Poland (NN-013-345/02). Subjects read the approved consent form and gave written informed consent to participate in the study.RM, RK, JJ, RJ, JM, MF, JN, PJ, and HN proposed the system idea, wrote, and edited the manuscript. RM, MF, and JN performed the experiments, developed, tested, and validated the software. HN, JJ, and PJ critically evaluated the scientific validity of the proposed system and the acquired vital data, wrote the manuscript, and performed its final edits.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AF and handling Editor declared their shared affiliation.