You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#Sec1}
============

A depressive disorder may influence the symptoms present in the course of a chronic illness. For example, the presence of depression was shown to be a stronger predictor of reporting diabetes symptoms than hemoglobin A~1C~ level and was more highly associated with chest pain than objective measures of coronary artery disease severity^[@CR1],[@CR2]^. Asthma is not an exception. Epidemiologic associations between depression and asthma symptoms have been recognized anecdotally for many years^[@CR3]--[@CR5]^. Dysregulation in key biologic systems, such as inflammation^[@CR6]^, neuroendocrine dysregulation^[@CR7]^, autonomic imbalance causing cholinergic activation^[@CR8]^, and genetics dysregulation^[@CR9]^, have been suggested as explanations of this co-occurrence.

A Bayesian network (BN) is a multivariate model of dependency among several variables, and the strength of the relationship between variables is represented by conditional probability distributions associated with each node^[@CR10],[@CR11]^. BNs have been applied in a variety of settings for the purposes of probabilistic prediction, including predictions of asthma exacerbations and control^[@CR12],[@CR13]^ and responses to short-acting bronchodilator and inhaled corticosteroids^[@CR14],[@CR15]^. Given that depression is associated with asthma symptoms, we hypothesized that combining information from multiple SNPs, demographic factors, depression, and anxiety can yield models predictive of symptoms. Using BNs, we developed a predictive model and identified one candidate single-nucleotide polymorphism (SNP), rs4672619, that showed possible combined effects with depression upon asthma symptoms. Rs4672619 is located on the intron of the Erb-B2 receptor tyrosine kinase 4 (*ERBB4*) gene and showed interactive effects with depression on asthma symptom severity in children from the Childhood Asthma Management Program (CAMP) trial^[@CR16]^. We confirmed that interactions between depression and rs4672619 in predicting asthma symptom severity were also noted in an independent cohort of elderly asthmatics, although the interactions were in the opposite direction.

Materials and methods {#Sec2}
=====================

Each study was approved by the Institutional Review Board of the corresponding institution and informed consent was obtained from all study participants. Detailed methods are described in the [supplementary material](#MOESM1){ref-type="media"}.

Identifying SNPs associated with asthma symptoms {#Sec3}
------------------------------------------------

To obtain reliable SNPs associated with asthma symptoms, we used the results of two genome-wide association study (GWAS) performed in the non-Hispanic white children with asthma enrolled in the CAMP trial (*n* = 438) and in the Childhood Asthma Research and Education (CARE) trials (*n* = 457). At baseline, all participants were asked to rate and score their asthma symptoms during the past 24 h on a diary card. Similar questions were used in both trials, and the symptom scores ranged from 0 (absent) to 3 (severe) in all trials^[@CR16]--[@CR18]^. Detailed methods of genome-wide SNP genotyping have been described elsewhere for CAMP^[@CR19],[@CR20]^ and CARE^[@CR21]^. A common set of SNP genotypes was obtained by imputation in each cohort using MaCH (version 1.0)^[@CR22]^ and the 1000 Genomes Project EUR reference-phased haplotypes based on Phase 1 low coverage data (20101123 release). SNPs with minor allele frequency \<1%, a Hardy--Weinberg equilibrium *P* \< 0.001, and/or an imputation quality score \<0.3 were excluded, resulting in a set of \~37 million variants per cohort. These data and quality control have been described previously^[@CR23]^. The association of SNPs with asthma symptom scores at baseline was measured with a linear regression model as implemented in PLINK^[@CR24]^ using additive genetic models. The regression models were adjusted for age and sex. From results of two GWAS, common SNPs with *P* values \<0.05 showing same directionality were obtained. As information on depression and anxiety was available only in the CAMP trial, BN analysis was performed in the CAMP cohort. Among common SNPs, the 100 top-ranked SNPs based on *P* values of CAMP were forwarded to BN analysis.

BN analysis {#Sec4}
-----------

We view the BN as primarily a hypothesis-generating step in the analysis. In the CAMP trial, measures of depression and anxiety status were collected at baseline and annually thereafter using the Children's Depression Inventory^[@CR25]^ and the Revised Children's Manifest Anxiety^[@CR26]^. Higher scores on each of these measures reflect increased problems. For better modeling, we drew 91 subjects with other ethnicities from the CAMP trial in addition to the 438 non-Hispanic white subjects, and resultantly, a total of 529 subjects who had a complete phenotype data set (symptom, depression, and anxiety scores) and genotype data were used for BN analysis. The baseline characteristics are presented in Table [1](#Tab1){ref-type="table"}. We used the open-source software package *bnlearn*^[@CR27]^ in the statistical and graphical environment R (<http://www.r-project.org>) for BN analysis. In BN analysis, we included 105 variables: 100 SNPs (homozygous minor alleles and heterozygous and homozygous major alleles); depression score (quartile 1--4); anxiety score (quartile 1--4); age (quartile 1--4); sex (male and female); and race (non-Hispanic white, African American, any Hispanic, and Asian). Prior distributions used were *bnlearn* defaults, and we learned a BN structure using *bnlearn*'s hill-climbing algorithm (a greedy optimizer for the Bayesian posterior probability of the data). Measuring the degree of confidence in a particular graphical feature is a key problem in inferring the network structure^[@CR28]^. We quantified such a degree of confidence by generating 1000 network structures based on nonparametric bootstrap to the data and by estimating the relative frequency of the feature of interest^[@CR28]^. Then, we removed undirected arcs and arcs with strength \<0.1 from the BN inferred. A previous report showed that removing the arcs with strength \<0.1 had a minimal effect on its classification accuracy^[@CR29]^. According to the Markov blanket property of the BN, only the parents (nodes connected above), children (nodes connected below), and the other parents of those children are required to predict the behavior of a node^[@CR30]^. We therefore selected the two nodes (depression and rs4672619) that were within the Markov blanket of the symptom node in BN and then performed interaction analysis. To calculate an adjusted *P* value, permutation tests for a linear model as implemented in the "lmPerm" R package^[@CR31]^ was done.Table 1Characteristics of childhood and elderly asthmaticsChildhood asthmatics, *N* = 26Elderly asthmatics, *N* = 96Age (years), median (IQR)8.8 (7.2--10.6)72.5 (70--77)Male gender, number (%)332 (62.7)38 (39.6)Symptom score, median (IQR)0.57 (0.14--1.00)^a^8 (6--10)^b^Race, number (%) Non-Hispanic white/African American/Hispanic/Asian389 (73.6)/58 (10.9)/40 (7.6)/42 (7.9)0 (0)/0 (0)/0 (0)/96 (100) Cognitive function (score), median (IQR)NA27 (24--29)^c^ Anxiety (score), median (IQR)10 (6--15)NA Depression (score), median (IQR)5 (2--10)19.5 (16--22)*IQR* interquartile range, *NA* not applicable^a^Range 0--3^b^Range 0--25^c^Range, 0--30 (any score ≥24 points indicates normal cognition)

Evaluating the interactive effects in another cohort {#Sec5}
----------------------------------------------------

We evaluated whether the interactive effects of depression status and rs4672619 genotypes on asthma symptom severity observed in the cohort of childhood asthmatics were sustained in another independent cohort. As no childhood cohort with both depression and genotype data was readily available, our evaluation was done in elderly asthmatics aged 65 years or older from a prospective, observational, and multi-centered cohort in Korea with the purpose of studying the natural history of asthma among elderly people^[@CR32]^. The baseline characteristics are presented in Table [1](#Tab1){ref-type="table"}. Symptom scores were measured using five questionnaires (range, 0--25; a higher score represents a more severe symptom), and depression status was assessed by the Korean version of the Geriatric Depression Scale Short Form (range, 0--30; a higher score represented more severe depression). Cognitive function was assessed using the Korean version of the Mini-Mental State Examination (range, 0--30; any score ≥27 points indicates a normal cognition). Details for symptom questionnaires are described in the online supplement. rs4672619 was genotyped, and the interactive effects between genotype and depression status on asthma symptom severity were evaluated.

Results {#Sec6}
=======

The lowest *P* value for GWAS analysis was 1.66 × 10^−6^ (rs2141189) in the CAMP trial and 1.52 × 10^−6^ (rs1429146) in the CARE trial. There were 413 common SNPs with *P* values \<0.05 with the same directionality. Of these SNPs, the 100 top-ranked SNPs based on the CAMP *P* values (Supplementary Table [1](#MOESM1){ref-type="media"}) were selected and forwarded for BN analysis. Figure [1](#Fig1){ref-type="fig"} shows the BN learned from the data of childhood asthmatics. We found two nodes directly attached to the asthma symptom (Sx node in Fig. [1](#Fig1){ref-type="fig"}): depression (Dp node in Fig. [1](#Fig1){ref-type="fig"}) and rs4672619 (S16 node in Fig. [1](#Fig1){ref-type="fig"}). The arc strengths between the Sx and Dp nodes and between the Sx and S16 nodes were 0.329 and 0.207, respectively. We then evaluated the interactive effects of depression severity and rs4672619 genotypes on asthma symptom severity in all CAMP subjects. As shown in Fig. [2a](#Fig2){ref-type="fig"}, in childhood asthmatics with homozygous reference alleles (frequency = 86.8%), severe depression was related to less severe asthma symptoms. However, in childhood asthmatics with heterozygous or homozygous variant alleles, depression and asthma symptom severity showed a positive correlation. The *P* values of the main effects were 0.009 for rs4672619 and 0.322 for depression, and the *P* value of the interaction was 0.014 (permutation *P* value = 0.019). rs4672619 was genotyped in 96 Korean elderly asthmatics, and we also found that the directions of correlations between depression status and asthma symptom severity differed according to rs4672619 genotypes (Fig. [2b](#Fig2){ref-type="fig"}). However, the direction of correlation was opposite to that of childhood asthmatics (Fig. [2](#Fig2){ref-type="fig"}). That is, elderly asthmatics with homozygous reference alleles (frequency = 92.4%) showed a positive correlation between depression and symptom severity. The *P* values of the main effects were 0.177 for rs4672619 and 0.125 for depression, and the *P* value of the interaction was 0.00014 (permutation *P* value = 0.003).Fig. 1Bayesian network inferred.A Bayesian network learned from the data of childhood asthmatics with a Hill-Climbing algorithm. Edges represent conditional dependencies, and connected nodes represent variables that are conditionally dependent on each other. Red edges represent edges with an arc strength \>0.5, blue edges represent edges with an arc strength between 0.2 and 0.5, and black edges represent edges with an arc strength between 0.1 and 0.2. Edges with an arc strength \<0.1 were removed from the network. For a certain node, nodes connected above (parent nodes), nodes connected below (children nodes), and the other parent nodes of these children nodes form a Markov blanket, which is required to predict the behavior of that node. Thus, we evaluated the interactive effects of depression severity (parent of the Sx node) and rs4672619 (S16) genotypes (children of the Sx node) on asthma symptom severity (Sx node). A age quartile, Ax anxiety quartile, Dp depression quartile, G gender (male/female), Sx symptom severity quartile, R ethnicity (non-Hispanic white/African American/Hispanic/Asian), and S1-100 top-ranked 100 SNPs (S16, rs4672619)Fig. 2Interactive effects of rs4672619 genotypes and depression severity on asthma symptom severity in cohorts of childhood and elderly asthmatics.**a** Childhood asthmatics. **b** Elderly asthmatics. Solid lines represent subjects with reference homozygous alleles of rs4672619, and dotted lines represent subjects with heterozygous or homozygous variant alleles of rs4672619. Q1 represents mild depression, and Q4 represents severe depression. The severity of asthma symptoms increases as symptom score increases

Discussion {#Sec7}
==========

Precise symptom perception would be an important component of asthma management, allowing asthmatics to use rescue medicine or seek medical help in a timely manner^[@CR33]^. However, there may be a discrepancy between asthma symptom perception and lung function^[@CR34]^. Therefore, asthma symptoms are a unique domain that should be evaluated for the proper management of asthma^[@CR35]^. β2 Adrenergic receptor gene methylation was reported to be associated with decreased asthma severity in inner-city schoolchildren^[@CR36]^, and genetic components are known to be involved in asthma symptom responses to medications^[@CR37],[@CR38]^. In addition, a recent report showed that psychosocial factors, such as harsh parent--child conflict^[@CR39]^ or targeted rejection^[@CR40]^, caused severe symptoms in children with asthma by decreasing anti-inflammatory gene expression. These findings motivated us to assess the interactive effects of multiple SNPs mapped to multiple genes and depression severity on asthma symptoms. Using BNs to guide our investigation, we found that rs4672619 was directly attached to the symptom node. Further analysis has shown that rs4672619 genotypes showed significant interactive effects with depression severity on symptom severity in both childhood and elderly asthmatics.

rs4672619 is on an intron of the *ERBB4* gene. *ERBB4*, along with its ligand neuregulin-1, is known to contribute to the pathophysiology of schizophrenia and bipolar disorders^[@CR41],[@CR42]^. A recent Bayesian modeling approach identified several variants in *ERBB4* with strong evidence for associations with childhood asthma^[@CR43]^. Recent increases in the understanding of dyspnea, one of the important asthma symptoms, suggest that there may be interactions between biopsychological factors and dyspnea perception^[@CR44]^. *ERBB4* regulates a thalamic reticular nucleus to cortical inputs at levels that can support sensory selection while allowing behavioral flexibility^[@CR45]^. In addition, crosstalk between the ErbB network and steroid hormone signaling pathway has been well known^[@CR46]^ and severe, steroid-dependent asthmatics had depression more often than non-steroid-dependent asthmatics^[@CR47]^. Taken together, rs4672619 is possibly involved in interactions between depression and asthma symptom severity by modulating symptom perception and steroid action.

One of the interesting findings of this study was that the direction of interactive effects of rs4672619 genotypes with depression severity on asthma symptoms was opposite between our childhood and elderly cohorts. The childhood asthmatics carrying two reference alleles showed significantly negative correlations between depression and symptoms scores, whereas elderly asthmatics with these alleles showed significantly positive correlations. Cognitive dysfunction, which is frequently encountered in elderly asthmatics^[@CR48]^, might affect asthma symptom presentation. However, we found that the Mini-Mental State Examination scores of all elderly asthmatics were greater than 24 (Table [1](#Tab1){ref-type="table"}), which suggested that they were cognitively competent^[@CR49]^. Many investigators have agreed that elderly asthma differs considerably from non-elderly asthma and that aging lungs^[@CR50]^, physiologic changes^[@CR51]^, and immunosenescence^[@CR52]^ are possible contributing factors. Likewise, the different interactive effects of rs4672619 genotypes and depression severity on asthma symptom severity between childhood and elderly asthmatics might come from the factors mentioned above. It is also possible that asthma symptom perception may be influenced by age, and a specific genetic mechanism regulating childhood asthma symptom severity may not carry over to old age. To date, no study has evaluated differences in the perception of asthma symptoms in childhood and elderly asthmatics. However, for childhood asthmatics, it was reported that adolescents (13--18 years) were more accurate in perceiving symptoms than school-age children (6--12 years)^[@CR31]^ and that the accuracy for perceiving symptoms increased with age in children aged 7 to 17 years^[@CR53]^. In our previous study, we found that genetic mechanisms underlying the symptomatic response to inhaled corticosteroids might be different between childhood and adult asthmatics^[@CR36]^. Interestingly, recent reports showed that some genetic variations in *ERBB4* showed age-by-genotype interactions on cortical brain morphology in a cohort of 3--20 year olds^[@CR54]^, and age-dependent differences in *ERBB4* expression were found in the brains of autistic patients^[@CR55]^. These findings suggest that the genetic effects of *ERBB4* may be different between childhood and elderly asthmatics, although we do not know whether the same differences exist in the lung.

Another plausible explanation is that the different interactive effects are due to ethnicity differences. Childhood asthmatics in this study were predominantly non-Hispanic white patients, and whereas elderly asthmatics were only Asian patients. Allele reversal at shared risk loci, the so-called flip-flop phenomenon, can be attributed to differences in the underlying genomic architectures at these loci according to ethnicity differences^[@CR56]^. For example, a previous report showed that variants of *ENND1B* were associated with asthma in children, but the association in the African Americans was linked to the opposite allele of that associated with asthma in subjects of European ancestry^[@CR57]^. Very little is known about ethnicity--gene interactions for *ERBB4*. Only one small-scale study conducted in non-Hispanic white and African-American schizophrenia patients reported that disease-associated SNPs on *ERBB4* showed no ethnicity--genotype effects on *ERBB4* splice-variant expression levels in the brain ^[@CR58]^.

There are a few potential limitations in generalizing our findings. Inevitably, the main effect and interaction effect are often correlated. It is possible that the significant effect of rs4672619 genotypes might have affected the interactive effect of depression severity and rs4672619 genotypes on asthma symptom severity in childhood asthmatics. Next, we could not replicate our results in another pertinent cohort of childhood asthmatics. The opposite directions of the correlations in childhood and elderly asthmatics was interesting. However, we cannot completely exclude the possibility that our results may be due to chance alone (false-positive associations). In addition, further mechanistic studies are needed to confirm the age-specific or ethnicity-specific effects of rs4672619 on the *ERBB4* gene. An incorporation of prior biological knowledge would help us overcome the reconstruction accuracy due to the complex nature of the network and the noise inherent in the data.

In conclusion, we have identified rs4672619, which showed significant interactive effects with depression severity on asthma symptom severity in both childhood and elderly asthmatics in opposite directions. This study is the first to show significant interactions between genetic factors and depression severity for asthma symptom severity. Our findings suggest that different strategies to decrease asthma symptom severity may be needed in these distinct age subsets of asthmatics.

Electronic supplementary material
=================================

 {#Sec9}

Supplementary material

**Publisher's note:** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material
=================================

**Supplementary information** accompanies this paper at 10.1038/s12276-018-0110-5.

This work was supported by the National Institutes of Health, US (R01 NR013391, R01 HL127332, and U01 HL065899) to K.G.T., by the Ministry of Health and Welfare, Republic of Korea (2008-E33028-00, 2009-E33022-00, and 2011-E33005-00) to H.-W.P., and by the Parker B. Francis Foundation to M.J.M.

H.-W.P. and K.G.T.: Conception and design of the study, data generation, analysis, interpretation, and preparation of the manuscript. W.-J.S. and S.-H.C.: Data generation and critical revision of the manuscript. M.J.M.: Data generation, analysis, interpretation, and preparation of the manuscript. F.M., D.M., and B.G.B.: Data generation, analysis, interpretation, and critical revision of the manuscript.

The authors declare that they have no conflict of interest.