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]


Two cohorts of naive macaques, which demonstrated different SHIV viral infection rates, had different viral target cells in the rectal mucosa

Two cohorts of naive Indian rhesus macaques were obtained from two different sources: the N7 group of seven animals were wild-captured from Morgan Island, while the N11 group of 11 animals were inbred from Alpha Genesis Inc (Supplementary Table 1). Before administration of SHIV SF162P4 viral challenges, the N7 and the N11 group of macaques were cohoused in the same room for 5 months. The repeated low-dose simian/human immunodeficiency virus (SHIV) challenge macaque model has been widely used to assess the HIV susceptibility.24,25 In this study, we utilized this model to test the viral susceptibility of these two cohorts of naive macaques. We gave the macaques low-dose SHIV SF162P4 virus intrarectally every week for 8 weeks. After 8 SHIV challenges, 17 out of 18 animals were infected, and the infection rate for all the animals was 27%. However, we observed that these two cohorts demonstrated significantly different infection rates. N7 had an infection rate of 47% per exposure, while N11 had only a 21% rate. Even with the small number of animals in each cohort, they showed a significant difference in viral acquisition (P = 0.04, Fig. 1a).

Fig. 1 Viral target cells in the rectal IEL correlated with SHIV acquisition. a Viral acquisition curves from two naive cohorts (N7 and N11) were demonstrated in a low-dose repeated intrarectal challenge model with weekly challenge of SHIV S F162P4 virus. b–f Multi-color flow cytometry techniques were used to measure and compare the frequencies of rectal LP CD25+FOXP3+ of CD4+T cells (b), rectal LP CD14 + MDSCs (c) and CD15 + MDSCs (d) cells; as well as the viral target cells (Ki67+CCR5+ in CD4+T cells) in PBMC (e) and rectal IEL (f) in N7 and N11 cohorts. Mann-Whitney tests were used for comparisons. g, h Correlations between viral target cells in PBMC (g) and rectal IEL (h) and number of viral exposures for the animals to get infected were performed using Spearman’s tests. Mean ± SEM are shown. Triangles denote the N7 group, and filled circles denote the N11 group Full size image

To explore the possible cause of this difference, we examined different cell subsets and the immune activation markers that are associated with the immune regulation in the colorectal tissues. Since CD4+ regulatory T cells (T regs ) are present at higher frequencies in the colon lamina propria (LP) than in other organs,26 and have important implications in inflammatory bowel disease, we first measured the frequencies of Tregs in the rectal mucosa. We found that the frequency of CD25+FOXP3+ cells among CD4+ T cells was about 4%, and no significant difference in Treg cell frequencies in the colon LP between the two cohorts were observed (Fig. 1b, Supplementary Figure 1).

We then tested myeloid-derived suppressor cells (MDSC), which not only are important immune regulators to suppress the immune activation, but also have been identified as viral target cells in the SIV-infected macaques.27 We measured two major subsets of MDSCs in the colorectal LP, and found that neither CD14+ MDSCs nor CD15+ MDSCs showed any significant difference between the two cohorts (Fig. 1c, d, Supplementary Figure 2). MDSC did not correlate with viral acquisition either.

We next measured the CD4+ T viral target cell (CD4+CCR5+Ki67+ T) frequencies in the blood and gut mucosal tissues based on our and other’s studies.28,29,30 We have shown previously that the viral target cells in the rectal mucosa determined the viral loads and viral eclipse time once the animals were infected, while Carnathan et al. found that the activated CD4+CCR5+ T cells in the rectum predicted increased SIV acquisition in SIVGag/Tat-vaccinated rhesus macaques.28,29,30 Indeed, we found that the original more susceptible N7 group had more activated CD4+CCR5+Ki67+ T cells (viral target cells) in PBMC (P = 0.003), and in the rectal intraepithelial lymphocytes (IEL) (P = 0.056) than those in the more resistant group of N11 (Fig. 1e-f, Supplementary Figure 3). Though the frequency of viral target cells in the rectal mucosa of the N7 group only showed a trend (p=0.056) of higher levels than that of the N11 group, it significantly correlated inversely with the number of intrarectal exposures needed for viral infection (Fig. 1h). Viral target cells in the PBMC showed a trend, but did not significantly correlate with viral acquisition (Fig. 1g). This supported the conclusion that the viral target cells in the rectal mucosa played an important role in determining viral transmission.

The mucosal immune activation status was significantly different in the two cohorts of macaques

Besides viral target cells, we further explored whether there were other immune activation markers that could be used to more precisely define the immune activation status of the rectal mucosa. We have measured five immune activation markers (Ki67, CD69, CD38, CCR5, and HLA-DR), which have been associated with HIV-1 infections, in three cell subsets (CD4+T, CD8+T, and CD14+monocytes). To generalize the idea, we analyzed the data using an unsupervised principal component analysis (PCA) with Qlucore Omics Explorer.31 With the first three PCA components explaining 67% of the total variance, the generated PCA plot revealed that the N7 and the N11 groups had distinct immune activation status (Fig. 2a). Highlighting a common signature of each cohort, the parameters listed in Fig. 2b accounted for most of the difference in the colorectal mucosa. The more resistant group of N11 had higher expression of CD38 in both CD4 and CD8 T cells, as well as higher HLA-DR expression in CD14+ monocytes, while the more susceptible cohort N7 had higher expression of CCR5 in all three cell types (CD4, CD8 T cells and CD14+ monocytes), and higher Ki67 expression in CD4 T cells. This suggested that the immune signature of the two cohorts primarily involved the expression of CCR5, Ki67, CD38, and HLA-DR. Overall, the two cohorts of macaques had different expression levels of immune activation markers on T cells and myeloid cells in the rectal mucosa.

Fig. 2 The immune activation status was significantly different in the two naive groups. a The frequencies of Ki67, CD69, CD38, HLA-DR, and CCR5 in CD4+, CD8+T cells, and CD14+ monocytes, as well as the percentages of the three cell subsets, were used to create a principal component analysis (PCA) plot. Magenta dots denote the N7 group, and yellow dots denote the N11 group. b The signature immune activation markers of the two cohorts (N7 and N11) were shown. c Immune activation PCA-1 was exported and compared between the N7 and N11 cohorts using a Mann-Whitney test. Mean ± SEM are shown. d Correlation between immune activation PCA-1 and numbers of viral exposures required for the animals to get infected were performed using Spearman’s test. The Qlucore Omics Explorer (Qlucore) was used to perform the PCA Full size image

We then exported the PCA-1 of the immune activation markers, and compared them in the two naive groups. As predicted, the N7 and N11 groups had significantly different PCA-1, which also showed a significant association with number of exposures needed for infection (Fig. 2c, d).

To summarize, SHIV viral susceptibility was associated with two markers: (1) the prevalence of activated rectal viral target cells, and (2) the PCA-1 of multiple immune activation markers in the rectal mucosa. Both could be used as surrogate markers to monitor the immune activation status.

Distinct bacterial taxa composition of fecal microbiome in the two naive cohorts

Because the two cohorts of naive groups came from two different sources, we hypothesized that the difference in immune activation in the GI tract might be due to distinct gut microbiota. To test this hypothesis, we examined the gut microbiome by 16S rDNA MiSeq. Total bacterial DNA was extracted from the fecal samples collected one-week before the serial challenges. Using the MiSeq protocol, we obtained an average of 108,096 ± 3121 operational taxonomic units (OTUs) per sample, which were subjected to subsequent analysis (Supplementary Table 2). We further examined the bacterial compositions at different levels, and investigated whether the gut microbiome was associated with local mucosal immune activation or SHIV susceptibility.

At the phylum level, 17 phyla have been identified. A compositional look at this level revealed that the top four phyla: Bacteroidetes, Firmicutes, Proteobacteria, and Spirochaetes represented more than 90% of the total bacteria (Fig. 3a, b). Among these four phyla, Firmicutes were significantly higher in the N11 group (the resistant cohort) than in the N7 group (Fig. 3c). Furthermore, Firmicutes correlated inversely with a low frequency of Ki67+CCR5+CD4+T cells (SHIV target cells) in the rectal mucosa and showed a correlation with mucosal immune activation PCA-1 (Fig. 3c), suggesting that Firmicutes might influence the frequencies of viral target cells and immune activation markers in the gut mucosa. However, Firmicutes did not correlate with numbers of exposures to SHIV virus, suggesting they unlikely affected the SHIV viral transmission directly (Supplementary Figure 4). The other top three phyla: Bacteroidetes, Proteobacteria, and Spirochaetes did not differ between the two cohorts (Fig. 3d).

Fig. 3 The analysis of relative abundance of the fecal microbiome at phylum level revealed that Firmicutes were higher in the more viral-resistant cohort, and correlated with viral acquisition. a Gut taxa composition from all 18 animals is shown. b Gut taxa composition from each animal is shown. c The relative abundance of Firmicutes was compared between the two cohorts using a Mann-Whitney test. Correlation between relative abundance of Firmicutes and viral target cells and immune activation PCA-1 were performed using Spearman’s tests. d The relative abundance of Spirochaetes, Proteobacteria, and Bacteroidtes were compared between the two cohorts using Mann-Whitney tests. Mean ± SEM are shown Full size image

Gut microbiota composition at the genus level showed that the bacterial PCA correlated with viral target cells/immune activation in the rectal mucosa

We then zoomed in on the bacterial compositions at the genus level, in which over 200 taxa were identified. We did not find significantly different taxa abundance between the two cohorts of macaques (Fig. 4a). However, the PCA plot of the gut bacteria showed different microbial compositions of the two cohorts, even after more than 5 months of co-housing (Fig. 4b). Exporting the bacterial PCAs, we found that PCA-1, representing 14% of total taxa, showed a correlation or a trend of correlation with mucosal immune activation PCA-1, or Ki67+CCR5+CD4+T SHIV target cells (Fig. 4c). However, bacterial PCA-1 did not directly correlate with the number of viral exposures.

Fig. 4 Principal component analysis (PCA) components at genus level correlated with rectal viral target cells and immune activation PCA-1 (in rectal IEL). a The number of total taxa identified in each animal of the two cohorts were compared using Mann-Whitney test. Mean ± SEM are shown. b PCA plot of gut microbiome at genus level is shown. Red triangles denote the N7 group, and black dots denote the N11 group. c Correlations between PCA component 1 of gut microbiome at genus level versus viral target cells or immune activation PCA-1 were performed using Spearman’s test Full size image

Among the taxa at the genus level, Anaerovibrio, Bacteroides, Bulleidia, Ruminococcus, Mogibacterium, and Symbiobacterium were significantly higher in the N11 group, while Anaeroplasma, Burkholderia, syntrophobacterwere significantly higher in the N7 group (Fig. 5). We also tried to identify the specific bacteria that might be associated with the viral infection. Mogibacterium, Hydrogenoanaerobacterium, and Orientia showed corrections with viral acquisition directly (supplementary Figure 5).

Fig. 5 Relative abundance of the fecal microbiome at genus level was compared in the two naive control groups. Gut bacterial composition at the genus level. a–d Firmicutes, e–g Proteobacteria, h Bacteroidetes, i–k others. Mean ± SEM are shown, and statistical analyses were performed using Mann-Whitney tests Full size image

The different sources of the two cohorts of animals might account for the different gut microbiome they had. However, we noticed that the average age of N11 cohort was relatively younger than that of the N7 cohort group (Supplementary Table 1). In the N7 group, three animals had ages similar to those of the N11 group, while the other four were older. Since a previous study has found that old age is associated with microbial dysbiosis and systemic inflammation,32 to determine whether age played any roles in determining immune activation and the composition of the gut microbiome, we have included another 21 animals, which were obtained from the same source (Morgan Island) and which arrived in the facility in the same shipment at the same time as the N7 group. As the macaques were all relatively young adults (aging from about 3–5.5 years), not elderly, we did not find any correlations between age and viral target cells in the rectal mucosa or PBMC (Supplementary Figure 6 a-b). Moreover, fecal samples from these 21 animals and the N7 groups were collected and sequenced for microbiome when they first arrived in the facility before any treatment or challenge. We did not observe any associations between age and the composition of gut microbiome either (Supplementary Figure 6 c-e). All animals in the N11 cohort were females, whereas there were both males (N = 5) and females (N = 2) in the N7 cohort. To test whether gender was a major factor in the observed differences in gut bacteria, we analyzed the data and did not find any gender-specific differences in gut bacteria: the two values of the females lay within the range of the five values of the males for PCA-1 and the Firmicute frequencies (P = 0.86 for each).

The ratio Bacteriodes /Prevotella inversely correlated with gut mucosal immune activation

Consistent with a previous report that Prevotella but not Bacteroides is the more common genus in the gut of macaques,33 we found that six out of seven macaques from N7 group hardly had any Bacteriodes in the gut (less than 1000 units), whereas more than half of the N11 group had more than 1000 units of Bacteriodes (Fig. 5). Since the ratio of bacteroides to prevotella has been previously shown lower in HIV-1 infected patients,20 we compared this ratio in the two cohorts. Notably, the two cohorts had different abundance of Bacteriodes, Prevotella, and the the ratio of bacteroides to prevotella was significantly lower in the more susceptible cohort, as in the HIV-infected patients20 (Fig. 6a). Though the ratio of bacteroides to prevotella did not directly correlate with number of viral challenges needed for the animals to get infected, it did show a trend of inverse correlation with viral target cells and did show a significant association with mucosal immune activation PCA-1 (Fig. 6b, c).

Fig. 6 The ratio of bacteroides/prevotella was higher in the more susceptible cohort and showed corrections with immune activation at genus level. a A Mann-Whitney test was used to compare the ratio of bacteroides/prevotella between the two cohorts. b, c Spearman’s tests were used to determine the correlations between the ratio of bacteroides/prevotella and viral target cells (b) or immune activation (PCA-1) (c) in the rectal IEL Full size image

We further identified the prevotella and bacteroides species that were different between the two cohorts. Among the ten species that had more than 1% of the total genus prevotella, P. copri ranked first and comprised 65% of the genus prevotella (Fig. 7a). However, only P. histicola, P. brevis, P. Oris,and P. albensis showed significant differences between the N7 and N11 groups (Fig. 7b). Nevertheless, none of them correlated with Ki67+CCR5+CD4+T cells in the rectal mucosa or mucosal immune activation PCA-1. In the genus bacteroides, the top four species: B. intestinalis, B. xylanolyticus, B. plebeius,and B. acidifaciens comprised more than 80% of the total genus (Fig. 7c). B. intestinalis,and B. plebeius showed significant differences between the N7 and N11 groups (Fig. 7d). B. intestinalis was associated with both Ki67+CCR5+CD4+T cells in rectal mucosa and the mucosal immune activation PCA-1 (Fig. 7e). We then asked whether mucosal viral target cells or immune activation PCA-1 correlated with the ratios of B. intestinalis to different species of prevotella. The ratios with all four species demonstrated associations with viral target cells or immune activation PCA-1 in the rectal mucosa (Fig. 7f). Overall, at the species level, B. intestinalis not only differed in two naive cohorts, but also was associated with viral target cells and mucosal immune activation PCA-1. Future studies should address whether B. intestinalis and its metabolites modulate the viral target cells and/or CCR5 expression on T and myeloid cells.

Fig. 7 The specific species of bacteroides and prevotella that correlated with viral target cells or immune activation PCA-1 were identified. a Gut bacterial composition of prevotella at the species level. b The four species of prevotella that showed difference in the two cohorts. c Gut bacterial composition of bacteroides at the species level. d The two species of bacteroides that showed differences in the two cohorts. e The species of bacteroides that correlated with viral target cells or immune activation PCA-1 were identified using Spearman’s tests. f Spearman’s tests were used to evaluated whether the ratio of specific species of bacteroides/prevotella correlated with the viral target cells or immune activation PCA-1 in the rectal IEL. Mean ± SEM are shown, and the comparisons of bacterial species between the two cohorts were analyzed using Mann-Whitney tests Full size image

Fecal samples from N7 and N11 groups induced different levels of immune activation markers on CD4+T cells of naive PBMCs

The correlations between immune activation and gut microbiome could not prove that the microbiome differences were causing the immunological differences. To investigate the potential impact of gut microbiome on immune activation markers, we treated naive PBMCs with supernatant of heat-killed fecal samples from each animal of the N7 and N11 groups, and compared the expression levels of immune activation markers on T cells, especially the viral target CD4+ T cells (CCR5+Ki67+). After co-culture, the frequencies of Ki67+, CD38+, CD69+, and CCR5+Ki67+ were significantly lower on CD4+ T cells stimulated with fecal samples from N11 group than those from N7 group, while the expression level of CCR5 and HLA-DR alone on CD4+ T cells were similar (Fig. 8). Importantly, the frequency of CD4+ T cells was not differentially affected by the microbiota from the two groups of macaques, indicating that the lower levels of activation by the N11 group fecal samples was not due to gross toxicity to CD4+ T cells (Fig. 8f). Most of the immune activation markers on CD8+ T cells did not significantly differ between N7 and N11 groups after stimulated with fecal samples from each group (Fig. 8, supplementary Figure 7). However, the CD8+ T cell frequency among the CD45+ live cell gating was significantly decreased after co-culture with fecal samples from N11 group, compared to the N7 group. Overall, exposure of naive PBMC CD4+ T cells to fecal bacteria from N7 and N11 cohorts resulted in significantly different expression levels of immune activation markers, consistent with the expression patterns observed in vivo. This result supported the interpretation that the gut bacterial composition might contribute to the immune phenotypes and/or infection outcomes of this study and could explain the correlations observed.