Clinical pathways (CPs) can improve health outcomes, but to be sustainable, must be deemed acceptable and appropriate by staff. A CP for screening and management of anxiety and depression in cancer patients (the ADAPT CP) was implemented in 12 Australian oncology services for 12 months, within a cluster randomised controlled trial of core versus enhanced implementation strategies. This paper compares staff-perceived acceptability and appropriateness of the ADAPT CP across study arms.Multi-disciplinary lead teams at each service tailored, planned, championed and implemented the CP. Staff at participating services, purposively selected for diversity, completed a survey and participated in an interview prior to implementation (T0), and at midpoint (6 months: T1) and end (12 months: T2) of implementation. Interviews were recorded, transcribed and thematically analysed.Seven metropolitan and 5 regional services participated. Questionnaires were completed by 106, 58 and 57 staff at T0, T1 and T2 respectively. Eighty-eight staff consented to be interviewed at T0, with 89 and 76 at T1 and T2 . Acceptability/appropriateness, on the quantitative measure, was high at T0 (mean of 31/35) and remained at that level throughout the study, with no differences between staff from core versus enhanced services. Perceived burden was relatively low (mean of 11/20) with no change over time. Lowest scores and greatest variability pertained to perceived impact on workload, time and cost. Four major themes were identified: 1) Mental health is an important issue which ADAPT addresses; 2) ADAPT helps staff deliver best care, and reduces staff stress; 3) ADAPT is fit for purpose, for both cancer care services and patients; 4) ADAPT: a catalyst for change. Opposing viewpoints are outlined.This study demonstrated high staff-perceived acceptability and appropriateness of the ADAPT CP with regards to its focus, evidence-base, utility to staff and patients, and ability to create change. However, concerns remained regarding burden on staff and time commitment. Strategies from a policy and managerial level will likely be required to overcome the latter issues.https://www.anzctr.org.au/.The study was registered prospectively with the ANZCTR on 22/3/2017. Trial ID ACTRN12617000411347. The online version contains supplementary material available at 10.1186/s12913-021-07252-z. Clinical pathways (CPs) are standardised, evidence-based multidisciplinary management plans, which identify an appropriate sequence of clinical interventions, timeframes, milestones and expected outcomes for one or more patient groups . CPs areOur group developed a CP for screening, assessment and management of anxiety and depression in adult cancer patients (the ADAPT CP), based on an evidence review and wide stakeholder input , 12. UtiWe implemented the ADAPT CP in a cluster randomised controlled trial (CRCT) in 12 oncology services in NSW Australia . The ADAProctor et al.  recommenFor the ADAPT CRCT, eligible services were public or private health services providing cancer care for at least 100 patients per year . In AustThe ADAPT CP and trial processes have been described in detail elsewhere , but in Following the CP launch, the two arms diverged. Services in the core implementation arm were able to initiate contact with the research team if help was required, were provided monthly portal reports , but were otherwise left to their own devices. Services in the enhanced implementation arm received proactive contact from the research team throughout the implementation period, with monthly review meetings at which data on service CP use was presented and discussed, and ongoing strategies such as newsletters and posters were provided to staff to encourage engagement.During the implementation (as outlined in site workflows), selected staff were responsible for registering patients on the ADAPT portal; patients then received an automated email to screen or were flagged within the ADAPT portal as due for screening using a validated tool (Distress Thermometer (DT)  or EdmonThe goal was to obtain a cross-sectional snap shot of staff perceptions at each time-point. The sample comprised lead team attendees at each of the monthly meetings, as well as staff who interacted in any capacity with ADAPT. An email was sent to all staff who had registered on the portal, attended training, or had an ADAPT role, as nominated by a Champion, to invite them to participate by completing questionnaires and interviews. Non-members of the lead team were included to ensure representation of views of staff on the ground actually enacting the ADAPT CP. Staff were assured of confidentiality. Non-responders were followed up with reminder emails twice. However, in response to staff movement and changes, staff who had employment contracts of less than 6 months were excluded.Staff were invited to complete a questionnaire just prior to the ADAPT CP implementation, but after the engagement meetings (T0), and again at 6 months (T1) and 12 months (T2) into the 12-month implementation. Demographic and professional details were elicited. Staff then completed 13 study-developed items to assess acceptability/appropriateness , a subset of staff, purposively selected, at each service were invited to participate in a semi-structured telephone interview exploring amongst other issues, their response to the tailoring of the ADAPT CP to their service context. The interviews were conducted by three female qualitatively trained researchers with no direct involvement with ADAPT CP delivery. Interviews were audio-recorded and transcribed.Quantitative data were analysed in Stata version 17. Summary statistics were generated, and the two arms of the study compared using Mann-Whitney test due to small and uneven sample sizes, and because data are not independent. Interviews were coded in NVivo12 . T0, T1 Framework analysis  was applServices: Twelve cancer services  participated in the ADAPT CRCT. Ten services were publicly funded, one was a public/private partnership, and one was privately funded. Five services had already conducted screening for anxiety and depression within the past 12 months using a validated tool . At T1, 58 staff (23 in core and 35 in enhanced services) completed questionnaires, and at T2, 57  did so. Reduced numbers over time were likely due primarily to staff movement and lack of time. See Table An exploratory factor analysis was conducted on the acceptability/approriateness items to assess their latent structure. Two factors (positive acceptability/appropriateness (or benefit) and negative acceptability/appropriateness (or burden)) were extracted ; these accounted for 84.5% of the variance and were weakly correlated (.29). Using Varimax rotation , most items factored well (Kaiser-Meyer-Olkin (KMO) = all items >.60, except for item 7 (0.53)). One item did not load on to any factor and was excluded. Internal consistency was acceptable (Cronbach’s Alpha for Factor 1 = 0.86 and for Factor 2 = 0.71).burden scores were moderate (12.6/20) dropping slightly across time (11.2 (T1) and 10.5 (T2)), with no differences between trial arms at any timepoint. Highest scores (with greatest variability) pertained to items addressing perceived impact on workload, time and cost. Mean perceived benefit scores were high at T0 (30.8/35) remaining very consistent over time, with no difference between arms.Total benefit and burden scores were calculated by summing scores within each factor Fig. . Mean peEighty-eight, 89 and 76 staff participated in interviews at T0, T1 and T2 respectively. The overall response rate across all time points was 64%, however response rates decreased slightly over time . A total of 122 unique staff participated; 44 staff 36%) were interviewed at all three time points, 43 (35%) at two time points, and 35 (29%) at one. Average interview duration was 22 min at T0 and 24 min at T1 and T2. Participants had an average of 6 years of experience in their role (range 0–33). For full demographic details of interview participants, see Table 6% were iQualitative analysis of staff interviews revealed four over-arching themes: 1) Mental health is an important issue which ADAPT addresses; 2) ADAPT helps staff deliver best care, and reduces staff stress; 3) ADAPT is fit for purpose, for both the cancer care setting and patients; 4) ADAPT: a catalyst for change. Themes generally remained stable over time and did not differ between study arms. Change and differences are noted where they occurred. Quotes are identified by arm of study: enhanced (E) versus core (C); profession, oncology service (S1–12); personal ID (PID); and time of assessment (T0–2). Additional quotes are provided in Additional file “everybody that has a cancer diagnosis will have some degree of anxiety and will need some support, and when they need a lot of support that needs to be identified quickly...” (C_ADMIN_S01P07T1)Almost all participants supported the importance of identifying and managing patients’ anxiety and depression. They believed these to be common co-morbidities which, if not addressed, make it significantly harder for patients to cope with their disease, its treatment and survivorship.Participants believed that addressing anxiety and depression was part of holistic, patient-centred care. Some staff felt that at their site, psychological morbidity was not addressed as well as it could be, and therefore ADAPT would provide a much-needed opportunity to remedy this gap.“It’s been one of the areas that’s been quite lacking, as far as, support for patients in this area. So I think that’s something that everybody is very excited about.” (E_NURS_S02P09T0)Staff believed that ADAPT was firmly based on good evidence, and trusted the research team’s expertise; this was primarily emphasised at T0.“I think it’s really useful and very evidence-based … it’s actually reassuring that what we’re doing is actually evidence-based.” (E_NURS_S08P01T0)falling between the cracks”. Staff noted that some patients have no visible signs of distress and therefore are hard to identify. While current practice picked up some cases, it was felt to be “adhoc”, “informal” and “uncoordinated”; some patients were missed because key staff were absent. Furthermore, the repeat screening step of the ADAPT CP was perceived to ensure adequate follow-up, to identify late-emerging symptoms.Staff noted that ADAPT would ensure all patients who are distressed and need support are identified, with none ““At the moment, we deal with anxiety and depression but it’s really ad hoc - sometimes we’re hit and miss. Because there could be patients that don’t show they're anxious or have any depression … and they just soldier on. This way … we’re going to pick up a lot more.” (E_ADMIN_S02P05T0)“We've picked up some things that wouldn't have, ordinarily, been picked up … and been able to provide services for those patients.” (E_AH_S03P06T2)“I think in my service in particular it is not exactly necessary … we are finding that a lot of the patients that are screening positive through the portal are already in touch with our psychology service.” (C_NURS_S11P03T1)However, some staff believed ADAPT to be unnecessary at their site, as their psychosocial care and referral processes already ensured that all patients who required support were identified.“So far all of the patients who’ve been identified are patients who are already known to us. So, so far the utility of it probably hasn’t been particularly helpful.” (C_PSYCH_S11P01T1)While in some sites this perception changed over time, in others staff remained confident that ADAPT was not needed throughout the implementation period.Some staff noted that ADAPT ensured early identification of distress in patients, thus preventing these symptoms from progressing to a full-blown clinical problem. This was seen as a way of reducing suffering, as well as reducing need for staff and service resources.“So if we have a system to certify the patient needs that help and refer to them early rather than late. And then, I think, make them, the journey a little bit easy.” (C_NURS_S12P05T2)“so it’s just that ADAPT– it’s just a slow moving beast. Whereas … the protocol that we had for distress … it allowed for quick reactions and referrals … rather than screen in a month and we’ll see where you’re up to … that just doesn’t seem to be a good fit for the surgical context.” (E_PSYCH_S07P01T1)However, some staff  felt ADAPT was less nimble than their current processes, potentially making it more likely that patients would not be identified until later in their treatment trajectory. This was primilarly because of the additional administrative steps involved in working with ADAPT’s online portal.Staff felt ADAPT, by normalising mental health needs, reinforcing the importance and validity of seeking help, and increasing awareness of mental health supports available, would empower patients to firstly identify when they were anxious or depressed, and secondly to seek appropriate help. On the whole, ADAPT was perceived as being highly acceptable.“People can often put on a brave face … until … the actual conversation is facilitated … I feel the pathway’s actually brought a lot of opportunities for people to get help that may … not have reached out for help otherwise. Or they didn’t know how to reach out for help.” (C_NURS_S12P01T1)However, some staff felt that ADAPT could not overcome the stigma of mental health.“To be honest … I think the last report said that we only had one person screened and I, personally, just think it’s our area, our demographics. It’s the stigma that goes with it. And I don’t think that will ever change.” (E_NURS_S04P02T1)“Two weeks ago the patient re-screened … and she screened high still … I was able to communicate with the doctor and offer her suggestions of what to do, and her treatment did change and she did have a better fortnight because of that.” (C_NURS_S12P01T1)Many staff thought that ADAPT was also acceptable and appropriate for staff. They felt ADAPT raised their awareness of psychosocial issues, and provided them with new skills and a well-planned, evidence-based intervention, ultimately improving their capacity to provide the best patient care. Having tried a different approach with ADAPT, staff felt they would have the confidence to continue raising mental health in routine care.their backfeet” in addressing them, and patients would be less likely to present in crisis.Staff felt their own work would be made easier and less stressful, as they would be informed upfront of patient concerns so they were not on ““They aren't asked early enough and then before we know it the patients have all these social issues at home and we're kind of on the back foot, we're not proactive about addressing it, we're just kind of reacting, so I definitely think the questions need to be asked.” (C_NURS_S01P06T1)Finally, staff felt that by raising the profile of mental health, creating more rigorous triage and establishing a whole-of-team approach, some staff would be better supported to deliver this care, while others felt that working with multidisciplinary colleagues enriched their working life.“I think the advantage will be that it will cut my load down … they can do the work themselves … through the resources, so I think that’s excellent.” (E_PSYCH_S05P01T0)“ … the referral pathways are working... [prior to ADAPT] I would get contacted and then I would be the one trying to figure out what to do next and who else to involve.” (C_MED_S01P09T1)Staff commented both positively and negatively regarding the fitness for purpose of ADAPT within the cancer care setting, and for their patients.Some staff noted over time that ADAPT fitted well, or had been made to fit well, into the existing workflow, complementing existing processes without adding significant burden. Some staff were surprised at how easy the process had been, while others found this took more time and effort. Having only a small number of patients score in the severe range of anxiety and depression made ADAPT more manageable.“the way that I see it is, um, that it's not going to increase workloads, it's just going to formalise workloads. So what we're already doing will just be documented better.” (C_AH_S03P06T0)“We don’t exactly know how to fit it into our normal routine just yet … it’s something new that we’ll have to adjust into our workflow. (E_NURS_S08P01T0)Sites where a pathway for psychosocial care and/or multidisciplinary collaboration were already in place, where minimal changes were required to accommodate ADAPT, were more likely to report positive experiences with workflow. In other sites, siloed clinicians or services made this a harder adjustment. Similarly, where staff felt they already had appropriate skills ADAPT was easier to accommodate, versus where additional training was required.“But at the end of the day there's only a certain number of hours in the day, and so many things to get done … fitting everything, has been difficult.” (C_MED_S01P09T2)In some sites, despite very positive attitudes to the philosophy, evidence and content of ADAPT, it was perceived as too burdensome, and unsustainable. This was either due to limited capacity in existing staff, or a lack of psychosocial staff to facilitate it. Some staff felt that processes that were in place before ADAPT were a better fit for purpose and thus more appropriate.“We’ve found that it hasn’t been a very good fit for the patients that come through … They … come in, they have their operation, they go home. And so there's not that kind of ongoing continuity of care that ADAPT would be quite well suited for.” (E_PSYCH_S07P01T1)In other sites with particular characteristics , ADAPT was seen as unfit for purpose, as it required ongoing follow-up and contact with patients.Some staff reported that ADAPT had a very good fit for patients, who expressed gratitude and relief that they had participated in ADAPT and had their needs met.“All three that spoke about it with me were grateful, one of them thought she didn’t need it but took our number and when she actually, when things got a bit hairy she gave us a call which I think is excellent … ” (E_PSYCH_S06P14T1)“The men tend to be, you know, more macho and not able to really express their feelings … Country folk I think are very different your more metropolitan, city people.” (E_NURS_S02P04T0)“We've had several patients who were quite distressed by the process … actually they’ve recorded that they found that quite anxiety inducing.” (C_PSYCH_S11P01T2)Other staff noted significant resistance to screening in some patients  which ADAPT failed to overcome. At one site, staff noted patients commented that ADAPT created a sense that they were not coping. It was not clear whether this could be overcome by improved communication when introducing ADAPT, or whether a different approach entirely was needed.“If you don’t speak English then … we can’t help you.” (E_NURS_S06P06T0)“We actually have patients that sometimes we can’t even get in contact with because they have no phone and they have no computer. They’re living in a tent or a car.” (E_NURS_S04P02T0)The inability of key groups of patients  to access ADAPT was also noted, reducing its fitness for purpose.“Honestly, the patients have been more willing than some staff … even the elderly patients, they might need a bit of help with these new fandangle things, but … . after the first couple of questions, they get the gist of it and they’re fine, they just fly through” (C_AH_S03P02T2)However, others predicted good uptake even in such groups, or noted that the service would try to address barriers to ensure ADAPT was fit for their patients, usually through family, staff or volunteer contact to facilitate questionnaire completion.Some staff  believed that ADAPT was an opportunity to gather data, highlight gaps in the service and advocate for change and improved resources to address psychosocial care.innovative ways of doing things” C_MED_S01P09T2) and advocate for more psychosocial staff to meet patient need. This acceptability of ADAPT at the service level is an encouraging finding and may be a contributor to longer term sustainability beyond the 12-months of implementation.Participants believed that the data generated by ADAPT would provide staff and services with more information about patients’ anxiety and depression, which could be used to inform and improve patient care “We probably have a better idea of how many people are actually needing a psychologist assessment so that maybe that will help us make a case to have a psychologist available for our service.” (C_MED_S01P09T1)At the service level, ADAPT was seen as a way to improve interdisciplinary communication, referral processes and co-operation.“Even within the corporate services, there seems to be, uh, not great communication. So we looked at this ADAPT trial as being quite a useful piece to help resolve that.” (C_ADMIN_S12P08T0)Some staff commented that ADAPT prompted the service to explore new referral pathways.“We knew that there would always be issues with the fact that we don't have a psycho-oncologist on site. But participating in ADAPT … made us explore alternatives to having a psycho-oncologist on site.” (C_MED_S01P09T2)“We did establish a pathway, and I think once the staff here saw that we could actually do something about it, we’ve even had patients referred inbetween a screening … now that we’ve got the pathway there and we have the support services mapped out.” (C_AH_S03P02T2)In this paper, we set out to describe staff perceptions of the acceptability and appropriateness of a clinical pathway (CP) for anxiety and depression prior to (T0), during (T1) and after (T2) implementation, and to compare services receiving core versus enhanced implementation strategies.There was high perceived acceptability and appropriateness of ADAPT, with some concerns pertaining to impact on workload and time. Staff universally expressed support for addressing mental health in their service, and that ADAPT was evidence-based, could improve the quality of patient care, enhance staff experience of delivering care, and ultimately improve their service. However, staff at some sites found ADAPT to be too burdensome, unsuited to their patient population or unnecessary, suggesting lower levels of appropriateness. This was observed particularly in settings with a belief that adequate psychosocial care was already in place.Our findings are in line with the Consolidated Framework for Implementation Research (CFIR) , which nThis study also revealed staff and system factors that contributed to perceived relative advantage, factors perhaps less well articulated in the CFIR framework. Staff in this study felt that ADAPT delivered both the ability to deliver better care and to work within a multidisciplinary framework. Furthermore, reducing stress in patients also reduced their own work-related stress. At the health service level, ADAPT was seen as an opportunity to accrue data to support requests for additional psychosocial staff, to enhance interdisciplinary co-operation and expand referral networks. This suggests that implementation of an appropriate intervention (ADAPT CP) encourages further innovation and adaptation within services. These novel findings reinforce the importance of considering, planning for and measuring staff and service-level variables in intervention implementation studies. Within the CFIR framework, these are described as inner and outer setting variables.not obviously in need, and are not intended to prevent usual care if greater speed is required. Perhaps a better articulation of how usual care and a new CP are intergrated, is required.A recent systematic review of hospital-based intervention implementation studies  noted thDespite enhanced sites being provided with additional support to recognise and address challenges to CP implementation as they arose, this did not impact perceived acceptability or appropriateness, with positive and negative views maintained over time, and with no discernable differences between study arms. This was apparent in both quantitative and qualitative data. This is surprising as the recent systematic review described above  noted thThis paper had a number of limitations. Thirteen services refused participation when approached, and thus potential bias through the inclusion of services with more interest in the ADAPT CP may have been introduced. However, as reasons for refusal were largely due to external influences  and there was considerable variability in participating services’ approaches to the ADAPT CP, we believe bias was minimal. While we obtained good multidisciplinary representation in a snapshot view of staff perceptions at each timepoint, staff movement meant that longitudinal data with a stable cohort was not possible. While interviewers were independent of the ADAPT research team who interacted with staff during implementation, social desirability bias may nonetheless have impacted responses, leading to a more positive perspective being presented than was actually felt. Due to more intensive interaction of ADAPT Team with Enhanced arm sites during implementation , there may have been issues impacting acceptability and appropriateness at Core services during the implementation phase to which we were not privy. Nevertheless, the qualitative analysis provided a rich and nuanced description of factors at the intervention, staff and service level impacting these outcomes.In conclusion, we found that ADAPT delivered an acceptable and appropriate program that enabled implementation of an anxiety and depression clinical pathway across 12 cancer services. Our study defined and operationalised these two implementation outcomes a priori and we were able to measure these over time using a robust combination of rich qualitative and quantitative data. Our findings reinforced that characteristics of the staff, service and its setting impact the acceptability and appropriateness of hospital-based interventions. Particular challenges are likely to be encountered in settings with high staff turnover and where continuity of care is more difficult to achieve, suggesting that adaptations of clinical pathways such as ADAPT will likely be required in such settings. Clinical pathways alone will not improve patient outcomes without effective and acceptable integration into existing workflows and service/system-level endorsement.Additional file 1. “CONSORT Flow Diagram”.Additional file 2. “Management recommendations for each ADAPT CP step allocation”. A figure that describes which ADAPT CP step yields which intervention.Additional file 3. “Acceptability/Appropriateness items”. A table outlining the thirteen study-developed items to assess acceptability/appropriateness.Additional file 4. “Additional quotes related to acceptability/appropriateness”. A table providing additional quotes related to acceptability/appropriateness.


Sarcoidosis is a systemic disease of an unknown cause that affects multiple organs, most commonly lungs, intrathoracic lymph nodes, eyes, and skin, which accounts between 20% and 25%. However, cutaneous sarcoidosis can present without any systemic involvement in 25% of cases. We present a case of a 53-year-old female patient with cutaneous sarcoidosis with no lung involvement. The patient presented to the family medicine department with non-itchy, tender, erythematous papules occurring at the dorsal part of the hands and the right foot for three months. Skin punch biopsy demonstrated multiple dermal-based nodules consisting of non-necrotizing granulomata. Serum angiotensin-converting enzyme level and a chest radiograph were normal and not consistent with pulmonary sarcoidosis. There are different cutaneous manifestations of cutaneous sarcoidosis and early identification helps in early intervention. Sarcoidosis is a chronic granulomatous disease that affects multiple organ systems in the body. It is characterized by the formation of epithelioid and non-caseating granulomas. The most common organs that are affected by sarcoidosis are the lungs, intrathoracic lymph nodes, eyes, and skin . The preA 53-year-old female patient presented to the family medicine department with “multiple small hard erythematous tender papules with no secondary changes over the dorsal aspect of the hands and right sole for three months increasing with time" (see Figures The skin is the second most common organ affected by sarcoidosis. According to PubMed published data by Karolyn A. Wanat, 25-30% of sarcoidosis cases had skin manifestations. The highest incidence of cutaneous sarcoidosis occurs in Black American females ,5. Our cSarcoidosis diagnosis is based on clinical manifestations by appropriate history taking, physical examination, radiological and histopathological findings, and, finally, exclusion of other granulomatous diseases. The clinical presentation of sarcoidosis is variable depending on the affected organs and the severity of the disease. Many patients are asymptomatic in the initial stages, and they are diagnosed incidentally based on a chest radiograph. On the other hand, up to half of the patients complain initially from respiratory symptoms such as shortness of breath and dry cough, and one-third of the patients may present with constitutional symptoms such as fever and significant weight loss . RadioloSerum ACE, which is derived from epithelioid cells of the granulomas, reflects the granuloma load in the patient and is usually elevated in around 60% of patients. Serum ACE levels are neither diagnostic nor predictors of systemic involvement, yet they may be useful for predicting disease progression, yet our patient presented with a normal level of serum ACE [Sarcoidosis is a disease with multiple organs' involvement. Cutaneous manifestations of sarcoidosis are different and non-specific. The diagnosis is confirmed by the presence of non-caseating epithelioid granulomas in histological findings. Topical and systemic corticosteroid and other immunosuppressive medications such as methotrexate, adalimumab, and infliximab are used in cutaneous sarcoidosis management.


Climate change has given rise to salinization and nutrient enrichment in lake ecosystems of arid and semiarid areas, which have posed the bacterial communities not only into an ecotone in lake ecosystems but also into an assemblage of its own unique biomes. However, responses of bacterial communities to climate-related salinization and nutrient enrichment remain unclear. In September 2019, this study scrutinized the turnover of bacterial communities along gradients of increasing salinity and nutrient by a space-for-time substitution in Xinjiang Uyghur Autonomous Region, China. We find that salinization rather than nutrient enrichment primarily alters bacterial communities. The homogenous selection of salinization leads to convergent response of bacterial communities, which is revealed by the combination of a decreasing β-nearest taxon index (βNTI) and a pronounced negative correlation between niche breadth and salinity. Furthermore, interspecific interactions within bacterial communities significantly differed among distinct salinity levels. Specifically, mutualistic interactions showed an increase along the salinization. In contrast, topological parameters show hump-shaped curves (average degree and density) and sunken curves , the extremums of which all appear in the high-brackish environment, hinting that bacterial communities are comparatively stable at freshwater and brine environments but are unstable in moderately high-brackish lake. In arid and semiarid areas, lake ecosystems are typically unproductive, and external disturbances dominate in-lake processes, making these systems ideal sentinels of environmental disturbance. As global climate change has intensified over the past several decades, arid and semiarid areas have been characterized by a negative moisture balance with evaporation exceeding precipitation . ConsequRelative to the macroorganisms, bacterial communities are extremely sensitive to environmental disturbances, whose change often integrates the biogeochemical processes of lake ecosystems . TherefoThe stability is critical to predict the bacterial community dynamics . For decUltimately, we hypothesized that climate-related disturbances induced profound effects on the bacterial communities with imbalanced influences of salinization and nutrient enrichment. To verify this, we applied a space-for-time substitution (SFT) approach, which is one of the most commonly encountered techniques in ecology to extrapolate a temporal trend. It is noteworthy that there is a premise to use SFT: biotic communities should be extensive homologous in their past history which means the environmental disturbances mainly regulate the community assembly, but not initial conditions and priority effects . Thus, wFour adjacent lakes were chosen in the Xinjiang Uyghur Autonomous Region, China . Lake BoIn situ measurements of water depth (WD), temperature (Temp), salinity, and dissolved oxygen (DO) were measured by a multiparameter water quality sonde . Five chemical parameters were measured upon return to the laboratory according to the standard methods described previously (4+–N), dissolved organic carbon (DOC), and chemical oxygen demand (COD).A total of 45 surface water samples  were collected with a 5 L Schindler sampler in September 2019. A 500 mL subsample for 16 S rRNA gene analysis was filtered onto 0.22 μm polycarbonate membranes  under a vacuum pressure of < 15 mm Hg. During transport back to the laboratory, the filters were stored at −20°C in a vehicle-mounted freezer; subsequently they were stored at −80°C in the laboratory for DNA extraction. The remaining water samples were transported to the laboratory for immediate further analysis. eviously : total n® Cycle-Pure kit . The V4 region of the 16 S rRNA gene was amplified using the primers 515F (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806R (3′-GGACTACNVGGGTWTCTAAT-5′) (2 (25 mmol L–1), 0.5 μL of each primer (10 μmol L–1 each), 30 ng of quantified template DNA measured by PicoGreen dsDNA Assay Kit , and 0.4 μL of Taq polymerase  (Crude DNA extracts were extracted by the E.Z.N.ATAAT-5′) . Unique  States) . PCR cycde novo assembled in a unique file using CLC Genomics Workbench  alignment and annotation tools. We removed sequences if they (1) contained more than one ambiguous nucleotide; (2) lacked a complete barcode and primer at one end; or (3) were shorter than 200 bp after removal of the barcode and primer. The overlap settings for this assembly were a mismatch cost of 2, an insert cost of 3, a minimum contig length of 200 bp, a similarity of 0.8, and a trimming quality score of 0.05. The sequences were clustered at 97% similarity cutoff into operational taxonomic units (OTUs). The representative sequence of each phylotype was aligned against the SILVA database (release 132) with a confidence threshold of 80%.Amplicon pools for all samples were paired-end sequenced (2 × 250) on an Illumina MiSeq platform at Magigen Biotechnology . The raw data for each pool of samples were separately trimmed and p-values from multiple interaction metrics were merged using the Simes method  plugin in Cytoscape software (v3.5.1), as previously described . To redus method , and fals method . A set os method . To test1) and the RStudio interface (version 1.1.463). To explore the major characteristics of aquatic physico-chemical parameters, principal component analysis (PCA) and hierarchical clustering analysis were utilized. A Kruskal-Wallis test was used to identify differences in individual parameters among distinct lakes. Data sets for multivariate statistical analyses, diversity estimates and interspecific interaction construction were rarefied according to the lowest numbers of reads among all samples . The richness, Shannon, and Simpson indices were representative of the alpha-diversity of bacterial communities. OTUs were binned into the “core community” if they were present in 100% (referred to as the Core100) and 80% (Core80) of all samples. To statistically test the difference of bacterial communities among distinct lakes, non-metric multidimensional scaling (NMDS) and PerMANOVA were applied based on Bray-Curtis dissimilarity with 999 permutations. To compare the significant differential abundance of bacterial communities between pairwise lakes, Statistical analysis of metagenomic profiles (STAMP) was applied at the phylum, class, order, family and genus levels  was the parameter that contributed the most, followed by TN (16.06%), DOC (15.45%), and TP (14.80%). The PCA results also indicated that these four lakes were significantly heterogeneous, although there was overlap between Lake R1 and Lake Bosten . This inp < 0.001). Temperature and DO were comparable in all lakes oscillating around 24.70°C and 8.73 mg/L, respectively. Salinity differed significantly between all pairs of lakes , with Lake R1 having a salinity of 0.47‰ (freshwater), Lake Bosten having a salinity of 0.74‰ (low-brackish water), Lake R2 having a salinity of 2.48‰ (high-brackish water), and Lake R3 having a salinity of 6.22‰ (brine water). Except for TP, the concentrations of nutrients  were similar between Lake R1 and Lake Bosten. Particularly, these nutrients were significantly higher in Lake R2 and Lake R3 (p < 0.05). Furthermore, nutrients except for TP and COD were much higher in Lake R3 than in Lake R2 (p < 0.05). Collectively, there was an increasing trend of salinity and nutrient concentration from Lake R1 to Lake Bosten, R2, and R3, suggesting simultaneous salinization and nutrient enrichment , and then increased slightly in Lake R3 with non-significant differences (p > 0.05)  . Furtherp < 0.001). Furthermore, we examined the relative contribution of deterministic and stochastic processes to the bacterial communities within individual lakes  to eliminate competitive microorganisms such as Burkholderia cepacia , suggesting that the bacterial assemblage was more tightly connected in the former lake. Simultaneously, the average path distance, which is biologically regarded as the length of the food chain flow of nutrients and energy, was shorter in Lake R2 than in Lake R3 (2.77 vs. 3.84). Together, these topologies demonstrated the most interactive communities in the high-brackish environment. Intuitively, close interactions lead to stable and robust communities; however, recent theories have challenged this intuition . Recent First, this study reveals that salinity rather than nutrient is the most variable in arid and semiarid areas in Xinjiang. Second, there are less diverse and less variable communities from freshwater to brine lakes, highlighting that salinization induces convergent response of bacterial communities. Third, the topologies of interspecific interactions along the salinity implied that salinization destabilized the bacterial communities. On particular, the bacterial communities were more unstable in the high-brackish lake than in the brine lake. Our findings have important implications for understanding how complex aquatic microbial communities respond to climate-related disturbances in arid and semi-arid area, which is pivotal for advancing our understanding of the evolutionary strategies of lake ecosystems.https://bigd.big.ac.cn/gsa, CRA004510.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: YH, GG, and BQ conceived and designed the experiments. XJ and KS performed sample collection. YH and XT analyzed the data. YH wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence  with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n = 40 samples) that were not used in model training . CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis-RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis-RE functions of rare cells without the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases. Non-coding DNA sequences serve different functional roles to regulate gene expression. For these sequences to be active, they must be accessible for proteins to bind and carry out specific regulatory functions. Even so, mutations or other regulatory events may modulate their activity or regulatory function, making it critical to infer their function to understand their regulatory impact. Current sequencing technologies capture accessible sequences from low cell numbers, enabling the study of clinical samples. However, determining their functional role remains a challenge. For example, enhancers and insulators serve distinct regulatory functions, yet both fall in open chromatin regions and have similar genomic annotations . Hence, alternative data sources and features  must be integrated to distinguish them. Towards this goal, we developed CoRE-ATAC to infer whether open chromatin regions correspond to promoters, enhancers, or insulators. We demonstrate that CoRE-ATAC can infer regulatory functions in diverse cell types, capture activity differences modulated by genetic mutations, and can be applied to single cell ATAC-seq data to study rare cell populations. These inferences will further our understanding of how genes are regulated and how these regulatory mechanisms are disrupted with disease. PLOS Computational Biology Methods paper.This is a Cis-Regulatory Elements (cis-REs) are non-coding DNA sequences that can be bound by transcription factors (TFs) and can take on different functional roles  to regulate gene expression programs. Over 88% of disease associated single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) are within non-coding region of the genome [cis-REs and their functions with increased precision to study how GWAS SNPs disrupt gene regulation in different cell types. Furthermore, genetic variation can impact the activity level of cis-REs [cis-REs will help to guide in-vivo and in-vitro functional studies, by prioritizing genomic loci that are most likely to impact gene regulation for experimental validation. Furthermore, identifying cis-RE functions in clinical samples will help uncover individual-specific and disease-associated elements and their functional roles in pathogenesis.e genome . In parte genome ,3. It is cis-REs –7. Uncovcis-REs for 127 reference human cell/tissue types by profiling their epigenomes and analyzing them using a hidden markov model (HMM) based approach: ChromHMM [cis-REfunctions. These reference epigenomes are valuableresources to uncover and study disease-relevant and cell-type-specific cis-REs. However, these maps serve as references and do not capture individual- or condition-specific  cis-REs. Moreover, these references do not include cis-REs of less frequently studied and/or rarer cells that are gaining attention with the advances in single cell profiling techniques. Although ChromHMM is very effective in inferring cis-RE function, it requires five or more ChIP-seq assays to be generated from the same sample, which is not always feasible due to the cost (both antibody and sequencing) and cell numbers required for ChIP-seq assays (104−106 cells per experiment).ENCODE  and RoadChromHMM . ChromHMcis-REs is through chromatin accessibility profiling, which identifies open chromatin regions that are accessible for binding of TFs or other regulatory proteins/RNAs. One of the most recent and most frequently used methods for identifying open chromatin regions is Assay for Transposase Accessible Chromatin using Sequencing (ATAC-seq) [cis-REs in individual epigenomes in health and disease. Furthermore, recent developments have enabled the generation of high-quality ATAC-seq maps from single cells ) [cis-REs from diverse cell types and cell states [cis-REs  cannot be inferred from these assays without the help of new computational methods that can fully interrogate ATAC-seq data features and integrate it with DNA sequence features.An alternative strategy for genome-wide interrogation of TAC-seq) ,12. ATACsnATAC))  and genel states ,11,14–19cis-REs can be predicted from ATAC-seq maps.Previously, we developed PEAS , a machicis-RE predictions, especially for loci with genetically modulated regulatory activity . In situations where it is not feasible to generate multiple ChIP-seq assays, CoRE-ATAC can effectively infer three active functional states  solely from ATAC-seq data. Despite the fact thatCoRE-ATAC does not fully capture all chromatin states that can be inferred via ChromHMM annotations, it captures three of the most important/relevant regulatory element types responsible for transcriptional regulation and chromatin architecture.Deep learning has revolutionized the machine learning field due to its ability to learn novel data features and has been successfully applied in genomics to predict chromatin accessibility  and enhacis-RE function  in 11 different cell types across 46 bulk ATAC-seq samples/replicates  irrespective of whether the cell type was used in model training. CoRE-ATAC predictions recapitulated ChromHMM enhancers from the cognate cell type as well as cis-REs inferred via different assays  [cis-RE activity linked to genetic variation. Finally, we showed that models built from bulk ATAC-seq data are also predictive on cell clusters from snATAC-seq data, by predicting cis-REs from snATAC-seq data in human Peripheral Blood Mononuclear Cells [In this study, we describe CoRE-ATAC and evaluate its ability to predict plicates . We demoys . We coms (MPRA)  to demonar Cells  for 7 blar Cells   and 5–55% are other functional annotations . The fun+ATAC-seq samples and model performanceswere evaluated using held-out test data   using ChromHMM and combined other states into a fourth class, named “other” which captures states such as repressed, transcribed, and low signal/quiescent regions, covering ATAC-seq peaks that do not correspond to the three major cis-REs   . After isamples)  Figs, wesamples) . We therMethods) . For thecis-REs , however, insulator states in islets were excluded from these analyses due to the poor quality of CTCF ChIP-seq data in islets  and three cell lines . These samples were not used in model training  in both model training and testing . When thWe further analyzed cross cell type performances by comparing enhancers versus other predictions of CoRE-ATAC with alternative models while incorporating cell types from 9 different tissues and cell types from ENCODE ,35,36: BFinally, we evaluated CoRE-ATAC’s cross-cell-type performance by stratifying ATAC-seq peaks by their cell-specificityacross 7 cell types and compared prediction performances. CoRE-ATAC predicted common promoters with higher average precision than cell-type-specific promoters. Interestingly, CoRE-ATAC predicted cell-type-specific enhancers more effectively compared to common enhancers (mean average precision 0.70 versus 0.48), emphasizing the utilityof this method to study disease-relevant enhancers that are typically cell-type-specific ,3 Fig 3ig 3F topcis-REs present in ATAC-seq data.We explored whether increasing the number of cell types could improve the performance of CoRE-ATAC. For this, we trained three different models for comparisons. The first model with 7 different cell types: GM12878, HSMM, Body of Pancreas, Stomach, Thyroid, Testis, and Transverse Colon. The second model was trained using the same cell types used previously, while including Heart (Left Ventricle) and Heart (Right Atrium). Finally, the third model was trained just as the second, but including Liver and HepG2 samples. We tested these models on held out cell types and chromosomes and determined that increasing the number of cell types had little impact for CoRE-ATAC’s predictive power . The ori+ T, and PBMC cell types to CAGE [via CoRE-ATAC using ATAC-seq data is a cost-effective alternative to annotations via ChromHMM that use multiple ChIP-seq assays.Several methods have been established for experimentally identifying enhancer sequences. The FANTOM5 project ,38 gener to CAGE ,25 enhan to CAGE ,38. We ovia MPRAs, we first compared enhancer predictions in A549 cells to enhancers identified from STARR-seq [Next, to understand whether CoRE-ATAC could detect enhancers identified TARR-seq ,41. The via chromatin accessibility quantitative trait loci (caQTL) analyses. Previously, we identified caQTLs from human islet samples (n = 19) [cis-RE activity levels within the same genotype. This heterogeneity likely stems from non-genetic factors including disease status  or other clinical information . Together, these results and findings establish CoRE-ATAC’s ability to predict individual level variability in cis-RE activity including heterogeneity stemming from genetic variation.Regulatory activities of certain open chromatin regions are genetically modulated, which can be detected (n = 19)  for whic(n = 19) . These dvia unsupervised clustering methods [cis-RE functions from human PBMC snATAC data [+ T, CD4+ T, effector CD4+ T, CD14+, and dendritic cells (DCs)  reveals chromatin accessibility at single nucleus resolution and enables 1) interrogation of chromatin accessibility at the single cell level; 2) identifying epigenomic maps of rare cell types  methods ; 3) enabTAC data  by firstTAC data ,17 reveals (DCs) . CoRE-AT41) Figs  and S16,41) Figs , confirmSimilar to enhancer predictions in bulk ATAC-seq, CoRE-ATAC enhancer predictions in snATAC clusters also included cell-specific enhancers for immune cell subsets within PBMCs . Cell-ty+ T and naïve CD8+ T). Immune cell enhancers predicted from snATAC-seq data were enriched in diseases related to the immune system  in CD4+ell maps . Despitecis-REs from clinically relevant samples. CoRE-ATAC harnesses the power of deep learning to integrate chromatin accessibility maps with DNA sequence and effectively predicts the functionality of cis-REs  across diverse cell types and individuals. We extensively evaluated CoRE-ATAC’s efficacy to predict cis-RE function across multiple cell types including those that are not used in model training. We established that CoRE-ATAC is an effective method for classifying the functional state of cis-REs, in new cell types  and in snATAC-seq data.Recent advances in ATAC-seq profiling and single cell genomics revolutionized the epigenomics field by enabling the generation of chromatin accessibility maps from small starting material, even at single cell resolution. Due to these advances, epigenomic maps of human cells/tissues from many individuals are being generated at an unprecedented rate, including our own studies, to study how epigenomic landscapes change with age, diseases, and upon in-vivo and in-vitro activation ,11,14–19cis-REs whose activity is modulated by genetic variation  are available for an individual, one can also leverage this information in CoRE-ATAC predictions by providing a reference specific to the individual. Genotype-aware methods can be effective in predicting the annotation and activityof , caQTLs –7). In acis-RE annotations. Although models from bulk data were effective in predicting functionality of cis-REs from single cell data, models built from snATAC-seq data might further improve the predictive performance. In the future, as more snATAC-seq data becomes available for more samples and more diverse cell types with established ChromHMM and CTCF insulator states, a CoRE-ATAC model can be trained solely on snATAC-seq data, potentially resulting in better classification performances for the purposes of annotating rare cell populations. Our predictions from snATAC-seq data were conducted at the cluster level. Given the sparsity of chromatin accessibility information at the individual single cell level , predicting functionality at the single cell level will remain a challenge.CoRE-ATAC also established a foundation for inferring functional annotations from snATAC-seq clusters, which can be useful to study rare cell populations that can be identified from these assays. For this analysis, we used CoRE-ATAC models trained on bulk data due to current limitations on the availability of snATAC-seq data that coincide with existing ChromHMM/Although CoRE-ATAC can effectively predict several functional classes of cis-REs, it has several limitations. First, CoRE-ATAC is limited to discriminating promoters, enhancers, and insulators, while categorizing the remaining open chromatin regions as ‘Other’. The ‘Other’ category includes multiple ChromHMM states . Data features that we extract from chromatin accessibility maps and DNA sequence alone may not be sufficient to further annotate these loci. Second, despite the fact that CoRE-ATAC can capture enhancer activity in an individualized manner, since CoRE-ATAC uses both ATAC-seq maps and DNA sequence for predictions, it may sometimes fail to learn the importance of DNA sequences that are especially correlated with ATAC-seq signal patterns . Hence, it can fail to detect SNP effects on chromatin accessibility levels; more appropriate methods should be used to detect these effects . Finallycis-RE function from ATAC-seq and further improves upon naïve methods as well as existing machine-learning models .CoRE-ATAC is widely applicable for inferring els Figs  and 3B trocessor . Our in-cis-REs in the final output dense layer using the Adam [CoRE-ATAC utilizes both data encoded in the deep learning framework and features extracted using our previous method PEAS . These tthe Adam  optimizaWe implemented a novel ATAC-seq data encoder that takes as input: paired-end reads, peaks, and the reference genome  to generate one 10x600 matrix per peak. Rows represent DNA sequence and ATAC-seq signal data, while columns represent the 300 base-pair positions downstream and upstream from the peak center . DetailsRows 1–4: The 0–1 normalized frequency of observing 1) adenine (A), 2) cytosine (C), 3) guanine (G), or 4) thymine (T) from ATAC-seq read pileups, which is calculated when there is enough read coverage (n > = 10). For low coverage loci; the frequency reduces to a one-hot encoding, representing the corresponding nucleotide in the reference genome.Row 5: The number of insert pileups within the 600 base-pair window, where each position is z-standardized with respect to pileups across all peaks for that position.Rows 6 and 7: The number of 5’ (row 6) and 3’ (row 7) cuts at a given position (z-standardized). Savitzky-Golay smoothing filter [g filter  is appliRows 8 and 9: The median fragment length for 5’ and 3’ cut site, applying the same standardization and smoothing used for rows 6/7.Row 10: The original peak region, setting values to 1 if a base-pair position is within the peak and 0 otherwise. This allows for distinguishing between peaks that are in close proximity to one another and providing an indicator of the most relevant positions.In all analyses, peaks used for data encoding were called with MACS2  using pahttps://github.com/UcarLab/ATAC-seq. First, read adapters were trimmed using Trimmomatic [+, and islets, version 0.7.12-r1039 for all other samples). Finally, duplicates were marked using the Picard Toolkit [Paired end ATAC-seq reads were processed using ATAC-seq pipeline available at mmomatic  (versionmmomatic  (version Toolkit  (version+ monocytes [https://genome.ucsc.edu/ENCODE/index.html). Hierarchical clustering of emission probabilities revealed 10 distinct clusters While training CoRE-ATAC, we envisioned two use case scenarios for annotations 1) use of a single ATAC-seq sample; 2) use of a set of samples  co-analyzed together. In the latter case, consensus peaks would be obtained from multiple samples and need to be annotated. In our experiments using consensus peaks, we noted that some “Other” regions are not called as peaks in all the samples, although they were among the consensus peaks. This could reflect individual-level heterogeneity and/or noise in either the data or peak calling step. To train a model that is robust to such instances, we incorporated non-peak regions for half of the samples (3 out of 6): HSMM, CD14+ Sample 2, and K562 replicate 2. Non-peak regions annotated as “Other” were selected among the consensus peaks that are not called as a peak in that cell type. Normalization steps are performed on each sample individually when encoding the data. Importantly, to not overwhelm the model with non-peak examples (since some of it could be noise), we restricted these examples to be at most 25% of all “Other” regions in that sample. In total, only 2,551 out of 57,870 (~4.4%) “Other” cases were such non-peaks. This step was implemented to help classify instances of non-peaks from a sample in case they are introduced to the algorithm, . Parameter tuning was performed in this stage of training to adjust i) the number of filters, ii) convolutional layer window size, iii) number of convolutional blocks, and iv) number of nodes within dense layers (where applicable) using held out validation data  . IndividHyperparameter selection was performed on i) the 1D convolution kernel size {9, 11, 19, 21}, ii) the number of convolutional layer block {3,4,5}, iii) intermediate dense layer size {1024, 2048, 4096} and iv) and the number of convolutional layer filters {32, 128, 256} where for each block the first two convolutional layers were set to the initial filter size and then doubled for the remaining two convolutional layers. Due to the long training time on these models, not every parameter combination  was tested. Instead, different parameters were tested one by one, and the best performing value for the parameter was kept. It is therefore possible that a more rigorous search for optimal hyperparameters could further improve CoRE-ATAC’s performance.cis-REs in ATAC-seq data obtained for 7 different cell types: MCF7 [+ T [+ T [To test the performance of CoRE-ATAC on datasets and cell types that were not used in model training, we initially predicted es: MCF7  : B Cells (E032), NK Cells (E046), CD8+ T , and Effector CD4+ T . Recalled ChromHMM states were converted to the 4 major class labels used by CoRE-ATAC based on emission probability correlations with the 10 cluster previously identified  ChIP-seq data obtained from public repositories . For theentified . The finChromHMM annotates the genome by assigning states for every 200bp interval. Consecutive states are merged by ChromHMM, and the size of these state assignments varies based on the number of times the same state is called in succession . ChromHMM states were then relabeled to Promoter, Enhancer, Insulator, or Other, based on their emission probabilities and our annotations e.g., . We over+ samples) (Tables https://hb.flatironinstitute.org/deepsea/). DeepSEA makes multiple predictions of activity for a wide array histone marks and transcription factors (TFs) across multiple cell types. We therefore selected enhancer probabilities by taking the maximum probability score for H3K4me1 and H3K27ac across all cell types predicted by DeepSEA. For promoters, probability scores were selected using H3K4me3 and pol2. For insulators, probability scores were selected using CTCF. The area under the receiver operating characteristic curve (ROC AUC) and average precision metrics were used to compare the four methods  Tables  and 2. Tods Figs  and S7..Naïve method comparisons focused on the 40 samples not used in model training to fairly assess CoRE-ATAC’s performance with respect to these methods. Multiple thresholds were applied for each Naïve method to identify the best threshold to set for predicting promoters, enhancers, and insulators. Promoters were tested using 1kb, 2kb, and 5kb distances to the nearest TSS, . Enhanc+ T cells, and PBMCs. We obtained STARR-seq active regulatory sites for A549 [Alternative enhancer definitions were explored to understand how well CoRE-ATAC can predict active regulatory elements  from FANTOM ,38 and Sfor A549  from thefor A549 ,69  were separated into two groups corresponding to the loss or gain of cis-RE activity (negative and positive correlations respectively) based on CoRE-ATAC predictions for different alleles. Finally, MPRA activity differences between alternative and reference alleles  were compared with the activity differences inferred from CoRE-ATAC cis-RE class probabilities. Student t-test, and Mann Whitney U test were used to calculate the significanceofMPRA log foldchange values observed for predicted loss and gain of cis-RE activity both individuallyas well as comparatively.Massively Parallel Reporter Assay (MPRA) data were generated in MIN6 pancreatic beta cell line to study the regulatory activity of variants associated with Type-2-Diabetes . BrieflySingle nuclei ATAC-seq (snATAC) PBMC data  was obtavia flow cytometry. To identify marker peaks for each cell type, we selected 2–3 representative and high quality ATAC-seq samples per cell type [cis-REs were predicted using CoRE-ATAC for 7 cell types by allowing duplicate reads. Predictions were evaluated using either ChromHMM annotations from Roadmap or in-house ChromHMM states (CD4+ T and CD14+ monocytes).Cell type annotations for these clusters were obtained by comparing snATAC profiles with ATAC-seq profiles of sorted immune cell typesell type ,17 for 1ell type , using iComparison of cell-specific enhancers were calculated using Fisher’s exact test. First, cell-specific ATAC-seq peaks were identified from union peaks by merging peaks called from all snATAC-seq clusters. Cell-specific peaks were identified by counting the number of overlapping peaks sets for each union peak. CoRE-ATAC and ChromHMM enhancers were identified from these cell-specific peaks, and Fisher’s exact test was applied to calculate the significance of CoRE-ATAC cell-specific enhancers overlapping with ChromHMM cell-specific enhancers. All remaining non-enhancer peaks were used as the background for these calculations.2 0.7 for the European (EUR) population). NHGRI-EBI GWAS Catalog SNPs [th 2020) for 3981 traits/diseases were used in enrichment analyses for different enhancer sets inferred by CoRE-ATAC.GWAS SNP enrichments were performed with GREGOR  softwarelog SNPs  Click here for additional data file.S2 FigTo select a ground truth for predicting the 10 functional states we identified, we corroborated our ChromHMM state calls with Roadmap 18 state models. (Top) Highlighted concordant recalled ChromHMM states with corresponding Roadmaps states to select active promoters (red), promoters (pink), flanking enhancers (orange), active enhancers (orange yellow), enhancers (yellow), genic enhancers (greenish yellow), transcribed (dark green), insulator (blue), repressed (dark gray), and low signal (light gray) functional states. (Bottom) The number of ground truth examples for each cell type used in model training and functional state examples selected. Note: Roadmap 18-state models do not include insulator states and we therefore chose insulators from our ChromHMM models that included CTCF ChIP-seq data. These predicted insulators mostly overlap with the Quiescent state in the 18-state models, suggesting that this ChromHMM state (Quies) is a miscellaneous functional state in the absence of CTCF ChIP-seq.(TIF)Click here for additional data file.S3 FigA) Confusion matrices of combined CoRE-ATAC performances for 10 state models. (B) Confusion matrices of CoRE-ATAC performance for 10 state models in GM12878. CoRE-ATAC 10-state models. Only 4 of the 10 chromatin states were predicted by CoRE-ATAC. Smaller subsets of these four functional states  were predicted as the state with the highest number of examples.((TIF)Click here for additional data file.S4 Fig(Top) Highlighted concordant recalled ChromHMM states with corresponding Roadmaps states to select promoters, enhancers, insulators and other. (Bottom) The number of ground truth examples for each cell type used in model training and functional state examples selected. Flanking enhancers were excluded as these regions are ambiguous and could be annotated as promoters. Merging states and relaxing concordance with Roadmap allowed for selecting more examples for model training. Note: Roadmap 18-state models do not include insulator states and we therefore chose insulators from our ChromHMM models that included CTCF ChIP-seq data. These predicted insulators mostly overlap with the Quiescent state in the 18-state models, suggesting that this ChromHMM state (Quies) is a miscellaneous functional state in the absence of CTCF ChIP-seq.(TIF)Click here for additional data file.S5 FigPrecision recall curves of held out test data for each sample used in model training. Individual class performances reveal that CoRE-ATAC predicts all classes with high average precision.(TIF)Click here for additional data file.S6 Fig(Left) The number of peaks identified as Transcribed, Repressed, or Low Signal by ChromHMM states (blue), overlayed with the number of these peaks predicted as “Other” by CoRE-ATAC. (Right) The fraction of Transcribed, Repressed or Low Signal states annotated as “Other” by CoRE-ATAC. The majority of these states  are predicted as “Other”.(TIF)Click here for additional data file.S7 FigA) and Precision Recall curves (B) for different enhancer prediction models: CoRE-ATAC components, PEAS, DeepSEA and LS-GKM. Models were evaluated for predicting enhancer versus “other” classes for chr3 and chr11 of the GM12878, HSMM, K562, and CD14+ datasets. Sequence based approaches had similar performances, including CoRE-ATAC’s sequence-based component. The ATAC-seq signal based  component of CoRE-ATAC alone outperforms all sequence-based approaches, however combining both sequence and signal greatly enhances predictive performances. PEAS captures more information than signal alone, however, is still underperforming compared to CoRE-ATAC-Sig+Seq model. Finally, the CoRE-ATAC model, which includes PEAS features, showed a slight improvement over the CoRE-ATAC-Sig+Seq model, likely taking advantage of the known features such as number of known motifs and conservation scores used in PEAS. (c) ROC AUC and (d) average precision scores for predicting promoters in chr3 and chr11. All models perform with high ROC AUC and Average Precision for predicting promoters due to distinct signatures for both sequence and ATAC-eq signal. (e) ROC AUC and (f) average precision scores for predicting insulators in chr3 and chr11. DNA sequence is the approaches are the best performing models for predicting CTCF insulators, while ATAC-seq signal alone provides the weakest predictive power for annotating these cis-REs.Receiver operating characteristic (ROC) curves ((TIF)Click here for additional data file.S8 Figcis-RE marker H3k27ac, transcribed marker H3k36me3, heterochromatin marker H3k9me3, and CTCF insulator marker CTCF (when available). Emission probabilities revealed consistent histone modification mark combinations present throughout these diverse cell types. Color annotations next to each heatmap of ChromHMM states represent the inferred ChromHMM state and the final 4 state relabeling.Recalled ChromHMM states for 10 of the 11 cell types used in this study. Each chromHMM run included enhancer marker H3k4me1, promoter marker H3k4me3, repressor marker H3k27me3, active (TIF)Click here for additional data file.S9 FigA) Cross cell type model performances when including islet insulators. Using poor quality CTCF ChIP-seq data (as evident from B) De novo motif enrichment for CoRE-ATAC insulator predictions in islets. Predicted insulators are highly enriched for CTCF with 76.1% of regions harboring a CTCF motif. Incorporating both DNA sequence and ATAC-seq signal enables CoRE-ATAC to detect a majority of insulators using the CTCF motif while detecting other insulators via other features learned in model training.((TIF)Click here for additional data file.S10 FigOverlap of CTCF ChIP-seq peaks with CoRE-ATAC insulator and non-insulator predictions. Majority of CoRE-ATAC insulator predictions overlapped with CTCF ChIP-seq peaks. Note: Overlaps between insulator and non-insulators are the result of CTCF peaks bridging the gap between these genomic regions when merging peaks for the union.(TIF)Click here for additional data file.S11 Figcis-RE function, outperforming commonly used methods for detecting promoters, enhancers, and insulators.CoRE-ATAC outperforms each threshold-based method for detecting promoters, enhancers, insulators, and combined annotations. Matthews Correlation Coefficient was used to measure performances as it accounts for true positives, true negatives, false positives, and false negatives simultaneously within its function and is an ideal measurement for performances when probabilities are static as in the case with all threshold-based approaches . CoRE-ATAC consistently had the best performance compared to each baseline. For detecting promoters, the best threshold was identified as ATAC-seq peaks within 2kb of the promoter, for enhancers, an FDR qval score is less than 0.0001, and for insulators, whether or not the region contained a single (n = 1) CTCF motif was the best performing threshold. Finally, combining all three of these thresholds confirmed that CoRE-ATAC improves our ability to predict (TIF)Click here for additional data file.S12 FigA) ROC AUC, (B) average precision, (C) F1, and (D) recall scores for predicting enhancers in chr3 and chr11 across different cell types not used in model training. CoRE-ATAC generalizes well for predicting enhancers in new cell types. CoRE-ATAC outperforms alternative sequence-based models . Note: The model for DeepSEA was trained using MCF7, A549, Stomach, Heart Left Ventricle, Heart Right Atrium, Pancreas, Liver, HEPG2, CD8 Naïve T Cells, and CD4+ T cells.(E-H) Performance scores for predicting promoters in chr3 and chr11 across different cell types not used in model training. All models predict promoters with high accuracy, irrespective of cell type. (I-L) Performance scores for predicting insulators in chr3 and chr11 across different cell types not used in model training. DeepSEA outperforms other methods for detecting CTCF insulators. CTCF has a well characterized DNA binding sequence that is very predictive.((TIF)Click here for additional data file.S13 FigThree CoRE-ATAC models were trained using: 1) 7 different cell types: GM12878, HSMM, Pancreas, Stomach, Thyroid, Testis, and Transverse Colon, 2) 9 different cell types: 7 cell type model with 2 Heart samples (Left Ventricle and Right Atrium Auricular Region) and 3) 11 different cell types: 9 cell type model with Liver and HepG2 samples. CoRE-ATAC performances did not significantly improve with increased number of cell types used for training for promoter, enhancer, or other prediction. Insulator prediction showed significant differences between models , however, the effect of these differences suggests that the model decreased in performance as more data was used for model training.(TIF)Click here for additional data file.S14 FigA) Distribution of CoRE-ATAC predictions for FANTOM enhancers in test chromosomes (chr3 and chr11). (B) Distribution of CoRE-ATAC predictions for test chromosomes. Pairs of bars represent comparisons between ChromHMM (H) and CoRE-ATAC (C), where each pair represents a sample/replicate for the respective cell type.  Histogram of distances to the nearest TSS for STARR-seq enhancers predicted as promoters by CoRE-ATAC for all chromosomes (C) and test chromosomes (D). Majority of predicted promoters are within 1kb of a TSS.  Histogram of distances to the nearest TSS for STARR-seq enhancers predicted as enhancers by CoRE-ATAC for all chromosomes (E) and test chromosomes (F). Majority of predicted enhancers are distal (> = 20kb) from the nearest TSS. STARR-seq enhancers annotated as promoters result from the close proximity these enhancers are to a TSS.  ChromHMM annotation (relabeled to 10 classes) distribution for STARR-seq enhancers in A549 for all chromosomes (G) and test chromosomes (H). Majority of STARR-seq enhancers are annotated as promoter or enhancer by ChromHMM.((TIF)Click here for additional data file.S15 FigMIN6 MPRA log fold change values for genomic regions predicted as losing or gaining cis-RE function based on CoRE-ATAC probabilities for reference and alternative alleles in test chromosomes (chr3 and chr11). Significance for predicted loss and predicted gain categories was calculated using student’s t-test for MPRA log fold change values being less than or greater than 0 respectively. Significance comparing the predicted loss and predicted gain of MPRA fold change distributions was calculated using Mann-Whitney U test. Concordant direction of effect  was observed for both for CoRE-ATAC predictions and MPRA activity levels for chromosomes not used in model training.(TIF)Click here for additional data file.S16 FigAverage precision, ROC AUC, F1 score, and recall for predicting cis-RE function in snATAC for 6 annotated clusters with available ChromHMM states. Model performances suggest that CoRE-ATAC is an effective tool for interrogating cis-RE activity from snATAC data.(TIF)Click here for additional data file.S17 FigTop de novo motif enrichments for all seven cell type clusters for snATAC data. Insulator predictions for all cell types are significantly enriched for motifs with high similarity with CTCF.(TIF)Click here for additional data file.S18 FigNumber of enhancers identified for the top 25 cell types and combinations by the number of enhancers predicted by CoRE-ATAC.(TIF)Click here for additional data file.S19 FigOverlap of CoRE-ATAC andChromHMM cell-specific enhancers. CoRE-ATAC predictions significantly overlap ChromHMM enhancers for all cell types . Overlaps were less significant for T cell comparisons, partially due to the differences in sorted cells used in ChromHMM annotations and total cells used in CoRE-ATAC predictions from snATAC-seq data.(TIF)Click here for additional data file.S20 FigA) promoters, (B) insulators, (C) other predicted annotations, and (d) all ATAC-seq peaks. Enhancers are more enriched for relevant diseases as expected (Selected disease SNP enrichments for (expected  and cont(TIF)Click here for additional data file.S21 FigWe measured the time (in seconds) to classify cis-REs using either an intel Core i9 CPU or a Tesla V100 GPU. Comparisons revealed a linear time increase as the number of peaks increases. Predictions with the CPU take ~2 minutes for 75000 peaks, which is reasonable for users of CoRE-ATAC. GPU based method comparatively take less than 20 seconds, showing the power of using a GPU for such analyses.(TIF)Click here for additional data file.


Strict social distancing measures owing to the COVID-19 pandemic have led people to rely more heavily on social media, such as Facebook groups, as a means of communication and information sharing. Multiple Facebook groups have been formed by medical professionals, laypeople, and engineering or technical groups to discuss current issues and possible solutions to the current medical crisis.This study aimed to characterize Facebook groups formed by laypersons, medical professionals, and technical professionals, with specific focus on information dissemination and requests for crowdsourcing.Facebook was queried for user-created groups with the keywords “COVID,” “Coronavirus,” and “SARS-CoV-2” at a single time point on March 31, 2020. The characteristics of each group were recorded, including language, privacy settings, security requirements to attain membership, and membership type. For each membership type, the group with the greatest number of members was selected, and in each of these groups, the top 100 posts were identified using Facebook’s algorithm. Each post was categorized and characterized . STATA  was used for statistical analysis.P<.01). Medical groups were significantly more likely to require security checks to attain membership  and less likely to be public  than layperson groups. Medical groups had the highest user engagement, averaging 502 (SD 633) reactions (P<.01) and 224 (SD 311) comments (P<.01) per post. Medical professionals were more likely to use the Facebook groups for education and information sharing, including academic posts (P<.001), idea sharing (P=.003), resource sharing (P=.02) and professional opinions (P<.001), and requesting for crowdsourcing (P=.003). Layperson groups were more likely to share news (P<.001), humor and motivation (P<.001), and layperson opinions (P<.001). There was no significant difference in the number of evidence-based posts among the groups (P=.10).Our search yielded 257 COVID-19–related Facebook groups. Majority of the groups  were for laypersons, 26 (10%) were for medical professionals, and only 2 (1%) were for technical professionals. The number of members was significantly greater in medical groups  than in layperson groups   and are more likely to use Facebook groups for education and information sharing, including academic posts, idea sharing, resource sharing, and professional opinions, which highlights the power of social media to facilitate CI across geographic distances. Layperson groups were more likely to share news, humor, and motivation, which suggests the utilization of Facebook groups to provide comedic relief as a coping mechanism. Further investigations are necessary to study Facebook groups’ roles in facilitating CI, crowdsourcing, education, and community-building. SARS-CoV-2, first discovered in Wuhan, China, on December 31, 2019, has quickly spread among >16 million individuals worldwide by June 2020 , includiHowever, despite the benefits of message sharing and crowdsourcing on social media platforms, studies have shown that social media platforms can lead to the propagation of misinformation . With thConsequently, it is critical to understand how social media can be used effectively, especially during unprecedented times such as the current COVID-19 pandemic. The CoV-IMPACT consortium has called for “the development of a real-time information sharing system, drawing from data and analyses from a range of social media platforms, in multiple languages and across the global diaspora” . FurtherFacebook was queried for user-created Groups with the keywords “COVID,” “Coronavirus,” and “SARS-CoV-2” at a single time point on March 31, 2020. The characteristics of each group were recorded, including language , privacy setting , security requirement to attain membership, and membership type . For each membership type, the group with the greatest number of members was selected, and in each of these groups, the top 100 posts were identified using Facebook’s algorithm. Each post was characterized by category and subcategory, whether it was evidence-based or crowdsourced, and whether the poster self-identified. The coding scheme for category and subcategory was developed independently by 3 investigators . Metricsα of .05. To compare membership volume across Facebook groups, a Mann–Whitney U test for nonparametric data was used. A Kruskal–Wallis test was used to compare the volume of reactions and comments for the top 100 posts across various types of Facebook groups. Planned posthypothesis testing was conducted using the Dunn test. Lastly, the chi-square test was conducted to compare evidence basis by group type for the top 100 posts.STATA  and Python  were used for statistical analysis. Demographic data were tabulated and stratified by the type of group . Hypothesis testing was conducted with a Cronbach U test indicated that overall, the number of members was significantly greater in medical groups  than in layperson groups  (P<.01) (P<.001). The majority of groups  predominantly operated in English, with no significant difference among layperson, medical, and technical groups  . The meal groups .P<.001). Among medical groups, the majority were private groups  that enforced security settings, with 4 groups (15%) that required requests, and only 1 (4%) that had no security settings. Similarly, both technical groups required security requirements to attain membership . In contrast, 98 (43%) layperson groups had security requirements, 110 (48%) had requests to join, and 21 (9%) had no security requirements.Nearly all groups required prospective members to submit a request or to answer security questions to attain membership  . MedicalP<.001). Male creators were more common in non-English layperson groups than female creators . Medical and technical groups were equally likely to be formed by a male or female creator. Facebook groups formed by organizations accounted for 14 (6.1%) layperson groups, 1 (5%) medical group, and no technical group.When investigating the gender of the Facebook group creator  was formed in late January 2020, while the largest medical (COVID-19 USA Physician/APP Group) and technical  groups were formed in mid-March 2020. As of this writing, CoronaVirus International had 185,340 members, averaged at 333 posts per day, operated predominantly in English, and required answering security questions to attain membership. COVID-19 USA Physician/APP Group had 140,018 members, averaged at 100 posts per day, operated predominantly in English, and required answering security questions to attain membership. Open Source COVID19 Medical Supplies had 61,935 members, operated predominantly in English, and required security questions to attain membership.P<.01) and 224 (SD 311) comments (P<.01) per post than layperson  and technical  groups  reactions  groups .P<.001), while medical groups had more evidence-based posts . Technical groups predominantly contained posts related to supply and equipment sharing (n=99) compared to layperson and medical groups (P<.001), the majority sharing ideas (n=72), followed by posts related to networking (n=11), requests for resources (n=9), and offers to provide resources (n=7). Medical groups had more posts related to supply and equipment than layperson groups , including posts sharing ideas . Medical groups provided more professional rather than layperson opinions , while layperson groups provided more layperson opinions . For each layperson or medical professional group, there was only one post. Layperson groups were more likely to share posts related to humor and motivation than medical groups , including humor  and inspiration .On comparing the characteristics of the posts by groups , laypersP<.001), and more likely to request crowdsourcing in the group  than their counterparts in layperson groups. There was no significant difference in the number of evidence-based posts among the 3 group types, with 28 evidence-based posts in layperson groups, 39 in medical groups, and 42 in technical groups (P=.10).Medical group posters were significantly more likely to self-identify , posts sharing ideas (P=.003), posts sharing resources (P=.02), and professional opinions (P<.001). Medical professionals were also more likely to request crowdsourcing than laypersons, asking questions about patient management and resources such as personal protective equipment. Together, this evidence suggests that medical professionals intentionally utilize Facebook groups as a forum to facilitate collective intelligence (CI) to compensate for the dynamic and unfamiliar evidence and guidance surrounding COVID-19 and associated treatments. CI is the “wisdom of crowds” [Medical groups are more likely to be private, and require security questions and agreement with group policies, and posters were more likely to self-identify  in accordance with the community rules, which is suggestive of a more professional community compared to layperson groups. Despite the heavier security, medical groups on average had more members than layperson groups  and high crowds” , which r crowds” , and has crowds” -14. Whil crowds” , social  crowds”  and CI a crowds” . Our finP<.001), humor and motivation (P<.001), and layperson opinions (P<.001) than medical groups. Layperson groups were less likely to crowdsource, and only 3% of posts were related to movement-based advocacy (such as “#stayathome”). This suggests that laypersons utilize the Facebook groups to form a community to share emerging news and share humor and inspiration, potentially to provide comedic relief as a coping mechanism. The COVID-19 pandemic has resulted in drastic shifts in day-to-day living for many individuals, including measures such as working from home, social isolation, adoption of hand hygiene, and wearing masks. These changes were rapid and may result in anxiety and distress among laypersons. Humor has been well evidenced as an adaptive mechanism for stress [However, layperson groups were more likely to share news . Only 1% of posts from both medical and layperson groups were conspiracy theories, suggesting that a potential paucity of information surrounding the COVID-19 pandemic may explain the low number of evidence-based posts. Regardless, the potential to rapidly propagate misinformation on social media could be dangerous, in both medical professional and layperson groups. Layperson groups in particular, may benefit from a moderator or peer “champions” [Recent studies evaluating the utility of information sharing on social media have focused on negative effects including rapid dissemination of false information ,7,9,10. ampions”  to encouPrevious studies have also described the potential of Facebook groups as support groups and for community-building among patients -33, or tA potential limitation to our study is the assumption that our findings are representative of the global culture of Facebook groups or other social media forums. We recognize that our study merely involves a small sample from among immensely diverse Facebook groups and the different communities that contribute to each group and the resulting culture. Furthermore, as a cross-sectional study, our data represent only 1 time point of the dynamic content on the social media platform. Our findings serve as a beachhead to establish the importance of understanding social media responses to the COVID-19 pandemic and its potential to facilitate CI, crowdsourcing, and community-building.In this study, we characterize how Facebook group activities surrounding the COVID-19 pandemic differ among layperson, medical, and technical groups. Medical professionals utilize Facebook groups as a forum to facilitate CI and are more likely to use the Facebook groups for education and information sharing, including academic posts, idea sharing, resource sharing, and professional opinions. Our findings highlight the power of social media to facilitate CI not only beyond geographic distances but also across additional physical barriers of strict social distancing practices resulting from the COVID-19 pandemic. Layperson groups were more likely to share news, humor, and motivation, which suggests the utilization of Facebook groups to provide comedic relief as a coping mechanism. Further studies are required to study the role of Facebook groups in facilitating CI, crowdsourcing, education, and community-building.


It implies that accuracy of the regression model for estimating PM10 and PM2.5 resuspension concentrations is 88% and 93%, respectively.Resuspension of particulate matter (PM) in classrooms, which increases the risk of negative impact on student health from exposure to PM, is influenced by humidity level in the indoor environment. The goal of this study is to investigate the properties of PM resuspension in accordance with relative humidity through classroom test chamber experiments. In actual classrooms, it is challenging to control factors influencing resuspension. Therefore, the classroom chamber that reflects the environment of elementary school classroom  is used in this study. The humidity of the classroom chamber is adjusted to 35%, 55%, 75%, and 85% by placing it inside a real-size environmental chamber, which allows artificial control of climatic conditions. At the respective humidity conditions, PM resuspension concentration and resuspension factor caused by occupant walking across the classroom chamber are analyzed. The results show that both of the resuspension concentration and resuspension factor reveal a linear negative correlation to humidity increase. Furthermore, coefficient of determination (R Because people of our day stay indoors a long time, management of indoor air quality is closely related to occupants’ health. Teenage students in particular are reported to spend most of their time indoors . As schoWhile there are a variety of causes of particulate matter (PM) emissions in classrooms, such as student activity , cleanliness of the floor, or the use of chalk for blackboards, PM resuspension from the floor is a major source ,9,10,11.The intensity of occupant activity, characteristics of the flooring , and humidity are known as major factors influencing PM resuspension ,13,14. OTemperature and humidity of indoor spaces such as a classroom are found to be influenced by outdoor air ,20. NguyOutlines of this paper are as follows. In Occupant activity, flooring properties, surface dust loading, and humidity are known as major factors influencing PM resuspension. Recently, several studied on the PM resuspension experiments under various influential factor conditions were reported ,22,23,24Rosati et al.  measuredA proposed procedure for measuring PM resuspension caused by the occupant activity under various humidity conditions is as follows. First, a classroom simulation chamber was constructed 9 m (L) × 3 m (W) × 3 m (H) in size, equivalent to one-third of an actual elementary school classroom. Then, this classroom chamber was placed inside the real-scale environmental chamber 34 m (L) × 6 m (W) × 6 m (H) in size. Based on South Korea’s weather data over the recent three years, the outdoor humidity conditions of the classroom chamber were set at 35%, 55%, 75%, and 85% or above. Next, an occupant whose body weight is 46 kg, similar to the average body weight of a sixth grader, walked across the chamber in one direction. The occupant walked at a pace of 2 m/s, similar to the average pace of an elementary school student in South Korea ,26. FinaA classroom simulation chamber that is one-third the size of an elementary school classroom in Suwon City, Korea was created. To measure PM resuspension concentrations from the classroom chamber, aerosol spectrometers  that use light scattering measurement were employed. This instrument divides particles that are 0.25–35 µm in diameter into 31 channels and then measures count concentrations and mass concentrations for each diameter every 6 s . In consTo adjust the classroom chamber’s outdoor humidity, a real-scale environmental chamber was used to realize weather conditions artificially. This weather environment test equipment is certified by the Korea Laboratory Accreditation Scheme (KOLAS) under the Korean Agency for Technology Standards of the Ministry of Trade, Industry, and Energy. It can simulate weather conditions such as temperature, humidity, sunlight, and freezing. This study aimed to measure PM resuspension by changing indoor humidity following changes in outdoor humidity. A solid aerosol generator  was used to disperse PM in the classroom chamber. The solid aerosol generator aerosolizes dry and easily flowing substances such as solid particles and powders at a designated feed rate . A1 ultrThe dust surface concentration settling on the floor is known as a factor influencing resuspension concentrations . For thi(1) Filters  were placed in the classroom chamber at an even distance;(2) Test dust was dispersed in the classroom chamber and allowed to settle;(3) Filter weights were measured;(4) Dust surface concentration of the classroom chamber was calculating using Equation (1)afterm and beforem refer to the filter weights before and after dispersing and settling of the test dust, and r refers to the filter radius.In Equation (1), 2, which is the average of previous studies [In this study, the classroom chamber’s dust surface concentration was set at 0.3 g/m studies ,13. ConsWeather conditions in Seoul and South Korea’s Gyeonggi Province over the most recent three years were analyzed to set the classroom chamber’s outdoor humidity conditions See . IrrespeAfter the classroom chamber’s humidity was set according to the designated conditions, the occupant walked across the chamber. PM resuspension concentrations were then measured. The step-by-step details were:(1) The occupant entered the classroom chamber and then waited for 5 min. This process was required to reduce changes in indoor PM concentrations caused by opening and then closing of the entrance door.(2) The occupant walked across the chamber once at a pace of 2 m/s.(3) The occupant exited the chamber after 10 min.(4) When PM concentration in the classroom chamber had stabilized, the above steps were repeated.10 and PM2.5 concentrations were measured with aerosol spectrometers  installed in the classroom chamber. The measured PM resuspension concentrations and durations were then analyzed (See Changes in PMyzed See .3): PM concentration increased after occupant had walkedResuspension concentration (µg/mResuspension duration (minute): Time spent to reach the resuspension concentrationLastly, PM resuspension factor was calculated under each humidity condition in the classroom chamber. Resuspension factor is the ratio of the measured airborne PM mass above the resuspension site to its surface mass and calculated with Equation (2).DpE refers to resuspension factor (in µg/µg), resuspensionC the resuspension concentration (in µg/m2), roomV the volume of the classroom chamber (in m3), loadingC the dust surface concentration (in µg/m2), and walkA the area in the chamber traversed by the occupant (in m2) [In Equation (2),  (in m2) .Influences of humidity on PM resuspension were quantified by comparing the resuspension factor of the classroom chamber under each humidity condition. The correlation between humidity and resuspension factor was analyzed using a regression line.10 concentrations in the classroom chamber under each humidity condition. The experiment was repeated 5 to 7 times under each humidity condition. At humidity levels of 35%, 55%, and 75%, PM10 concentrations surged immediately after the occupant walked across the chamber and then tended to reach the maximum concentration after about 1 min. On the other hand, at a humidity of 85% or above, PM concentration did not change much after the occupant walked across the chamber. This is because high humidity causes capillary coagulation between particles and hinders resuspension by increasing particle surface adhesion [10 resuspension concentrations decreased as humidity increased .2.5 concentrations in the classroom chamber under each humidity condition. As was the case for PM10 concentrations, PM2.5 concentrations surged immediately after the occupant walked across the chamber and tended to reach maximum concentration after about 1 min. However, resuspension concentrations of PM2.5 were lower than those of PM10 , which shows the goodness-of-fit of a regression equation, was 0.88 in the case of PM10 and 0.93 in the case of PM2.5. It indicates that the accuracy of regression equations between humidity and resuspension concentrations of PM10 and PM2.5 was 88% and 93%, respectively.10 and PM2.5 resuspension factors under each humidity conditions. Please note that the PM resuspension was measured by realizing the indoor humidity changes as influenced by outdoor humidity. Therefore, 10 and PM2.5.Equations (5) and (6) below show regression equations between the indoor humidity and resuspension factors of PMPME10 and PME2.5 mean resuspension factors of PM10 and PM2.5, and Rh refers to classroom chamber’s indoor humidity. Coefficient of determination (R2) was 0.91 in the case of PM10 and 0.95 in the case of PM2.5, which means that the accuracy of regression equations between humidity and resuspension factors of PM10 and PM2.5 was 91% and 95%, respectively. These regression equations will enable estimating PM resuspension factors under various humidity conditions.In the above equations, The resuspension factor in this study is lower than that in a previous study   will be carried out for the practical application of this study. Then, these results can contribute to estimation of particle resuspension in classrooms according to humidity conditions.An experimental study was conducted in a classroom simulation chamber to identify the characteristics of PM resuspension in such a classroom under various humidity conditions. This study differentiates itself in that it measured PM resuspension in indoor humidity changes that were influenced by outdoor humidity changes. This was achieved by placing the classroom chamber in a real-scale environmental chamber that could artificially realize weather conditions, and PM resuspension was measured when the classroom chamber’s indoor humidity had changed under the influence of the outdoor humidity. Resuspension factor, a ratio of the airborne PM concentration measured above the resuspension site to its surface concentration, was measured under each humidity condition. The PM resuspension concentration and factor tended to show a linear decrease as humidity increased. In particular, the accuracy of regression equations between humidity and resuspension concentration was 83% for PM