Self-regulation refers to a person’s ability to manage their cognitive, emotional, and behavioral processes to achieve long-term goals. Most prior research has examined self-regulation at the individual level; however, individual-level assessments do not allow the examination of dynamic patterns of intraindividual variability in self-regulation and thus cannot aid in understanding potential malleable processes of self-regulation that may occur in response to the daily environment.This study aims to develop a brief, psychometrically sound momentary self-regulation scale that can be practically administered through participants’ mobile devices at a momentary level.This study was conducted in 2 phases. In the first phase, in a sample of 522 adults collected as part of a larger self-regulation project, we examined 23 previously validated assessments of self-regulation containing 594 items in total to evaluate the underlying structure of self-regulation via exploratory and confirmatory factor analyses. We then selected 20 trait-level items to be carried forward to the second phase. In the second phase, we converted each item into a momentary question and piloted the momentary items in a sample of 53 adults over 14 days. Using the results from the momentary pilot study, we explored the psychometric properties of the items and assessed their underlying structure. We then proposed a set of subscale and total score calculations.In the first phase, the selected individual-level items appeared to measure 4 factors of self-regulation. The factors identified were perseverance, sensation seeking, emotion regulation, and mindfulness. In the second phase of the ecological momentary assessment pilot, the selected items demonstrated strong construct validity as well as predictive validity for health risk behaviors.Our findings provide preliminary evidence for a 12-item momentary self-regulation scale comprising 4 subscales designed to capture self-regulatory dynamics at the momentary level. Self-regulation refers to a person’s ability to manage emotions, cognition, and behavior to avoid immediate gratification, which may interfere with achieving long-term goals. People with self-regulatory competence tend to make goal-oriented decisions and inhibit impulsive behavior that is incompatible with their long-term goals . Self-reAn individual’s level of self-regulation is likely responsive to internal factors, such as negative affect or stress, which may lessen an individual’s determination or available resources to focus on self-regulatory behavior, as well as external environmental factors, such as being in a location with many temptations for risk behaviors. However, published measures of self-regulation are typically based on retrospective self-reports obtained through cross-sectional surveys or task-based methods. As in other assessments of psychological constructs , retrospective self-report methods collected at 1 time point are helpful in measuring dispositional states, allowing researchers to quantitatively describe an individual and examine interindividual variability, but are limited in measuring the changing states of such constructs within an individual ,6. Task-A methodologically and psychometrically sound metric that precisely and sensitively captures malleable processes involved in self-regulation in a real-world setting may enable a more contextually informed understanding of self-regulatory processes. Developing a valid assay for measuring changes in self-regulation in a nonlaboratory, everyday setting may help researchers better identify the construct’s responsiveness to internal and environmental factors and thus more effectively intervene in self-regulation as a putative mechanism that may play a causal role in facilitating health behavior change. Proliferation in information and communication technologies, combined with novel measurement methods, such as smartphone-based ecological momentary assessment (EMA), enables researchers to examine and assess self-regulatory processes and dynamics at a momentary level as people move through their lives in various real-world environmental contexts .This study aimed to develop a brief, psychometrically sound momentary self-regulation scale that can be practically administered through participants’ mobile devices at a momentary level. The objective was for the momentary scale to capture the constructs measured in existing self-regulation measures and capture both intra- and interindividual variability in self-regulation as it occurs in naturalistic settings. This work is part of a broader exploration of the ontology of self-regulation supported by the National Institutes of Health’s Science of Behavior Change initiative . The worBeginning with a broad representation of items putatively measuring self-regulation at the individual level, we aimed to understand the underlying dimensions measured by these items and then select a smaller subset of items that capture these dimensions well and that could be studied at the moment level in a naturalistic setting.Beginning with the items selected in phase 1, we aimed to modify the items for measurement at the moment level, pilot their use via momentary assessment methods, construct a momentary scale, and preliminarily assess its psychometric properties at the moment level.Starting with a comprehensive set of existing scales that purport to measure self-regulation at the individual level, we aimed to confirm each scale’s factor structure and characterize its item characteristics. We then assessed the underlying constructs measured by the full set of items from all scales and determined their factor structure. Considering this as the underlying structure of self-regulation, we made an initial selection of the best-performing items from each factor. We aimed to confirm that the factor structure of self-regulation was preserved when using this limited set of items. Once we finalized a set of items that performed well and together measured all factors identified as part of the constructs measured at the individual level, we moved to study them further at the moment level in phase 2.self-regulation. Self-regulation refers to a person’s ability to manage cognitive, motivational, and emotional resources to act in accordance with their long-term goals. The constructs were operationalized as cognitive functions that allow an individual to engage in effective self-regulatory behaviors. Measures that focused on aspects of self-regulation such as goal planning, self-regulation failures, impulsivity, cognitive control, and temporal discounting were sampled.The larger self-regulation initiative began with a comprehensive review of the scientific literature of assessments (both survey based and cognitive task based) used in the domain of self-regulation research and related constructs . This review outlined the origin of all assays and the conceptual and empirical associations between the data from each measure and health and social behaviors. This study identified 23 self-report surveys and 37 cognitive tasks that purport to measure some aspect of self-regulation. The process for selecting the scales has been described in detail elsewhere ,10. BrieNext, a sample of 522 adults was recruited through Amazon Mechanical Turk (MTurk), a crowdsourcing website, and the 594 items from the 23 surveys were administered. A subset of these individuals  was selected to complete the surveys again 3 months later to enable the assessment of test-retest reliability . A descrTo support the dimension analysis via exploratory factor analysis (EFA), the observations (participants) were partitioned into a discovery set  and a validation set . The complete set (N=522) comprised the discovery and validation sets.n<p problem describes the challenge of having more variables than observations on which the variables are measured. With the MTurk sample of 522, we encountered such a challenge in using all the variables (items) to perform a dimension analysis of self-regulation using all 594 items from the 23 self-regulation surveys. Therefore, a precursor to performing exploratory factor analyses was the reduction of the number of evaluated items while still maintaining items in each potential self-regulation domain. To facilitate this process, scale-level analyses were performed on each self-reported scale to first confirm the structure of the scale and then identify a subset of well-performing items to carry forward as candidates for further self-regulation dimension analysis. This process began with research on the scale’s derivation and a qualitative review of the scale to ensure self-regulation was indeed the scale’s target. Next, for each scale, correlated factor analysis was performed to confirm the scale’s factor structure in the sample. Finally, an item response theory (IRT) analysis was performed within each scale to identify a set of approximately 3 items per scale to inform the development of a measure that succinctly captures various dimensions of self-regulation. Items were considered to perform well in the factor analysis if they loaded primarily on one factor of the scale and did not have high loadings on other factors and were considered to perform well in the IRT analysis if they had high information and discrimination. The goal of the item reduction process was to keep items from each subscale or construct measured to retain full coverage of scales in the final candidate set while limiting them to well-performing items.In statistics, an The MTurk data on the reduced set of items (116 items) were subjected to dimensional analyses. The discovery (n=200), validation n=322), and complete data sets (N=522) were used to perform the EFA of the reduced set of items. The goal of the EFA was to identify the number of underlying factors in the sample and interpret the content of each factor where possible. EFA was performed using Mplus “I usually think carefully before doing anything.” [UPP49]“Before making up my mind, I consider all the advantages and disadvantages.” [UPP56]Perseverance“I generally like to see things through to the end.” [UPP05]“I finish what I start.” [UPP28]“I almost always finish projects that I start.” [UPP43]Negative urgency“When I feel bad, I will often do things I later regret in order to make myself feel better now.” [UPP18]“When I am upset I often act without thinking.” [UPP30]“I often make matters worse because I act without thinking when I am upset.” [UPP45]Positive urgency“When I get really happy about something, I tend to do things that can have bad consequences.” [UPP41]“When overjoyed, I feel like I can’t stop myself from going overboard.” [UPP46]“I tend to act without thinking when I am really excited.” [UPP53]Sensation seeking“I quite enjoy taking risks.” [UPP24]“I welcome new and exciting experiences and sensations, even if they are a little frightening and unconventional.” [UPP32]“I sometimes like doing things that are a bit frightening.” [UPP42]Scree plots of eigenvalues from the discovery, validation, and complete sets (reduced items) showed a decreased rate of change after 3 eigenvalues and were almost flat after 6 eigenvalues. Given this, 3- and 4-factor EFAs were completed.Patterns of factor loadings were used to interpret the measured factors across several factor solutions. For each solution, all items loading on a factor were reviewed, and an attempt was made to identify the construct under study and assign a name to each factor through a qualitative assessment of the item text. In the 3-factor solution, in both the discovery and validation sets, as well as the complete observation set, the factors appeared to represent (1) perseverance or lack of impulsivity, (2) sensation seeking, and (3) inhibition or mind over matter. In the 4-factor solution, in both the discovery and validation sets, the factors appeared to represent (1) perseverance, (2) impulsivity or sensation seeking, (3) inhibition or mind over matter, and (4) negative rumination or self-judgment. However, in the complete observation set, the factors differed, appearing to represent (1) impulsivity, (2) sensation seeking, (3) inhibition or mind over matter, and (4) negative rumination or self-judgment. Overall, the 3-factor solution had the most support based on the scree plot, consistency of results across observation sets, and additional exploratory factor analyses not presented. Therefore, we moved to the next step, assuming a 3-factor solution.As 3 factors appeared to have the most support, and among the 3-factor solutions, the discovery, validation, and complete data sets yielded similar factors and results, we considered the complete set and 3-factor solution as the results from which we would identify items for further study at the momentary level. The selection process is detailed in Item selection process based on the complete data set and 3-factor solutionFrom each factor, we removed items with factor loadings <0.5 or with factor loadings >0.5 on >1 factor from further consideration.Among the remaining items, the perseverance factor had many more items with factor loadings >0.5 (49 items), whereas the other 2 factors (sensation seeking and inhibition or mind over matter) each had a smaller number of items with loadings >0.5 . As we wanted to represent all 3 factors in our selected items, we only considered the first 21 items  that loaded onto the perseverance factor . For sensation seeking and inhibition or mind over matter, we evaluated all items with loadings >0.5 onto the factor.Among the resultant 21+15+9 items, we aimed to select items in proportion to the number loading on the 3 factors; hence, we sought to select 10 perseverance or impulsivity items, 3 sensation-seeking items, and 2 inhibition or mind over matter items. The selection procedure was as follows:We removed items with a large intraclass correlation coefficient (ICC) value (>0.7).We removed items referring to a specific activity , which is common among sensation-seeking items.We added items outside of the initial selection from each factor if they narrowly missed the selection but had available ICCs that were <0.4.We then selected items that represented a variety of themes within the factor, favoring items with lower ICCs.impulsivity  were not selected, we call the first factor perseverance rather than perseverance or lack of impulsivity.The selection resulted in 20 initially selected items: 10 items from the perseverance or impulsivity factor, 5 items from the sensation-seeking factor, and 5 items from the inhibition or mind over matter factor. Given that the items that appeared to represent We performed a CFA to determine whether the identified factors in the larger set of items were confirmed in the selected 20 items. The 3-factor confirmatory model did not fit the data well . Therefore, we performed a 3-, 4-, and 5-factor EFA of the selected items. In the exploratory analysis, the 4-factor solution best fit the data . Qualitative examination of items that had previously made up the third factor (inhibition or mind over matter) showed a split across the third and fourth factors in the EFA, suggesting 2 separate factors: emotion regulation and mindfulness. To accommodate this new structure, from the originally selected 20 items, 2 items were removed from the perseverance factor, and 1 item each was selected  to measure the emotion regulation and mindfulness factors. Furthermore, a 4-factor CFA was performed on the revised 20-item set, and the model fit the data well , and we considered our selection complete. 2, falling in the overweight category. Approximately 30% (16/53) of the participants reported that they smoked cigarettes every day, 40% (21/53) had used cannabis, and 36% (19/53) reported that they had not limited their alcohol intake to less than monthly in the past year. Approximately 28% (15/53) of participants reported that they had used drugs other than those required for medical reasons.Approximately 12% (7/60) of participants who enrolled in the study did not complete any of the 42 microsurveys, resulting in 53 participants with momentary data. Participants were mainly White , non-Hispanic , married , and female  and aged between 22 and 48 years at baseline. These 53 participants completed a median of 42 of the 42 microsurveys (range 1-42). The mean BMI was 27.18  kg/mAnalyses examining the construct validity of the individual items assessed the association between the baseline trait-level self-regulation item and momentary-level self-regulation item. They showed a significant association with the exception of 1 item (item 3), indicating that for all other items, the concept operationalized and measured through momentary items reflects the original construct (trait-level self-regulation) measured at the baseline survey .Unconditional multilevel models confirmed that there was significant variation at the individual item level for all items with ICCs ranging from 37% to 64%, indicating that a large proportion of the variability in the items was because of within-individual fluctuation rather than between-individual variation.Multilevel EFA models with between 3 and 5 within-individual factors and between 3 and 4 between-individual factors were explored. We used iterative processes to report the best factor solutions for the momentary self-regulation scale. For example, although the 5-factor within-individual structure seemed to fit the data well, having 5 factors resulted in separating the emotion regulation factor into 2 factors based only on the scales from which the items originated, which is unlikely to be a meaningful distinction. The 3-factor solution at the between-individual level appeared to fit the data well. The difference between the 3- and 4-factor solutions is that emotion regulation and mindfulness factors may be combined into 1 factor at the between-individual level. Given these results, we examined the 4-factor EFA results for the item selection steps that followed. The 4 factors identified were very similar to those present in phase 1: perseverance, sensation seeking, mindfulness, and emotion regulation.As we aimed to retain only items that performed well at the individual and moment levels in a momentary self-regulation scale, we examined the factor loadings and communalities of the items in the multilevel EFA. We selected items that consistently showed high communalities at both within- and between-individual levels, indicating that variability in these items is reasonably explained by the underlying factors, as well as items that consistently loaded highly on the factors of interest and did not simultaneously load highly on the other factors being measured. This process yielded a set of 12 items—3 selected for each underlying factor.A multilevel CFA was fit to these 12 items. The fit statistics from this multilevel CFA model were comparative fit index 0.989, standardized root mean squared residual (between) 0.083, standardized root mean square residual (within) 0.083, RMSEA 0.012, and TLI 0.985. The significant path coefficients and covariances between the factors are shown in To create subscale scores based on the 4 identified factors, we calculated the mean of the scores of all items loading onto a factor and reverse coded the items so that all items measured the construct in the same direction. For example, the sensation-seeking subscale comprised the mean of items 7 and 8, which measure sensation-seeking behavior, and reverse-coded item 17, which measures a lack of sensation-seeking behavior. In addition, we reverse coded all mindfulness items  to measure mindfulness rather than lack of mindfulness.We also created a total momentary self-regulation scale score that was a composite  of all subscale scores using 2 methods: literature-based total score and data-based total score. On the basis of the existing literature, we would expect that perseverance, mindfulness, emotion regulation, and lack of sensation-seeking factors would all have positive correlations. Therefore, we created a literature-based total by calculating the mean of the perseverance, mindfulness, emotion regulation, and reverse-coded sensation-seeking subscales .In the CFA (and EFAs), we found that the emotion regulation and perseverance factors had a negative correlation. Therefore, we examined the items that make up the emotion regulation factor, and instead, they appeared to measure self-judgment to some extent. Indeed, the items were from the nonjudging scale of the FFMQ. Self-judgment could be negative rather than positive in terms of self-regulation; thus, we created another total score comprising the mean of the perseverance, mindfulness, reverse-coded emotion regulation, and reverse-coded sensation-seeking subscales . Higher literature-based and data-based totals each indicate greater momentary self-regulation. We examined the relationships between the momentary self-regulation subscales and the trait-level self-regulation subscales by modeling the associations between the momentary subscales and the literature-based and data-based total scores with the trait-level subscale scores assessed at baseline. The results are presented in P=.05), the MAAS, the SSRQ, and the lack of premeditation and lack of perseverance subscales in the UPPS-P, indicating various convergent features of the momentary mindfulness subscale with trait-level surveys.The momentary mindfulness subscale was significantly associated with the trait-level suppression subscale of the ERQ, the nonjudging subscale of the FFMQ, the venturesomeness subscale of the I-7 , with those in the US $30,000 to US $50,000 income category reporting a significantly lower literature-based total score than those in the ≥US $70,000 income category.The data-based total score  had the most associations with negative health behaviors and is therefore considered the optimal way of combining the subscales into a total score for the predictive ability for risk behaviors. This data-based total score was significantly negatively associated with the AUDIT total, TFEQ-R18 total, TFEQ-R18 uncontrolled eating, TFEQ-R18 emotional eating, and having never smoked and positively associated with low categories of the AUDIT. The data-based total also had a significant association with older age. Although the pairwise comparison between those in the US $30,000 to US $50,000 income category and those in the ≥US $70,000 income category had significantly different data-based total scores, income was not significantly associated with the data-based total score overall. In contrast, the literature-based total  was not related to any negative health behaviors. The literature-based total had a significant association with income  study did not measure environmental contextual factors or social interactions of participants, which may have affected the responses. Future efforts may adapt the study methods and analytic procedures to capture these external factors as momentary self-regulatory dynamics are likely to be interactive with, and reactive to, environmental and internal factors.In addition, in phase 2, we recruited a sample of 60 individuals who resided in states in the Eastern time zone in this study because of the operational capacity of the research team and the manual process involved in sending text prompts with microsurveys at randomized times. Although we do not have reason to believe that persons from states in the Eastern time zone have significantly different self-regulatory dynamics and characteristics than those in other areas, future studies should examine the validity and reliability of our scale among individuals recruited from geographically diverse areas and groups, including rural and urban areas.Beginning with existing self-regulation scales, we examined the underlying constructs measured by the full set of items in the scales and then identified a reduced set of items that captured the underlying constructs measured across the scales. After confirming that the construct structure was retained within the reduced set of items, we further piloted the set of items in a momentary study in which we captured data from individuals in the moment, in their naturalistic contexts. Using the momentary study results, we further reduced the item set and demonstrated the initial validity in this sample of a momentary self-regulation scale comprising 12 items spanning 4 momentary self-regulation subscales. To further evaluate the validity and reliability more generally, the momentary self-regulation scale should be evaluated in other samples and contexts. This novel momentary self-regulation scale measures self-regulation on a momentary basis as individuals move through their daily lives and can be administered on mobile devices.


Picking single-protein particles from cryo-EM micrographs (images) is a key step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though the emerging machine learning-based particle picking can potentially automate the process, its development is severely hindered by lack of large, high-quality, manually labelled training data. Here, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for single protein particle picking and analysis to address this bottleneck. It consists of manually labelled cryo-EM micrographs of 32 non-redundant, representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). It includes 9,089 diverse, high-resolution micrographs (~300 cryo-EM images per EMPIAR dataset) in which the coordinates of protein particles were labelled by human experts. The protein particle labelling process was rigorously validated by both 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of machine learning and artificial intelligence methods for automated cryo-EM protein particle picking. The dataset and data processing scripts are available at  With the advent of cryo-EM, there has been a boom in structural discoveries relating to biomolecules, particularly large protein complexes and assemblies. These 3D structures of proteins 2 are important for understanding their biological functions 3 and their interactions with ligands 5, which can aid both basic biological research and structure-based drug discovery 6. A key step of constructing protein structures form cryo-EM data is to pick protein particles in cryo-EM images (micrographs). Before diving into recent developments in protein particle picking and the bottleneck it faces, it is important to understand the physics and chemistry behind the grid preparation and micrograph image acquisition in cryo-EM experiments.Cryo-electron microscopy (cryo-EM) is an experimental technique that captures 2D images of biological molecules and assemblies  at cryogenic temperature using ‘direct’ electron-detection camera technology I.7; the next step of the single-particle procedure is to prepare the cryo-EM specimen. The grid preparation process, also known as vitrification, is straightforward. An aqueous sample is applied to a grid, which is then made thin. Eventually, the grid is plunged frozen at a time scale that inhibits the crystalline ice formation. Additionally, the particles must be evenly distributed across the grid in a wide range of orientations. It is very difficult to achieve a perfect cryo-EM grid because particles may choose to adhere to the carbon layer instead of being partitioned into holes. They may also adopt preferred orientations within the vitrified ice layer, which reduces the number of unique views 8. The grid is ready for analysis once the cryo-EM sample is successfully inserted into the electron microscope 9. Images are routinely captured during the screening phase at various magnifications to check for ice and particle quality. After the grids are optimized and ready for cryo-EM data collection, they are taken to a cryo-EM facility where qualified professionals load specimens into the microscope. To enable the best high-quality image capturing, experts adjust several parameters such as magnification, defocus range, electron exposure, and hole targeting techniques  can be summarized in four brief steps: (1) sample purification, (2) cryo-EM grid preparation, (3) grid screening and evaluation, and (4) image capturing. Once the sample is purified according to the standard protocols II.When the electron beam passes through a thin vitrified sample, it creates 2D image projections . So subsequent manual particle selection is necessary.17, APPLE picker 18, DeepPicker 19, DeepEM 20 , FastParticle Picker 21, crYOLO 22 , PIXER 23, PARSED 24, WARP 25, Topaz 26, AutoCryoPicker 27 , and DeepCryoPicker 28, can be taken as good examples of such efforts.To deal with the issue, artificial intelligence (AI) and machine learning-based approaches have been proposed, which can be less sensitive to impurities and more suitable for large-scale data processing and therefore hold the potential of fully automating the particle picking process. XMIPP 29. It contains almost all the publicly available raw cryo-EM micrographs. It is a public repository containing 1,159 entries/datasets (2.39 PB) as of Jan 29, 2023. It includes not just cryo-EM images of proteins, but also Soft X-ray Tomography (SXT), cryo-ET and many other microscopic projections of other biological samples. Only some cryo-EM images of a small number of datasets in EMPIAR contain particles manually labelled by the original authors of the data. Therefore, most existing machine learning methods for particle picking were trained and tested on only a few manually labeled datasets of a few proteins like Apoferritin and Keyhole Limpet Hemocyanin (KLH). The methods trained on the limited amount of particle data of one or a few proteins cannot generalize well to pick particles of various shapes in the cryo-EM micrographs of many diverse proteins in the real world. Therefore, even though machine learning particle picking is a promising direction, no machine learning method has been able to replace the labor-intensive template-based particle picking in practice. Therefore, the lack of manually labelled particle image data of a diverse list of proteins is a key bottleneck hindering the development of machine learning and AI methods to automate protein particle picking.The datasets used to train and test machine learning particle picking methods were curated from EMPIAR 30. This is one reason there is still a lack of large manually curated protein particle datasets in the field.Creating a high-quality manually labelled single-protein particle dataset of a large, diverse set of representative proteins to facilitate machine learning is a challenging task. Single-particle cryo-EM images suffer from high background noise and low contrast due to the limited electron dose to minimize the radiation damage to the biomolecules of interest during imaging, which makes particle picking difficult even for human. Low signal-to-noise ratio (SNR) of the micrographs, presence of contaminants, contrast differences owing to varying ice thickness, background noise fluctuation, and lack of well-segregated particles further increases the difficulty in particle identification 31. For instance, the methods: DRPnet 32, TransPicker 33, CASSPER 34, and McSweeney et al.’s method 35 that made significant contributions to the particle selection problem suffered the two similar problems. Firstly, there is not a sufficient and diversified dataset to train them. Secondly, there is no gold standard to test them. The similar problems happened to other supervised and unsupervised machine learning methods, such as an unsupervised clustering approach 36, AutoCryoPicker 27, DeepCryoPicker 28, APPLE picker 18, Mallick et al’s method 37, gEMpicker 38, Langlois et al.’s method 31, DeepPicker 19, DeepEM 20, Xiao et al.’s method 21, APPLE picker 18, Warp 25, SPHIRE-crYOLO 39, and HydraPicker 40 all encountered similar problems. They usually perform well on the small, standard datasets used to train and test them , but may not generalize well to non-ideal, realistic datasets containing protein particles of irregular and complex shapes, which are generated daily by the cryo-EM facility around the world.A common problem of the particle picking algorithms trained on a small amount of particle data of a few proteins is that they cannot distinguish ‘good’ and ‘bad’ particles well, including overlapped particles, local aggregates, ice contamination and carbon-rich areas To address this key bottleneck hindering the development of machine learning and AI methods for automated cryo-EM protein particle picking, we created a large dataset (CryoPPP) of cryo-EM micrographs in which protein particles were manually labelled. The micrographs are associated with 32 representative proteins of diverse sequences and structures that cover a much larger protein particle space than the existing datasets of a few proteins such as Apoferritin and KLH. The quality of the manually labeled particles of selected proteins was rigorously validated against some particles labelled by the authors who generated the cryo-EM data by both 2D particle class validation and 3D cryo-EM density map validation. The quality of our manual annotation is in pair with the annotations provided by the experts who created the data in the first place, which confirms our manual particle labelling process is effective. Therefore, we believe CryoPPP is a valuable resource for training and testing machine learning and AI methods for automated protein particle picking.CryoPPP was created through a series of steps as shown in After importing the micrographs with all the physiochemical parameters gathered from the corresponding published literature, we performed motion correction and Contrast Transfer Function (CTF) estimation for them. Once the micrographs were prepared, two human experts manually picked the particles after setting up the low pass filter values and proper diameter for picking particles.The expert-picked particles were cross-validated and then went through 2D particle classification. The best particles based on resolution, particle count, and visually appealing and sensible 2D classes were selected and further used for template-based particle picking and further human inspection. After iterating the 2D classes from template-based picking and human inspection, we ultimately obtained the final set of highly confident protein particles as ground truth and exported them in the files in star, csv and mrc formats. The first two files (.star and .csv) contain the coordinates of the protein particles and the latter (.mrc) store particles stacks. The process of creating CryoPPP in I.29. The metadata includes EMPIAR ID of each cryo-EM dataset of a protein, the corresponding Electron Microscopy Data Bank (EMDB) ID, Protein Data Bank (PDB) ID, size of dataset, resolution, total number of micrographs, image size/type, pixel spacing, micrograph file extension, gain/motion correction file extension, FTP path for micrograph/gain files, Globus path for micrograph/gain files, and publication information.The process of preparing the dataset began with collecting metadata about cryo-EM image datasets in EMPIAR. Data collection scripts that use API and FTP protocols were used to automatically download the metadata from the EMPIAR web portal (Steps 1 – 5). First, we only chose EMPIAR IDs (datasets) that have their volume maps deposited in EMDB. From the chosen EMPIAR datasets, we only selected ones that had corresponding protein structures in the Protein Data Bank (PDB).Following the metadata collection, the individual cryo-EM datasets in the collection were filtered as depicted in EMPIAR_metadata_335.xlsx file in CryoPPP for further information about the list of 335 datasets of 355 proteins.To ensure high data quality, we then retained only the EMPIAR datasets whose resolution was less than 4 Angstrom (Å). We observed that there were some redundant EMPIAR datasets  that correspond to the same biomolecule with the same PDB and EMDB IDs. Hence, we eliminated those duplicate entries. After removing duplicate records, we selected only EMPIAR datasets that contained micrographs of protein particles, excluding other biological samples such as viruses. This filtering step required some literature study of individual EMPIAR datasets. The motion correction and gain correction files for the selected datasets were extracted from the EMPIAR if required. The final list of meta data includes 335 EMPIAR entries, 32 out of which were used for manual labelling. Refer to the II.16 , EMAN2 14 and RELION 15 is very time consuming. Additionally, it is highly challenging to import micrographs, carry out motion correction, and estimate CTF for large micrographs. Furthermore, it takes a lot of disk space to store the labelled particle data together with the corresponding micrographs and particles stack files. Therefore, we chose 32 representative EMPIAR datasets out of 335 entries selected in the previous section for manual particle labelling to create the CryoPPP dataset, considering diverse particle size/shapes, density distribution, noise level, and ice and carbon areas. Moreover, proteins from a wide range of categories, such as: metal binding, transport, membrane, nuclear, signaling, and viral proteins were selected. See 16 , EMAN2 14, and RELION 4.0 15.Manually picking particles in cryo-EM micrographs through the GUI interfaces of cryo-EM analysis tools such as CryoSPARC CryoPPP includes a total of 9,089 micrographs (~300 Cryo-EM images per selected EMPIAR dataset). We labelled ~300 micrographs per EMPIAR data because using all the micrographs in each dataset would result in 32.993 TB of data, which would be too big for most machine learning tasks. Another reason is that many micrographs in the same EMPIAR dataset are similar and therefore it is not necessary to include all of them. The particle labelling process is described in detail as follows.1.2) for the micrographs in the dataset.This is the crucial first step of particle labeling. For each EMPIAR dataset, we import two inputs: micrographs and gain reference (motion correction files). We analyzed the description of the EM data acquisition and grid preparation for each dataset in order to collect the important information such as raw pixel size (Å), acceleration voltage (kV), spherical aberration (mm), and total exposure dose  to .mrc file since CryoSPARC accepts only .mrc extension. Then, based on the microscope camera settings and how the data was acquired during the imaging process, we applied geometrical transformations (flip gain reference and defect file left-to-right/top-to-bottom (in x/y axis) or rotate gain reference clockwise/anti-clockwise by certain degrees) relative to the image data. Furthermore, we obtained gain reference for micrographs if their motion was not corrected before. We used 2.41. Hence, it is necessary to correct the movement of particles (referred to as ‘beam-induced motion’).When specimens are exposed to an electron beam, the mobility of sample molecules (protein particles) during data acquisition can affect the overall quality of electron micrographs and lower the final resolution Motion from Microscope: It is caused by stage drift and usually occurs in microscope due to little amount of vibration left over after the stage has been aligned to a new position 42. It moves the sample relative to the beam and optical axis. This motion is quite jagged in time, with sharp accelerations or twitches, but is consistent. The entire image will move in the same direction over time. (2) Motion from sample deformation: This motion is caused by the energy deposited into the ice by the beam, or energy already trapped in it, due to strained forces locked in during freezing. It is eventually released during the image capturing process. As the electrons pass through the samples, the energy from the beam and the temperature change causes the ice to physically deform and bend. That deformation is often smoother over time, but it can be highly anisotropic in space. In this case, various parts of the same image can move in different directions at the same time.The causes behind this motion can be categorized into two types: (1) Both motions must be estimated and corrected to obtain high-resolution reconstructions from the data. In the patch-based motion correction step, we corrected both global motion (stage drift) and local motion (beam-induced anisotropic sample deformation) for the micrographs (as shown in Left), each dot represents the sample’s position from frame to frame. Here, the x and y axes represent the units of pixels in the raw data’s pixel size. The sample begins at point (X), moves downward, makes a curve and again changes direction toward the left-top, and then continues to descend to the left. We apply this trajectory to the input data by shifting each image in reverse of what the motion trajectory suggests and finally averaging images together. In other words, we track a sample’s motion during the exposure to undo it.3.The contrast of images captured in the electron microscope is affected by imaging defocus and lens aberrations, which are adjusted by microscope operators to enhance the contrast. The relationship between lens aberrations and the contrast in the image is defined by the CTF. It explains how information is transferred as a function of spatial frequency.It is important to estimate CTF, which is then corrected during 2D particle classification and 3D reconstruction steps. Otherwise, the feasible reconstruction will have extremely low resolution. A full treatment of the effects of the CTF usually proceeds in two stages: CTF estimation and CTF correction. In CryoSPARC the CTF model is given by the 44. Because defocus has an impact on the CTF, distinct particles can have various defoci and hence various CTFs within a single image. To address this problem, CryoSPARC offers a patch-based CTF estimator that analyzes numerous regions of a micrograph to calculate a “defocus landscape”.Most cryo-EM samples are not ‘flat’. Before a sample is frozen, particles tend to concentrate around the air-water interfaces, and the ice surface itself is usually not flat 45. This plot helps identify a particular defocus value that stands out among a variety of other defocus values (x-axis). Patch CTF creates a plot showing how closely the input micrographs’ observed power spectrum and the calculated CTF match. The CTF fit plot in We performed a 1D search over defocus for every micrograph. We executed the patch CTF to obtain the output micrographs with data on their average defocus and the defocus landscape. When particles were extracted, this data was automatically used to assign each particle a local defocus value based on its position in the landscape.4.After performing the motion correction and CTF estimation, we manually picked particles interactively from aligned/motion-corrected micrographs with the goal of creating particle templates for auto-picking. Depending on the size and shape of the protein particles, we adjusted the box size and the particle diameter. Since picking particles on raw micrographs is extremely difficult, we tweaked the ‘Contrast Intensity Override’ while viewing micrographs in order to obtain the best distinctive view for picking particles.45. Hence, to generate comprehensive templates from a wide range of defocus values, we manually picked particles from multiple micrographs with diverse defocus and CTF fit values.It is particularly challenging to manually pick particles from micrographs with smaller defocus levels, and vice versa. As manual picking was very time intensive, we selected a subset of micrographs (around 20 micrographs of each EMPIAR dataset) for manually picking initial particles for the subsequent template-based particle picking. More details regarding the total number of particles picked manually including the total number of micrographs considered for manual pick are provided in the 5.The manually picked particles went through the 2D classification step. This step helped to classify the picked particles into several 2D classes to facilitate stack cleaning and junk particles removal. To analyze the distribution of views within the dataset qualitatively, we specified a specific number of 2D classes. By doing this, we investigated the particle quality and removed junk particle classes, which ultimately facilitated the selection of good particle classes.We specified the initial Classification Uncertainty Factor (ICUF) and maximum alignment resolution to align particles to the classes with 40 expectation maximization (EM) iterations. The diameter of the circular mask that was applied to the 2D classes at each iteration was controlled using the circular mask diameter in the case of crowded particles.46. We used three diagnostic measures to select the 2D classes: resolution (Å) of a class, the number of particles of a class , visual appearance of a class. Considering only the number of particles in a class is not sufficient because some classes containing a small number of particles may represent a unique view of the protein.After the 2D classes were formed, we selected the best particle classes interactively to remove the junks. 6.Forming and Selecting Best 2D ‘ step to pick more particles. The process was iterative, meaning that the output of a round of ‘template-based picking and inspection’ was again utilized for ‘2D class formation’ step to form and select best 2D classes under the human inspection. This process was repeated until we acquired high resolution particles that include all possible particle projection angles.After the best particle classes were selected and exported, we used a template generated from the ‘46 that work as templates to facilitate template-based picking that eventually results in template-based picked particles ready for human inspection as shown in 32 to remove any signals from the corners and prevent crowding. We observed that the blob-based in picking in RELION required minimum and maximum allowed diameter of the blobs, whereas defining a single value for particle’s diameter worked well in CryoSPARC.The final templates with green boxes , and power threshold to improve the visibility of the picks and removed false positives as shown in The CryoPPP dataset consists of manually labelled 9,089 micrographs of 32 diverse, representative cryo-EM datasets of 32 protein complexes selected from EMPIAR. Each EMPIAR dataset identified by a unique EMPIAR ID has about ~300 cryo-EM images in which the coordinates of protein particles were labeled and cross-validated by two experts aided by software tools.Each data folder (named by its corresponding EMPIAR ID) includes the following information: original micrographs (either motion-corrected or not), gain motion correction file, new motion-corrected micrographs , ground truth labels , and particles stack. The directory structure of each data entry is illustrated in I.gain’ folder, it includes both raw non-motion-corrected micrographs and their motion-corrected counterparts created by us. Users are supposed to use the motion corrected micrographs as input for machine learning tasks. The scripts for the motion correction are available at CryoPPP’s GitHub website.These are the two-dimensional projections of the protein particles in different orientations stored in different image formats . They can be considered as the photos taken by cryo-EM microscope. Original micrographs are from EMPIAR and can be either motion corrected or not. If an entry has a ‘II.It contains motion correction files  stored in different formats like dm4 and mrc. It is used to correct both global motion (stage drift) and local motion (beam-induced anisotropic sample deformation) that occur when specimens (protein particles) are exposed to the electron beam during imaging. Correcting the motion enables the high-resolution reconstruction from the data.III.14, UCSF Chimera 47, or UCSF ChimeraX 48.Particle stack comprises of the mrc files  of manually picked protein particles (ground truth labels). These are three-dimensional grids of voxels with values corresponding to electron density . To browse and examine this file, utilize EMAN2 IV.Ground truth data contain the star and CSV files for both all true particles (positives) and some typical false positives . The positive star (and corresponding CSV) files are the ground truth position of the picked particles combined in a single file for all ~300 micrographs per EMPIAR ID. While the negative star file consists position of the false positive particles. These star files contain information like X-coordinate, Y-coordinate, Angle-Psi, Origin X (Ang), Origin Y (Ang), Defocus U, Defocus V, Defocus Angle, Phase Shift, CTF B Factor, Optics Group, and Class Number of the particles.particle_coordinates inside ground_truth, which contains csv files, with same name as raw micrographs, which contain individual protein particle’s X-Coordinate, Y-Coordinate along with their diameter and other relevant information.Besides, there is a subdirectory called To ensure that the dataset is of high quality, we applied numerous validations and statistical analyses throughout the data curation process.I.As noted in II.a)To be inclusive and ensure unbiased data generation, we selected the cryo-EM data of 32 different, diverse protein types  to manually label protein particles, which can enable machine learning methods trained on them to work for many different proteins in the real-world. We selected the datasets covering different particle size, distribution density, noise level, ice and carbon areas, and particle shape as they are influential in particle picking.b)The variance in micrographs’ defocus values within a EMPIAR dataset is not accounted for by majority of the particle picking methods. This defocus variation causes the same particles to appear differently, altering the noise statistics of each micrograph. This makes it challenging to create thresholds to select high quality particles. 48) to assess the shape and density of a particle candidate independently. After calibration, the scores of each particle are recorded relative to the calibration line, and these values are used to define thresholds on the parameters.We recorded the correlation between defocus levels and the pick scores / the power scores (shown in III.a)All the input parameters used to prepare for loading micrographs into the CryoSPARC system were gathered from the appropriate literature. We adhered to the standards in the publications including data acquisition and imaging settings such as the microscope used, defocus range, spherical aberration, pixel spacing, acceleration voltage, electron dose and the correct usage of motion correction. Based on the microscope settings during the imaging process, we applied appropriate geometrical transformations. The defect files and the motion-correction files were flipped left-to-right or top-to-bottom and also rotated by specific degrees in clockwise/anti-clockwise direction as required. All these factors were thoroughly investigated and used during the data loading process in CryoSPARC.b)The picked particles were inspected using a 2D colored histogram, as shown in bottom), we interactively specified the upper and lower thresholds for both the Power score and NCC score for each dataset improving the accuracy in the manual particle picking.As shown in c)49 and EMPIAR-10081 50 with 300 micrographs  were used in cross-validation. The results of the 2D classes were compared based on total number of particles in each class, relative resolution of particles in the class, and distinct views of the structure of particles. Similar 2D classes, as shown in The results of the particles picked by the two Cryo-EM experts were compared to each other to make sure they are consistent. For example, two EMPIAR IDs: EMPIAR-10028 IV.51 and EMPIAR-10406 52 to validate our particle labelling process as follows.Gold standard particles are those particles that were picked by the Cryo-EM experts who generated the cryo-EM data. There are only a few EMPIAIR IDs deposited in EMPIAR that have both the micrographs and the gold standard particles. To validate the accuracy of our picked particles, we compared our results with the already-existing gold standard particles that are publicly available through the EMPIAR website. We carried out 2D and 3D validation for EMPIAR-10345 a)In order to get the gold standard 2D particles of the dataset, we downloaded the particle stack image files (.mrc) and .star file with the attributes of picked particles from EMPIAR. We used the particle stack and the star files to create the 2D classification results using CryoSPARC. Eventually, we compared our 2D class results with the gold standard. We performed the comparison based on the total number of classes, total number of picked particles, resolution, and visual orientation of the protein particle for each EMPIAR ID. Our results and the gold standard results exhibit strong correlations. It is worth noting that a high number of particles alone does not necessarily yield high resolution. Selecting a decent number of high-quality particles spanning a wide angular distribution is important for generating high 2D and 3D resolution.b)We performed an ab-initio reconstruction of the 3D density map using CryoPPP’s picked particles and compared the results with the gold standard 3D density maps from the EMPIAR website. The comparison of the 3D maps between EMPIAR and CryoPPP for EMPIAR-10345 and EMPIR-10406 is depicted in The detailed comparison results are reported in Supplement 1


Misinformation and conspiracy theories related to COVID-19 and electronic nicotine delivery systems (ENDS) are increasing. Some of this may stem from early reports suggesting a lower risk of severe COVID-19 in nicotine users. Additionally, a common conspiracy is that the e-cigarette or vaping product use–associated lung injury (EVALI) outbreak of 2019 was actually an early presentation of COVID-19. This may have important public health ramifications for both COVID-19 control and ENDS use.Twitter is an ideal tool for analyzing real-time public discussions related to both ENDS and COVID-19. This study seeks to collect and classify Twitter messages (“tweets”) related to ENDS and COVID-19 to inform public health messaging.Approximately 2.1 million tweets matching ENDS-related keywords were collected from March 1, 2020, through June 30, 2020, and were then filtered for COVID-19–related keywords, resulting in 67,321 original tweets. A 5% (n=3366) subsample was obtained for human coding using a systematically developed codebook. Tweets were coded for relevance to the topic and four overarching categories.A total of 1930 (57.3%) tweets were coded as relevant to the research topic. Half  of these discussed a perceived association between ENDS use and COVID-19 susceptibility or severity, with 42.4% (n=818) suggesting that ENDS use is associated with worse COVID-19 symptoms. One-quarter  of tweets discussed the perceived similarity/dissimilarity of COVID-19 and EVALI, and 13.8% (n=266) discussed ENDS use behavior. Misinformation and conspiracy theories were present throughout all coding categories.Discussions about ENDS use and COVID-19 on Twitter frequently highlight concerns about the susceptibility and severity of COVID-19 for ENDS users; however, many contain misinformation and conspiracy theories. Public health messaging should capitalize on these concerns and amplify accurate Twitter messaging. The COVID-19 pandemic has spread rapidly, with over 490 million confirmed cases and over 6.1 million confirmed deaths worldwide at the time of this writing . EmerginThe research on the potential associations between tobacco and nicotine use and COVID-19 risk has been mixed. Some published research has indicated that self-reported COVID-19 infection is greater among current cigarette smokers and former smokers compared to nonsmokers , that ciLikewise, research on the impact of COVID-19 on ENDS use has been mixed. A survey of a small convenience sample of US adult dual cigarette and ENDS users found that approximately one-quarter of participants attempted to reduce their tobacco and nicotine use during the pandemic . ResultsThe COVID-19 pandemic has been accompanied by an “infodemic” in which a substantial amount of information has been spreading both online and offline . In partTherefore, the purpose of this study was to systematically collect tweets related to COVID-19 and ENDS during the height of the pandemic in the United States and qualitatively analyze them to classify user discussions related to perceived associations between ENDS use and COVID-19. Using a “social listening” approach on Twitter can lead to a better understanding of tobacco-related topics of current importance . AdditioWe used the open source real-time infoveillance of Twitter health messages (RITHM) framework  to colleInitial codebook development involved a separate pool of random tweets . Individual codes were developed through a hybrid process, using both the themes identified by previous research and an examination by two independent coders of the pool of random tweets . Coders After initial codebook development, the two coders were provided with a spreadsheet containing the tweet text and a link to each tweet online. The tweet text was initially coded for relevance, defined as discussing a perceived association between COVID-19 and ENDS . Tweets that discussed ENDS or COVID-19 but not a perceived association between the two were excluded . Coders viewed all relevant tweets that remained publicly available at the time of coding on Twitter so that links to external content could be assessed. However, coders included the text from unavailable tweets to preserve the comprehensiveness of the original data.Relevant tweets were then coded as to whether they referenced four overarching categories: discussions about associations between COVID-19 severity and ENDS use; discussions about COVID-19 and e-cigarette or vaping product use–associated lung injury (EVALI) symptom similarity; discussions about COVID-19 affecting ENDS use; and discussions about personal or proximate experiences . Additionally, substantial misinformation related to COVID-19 and ENDS was found during coding and tweets containing potential misinformation—defined as statements not supported by the current peer-reviewed literature or exaggerations of research findings or public health findings—were identified and tagged by coders. Tweets containing potential misinformation were analyzed by an experienced graduate-level coder and the first author as themes within the major coding categories. All codes and subcodes are described in Codes were not mutually exclusive. For example, a tweet that stated, “Coronavirus attacks the lungs so one of the most important things you can do is to quit smoking and vaping. I’m in day 5 – join me!” would be coded as discussions about the association between COVID-19 severity and ENDS use (subcode: perception that ENDS use is associated with worse COVID-19 symptoms), discussions about COVID-19 affecting ENDS use (subcode: quitting ENDS because of COVID-19), and discussions about personal or proximate experience. We coded both textual and visual  content .The iterative coding process involved double-coding 100 tweets by two independent, experienced Twitter coders that were guided by a senior-level coder. All disagreements were discussed with the senior-level coder and adjudicated with the lead author, after which the codebook was modified accordingly. Interrater reliability was assessed using Cohen κ , and it Frequencies and percentages were calculated for each code. A thematic qualitative content analysis approach was used to inductively assess the tweets and refine thematic units within codes . The theOf 3366 human-coded tweets, 1930 (57.3%) were coded as relevant  and were included in the analysis . A totalSome tweets  suggested that ENDS use protects individuals from COVID-19 infection and progression. One theme focused on the potential curative effect of ENDS, with references to early research suggesting the protective effect of nicotine  and the components of ENDS that may cure those with COVID-19 . Other themes suggested that ENDS use protects users from infection and that there was no link between COVID-19 and ENDS .Fewer tweets  suggested that ENDS use actually is the cause of COVID-19, with themes focused on the possibility that the COVID-19 virus was in ENDS liquid  and that secondhand vapor might be contributing to the spread of COVID-19 .A total of 479 (24.8%) tweets discussed the perceived similarity (or dissimilarity) of the symptoms of COVID-19 and EVALI. Of these, a greater number of tweets  suggested that COVID-19 and EVALI are actually the same disease, with overarching themes focused on government deception  and similarities of symptoms and medical imaging. A smaller number of tweets  focused on distinctions between COVID-19 and EVALI, with overarching themes mentioning how EVALI was not infectious  and differences in age groups affected .Approximately 13.8% (n=266) of tweets discussed a potential relationship between ENDS use behavior and the pandemic. Of these, most mentioned quitting ENDS in response to the pandemic . The most prominent theme in this category was quitting ENDS because of its effects on respiratory health . Fewer  mentioned starting or continuing ENDS because of the pandemic  and the perceived health benefits of nicotine . Finally, 33 (1.7%) tweets mentioned switching from traditional cigarettes to ENDS, with all tweets in this coding category containing the theme of ENDS being a safer alternative to cigarette smoking.Approximately 12% (n = 231) discussed a personal or proximate experience. Among users who mentioned ENDS use themselves, one theme focused on limiting the sharing of their ENDS because of COVID-19 . A total of 40 (2.1%) tweets discussed respiratory symptoms that users believed could be due to either COVID-19 or ENDS use . Another theme focused on possibly having COVID-19 in the past but attributed symptoms to ENDS use at the time. Additionally, some users expressed concern about COVID-19 for friends/family who use ENDS  and relief about not using ENDS themselves in light of COVID-19.In this study, approximately half of the tweets that discussed perceived associations between ENDS use and COVID-19 contained language suggesting the perception that ENDS may worsen COVID-19—specifically that the use of ENDS by predominantly younger individuals may increase risk of severe COVID-19 symptoms. This is consistent with recent research finding that, among those aged 13 to 24 years, current ENDS users and current ENDS/cigarette dual users are 5 and 6.8 times more likely to be diagnosed with COVID-19 compared to nonusers, respectively . LikewisThe themes emerging from this study, combined with previous research, suggest that focusing public health messaging on the potential for worse COVID-19–related health outcomes among ENDS users may resonate with those discussing this topic on social media. Specific strategies, such as magnifying accurate Twitter messages linking ENDS use with COVID-19 and disseminating messages via social media with clear actionable public health advice linked to credible sources may be important health communication tools moving forward .Our findings were also consistent with others who have reported rapid spread of misinformation related to COVID-19 in general ,30. In oAdditionally, our analysis uncovered conspiracy theories related to the origins of the virus, its transmissibility, and potential treatments ,33. For Several tweets suggested that propylene glycol may kill the virus, thus protecting ENDS users from infection, which relates to a popular misperception of the antiviral and antibacterial properties of propylene glycol gas . Some ofOur study was limited in that these results are neither generalizable to non-Twitter users nor the general population. Twitter users tend to be younger and more educated than the general population , and theIn conclusion, discussions about the perceived associations between ENDS and COVID-19 on Twitter are often conflicting. These conflicts reflect the lack of consistent health communication messaging, which may have facilitated the spread of speculation and misinformation. The results suggest the need for further research to investigate the spread of information and misinformation about ENDS use and COVID-19, especially on social media platforms. They also suggest potential targets for evidence-based clarifications public health providers can implement.


This ability allows HICs to delay or avoid progression to AIDS. HIV control is strongly associated with the expression of human leukocyte antigen (HLA) alleles in HICs. From the HIV protective HLAs described, HLA-B57 is the most frequent in HIC patients. HLA-B57 can present a large range of highly conserved Gag-derived HIV peptides to CD8+ T cells and natural killer (NK) cells, both the focus of this review. So far there are limited differences in the immune response strength, magnitude, or receptor repertoire towards HIV epitopes that could explain viral control in HICs. Interestingly, some studies revealed that during early infection the large breadth of the immune response towards HIV mutants in HLA-B57+ HIC patients, might in turn influence the disease outcome.Understanding the basis of the immune determinants controlling disease outcome is critical to provide better care to patients and could be exploited for therapeutics and vaccine design. The discovery of the human immunodeficiency virus (HIV) virus as the causing agent of acquired immunodeficiency syndrome (AIDS) decades ago, led to a tremendous amount of research. Among the findings, it was discovered that some rare HIV Antiretroviral therapy (ART) has dramatically improved the health of HIV-infected individuals; however, many side effects arise from prolonged ART use . Th. Th63]. + patients.The ability of both KIR and T cells to recognise HIV epitopes bound by HLA-B57 could potentially reduce viral escape in HLA-B57Despite the research interest in protective HLAs, such as HLA-B57, including their role in HIV control and the potential to advance new therapeutics, there are still many questions remaining. Surprisingly, even for conserved potent Gag-derived epitopes such as TW10, KF11, and IW9, there are gaps in knowledge regarding T cell function, cross-reactivity patterns, TCR repertoire diversity, affinity, and magnitude of the response. These parameters are critical to enable a clear comparison between HIC and progressor individuals and to understand if the differences occur early during infection as previously suggested ,44. Diff+ and HLA-B*57:03+ individuals, despite presenting peptides in similar fashion, there are differences that T cells can ‘detect' and provide an advantage to HICs. While the mechanisms are not fully clear, HLA-B57 molecules provide an advantage in HIV infection as well as in HCV infection [+ T cell function such as epigenetic modifications that allow for effective function in chronic HIV infection [+ HIC individuals have shown that the host immune factors might drive the restriction of viral replication, while the mechanism remains unclear [There are some differences between HICs and progressors, but not significant yet to draw any conclusions. A recent study showed that across HIV epitopes presented by HLA-B57, progressors were selecting TCRs expressing the TRBV7-9 gene family more than HIC individuals . Given tnfection  and thernfection . Several unclear ,34.The research reviewed here clearly highlights the subtle differences in the epitope presentation of the HLA-B57 allomorphs which impact their recognition by T cell and NK cell receptors. In addition to the highly polymorphic nature of HLA molecules, KIR receptors on the surface of NK cells are also polymorphic, similar to TCRs, which adds some layer of complexity in the search for a common feature that could define the basis of the HLA-B57 protective nature. These factors combined seem to have, to date, prevented the development of interventions based on the protection offered by HLA-B57 carriage in some individuals. However, further dissection of the immune response in HIV-1 controllers may yield insights to allow for the successful modulation of the immune response to afford this protection to broader populations.Better understanding of the immune response to protective HLAs, such as HLA-B57, and their role in HIV control has the potential to advance new therapeutics.The data currently available on protective HLA alleles does not allow to conclude on the mechanism for viral-load control observed in HIC individuals.More work on exploring the basis of HIV control is worth pursuing as this could reveal some critical information to better fight HIV.


Drosophila melanogaster a suitable model for studying the innate immune response and learning general mechanisms that are also relevant for humans. In this review, we summarize what is known about the dynamic regulation of the central NF-κB-pathways and go into detail on the molecular level of the IMD pathway. We report on the role of the nuclear protein Akirin in the regulation of the NF-κB Relish immune response. The use of the Drosophila model allows the understanding of the fine-tuned regulation of this central NF-κB pathway.Metazoans have developed strategies to protect themselves from pathogenic attack. These preserved mechanisms constitute the immune system, composed of innate and adaptive responses. Among the two kinds, the innate immune system involves the activation of a fast response. NF-κB signaling pathways are activated during infections and lead to the expression of timely-controlled immune response genes. However, activation of NF-κB pathways can be deleterious when uncontrolled. Their regulation is necessary to prevent the development of inflammatory diseases or cancers. The similarity of the NF-κB pathways mediating immune mechanisms in insects and mammals makes  The immune system is composed of tissues, cells and molecules within an organism that collectively aim to detect agents that are different from the organism’s healthy tissues and organize a response to counteract them and maintain homeostasis. There are two main types of systems: innate and adaptive. The innate immune system precedes the adaptive response and presents conserved mechanisms . It invoThe innate immune pathways mainly involve three types of proteins: sensors, which are able to detect microbial patterns or danger signals; adaptors, which are able to transduce the signal downstream of the signaling pathway; and effectors and regulators, which are crucial to the immune response and its dynamicity. When abnormally regulated, innate immune responses contribute to the development of pathologies including chronic inflammation, autoimmune diseases and cancer ,6. The nDrosophila are a well-suited model to unravel the fundamental mechanisms that constitute the innate immune response. They share many molecular pathways underlying the activation of their innate immune systems with humans [Drosophila as a model insect in the field of oncology [h humans , and stuh humans . On a sioncology ,10,11,12oncology ,14,15.The flies present cellular local responses  and a syDrosophila system in respect to mammals allowed the identification or analysis of processes and molecules, as exemplified by the Nobel Prize attributed to Jules A. Hoffmann and Bruce A. Beutler in 2011 for their discovery of the role of Toll receptors in the activation of the innate immune response [The simplicity of the response . It is tDrosophila that play a fundamental role in their immune response. The IMD and Toll pathways are able to recognize three main pathogen families: mostly Gram-negative bacteria for IMD, Gram-positive bacteria and fungi for Toll [NF-κB was originally identified as a DNA-binding activity protein in activated B cells . There afor Toll .Drosophila, at least 13 genes encode 17 PGRPs isoforms through alternative splicing [Microbial-associated molecular patterns (MAMP) of Gram-negative and some Gram-positive bacteria activate the IMD pathway . Meso-displicing .The activated receptors PGRP-LC or -LE will interact with the protein adaptor IMD. The cleaved N-terminal of IMD exposes an Inhibitor of Apoptosis 2 (IAP2) binding motif (IBM). That will lead to the recruitment of a tetrameric protein complex composed of DIAP2, Ubiquitin-conjugating variant 1a enzyme (Uev1a), Bendless and Effete complex . The rolIn the nucleus, Relish proteins form homodimers that control the expression of hundreds of target genes, affecting various immune functions such as microbial recognition, melanization or production of reactive oxygen species ,32,33. SThe Toll pathway is activated upon the sensing of fungi, Gram-positive bacteria and some Gram-negative bacteria. The transmembrane receptor Toll is activated by an extracellular proteolytic signaling cascade in two ways: circulating PRR recognizes Lys-type peptidoglycan (Lys-PGN) from Gram-positive bacteria or β-glucans from fungi ; proteasThe extracellular signaling cascade leads to activation of Spätzle (Spz) and its binding to Toll. This initiates the internalization of the receptor and recruitment of the adaptor protein Myeloid differentiation primary response gene 88 (MyD88) through their common TIR domains ,45,46,47Toll pathway activation must also be controlled to prevent putative harmful activations. Only one negative regulator has been identified up until now: Pellino, which regulates Myd88 protein stability . While tA difference between the two pathways is the timing of activation: the IMD pathway is rapidly activated within minutes in cell cultures and AMP gene expression culminates around 6 h post stimulation, whereas the Toll pathway takes longer to get activated, with a peak of gene expression around 24 h post stimulation. We have no clue if this is due to differences in the mode of activation of the pathways or something else.Recent work in various laboratories provides a more comprehensive view of the IMD pathway, its regulation and the dynamic control of gene expression by its NF-κB factor, Relish. We decided to focus the rest of the review on what is known about Relish regulation as a case study to understand the regulation of NF-κB response.Activation of the IMD pathway requires the formation of a high molecular weight protein complex, described as a signaling amyloid complex. Through homophilic interactions between proteins bearing the same homology domain and poly-ubiquitin chains, the various proteins of the pathway aggregate, allowing the activation of Relish. This process is detailed in  and can Binding of DAP-type peptidoglycan to PGRP-LC at the cell membrane or to PGRP-LE in the cytosol leads to multimerization of the receptor and activation of the pathway . Given tThe C-terminal domain of IMD contains a death domain (DD) that interacts with the DD of FADD , which is responsible for binding to DNA and homo or heterodimerization . There aIn the case of Relish, instead of association with the other ΙκB family genes, such as Cactus, Relish itself contains a C-terminal ankyrin-repeat domain. Thus, when Relish gets activated, it must be phosphorylated and cleaved in order for the transcription factor domain to be able to enter the nucleus. The complexity of the IMD pathway is related to this complex mode of activation of Relish.After its cleavage, the Relish N-terminal domain (RelN) is translocated to the nucleus, which opens the possibility of target gene transcriptional activation, but only if Relish is allowed to access its targets .Ecdysone is a developmental hormone required for metamorphosis. It is sensed by a nuclear receptor, which activates some early response genes such as the Broad-complex (BR-C). In both S2 cells  and MalpFurthermore, several other transcription factors, such as the GATA factors Pannier and Serpent, are required in addition to BR-C for the immune response in S2 cells, in the fat body and for the survival of flies after infection . One hypOther than downstream of the IMD pathway, Relish can also be activated in different circumstances by mechanisms that are not well understood. It is expressed as a response to hypoxia and the activation of FOXO transcription factor , but we Our laboratory in Strasbourg recently conducted a study focused on the response of drosophila adult flies to oncogenic RasV12-expressing cells injected into the body cavity . Eleven Different mechanisms are involved in keeping the pathway silent in the absence of infection, in tolerance to commensals or in rapid resolution of the response after an infection through auto-regulatory loops . In thosThe PGRP family of proteins is subdivided into two categories: some, such as PGRP-LC or PGRP-LE, bind the ligand, whereas others, such as PGRP-SC and PGRP-LB, are amidase enzymes that cleave the ligand and are unable to activate the pathway . This sePGRP-LF is a transmembrane protein that lacks the intracytosolic RHIM domain. In its absence, the IMD pathway is constitutively activated . PGRP-LFMany key steps of IMD pathway activation involve the deposition of K63 poly-ubiquitin chains. K48 poly-ubiquitinations are known to activate the degradation of proteins. Therefore, it does not come as a surprise that several negative regulators of the pathway have been identified to regulate those processes. As explained, once recruited to the IMD amyloid, TAK1 phosphorylates the IKK complex, leading to Relish activation and IMD itself leading to K63-deubiquitination. Drosophila ubiquitin-specific protease 36 (dUSP36) prevents the accumulation of K63-activated IMD, thus promoting its degradation . Fat facThe deubiquitinating enzyme cylindromatosis (CYLD) has been reported to inhibit IMD pathway activation, possibly by targeting the IKK complex , which iCaspar, a ubiquitin-related domain bearing protein, inhibits the cleavage of Relish by the caspase Dredd . AlthougSimilarly to ubiquitination, several phosphorylation reactions also play a pivotal role in IMD pathway signaling. Recently, our group published the identification of the protein phosphatase PP4 as a new negative regulator of the IMD pathway . InhibitThe transcription factor of the homeobox family Caudal has been shown to negatively regulate the IMD pathway in the intestine . Indeed,Drosophila high mobility group (HMG) protein named Dorsal switch protein 1 (DSP1) [Additionally, the transcription factors downstream of the JNK and of the JAK/STAT pathways have been shown to negatively influence the transcriptional activity of Relish. Drosophila activator protein 1 (dAP-1) and Stat92E, the specific transcription factors of the JNK and JAK/STAT pathways respectively, have been proposed to form a repressosome complex with a 1 (DSP1) . This re1 (DSP1) . The exp1 (DSP1) .The IκB family member Pickle binds to Relish and inhibits its activity, possibly via the recruitment of the histone deacetylase HDAC1. Loss of Pickle results in hyperactivation of the pathway after infection and constitutive activation by commensal bacteria. Pickle is induced in the gut by bacteria in an IMD-independent manner and is not induced after systemic infection . This fuPseudomonas aeruginosa [Finally, two studies pointed out the role of SUMOylation in IMD pathway regulation. A small ubiquitin-like modifier (SUMO) is a short ubiquitin-like protein which also covalently attaches to proteins . First, ruginosa .In the quite impressive list of regulators identified so far , we obseOf note, the same regulator can have a constitutive basal expression as well as an induced expression. Pirk and PRGP-LB are good examples of genes whose expressions are rapidly induced by infection in a PGRP-LC-Imd-Relish dependent manner and are involved in the resolution of infection. Pirk and PGRP-LB are also involved in blocking even low levels of activation. Their absence does not lead to a strong systemic activation of the pathway, but to a low level of detectable antimicrobial peptide expression due to commensal bacteria, ultimately affecting the lifespan. It points to the possibility of their expression at a background level when there are not enough elicitors to strongly activate the pathway and express immune effectors, but a sufficient amount to activate some genes, including inhibitors. It prevents a dangerous background level of immune activity and allows tolerance to gut commensal flora. Pickle has a similar effect but its expression is independent of IMD.Furthermore, commensal bacteria only induce the expression of a subset of genes, including PGRP-SC and PGRP-LB, but not of genes coding for antimicrobial peptides . This suThe concomitant expression of effectors and regulators after infection and their differential basal or commensal-activated expression raises the question of the dynamical and precise control of the expression of different classes of genes responding to the same activation pathway.Drosophila led to the identification of the nuclear protein Akirin by our laboratory. This evolutionarily conserved player in the NF-κB pathway is required for IMD target gene expression by the Relish transcription factor (The study of the molecular cascade of the IMD pathway in n factor . Its knoDrosophila Schneider 2 (S2) cells reduced the induction of specific IMD pathway regulated antimicrobial peptides, like AttacinA, by 90%. Subsequent epistatic analysis using S2 cells indicated that Akirin acts downstream of or at the level of Relish. Akirin encodes a putative 201 amino acid protein with no recognizable domains but with a clear nuclear localization signal (NLS). This small nuclear protein is highly conserved in metazoan species and consists of one copy in insects and worms and two in vertebrates (except for birds), but none in plants, yeast or bacteria. Complete knockouts of Drosophila Akirin and mouse Akirin 2 led to early embryonic lethality at an early or middle stage, but it was not the case in Akirin1 KO mice [Akirin was identified by a genome-wide RNAi screening as a positive regulator of the IMD pathway . Knock-d KO mice .Drosophila Akirin and mammalian Akirin2 are required for the innate immune response. Since Akirins have no obvious DNA binding domain, it was proposed that Akirin could be a cofactor that regulates or fine-tunes NF-κB transcriptional activity by interacting with chromatin remodeling factors and/or the transcriptional machine [Both  machine ,105,106.After stimulation, Akirin is K63-polyubiquitinalyted through the activity of the Hyd E3 ubiquitin ligase . This leOne interesting hypothesis would be that a moderate or short-time induction of the pathway would only activate, in an Akirin-independent process, a few AMPs to fight infection and most of the regulators of the pathway to maintain the homeostatic state, whereas a strong or prolonged infection would lead to the full Akirin-dependent activation of the pathway, efficient immune response and its subsequent resolution. This would protect against unnecessary activation by commensal or weak infections easily handled by epithelial and phagocytic immune responses. In this model, Akirin-independent genes would be easier and therefore quicker to activate than Akirin-dependent genes. This points to a different epigenetic state of these two classes of genes and a specific function of Akirin to allow activation of less prone-to-activation genes.Drosophila, mammalian Akirin-2 acts downstream of the TLR, TNFR and IL-1R signaling pathways [akirin-2 in macrophages compromised the immune response of mice against Listeria monocytogenes intra-peritoneal infections in vivo. Interestingly, mAkirin-2 is required for the regulation of only a subset of LPS and IL-1 inducible genes with mainly pro-inflammatory activity. Moreover, like in Drosophila, mAkirin-2 bridges the NF-κB factor and the chromatin remodeling SWI/SNF complex. It also appeared to participate in the innate immune response through its interaction with the nuclear IκB protein IκBζ, an atypical member of the IκB protein family [As in pathways . A condin family .Drosophila, another cofactor could be implicated and remains to be identified. Other studies show a NF-κB-dependent immune function of Akirins against Gram-negative bacterial infections or more generally, an indispensable role for the expression of innate immune defense genes [It was suggested that IκBζ may influence the regulation of histone modification through selective H3K4 tri-methylation of TLR-induced promoters . An incrse genes ,114. TheDrosophila [Drosophila against Gram-negative bacteria and worsening survival after infection [Devoid of known predicted functional domains in their sequence, Akirins might gather chromatin-remodeling complexes with sequence-specific targeting transcription factors  Figure . Using aosophila  to mammaosophila , link chnfection .Another group also found a diminution of IMD pathway activation after inhibition of BAP complex genes in cells but, surprisingly, an increase in several IMD genes in vivo . We cannAnother insight into the pivotal function of chromatin regulation during inflammation lies in the role of a distinct SWI/SNF chromatin remodeling complex, the Poly-Bromo associated Brahma (PBAP) complex, as a negative regulator of IMD signaling in the gut . Bap 180Drosophila innate immune response is still a pending question. Moreover, the precise histone-modifying enzymes involved in the deposition of epigenetic marks in Drosophila remain to be identified. Similarly, whether other chromatin remodeling complexes, in addition to the SWI/SNF complexes, participate in a chromatin remodeling ballet to regulate the expression of immune genes in a dynamic manner remains to be explored.Whether other epigenetic marks, in addition to H3K4ac, contribute to coordinate Of note, mAkirin2 has recently been shown to be required for nuclear entry of proteasomes and turnover of some nuclear factors to control their short-lived activity . WhetherDrosophila remains an interesting model to explore this dynamic regulation and help address questions in the always important fields of NF-κB and cancer research.The key to understanding the activation and resolution of the innate immune response seems linked to the characterization of the dynamics that are behind its modulation, particularly at the level of epigenetic regulation of gene expression . Data po