The conceptual framework used to develop the Bayesian hierarchical model. The model comprised three common key events shown in green selected based on topology analysis and expert knowledge to describe developmental neurotoxicity. These served as a basis to define the structure of the quantitative model. Appropriate data were collected to fit the model with the aim of predicting the probability of whether a compound triggers the biological path. •A developmental neurotoxicity Adverse Outcome Pathway network was simplified.•Common key events were chosen based on topology analysis and expert judgement.•Quantification of causal relationships was informed by key event relationships.•Various types of information were integrated for probability prediction.•Bayesian hierarchical modelling was applied for hazard identification. in silico and in vitro information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as  Whilst NAMs for DNT are not yet standardised or required by regulatory authorities, they can provide valuable mechanistic insights regarding potential developmental neurotoxicants Neurodevelopmental disorders such as impairment of learning and memory, and cognitive dysfunction, are of serious concern due to the health risks and consequences on the developing brain resulting from exposure to exogenous chemicals Frameworks are required to organise information from NAMs to predict complex toxicological endpoints, including neurotoxicity and DNT. A promising approach to organise DNT information and subsequently use the information to develop a predictive model is provided by the adverse outcome pathway (AOP) concept Quantitative AOPs (qAOPs) and qAOP networks are increasingly being modelled using probabilistic methods in vivo and in vitro data, and the potential for Bayesian machine learning to investigate chemical-induced DNT, the aim of this investigation was to quantify a simplified, reduced version of the AOP network for neurotoxicity developed by Spînu et al. in silico and in vitro data. This investigation also aimed to explore whether Bayesian hierarchical modelling is fit-for-purpose in chemical risk assessments informed by qAOP models.Given the limited availability and heterogeneity of DNT information, i.e., 22.1e-Resources to Revolutionise Toxicology: Linking Data to Decisions”, held at the Lorentz Center  in October 2019 https://aopwiki.org/aops/13; The AOP network for neurotoxicity developed and analysed by Spînu et al. 2.2in vitro studies in vitro studies differed in the concentration ranges used, type of viability assay applied, plating densities of the cells, calculation of the effective concentration (EC), and the exposure period (five days vs 12 days). Synaptogenesis was measured by a battery of high content imaging and microplate reader-based assays, while the viability was based on the number of cells per field or determined in sister plates using a luminescent assay x values of the range of higher concentrations tested, and associated responses, for valproate were retained. The difference in the concentration range and time of effect shows that impacts on synaptogenesis are observed at lower levels than impacts on neural network formation. This concentration–response concordance fits the expected pattern for a causal relationship between the two key events in an AOP Two in vivo studies summarised and evaluated in a literature review by Mundy et al. in vitro and/or in vivo studies that showed a decreased or increased level of BDNF for each chemical included in the test set. Compounds were further classified as active/inactive for the CKEs relating to the decrease of synaptogenesis and neural network formation; this was based on the selectivity and specificity identified from the corresponding in vitro studies taking into account cell viability and the results of toxicity assays.The compounds were assigned as positive or negative for DNT induction based on the in silico models based on curated SMILES Other data collected included the calculated logarithm of the distribution coefficient , a measure of lipophilicity for ionizable compounds; blood–brain-barrier (BBB) permeability; and the capability to bind to the P-glycoprotein (P-gp) transporter, i.e., if the compound acts as a P-gp inhibitor or substrate, or is (non-)active against P-gp. Predictions for BBB permeability and P-gp interactions were made using in silico models for e.g., inorganic compounds, and there were compounds with no available evidence in the literature for the reduction of BDNF. In addition, inactive compounds in any of the in vitro assays were treated as missing information. Eight compounds contained complete details for this biological path. The final data set is provided in The final data set contained missing information given by the 2.3Exploratory data analysis was applied to analyse the data collected and to summarise their main characteristics, including the types of variables ; the shape of the empirical distributions  of dependent variables; correlations between the variables using the Pearson correlation coefficient; the distribution of the missing values; chemical characteristics described by physicochemical properties associated with the categorical classification, i.e., positive/negative, of the CKEs and DNT; and the presence of (un)balanced categories in the dataset. The analysis was conducted to help choose the appropriate priors and define regression models and the overall strategy for computational modelling.2.4A Bayesian hierarchical model is a combination of sub-models in which the parameters are informed by a common hyperprior. The model is structured into exchangeable levels/groups, e.g., CKEs, categories of chemicals, taking into account the inter-independencies and interactions between those groups, leading to improved inferences A single nested partial pooled Bayesian hierarchical model was formulated as outlined in Each The missingness was treated by Bayesian imputation. Each missing data point in the design matrix The matrices of The three CKEs were generated from a Bernoulli distribution of deterministic relationships estimated from the data. The AO was also generated from a Bernoulli distribution of the deterministic relationship that summed up the logistic regression of all Model building and inference was carried out using PyMC3 version 3.9.3 2.5The Bayesian hierarchical model had 783 parameters in total for a dataset of 88 compounds that described the CKEs as well as each chemical independently . The num(1)The Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV) statistic, which measures the predictive accuracy of a Bayesian model by fitting a Pareto distribution to the upper tail of the distribution of the importance weights to estimate pointwise predictive density (2)The Widely applicable information criterion (WAIC), which is an alternative approach for estimating the out-of-sample expectation based on pointwise calculations considered to be asymptotically equal to leave-one-out cross-validation (3)The Brier score To show that the model is data-driven and not significantly influenced by the choice of hyperpriors, a sensitivity analysis was performed. Two additional weakly-informative hyperpriors were chosen. The comparison of the models was conducted for three different metrics including:In addition, model fitting was evaluated for its convergence to the target distribution using several statistical measures. The Gelman-Rubin diagnostic, also known as the R-hat statistic, measures how similar different MCMC chains are, i.e., within and between chains and thus whether the chains converge to the same distribution 2.6Four different metrics were employed for the evaluation of the model performance:(1) Sensitivity, also referred to as true positive rate, measures the ability of a model to correctly detect a positive sample as positive:(2) Specificity, also referred to as true negative rate, measures the ability of a model to find all negative samples:(3) Accuracy measures the number of correctly predicted data points out of all the data points:where FN stands for false negatives, FP stands for false positives, TN stands for true negatives, TP stands for true positives.(4) Balanced accuracy quantifies the average of sensitivity and specificity and is robust against data imbalances between the two classes.These metrics were reported for two CKEs and the AO. It was not possible to evaluate the CKE reduction of BDNF because of the presence of the missing information about the activity of the studied compounds for this protein.3The exploratory data analysis allowed for a better understanding of the data collected and utilised for the Bayesian analysis . The staThe posterior distributions,  and posterior predictive distributions were obtained from the Bayesian analysis. The posterior distributions represent the evidence provided by the data combined with the prior that incorporates our knowledge before analysing the data. The posterior probability distributions for each CKE, including the AO, are summarised in in vitro studies for the other two CKEs and expressed as a Bernoulli distribution to describe the outcome in a Boolean way, as explained in the Materials and methods section. Two thresholds derived from the results of the predicted posterior distributions were used to classify the compounds for the low, medium or high probability of inducing a CKE and the AO. Such a classification might be used for screening and prioritisation purposes. All compounds with a probability between 0.59 and 0.60 for posterior predictive distributions were classified overall as having a medium probability of inducing a reduction of BDNF . The full results for the prediction of CKEs and the AO are provided in The posterior predictive distributions are given by the binary classification of compounds that were analysed against the likelihood. The binary classification was informed by the literature review for the CKE reduction of BDNF and by the  of BDNF . This mi of BDNF . For the of BDNF . An over of BDNF .B. Relatin silico model used for the prediction of P-gp, which classified the compound as an inhibitor, substrate and active, (2) the absence of data for the CKE of reduction of BDNF, (3) the in vitro assays that identified it as active, (4) the chosen threshold for classification. However, the large CI associated with the predicted mean value of 0.36–0.99 emphasises the caution required in making decisions for this compound.The sensitivity analysis underlined that weakly-informative priors did not have a significant influence on the model, which is instead data-driven . The ove4The AOP paradigm has provided an opportunity to organise data and information for over a decade. AOPs have developed from the original “linear” concept exemplified by Ankley et al. The approach for modelling the AOP network was based on Bayesian modelling. There are three ways to assign probabilities in a Bayesian model depending on the research question, available evidence and mathematical approach: (1) making subjective assessments (expert judgement), (2) using empirical probabilities based on observed data, and (3) constructing a parametric probability model Bayesian machine learning has been increasingly applied in toxicology using the parametric approach, including: improving the modelling of physiologically-based kinetic (PBK) models of inter- or intra-individual variability across a population in vivo mammalian studies. The requirement for reliable and meaningful data is especially relevant for the mechanistic understanding of the endpoints related to DNT. Thus, the development and use of in vitro models could facilitate the generation of relevant and reliable mechanistic data as indicators of DNT, especially when data have been produced under good cell culture practice in vitro testing strategy has been proposed and evaluated for both the purposes of screening and prioritisation, and hazard characterisation A qAOP model is not only knowledge-based but also data-driven. Thus, initiatives that focus on data generation, collection and quality control are crucial for the development of qAOPs. A good example of a data driven approach to support DNT evaluation is the collaborative Developmental Neurotoxicity Data Integration and Visualisation Enabling Resource (DNT-DIVER) project of the National Toxicology Program (NTP) of the National Institute of Environmental Health Sciences (NIEHS) The model proposed herein analysed a series of compounds for a simplified biological path predicting the probability of inducing each key event. The model represents an attempt to describe a complex endpoint on the basis of limited NAM data. The best predicted event was the AO of DNT . This unA Bayesian model applied to toxicology should answer causal, rather than purely associative, questions that cannot be computed from data alone. Causal inference can be defined as a way of predicting what would happen, or what might have occurred, to produce an outcome 1.A qAOP model must allow for an objective scientific evaluation of the potential toxicity of chemicals 2.in vitro information and physicochemical properties can inform better toxicity predictions associated with DNT as presented herein. It can also include missing information, and can cope with the complexity of mechanistic knowledge. Thus, the Bayesian modelling approach offers an understanding of the likelihood of effects and the level of perturbations at different biological levels. It also represents a means to determine whether the available information is sufficient to address a question, and what kind of additional information might be needed. It can also help to screen a large number of compounds and identify tailored toxicological tests for individual compounds or mixtures.There is a demand for strategies that better integrate the variety of information sources, including NAMs, available in risk assessment. The aim is to achieve an informed assessment and a structured decision-making process 3.in vivo, had a predicted probability of 76% to induce DNT because of the hierarchical structure even though it had missing information.A Bayesian hierarchical model is an extension of regression in which data are structured in groups and coefficients can vary by the group Modelling a qAOP in a Bayesian  manner has several advantages, including:A drawback of Bayesian models is often considered to be the subjectivity of priors. Even though the selection of priors is always debatable, ultimately, the role of priors is to improve the predictions, e.g., the process of data shrinkage towards a prior group-mean to represent the common group distribution Further modelling efforts should also consider ADMET, kinetics and types of exposure (acute vs chronic). Since DNT effects occur primarily in the offspring following exposure (of the parent) to chemicals, it is essential to study metabolism and other kinetic properties of chemicals, e.g. by using pregnancy-specific PBK models. In other words, once other important kinetic and dynamic types of information, as well as tools to facilitate this understanding, become available, the Bayesian model could be updated accordingly and/or coupled with a PBK model. Importantly, the biological path studied herein does not include any MIEs of the initial AOP network. Linking the Bayesian model to quantitative structure–activity relationship (QSAR) models that evaluate the MIEs could expand its applicability domain.in vivo models towards a better-informed and improved assessment of human health and environmental risks resulting from chemical exposures. Thus, there is momentum to bring CRA up to date with modern technologies and  tools such as qAOPs. Shifting CRA towards probabilistic thinking allows details regarding the level of uncertainty and confidence to be accommodated in the decision-making process. Therefore, a Bayesian hierarchical model can contribute to the paradigm shift towards a mechanistically-driven assessment in modern toxicology and translate a qualitative AOP into a quantitative computational predictive model for potential use in CRA. This study has shown that a qAOP model can serve as a bridge between NAMs and the current regulatory landscape that can help to translate the new science into internationally accepted standard methods with applications in CRA. As such, the model developed in this study demonstrates that qAOPs can become informative tools to determine the potential hazards arising from chemical exposure, and hence support actions to prevent or reduce the associated risks.Today, chemical risk assessment (CRA) tends to be pragmatic, with different methods being combined within an IATA 5A Bayesian hierarchical multiparameter model was developed for a simplified AOP network for DNT derived from a topology analysis, combined with expert judgement. The modelling workflow achieved three goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a DAG to simulate the biological path. The model itself, derived from the workflow, can be used to predict the DNT potential of a compound as well as the probability of triggering each of the three upstream CKEs with the associated uncertainty resulting from different sources of data variability. In addition, the model can guide further data generation to better understand DNT mechanistically and support decision-making in the regulatory assessment of chemicals, e.g., by supporting the development of chemical categories and the application of read-across. The methodology can be applied to other endpoints of interest and can be updated to accommodate new evidence. Future directions include the addition of other biological paths and kinetic information to extend the applicability domain and utility as a qAOP model.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Motion adaptation has been attributed in flying insects a pivotal functional role in spatial vision based on optic flow. Ongoing motion enhances in the visual pathway the representation of spatial discontinuities, which manifest themselves as velocity discontinuities in the retinal optic flow pattern during translational locomotion. There is evidence for different spatial scales of motion adaptation at the different visual processing stages. Motion adaptation is supposed to take place, on the one hand, on a retinotopic basis at the level of local motion detecting neurons and, on the other hand, at the level of wide-field neurons pooling the output of many of these local motion detectors. So far, local and wide-field adaptation could not be analyzed separately, since conventional motion stimuli jointly affect both adaptive processes. Therefore, we designed a novel stimulus paradigm based on two types of motion stimuli that had the same overall strength but differed in that one led to local motion adaptation while the other did not. We recorded intracellularly the activity of a particular wide-field motion-sensitive neuron, the horizontal system equatorial cell (HSE) in blowflies. The experimental data were interpreted based on a computational model of the visual motion pathway, which included the spatially pooling HSE-cell. By comparing the difference between the recorded and modeled HSE-cell responses induced by the two types of motion adaptation, the major characteristics of local and wide-field adaptation could be pinpointed. Wide-field adaptation could be shown to strongly depend on the activation level of the cell and, thus, on the direction of motion. In contrast, the response gain is reduced by local motion adaptation to a similar extent independent of the direction of motion. This direction-independent adaptation differs fundamentally from the well-known adaptive adjustment of response gain according to the prevailing overall stimulus level that is considered essential for an efficient signal representation by neurons with a limited operating range. Direction-independent adaptation is discussed to result from the joint activity of local motion-sensitive neurons of different preferred directions and to lead to a representation of the local motion direction that is independent of the overall direction of global motion. Neuronal adaptation, i.e., the adjustment of the response properties of neurons according to their recent stimulus history, is a general feature of neurons . Motion From a functional perspective, adaptive processes in sensory systems often come into play when a very large stimulus range must be mapped onto the operating range of neurons limited by the underlying biophysical mechanisms. Adaptation then leads to the neuron’s working range being adjusted to the prevailing stimulus conditions. Although this functional aspect might play a role, various other functional consequences were also discussed with regard to movement adaptation. These range from the energy required for signal representation  to an enThe experimental evidence for all these conclusions is based for methodological reasons mainly on electrophysiological recordings from LTPCs, i.e., at a processing stage where the local movement detectors sensing motion in a retinotopic way across the entire visual field have already been spatially pooled to a large extent reviews: . WhereasTherefore, we designed a novel stimulus paradigm, which allowed us to exclude largely the effects of local motion adaptation and to compare the consequences of adaptation with motion stimuli that avoided local motion adaptation to those evoked by stimulus conditions that are identical in all other aspects, except that local motion adaptation was not excluded. In this way, we could pinpoint by intracellular recording from a prominent LPTC, the HSE-cell , for the2, respectively. To stimulate the right eye, the monitor was placed at the right side of the animal. The perpendicular from the fly’s eye to the monitor screen was 12.7 cm and virtually divided the screen into four quadrants of different size  at a rate of 144 fps and a luminance of white and black stimulus areas of 250 and 2 cd/mTo investigate the impact of adaptive motion on the response gain of the HSE-cell, a 500 ms reference and a 500 ms test stimulus were presented before and after a 3 s adaptation motion stimulus, respectively. A 1 s interval without any motion separated the end of the reference stimulus and the start of the adaptation period; the last frame of the reference stimulus was constantly shown in this interval. In contrast, the test stimulus directly followed the adaptation period . Two secTo disentangle the adaptive effects caused by local motion adaptation from those caused by global adaptation, we designed two types of motion stimuli, which had identical overall stimulus strength, but only differed in the adaptation they induced: one elicited local motion adaptation (LA conditions), while the other avoided local motion adaptation (nLA conditions). This means that it had to be ensured that in the LA case the same areas in the visual field were repeatedly stimulated and in the nLA case exactly that had to be prevented, whereby the overall stimulus was not allowed to differ—apart from statistical details. This was accomplished by subdividing for both stimulus types the 3 s adaptation period into six consecutive 500 ms time intervals and by using stimulus patterns composed of black dots on a white background , with alTo reduce flicker responses of the recorded neuron due to a synchronous appearance and disappearance of the different dots when they begin and end their movements, each started and ended moving at a different phase randomly chosen from 16 phases equally distributed within the 500 ms interval. The flickering effects were further reduced by gradually increasing and decreasing the gray values  or the null direction  of the right HSE-cell. Accordingly, the fly was confronted with four different stimulus conditions: LA and nLA with motion during the adaptation period in the preferred direction and null direction, respectively. To adapt the same location for the two adaptation directions, under the PD and ND condition a dot traversed the same segment in the opposite direction. For the LA condition, the segments traversed by the dots were identical for the reference, adaptation, and test phase. Since for the nLA condition repetition of movement in the same segment was avoided, the segments that the dots traversed never matched over the entire reference, adaptation, and test phases. As a consequence, the traversed segments had to differ between the reference and test stimuli during nLA motion. Therefore, pairs of nLA motion stimuli were generated where the reference and test patterns were exchanged between consecutive stimulus presentations to ensure that the test and the reference stimulus were overall the same apart from their temporal order. Eight pairs of stimuli of different dot constellations were generated to reduce the effect of pattern-specific modulations in the average neuronal response that can be observed in LPTCs if stimuli contain low spatial frequency components .Calliphora vicina) from our laboratory stock were dissected according to standard procedures for intracellular recording of HSE-cells  was applied via the indifferent electrode, which was connected via an electrode holder and a silicone tube to a syringe, to prevent the brain from drying out. The proboscis and the antennae were removed. Tissues bridging both hemispheres were partially removed to make the esophagus accessible, which was pulled out of the head and fixed to the dorsal part of the thorax with wax. The wounds were waxed. Air sacks, fat bodies, and part of tracheas were removed to make the axon of the HSE-cell accessible. The electrode for intracellular recording was pulled from a borosilicate thin-walled glass pipette  with a Flaming/Brown Micropipette Puller . The electrodes were filled with 1 M KCl and had resistances ranging from 27 to 45 MΩ. Intracellular recordings were obtained from the axon of the right HSE-cell. The cell could be identified unambiguously by its characteristic response properties  and the test response (Rtest) were calculated as time-average over the respective last 400 ms of the stimulation period, thus not taking into account the initial 100 ms response transients. Similarly, the responses at the beginning and the end of the adaptation period  were calculated as an average over 400 ms with the averaging starting only 100 ms after motion onset. Changes in the HSE-cell response evoked by motion adaption were assessed in two ways:The analysis of recorded neuronal responses, the statistics as well as the model simulations were done with programs written in Matlab, 2019b. 13 HSE-neurons from different flies were recorded . Another set of recordings using the same design, but not exactly the same stimuli, were obtained in the Ph.D. project of (1)adpt) was calculated as the difference between average response at the end of adaptation (Readp) and the average response at the beginning of the adaptation period (Rbadp) and normalized to the average reference response:The relative amplitude reduction of the response during adaptation (RAR(2)How much the test response was smaller than the reference response was determined in a similar way as the normalized difference between the test and reference response:To determine the time constant for the decay in the response amplitude during the adaptation period, the HSE-cell responses were fitted by the formulaRHSE being the average membrane potential of 13 HSE-cells, t the time, τ the approximated time constant, and a, b are parameters for curve fitting with the scaling factor a and b the level where the exponential curve settles.with The experimental data were interpreted on the basis of a computational model of the visual motion pathway of flies, which has been developed in two previous studies , 2017 apAccording to the overall structure of the fly visual system, the model of the visual motion pathway is composed of successive layers of retinotopic arrays of model photoreceptors (PRs), the lamina with its characteristic large monopolar cells (LMCs), the medulla with the elementary motion detectors (EMDs), and the lobula plate with a characteristic LPTC, the HSE-cell, integrating the output signals of a large array of EMDs . The modThe peripheral visual system of the model consisted of PRs with low-pass-like temporal characteristics and the LMCs with their high-pass-like temporal characteristics. It was modeled according to In the medulla, the outputs of the ON and OFF pathways, respectively, were fed into the adaptive motion detector model. This motion detector model was of the correlation-type consisting of two mirror symmetric half-detectors sensitive to motion in opposite directions . Motion EMD) a low-pass filtered version of them. During hyperpolarization the summated EMD response was just scaled to the membrane potential as has been measured in the experiment:In the LPTC model, for simplicity, we assumed that the outputs of the local motion detectors are linearly summated in the lobula plate. Here, both half-detectors, i.e., ON and OFF, responding best to preferred-direction motion of the LPTC contributed to the sum with a positive sign, whereas both half-detectors responding best to null-direction motion of the LPTC contributed with a negative sign . The simmV representing the scaling factor transforming the model response into mV, and Fratio representing the ratio between the asymmetric depolarizing and hyperpolarizing response amplitude—as averaged across all neurons used for our analysis—to a given stimulus moving in the preferred and null direction, respectively. FmV = 842 and Fratio = 1.97 were determined by scaling the Rref of the model under nLA conditions to that of the cell. The time constant of the first-order low-pass filter (indicated by LPF in eq.4) was 1,170 ms as determined from the neural responses averaged across all cells used for our analysis under the condition without local motion adaptation (see Eq. 3). Fadapt = 0.32, the scaling factor for the magnitude of the adaptive decay was determined by scanning through the parameter space in steps of 0.01 for the minimal difference between the model response and the cell response.with FTo pinpoint the role of local vs. wide-field motion adaptation for shaping the responses of LPTCs we specifically designed a visual stimulus paradigm, which allowed us, while stimuli being identical with respect to their overall motion strength, to include (LA) or exclude (nLA) local adaptation. The graded membrane potential changes of the HSE-cell, recorded intracellularly under our nLA conditions were, therefore, shaped only by wide-field motion adaptation at the level of this LPTC. However, since wide-field motion adaptation cannot be avoided when analyzing motion adaptation on the basis of HSE-cell responses, the responses obtained under the LA conditions were not only the consequence of local motion adaptation, but inevitably also of wide-field adaptation. Therefore, we inferred the characteristics of local motion adaptation presynaptic to the HSE-cell indirectly by comparing the corresponding responses obtained under nLA and LA conditions.time constant of wide-field adaptation; it was estimated on this basis and approximates 1.17 s (see gray curve in p = 3.31 × 10–4 < 0.001). Accordingly, also the test response for LA (p = 1.65 × 10–5 < 0.001). This difference between the HSE-cell responses obtained under the nLA and LA conditions, respectively, reflects the consequences of local motion adaptation. The time-dependent response profiles and mean adaptation-dependent response changes of the HSE-cell are qualitatively well reflected by the corresponding model responses (During PD motion adaptation  the respesponses . In the p = 0.014 < 0.05). Without local motion adaptation it even increased slightly by about 7% in relation to the reference phase (p = 2.08 × 10–5 < 0.001).During adapting motion in the ND , the HSEce phase  and decrce phase  center. p = 7.13 × 10–4 < 0.001, test response p = 9.72 × 10–5 < 0.001), it is suggested that wide-field motion adaptation is strongly direction dependent. This might be a direct or indirect consequence of the depolarization level of the cell. In contrast to wide-field motion adaptation, which can be characterized with our stimulus paradigm largely uncontaminated by local motion adaption, this is not possible for local motion adaptation. As a proxy for the properties of local adaptation, we determined the difference in adaptive effects between LA and nLA stimulation based on the simplifying assumption that the two adaptive components superpose linearly , local motion adaptation can be suggested to be largely independent of the direction of motion, unlike the highly direction dependent wide-field motion adaptation. since there was only little wide-field ND motion adaptation, if local adaptation was largely prevented . The time constant was estimated as 1.47 s  that pool the outputs of local motion-sensitive cells from large parts of the visual field . By intr2+ accumulation in the dendrite of the HSE-cell (Various aspects of motion adaptation have been characterized in other contexts and with different types of stimuli leading to a variety of interpretations. How do these concepts relate to our conclusions? HSE-cell  and causHSE-cell .In the previous studies, contrast gain modulation induced by motion adaptation has been assessed by varying the pattern contrast of the reference and the test stimuli presented before and after a strong preferred direction adaptation and by comparing the response amplitude for each contrast condition before and after adaptation . The dirWhile motion adaptation has originally been suggested to be a consequence of the adaptation of the motion detector time constant , later eMotion adaptation in the insect visual motion pathway has been studied mostly based on recordings of wide-field motion-sensitive LPTCs. These studies used either relatively simple system-analytical stimuli , or moreHowever, the neuronal representation of brightness and motion differ in at least one fundamental aspect, leading us to suggest an additional novel functional role of motion adaptation. Unlike local brightness, local motion is represented by four subtypes of local motion-sensitive neurons each tuned to one of four cardinal directions . If motiWe have refrained here from assigning the individual elements of the computational model to specific local interneurons in the visual motion pathway. Although the properties of important cellular elements involved in motion detection is increasingly being elucidated thanks to innovative genetic, imaging, and electrophysiological approaches  mapping The local motion-sensitive cells are then spatially pooled by the population of LTPCs, which have either a predominantly horizontal or vertical preferred direction . BecauseThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.JL, MN, RK, and ME conceptualized and designed the project and wrote the manuscript. JL performed the preliminary electrophysiological experiments, developed the model, and conducted the model simulations. MN collected the electrophysiological data shown in this study. JL and MN analyzed the data. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The ability of various commercial fibrous carbon materials to withstand stress and conduct heat has been evaluated through experimental and analytical studies. The combined effects of different micro/macro-structural characteristics were discussed and compared. Large differences in mechanical behavior were observed between the different groups or subgroups of fibrous materials, due to the different types of fibers and the mechanical and/or chemical bonds between them. The application of the Mooney–Rivlin model made it possible to determine the elastic modulus of soft felts, with a few exceptions, which were studied in-depth. The possible use of two different mechanical test methods allowed a comparison of the results in terms of elastic modulus obtained under different deformation regimes. The effective thermal conductivity of the same fibrous materials was also studied and found to be much lower than that of a single carbon fiber due to the high porosity, and varied with the bulk density and the fiber organization involving more or less thermal contact resistances. The thermal conductivity of most materials is highly anisotropic, with higher values in the direction of preferential fiber orientation. Finally, the combination of compression and transient thermal conductivity measurement techniques allowed the heat conduction properties of the commercial fibrous carbons to be investigated experimentally when compressed. It was observed that thermal conductivity is strongly affected under compression, especially perpendicular to the main fiber orientation. The competitiveness in many industrial applications of carbon felts (CFs), i.e., nonwovens based on carbon fibers, is due to the combination of unique structural and physical properties, giving them in particular extreme lightness together with good mechanical strength and excellent chemical and heat resistance. The main aspect to be considered when selecting a fibrous carbon structure for a given application is the level required for these materials properties. For example, the mechanical response of different nonwovens to transverse compressive forces plays an important role in almost all applications involving liquid infiltration processes for the preparation of composites , whereasTheoretically, changes in any of the raw materials and manufacturing steps should result in subtle differences in the structure and performance of the final fibrous carbon materials. Previous studies have examined the influence of differences between types ,6 and pr−1·K−1 [−1·K−1 [As previous studies on thermal conductivity have shown , the ani−1·K−1 ,23. The  [−1·K−1 . ThermalSince suppliers of fibrous carbons give numerical values for only a few of the physical properties of the materials they sell, such as weight per unit area, average thermal conductivity, felt thickness and carbon content see , choosinThe results of these applied experimental and analytical studies are presented and discussed in this paper. To our knowledge, no other study has dealt so thoroughly with the properties of commercial fibrous carbons. This paper is the third of a series on the broad evaluation of the properties and use of such materials in new or improved applications ,26. The Samples of 18 commercial fibrous carbons see  were usePrior to any measurement on the materials, a preliminary desizing step was performed to guarantee the purity in terms of carbon content of the commercial materials, thus avoiding the risk of an additional and undesirable influence of the sizing layer possibly present at the surface of the fibers. For this reason, slow pyrolysis has always been applied to the samples , before A classical method for studying the compressive mechanical properties of materials is to apply an increasing load to them at a moderate and constant strain rate, and by measuring the resulting deformation. Using this method, stress-strain curves are obtained, from which different quantities can be derived. Despite its destructive nature, this technique is still the most frequently used for both soft and rigid fibrous materials . It is n−1, following the ASTM D5729-97(2004)e1 standard [E (MPa), within the limits of the application of Hooke’s law [F (N) being the compression force applied to a defined cross-section S0 (m2), and where ϵ (dimensionless) is the strain, i.e., the relative deformation ). The yield stress (MPa), also called the elastic limit, has been defined as the maximum stress in the elastic region: it thus corresponds to the end of the elastic behavior and the beginning of the plastic behavior.In the present case, the specimens were studied by axial compression using an Instron 5944 universal testing machine equipped with a 2 kN head see . The comstandard  for the Three cylindrical samples of diameter 44.45 mm (1 ¾ inch) and a thickness measured according to the ASTM D5729-97(2004)e1 standard  by use oA non-destructive technique, based on the damping of mechanical vibrations, was also examined in the case of soft CFs. These materials indeed present mechanical properties that can vary from purely to partly elastic over the entire range of deformation, depending on their manufacturing process.The QMA is a rather recent and uncommon method for determining the elastic modulus of porous materials. The process of measuring the properties of poroelastic materials by this technique is explained in detail elsewhere . BrieflyThe procedure for performing QMA tests is detailed in The Hot Disk technique is a transient plane source method for the rapid measurement of the thermal conductivity of solid, granular or paste-like materials and even liquids. The basic theory behind its principle has been discussed elsewhere . Brieflyxy–in-plane) are considered to be identical , and therefore different from those along the third axis (z—out-of-plane)  [Thermal properties are strongly influenced by the anisotropy of the materials , which makes it difficult to measure them exhaustively using conventional instruments . Indeed, most of them give either the conductivity along only one direction, or an apparent overall conductivity. On the other hand, on the condition that the volume heat capacity of the material is known, the transient plane source method can be applied to the case of anisotropic materials. In the case of the present carbon nonwovens, the thermal properties along two of the orthogonal and main axes  .The measurement protocol and procedure are explained in The objective of this part was to study the mechanical properties of porous nonwoven carbon materials as a function of their structural characteristics. Thus, the capacity of commercial fibrous carbons to withstand the stresses applied in the OP direction was evaluated by two different characterization methods, in order to compare the recovered data.The stress-strain curves of all soft carbon felts (CFs) are shown in The logarithmic presentation of the stress-strain results suggests that at least two compression regimes govern the deformation of soft CFs. These consecutive deformation regimes are important characteristics addressed by several previous studies ,34,35. FEffect of fiber orientation on compression propertiesFiber orientation and felt bulk density influence the uniaxial compressive mechanical properties of all soft CFs studied with the conventional method, and their effects are detailed below:Different fiber orientation distributions have been observed in CFs manufactured by different needle-punching processes in terms of needle-punching density, penetration depth and needle type, as also reported in previous studies ,36,37. AEffect of felt bulk density on compression propertiesHerein, the averaged fiber orientation was studied quantitatively by micro-computed tomography (µ-CT) analysis see . This stAs shown in For rigidized, PAN-derived, soft CFs, a higher compression stress is observed at much lower strain, compared to the previous groups of soft CFs. Differences in manufacturing process, such as the rigid bonds introduced on the upper and lower surfaces of initially soft felts, and between two or more sheets needle-punched on top of each other along the OP direction , preventIn contrast to soft CFs, where the distribution of fiber orientations has a major effect, the mechanical properties of rigidized soft CFs and rigid boards depend mainly on the bond strength at the interface between the fibers and the carbon binder. The phenomenon of binder distribution and accumulation at the fiber intersections contributes, as expected, to the overall mechanical properties of the materials. As previously reported, this is due to the increase in effective load transfer between the fibers when exposed to external compression or other mechanical forces . TherefoIn the case of rigidized soft CFs and rigid boards, we have considered the materials evaluated as initially linear elastic or Hookean, i.e., where the force required to compress them to a certain thickness is proportional to that thickness . In contThis compression behavior of fibrous nonwovens was first studied by Wyk , who conAs described earlier , the sofThe elastic modulus is estimated in practice simply by fitting the Moonley-Rivlin model to the experimental data . When stHyperelastic materials can be described under the assumption of the existence of a deformation energy potential . In the z-axis. With a known density of strain energy, it is possible to determine the dependence of the nominal stress on the strain. Therefore, using the compressive stress F is the force applied on the sample of cross-section S0, the modified Mooney-Rivlin equation reads:C10 and C01 to be determined. Therefore, a numerical function was created for the simultaneous identification of the two Mooney-Rivlin constants, using Equation (4):The left-hand side of Equation (3) represents the resultant stress and the right side is linear with respect to the variable µ, which has the same meaning as the nonlinear shear modulus, by Equation (5):Fitting the model to the experimental stress-strain curves of the soft CFs see  allows tE*, can be defined as twice the shear modulus by the relationship:Finally, a single value of the elastic modulus, E*, for PAN-derived (thin fibers) CFs are lower than expected, and this is the result of the model’s assumption of purely non-linear material deformation, with rubber-like properties. Yet, the tested PAN (thin fibers) nonwovens exhibit somewhat different elastic fiber deformations. Therefore, as presented in When the values of elastic modulus obtained from Equation (6) are plotted against bulk density, see We saw that the particular shape of the stress-strain curves  leads toThe excellent correlation between the modulus values determined by Equation (6) for carbonized and graphitized felts from the same precursor, such as SFC1aC↔SFG1aC, SFC1aBG↔SFG1aBG and SFC1aSI↔SFG1aSI see , is probThe slope values of the initial parts of the stress-strain curves are shown in All soft CFs were investigated not only with the aforementioned destructive method, but also by a quasi-static, non-destructive method. Soft, porous materials are sometimes characterized by QMA to initially identify the Poisson ratio, allowing the elastic modulus and damping loss factor to be calculated . HoweverAs explained in The damping loss factor of the soft CFs was also obtained by QMA measurements see b. The loInformation on the intrinsic mechanical parameters of CFs is crucial for a prediction of their performance that is more accurate, e.g., in composite preparation, vibro-acoustic applications and many others. This is why, using the broad variety of soft CFs, we have attempted to compare directly the results of the two methods used to determine the elastic modulus. The novelty is to provide important information on the accuracy and/or dispersion of the elastic properties of soft CFs, measured with either a dynamic and destructive method or a quasi-static and non-destructive method.The elastic moduli calculated by QMA were plotted against those obtained by the dynamic compression method. This is shown in Although destructive, the dynamic compression method is more suitable for determining a global elastic modulus, using Equation (6) for hyperelastic materials or Equation (1) for materials with an initial linear response under constant load increase. In contrast, QMA is only suitable for determining the elastic modulus of soft CFs at a defined pre-compression load, without giving the global elastic modulus of the materials.When working with highly compressible materials, it is very important to know the value of the force at which the initial thickness of the materials was measured, before carrying out compression tests. In measurements made with the dynamic compression method, the initial contact was obtained at a predefined force value, as explained in The results obtained also indicate which of the two methods is the most appropriate for studying the elastic properties of soft CFs. If the materials are used for static purposes, where creep is expected under persistent mechanical load, the study of the materials should be carried out by the dynamic mechanical compression method. Otherwise, the materials should be examined by QMA in a vibro-acoustic application. Moreover, it has been previously observed that the QMA method is suitable for measuring the elastic modulus of viscoelastic materials only in the absence of brittle mechanical deformation .Thermal conductivity measurements were carried out for all commercial fibrous carbons, and the corresponding data are given in xyzκ is the overall thermal conductivity calculated from the experimental measurements of the thermal conductivity carried out on the one hand in the IP direction, leading to xyκ, and on the other hand in the OP direction, leading to zκ.z) direction than in the IP (xy) direction. The anisotropic thermal behavior is due to the preferential orientation of the fibers, which are much more numerous in the IP direction than in the OP direction, in good correlation with the µCT results already presented in The validated measurements, obtained in the anisotropic analysis mode, can therefore be used to discuss the different heat transfer behaviors of the examined materials. As can be read in the literature ,61, all xyκ), differences in the results can be attributed to the conductive nature of the individual fibers, provided that all other felt formation parameters are similar. The influence of fiber conductivity is observed from the corresponding soft CFs, i.e., without or with graphitization, such as SFC1aC↔SFG1aC, SFC1aBG↔SFG1aBG, SFC1aSI↔SFG1aSI and SFC2bC↔SFG2bC. Produced only by increasing the final temperature, graphitized CFs show increased thermal conductivity and greater differences in values between the IP and OP directions. Exceptions, such as SFC2bBG, even though only carbonized at about 1200 °C (information from the manufacturer) showed a highly directional and anisotropic thermal conductivity similar to that of the corresponding graphitized SFG2bBG. The latter correlates well with the examined structural properties of SFC2bBG [In the case of the IP thermal conductivity  than in the IP direction (xyκ) by changes in the tortuosity of the felts and the reorientation of the fibers.On the other hand, the thermal conductivity in the OP direction depends mainly on the thermal resistance between the soft or rigid contacts and the proportion of carbon fibers reoriented along the lsewhere , showingIn the case of all carbonized CFs (except SFC2bBG and SFC2aZF), i.e., heat-treated at a temperature ≤1200 °C, we obtained close thermal conductivity values in both IP and OP directions compared to the graphitized ones. This observation is explained by the less conductive nature of the constituting carbon fibers compared to their graphitized counterparts. Although this may seem obvious, the difference in thermal conductivity in the IP and OP directions is smaller for highly needle-punched CFs, such as SFC2aZF, compared to other materials whose fibers are more oriented in the IP direction. These results correlate well with the results obtained by µCT analysis. A similar effect has been observed in previous studies ,65,66, wThe soft Rayon-derived CFs evaluated are generally much less conductive in the IP direction than the soft PAN-derived CFs, when compared to CFs manufactured with similar final heat treatment. This may be due to the higher aspect ratio of the PAN-derived carbon fibers and their circular cross-section, but mainly to the graphitic structure, which results in higher thermal conductivity in the axial direction of the fibers compared to the Rayon-derived fibers. A similar comparison cannot be made with rigid boards and rigidized soft CFs, due to the effect of the binder (possibly introduced in different amounts) and to the different methods to prepare them.The thermal conductivity of the rigid boards again demonstrates their anisotropic structure, due to the preferential orientation of the fibers and the inhomogeneous dispersion of the binder at the interconnections. A better thermal conductivity in the IP direction is observed due to a higher proportion of interconnected fibers connections, as in ex-Rayon materials. The observed results are thus in good agreement with former studies, suggesting the strong influence of fiber and binder continuity on the thermal conductivity of fibrous carbons ,67.In many applications, such as fuel cells or redox flow batteries ,61,68,69In order to analyze the influence of compression on the effective thermal conductivity, the experimental set-up described above was reused in a modified version. The measurements were carried out on samples from a single subgroup of the Rayon-derived soft CFs. This subgroup was chosen because it is the only containing three pairs of graphitized -non-graphitized CFs. The method used, described in According to our previous research on mechanical properties, the decrease in thickness, and therefore porosity, of materials under compression should not lead to changes in the morphology of fibers in terms of cross-section or length. Moreover, the observed linear change of the thermal conductivity, in both IP and OP directions, should only result from the change in the structure of the felts and the improvement of the contacts between the fibers, i.e., the decrease in thermal resistance at the contacts between the fibers. Deviations from the linear variation mentioned above are only observed for SFC1aSI and SFG1aSI. Thus, these deviations correlate well with the calculated elastic limit of these materials , which wSeveral analytical equations widely used in the literature were then tested to account for the experimental results shown in In this study, the initial focus was on identifying the uniaxial compressive behavior, governed by different mechanisms, of various commercial fibrous carbon materials. It was seen that the elastic properties of most soft CFs are very complex, with an almost continuous non-linear evolution of stress at constant deformation rate, in contrast to the successive regions of linear compression, buckling and densification observed for rigid boards and rigidized CFs.Due to the need to model the elastic behavior of soft CFs and the impossibility of determining a single value of elastic modulus with the commonly used Hooke’s law, the Mooney–Rivlin model was chosen and applied because of its mathematical simplicity. It allowed us to determine analytically the effective elastic modulus of soft CFs. In addition to the “dynamic” method, the compressive behavior was also studied by a little-known “quasi-static” method. The possibility of using two different test methods made it possible to compare the results of effective elastic modulus obtained under different test regimes. A good correlation was found between the values calculated from the stress-strain curves and from the QMA. However, the QMA method did not provide an overall elastic modulus of the materials, and it is only appropriate for determining the effective elastic modulus of soft CFs at a predefined strain. In addition, and for the first time, the QMA method was successfully used to study the damping loss factor of commercial CFs.The effective thermal conductivity of the commercial CFs examined was also considered in the second part of the study, and was found to be much lower than that of the constituent carbon fibers. This observation is due to the very high porosity of the materials studied, but also to various structural defects in the fibers and their organization in the felts, implying high thermal contact resistances. These results also show that the thermal conductivity of most CFs is highly anisotropic, with higher values in the IP direction than in the OP direction. Therefore, a higher heat transfer in the plane of the felts is always to be expected, and the materials should be properly adapted to the conditions of use of their final application. In addition, the thermal conductivity of the commercial CFs tested change due to compression. Thus, in most cases, the thermal contact resistance decreases linearly with decreasing material thickness (porosity), and more in the OP than in the IP direction. Finally, the unsuccessful fitting of the results by different thermal conductivity models found in the literature requires further research to develop additional characterization tools adapted to this type of complex materials. To date, the results of effective thermal conductivity at different material thicknesses (porosities) are not available in the literature.Through this detailed study, the structural and physical properties of fibrous carbons can be adjusted to develop materials with optimal performance in various applications.


This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks  were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to  The preretinal space of the human eye and the pathologies connected with its improper changes have become of interest in recent years . The fieAs one of the significant advantages, the OCT allows to visualize the progression of posterior vitreous detachment (PVD) and to monitor its possible pathological outcomes ,5,6,7. IAlthough the complete posterior detachment is prevalent in over 40% of healthy subjects at the age of 60 , its abnIn VMT, the posterior vitreous with continuous adhesion to the macula exerts traction on the retina (mainly fovea—the most sensitive part of the macula). Persistent traction can lead to deformation of the fovea, cystoid foveal thickening, and disorganization of retinal layers. Such problems manifest with metamorphopsia (image deformity), deterioration of visual acuity, blurred or impaired central vision that significantly impair daily tasks  .It has also been reported that the prevalence of VMT increases significantly with age, from 1% in subjects of 63–74 years old to 5.6% in patients over 85 years old . The chaCurrently, VMT advancement is determined based only on a single cross-section through the center of the macula. To the best of our knowledge, no research or commercial image informatics solutions allow for automatic segmentation of the vitreous cortex and thus the preretinal space. Therefore, manual measurements can only be made in a few points of the volume and are not sufficient to quantify the profile of this epiretinal pathology. A numerical analysis of preretinal space volume and adhesion area is required to assess precisely the development and current stage of VMT. Such investigation can include: quantification of the preretinal space volume , statistEvaluation of state-of-the-art neural network methods employed for the new task of preretinal space segmentation to the best of our knowledge has not been previously attempted. The conducted experiments demonstrate that the deep learning approach is better suited for segmenting preretinal space than a standard graph-search method.Analysis of the influence of kernel shape and size on convolutional network performance for OCT images. With the experiments described in Analysis of relative distance map calculation to improve retina layers topology preservation using various network architectures. We propose two methods of obtaining a distance map for any given image. They do not require any given knowledge about the retina layers (as is the case in the compared methods), are less computationally expensive, and give similar (or in some cases better) results.Collection of a unique dataset of 3D OCT images from 50 VMA and VMT subjects (7050 2D B-scans) with manual segmentation data of preretinal and retinal volumes . The gatThe focus of this paper is the development of an automatic image informatics approach for the segmentation of the preretinal space in both healthy and pathological cases. The major contributions of this study are:The rest of the article is organized as follows. First, the recent state-of-the-art approaches for retina layers segmentation as well as the methods selected for preretinal space segmentation are described in With the increased availability of OCT imaging devices, the detailed analysis of retina pathologies became possible. However, manual segmentation and measurement of tissue biomarkers are very time-consuming, and with an increased number of pathological subjects, not always an option. Thus, multiple image informatics algorithms have been developed in recent years to support the effort of ophthalmologists.Development of the algorithms for retina layers segmentation started over 20 years ago with classical image informatics approaches, that can be divided into: edge detection methods ,18,19,20As can be derived from analysis of the literature, the complexity of proposed methods advanced with years, as well as their accuracy of retina layers segmentation and number of layers that could be segmented. Since 2012 the graph-search methods proved one of the most accurate retina layers segmentation for healthy and pathological cases. Their disadvantage, however, is the need for extensive image preprocessing (primarily noise suppression) ,28 and cThe progress has also been made with machine learning, pattern recognition, kernel, and clustering techniques ,30,31. FThe reviewed works focus on segmenting main retina layers in normal and pathological subjects and, in some cases, accompanied fluids. Although the available databases consist of a limited number of training images, researchers compensate for that with data augmentation methods. The majority of published methods are based on UNet architecture , its modDespite the plethora of available retina layer segmentation methods, preretinal space segmentation from OCT images is not widely researched. To the best of our knowledge, deep learning methods have not yet been used for this task, and only a handful of reports of other approaches can be found in the literature.For instance, Malagola et al.  showed tThe first graph search-based approach that segmented both the retina borders , and retinal pigment epithelium (RPE)) and the posterior vitreous cortex was published in 2014 . HoweverFurthermore, automated segmentation was used for quantitative analysis of epiretinal abnormalities only in several studies ,49,50  coordinate of previously segmented ILM and RPE lines in the image column x. According to this equation, the pixels above the ILM take value The relative distance map provides a way of introducing additional spatial information alongside the input image. Each pixel value of the map corresponds to the pixel position in the image as a function of retina location. Thus, knowing the coordinates of inner and outer retina borders (namely ILM and RPE) across a B-scan, the intensity values of the relative distance map are computed for each pixel with indexes Our work explores the possibility of utilizing deep neural networks (DNN) for the task of preretinal space segmentation and investigates the challenges connected with the specificity of the preretinal space (shape and image intensity variations). We present the research results for preserving the topological correctness of the segmented OCT image areas, including the implementation of four different distance maps (based on prior segmentation or without it). We propose investigating the influence of convolutional kernel size and shape on topology correctness and overall segmentation accuracy.The goal of conducting research in OCT retina layers segmentation creates a need to obtain numerous images with annotated biomarkers searched for in a specific task. Since 2018 only several public OCT databases with 3D images have been established, and most of them are aimed at classifying a specific disease from a single B-scan. Other cohorts, focused on automatic retina layer segmentation, provide manual segmentations for 3 to 8 retina borders. Most contain images of 10 to 25 volumes ,54,55,56Thus, a CAVRI  dataset  of OCT iy), and x) and non-fast scanning (z) directions, respectively. No multi-sampling and noise-reduction protocols were used during acquisition or preprocessing. All 7050 cross-sections , visualized as gray-scale images, were analyzed separately.The CAVRI database contains 3D images of the macula obtained with the Avanti RTvue device  using the 3D Retina scanning protocol. For this research, from a group of 73 cases a set of 50 OCT volumes was selected: 25 examples of the healthy retina (with asymptomatic vitreomacular adhesion (VMA)) and 25 subjects with VMT. Each 3D scan consists of 141 cross-sections with p-value close to zero  are data examples outside the normal distribution of data features ,62. For As was established in  the presA wide variety of outlier detection algorithms are constantly developed and compared by many researchers ,68,69. IUtilizing the anomaly detection implementation provided by Kucukgoz , five imTo segment the PCV using a deep learning approach, we trained four state-of-the-art convolutional neural networks. Then, we compared their output with the previously described graph search-based approach and the ground truth. This section gives a general description of the utilized fully convolutional network architectures, including UNet, Attention UNet, ReLayNet, and LFUNet.The processing pipeline of the proposed system is presented in The classical image segmentation utilizing graph search and dynamic programming is described in . Here, bAs was described in , the samUNet is an architecture proposed by  that obtOne of the beneficial procedures introduced in UNet is a skip connection: e.g., the feature maps at the end of each encoder level are concatenated to the upsampled decoder maps before being processed by the convolution blocks. Such operation allows preserving relevant information from the input features. The probability maps are obtained after applying a final An extension of the UNet architecture is the Attention UNet proposed by . It intrThe general design of this network is similar to the baseline UNet, with five double ned from .ReLayNet  was the The ReLayNet also differs from the original UNet with the kernel size used for each convolution, which is orted in , this enThe LFUNet network architecture is a combination of UNet , and FCNThe decoder part consists of two parallel paths for UNet and FCN. The UNet path utilizes concatenation of up-sampled feature blocks with the corresponding blocks from the encoder part . The FCN path performs the addition of up-sampled feature blocks with the matching encoder blocks. The upsampling in both paths is performed with the C classes was obtained with the Softmax function. ReLu was used as all activation functions in the hidden layers.The additional strength of this network introduces the last part, which is a concatenation of final feature maps obtained from both decoder paths. They are subsequently dilated with three separate kernels, and the resulting matrices are again concatenated before final convolution. The output probability map for each pixel belonging to one of the It should be noted that the problem of preserving correct topology in retina layers segmentation is even more pronounced for the preretinal space since it has almost the same intensity range as the vitreous. Hence, in this work, we employed the available approach for preparing the RDM (here referred to as “2NetR”) based on prior information of retina borders and proposed a modified version tailored to the problem of segmenting the preretinal space. In addition, we also tested if a more straightforward map that does not require two cascaded networks and is computationally less expensive could also facilitate this task.For vitreous region: For preretinal space: For retina: For region below retina: To increase the significance of preretinal space as a region below the vitreous but above the retina, we propose to utilize a distance map , that would take the following values:This can be defined for each pixel with the following formulations:Nevertheless, as efficient an idea as this is, it still requires prior knowledge of retina borders in a given cross-section. As reported, this information can be obtained via graph search approach , or by parctan of the vertical and horizontal image edges subsequently obtained with the use of Sobel edge detection. This map will be further called “BasicOrient”.Basic Map with Orientation—Firstly, we investigated if a map of linearly spaced values in the range of Cumulative Sum Map—The second method incorporates calculating a cumulative sum of intensity image values for each column of the image. This is based on the assumption that pixels in the vitreous and preretinal space region have very low-intensity values, as opposed to the retinal region. Additionally, the pixels below the retina have average intensity, hence providing lower variations in the cumulative sum. Furthermore, by performing a scaling operation, it is possible to obtain values similar to those produced by Equation , but witThus, we also investigated an approach that does not require any a priori knowledge about the retina position within the analyzed image. Two following solutions are evaluated:The pixel intensity values of the preretinal space are similar to those of the vitreous. Therefore, the network may not have enough information about the surroundings to correctly assign a given pixel to a class. Furthermore, the area and shape of the preretinal space differ from B-scan to B-scan.Another way of providing the network with the information of where a given pixel belongs within the image is using a bigger convolution kernel. In contrast to works described in the literature review in ReLayNet , the netsquare kernels: vertical kernels: horizontal kernels: Within the retina scan pixel intensities vary significantly in the vertical direction, therefore it can be beneficial to utilize a bigger kernel to detect those changes. In our experiments we check the influence of:Bearing in mind the computational cost of incorporating a bigger convolutional kernel, we pose that even a non-uniform filter will significantly improve the accuracy of pixel-wise segmentation.We have performed a comprehensive set of experiments designed to measure the performance of various deep neural networks employed for the segmentation of preretinal space from OCT images. We evaluated the effect of removing anomalous data samples from the training set and the influence of data augmentation on the model accuracy. We addressed the issue of incorrect class topology common in pixel-wise image segmentation with the calculation of a relative distance map as guidance information for the system. With a set of tests we compared our proposed solution to a state-of-the-art method. Furthermore, we also proposed and evaluated an alternative method of changing the size of the convolution kernel while measuring the computational complexity.In this section, we describe the experiment setup and parameters of the system for all segmentation methods. Next, we describe data augmentation techniques utilized in this research, and finally, we provide information regarding the evaluation metrics used to compare the obtained results quantitatively.The goal of the segmentation task is to predict 4 separate areas in the image, further described as set of classes z-score normalization.Bearing in mind the specificity of preretinal space, we consider the possibility that the PCV line is not sufficiently visible throughout the scan or is partially connected to the ILM. In such a situation, using narrow patches could mislead the network. Hence, we input to the network an entire B-scan  with the resolution of All neural networks described in this paper were implemented using Python 3.7 with PyTorch 1.8.1 and NVIDIA CUDA 11.2 libraries. The experiments were conducted on a 64-bit Ubuntu operating system with an Intel Core i7-7700K 4.20GHz computing processor and 32 GB RAM. The NVIDIA GeForce GTX 1080 Ti GPU card with 11 GB memory was used during training and evaluation.The CAVRI dataset was randomly split into training, validation, and testing subsets with the ratio of https://github.com/krzyk87/pcv_segmentation, (accessed on 5 November 2021).Using the PyTorch Lightning 1.3.5 library, we trained each network with an Adam optimizer and the following parameters: learning rate The graph search algorithm was implemented in the Matlab/Simulink environment  on a 64-I and prediction w and h denote the width and height of the image, respectively; x and y belonging to class c; c; and The training is aimed at minimizing a loss function  methods ,40 to suIn detail, since the PCV line is very often on the level of noise in the image and due to OCT characteristics, the edges of the regions can be blurred. To boost the network’s sensitivity to class boundaries, the pixels at the edges are given an additional weight Equation  describec.The second component of the loss function is computed based on a Dice coefficient. The Dice as region-based metric measures the overlap of two regions according to the equation:Consequently, the Dice loss  follows:LDice, preserving the morphology of retinal structures (such as vessels and layers topology).Rotation—Slight variations in retina orientation are natural when acquiring an OCT scan. Thus, augmenting images in this way allows training the model to anticipate any future examples in a wide variety of retina orientation angles. In order to determine the range of random orientations to apply, we performed a statistical analysis of a retina orientation distribution within the CAVRI dataset. As can be seen in Vertical Translation—Automatic acquisition protocol in an OCT device aims at focusing the device’s optics on the retina. Notably, the thickness of the retina tissues extends to an average of 200 Random Crop—The wide variety of OCT acquisition devices allow for an even greater number of scanning protocols, with various image sizes and scanning widths. Thus, performing an augmentation technique of random cropping, we train the network to perform well on any input image regardless of its size or fovea width to image width ratio. In our experiment, we employed a crop with randomly selected values for both width and height .We utilized data augmentation techniques during training to improve the model’s generalisability and increase the segmentation accuracy. Thanks to this, the number of the image examples expanded artificially with each technique while maintaining the data characteristics that may naturally occur in the data. The following transformations performed in 2D for each cross-section were used:Utilizing such data augmentation techniques allowed to increase the number of training examples, as shown in 1.Dice Coefficient—A measure of overlap between the segmented region in the ground truth and prediction. It is calculated for each of the segmented classes  based on Equation  separate2.x stands for the image column index within the image width w; and z denotes an individual image index. The MAE is computed for the three segmentation lines of interest, namely the Mean Absolute Error (MAE) with standard deviation (SD)—The average vertical distance between an annotated line and a segmented boundary. It represents the error of the segmentation result relative to the ground truth. It is calculated as follows:3.Topology Incorrectness Index (TII)—that indicates what percentage of tested images has incorrect layers topology in the vertical direction. It is computed based on a vertical gradient of the predicted segmentation mask.To compare the correctness of the proposed segmentation methods with the manual annotations, we employ the following evaluation metrics:We conducted multiple experiments using several networks to segment the preretinal space and the retina in the OCT images. This section presents a qualitative and quantitative comparison of preretinal space segmentation with a graph search approach and four DNN methods. Specifically, UNet, LFUNet, Attention UNet, and ReLayNet networks were used to obtain precise segmentation masks. As can be noticed, all neural networks perform better than the graph search-based method, and from CNN, the UNet has the best performance in all segmented areas and borders. On the other hand, the ReLayNet gives the worst results, which may be explained by a relatively lower number of features compared to other architectures. Additionally, the preretinal space boundary of PCV and the image classes it separates  have worse accuracy than the clearly defined ILM and RPE borders and the two image regions they define. This confirms the difficulty of determining preretinal space boundary due to similar pixel intensities of this region to the vitreous part.In As can be expected, the addition of more varying images helps to train the network. This strategy boosts the segmentation outcome in all the methods. A detailed analysis revealed that rotation and random cropping are the two strategies that best improve the segmentation results. This supports the observation that the angle setting for each patient is an individual parameter that can change even between examinations. f-ratio value is 40.91. The p-value is <0.00001. The result is significant at We performed a statistical test of significant differences among models . The data used for the test is the same as those used to calculate the average values of Dice scores for Preretinal Space in Standard UNet architecture and LFUNet provide the best probability maps, although UNet has slightly better performance in segmenting preretinal space, retinal area, and PCV border. The Attention UNet and ReLayNet performed poorly here, even if their scores are better when employing data augmentation. Examples shown in In the presented images, it is visible that poor evaluation scores in Both UNet and LFUNet correctly learn the preretinal and retinal space borders regardless of the PCV intensity in the image, which is a significant improvement over the graph search-based method. Visually, those networks perform very well for both VMT and VMA cases. Furthermore, their accuracy is not affected by the placement of preretinal space in the image or the area it spans.Compared with the classical approach based on image intensity gradient, the neural network learns smooth region borders and is not affected by slight local intensity variations—it can robustly generalize the preretinal space structure. Moreover, the graph search-based approach has difficulty correctly detecting the PCV border whenever it connects with the ILM line. This is a distinct disadvantage compared to the neural network methods that do not present such hindering.On the other hand, it should be noted that in cases when the preretinal space takes only a narrow area of the image  perform better than the state-of-the-art approach of “2NetR”. Furthermore, the “CumSum” does not require an initial segmentation and is less computationally expensive.Additionally, we observed that a simple linear map (“BasicOrient”) not only does not preserve correct layers topology but, in most cases, hiders network ability to segment the OCT image properly. On the other hand, for UNet, and LFUNet, this map lowered the number of images with incorrect topology.Previously described experiments showed the best performance of segmenting preretinal space and PCV line when utilizing UNet architecture. Therefore, this network will be used for further improvement with various convolutional kernel sizes.In this work, we have evaluated a set of methods employed for the segmentation of the preretinal space in optical coherence tomography images of the retina. We proposed an OCT image segmentation system that can help doctors automatically quantify morphological parameters of the preretinal space in healthy eyes and pathological cases of vitreomacular traction. Our approach provides robust end-to-end learning of preretinal space borders, with performance higher than in previous works. Employing CNN for this task does not require image preprocessing in the form of denoising, thresholding or other methods as is with standard computer vision algorithms.We have shown the challenges associated with the segmentation of the preretinal space and proposed a solution in applying convolutional neural networks to this task. With quantitative and qualitative tests, we analyzed four neural network architectures, including UNet, LFUNet, Attention UNet, and ReLayNet for this task. Two standard metrics of Dice score and MAE were used for evaluation. An additional discussion on their interchangeability and limitation for preretinal space analysis was shown. The evaluation tests were conducted on a unique dataset of OCT images with 25 vitreomacular traction patients and 25 vitreomacular adhesion subjects. By performing the segmentation at a 2D level, thus utilizing 7050 images, we avoid computationally expensive 3D convolutions.In general, the original UNet and LFUNet have performed relatively well, correctly segmenting preretinal space borders, with Mean Absolute Error of 1.33 px and 1.5 px, respectively, whereas the Attention UNet and ReLayNet gave MAE results of 4.02 px and 12.63 px, respectively. Nevertheless, all networks faced the challenge of incorrect vertical topology associated with the semantic image segmentation, but unacceptable in biological image analysis.We have proposed two new approaches for improving the topological correctness of OCT image segmentation, namely two relative distance maps and the use of a non-typical convolution kernel. Extensive experiments for all network architectures show that both of the proposed relative distance maps tailored for preretinal space better preserve the correct layers’ topology , than the state-of-the-art approach (9.4% for UNet and 6.3% for LFUNet). Additionally, we conclusively demonstrate that using a bigger kernel for a UNet-type network allows improving topological correctness of segmentation to a greater extent than utilizing an additional distance map (improvement to only 2.4% of images with incorrect topology).The presented results confirm that CNN can reliably segment preretinal space and with significantly better performance than the graph-based approach. The conducted experiments show that the best-obtained Dice score of preretinal space segmentation is up to


Flying animals such as insects display great flight performances with high stability and maneuverability even under unpredictable disturbances in natural and man-made environments. Unlike man-made mechanical systems like a drone, insects can achieve various flapping motions through their flexible musculoskeletal systems. However, it remains poorly understood whether flexibility affects flight performances or not. Here, we conducted an experimental study on the effects of the flexibility associated with the flapping mechanisms on aerodynamic performance with a flexible flapping mechanism (FFM) inspired by the flexible musculoskeletal system of insects. Based on wing kinematic and force measurements, we found an appropriate combination of the flexible components could improve the aerodynamic efficiency by increasing the wingbeat amplitude. Results of the wind tunnel experiments suggested that, through some passive adjustment of the wing kinematics in concert with the flexible mechanism, the disturbance-induced effects could be suppressed. Therefore, the flight stability under the disturbances is improved. While the FFM with the most rigid spring was least efficient in the static experiments, the model was most robust against the wind within the range of the study. Our results, particularly regarding the trade-off between the efficiency and the robustness, point out the importance of the passive response of the flapping mechanisms, which may provide a functional biomimetic design for the flapping micro air vehicles (MAVs) capable of achieving high efficiency and stability. Unmanned aerial vehicles (UAV), also known as drones, are widely used for various missions such as filming, surveillance, and transportation . BecauseAnimals such as insects and birds inhabit where drones are expected to operate. Their flight apparatus is thought to adapt to the flight in the unpredictable aerial environment through natural selection. Therefore, the bio-inspired strategy, such as flapping-wings, can greatly enhance the stability and reliability of conventional UAVs. For example, computational studies suggested that the unsteady separated flows on the flapping-wings can reduce the force fluctuation caused by the relatively large-scale turbulent flows . The flaInsects flap their wings by transmitting the alternative muscle contraction at the flexible skeletons . Due to In addition to the power economy, the flexible structures may enhance the insects’ stability by responding to aerial disturbances passively. Flexible wings can respond to the gust by adaptively changing their shapes to reduce the force changes due to the gust , enhanciIn this study, the effect of the flexibility in the flapping mechanism on the gust response of a flyer is investigated experimentally. We have fabricated a robot with insect-inspired flexible flapping mechanisms, slightly modifying the existing mechanism . The aerWe have fabricated a motor-driven flexible flapping mechanism FFM;  by slighThe wing part also has the flexibility at the wing base to increase the wingbeat amplitude and to adjust the feathering angle passively. The wing shape is a quarter ellipse with the length of semi-major and semi-minor axes of 70 and 25 mm, respectively . While tThe whole mass of FFM is 4.0 g without a power supply cable. Additional electric components like battery, sensor, and control board would be equipped for free flight experiment in future work.The “Constrained” FFM, shown in In this study, we have evaluated the flight efficiency and robustness by fixing the FFM via force balance. Therefore, we did not include further complexity, such as body motions and active responses, in the evaluation. The “steady” setup is still useful because it simplifies the phenomenon to evaluate the performances separately from its flight dynamics.A high-speed camera was placed above the system to film the wing kinematics at 2,000 fps. We tracked the five landmarks ; rotatioIn this study, three types of torsion spring and input voltage were tested to evaluate lift and efficiency at various wingbeat frequencies in the range of 12 to 25 Hz with an increment of 1 Hz. Each combination was measured three times, with a minute interval to cool the motor.We used a low-speed wind tunnel  to invesIn this experiment, the input voltage was adjusted so that the lift-to-weight ratio is one without the wind. In order to confirm the reproducibility of the experiment, the measurement was conducted six times with each torsion spring stiffness. After each three-time measurement, the FFM was detached from the load-cell and attached again.The obtained data from previously described experiments were processed to calculate the efficiency. In this study, efficiency is defined as:PRF is the absolute minimum value of induced power, which is derived on the basis of the Rankine-Froude momentum (RFm) theory  theory , and PinThe aerodynamic performance of FFM with various torsion spring stiffnesses and frequencies was evaluated in terms of the wing kinematics, the lift, and the efficiency. The wingbeat amplitude was increased by increasing the input voltage from 6 to 7 V, and was increased with decreasing wingbeat frequency from 25 Hz . After rThe maximum wingbeat amplitude of each input voltage was achieved by “Soft” torsion spring at around 14 Hz. Unsurprisingly, the large lift was generated when the wingbeat amplitude was larger, as shown in The FFM can generate lift force with the highest efficiency at 15 Hz with “Soft” torsion spring, while, with “Hard” and “Medium” torsion spring, the highest efficiency was achieved at 16 Hz . Since tThe pitching torques due to the frontal wind were negative , and theFy, was also reduced in the “Normal” FFM compared to the “Constrained” FFM at higher wind speed  inspired by a flexible insect musculoskeletal system. The mechanism in this study was the modification from the original design of The flapping robots and animals can benefit energetically from the flexibility in the mechanisms. The FFM in this study is qualitatively similar to the insect thorax, having three different elastic elements: a torsion spring, a cantilever, and a hinge . As a reThe passive change of the wing kinematics in response to the aerial disturbances affects the stability of the flapping-wing flyer. In this study, the passive feathering reduced the forces and the torques induced by the frontal or lateral wind. A similar effect by the flexible wing has been suggested by a computational study . While wThe pitching torque due to the frontal gust can be reduced by the passive adjustment of the feathering angle. The increase in the feathering angle during downstroke  reduces The passive adjustment of feathering can respond to the lateral wind similarly to the frontal wind. The relative increase in feathering angles, or the reduction in angle of attack, reduces the drag on the upstream wing during the early half of each half-stroke (especially p3 in There is a trade-off between efficiency and robustness against the disturbances. While the FFM with the “Hard” spring was most robust against the frontal and lateral winds , 7A, theThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.TN conceived the study with SK and HL. SK, TN, and HL designed the experiments. SK performed the experiments. SK and TN analyzed the data. All authors contributed to the final version of the manuscript and approved the submission.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.