These patients were assessed for delirium at 30 min following arrival in the PACU, if they were adequately arousable (Richmond Agitation Sedation Score ≥ −2). All tests for delirium diagnosis  and the sub-domains assessed were compared to understand temporal recovery of neurocognitive domains. These data were also analysed to determine the best predictor of PACU delirium. We found the incidence of PACU delirium was 35% (3D-CAM). Individual cognitive domains were affected differently. Few individuals had vigilance deficits  or disorganized thinking , in contrast attention deficits were common  and most of these patients  were not sedated (RASS ≥ −2). CAM-ICU (27%) and NuDESC (52.8%) detected fewer cases of PACU delirium compared to 3D-CAM. In conclusion, return of neurocognitive function is a stepwise process; Vigilance and Disorganized Thinking are the earliest cognitive functions to return to baseline and lingering deficits in these domains could indicate an abnormal cognitive recovery. Attention deficits are relatively common at 30 min in the PACU even in individuals who appear to be awake. The 3D CAM is a robust test to check for delirium in the PACU.The course of neuro-cognitive recovery following anaesthesia and surgery is distinctive and poorly understood. Our objective was to identify patterns of neuro-cognitive recovery of the domains routinely assessed for delirium diagnosis in the post anaesthesia care unit (PACU) and to compare them to the cognitive recovery patterns observed in other studies; thereby aiding in the identification of pathological (high risk) patterns of recovery in the PACU. We also compared which of the currently available tests  is the best to use in PACU. This was a  The mechanistic details of the patterns and processes of “neurocognitive recovery” after general anaesthesia and surgery are not yet well characterized . In a stThe diagnosis of delirium is based on clinical assessment guided by standard criteria. Whilst the ICD or DSM criteria are widely accepted as “gold-standard” for the diagnosis of delirium, this is not similarly established for emergence or PACU delirium. Additionally, the DSM criteria have changed with time and will continue to do so as studies attempt to characterize the core domains of delirium more rigorously. Of note, “experts” at the diagnosis of delirium will be unfamiliar with the immediate postsurgical patient . Most tools used for formal delirium assessment are too complex, non-specific and time consuming for use in distressed patients in a busy PACU. Additionally, they conflate different subdomains, and force a binary construct onto a continuum of several parallel cognition recovery streams.However, there are various established delirium screening tests currently widely used in the PACU,  which assess the four cognitive domains defined by DSM-5  < −2) or in severe pain  the researcher would attempt to repeat the assessment after another half an hour (n = 16). Individuals who failed to complete 3D-CAM and CAM-ICU assessments due to a low RASS score even at 1 h after extubation (n = 8) were graded as having delirium . As per the DSM-5, a severely reduced level of arousal above the level of coma should be considered as having severe inattention and thus delirium  score. A score of less than 23 was considered an impaired MOCA for the study, MOCA scores were adjusted for level of education . Short 3Assessments of delirium in the PACU at 30 min included:1.3D-confusion assessment method (3D-CAM) and the 3D-CAM severity score. Information recorded included deconstruction of the features of delirium, along with a severity score for each feature. This feature severity score was calculated based on the CAM-S SF . More de2.CAM-ICU. Special focus was the attention test part of the CAM-ICU, also known as the “Ten-letter vigilance “A” task ”. Here tWe extrapolate our results from tests of disorganized thinking on the CAM-ICU and the 3D-CAM as being conceptually similar – but not identical – to the Abstract Matching test that Basner et al. performed, since both reflect the same neurocognitive domains of higher mental functions .3.The NuDESC .5.Speech Language Assessment  assessment, with reported sensitivity of 95% and specificity of 94% has been widely validated as a tool for diagnosing delirium. In a pilot study, we found it to be more sensitive than CAM-ICU, which is only validated for the ICU population and so doesn’t include any testing that require verbal responses from participant, thus missing interesting and important information. In the absence of any more validated tools, we decided to use 3D-CAM as the primary outcome measure.It incorporates four cognitive domains  required for the diagnosis of delirium as per the DSM-5. These domains of cognition are closely linked to one another, and a hierarchical relationship exists between level of arousal and cognition. A certain level of arousal is necessary prior to the assessment of other cognitive domains, especially attention . ArousalIn our study individuals were administered the 3D CAM assessment after a RASS score of ≥ −2 was achieved. At that time this was considered an adequate level of arousal to assess for attention. We assumed all individuals experienced the same mental status change due to exposure to anaesthesia, so only those who exhibited additional changes (after approximately 30 mins following extubation in the PACU) based on responses to the 3D CAM questionnaire were classified as having an acute change in mental status.The four 3D-CAM features  were inspected individually for their incidence and relation to one another. The performance under the individual cognitive domains of the other tests (CAM-ICU and NuDESC) were also analysed for a better understanding of cognitive recovery patterns.P values of less than 0.05 are considered statistically significant. Statistical analyses included t-test (unpaired) for normally distributed continuous data, chi square or Fisher’s exact test for categorical data, and McNemar test for paired nominal data. Sensitivity, Specificity, Positive Predictive Value (PPV) and NPV  were used to compare the various delirium tests.Individuals were divided into two groups using the conventional criteria for delirium in the 3D-CAM system. Statistical analysis was carried out using MATLAB R2021a. Data has been presented as mean and standard deviation for continuous data. Categorical data are presented as numbers or percentages (%). n = 70, red section in The 3D-CAM has the highest reported specificity and sensitivity for detecting delirium; therefore, we used this metric as the primary outcome measure to which we compared other delirium assessments . The mean = 144) displayed inattention in the PACU  exhibited an acute change in mental status (after applying the modification to the 3D CAM used for the Alpha Max study). In contrast, only around a quarter of patients  showed signs of disorganized thinking (red region in n = 2), altered level of arousal (n = 4) or an acute change in mental status (n = 6). Note, the 6 patients who were scored as having an isolated acute change in mental status claimed to feel confused, however, observer scoring in the 3D-CAM, NuDESC as well as CAM-ICU did not find them to be categorized as confused. The 3D-CAM algorithm is such that patients reporting subjective confusion only and “passing” the objective confusion questions are still assessed as “positive” for that feature.n = 187) at the time of assessment (based on the vigilance A task). Vigilance is proposed to be the ability to be aware of relevant, unpredictable changes in one’s environment and is a prerequisite for sustained attention (n = 162) exhibited no signs of disorganized thinking (Disorganized thinking was present in 19% (n = 38) CAM-ICU vs 27.5% via the 3D-CAM).The CAM-ICU test established that almost all the patients had an intact level of vigilance  of those considered fully awake (RASS = 0) also failed the attention tests, suggesting that a mechanism other than sedative effect of residual drugs has an important role in causing attention problems post operatively. n = 129) of patients who had inattention had a RASS score ≥ −2.To understand the role of residual sedation in causing inattention, we compared attention tests with RASS scores. There was a statistically significant association between those who were sedated and had inattention (3D-CAM). Even mild sedation caused attention difficulties , but almost half , and 93.5% (n = 187) had intact vigilance based on CAM-ICU. (Awake enough to be able to follow commands and squeeze fingers when instructed to do so).2.Attention deficits were present at 30 min in 72% (n = 144) of individuals. Of the individuals who failed attention tests on the 3D-CAM, 89.5% (129) had an adequate level of arousal (RASS ≥ −2). Of all the individuals with inattention 36.8% (n = 53/144) also displayed signs of disorganized thinking.3.Disorganized thinking was more prevalent  at 30 min in our study. Additionally in our study almost all individuals (53 out of 55) had inattention along with disorganized thinking.n = 63), 47% (n = 30) developed delirium in the PACU . The incidence of PACU delirium among those with a normal MoCA (n = 137) was 29% (n = 40). Individuals with an impaired MoCA had a higher incidence of inattention  and disorganized thinking . There was a large discrepancy in the numbers of patients diagnosed with delirium based on the 3D-CAM vs the CAM-ICU. A total of 70 patients were graded as having delirium by the 3D-CAM; the CAM-ICU picked up only 19, while the NuDESC was able to identify 37. We feel that individuals with cognitive deficiencies in the PACU are best picked up by the 3D-CAM when compared to NuDESC and CAM-ICU.We compared the various assessments of delirium with one another . Of thosOverall, we found a 35% prevalence of delirium in the PACU using the 3D-CAM assessment, similar to results from other studies . When sen = 187) of individuals. Although the tests of disorganized thinking used in the CAM-ICU and the 3D-CAM are relatively simple, they may be extrapolated as mapping onto a fairly similar domain to the Abstract Matching test (n = 162); 3D-CAM 72.5% (n = 145). The cognitive assessments used for comparison in other studies were more challenging and complex compared to the ones used to derive information in our study. In our situation it was not feasible to subject elderly individuals following 2 h of surgery to an exhaustive battery of complex cognitive tests. Mashour’s study concluded that abstract matching speed and accuracy was relatively normal within 30 min following anesthesia, while in our study a sizable minority (18–20%) still showed signs of disorganized thinking (disoriented/unable to think in an organized fashion). This suggests that signs of disorganized thinking could indicate an abnormal pattern of recovery following anesthesia and surgery.Multiple studies have found early engagement of the pre-frontal cortex after surgery, as demonstrated by early return of abstraction (Abstract Matching test) and vigilance (Psychomotor Vigilance Test), leading them to believe that the pre-frontal cortex has an important role in arousal from anaesthesia . Our assing test . We founnot an indicator of pathological passive delirium.Working Memory , took up to 2 h to return to baseline in healthy young individuals . These aintact attention at pre-operative assessment, and post operatively most of those who failed the attention tests were not sedated. It is believed that tests of attention measure working memory capacity, which includes the ability to control attention  and method of assessment  of the patients which had been detected as having delirium by the 3D-CAM, possibly because the assessments are much simpler and focus on domains of cognition that return much earlier . Hight eOur article aims to highlight the fact that there are normal as well as abnormal patterns of cognitive recovery after anaesthesia. Slow recovery of attention is probably normal. More research is required into these cognitive recovery patterns following anaesthesia and establishing a timeline of the sequence of return of various cognitive domains would greatly help formulate a more specific tool to detect those at risk of pathological post-operative delirium. We believe that the PACU is the ideal location to identify such individuals. To develop an appropriate tool it is essential to understand the pattern and time course of cognitive recovery, its neuroanatomic basis and its implications. The results we have presented here are the first step toward developing such a tool.We acknowledge that DSM-5 is the gold standard for diagnosing delirium in the ward. It is a lengthy assessment best performed through periodic assessments over several days. This is not the situation in the PACU, where patients are too ill to be able to tolerate the prolonged assessment required for DSM-5. Currently the role of DSM-5 in this situation is unknown. Our study has based delirium diagnosis on the 3D-CAM, which has been validated in a non-PACU setting. PACU patients are different from those on whom the 3D-CAM was validated; as they are still recovering from the effects of surgery and anaesthesia. Since there is no specifically designed tool available for assessing PACU delirium, we relied on the 3D-CAM.Our tests for assessing the various cognitive domains were simpler tests and were performed at one single time point in the PACU. But these tests, which are routinely used to screen individuals, are validated across multiple delirium studies, and are also more practical to perform in a clinical setting. The choice of the single evaluation at 30 min was based on the fact that this time point showed maximal separation in recovery between cognitive domains in Mashour’s study, and also it is sufficient time to minimize the effect of slow arousal to confound the results. It is difficult to confidently localize cognitive processes to particular brain regions, primarily because they involve multiple interconnected networks.Our study highlights the fact that following anaesthesia and surgery, cognitive domains recover in a piecemeal fashion. Arousal, organized thinking and vigilance are among the earliest cognitive functions to return. So, continued deficits in these domains in the PACU suggest a pathological recovery. Attention is the most frequently affected domain and it is possible that individuals who show deficiencies in this domain are simply recovering “physiologically” from anaesthesia and surgery. Among the tests commonly available to assess delirium in the PACU, it would appear that the 3D-CAM is most suitable, since it incorporates a comprehensive (subjective as well as objective) assessment of all cognitive domains.The original contributions presented in this study are included in the article/The studies involving human participants were reviewed and approved by New Zealand Health and Disability Ethics Committee ref. 17/NTA/56 and had local institutional approval at Waikato Hospital. Australian and New Zealand Clinical Trial Registry, ID:12617001354370, registered on 27/09/2017. The patients/participants provided their written informed consent to participate in this study.JS and AG: concept, study design, and initial protocol for the AlphaMax trial. AB: data analysis and manuscript preparation. All authors: manuscript review, revisions and approval of the manuscript.


Drosophila melanogaster. In this epithelium, the cadherin Fat2 localizes to the trailing edge of each cell and promotes the formation of F-actin-rich protrusions at the leading edge of the cell behind. We show that Fat2 performs this function by acting in trans to concentrate the activity of the WASP family verprolin homolog regulatory complex (WAVE complex) at one long-lived region along each cell’s leading edge. Without Fat2, the WAVE complex distribution expands around the cell perimeter and fluctuates over time, and protrusive activity is reduced and unpolarized. We further show that Fat2’s influence is very local, with sub-micron-scale puncta of Fat2 enriching the WAVE complex in corresponding puncta just across the leading-trailing cell-cell interface. These findings demonstrate that a trans interaction between Fat2 and the WAVE complex creates stable regions of protrusive activity in each cell and aligns the cells’ protrusions across the epithelium for directionally persistent collective migration.For a group of cells to migrate together, each cell must couple the polarity of its migratory machinery with that of the other cells in the cohort. Although collective cell migrations are common in animal development, little is known about how protrusions are coherently polarized among groups of migrating epithelial cells. We address this problem in the collective migration of the follicular epithelial cells in  Collective cell migration is essential for a variety of morphogenetic processes in animals . As withDrosophila melanogaster ovary are a powerful experimental system in which to investigate how local interactions among migrating cells establish and maintain group polarity. Follicle cells are arranged in a continuous, topologically closed monolayer epithelium that forms the outer cell layer of the ellipsoidal egg chamber—the organ-like structure that gives rise to the egg  whether the WAVE complex could still form regions of enrichment in individual cells and (2) whether these WAVE complex-enriched regions were active and responsible for templating unpolarized protrusions. To evaluate the WAVE complex distribution along the edges of individual cells, we generated entirely fat2 mutant epithelia in which patches of cells expressed Sra1-GFP. At cell-cell interfaces along Sra1-GFP expression boundaries, we found that the boundary cells often had cortical regions devoid of Sra1-GFP , which distributes more broadly around the cell perimeter than wild-type Fat2 , but remEna-dependent filopodia are embedded within and grow from the lamellipodia . The WAVena-RNAi-expressing epithelia, in which filopodia are strongly depleted , membrane-localized proteins that directly bind the WAVE complex, and the WAVE complex itself    was usedT method , using FGCTTAAATGCATCCCTTTCCGGG-3’5’- was chosen with flyCRISPR Target Finder  following the general approaches described by t Finder . The undt Finder  backgrouDrosophila medium with 15% fetal bovine serum and 200 μg/mL insulin) in a spot plate using 1 set of Dumont #55 forceps and 1 set of Dumont #5 forceps. Ovarioles were removed from the ovary and from ovariole muscle sheathes with forceps. For live imaging, egg chambers older than the egg chamber to be imaged were removed from the ovariole strands by cutting through the stalk with a 27-gauge hypodermic needle. For fixed imaging, egg chambers older than stage 9 were removed prior to fixation. Removal of older egg chambers allows more compression of the imaged egg chamber between the slide and coverslip so that the basal surface of a field of cells can be imaged in a single plane. For a more detailed description and movies of dissection, see All data come from stage 6–7 egg chambers. To obtain these, ovaries were dissected into live imaging media  was added and ovarioles incubated for 15 min, followed by a wash in live imaging media to remove excess stain before mounting. Ovarioles were then transferred to a glass slide with 20 μL of live imaging media. For CK-666 treatment, following plasma membrane staining, ovarioles were transferred to live imaging media with 750 μM CK-666  and then mounted in the same media. Glass beads with diameter 51 μm were added to support the 22x22 mm #1.5 coverslip and limit egg chamber compression. Coverslip edges were sealed with melted petroleum jelly to prevent evaporation while imaging. Samples were checked for damage using the membrane stain or other fluorescent markers as indicators, and excluded if damage was observed. Slides were used for no more than 1 hr.Following dissection, ovarioles were fixed in 4% EM-grade formaldehyde in PBT  and then washed 3x5 min in PBT at room temperature. Egg chambers were incubated with primary antibodies in PBT overnight at 4° C  or for 2 hr at room temperature  while rocking. Ovarioles were then washed 3x5 min in PBT and incubated in secondary antibody diluted 1:200 in PBT for 2 hr at room temperature while rocking. F-actin staining was performed using either TRITC phalloidin  or Alexa Fluor 647 phalloidin . If TRITC phalloidin was the only stain or antibody used, it was added directly to the fixation media for 15 min of staining concurrent with fixation. Otherwise, TRITC phalloidin was added for 15–30 min at room temperature as the final staining step. Alexa Fluor 647 phalloidin staining was performed for 2 hr at room temperature while the sample was rocking, concurrent with secondary antibody staining where applicable. Ovarioles were then washed 3x5 min in PBT and mounted in 40 μL SlowFade Diamond antifade on a slide using a 22x50 mm #1.5 coverslip, sealed with nail polish, and stored at 4° C until imaged.Laser scanning confocal microscopy was used for all fixed imaging and for live imaging of membrane-dyed egg chambers. Imaging was performed with a Zeiss LSM 800 upright laser scanning confocal with a 40 x/1.3 NA EC Plan-NEOFLUAR oil immersion objective or a 63 x/1.4 NA Plan-APOCHROMAT oil immersion objective, diode lasers , and GaAsP detectors. The system was controlled with Zen 2.3 Blue acquisition software (Zeiss). Imaging was performed at room temperature. All images show the basal surface of stage 6–7 egg chambers except for Near-TIRF microscopy was used to visualize Fat2-GFP, Sra1-GFP, Abi-mCherry, and F-Tractin-tdTomato  dynamicsProtrusions from timelapse datasets of the follicle cell basal surface stained with CellMask Orange (see Live imaging sample preparation) were segmented with the Python scikit-image and scipy libraries . First, protrusive or non-protrusive throughout the study, we used the ‘average length’ metric. We measured the average length distribution in CK-666-treated epithelia, which are nearly non-protrusive and so provided a measure of the width of the cell-cell interface alone. For all conditions, we categorized a membrane extension as protrusive if its average length was greater than the 98th percentile of length of CK-666-treated epithelia. We then defined the protrusivity of an entire epithelium as the ratio of protrusive to total cell edges in the field of view. We also report two alternate measurements of the protrusivity of an epithelium. In one, We calculate epithelial protrusivity as above, but substitute the longest length as our length measurement  tissue surface. For F-actin cell-cell interface enrichment measurements, the overall brightness of the phalloidin staining varied between epithelia independent of genotype. To control for this variation we subtracted the mean intensity of the epithelium’s non-interface basal surface from its mean interface intensity measurement. This value, the degree of F-actin interface enrichment, was used as a proxy for F-actin protrusivity.fat2 datasets are rescaled separately so that each have a mean value of one. As a summary statistic for the degree of planar polarization of F-actin or Sra1-GFP in each egg chamber, we found the average fluorescence intensities of interfaces with angles between 0° and 10° and between 80° and 90°. These correspond with leading-trailing and side interfaces, respectively, in migratory epithelia. The leading-trailing interface enrichment is the ratio of these numbers.As a simple planar polarity measurement, we quantified mean F-actin  or Sra1-GFP fluorescence intensity along each cell-cell interface as a function of the interface’s orientation with respect to the anterior-posterior axis. To do this, cells and cell-cell interfaces were segmented as described above. For interface angle measurements, the angular distance between the line defined by the interface-bounding vertices and the anterior-posterior  axis was calculated. For interface fluorescence intensity measurements, interface regions were identified as segmented interfaces dilated by 5 pixels. Vertices, dilated by 10 pixels, were excluded from interface regions. Mean fluorescence intensity was calculated within each interface region, and background  was subtracted. Polar bar plots, generated in Python with matplotlib, show the mean interface intensity as a function of interface angle. In these plots, bar area is proportional to intensity, and Control and fat2 cells that had global stress fiber alignment orthogonal to the anterior-posterior axis, indicating global planar polarity. Since migration is required to maintain planar polarity  and continuing counter-clockwise. Each row shows the fluorescence intensity of the perimeter pixels in sequence. Cell perimeter lengths varied between frames, so kymograph row lengths varied and were aligned to their center position.At the spatial and temporal resolution of the timelapses and corresponding kymographs, we cannot evaluate differences in the dynamics the puncta-scale WAVE complex accumulations highlighted in As a measurement of the stability of protrusive regions over time, we quantified the frequency with which the direction of protrusion switched across a cell-cell interface. These switching events occur when one cell and then its neighbor protrude across their shared interface, and they serve as a score-able indicator of a change in the polarity state of the cells that bound the interface. To determine the switching frequency, we counted the number of switching events that occurred in each timelapse, and then determined an interface protrusion polarity switching rate by dividing the number of switches by the number of interfaces identified with cell and protrusion segmentation. We chose a hand-counting method because the protrusion segmentation error rate from the inclusion of retraction fibers and other sources  was sufficiently high that automated measurement of dynamic features of protrusions was unreliable.ΔICD condition, in which the distribution of Fat2 expands beyond leading-trailing interfaces, we measured colocalization either along randomly oriented interfaces , statistical tests performed, and their significance can be found in figures or figure legends. Based on visual inspection, all data on which statistical tests were performed followed an approximately normal distribution, so tests assuming normalcy were used. Alpha was set to 0.05 for all statistical tests. Paired statistical tests were used for comparisons of cells of different genetic conditions within mosaic epithelia. All t-tests were two-tailed. One-sample t-tests were used when comparing a distribution of ratios to a null expectation of one. A one-way ANOVA was used when more than two conditions were compared. Welch’s corrections were performed for the t-tests or ANOVAs of data plotted in  Drosophila genetic tool box. It adds to our growing knowledge of how collective cell migration is regulated and introduces an exciting new line of inquiry.This paper addresses a fundamental aspect of cell migration, how the direction of cell migration is established. It links molecules involved in planar polarity to the migration machinery using quantitative imaging techniques capitalizing on the  public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Derek Applewhite as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Jonathan Cooper as the Senior Editor.Thank you for submitting your article "Fat2 polarizes the WAVE complex in trans to align cell protrusions for collective migration" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:Broader/Mechanistic Comments:A. How does Fat2 restrict WAVE activity in the trailing cell?1. To test whether the proposed sequestration-based mechanism is feasible, quantitative imaging of the amount of WAVE recruitment to the membrane in wildtype cells adjacent to Fat2-perturbed vs control cells should be performed. Hypomorphs of WAVE complex with or without Fat2 perturbations would also be valuable if the authors are proposing sequestration.2. Beyond sequestration of WAVE, another perhaps more likely scenario is that Fat2 alters the polarity of WAVE complex regulators such as PIP3 and active Rac. Examining the spatial dynamics of these molecules in addition the WAVE complex is the single addition that would most significantly strengthen the work. Probing these signaling nodes would have the additional benefit of revealing whether Fat2 has inputs to the trailing cell beyond the WAVE complex. This could be tested by analyzing these biosensors in WAVE complex null cells adjacent to control or Fat2 deficient cells.B. What is nature of Fat2 coupling to WAVE/protrusions?1.The colocalization of Fat2 versus WAVE is such an important point for this paper, it would be useful to firmly establish this relation. It would be helpful to show supplemental data normalizing Fat2 and WAVE to membrane or Cadherin to ensure their distributions reflect true enrichment rather than cell morphology. This seems to be the case, but it would be helpful to confirm.Specific Comments:(1) In general the N for some of the experiments seem low. I understand how difficult these experiments are to perform but some of the findings could be strengthened by more experiments. In Figure 2C, I'm a little concerned about the differences in sample size per condition. Sampling at least 5 egg chambers for the CK-666 treatment would make these data stronger. Additionally, Figure 2C shows the ratio of protrusive/total edges, but it would be helpful to include the total number of protrusive edges in each condition.(2) The manuscript is short, but there was also more supplemental figures than full figures. Supplemental figures in general are not given as much weight so the impact of this story  is somewhat lost. Re-organization of figures would greatly strengthen the manuscript.3) In the cases of Figure 4D and S7, which are looking at the stability of Sra1 localization, one way to quantify this could be by measuring the Pearson correlation of Sra1-3xGFP intensity between each frame of the timelapse image of the interface to the first frame. Stable Sra1-3xGFP enrichment will result in this correlation to remain relatively strong throughout time. However, unstable or transient Sra1-3xGFP will result in a dramatic decrease in correlation over time.4) In the case of Figure 4E, cell edges could be tracked over time and stability of orientation can shown by the difference in the angle of protrusion formed at that edge between time points or some appropriate time window. Cell edges with stable protrusions would have small differences in angle over time, whereas edges with unstable protrusions will have large differences in angle. Essential revisions:Broader/Mechanistic Comments:A. How does Fat2 restrict WAVE activity in the trailing cell?1. To test whether the proposed sequestration-based mechanism is feasible, quantitative imaging of the amount of WAVE recruitment to the membrane in wildtype cells adjacent to Fat2-perturbed vs control cells should be performed.We were unsure which membrane region this request referred to—membrane at the leading edge or membrane away from the leading edge—and have addressed both possibilities below.fat2 cells adjacent to wild-type cells, this measurement was in our original manuscript. WAVE levels are reduced at the leading edges of cells directly behind fat2 mutant cells, regardless of the cells’ own genotype .If this request referred to WAVE levels at the leading edge of fat2 and wild-type cells, but did not include cells at clone boundaries because the small number of these cells makes it challenging to detect subtle changes in fluorescence intensity. Originally, we found a slight increase in WAVE levels at the medial basal region of fat2 cells relative to wild-type ones, consistent with a broadening of the WAVE distribution in the absence of Fat2 . We have now measured more clones so that we can make paired comparisons of WAVE levels at the medial basal surface of cells at the clone boundaries , and found no significant change in WAVE levels between genotypes in these cells. With the expanded dataset, we also no longer detected significant differences in WAVE levels in wild-type or fat2 cells away from genotype boundaries . We have updated Figure 5F and the associated results text (lines 186-196) and added Figure 5S2G to reflect these changes.We also considered that the request might refer to WAVE levels at the medial basal surface, where WAVE could redistribute if no longer recruited to the leading edge. Our original manuscript showed this analysis for Our data make a strong case that Fat2 at the back of one cell recruits the WAVE complex to the leading edge of the cell behind to form a long-lived protrusive domain. However, whether this behavior is also sufficient to suppress protrusions from forming elsewhere along the cell perimeter is not clear. There was one line in the Discussion section of the original manuscript suggesting that Fat2 might sequester WAVE away from other sites. We have expanded this paragraph to include other ways in which protrusions could be suppressed at a distance (lines 371-375).Hypomorphs of WAVE complex with or without Fat2 perturbations would also be valuable if the authors are proposing sequestration.We were initially unsure of the rationale for this request and asked for clarification from Dr. Applewhite, who provided this additional explanation from an anonymous reviewer:The authors are proposing that Fat2 limits protrusion formation in adjacent cells through WAVE complex sequestration. Under this model, the increased protrusion formation that accompanies loss of Fat2 is a consequence of the increased cytosolic pool of WAVE complex. So if Fat2 normally sequesters say 50% of the WAVE complex, then a WAVE complex heterozygote (loss of 50% of protein) should rescue the enhanced protrusion elicited by loss of Fat2. In contrast, if Fat2 is playing a role in regulating cell polarity beyond WAVE sequestration, then adjusting the WAVE dose to compensate for the amount normally recruited to Fat2 should have no effect.fat2 mutant epithelia seems unlikely to shed light on Fat2’s mechanism of action.As noted in point 1, the manuscript has been modified to no longer propose a sequestration based mechanism. It should also be noted that, although the WAVE complex and protrusions are more widely distributed around the basal cell perimeter in the absence of Fat2, their overall levels are reduced when compared to the wild-type condition, not increased . Given this relationship, further reducing WAVE levels in 2. Beyond sequestration of WAVE, another perhaps more likely scenario is that Fat2 alters the polarity of WAVE complex regulators such as PIP3 and active Rac. Examining the spatial dynamics of these molecules in addition the WAVE complex is the single addition that would most significantly strengthen the work. Probing these signaling nodes would have the additional benefit of revealing whether Fat2 has inputs to the trailing cell beyond the WAVE complex. This could be tested by analyzing these biosensors in WAVE complex null cells adjacent to control or Fat2 deficient cells.Drosophila. We think it is highly likely that active Rac and PIP3 are polarized in this system, but we are unable to confirm this or test the role of Fat2 in that polarization with the accessible tools.We agree that Fat2 could act more directly on PIP3 or Rac nodes of the lamellipodia circuit, and thereby polarize WAVE, and we appreciate the reviewer’s suggestion to delve into this. To evaluate Rac polarization, we imaged the FRET sensor reported by the Montell lab , expressed either in all follicle cells or mosaically, allowing us to distinguish leading edge signal from that of the adjacent trailing edge. We measured the FRET ratio using the approach described here: (doi:10.1038/nprot.2011.395) after seeking advice from a senior postdoc in the Montell lab. However, we were unable to detect any FRET signal polarization. We likewise looked for polarized PIP3 using a Grp1-PH-based probe (tubP>GFP-Grp1-PH: BDSC8163), but were again unable to detect polarized signal—in this case the probe had the same distribution as a plasma membrane label. Unfortunately, these are the only two biosensors that are currently readily available for Our initial submission included a paragraph in the Discussion noting the possibility that Fat2 could act through Rac or PIP3, but this came late in the manuscript. We have now modified our Results section to emphasize that we are using WAVE complex localization as a readout for the entire circuit (lines 156-161).B. What is nature of Fat2 coupling to WAVE/protrusions?1.The colocalization of Fat2 versus WAVE is such an important point for this paper, it would be useful to firmly establish this relation. It would be helpful to show supplemental data normalizing Fat2 and WAVE to membrane or Cadherin to ensure their distributions reflect true enrichment rather than cell morphology. This seems to be the case, but it would be helpful to confirm.To control for effects of morphology on the apparent colocalization between Fat2 and WAVE, we measured the colocalization between Abi-mCherry and E-cadherin-GFP, which is near-uniformly distributed along interfaces. Abi and E-cadherin show a positive Spearman’s correlation, indicating that interface morphology may contribute to positive Fat2-WAVE correlation . But the correlation between Fat2 and the WAVE complex is significantly higher than this negative control in all backgrounds we measured, showing that Fat2 and WAVE do colocalize . These data were present in our original submission, but we only discussed them as a control for our region selection method. We have now added text in the Results section stating that these data also serve as a control for cell morphology (lines 261-262).Specific Comments:1) In general the N for some of the experiments seem low. I understand how difficult these experiments are to perform but some of the findings could be strengthened by more experiments. In Figure 2C, I'm a little concerned about the differences in sample size per condition. Sampling at least 5 egg chambers for the CK-666 treatment would make these data stronger.We have now collected a total of 5 CK-666 timelapse videos and reanalyzed membrane protrusion data with this expanded dataset .Additionally, Figure 2C shows the ratio of protrusive/total edges, but it would be helpful to include the total number of protrusive edges in each condition.We have added a supplemental table  that includes these values. The table is referenced in the legend of Figure 2C .2) The manuscript is short, but there was also more supplemental figures than full figures. Supplemental figures in general are not given as much weight so the impact of this story  is somewhat lost. Re-organization of figures would greatly strengthen the manuscript.We have reorganized the manuscript so that data from 3 formerly supplemental figures are now included in the main figures . There are now a total of 8 main figures and 6 supplemental figures .3) In the cases of Figure 4D and S7, which are looking at the stability of Sra1 localization, one way to quantify this could be by measuring the Pearson correlation of Sra1-3xGFP intensity between each frame of the timelapse image of the interface to the first frame. Stable Sra1-3xGFP enrichment will result in this correlation to remain relatively strong throughout time. However, unstable or transient Sra1-3xGFP will result in a dramatic decrease in correlation over time.fat2, epithelia, we cannot use simple intensity correlation over time to compare Sra1 subcellular dynamics. We attempted to register the migrating cells to isolate the contribution of within-cell Sra1 distribution changes, but control cells still tend to deform more over time, so this didn’t fully remove the confound. As an imperfect stand-in for quantification, we have included a video showing Sra1 dynamics in a field of control and fat2 cells, allowing the reader to assess the generality of fat2 domain fluctuations among tens of cells in a shared epithelium. We have also added a quantification of membrane protrusion stability (see point 9). Sites of Sra1 enrichment correspond closely with sites of membrane protrusion , so we can strongly infer that the Sra1 distribution is reflected in the membrane protrusion distribution.We agree that quantification of Sra1 dynamics would strengthen the manuscript. However, the migratory state of the tissue confounds many measurements of these types of subcellular dynamics. Because the entire tissue is migrating in control, but not 4) In the case of Figure 4E, cell edges could be tracked over time and stability of orientation can shown by the difference in the angle of protrusion formed at that edge between time points or some appropriate time window. Cell edges with stable protrusions would have small differences in angle over time, whereas edges with unstable protrusions will have large differences in angle.fat2 epithelia than controls . These data are now plotted in Figure 7C with an accompanying example of a switching event in Figure 7B.We have now added a measurement of the stability of membrane protrusion distribution, in which we quantify the frequency of events in which the direction of protrusion from an individual interface switches. We chose this measurement because, as a binary event, it was more readily score-able than the related quantity of changes in net cell polarity exemplified in Figure 7A. The frequency of polarity switches per interface per hour was significantly higher in Ideally, we would have used our membrane protrusion segmentation and polarity data as a starting point for measuring protrusion stability. However, although the segmentation was reliable enough to detect a bias in protrusion polarity shown in the rose plots, there are errors. These errors have an outsized impact on measurements of traits with a time dimension like stability because they depend on successful segmentation of a protrusion for many consecutive frames. Measurements of protrusion orientation stability over time were strongly influenced by these errors. For this reason, we manually counted all polarity switches in the new analysis.


A detailed presentation of the automatic data analysis pipelines for the BioSAXS beamline at the European synchrotron. FreeSAS, an open-source collection of various small-angle scattering analysis algorithms needed to reduce and analyze BioSAXS data, and Dahu, the tool used to interface data analysis with beamline control. It further presents the data-processing pipelines for the different data acquisitions modes of the beamline, using either a sample changer for individual homogeneous samples or an inline size-exclusion chromatography setup.The second phase of the ESRF upgrade program did not only provide a new storage ring  but also allowed several beamlines to be refurbished. The BioSAXS beamline (located on port BM29) was upgraded with a new wiggler source and a larger detector. All analysis software has been rewritten to cope with the increased data flux and continues to provide beamline users with reduced and pre-processed data in real time. This article describes  Unlike single-crystal diffraction or nuclear magnetic resonance (NMR) which offer atomic resolution, BioSAS provides only information on the envelope of macromolecules: it can be used to validate the relative position of large structures in the assembly of biological complexes  was re-implemented with web-technology, replacing the PyQt4 interface. To meet the real-time feedback requirement, the software performing the analysis was also re-written.During the upgrade of BM29 beamline, most software have been replaced by completely new developments: the sequencer EDNA-based pipeline , all source code is available publicly on github  and from the processing pipelines so that scientists can reprocess their data and compare their results with those of other analysis software like ScÅtter , Fig. 1I = f(q), where q = 4πsin(θ)/λ is the amplitude of the scattering vector, θ is half the scattering angles, λ the wavelength and I the recorded intensity at a given q, alongside some basic analyses which are described in the next sections. The program takes as entry a three-column ASCII file containing q, I and σ.The 2.1.2.Rg, of the solvated macromolecule and the forward-scattering intensity, I0 ] = log(I0) − 1/3(qRg)2 on the proper q-range, at small angles, called the Guinier region. The selection of the Guinier region is far from obvious due to the beam-stop shadow and aggregation effects, and its upper limit depends on Rg itself: qRg < 1.3. Thus multiple implementations are provided: BioXTAS-RAW  is the Fourier transform of the pair distribution function p(r), the latter cannot directly be obtained from an inverse Fourier transform (IFT) due to the loss of phase information and the limited amount of information in the scattering curve. This ill-posed mathematical problem has no exact solution and is usually inverted using some extra constraints, like the finite size of the macromolecule . FreeSAS proposes an IFT based on Bayesian statistics and derived from BIFT  algorithm implemented from the publication by Franke et al. (2015(i)  al. 2015.et al., 2016(ii) q, Iavg and σ(I)] text files (with headers) for compatibility with third-party tools. Following ESRF’s data policy  FreeSAS and ATSAS packages. The latter toolbox is built on top of several decades of experience acquired by the EMBL-Hamburg group and features some 60 programs to perform BioSAS data analysis. FreeSAS cannot compete on the number of features but tries to offer a clean Python interface to build pipelines in an efficient manner and fully open (both for usage and modification).Table 12.2.BsxCuBE3, the graphical user interface) are actually performed, informs the client about the finished jobs and warns it in case of an error.The role of the job manager is to ensure all processing requested by the client  is released, like in Cython extensions  and provides a unique identifier which gives access to the status and output of the processing. Jobs see their input and output saved onto disk, which allows offline reprocessing in case of an issue during online data analysis.The 2.2.2.Dahu plugins implement the processing logic of the different pipelines. Written in simple Python and fairly independent of the Dahu framework, those plugins are often written or modified by beamline scientists themselves.2.2.3.et al., 2016Dahu has virtually no dependencies, it can be deployed on any computer to reprocess data. Nevertheless, to reprocess data acquired at the BioSAXS beamline, one would need FreeSAS and all the other dependencies of the BioSAXS plugins, which are documented in the Offline re-processing is made possible by the 3.integrate multi-frame.There are two main experiments performed at the BioSAXS beamline – using the sample changer (referred to as SC) or the inline-chromatography setup (referred to as HPLC since it uses a high-pressure liquid chromatograph). Thus, two analysis pipelines were built, one for each of these experimental modes. The common part, mainly dealing with azimuthal integration, is integrated into a pre-processing pipeline called LIMA software , beam-stop diode intensities and other processing parameters have to be provided by the experiment sequencer, BLISS, as part of the job description when triggering the process. This configuration can be retrieved from the data portal web page , together with the configuration associated with each processing. Input data sets are referenced using external links, which avoids data duplication while keeping traceability. Finally, metadata describing the sample, its buffer and the configuration of the beamline are also recorded using the Nexus convention  and additional metadata describing the sample and the experiment. This additional information is either collected by the sequencer, BLISS, like the beam-stop diode intensity, or read from the user interface, BSXCube3, like the sample name and concentration. Samples and experimental conditions for all examples and figures are described in Appendix AThe multi-frame integration pipeline, Fig. 4I(q) viewed with silx. This pipeline is built of four subsequent analysis steps, as illustrated in Fig. 4Fig. 5pyFAI  Each recorded image is azimuthally integrated with CorMap algorithm  Scattering curves are compared, searching for radiation damage using the B(iii) Equivalent images are averaged pixel-wise, weighted by the beam-stop diode intensity; variance is assessed assuming Poisson statistics. Since time-averaging and azimuthal-integration are not commutative (see Appendix (iv) The averaged frame is finally azimuthally integrated and uncertainties propagated accordingly.The plot of the azimuthally integrated averaged frame (at stage 4) is set as the default display when processing data in sample-changer mode. In HPLC mode, the default plot represents the summed intensity as a function of time, which is a fraction of the complete chromatogram. The HDF5 file additionally includes external links to the raw frames as acquired by the detector (stage 0).3.2.i.e. the name of the file containing the sample data after azimuthal integration) and a list of buffer files corresponding to the different acquisition of buffers, usually the buffer before and the buffer after the sample acquisition.In sample-changer mode, solutions containing samples are acquired alternatively with pure buffer solutions. The throughput of the beamline is then limited by the pipetting system of the robot and the delay for cleaning the exposure chamber. The processing is triggered with integrated data from the sample  Comparison of buffer curves using the (2) Buffer frames are averaged together and subtracted from the sample-averaged frame (restarting from 2D raw frames).pyFAI  Azimuthal integration of the subtracted frame with I(q)] versus q2 providing Rg and q as default plot.(4) Guinier analysis with the associated linear regression of log[qRg)2I/I0 versus qRg to assess the flexibility of the macromolecule.(5) Dimensionless Kratky plot: ((6) Porod .(7) Indirect inverse Fourier transform using the BIFT algorithm  Transfer of reduced data to ISPyB for BioSAXS (compatibility layer with legacy ISPyB).multi-frame processing pipeline , and make them instantly available to the user via the data portal , they receive a unique DOI which can be referred to when users are publishing results to the SASBDB  Concatenate partial chromatograms (1D curves) provided by the et al., 2017(2) Perform a singlar value decomposition (SVD) on the chromatogram to assess the number of components and extract the scattering from the background  Perform a non-negative matrix factorization (NMF) to provide the scattering curve of the different pure components and their associated chromatograms (Bunney (4) Select points belonging to the background by comparing experimental scattering with the first singular vector from the SVD; average selected curves.(5) Perform peak-picking on subtracted curves to define fractions of the chromatogram.(6) Analyse each fraction with a similar pipeline to the one presented in Section 3.2(7) Finally, export data and send them to ISPyB for BioSAXS.et al., 2014NumPy  is performed on the total scattering chromatogram, smoothed by median filtering. A peak search is performed with the 4.4.1.The processing described in Section 3multi-frame integration presents a clear bi-modal distribution since the same code is used in sample-changer mode (10 frames per acquisition) and in HPLC mode where 100 frames are acquired per file. From the figures in Table 2The run time for 4.2.et al., 2016EDNA, is difficult since their structure is very different and the computer equipment has evolved. EDNA was processing frame per frame and used to take 2.3 s to integrate a single image from the former Pilatus 1M detector. With this new pipeline, integration occurs at more than 13 frames s−1  can be directly compared with two of the the former EDNA pipelines which were called autoSub (2.1 s) and SAXSAnalysis (9.7 s).Performances for the All computations are now executed on a single computer equipped with a single hexa-core processor and an entry-level graphics card (Intel Xeon E5-2643 v3 + Nvidia Quadro M2000) which have replaced the previous setup (based on Intel Pentium4 + Quadro4000).4.3.The foreseeable future should see a better integration of BioSAXS data into the data portal with better web visualization capabilities of the processed HDF5 files.Bab initio shape reconstruction, based on the DENSS  exact since it is based on integrated curves which have been normalized. It should be possible to weight properly those curves to obtain an average which is exactly the same as if one would have averaged or subtracted 2D frames and integrated the result (discussed in Appendix 5.FreeSAS and the Dahu software packages which are used, respectively, to analyse BioSAS data and control online data analysis. These two packages are used at the BioSAXS beamline at ESRF, combined with others, to provide complete data-analysis pipelines. The three pipelines described in this contribution have been used in production since 2020, and provide real-time feedback of ongoing experiments to the user. All metadata, all parameters and all references to the source data are recorded together with the processed data into single HDF5 files, which offers not only convenient storage but allows also reproducible science following the FAIR principle. This document introduces the


Cryptosporidium is a leading infectious cause of diarrhea around the world associated with waterborne outbreaks, community spread, or zoonotic transmission. The parasite has significant impact on early childhood mortality, and infection is both a consequence and cause of malnutrition and stunting. There is currently no vaccine, and treatment options are very limited. Cryptosporidium is a member of the Apicomplexa, and, as typical for this, protist phylum relies on asexual and sexual reproduction. In contrast to other Apicomplexa, including the malaria parasite Plasmodium, the entire Cryptosporidium life cycle unfolds in a single host in less than 3 days. Here, we establish a model to image life cycle progression in living cells and observe, track, and compare nuclear division of asexual and sexual stage parasites. We establish the length and sequence of the cell cycles of all stages and map the developmental fate of parasites across multiple rounds of invasion and egress. We propose that the parasite executes an intrinsic program of 3 generations of asexual replication, followed by a single generation of sexual stages that is independent of environmental stimuli. We find no evidence for a morphologically distinct intermediate stage (the tetraploid type II meront) but demonstrate direct development of gametes from 8N type I meronts. The progeny of each meront is collectively committed to either asexual or sexual fate, but, importantly, meronts committed to sexual fate give rise to both males and females. We define a Cryptosporidium life cycle matching Tyzzer’s original description and inconsistent with the coccidian life cycle now shown in many textbooks. Cryptosporidium is a leading infectious cause of diarrhea around the world. This imaging study of Cryptosporidium development reveals an intrinsic program of asexual and sexual commitment and replication, requiring revision of the lifecycle currently shown in most textbooks for this important parasite. Cryptosporidium, and Shigella, which together account for more than half of all diarrheal deaths in this age group " I think this needs a bit more explanation without the reader having to go to this paper - e.g. that the strain expresses markers tagging male (Hap2?) and female (cowp1?) specific genes that allows the observer to discern male vs female gametes.11) line 464 contrast should be contrasts12) line 500 - see Bancells Nat Microbiol . 2019 Jan;4(1):144-154 for a potential exception  Figure 5 - Perhaps the figure would benefit from some arrows to more explicitly depict the sequence, as well as some annotation to clarify that the oocyst is a source of ongoing autoinfection as well as an infectious form that exits the host - I think this could be important for the many readers who aren't already familiar with the lifecycle.aReviewer #3 (Guan Zhu): This manuscript presents solid and exciting data that corrects some long-term (mis)conception of some fundamental biology  on Cryptosporidium parvum - an important and globally distributed zoonotic apicomplexan parasite. It was a generally accepted/described (mis)conception that the life cycle of Cryptosporidium parasites undergoes 2-3 or more cycles of merogony  to produce type I (8 nuclei) and type II (4 nuclei) meronts that produce 8 or 4 merozoites at the end of the merogonic cell cycles, but only merozoites produced by type II meronts enter gametogenesis  to produce microgametes  and macrogametes . Using live imaging to track individual parasite cell development and progression, the authors provide solid data to clarify that: 1) The parasite only produces 8-nuclear merozoites per asexual cell cycles, and the 4-nuclear meronts are mostly  transit form in the development from a 1-nuclear trophozoite to 8-nuclear meronts.2) The merozoites produced after the third cycle of merogony could enter sexual development to form micro- or macrogametes. The general concept that sexual development starts from merozoites produced from type II meronts was incorrect.3) The 8 individual merozoites produced from a single meronts may develop into either male or female gametes, rather than all or none to male or female. 4) There are no external factors that initialize/trigger the sexual development. The sexual-ready merozoites are intrinsically and individually programmed for the fate to enter male or female development in the coming cell cycle. The findings are of high impact on the biology field in general, and on the parasitology field in particular. I have no major concerns on the data and conclusions, but a few minor comments as described below.1) Figure 1, E-F (Super-infection assay data): Infection at 0 h = infection with H2bNeon strain of C. parvum (Cp) only; infection at 24 h = Mixed infections with both WT Cp (0 h) and H2bNeon strain Cp (infection at 24 h post-infection with WT Cp. It may be better to have a timeline bar (or may be another way) to clarify the two different types of h/hours in the figures.2) Line 178 "found a mean time to egress of 12.57 hours": from which time point ?3) Figure 2 (time points in lapse images and bar charts): In each panel, please clarify/calibrate the start point of the time in relationship to the h post-infection (hpi). Based on the description in lines 167-169 "We began imaging at 11 hours of cultures … also … imaging from 29 or 40 hours of culture onwards", the 0 h or 0:00 h start point could be equivalent to 11, 29 or 40 hpi.4) Lines 208-218 (microgamete development): Please clarify the number of observed cell cycles for male parasites (4 cycles of cell division?).5) Figure 3A, Pink/red lines: Pink lines are not well shown. Will another color work better, e.g., orange, plus some use of opacity? 6) Figure 4: Please clarify the time points in each panel . Figure 4B: Please clarify the meaning of the daggers. Figure 4C: Please clarify the "grey" zones (non-egressed merozoites?). Do all tracked meronts produced 8 merozoites?7) Please define the "hours" in the manuscript when necessary (relationship with h post-infection [hpi]). Also most the words "hours" may be changed to "h".8) Figure 6A: It may help to add arrows to between developmental stages, and mark both male and female gamonts and gametes. In current form, it appears that the female gametes are developed from male gamonts. 9 Mar 2022AttachmentRebuttal_English.docxSubmitted filename: Click here for additional data file. 11 Mar 2022Dear Dr Striepen,On behalf of my colleagues and the Academic Editor, Gary Ward, I am pleased to say that we can in principle accept your Research Article "Live imaging of the Cryptosporidium parvum lifecycle reveals direct development of male and female gametes from type one meronts" for publication in PLOS Biology, provided you address any remaining formatting and reporting issues. These will be detailed in an email that will follow this letter and that you will usually receive within 2-3 business days, during which time no action is required from you. Please note that we will not be able to formally accept your manuscript and schedule it for publication until you have any requested changes.http://www.editorialmanager.com/pbiology/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.Please take a minute to log into Editorial Manager at PRESSbiologypress@plos.org. If you have previously opted in to the early version process, we ask that you notify us immediately of any press plans so that we may opt out on your behalf.We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with http://www.plos.org/about/media-inquiries/embargo-policy/.We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit Thank you again for choosing PLOS Biology for publication and supporting Open Access publishing. We look forward to publishing your study. Sincerely, DarioDario Ummarino, PhD Senior Editor PLOS Biologydummarino@plos.org


Oral anticoagulants (OAC) effectively reduce the risk for ischemic stroke in patients with atrial fibrillation (AF). We aimed to assess OAC treatment adherence in secondary stroke prevention and to find predictors of adherence using individualized patient data.  This retrospective cohort study included patients with a discharge diagnosis of ischemic stroke and AF from Tartu University Hospital from 2017 to 2018. Data from patient charts and the Electronic Hospital Information, Estonian Electronic Prescription, and Estonian Electronic Health Record systems were registered.  Of the 353 patients, 237 (67%) were prescribed OAC treatment at discharge and during the first year after stroke, 202 (85%) of them used OAC treatment. The mean adherence was 81%, while only 68% had good adherence. Reduced non-vitamin K antagonist OAC (NOAC) dose was used in 68 patients (39%), which was justified in 23 (34%). First-ever stroke occurrence was the only significant factor for good treatment adherence in logistic regression analysis. There were 47 patients (23%) with complications among the patients on OAC treatment. Majority of the patients (70%) with hemorrhagic complications and 52% of patients with thromboembolic complications had good treatment adherence  Our study showed that OAC treatment adherence following stroke was modest and first-ever stroke was the only predictor of good or full treatment adherence. Oral anticoagulants (OAC) effectively reduce the risk for ischemic stroke in patients with atrial fibrillation (AF) . There aPoor adherence to treatment, including OACs, is a worldwide problem when it comes to chronic diseases . HoweverWhile there have been numerous studies reporting real-world data on OAC use and therapy adherence in AF patients, much less is known about OAC treatment following an ischemic stroke –14. ThesWe included patients hospitalized in the Department of Neurology at Tartu University Hospital with a discharge diagnosis of ischemic stroke and AF from January 1, 2017, to June 30, 2018. All of the patients were managed by the stroke neurologist in the stroke unit. The follow-up period was 1 year from the diagnosis of index stroke.Data were obtained from four databases: (a) the patients' charts from Tartu University Hospital, (b) Electronic Hospital Information system, (c) Estonian Electronic Prescription (EP) system, and (d) Estonian Electronic Health Record (EHR) system. Individual patient data from the aforementioned sources were reviewed and analyzed in detail.2DS2-VASc)  at discharge 12 [p < 0.001), 82% died within 1 year following the index stroke. The characteristics of patients who were not prescribed OAC and those who did not purchase the prescription were not significantly different.The patients who did not start or were not prescribed OAC after stroke occurrence n = 11 were siharge 12 ; p < 0.0p = 0.005). The proportion of patients in each treatment adherence group is shown in The mean treatment adherence was 81% . A total of 136 (68%) patients had good or full treatment adherence (treatment available ≥ 80% of the time). Patient characteristics according to adherence are shown in For the assessment of factors related to good treatment adherence, a multiple logistic regression model was used. Only first-ever stroke remained a significant factor for good treatment adherence see .Reduced NOAC dosage was prescribed for 68 patients (39%). This was justified only in 23 (34%) patients.There were 47 patients (23%) with complications among the patients on OAC treatment. During the 1-year follow-up period, 21 (10%) patients died. The cause of death were complications in 10 (5%) patients . In 11 (5%) patients, the cause of death was unavailable. Among the surviving 181 patients, there were 20 (11%) thromboembolic events and 6 (3%) hemorrhagic events. Recurrent stroke or transient ischemic attack was diagnosed in 16 (8%), myocardial infarction or systemic embolism in 4 (2%), intracranial hemorrhage in 1 (0.5%), gastrointestinal hemorrhage in 3 (1.5%), and other hemorrhagic complications in 2 (1%) patients.The treatment adherence of patients with any complications is shown in Our study using real-world individual patient data revealed that treatment adherence to OACs in patients with AF following stroke was modest: the mean treatment adherence was 81%. Although the mean adherence was good, only 68% of the patients had good or full adherence during the first year after stroke. Moreover, if we take into account the correct dosage of OACs, only 50% of patients received sufficient prevention. The only factor contributing to good adherence was first-ever stroke. In majority of the patients, AF had been diagnosed before stroke, but less than half of these patients were on anticoagulants. The underuse of OACs in patients with AF prior to stroke has also been shown by other studies , 20.Adherence to a medication is the mainstay of effective treatment. Poor adherence to any medication can be intentional or unintentional and can be affected by many patient-related factors . While a2DS2-VASc, or the HAS-BLED scores.Most previous studies with stroke patients have not reported differences in sex –9, 12. WWe found a tendency of better adherence to apixaban but not to other OACs. A Swiss registry including stroke patients did not find a significant difference in the adherence to various OACs, but the proportion of fully adherent patients was lowest in the dabigatran group . A trendIt is crucial to start the treatment during hospital stay if the stroke is not too severe. In our study, the treatment was started only by 65% of those who were prescribed treatment initiation at home. Similar results were reported by two recent German studies , 8.We used individual patient follow-up data to assess correctness of prescribed dosages and found that in majority of the patients (66%), the prescribed reduced dose was not justified. In these cases, the patient may be adherent to treatment, but the incorrect dosage does not provide the efficacy needed.It is difficult to compare the results with other studies because of several methodological differences. Abdou et al. recommended using different data sources to assess medication adherence to ensure more solid results . Most of2DS2-VASc were higher and the sample size is smaller than in the randomized trials, which could have contributed to this result. In addition, the randomized controlled trials used CHADS2 scoring which takes into account less risk factors and thus gives lower scores compared to the CHA2DS2-VASc. While most of the patients with hemorrhagic complications were adherent to treatment, there were still approximately half of the patients with thromboembolic events who were nonadherent.Most of the real-world studies available did not evaluate the complications of OAC treatment following stroke, and this should be taken into account when comparing the results. The rate of complications (both thromboembolic and hemorrhagic) was somewhat higher in our cohort compared to that in randomized controlled trials . It is iThe strength of our study was the use of real-world individual patient data from different sources, resulting in high-quality data. In this study, we used not only administrative data but also individual case histories, clinical data, and information about any hospitalizations and outpatient visits during the follow-up period that we carefully reviewed and analyzed to detect cases with complications and information about factors that could influence the treatment adherence. If we had not looked into patient files and used only administrative data, we would have overestimated the proportion of patients with sufficient prevention by 50%. In addition to adherence, we evaluated treatment complications and the validity of the prescribed dosage in order to assess both the risks and benefits associated with OACs. As all health information for each individual in Estonia is digital, we did not lose any cases during the follow-up.One limitation of our study might have been that we measured adherence based on dispensed prescriptions, expecting that the medications were taken by the patient. This approach could have caused us to overestimate drug adherence, though we considered this possibility highly unlikely. Also, patients who were prescribed OAC but never started the treatment were excluded from the analysis. Including those patients in the nonadherent group would have further decreased the adherence rate. In addition, we did not assess the adherence to OAC treatment prior to stroke occurrence, which could have added valuable information and could have explained the causes for stroke recurrence. While it is known that OACs do not prevent all strokes even if used correctly, a relatively high proportion of patients (33%) in our cohort on prior OAC treatment suffered from stroke. An even higher proportion has been reported by the Erlangen cohort . SecondlOur study showed that OAC treatment adherence following stroke was modest and must be improved in order to prevent recurrent stroke. While it is generally accepted that adherence ≥ 80 % is considered sufficient, this means that 20% of the time, the patient is still at risk of thromboembolic complications. It is important to be knowledgeable of modifiable factors of treatment adherence and to manage them in everyday practice. Due to the fixed dosing and stable pharmacokinetics, NOACs do not require monitoring in principle, but at the same time, patient monitoring and long-term surveillance seem obligatory. As a future perspective, patients should be educated and reminded of the efficacy of OAC treatment for AF and the importance of treatment continuity as well as physicians should be encouraged to use OAC in patients with AF according to guidelines, to evaluate the risks and benefits of the treatment in each individual patient before discontinuing the treatment or using the reduced dosage.


Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems. Human emotion recognition is a critical research topic in brain–computer interaction (BCI) ,2,3,4. MAmong the various physiological signals, electroencephalogram (EEG) has been frequently reported to be closely and directly related with human emotions in previous studies ,7,8,9,10However, most of these studies use the complete EEG samples with different time durations as inputs for model training. This treatment would make the trained model not suitable to be applied for online or real-time emotion recognition because the learned knowledge of the trained model is all about the characteristics of the complete samples for post-detection which is not suitable for online or real-time applications . MoreoveTo this end, various time windows (TWs) have been developed and used in signal analysis of EEG temporal sequences. Abtahi et al.  used a 2Moreover, in the development of emotion recognition with EEG data, a significant problem is the negative influence of individual differences ,16 whichTherefore, this paper aims to examine the effectiveness of different TW lengths on emotion recognition based on batch normalized EEG signals for better individualized emotion recognition performance. The main contributions of this paper are two-fold. Firstly, the best TW length to extract features for EEG-based emotion recognition is determined. This would fill the research gap on the selection of TW for feature extraction to facilitate EEG-based emotion recognition. Secondly, an experiment-level batch normalization (ELBN) method is newly applied in EEG feature processing to alleviate the negative impact caused by individual differences in different experiments. This method can help extract useful features without being greatly affected by human behavioral differences across experiments, which would greatly improve emotion recognition performances in online or real-time applications.EEG signals are non-stationary, non-linear, and non-Gaussian . RecordeAnother significant characteristic of EEG signals is that EEG is highly subject to various individual differences, which brings difficulties when the pre-trained model is applied on a new subject directly. To address this problem, previous studies have proposed a variety of approaches for EEG data processing. Li et al.  proposedTo analyze EEG signals from different perspectives, various features have been extracted to describe brain activity information. Unde et al.  used powAmong these various EEG features, PSD and DE are two commonly used and well-accepted features used to analyze human’s EEG activities. PSD features are used to represent the distribution and energy strength of signal power over a frequency ,9. DE arThe SJTU emotion EEG dataset (SEED)  is a popResearchers usually elicit specific emotions of subjects by audio or video clips and extract the corresponding EEG data for analysis ,29,38. SThe 15 film clips in There were 15 young subjects  participating in the experiments in the SEED dataset. Each subject repeated the experiment three times with an interval of one week or longer. Therefore, in the SEED dataset, there were 45 experiments across the 15 subjects. Since each experiment had 15 trails, there were 675 trials in total across the 15 subjects. For each emotion category , there were 225 trials, which means that the emotions were balanced in the SEED dataset.PSD and DE features ,37,40 weThe minimum duration of the collected trials was about 180 s; hence, each channel of the EEG signals can be segmented into 180 1-s epochs without overlapping. Therefore, there would be 58 × 180 epochs from the collected EEG signals in each trial. PSD features and DE features were calculated in each epoch in the five given frequency bands, respectively. Therefore, the format of features in each trial was defined as , where 58 represents 58 channels, 5 represents 5 frequency bands, and 180 represents the total number of features extracted from the epochs in the corresponding trial. In total, there were 675 trials in all the 45 experiments. A total of 11 different lengths of TW were examined to investigate the optimal TW length for EEG data extraction in this study. See iF and BNiF represent the original value of a specific feature and the value of the feature with ELBN in an experiment, respectively, while minF and maxF represent the minimum and maximum values of the corresponding feature in the same experiment, respectively. Features extracted from one frequency band in one channel are calculated in each trial, and the features from all 15 trials in the same experiment are normalized following Formula (1). The normalization occurred across 15 trials in one experiment. Each trial can provide one feature from one frequency band in one channel when using a selected TW, and then the 15 trials contribute to the 15 features for normalization. As for the 180 s TW where there is just one TW, the normalization will be performed one time in one experiment with the minimum and maximum values across the 15 trials. Our proposed ELBN is conducted within an experiment to avoid interference or noise from other factors , which is newly developed to solve the individual difference and baseline deviation problems in the collected data from different subjects on different days. The protocol of ELBN is shown in To reduce the impact of individual difference on EEG-based emotion recognition , an expeProcessed PSD features and DE features with different TW lengths were separately fed into the six classifiers. The training data contain features extracted from 12 trials in each experiment, while the testing data contain features extracted from the other 3 trials from the same experiment. Both training data and testing data are evenly distributed across the three different emotions. A total of 10 random sets see  of possiThe recognition results of PSD features and DE features when using different TW lengths are shown in PSD features and DE features with ELBN were separately fed into the six classifiers. Features from the 10 given random sets were used as inputs and their averaged accuracies in each classifier were used as the final recognition results. The recognition results of PSD features and DE features are shown in When using different TW lengths for emotion recognition, the results shown in Our results show that the 2-s TW with ELBN has the best emotion recognition performance. In general, a longer length of TW will lead to fewer amounts of input, which is beneficial for reducing computational cost , while aELBN performs well on improving EEG-based emotion recognition according to the results in A 2-s sliding TW with a 1-s step was used for online emotion recognition with the LR classifier. The 10 random sets were used with 540 × 10 samples for training and 135 × 10 samples for testing, where 540 and 135 are the numbers of samples for training and testing, respectively, in one random set, and the number 10 means that there are 10 random sets. In total, 675 × 10 × 179 TWs  were used to examine the online emotion recognition performance, and the mean accuracy of the testing samples was used as the final output. The results when using PSD features or DE features are illustrated in As a common sense, human emotion usually lasts much longer than 10 s and can even last several minutes . Using aThe selection of TW length mainly affects emotion recognition accuracy as well as model complexity caused by the number of input features. As shown in Given that a shorter TW can capture more features with a higher model complexity, but the recognition accuracy would not continuously increase with the shortening TW length, a balanced TW length selection strategy should be considered, which would be another interesting topic for further investigation. Although the LR results in Another problem is that different frequency bands with different characteristics have different sensitivities to emotions , leadingThe effectiveness of different time window (TW) lengths on emotion recognition is examined based on EEG signals before and after applying experiment-level batch normalization (ELBN). The results show that the highest recognition accuracy is achieved when using the 2-s TW for feature extraction. The highest accuracies when using PSD features and DE features are 79.48% and 82.96%, respectively. The developed ELBN increases the feature sensitivities to emotion discrimination, which greatly contributes to the recognition accuracy improvement. A limitation of this study is that only the classical classifiers are used for emotion recognition. Advanced algorithms based on neural networks and deep learning have been extensively developed for emotion recognition in recent years ,50,51. O


Our results show that solvents with medium and low Gutmann donor and acceptor numbers provide PL intensity increase for both single halide perovskites by passivating the surface defect sites. Among the single halide perovskites, MAPbBr3 is more attracted to hydrogen bonding solvents, in contrast to MAPbI3 that is preferred by Lewis bases. This halide selective attraction also has an influence on the mixed-halide composition. Identifying these interaction mechanisms provides new insights into passivating the surface of perovskites for future device design.Surface passivation is one of the commonly used approaches to reduce the density of defects on the surfaces and interfaces hindering the performance and stability of perovskite optoelectronic devices. Although surface passivation leads to performance improvement for the targeted devices, details of the complex intermolecular interactions occurring between the molecules and perovskites are not entirely known. Here, we investigated a variety of commonly used solvents in the post-processing of perovskites by using photoluminescence (PL) spectroscopy on single and mixed halide perovskites (MAPbI Various surface passivating solvents with different functional groups were used to investigate solvent–perovskite interactions. The identification of the underlying mechanisms provides insight for new surface passivation strategies. Although a variety of approaches are used to modulate the nucleation-growth mechanism, improved film properties are mainly observed due to the reduction of defect density. The most common defect types encountered in the MAPbX3 based perovskites that influence the frontier orbitals of the Pb–X network  are uncoordinated lead and uncoordinated halide ions.10–12 Grain boundaries and surfaces are rich with these type of defects, and also the place where the structural deterioration starts by the adsorption of water and oxygen.13,14 Bulk and surface passivation of these defect states are the main requirements to boost the performance and the stability of perovskite devices. For this purpose, a variety of techniques have been applied in recent years, often involving multistep processes with various solvents and solutions.15 These techniques have been optimized towards practical purposes and the required macroscopic parameters  were chosen accordingly.Organic–inorganic halide perovskites have been playing a significant role in photovoltaic applications in the past years owing to their compositional flexibility, solution processability and easier adaptability to scale up production methods. Single junction solar cells have already reached 25.7% conversion efficiency owing to defect passivation and crystallization strategies.16 Such interactions play a principal role in both perovskite deposition and passivation studies. The affinity of a Lewis base to a Lewis acid is generally described by the Gutmann donor number,17–19 and it allows estimation of the strength of the dative or coordinate bonding. Besides the experimentally determined donor number, Gutmann also introduced an acceptor number for Lewis acids,20 that include the proton and thus can be applied to hydrogen bonds as well. We will use these parameters to understand the effect of commonly used solvents on the surface of single and mixed-halide perovskites 3) in post-processing under continuous illumination by following their steady-state photoluminescence. With these experiments, we model both environmental effects and targeted passivation. We study the interaction of uncoordinated lead atoms with Lewis donors and that of uncoordinated halides with Lewis acceptors (including hydrogen-bond donors).In this paper, we take another approach and concentrate on microscopic processes between the perovskite layer and pristine solvents in the vapor phase. The starting point in describing the effect of solvents on perovskites is based on Lewis acid–base interactions.All chemicals used are from commercially available sources without any further purification.3 precursor solutions  are prepared in dimethylformamide (DMF) in a N2 filled glovebox. After stirring overnight, equivalent volumes of these solutions are mixed to form 1 M of MAPb(Br0.5I0.5)3 perovskite precursor solution and stirred for 30 min. To prepare perovskite thin films, 10 μL of precursor solutions are statically dropped onto prepared silica substrates, then a spin coating process is initiated at 2000 rpm for 9 seconds, followed by 4000 rpm for 30 seconds. When the spin coating process is completed, samples are transferred to a hot plate and annealed at 100 °C for 30 minutes.Undoped silica substrates are cleaned by ultrasonic bath sonication for 10 minutes in each solvent acetone, ethanol and distilled water, then plasma cleaned for 15 seconds to form hydrophilic surfaces. 1 M of MAPbX−2 illumination intensity in all measurements unless otherwise mentioned. Prepared perovskite thin films are put into a sealed cryostat in a N2 filled glove box and transported to the spectrometer, then N2 flow connections are set up for the cryostat and it is kept under N2 flow in the dark for a couple of minutes before the beginning of the measurement. Solvents are introduced into the sample chamber by continuous N2 bubbling during the designated exposure period. The schematic of the PL measurement is illustrated in Photoluminescence of perovskite samples is recorded by a Horiba Jobin Yvon Nanolog Fluorimeter with 0.5 seconds integration time at 430 nm illumination wavelength and 3 mW cmPerovskite solutions are recorded by an Ocean Optics fiber optics spectrometer under laboratory conditions of 23 °C with 30% relative humidity. Samples are prepared in a glove box, then placed into cuvettes filled with the selected solvents. The cuvettes are lightly sealed to prevent the evaporation of solvents during the measurement. The solutions are kept for 2 hours dark to observe the spectral changes over time caused by the dissolution of surface species. The schematics of the UV measurement is illustrated in Fig. S6.†−1 mid-infrared region with an average of 256 scans. Exposure of the sample to water is conducted in the same way as with photoluminescence measurements and with the same exposure unit.Powdered perovskites are placed onto a molybdenum crystal to measure the attenuated total reflection infrared (ATR-IR) spectra using a Bruker Tensor 37 FTIR instrument in the 800–4000 cm−1 resolution.Far-infrared spectra are measured using a Bruker IFS66v spectrometer with 6 μm mylar beamsplitter and an average of 128 scans. All measurements are carried out with 4 cm2+ in perovskites acts as a Lewis acid and interacts with electron donors,21,22 while uncoordinated halides behave as Lewis bases. Hydrogen bond donor molecules can behave as Lewis acids and accept electrons from uncoordinated halides. We evaluated the Lewis acid–base interactions of commonly used solvents in perovskite post-processing with halide and lead defects on the surfaces and interfaces based on their electron donor–acceptor abilities by using donor and acceptor numbers given in Table S1.†In situ PL spectroscopy allows us to examine the effects of the ongoing passivation processes almost instantly, on a scale of seconds, by causing PL intensity increase or decrease related to defect passivation or formation during the exposure and recovery periods. We start this section with the effect of electron donor solvents interacting with uncoordinated Pb2+, then we examine the effects of electron acceptor solvents on uncoordinated X− in single halide perovskites. These will be followed by the evaluation of the effects of both electron donor and acceptor solvents on mixed halide perovskites. Possible interactions of the solvent molecules with the uncoordinated atoms on the perovskite surfaces are illustrated in Molecules that can donate and accept lone electron pairs are defined as Lewis bases and acids, respectively, and the transfer of a pair of electrons from an occupied orbital of a base to an unoccupied orbital of an acid is called a Lewis acid–base reaction. Uncoordinated Pb3 and MAPbI3 PL intensity during exposure to electron donating solvents. Full PL mapsfor are shown in Fig. S3.23 reacting with all components of the perovskite, therefore the interactions go beyond surface passivation. During the exposure of the surface of MAPbI3 to DMF, quenching of the PL is observed after a quick PL intensity increase at the beginning of the exposure period  on the perovskite surface, which makes it elusive to interpret from which part of the hydroxyl group the PL intensity changes are originating in the spectra. As can be seen in 3 and MAPbI3, which can result from either electron donor or acceptor interactions, or both 2-methoxyethanol (2-MetOH) has a chemical structure similar to ethanol with an additional ether functional group that behaves as an electron donor due to the lone electron pairs on the oxygen. This makes 2-MetOH a multifunctional molecule, capable to interact with both uncoordinated Pb2+ and X− ions simultaneously. By taking advantage of its special chemical structure, we used this molecule to show that the selection of halide in perovskites dominantly affects which type of defect interacts with a passivating molecule and contributes to the PL changes. In 3 can be seen after the surface is exposed to 2-MetOH, that is later followed by a gradual decrease until the end of the interaction period, indicating a slight disruption of the surface by the methoxy group. MAPbI3, on the other hand, shows significant PL intensity increase in the first couple of minutes, followed by a fast-paced decrease. This difference shows that the methoxy group affects MAPbI3 more significantly than MAPbBr3. As we already discussed earlier, the iodide ion shows higher affinity to electron donor groups and less tendency toward hydrogen-bond formation. Therefore, it is plausible to interpret that the hydroxyl group interacts selectively with perovskites based on the halide ion, where hydrogen as an electron acceptor is dominant on MAPbBr3 and oxygen as an electron donor is dominant on MAPbI3. It is also known that 2-MetOH is a good solvent for MAPbI3 precursor salts,31,32 and detecting a PL intensity response similar to the effect of DMF is reasonable  should cause PL intensity increase even if it partially dissolves the surface as we experienced earlier with 2-MetOH  measurements are taken during exposure to water of both MAPbBr3 and MAPbI3 by in situ ATR spectroscopy , Pb(OH) Fig. S11. Water ie.g. tandem solar cells, as their band gap can be easily tuned in the range of bromide and iodide single halide perovskites (530–780 nm) by changing the stoichiometry. The ionic potential difference of bromide and iodide causes halide mismatch in the nucleation-growth stage,37 which makes mixed halide perovskites prone to be highly defective compared to single halide perovskites. Upon illumination, certain mixed-halide compositions become unstable and single-halide regions form. A small portion of the iodide ions migrates to the surface and grain boundaries with the assistance of defects and forms clusters at thermodynamically stable iodide-rich compositions. The excited charge carriers funnel into these newly formed, lower-bandgap regions and recombine there. This process is known as halide segregation or Hoke effect,38 and causes the PL spectra to be dominated by the emission of the newly formed iodide-rich phase (segregate phase), independent from the initial composition of the mixed halide perovskite. Despite the uniqueness of the segregate phase formation in mixed-halide perovskites, single-halide perovskites also suffer from similar vacancy-mediated ion migration, which causes hysteresis, and was even pointed out as one of the reasons of the degradation under prolonged illumination.29,39 Passivation of the defects has a significant impact on this process, because it can prevent or slow down the initial formation of the segregate phase by blocking ion migration pathways, or it can disrupt the charge funneling process by altering charge diffusion lengths (formation of electric field and dipole by the donor–acceptor interactions).40,41In this last part, we discuss the effects of the donor–acceptor interaction on the segregation of mixed halide perovskites. Mixed halide perovskites are potential candidates for several applications, Fundamentally, the aspects of donor–acceptor interactions that we observed on single-halide perovskites are similar in the case of mixed halide perovskites. On the PL spectra, however, we observe the interactions from the side of the minority segregate phase, not from the mixed halide phase itself. In a simplistic assumption, the donor–acceptor interactions should cause PL changes which are the mirror image of the main single halide phase interactions. Passivation of the surface defects should reduce the segregate phase and increase the mixed halide phase intensity by healing the defects. Conversely, with increased defect concentration, the halide segregation should accelerate.0.5I0.5)3 surface interacts with 2-MetOH and ethanol. 2-MetOH is a good example for the effect of surface passivation because the PL intensity of the segregate phase immediately decreases 3 is given in + (Lewis acid) added mixed halide perovskites.42Last, we are going to evaluate the effect of water and acetic acid on the mixed halide perovskites. As we mentioned earlier, water and acetic acid have relatively close donor and acceptor numbers, and we could expect a similar effect on the segregate phase intensity. The effect of both solvents on the PL spectra of MAPb3 perovskites by using a variety of solvents with known donor and acceptor numbers on the Gutmann scale. Our results show that solvents with medium-low donor or acceptor numbers are optimal for surface passivation. Surface interactions highly depend on the selection of the halide in methylammonium-based lead perovskites, especially on its electronegativity. Donor functional groups such as carbonyl and ether tend to interact and bond with uncoordinated Pb+ ions atoms in perovskites containing iodide. Conversely, molecules with a functional group that can behave as an acceptor tend to be attracted to perovskites containing bromide. Our evaluation with the hydroxyl group molecules also indicates the same tendency towards the halide with a higher electronegativity to form a coordinate bond on the surface, which highlights the degradative contribution on MAPbBr3 when the acceptor number is high.We evaluated surface interactions and their effect on the PL properties of MAPbBr0.5I0.5)3, considering the halide selective tendency of functional groups to interact with the surface. Our measurements showed that donor–acceptor interactions of the solvents such as acetic acid and 2-MetOH not only contribute to the passivation of the defects on the surface, but also to the migrating species. Water (with high acceptor number), even tends to quench the segregate PL intensity rapidly when it is exposed to the mixed halide perovskite surface.In addition, we have evaluated a mixed halide perovskite, MAPb(BrIn the light of these findings, we believe that selective or halide-dependent passivation strategies can be applied effectively in future devices by designing new molecules to passivate and protect the perovskite surface.M. D. Ö., B. B. and Á. P. conceived the project. M. D. Ö. did the sample preparation, photoluminescence, infrared and UV-visible spectroscopy measurements. M. D. Ö., Á. P. and B. B. interpreted the results. All authors took part in formulating the final conclusions and writing the paper.There are no conflicts to declare.RA-012-D2RA04278A-s001