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]


MicroRNAs (miRNAs) are derived from endogenous genes that are initially transcribed as longer RNA transcripts (Lee et al. 1993) (Wightman et al. 1993) (Reinhart et al. 2000) (Lagos-Quintana et al. 2001) (Lau et al. 2001) (Lee and Ambros 2001) (Mourelatos et al. 2002) (reviewed in (Nelson et al. 2003), (Bartel 2004)). In mammals, the primary miRNA transcripts (pri-miRNAs) are processed by the nuclease Drosha (Lee et al. 2003) into ˜70 nt precursor miRNAs (pre-miRNAs) that are exported by exportin-5 to the cytoplasm (Yi et al. 2003; Lund et al. 2004) (Bohnsack et al. 2004). The Dicer nuclease excises the mature miRNAs from pre-miRNAs (Hutvagner et al. 2001) (Ketting et al. 2001) (Knight and Bass 2001) (Grishok et at. 2001). miRNAs are bound to proteins that belong to the Argonaute family and, in humans, may also assemble with other proteins, including the Gemin3 and Gemin4 proteins, to form micro-Ribonucleoprotein complexes (miRNPs) (Mourelatos et al. 2002) (Nelson et al. 2004). Dicer also processes another class of ˜22 nt RNAs termed short interfering RNAs (siRNAs) (Elbashir et al. 200)) (Hamilton and Baulcombe 1999) from double stranded RNAs (Bernstein et al. 2001). Analogous to miRNAs, siRNAs are bound to Argonaute proteins (Hammond et al. 2001) (Martinez et al. 2002) and may also assemble with additional proteins to form RNA-induced Silencing Complexes (RISCs) (Hammond et al. 2001). siRNAs and miRNAs (and RISCs and miRNPs) are functionally equivalent and the main difference between the two classes of small RNAs are the fact that miRNAs are derived from endogenous genes (Ambros et al. 2003a).
Many miRNAs and siRNAs function by base pairing with miRNA-recognition elements (MREs) found in their mRNA targets and direct either target RNA endonucleolytic cleavage (Elbashir et al. 2001) (Hutvagner and Zamore 2002) or translational repression (Olsen and Ambros 1999; Seggerson et al. 2002; Zeng et al. 2002; Doench et al. 2003). The manner by which a miRNA or siRNA base pairs with its mRNA target correlates with its function: if the complementarity between a miRNA and its target is extensive, the RNA target is cleaved (Hutvagner and Zamore 2002) (Rhoades et al. 2002) (Llave et al. 2002) (Tang et al. 2003) (Xie et al. 2003); if the complementarity is partial, the stability of the target mRNA in not affected but its translation is repressed (Olsen and Ambros 1999; Seggerson et al. 2002; Zeng et al. 2002; Doench et al. 2003). However, how general this correlation is and the factors and mechanisms that determine the function of any given miRNA are unknown.
In plants, the computational identification of miRNA targets was facilitated by the extensive complementarity between plant miRNAs and their mRNA targets (Llave et al. 2002). (Rhoades et al. 2002). Plant miRNA targets have been verified experimentally (Llave et al. 2002) (Xie et al. 2003) (Kasschau et al. 2003) (Palatnik et al. 2003) (Aukerman and Sakai 2003) (Chen 2004) reviewed in (Bartel and Bartel 2003). Two mouse miRNAs (miR-127 and miR-136) show perfect antisense complementarity with the coding region of a retrotransposon-like gene (Rtl1) (Seitz et al. 2003). However, most animal miRNAs are thought to recognize their mRNA targets via partial antisense complementarity (Lee et al. 1993) (Wightman et al. 1993) (Moss et al. 1997) (Reinhart et al. 2000) (Olsen and Ambros 1999; Zeng et al. 2002; Doench et al. 2003). Because of this partial complementarity, simple homology-based searches have failed to uncover targets for miRNAs in organisms other than plants (Bartel and Bartel 2003) (Ambros et al. 2003b). Animal miRNA targets were initially identified in genetic screens. In particular, genetic dissection of the heterochronic gene pathway in C. elegans identified the lin-14 and lin-28 mRNAs as targets for the lin-4 miRNA (Lee et al. 1993) (Wightman et al. 1993) (Moss et al. 1997), and the lin-41 mRNA as a target for the let-7 miRNA (Reinhart et al. 2000). In Drosophila, the bantam miRNA regulates the pro-apoptotic gene hid (Brennecke et al. 2003). Importantly, these and other studies demonstrated that MRE sequences are necessary and sufficient to confer miRNA-dependent gene expression regulation in MRE-bearing target mRNAs (Moss et al. 1997) (Reinhart et al. 2000) (Zeng et a). 2002) (Doench et al. 2003) (Vella et al. 2004). Putative targets for other miRNAs have been proposed (Lai 2002; Abrahante J E 2003; Lin S Y 2003; Xu et al. 2003), but these are predominantly based on visual inspection of putative mRNA targets for partial complementarity with miRNAs and lack experimental verification of specific miRNA:MRE interactions.
Very recently, carefully designed bioinformatic approaches have been used to predict mRNA targets for Drosophila (Stark et al. 2003) (Enright et al. 2003) and mammalian miRNAs (Lewis et al. 2003). In particular, Bartel, Burge and colleagues have presented a robust bioinformatics strategy that allows prediction of conserved, mammalian miRNA targets along with accurate estimates of false positive rates (at 31% for miRNA targets identified in human mouse and rat and 22% for targets identified in mammals and in pufferfish) and experimental validation of 11 (out of 15 tested) predicted targets (Lewis et al. 2003). Most of the targets identified by Lewis et al. contain multiple MREs for the same miRNA or are regulated by more that one miRNA. The targets reported for Drosophila miRNAs also contain, for the most part, multiple MREs (Stark et al. 2003) (Enright et al. 2003). However, the rules guiding single miRNA:MRE (target mRNA) interactions have not been investigated and as a result predictions of miRNA targets containing single MREs are lacking.
There remains a need for an algorithm providing rules that guide single miRNA:MRE (target mRNA) recognition. There remains a need for methods, systems and computer programs which use the rules to identify MREs.