Mesodesma donacium, were shown to accumulate toxins from Dinophysis acuminata blooms. Only pectenotoxin 2 (PTX2) and some of its derivatives were found, and no toxins from the okadaic acid group were detected. PTX2 seems to be transformed to PTX2 seco-acid (PTX2sa), which was found in concentrations more than ten-fold those of PTX2. The seco-acid was transformed to acyl-derivatives by esterification with different fatty acids. The estimated amount of these derivatives in the mollusks was much higher than that of PTX2. Most esters were originated by even carbon chain fatty acids, but some originated by odd carbon number were also found in noticeable concentrations. Some peaks of toxin in the bivalves did not coincide with those of Dinophysis abundance, suggesting that there were large differences in toxin content per cell among the populations that developed throughout the year. The observed depuration (from the digestive gland) was fast (more than 0.2 day−1), and was faster for PTX2 than for PTX2sa, which in turn was faster than that of esters of PTX2sa. PTX2 and PTX2sa were distributed nearly equally between the digestive gland and the remaining tissues, but less than 5% of the palmytoyl-esters were found outside the digestive gland.Surf clams,  Dinophysis frequently accumulate in bivalves making them unsafe for human consumption and leading to closures of fisheries or marketing of aquaculture products. The impacts of these toxins are widely distributed across the oceans, but some areas are particularly affected, as is the case in Southern Chile and North-Western Spain week0 − [Tox]week1)/7 days and was expressed as day−1.Rough estimates of depuration rates were obtained by using concentration values in two consecutive weeks based on the following selection criteria: (a) that the first observation had a high concentration value; (b) that the R [Regression, correlation and ANOVA analyses were carried out with R . DescripR .


In the era of technological advances and innovations in transportation technologies, application of driving simulators for the investigation and assessment of the driving process provides a safe and suitable testing environment. Although driving simulators are crucial for further improvements in transportation, it is important to resolve one of their main disadvantages–simulator sickness. Therefore, suitable methods for the assessment of simulator sickness are required. The main aim of this paper was to present a non-invasive method for assessing simulator sickness by recording gastric myoelectrical activity–electrogastrography. Open-source hardware for electrogastrography together with recordings obtained in 13 healthy volunteers is presented, and the main aspects of signal processing for artifact cancellation and feature extraction were discussed. Based on the obtained results, it was concluded that slow-wave electrical gastric activity can be recorded during driving simulation by following adequate recommendations and that proposed features could be beneficial in describing non-ordinary electrogastrography signals. Driving simulation is a tool that provides an alternative to on-road testing in a safe, reliable, and efficient manner . Since iNon-invasive recording of myoelectrical potentials from stomach smooth muscles (EGG) was first described in the literature by Alvarez in 1921 ,11. The In this paper, we present open-source hardware dedicated for the acquisition of EGG signals. We also report on its performance in SS assessment when driving in Nervtech’s 4DOF motion-based car driving simulator with haptic feedback  . Is it possible to reliably acquire slow-wave activity using a custom-made EGG sensing system during driving simulation?What EGG parameters are suitable for the analysis of recorded signals and, is there a correlation between them and subjective sickness assessment?Is there a clear difference in signals for resting, and the motion and no-motion drive?The three main research questions are:Our protocol for SS assessment was tested in 13 healthy volunteers and included three testing conditions: (1) resting; (2) drive with haptic feedback (motion drive); and (3) drive without haptic feedback (no-motion drive). EGG signal analysis consisted of two parts. The first part included pre-processing methods for artifact cancellation, and the second part incorporated methods for the extraction of the following features: (1) dominant frequency (DF); (2) median frequency (MF); (3) crest factor (CF); (4) root mean square (RMS); and (5) percentage of spectral power in the normogastric range (2–4 cpm). All parameters were calculated for three testing conditions, and the results were thoroughly compared and discussed. In addition to EGG-based measurements, the subjects filled out Simulator Sickness Questionnaires (SSQ)—a widely used subjective measure to quantify simulator sickness . QualitaThis study was conducted in Ljubljana, Slovenia, in compliance with the Code of Ethics of the University of Ljubljana, which provides guidelines for studies involving human beings and is in accordance with the Declaration of Helsinki.The subjects were recruited from generally healthy Slovenian citizens, mostly from the students and staff of the University of Ljubljana. The exclusion criteria were pregnancy, disorders of gastrointestinal tract and vestibular system, so as other chronic and acute pathologies. All subjects reported that they did not use any medications at least one week prior to testing . EGG signals were recorded in healthy subjects during driving simulation using the presented open-source EGG sensing system. Prior to the recording, the researchers explained the recording protocol to the subjects and presented the basic properties of the driving simulator. The test study group consisted of 13 healthy participants (ID1-ID9 and IDN1-IDN4), 4 females and 9 males, with age ranging from 23 years to 47 years (mean 29 ± 8 years), weight from 49 kg to 115 kg (mean 73 ± 19 kg), and height from 160 cm to 192 cm (mean 177 ± 8 cm). In In this chapter, the architecture of open-source hardware for the acquisition of EGG signals is presented. Low amplitude of EGG signals dictates that high gain should be obtained. Regardless of the fact that EGG could be recorded with a relatively low sampling frequency (< 4 Hz), low-pass (LP) filters are needed for the cancellation of high-frequency components coming from other physiological signals as well as possible artifacts originating from power hum. The most challenging step is baseline drift removal because it requires a high-pass (HP) filter with a cutoff frequency low enough to preserve the EGG frequency spectrum . Having that in mind, hardware was designed out of three main components: (1) amplification; (2) a HP filter; and (3) a LP filter. Due to the vulnerability of EGG recordings to artifacts, the application of more than one recording channel was indicated and therefore a three-channel EGG architecture was proposed . Each chg resistor for gain setting–G = 1000  in Our setup included a Force Sensing Resistor (FSR) as a pushbutton for manual synchronization between the driving simulator and EGG recordings. FSR was digitized by an ELVIS A/D connector (NI) together with three EGG channels.We used Nervtech’s 4DOFmotion car driving simulator that includes a racing car seat and a three-pedal set with a steering wheel by Fanatec . The display is composed of three curved 49-inch screens and SCANeR software . This device is capable to deliver realistic simulation of different roads and driving conditions, and its main component is a fast and powerful moving platform that provides haptic feedback to the user .In order to prevent excessive movement artifact occurrences, the subjects were instructed to restrict sudden movements, talking, and laughing. Volunteers reported that they did not eat for 6 hours or drink for 2 hours before recording, so EGG was recorded in the fasting state. Additionally, the subjects confirmed that they had not used any medications in the past 6 months.iliac crest;reference electrode—tissue covering the common electrode—on the stomach, 8 cm straight above the navel;sternum;channel 1 electrode—8 cm left of the common electrode inclined by 20 degrees from the line that connects the navel and the sternum;channel 2 electrode—8 cm left of the common electrode inclined by 55 degrees from the line that connects the navel and the sternum.channel 3 electrode—8 cm left of the common electrode inclined by 90 degrees from the line that connects the navel and the Prior to the placement of recording electrodes, adequate skin preparation was performed, i.e. shaving (if needed) with alcohol application or skin cleaning with abrasive gel . Standard surface self-adhesive Ag/AgCl electrodes H135SG  were applied. For electrode placement, positions recommended in  were useElectrodes were placed above the dominant pacemaker region of the stomach rich with intestinal cells of Cajal. More specifically, channel 1 was placed towards the part of the stomach called the lesser curvature, while channel 3 was placed above the greater curvature region. The channel 2 electrode was placed between channels 1 and 3 .Test drive—in order to enable participants to become familiar with the driving simulator operation (~ 5 min);Resting sequence—baseline slow-wave activity before driving simulation was recorded (~ 5 min);Drive with motion—driving simulation with haptic feedback included (~ 5 min);Drive without motion—driving simulation with no haptic feedback (~ 5 min).EGG signals were continuously recorded during the session for approximately 30 minutes. The protocol was divided into the following four segments (approximate duration of each segment is provided in brackets):In order to counterbalance the testing protocol, the order of the last two sequences was altered for each volunteer. Between consecutive segments, the subjects were asked to complete a standard SSQ . The parrd order band-pass filter with cut-off frequencies from 0.03 to 0.25 Hz as suggested in [A complete software analysis was performed in Matlab ver. R2013a . In order to cancel out the frequency content outside the EGG range, EGG signals were filtered using Butterworth 3automatic selection of a channel that was least affected by artifacts, andmanual cancellation of the remaining artifacts on the chosen channel.Recordings were obtained using three channels in order to increase robustness of the system and decrease sensitivity to motion artifacts. The first two steps in the analysis were:For automatic channel selection, the amplitude range and power of the signal were calculated. A higher amplitude range in one of the channels suggested the presence of non-physiological peaks, and therefore signals from the channels that had the amplitude range 100% higher compared to the other channels were eliminated. In order to select a channel from the non-eliminated group of signals, the lowest power criterion was applied .Although automatic channel selection is more desirable, it is stated in  that theDF was calculated as the position of the maximum peak in the frequency spectrum of the EGG signal . MF was CF was obtained by dividing the magnitude of the maximum peak in the frequency spectrum by its RMS value. It was calculated for each segment in the EGG dataset with the aim to assess peak prominence in FFT. The percentage of spectral power in the normogastric range (2–4 cpm) of the EGG signal was derived from the power spectrum density (PSD) function calculated using Welch windowing. Additionally, for each segment in the EGG dataset, the RMS value was calculated. To determine a statistically significant difference between features calculated for various sequences, a paired-sampled t-test was applied. Results that had p-value < 0.05 were considered significant.In Drop-out due to severe nausea and anxiety symptoms (in one subject);Severe artifacts present in the signal, most probably due to the electrodes detachment and movements (in three subjects).Four out of the 13 subjects  were excluded from the study as their EGG signals were not suitable for the analysis. This was a consequence of the following problems:Feature extraction and further analysis was performed on EGG signals recorded in the remaining nine participants (ID1-ID9). Channel 1 (upper part of the stomach) was indicated as noise-free in one subject, and channels 2 and 3 (lower part of the stomach) were indicated as suitable for processing in eight subjects (4 for each channel).Scatter plots of RMS values and the percentage of spectral power in the normogastric range for all segments  in nine subjects are presented in Graphical representation of DF and MF values is shown in A statistically significant difference was obtained only between RMS values for resting and no-motion drive sequences (p = 0.03). For the other calculated features , a paired-sampled t-test did not give any significant results regarding variation between different sequences. Although the results presented in The results of the SSQ for the nine subjects that successfully finished the test protocol (ID1-ID9) are presented in In this section learned lessons are discussed by following the order of appearance of topics as presented in This study demonstrated that our open-source EGG sensing system can be used for the acquisition of EGG activity signals during driving simulation. There are a few issues that need to be addressed for the future device improvement. Firstly, the device should be realized on a printed-circuit board (PCB) to make it more resistant to external noises and also to make it more suitable for transportation and less vulnerable to hazards that may occur during manipulation. The implementation of a HP filter with an extremely low cut-off frequency as presented here (0.016 Hz) required high-resistance values (10 MΩ) that may produce current leakage in PCB implementation, which is why alternative filtering designs should be considered.Firstly, subject’s movements can cause erroneous EGG, so body movements should be carefully controlled when recording EGG in a dynamic environment, such as driving simulation with haptic feedback.Secondly, EGG could be affected by the posture as stated in . AlthougThirdly, we faced a problem with electrode detachment in two study participants, which caused the drop-out of those two subjects. Hence, we propose the application of an additional protective layer of adhesive bandage over the surface electrodes.The percentage of recording sessions that were not successfully obtained and did not provide EGG signals suitable for further analysis is 31%, which is not insignificant. We identified and discussed reasons that caused unreliable EGG recordings:In this study, the resting sequence was recorded after the test drive see , which wChannel 2 was previously recommended in  as the mOut of the 27 signal sequences analyzed (9 subjects × 3 sequences), motion artifacts were visually detected in four of them (16%). Following the recommendations of , our expWhile DF is a commonly used EGG parameter ,21, its CF can be used to assess the prominence of peaks , so it cWe used the RMS value as an estimator of power changes in EGG signals since it has been proved that RMS increase correlates with nausea occurrence ,28. The The percentage of spectral power in the normogastric range presented in The absence of statistically significant results may be a consequence of a relatively small study group. That is why we presented a box plot only for RMS values–the parameter that showed a statistical significance between resting and the no-motion drive.There was no noticeable difference in the observed parameters between the no-motion and motion driving session in all parameters. Although it was previously concluded that haptic feedback could reduce SS , our proWe did not encounter any significant artifacts in EGG signals related to haptic feedback. Therefore, our EGG open-source system does not have contraindications for this kind of application.ID4 after driving with motion,ID5 after driving with motion, andID9 after both driving with and without motion.The SSQ revealed that the proposed protocol induced moderate sickness symptoms in three study participants :ID4 afteID5 revealed a slight RMS increase during the driving sequence, while ID9 had a higher RMS increase for both driving sessions compared to the resting period. These RMS values in ID9 can be observed in A decrease in RMS in subject ID4 for driving sequences was unexpected. This could be a consequence of signal irregularity during the resting sequence–RMS, MF and DF were higher than expected (see 4.5).It should be mentioned that subject IDN3 reported severe nausea during the motion drive. Therefore, the recording was not completed and the SSQ was not filled out. These signals were not included in the statistical analysis due to the presence of excessive motion artifacts being a result of subject’s exit from the driving simulator.In order to provide more reliable recommendations on the relationship between SS symptoms and EGG features, it is necessary to have a larger study group and a protocol that has a higher probability of nausea induction.Recording of EGG during driving simulation is possible using our custom-made open-source device with careful considerations regarding recording setup and the protocol. Despite that, its effectiveness for SS assessment is yet to be shown.RMS values might be used for the estimation of amplitude variations in EGG signals. Correlation between RMS and nausea should be examined in a future study. MF, CF, and DF could be used for the assessment of the EGG spectrum. In order to confirm these findings, future study should be performed on a larger sample.There was no clear difference between resting, motion, and no-motion sequences, except for an increase in RMS for driving sessions compared to resting.Based on the presented results and previous discussion, the following answers to the research questions are proposed:EGG under dynamic conditions should be recorded by carefully following protocol recommendations and the application of more than one EGG channel.The resting sequence of EGG recording should be obtained prior to any simulator activity.EGG signals should be visually examined in order to detect and manually extract motion artifacts.Features for a description of frequency content  should be carefully examined prior to any conclusions.Further improvement of the EGG device, primarily realization on a PCB with consideration regarding filter design.Assessment of nausea on a larger study group with higher statistical power in order to divide subjects into nausea and non-nausea groups. For the selection of target population, it could be beneficial to use a Motion Sickness Susceptibility Questionnaire to estimate susceptibility to suffer from SS.Recommendations for future research are:


In clinical practice the measurement error of an instrument has special importance in analyzing and interpreting data, and acknowledging limitations. The purpose of this study was to evaluate intrasession and between-visit reproducibility of OCT angiography measurements in diabetic patients. A total of 54 eyes of 27 diabetic patients underwent OCT angiography imaging. Foveal avascular zone (FAZ) area and superficial retinal vessel density (VD) at 3 mm were calculated using the AngioAnalytics software. Three consecutive images were acquired at first visit and one image 1 month later. Intrasession and between-visit reproducibility of parameters were characterized by intraclass correlation coefficient (ICC), coefficient of variation (CV), and coefficient of repeatability (CR) values. We measured excellent (>0.90) ICC values both in intrasession and between-visit comparisons. CV was higher for the FAZ area compared to VD both in intrasession (7.79% vs. 2.87%) and in between-visit (12.33% vs. 2.95%) comparisons. Between-visit CR value for VD was 4.53% (95% CI: 3.72–5.79%). These data suggest that OCT angiography shows excellent repeatability in diabetic patients, indicating that this non-invasive technology might be suitable for longitudinal assessment of microvascular complications. According to a recent study each year in the duration of diabetes represents a 6% increase in the onset of diabetic retinopathy3 and DR has a significant effect on life quality, particularly at more advanced stages with vision-threatening complications 4. Microvascular damage in diabetes leads to capillary nonperfusion and ischemia, upregulating the production of vascular endothelial growth factor (VEGF) subsequently leading to pathologic neovascularization and increased vascular permeability5. Diabetic macular edema (DME), which can develop at any stage of DR is responsible for 4.8% of blindness worldwide6.Diabetic retinopathy (DR) is a common microvascular complication that develops in almost 80% of diabetic patients after 15 years7. Since the availability of OCTA, numerous studies have reported the changes of retinal microvasculature of patients with diabetes mellitus such as vascular remodeling, enlarged FAZ, and capillary tortuosity and dilation17.Better identification of diabetic patients at high risk of vision-threatening complications is crucial for the prevention of vision loss. Ocular imaging by fluorescein angiography and optical coherence tomography has been playing a significant role in the visualization of retinal vascular perfusion and macular edema, respectively. Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique that is able to visualize separately the different retinal and choroidal vascular layers using motion contrast technology to detect blood flow without intravenous dye injection25, there is a lack of data on the reproducibility of OCTA measurements in diabetic patients. The purpose of this study was to evaluate the intrasession and between-visit reproducibility of quantitative retinal microvasculature parameters using OCTA in patients with non-proliferative diabetic retinopathy.In clinical practice the measurement error of an instrument has special importance that depends on the context in which the measurements in question are to be used. Usually, the same degree of measurement error that is acceptable in a comparative study, such as a clinical trial, may be too large in individual patient management, such as in a follow-up or the assessment of a therapeutic response. Moreover, it is important to know the measurement error of an instrument in designing clinical trials, analyzing and interpreting data, and acknowledging limitations – all of which are essential for clinical research. Although the high accuracy and reproducibility of OCTA parameters have already been described in normal subjects2) using the automatic non-flow tool of the software. In this study we analyzed retinal vessel density in the central 3 mm diameter area as the current version of the AngioAnalytics software acquires scans with the highest resolution in the central 3 × 3 mm area enabling confident evaluation of fine vessels in this region of the macula. In addition, we evaluated changes solely in the superficial capillary plexus, with the level at which the segmentation line was positioned with an inner boundary being 3 µm below the internal limiting membrane (ILM) and an outer boundary 15 µm below the inner plexiform layer (IPL), as quantitative analysis of the deep capillary plexus is unreliable using the current AngioVue software due to projection artefacts. Moreover, images with pronounced diabetic macular edema, which generated consecutive segmentation errors at the level of the superficial vascular plexus, were excluded from the study. As a result, no segmentation line needed manual modification and positional change of the retinal blood vessels due to DME was not observed. Since image artefacts – such as movement artefacts , projection artefacts and segmentation errors – can provide false and misleading information, in patients with initially poor images, we repeated the scans until good image quality could be obtained. The selection of images for the purpose of exclusion was conducted by one individual trained examiner, with poor quality images, which were excluded from the analysis being defined as those: with a Signal Strength Index (SSI) below 50; autosegmentation alignment errors on OCT B-scans at the level of the superficial capillary plexus induced by DME; and, images with a presence of artefacts, such as double vessel pattern and dark areas from blinks or media opacities that obscure vessel signal. Although the recommended cut-off value of the SSI is greater than 39 on a scale from 0 to 100 according to the manufacturer’s recommendation, in previous research papers the generally accepted threshold value was 50; thus in our study we have adopted this limit. Each study subject underwent two sessions of imaging; in the first session three OCTA images were obtained and one month later, in the second session one OCTA image was acquired.In this prospective, observational cross-sectional institutional study, a total of 54 eyes of 27 subjects  with type 1 and type 2 diabetes were recruited from the outpatient clinic of the Department of Ophthalmology at Semmelweis University. The study followed the tenets of the Declaration of Helsinki, applicable national and local requirements, and was approved prospectively by the Ethical Review Board for Human Research of the National Drug Agency. All patients signed their written informed consent. Diabetic patients without any retinopathy and patients with clinically detectable retinopathy, as defined by the International Clinical Diabetic Retinopathy Disease Severity Scale of the American Academy of Ophthalmology, were recruited in the study. Exclusion criteria were the following: any history of intraocular surgery, other ocular disease , previous intraocular anti-VEGF, steroid or laser treatment, the presence of clinically significant lens opacities, or refractive error >6 diopters. All subjects underwent a comprehensive ophthalmic examination including slit lamp and fundus examinations using indirect ophthalmoscope. Optical coherence tomographic angiography imaging was performed using the AngioVue OCTA system  with an SSADA (split-spectrum amplitude decorrelation angiography) software algorithm. This device obtains volumetric scans of 304 × 304 A-scans at 70 000 A-scans per second in approximately 3.0 seconds. Full retinal thickness was measured in the central 1.0 mm area. Superficial vessel density (VD) was evaluated in the central 3 mm and parafoveal area, and foveal avascular zone (FAZ) area was measured using the built-in AngioAnalytics software of OptoVue system with an automated segmentation algorithm . The Shapiro-Wilks W test was used to test the normality of the data.a priori by statistical power calculation  using data from previous studies at our institution and the method proposed by Lu et al. for repeated measurements26. The minimum number of eyes to enroll in this study was calculated to be 37 eyes with a maximum allowed difference of 5% in vessel density values between two consecutive measurements on the same subject.Sample size was determined 27. The ICCs could range from 0 to 1, with a higher value indicating better reliability and an ICC above 0.9 indicates excellent clinical repeatability28. Intrasession and between-visit CVs with their corresponding 95% confidence intervals were calculated according to the procedure described by Bland and Altman29. For this purpose, the within-subject standard deviation (Sw) was calculated as the square root of the within-subject mean square of error in a two-way mixed-effects model, and then CV was determined as 100× within-subject standard deviation/overall mean30. Finally, Bland- Altman plots were created by plotting the coefficient of repeatability (CR), which is a useful index that quantifies measurement error in the same units as that used in the measurement tool. The CR of an instrument corresponds to the 95% limits of agreement on Bland-Altman plots and contains 95% of differences between repeated measurements on same subjects. The CR is the value below which the absolute differences between two measurements would lie with 0.95 probability33 and can be calculated by multiplying the within-subject standard deviation (Sw) by 2.77 (√ 2 times 1.96)33.Intrasession and between-visit reproducibility were characterized by the corresponding intraclass correlation coefficient (ICC) and within-subject coefficient of variation (CV) values. Intrasession and between-visit intraclass correlation coefficients (ICC) were calculated using two-way mixed-effects models with their 95% confidence intervals on absolute agreementIn all statistical analyses, a P-value of less than 0.05 was considered to be statistically significant.2 vs. 0.31 ± 0.11 mm2; p > 0.05). Similarly, there was no significant difference in 3.0 mm vessel density  and in parafoveal vessel density  between the two visits. However, as an indicator of intrasession variability of measurements, fluctuation in OCTA metrics could have been observed on images taken consecutively from the same subject as one can see on the representative Fig. Patients characteristics are summarized in Table Intraclass correlation coefficient values were excellent (>0.90) both in intrasession and between-visit comparisons of measurements Table . However2 for the FAZ area. In addition, the presence of any proportional error during the repeated measurements was assessed by drawing a regression line on the differences seen in Fig. Next, Bland-Altman analysis was used to describe between-visit agreement of the repeated measurements. As it is shown on the Bland-Altman plots, the mean difference was 0.07% for the VD in the whole macula and 0.42% for VD in the parafoveal ring and 0.01 mmGiven the possible effect of image quality on the repeatability of measurements, we additionally assessed the variability of image quality expressed by their corresponding SSI values and its influence on vessel density measurements. The intrasession CV value for SSI was 3.59% (95% CI: 2.91–4.27%) suggesting that image quality had excellent reproducibility in this study. However, by analyzing the triplicate measurements from the first visit, we found a significant correlation between the variability of SSI and the variability of vessel density values in the whole macula , and also in the parafoveal ring  and without n = 33) macular edema. We found that image quality was significantly lower in eyes with macular edema compared to those without 33. In our study, the between-visit CR of the vessel density in the 3 mm macular area was similar (±4.53%) to that for the parafoveal ring (±4.73%). The CR (±4.53%) means, that vessel density values in any individual patients would be of this scale, being within 4.53% of this bias. Based on the CR of the 3 mm macular area, a clinician using the retinal vessel density in a diabetic patient would need to measure a change of at least 4.53% at re-assessment to be 95% confident that the patient had, in fact, progression in diabetic microangiopathy. A change of less than 4.53% might simply be due to the measurement inaccuracy of OCTA, which is unable to reliably detect change of less than 4.53%. Our results are in a good harmony with the findings of a previous study on healthy subjects, where a CR of 4.1–4.64% for the whole scan and a CR of 4.4–4.85% for the parafoveal VD in the 3 × 3-mm OCTA images were calculated34.In addition, we have provided coefficient of repeatability (CR) values for different OCTA metrics as well, since these data may have direct implications on the follow-up of this population. The coefficient of repeatability can be interpreted as the smallest change in OCTA metrics that represents true change35 with high reproducibility and reliability36. The AngioAnalytics flow density map software released by the AngioVue system allows the quantification of flow area, FAZ area, and flow density around the fovea. Vessel density measurements estimate the degree of capillary loss over an area as a percentage of the total evaluated area35, however, OCTA cannot directly detect leakage from the retinal blood vessels and cannot image early signs of peripheral retinal nonperfusion37. In this study we analyzed retinal vessel density in the macular area focusing on the changes in superficial retinal vascular bed as quantitative analysis of the deep capillary plexus is not reliable using the AngioVue software due to projection artefacts38. Apart from confirming previous results on the negative effect of projection artefacts on quantitative results39, we also confirmed findings that the variability of vessel density measurements is significantly related to the variability of SSI values23, showing the importance of high quality image acquisition during OCTA. We also found, that image quality was significantly lower in eyes with macular edema compared to those without, although neither intrasession nor between-visit variability of image quality was affected by the presence of diabetic macular edema. Cystoid macular edema in diabetes is located in different retinal layers, especially in the outer plexiform layer40. While the retinal layers are easily distinguishable in healthy retinas, the presence of macular edema decreases the contrast between different layers and the segmentation remains very challenging for automated methods. Although recent studies have introduced automated segmentation methods for eyes with severe macular edema, their availability remains limited41, thus, before quantitative analysis of OCTA images any segmentation error due to DME should be identified. The decreased reliability of AngioFlow measures due to compromised image quality and projection artefacts can be particularly important during the analysis of deep capillary plexus, which has already been described as helping in the identification of eyes at increased risk of developing DR42. The possible effects of segmentation error on our results could be dismissed from consideration since in this study we evaluated changes in the superficial capillary plexus, and images with misidentification of segmentation lines on OCT B-scans induced by macular edema were excluded.Diabetic retinopathy was previously mainly associated with visible pathology of the retinal vasculature; however, OCT angiography is a sensitive tool that can indicate subtle microvascular damage even before clinical vascular abnormalities can be seen. OCTA technology enables us to study, both qualitatively and quantitatively, the retinal microcirculation44. In addition, in designing clinical trials with OCTA, knowledge of the repeatability of measurements is crucial in order to calculate the sample size required to demonstrate disease progression or the impact of an intervention that is intended to reach a clinically relevant threshold. Repeatability of measurements refers to the variation in repeated measurements made on the same subject under identical conditions and is particularly important in clinical settings. To assess repeatability, measurements should be taken by the same instrument over a short period of time, over which the underlying value can be considered to be constant. Therefore, variability in measurements made on the same subject in a repeatability study can then be attributed only to errors due to the measurement process itself. In this study, repeated OCTA measurements made during the first visit corresponded to intrasession repeatability, while the second visit - scheduled approximately one month after the first visit - was used to assess between-visit repeatability and mirrored the usual follow-up intervals of diabetic patients . Therefore, it is our opinion that the results on the repeatability of OCTA metrics can be generalized to a clinical population of diabetic patients, and thus helping the clinical care of this population.Knowledge of the measurement error of an instrument is especially important both in the detection of retinal abnormalities at their earliest stages and in patient follow-upAs a limitation of the present study, the data is obtained from a cohort from a single center, which may limit the generalizability of our results to other populations. Nevertheless, we assume that given the number of eyes included in this study the conclusions drawn from our results are reliable. Another significant limitation of the study is the absence of information on the patient’s nutritional status, concomitant cardiovascular and other non-cardiovascular conditions that may contribute to the between-visit variability of OCTA parameters. Although this information would have helped elucidate the relationship between systemic factors and the repeatability of measurements, the primary purpose of this study was to assess the reliability of OCTA in detecting retinal microvascular damage to make both screening and assessment of progression more sensitive.The fact, that the repeatability of retinal vessel density measurements was not affected by the presence of diabetic macular edema supports the assumption that the progression of microvascular damage can be evaluated accurately in different stages of non-proliferative diabetic retinopathy. However, further studies are recommended to examine whether the between-visit repeatability of OCTA measurements could be improved by using average values of consecutive measurements during each visit.In conclusion, repeated OCT angiography measurements of macular vessel density revealed excellent repeatability, indicating that this non-invasive technology is sufficient for longitudinal assessment of microvascular complications in diabetes. However, a change of less than 4.5% in superficial vessel density might simply be due to the measurement inaccuracy of OCTA, thus a change of at least 4.5% at re-assessment is required to indicate with 95% confidence that the patient had, in fact, a true change in retinal capillary blood flow.


Voltage imaging of many neurons simultaneously at single-cell resolution is hampered by the difficulty of detecting small voltage signals from overlapping neuronal processes in neural tissue. Recent advances in genetically encoded voltage indicator (GEVI) imaging have shown single-cell resolution optical voltage recordings in intact tissue through imaging naturally sparse cell classes, sparse viral expression, soma restricted expression, advanced optical systems, or a combination of these. Widespread sparse and strong transgenic GEVI expression would enable straightforward optical access to a densely occurring cell type, such as cortical pyramidal cells. Here we demonstrate that a recently described sparse transgenic expression strategy can enable single-cell resolution voltage imaging of cortical pyramidal cells in intact brain tissue without restricting expression to the soma. We also quantify the functional crosstalk in brain tissue and discuss optimal imaging rates to inform future GEVI experimental design. However, the appropriate GEVI imaging sampling rate further depends on the time course of the optical signal generated by the GEVI used. Due to the non-instantaneous kinetics of GEVIs the optical signal is generally low-pass filtered relative to fast voltage signals . Imaging at rates higher than necessary degrades SNR by increasing the proportion of time spent on image readout relative to signal integration and increases accumulated read noise. Increasing frame rates without reducing the SNR also necessitates an increase in illumination intensities, which will increase the bleach rate and reduce the available imaging time. Imaging at frame rates above 100 Hz is also limited by image sensor technology and often requires using fast, low pixel-number charge coupled-device (CCD) cameras, or pixel sub-arrays from most modern sCMOS cameras.When imaging with wide-field illumination, photons are integrated throughout virtually the whole frame period, and therefore fast fluorescent transients can be detected even if the imaging rate is below the nominal Nyquist rate of the optical signal. This is because the integration of detected photons over the frame period applies an effective low-pass filter to the collected fluorescence signal. This contrasts with laser scanning illumination techniques where photons are collected for each pixel for only a short fraction of the frame rate, and a frame rate above the Nyquist frequency must be used to ensure detection of fast transients. That is, a neuron's action potential may escape detection if it occurs between visits of the neuron by the laser spot in LSM. The choice of wide-field imaging speed appropriate to the indicator and experimental question is therefore important.Improvement of SNR has been the driving force for much of the previous work on voltage imaging. Recently, new photostable low molecular weight voltage sensitive dyes and genetically encoded voltage indicators (GEVIs) have been developed ΔF/F where fb is the fraction of fluorescence arising from non-signaling structures. Background fluorescence also has a detrimental effect on SNR. In a shot noise limited imaging system, SNR will be reduced proportionally to the SNR measured in the absence of background fluorescence (SNR0) as SNR0 . The fractional change in collected fluorescence, ΔF/F, will be reduced to  imaging requires not only sparse, but also strong expression to enable an adequate collected photon flux. Gene delivery via intracortical injection of viruses produces expression patterns where the expression strength and likelihood decreases with increasing distance from the injection site and virus titer. Sparse expression can therefore be achieved with modifications to injection protocols, albeit in a local and highly variable way and with low expression levels (on average < 1 virus particle per cell and therefore often only 1 virus per expressing cell). Another strategy to limit the density of indicator expressing membranes is the targeting of a GEVI to specific compartments of the cell. Recent works showed that, indeed, soma targeting of GEVIs can reduce the number of labeled membranes and enable single-cell signals to be extracted from sequences of images  Act 1986 under Home Office Project and Personal Licenses (project licenses 70/7818 and 70/9095). The protocol was approved by the UK Home Office. Transgenic mice were bred to express VSFP Butterfly 1.2 in cortical layer 2/3 pyramidal neurons under the intersectional control of TetO and Cre-recombinase  – fi[n])/, where 1,600 is the camera offset for 4 × 4 binning and n is the frame number. Only averaged traces were analyzed and all traces plotted are averages unless explicitly noted. Number of averages for traces are noted in captions. We plot all fluorescence traces on inverted y axes.Imaging trials were repeated between 5 and 20 times and the individual trial image sequences averaged. Linear fits, fi[n] – fi[n]). The image time series was downsampled to 50 Hz by averaging to improve the SNR. To create maps for visualization and segmentation the image sequences were spatiotemporally filtered with a Gaussian filter with sigma of 1 time point/1 pixel 20 ms/1.04 μm and for functional spread characterization they were temporally filtered with a 3 point median filter. 2D activation maps were then found from 3D image sequences by summing the frames collected during stimulation periods.Image analysis was conducted with Python. Activation maps were found by considering ΔF image sequences calculated pixel-wise as  with the median value of the preceding 10 time points subtracted. For subsequent spikes in the spike train the signal size was calculated as the minimum of the 50 ms following the stimulus with the maximum of the 20 ms time points before the stimulus subtracted. The noise level of traces was calculated as the standard deviation of 20 samples before the stimulus was applied.0 trace acquired at 500 Hz and downsampled it by averaging. To avoid overestimating the noise level for larger averaging periods we first flattened the trace to remove bleaching remnants by dividing it by a polynomial fit to the trace with the voltage signals removed. We then calculated n downsampled traces for downsampling by integer factors of 1, 2, 3, 4, 5, 10, and 20, where each of the n traces corresponds to a different downsampling phase. We calculated the noise level as the standard deviation of 100/n points with no voltage signal and the signal level as the peak value during the stimulus period subtracted by the median value of the 20/n preceding points. AP timing detection jitter was calculated as the imaging frame period multiplied by the range in frame number of the location of the optical AP signal peak. Least-squares log-log-linear fits were then calculated for the noise and 1-signal size.To examine the effect of decreased sampling rate on GEVI traces we took an example ΔF/Fri to be equal to We estimated the sparsity level required to separate signals between adjacent cells using the mean autocorrelation of the activation maps. Autocorrelations of spatially unfiltered maps were calculated and a 1-pixel central peak arising from noise in the image was removed by replacing the central pixel with the mean of the surrounding pixels. The autocorrelation images were then normalized to between 0 and 1, rotated such that their longest axis faced the same direction before taking an average over all cells. This generated a roughly elliptical autocorrelation image with orthogonal axes representing our estimate of the largest and smallest signal mixing lengths. We calculated the amplitude of the crosstalk for a neuron at the origin due to other neurons at positions i))  see . We can As explained in the introduction, it is difficult to optically resolve fluorescent plasma membranes if a GEVI is targeted to all cells in a dense population of neurons, such as cortical pyramidal cells. To illustrate this, 0 after averaging the images from up to 80 trials . In response to short single spike-triggering current pulses (0.5 ms current injections to induce spiking at 20 Hz), we observed signals (n = 4 cells out of 4 cells attempted), with 2 cells with a signal-to-noise ratio consistent with spike detection without trial averaging (see below). Overall, we resolved single cell voltage signals in 13 patched cells in 13 slices at imaging depths of up to 77 μm.In response to long lasting depolarizing current injections , we resolved voltage signals in single trials  reveal cellular structures that are not always readily apparent in raw wide-field images. Maps of GEVI responses allow identification of processes belonging to active cells . We calcIexΦBC, where Iex is the illumination intensity, ΦB the bleaching quantum yield, and C the concentration of unbleached GEVIs. Ideally the quantum yield of bleaching would be measured for each GEVI as it would allow independent comparison of expression density; however, this is not known for the GEVI used in this study or, to our knowledge, any other GEVI. Normalizing the SNR to the bleach rate, however, can help to control for the GEVIs bleaching propensity and concentration along with the illumination level in one step with an easily calculable number.Comparing experimentally assessed SNR values between different imaging systems and preparations is complicated by differing fluorescence collection efficiencies, illumination conditions, and GEVI expression levels. Optical signals collected with high quality sCMOS cameras operating in a high photon count regime [>>10 photons per pixel per frame . We calculated the signal and noise levels for 9/15 cells for which we collected data at 100 Hz with comparable stimulus regimes, and used image sequences of the average of four trials for each cell in the analysis. These SNR values come from an early generation GEVI with low sensitivity. As SNR increases linearly with indicator sensitivity, our values and conclusions can easily be scaled for more recent GEVIs with much higher reported sensitivity under similar experimental conditions.n of the n-point moving average. A fluorescent time course containing 3 AP-evoked fluorescence transients was downsampled by averaging to frequencies as low as 25 Hz (2xb2 (log-log linear r2 = 0.999), with b2 = 0.45 ≈ 0.5 as predicted by theory , with b1 = −0.13. Combining these and plotting again the SNR gave a predicted ideal imaging speed for spike detection of 99 Hz  CCD camera , images.as 25 Hz  and the as 25 Hz . The noiof 99 Hz . We noteof 99 Hz  is desirWe validated this analysis by modeling the GEVI signal as a series of decaying exponentials. We calculated the fraction of aliased power compared to total sampled power as a function of sampling frequency when sampling by integrating through the sampling period. We found that severe aliasing occurs when the sampling period used is more than approximately half the decay constant of the optical signal. The signal in In order to guide future levels of sparsity we developed a method to estimate the level of signal mixing between cells as a function of distance. Neurons are highly variable in shape and, in the cortex, their extent varies with the orientation of the imaging focal plane relative to the cortical column. i other neurons separated by distances, ri, of the same shape and with similar fluorescent time courses. We wanted to quantify crosstalk, that is the signal amplitude  based expression strategies enable single-cell resolution voltage imaging of cortical pyramidal cells in acute brain slices. This expands the current repertoire of single-cell voltage imaging from interneurons and other naturally sparse cell classes to non-sparse layer 2/3 cortical pyramidal cells. We have not verified this approach with other cell classes, although we expect no differences in the strategy relevant to functional imaging. This powerful approach allows the sparsity of expression to be varied by the titration of the dosage of TMP which stabilizes the dCre and, in combination with strong transgene inducers systems, such as tetO, enables sparse but strong GEVI expression. Different levels of sparsity allow the experimenter to trade off the level of population sampling and functional crosstalk between cellular voltage traces. The level of sparsity can also be adapted to the imaging approach used, with increased specificity from, for example, holographic imaging allowing denser cellular expression  active cells spread in order to guide future experimental designs. To choose a specific sparsity level, however, experimenters must also consider other factors, such as the characteristics of the membrane dynamics of the studied cell class, as well as the specific experimental question. More crosstalk can be tolerated, and therefore more dense cells studied, if factors, such as AP amplitude can be used to discriminate between signals from different cells and subthreshold fluctuations are less important.Sparse transgenic approaches could also be beneficial to opsin photostimulation approaches. Light shaping methods using computer generated holography (Papagiakoumou et al., The datasets generated for this study are available on request to the corresponding author.PQ, CS, SA, AF, and TK conceived and designed the experiments. PQ, CS, EM, MM, and CH performed experiments. PQ, SA, MN, SS, AF, and TK designed the analysis. PQ, SA, and TK analyzed the data and wrote the paper. All authors contributed to manuscript revision and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The IPCC Third Assessment Report presents a conceptual framework for vulnerability to climate change with the three attribute components of exposure, sensitivity, and coping. Since the vulnerability assessments have been conducted mainly by the composite indicators aggregated from the IPCC’s components, it is necessary to assess aggregation frameworks for constructing the composite indicators that have an influence on vulnerability assessment outcomes. This study therefore investigates the robustness of assessment outcomes for flood vulnerability to climate change through a comparative analysis of the six vulnerability indicators aggregated from the IPCC’s components by the conventional aggregation frameworks. The comparative analysis has been illustrated through both the possible combinations of reference values for vulnerability attribute components and a case study on the flood vulnerability assessment to climate change for coastal areas in the Republic of Korea. The study demonstrates that there can be large fluctuations and reversals in ranking orders across the six vulnerability outcomes by different aggregation frameworks. It concludes that for flood vulnerability assessment to climate change in coastal areas, the vulnerability indicator needs to be aggregated by a multiplicative utility function from all the three assessment components with positive elasticity to vulnerability. One of the most outstanding definitions and frameworks for vulnerability assessment to climate change is discussed in the Intergovernmental Panel on Climate Change Third Assessment Report (IPCC TAR)1, which states that the vulnerability assessment components are exposure, sensitivity , and coping . This has also been described in some selected literature, including the IPCC Fourth and Fifth Assessment Reports5. As climate change has a large impact on many sectors important to society and the environment, this conceptual framework for vulnerability to climate change has been used in various vulnerability assessments for the purpose of targeting multi-dimensional issues in a wide range of fields6. In particular, many coastal areas have already experienced severe weather conditions, such as strong winds, heavy rains, and huge storm surges, due to climate change, causing devastating flooding in low-lying areas of coastal regions5. Hence, it is necessary to develop flood damage mitigation strategies in coastal regions through an appropriate assessment of flood vulnerability that indicates a degree of potential damage at flood risk.Climate change is one of the greatest environmental threats, already having visible impacts on all countries worldwide; thus, vulnerability assessment to climate change has become one of the most important tools for providing fundamental information on mitigation and adaptation measures concerning climate variability, changes, and impacts7. A single composite indicator is usually compiled from individual component indicators to measure the multidimensional concept8, as multiple-criteria decision making or analysis is required to evaluate multidimensional issues and multiple conflicting criteria in decision making9. Hence, multi-attribute utility functions are most commonly and powerfully used to combine assessment attribute components into a composite indicator for the overall performance information that can provide ranked alternatives for deterministic policy and decision problems8. The general procedure for constructing a composite indicator for policy and decision making is as follows:7 1) a theoretical framework is established to select and classify appropriate assessment components that fulfill the purpose of generating a meaningful composite indicator; 2) proxy variables relevant to individual components are selected and identified by multivariate analysis in the context of availability, measurability, representatives, soundness, and mutual independence; 3) a selected normalization method transforms variables measured at different units or scales into a common domain to affect the analysis in an equal scale; 4) reasonable weightings are estimated and assigned for the importance of the associated variable based on correlations between variables and the phenomena; 5) a multi-attribute utility function is employed to aggregate individual attribute components into a composite indicator to measure overall performance; and 6) sensitivity and robustness analysis is conducted to prevent the derived composite indicator from providing false or misleading information.Vulnerability to climate change has been evaluated mainly with a composite indicator compiled from multiple assessment components by one of the various aggregation frameworks and methods7. Some of the previous studies addressed multiple frameworks for constructing vulnerability composite indicators11, but it is deemed acceptable in most studies that a single aggregation method is simply selected without considering the purpose and characteristics of each vulnerability assessment. Although there is limited comparison of the vulnerability outcomes from different aggregation methods by using only the correlation coefficients between them11, the robustness and sensitivity of vulnerability scores with varied types of frameworks and utility functions have not been explicitly explored.Since there is no prominent scheme or standard method for constructing the vulnerability composite indicator from individual attribute components, vulnerability to many aspects of natural and social sciences and humanities has been estimated somewhat discretionally by simply selecting one of several aggregation frameworks and methods. Many vulnerability assessment studies have presented various vulnerability composite indicators generated from the constituent components by either additive utility functions or multiplicative utility forms conventionally and in general7. The comparative analysis has focused on the vulnerability ranking fluctuations across the six composite indicators by the conventional aggregation frameworks to facilitate the understanding of the interaction among the constituent components and assessment outcomes with respect to various multi-attribute utility forms. The analysis is illustrated by tackling the possible reference cases for combinations of the vulnerability attribute components and by a case study based on actual observations and data in multidimensional situations for the coastal flood vulnerability assessment to climate change in the Republic of Korea.The aim of this study is to evaluate various kinds of composite indicator products for assessing vulnerability to climate change. It uses different conventional aggregation frameworks to better understand the importance of composite indicators used in decision making for selecting alternatives or a preferred solution. Therefore, for evaluating the various composite indicators, this study investigates how frameworks of assessment components and multi-attribute utility forms for combining them affect the vulnerability composite indicator outcomes to estimate the overall performance. This study considers that all the attribute components are normalized into a commensurate scale from 1 to 100, indicating the least to most preferred, and weighted equally to individual components in order to avoid any other effects of normalization and weightings on composite indicator outcomes. It is feasible to assign equal weights to all assessment indicators in the absence of information or consensus for different weights8. It is obvious that the aggregation frameworks and multi-attribute utility functions for individual assessment components can directly affect composite indicator outcomes, although it is also important to appropriately utilize other factors such as proxy variables, normalization methods, weighting methods, etc. Given that weighted and normalized individual attributes are under mutually independent conditions, they can be in general and conventional combined into a composite indicator by either the linear additive utility function7 as:7 as:CA or CM is additively or multiplicatively aggregated from individual weights αi or βi, respectively, and component indicator scores χi of attribute i in a common scale for component numbers n.When individual attribute indicators cannot satisfactorily represent the multidimensional concept, they are compiled into a composite indicator that can measure the complex phenomenon2, many vulnerability indicators have been aggregated from the IPCC’s three components by the conventional utility functions. The vulnerability indicator to climate change can be expressed in Eq.  framework, the average of the sensitivity and coping components is combined with the exposure component as in Depietri et al.26 On behalf of the three modified frameworks where the two components are combined to be compiled with the other component, the framework using the potential impact PI as a composite component of exposure and sensitivity is selected for one of the conventional frameworks for the vulnerability indicator. Thus, Eq.  (where “||” denotes “OR”).To investigate the performance of vulnerability indicators aggregated from the three components of exposure, sensitivity, and lack of coping (or coping) by the six multi-attribute utility functions in Eqs.  through VA1 and VA2) and the two multiplicative forms (VM1 and VM2) in Fig. 2 and VM2) and the two components (VA3 and VM3) in Fig. In Table VA1 and VA2, by additive aggregations in Eqs. . Figure VA1, VA2, VM1, and VM2, from the three constituent components. As shown in Fig. VA1 and VA2 values aggregated by the two additive multi-attribute utility functions is completely coincident between the two results. Note that the outcome values of VA1 and VA2 can differ (see Table Table  in Eqs.  and 5),,VA1 and  in Eqs.  and 6)  VA1 and VM1 and VM2, some reference cases of the three attribute components show inconsistent outcomes in VM1, which incorporated the coping component as a divisor. The composite indicator outcomes are expected to be identical for the reference cases that comprise the three components of the same score levels in different orders, such as cases (3) and (2) with H-H-M(M) and H-M-H(L), cases (9) and (10) with H-L-M(M) and M-L-H(L), cases (13) and (12) with M-L-M(M) and M-M-L(H), and cases (17) and (16) with L-L-M(M) and M-L-L(H), respectively. However, the vulnerability outcome of each of the former cases is much lower than that of the latter (i.e. cases (3) < (2), (9) < (10), (13) < (12), and (17) < (16)) as presented in Table VM1 as a result of a multiplicative aggregation function using a divisor component of coping in Eq. (VM1 produces much higher vulnerability in case (5), with M-M-H(L), than case (4), with H-M-M(M) ) and case (3), with H-H-M(M) (a higher level combination than the case (5)). This is due to the fact that the multiplicative composite indicator VM1 would generate the overestimated outcomes in such combinations as in case (5), including a low denominator of coping without any comparable low numerators of exposure or sensitivity that cannot cancel out each other. Abnormally large outcomes could lead to ranking reversals in vulnerability assessments measured by VM1. Such large vulnerability outcomes are also generated in cases (1) and (2), but ranking reversals might not occur since these cases have higher score levels than other reference cases. Note that as shown in Table VA1, VA2, and VM2 produce the same vulnerability outcomes between each of the two cases in the above five sets of reference cases, such as cases (2) & (3), (4) & (5), (9) & (10), (12) & (13), and (16) & (17), while there is a difference between each of the two cases in VM1 outcomes only. Consequently, the vulnerability outcomes VM1 aggregated from the IPCC’s components E, S, and C by the well-known form of a multi-attribute utility function in Eq.  generates higher vulnerability scores in case (12) with M-M-L(H) and case (13) with M-L-M(M) than cases (14) with L-L-H(L) and (15) with H-L-L(H). These four reference cases all have the same vulnerability scores in the additive composite indicators VA1 and VA2. Case (6) with M-M-M(M) is higher than case (7) with H-H-L(H) in the multiplicative outcomes by VM2, but case (6) is less than case (7) in the additive outcomes by VA1 and VA2. Thus, the outcomes of the additive composite indicators could be compensated with a higher score in any of the three components, while a lower score in any of the three components could drastically reduce the outcomes of multiplicative composite indicators. This is due to the fact that the additive aggregation functions show the higher compensability in that the composite indicators from deficient components can be compensated for by surplus scores of other components, which can hardly compensate for low components in the multiplicative aggregation functions.As denoted in Table VA2 and VM2 aggregated from the three components, exposure, sensitivity, and lack of coping, with VA3 and VM3 aggregated from the two components, potential impact (combined from exposure and sensitivity) and lack of coping. The framework comprising the two components with more weighted lack of coping can lead to different vulnerability ranking than that of results combined when the three components are equally weighted. The vulnerability indicators VA3 and VM3 can be more sensitive to the lack of coping component weighted at one-half of the exposure and sensitivity components with one-quarter weight for each, given the equal weights of 1/2 as denoted in Table VA2 and VA3 by additive aggregations and between VM2 and VM3 by multiplicative aggregations. As for the perspective of the lack of coping scores, the vulnerability ranking of VA3 and VM3 is higher in cases (5), (8), (10), and (14) with the high level scores of lack of coping, while lower in cases (7), (11), (12), (15), and (16) with the low level scores of lack of coping, compared with that of VA2 and VM2, respectively, as shown in Fig. Table 1.Flood vulnerability assessment is one of the essential tools for providing preemptive information on current flood mitigation plans, wherein severe flood damages occur more frequently. Flood vulnerability has been commonly evaluated with a composite indicator compiled from multiple assessment components by easily selecting one of the various aggregation schemes. Although it is natural that different aggregation schemes may generate different composite indicators, it is necessary to understand the impact of different aggregation schemes on vulnerability assessment outcomes. Therefore, a case study on flood vulnerability assessment to climate change is conducted by various aggregation frameworks for the three relevant components in the IPCC TARAs shown in Fig. 7. However, there will be some hurdles in the selection of proxy variables for every study on vulnerability assessments despite the best possible efforts and intentions to avoid such issues. In the selection of proxy variables for this study, the main focus was on human and property damage by coastal flooding due to climate change. With reference to previous studies34 and data acquisition availability in the study site, this study carefully selected and collected the six proxy variables for the three attribute components of exposure, sensitivity, and coping in the well-known conceptual framework of vulnerability to climate change in the IPCC TAR1, as shown in Table The proxy variables need to be carefully selected and constructed based on the full context of availability, representatives, measurability, integrity, analytical soundness, relevance to phenomena, and relationship to each other35. The storm surge height data above ground level are constructed from the storm surge data across a 100-year return period using a non-stationary GEV  distribution model36. The sensitivity component comprises the human and property factors likely to be damaged by hazardous floods, such as the children and elderly population, and major facilities and infrastructure data, which were collected from the Korean Statistical Information Service37. On behalf of systems for coping with hazardous floods, the medical and evacuation facilities for emergency services and the river improvement length for flooding countermeasures were selected in the coping component against casualty losses and property damage, respectively. Figure Due to the lack of data on hydrological modelling for the entire study site, for the geographical range exposed to hazards, the exposure component comprises flood-causing climatic variations in coastal regions, such as the rainfall and storm surge data. Aside from heavy rainfall being the main cause of flooding, storm surge is one of the most dangerous perturbations causing flooding in coastal areas, since it can cause a significant rise in sea level. The future daily rainfall data by climate change based on the IPCC AR5 scenario are collected from the Korea Meteorological Administration Global Atmosphere Watch38, which transforms all proxy variables into a common scale between 1 and 100 as follows:xi is the original value of the proxy variable i, ui is the standardized value of xi, max(xi) is the maximum value of the proxy variable i, min(xi) is the minimum value of the proxy variable i, smax is the standardized maximum value of 1, and smin is the standardized minimum value of 100.For standardization of all the proxy variables in exposure, sensitivity, and coping components, this study uses the min-max normalization method as employed in previous studies38:The lack of coping component for a positive contribution to vulnerability is standardized into reversal values of the coping proxy variables having a negative relationship with vulnerability as used in previous studiesχj for exposure E, sensitivity S, coping C, or lack of coping LC with the weighting factor wi for the total variable number k as used in previous studies38:The standardized proxy variables are then compiled into each component indicator VA1, VM1, VA2, VM2, VA3, and VM3 are derived by various aggregation frameworks in Eqs.  by Eq. (VA3 from the two components (PI and LC) by Eq. ,,VA1, VM12 by Eq.  and VM2 ) by Eq.  and VA3 s by Eq. . Some diE, S, and LC(C), and the flood vulnerability rankings by the six composite indicators VA1, VA2, VA3, VM1, VM2, and VM3. Higher flood vulnerability rankings are determined by the multiplicative aggregation forms VM1, VM2, and VM3, compared to the additive indicators VA1, VA2, and VA3, as shown in districts 2620, 2811, 3114, 4122, and 4127, where the value of neither E nor S is too low. On the contrary, the flood vulnerability rankings of districts 2611, 4678, 4682, and 4885 are mostly higher in the additive indicators VA1, VA2, and VA3 that have higher compensability by the highest level value for either E or S, as compared to the multiplicative outcomes by VM1, VM2, and VM3. Table VM1 and VM2 in district 2611, which has the three component values comparable to reference case (11) in Table VM2 produces an identical vulnerability for both reference case (11) with H-M-L(H) and reference case (9) with H-L-M(M), in which both combinations are equivalent to each other. However, the vulnerability of reference case (9) is much lower than that of reference case (11) in VM1, although the two cases have equivalent scores for the three constituent components. A similar ranking fluctuation between VM1 and VM2 occurs in district 5011, ranked as the most vulnerable district by VM1, where a low denominator value of the coping component C will generate abnormally large composite indicator outcomes, as addressed in Section 3. In some districts, the flood vulnerability rankings of the VA2 and VM2 aggregated from the three components E, S, and LC with the equal weights of 1/3 are different from those of the VA3 and VM3 derived from more weighted LC by 1/2 and less weighted E and S by 1/4. As compared to VA2 and VM2, VA3 and VM3 showed lower flood vulnerability rankings in districts 2611, 2811, 3144, and 4122, with relatively low values of LC, while higher flood vulnerability outcomes were observed in districts 4678, 4682, 4885, and 5011, with relatively high values of LC. Such ranking changes appear more prominently in comparing the two multiplicative indicators between VM2 and VM3, rather than in the two additive indicators between VA2 and VA3. The flood vulnerability outcomes are similar between VA2 and VA3 and between VM2 and VM3 in districts 2620 and 4127, respectively, with middle level values of LC.For further comparative analysis of various flood vulnerability outcomes as shown in Fig. 1, the vulnerability assessments to climate change have been conducted in various fields, such as human health, ecosystem, water, agriculture, fishery, etc., including floods. This study has therefore assessed the influence of frameworks and utility function forms for constructing a composite indicator on measuring the overall vulnerability to climate change. In the framework for the three vulnerability assessment components in the IPCC TAR1, the exposure and sensitivity components have a positive elasticity to the vulnerability, while the vulnerability decreases with the increasing coping score that needs to function as a subtrahend in additive multi-attribute utility forms and a divisor in multiplicative multi-attribute utility forms. Thus, lack of coping with a positive elasticity to the vulnerability like other two components can be used in the opposite concept to coping in the construction framework for composite indicators. The vulnerability ranking orders based on composite indicators VA1 and VA2 derived by the two additive aggregation forms are completely coincident regardless of the use of coping or lack of coping component. The two outcome values of VA1 and VA2 will naturally differ, but the magnitude sequences are nevertheless unchanged in each vulnerability indicator. However, the volatility of rankings and rank reversals occur in some vulnerability outcomes by a multiplicative multi-attribute utility function with a denominator coping component, since low level coping values will generate abnormally large composite outcomes and middle level coping values will underestimate the vulnerability numerically. Hence, for robustness and confidence in obtaining the vulnerability outcomes by a multiplicative composite indicator, all the assessment components need to have the same directional elasticity to vulnerability. There are also obvious differences in some vulnerability ranking orders between the two composite indicators from additive and multiplicative aggregations. This is due to the fact that additive multi-attribute utility functions will generate composite indicators with higher compensability of attribute components, while multiplicative multi-attribute utility forms will induce one of the low score components to reduce the composite indicator drastically7. In the framework for the use of potential impact as a composite component of exposure and sensitivity, the vulnerability indicator may provide misleading information in assigning more weights to the coping component, although these issues can be somewhat mitigated by the use of proper weighting methods as presented in some literature29.The flood vulnerability assessment is one of the key non-structural measures against flood damage by climate change, a more serious threat in coastal areas than in inland regions due to regional climatic and topographic features. Base on the IPCC’s conceptual framework comprising exposure, sensitivity, and coping componentsVM2 can be suggested to be more appropriate for measuring the flood vulnerability in the coastal areas under study, given that districts where all of the assessment components are more vulnerable can have higher vulnerability than the ones being compensated for by any larger component. Table VM2, as compared to ranking orders by other five flood vulnerability indicators, along with the two selected administrative districts that show extreme fluctuations in ranking orders across different flood vulnerability outcomes in the study site. Although it might be difficult to validate the assessment outcomes of the flood vulnerability to future climate change, the historical flood damage data are collected from the National Disaster Information Center39 as a reference for the review on the flood vulnerability results in the study site. Note that for the past five years during 2013 and 2017, the average number of flood damage reports is 6.4 for the top ten administrative districts in Table As shown in a case study involving the Republic of Korea, coastal regions tend to have more severe weather events and more barren areas undeveloped in a natural state or unimproved by forced displacement and planned relocation, as compared to inland regions. On the other hand, most of the densely populated and developed areas along the coastlines have adequately adapted or coped with flooding in the study site. In light of such circumstances, a multiplicative composite indicator VM1 is problematic in that low-level coping values generate undesirable ranking changes as typically denoted in district 5011. The undesirable impact of this problem can also reach the unfavorable ranking alterations in districts 2814, 2650, 2629, 2638, 2617, and 2611. Meanwhile, the additive vulnerability indicators generate higher vulnerability outcomes by one of the much higher-level values in the constituent components, which results in the overestimated districts 2611 and 5011, where one of the assessment components is at the highest level. On the contrary, there are the underestimated vulnerability outcomes by the additive vulnerability indicators in some districts, 2629, 2638, 2617, and 2818, without very high level components. District 2611 has an abnormally higher ranking in VM1 due to ranking changes with other underestimated districts. It likewise has a higher ranking in VA2 by compensation of the maximum S than in VM2, which is also higher than in VM3 with more weights to the minimum LC. As low-level coping values will generate undesirable ranking changes in VM1, district 5011 with the minimum C shows the abnormally highest ranking in VM1. District 5011 also has higher rankings in VA3 and VM3, with more weights to the maximum LC, compared to rankings in VA2 and VM2, respectively. Note that districts 2611 and 5011, with the extreme level coping values, might provide much different flood vulnerability outcomes in the aggregation framework using a denominator coping or a more weighted coping.Table 40. Although both additive and multiplicative aggregation schemes are compensatory schemes, the latter offers lesser compensation for low value components compared with the former with constant trade-offs across all components. In addition, the multiplicative aggregation scheme requires that all components have the same directional elasticity to a composite indicator in order to avoid undesirable ranking reversal problems. As selected for the coastal flood vulnerability to climate change in this study, the multiplicative scheme can provide a better solution to avoid full compensability. Under certain circumstances, however, it would be a partial solution still with some degree of compensability. When it should be interpreted as symmetrical importance of components, it can be solved by a non-compensatory multi-criteria approach41. All studies on vulnerability assessments may have limitations and drawbacks in processes for constructing composite indicators. This study employs the min-max normalization method where proxy variables are transformed into a common scale between 1 to 100 and also adopts an equal weighting assumption, the possible weighting systems in lack of a statistical or an empirical basis on causal relationships or consensus of assessment components. Therefore, this comparative case study for various composite indicators needs further investigation on several important processes affecting composite indicator outcomes, particularly for the selection and uncertainty in representative proxy variables for each attribute component, normalization methods for transforming variables measured at different units or scales into a common domain, and weighting schemes for variables and components to contribute to the analysis in different scales.This study has compared and evaluated the composite indicators aggregated by the three additive and the three multiplicative utility functions for the IPCC’s vulnerability assessment components. The selection of aggregation frameworks needs to be carefully determined according to the purpose of vulnerability assessments and the characteristics of assessment components, and compensability is one of the basic selection criteria in various aggregation schemes, each with its own benefits and limitations. As an example case, the Unite Nations Development Programme (UNDP) revised the Human Development Index (HDI) by a multiplicative composite scheme, since the additive structure of the original HDI had been criticized for its drawback that allowed for perfect substitution across the three assessment components of health, education, and income8, the vulnerability to climate change has been generally measured by a composite indicator. A prominent framework for vulnerability assessment to climate change comprises the three attribute components of exposure, sensitivity, and coping, as presented in the IPCC TAR1. There has been a variety of aggregation methods for the constituent components into a composite indicator to measure the overall vulnerability to climate change. The conventional frameworks commonly used for aggregating the IPCC’s assessment components can be classified as: 1) an additive form VA1 and a multiplicative form VM1 for the three individual components as originally, 2) an additive form VA2 and a multiplicative form VM2 for the three individual components by replacing coping with lack of coping, and 3) an additive form VA3 and a multiplicative form VM3 for a composite indicator combining the two components, as well as the other one. However, it has not been explicitly interpreted how the construction frameworks and aggregation methods influence the vulnerability composite indicator outcomes. This study has therefore evaluated the robustness and sensitivity of the vulnerability composite indicators aggregated by the conventional additive and multiplicative multi-attribute utility functions from the vulnerability assessment components. The analysis is especially focused on the vulnerability ranking alteration across the six composite indicators by the conventional aggregation frameworks and processes. The effects on the vulnerability outcomes across different construction frameworks are first examined by a design of the possible reference cases of the vulnerability assessment components, and then demonstrated through a case study on the coastal flood vulnerability assessment from actual data to climate change.As the individual attribute components can be compiled into a composite indicator by multi-attribute utility functions to effectively represent multidimensional complicated issues42. Multiplicative aggregation allows lesser compensation for low value components whereas additive aggregation provides constant trade-offs among constituent components. When the assessment components are mutually preferentially independent, full substitution should not be allowed across components in general. In such contexts, this study suggests that the coastal flood vulnerability to climate change be evaluated by a multiplicative composite indicator VM2 from the IPCC’s three components with all the positive elasticity to vulnerability, since regions with a lower component value cannot be fully compensated for by other component values. If trade-offs are not allowed at all, the vulnerability should be assessed by non-compensatory multi-criteria methods. It is important to select a proper framework and aggregation method to construct vulnerability composite indicators, since an undesirable composite indicator outcome due to overlooking irrational features in some aggregations may cause preference reversals in policy and decision making. It should therefore be prudent to select aggregation frameworks for composite indicators with careful consideration of the objectives and features of the vulnerability assessments, as well as the advantages and disadvantages of each aggregation method that may have problematic limitations in construction processes and schemes.It is found that it is desirable for all constituent components to have the same directional elasticity to a composite indicator for robustness of vulnerability scores generated by multiplicative multi-attribute utility forms in order to avoid undesirable ranking reversals. It is also implied that the uncertainty in vulnerability outcomes can increase with the use of potential impact with more weighted coping that tends to have more uncertainty