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]


**(**Please cite as: **Fathi F, <PRESIDIO_ANONYMIZED_PERSON>, Tafazzoli M, Naderi N, Sohrabzedeh K, Fathi S, et al. Metabonomics based NMR in Crohn's disease applying PLS-DA. Gastroenterol Hepatol Bed Bench 2013;6(Suppl.1):S82--S86).**

Introduction {#S0001}
============

Crohn\'s disease (CD) is a type of inflammatory bowel disease (IBD) that is found in the last part of the small intestine and the first part of the large intestine ([@CIT0001]--[@CIT0003]). Both the host genotype and environmental factors play a role in etiology of CD. Also presence of bacteria needs for disease induction.

In order to exact diagnosis of CD, several serological biomarkers have been proposed. But they are used in conjunction and as a supplement to endoscopy. Thus metabonomics as a monitoring tool are needed for early diagnosis ([@CIT0004]). Metabonomics is defined as "the quantitative measurement of the dynamic multi-parametric response of living systems to patho physiological stimuli or genetic modification" ([@CIT0005]). One of the most commonly applied for metabonomics is proton nuclear magnetic resonance (^1^H NMR). This analytical technology has several advantages as follows: It provides quantitative and reproducible information with little sample preparation, and hence it is widely used to build metabolic profiles in diverse metabolic studies. ^1^HNMR spectroscopy of biofluid and tissue samples has been applied to the investigation and diagnosis of many diseases of gastrointestinal ([@CIT0006]--[@CIT0008]).

So far several study has investigated metabolite profiles in serum of patients with CD in the literature ([@CIT0009]). In the study by Schicho et al. metabolites in serum and plasma of IBD subjects were analysed. They obtained regular one-dimensional proton NMR spectra using a standard pulse sequence (Bruker pulse program prnoesy1d) ([@CIT0009]). In other survey Fathi et al. applied classification and regression tree (CART) to explore the metabolic biomarkers causes of CD compare to control group ([@CIT0010]).

In this study, to determine the metabolites, we used ^1^H NMR spectroscopy and performed quantitative analysis of metabolites in the serum of patients with active CD. PLS-DA was employed as a powerful classification method. Our aim was to search for metabolic biomarkers of CD to classify the control and CD groups.

Patients and Methods {#S0002}
====================

Sample collection {#S20003}
-----------------

Twenty-six adult patients with mean age (± standard deviation) of 33.6 ± 11.3 years diagnosed with Crohn\'s disease and twenty-nine healthy subjects with mean age (± standard deviation) 34.7±12.2 years were recruited from Gastroenterology and Liver Disease Research Center, Shahid Beheshti University of Medical Sciences. To avoid the affect of aging and gender influence on metabonomics, the healthy subject were matched with CD subjects ([@CIT0009]). Experienced gastroenterologists made diagnosis of CD on the basis of radiographic, clinical findings and often colonoscopy criteria. Both CD and healthy cohort who entered to study had not significant other past medical history including hypertension, diabetes mellitus or hyperlipidemia. Serum samples were collected in the morning after a 12 hour fast and store at −70°C till measurement. ^1^HNMR spectroscopy and data preprocessing were thoroughly explained in our previous study ([@CIT0011]).

Statistical analysis {#S20004}
--------------------

Partial least squares discriminate analysis (PLS-DA) was employed using PLS_Toolbox method (Version 2, Eigenvector Research Inc., Manson, WA) within MATLAB (version 6.5.1, The Mathworks, Cambridge, U.K.). This method is a regression technique adapted to a supervised classification task. Thus it is a frequently used for classification method. It is on the basis of the partial least squares (PLS) approach ([@CIT0012]). The standard PLS algorithm can be utilized and class labels can be used for the dependent Y vector. Usually in the two-class case, the values of the Y are given 1 for one class and 0 or -1 for the other class. Using this supervised analysis technique; we can identify those metabolites which show a discrepancy between diagnostic groups ([@CIT0013]). In this study, ^1^HNMR data and class labels were used as x matrix and y matrix respectively. The dataset was divided into two parts training set and test set. Training set was used to build a model and identify the most relevant metabolites and in order test predictive ability of the classification model, test set was employed.

Results {#S0005}
=======

In classification part, PLS-DA was used to classify CD and healthy samples. Based on a randomly choice, approximately 30% of samples are left out to form the test set. Consequently, the training set was included of 39 ^1^H NMR spectra and the test set was composed of 16 spectra. By means of this procedure we can reduce the risk of over-fitting and avoid any possibility that the best classification models selected had a chance correlation to peculiarities in the capacity of the test set([@CIT0010]). [Figure 1](#F0001){ref-type="fig"} and [Figure 2](#F0002){ref-type="fig"} present the score plots of the related PLS-DA of the ^1^H NMR spectra of the serum for training and test sets respectively.

![PLS-DA sores plot of serum dataset: training set](GHFBB-6-S082-g001){#F0001}

![PLS-DA sores plot of serum dataset: test set](GHFBB-6-S082-g002){#F0002}

In [Figures 1](#F0001){ref-type="fig"} and [2](#F0002){ref-type="fig"}, the first two latent variables LV1 versus LV2 map the representative points of the serum samples in the space were spanned. Also scores plot was elucidating a logical clustering appearing according to class (CD and healthy subject). To explore exactly which metabolites have caused the separation between CD and healthy subjects, the loadings plot of PLS-DA model is depicted in [Figure 3](#F0003){ref-type="fig"}. [Table 1](#T0001){ref-type="table"} reveals that the metabolite was the most prominent in serum separation with P-value \< 0.00001.

![PLS-DA the corresponding loadings plot: training set](GHFBB-6-S082-g003){#F0003}

###### 

Specifications of the selected PLS-DA descriptor

  Descriptors   Assignment             ^l^H chemical shift (ppm)
  ------------- ---------------------- ---------------------------
  Lipid         CH~3~CH~2~(CH~2~)~n~   1.26

A confusion matrix ([@CIT0017]) includes knowledge about the number of correct and incorrect predictions compared to the real outcomes by a classification model. Performance of such systems is commonly evaluated using the data in the matrix. The [Table 2](#T0002){ref-type="table"} shows the confusion matrix for a two class classifier. As it is clear from [Table 2](#T0002){ref-type="table"}, PLS-DA model has an accuracy of 0.94 in detecting CD patients for external test set.

###### 

Confusion matrix for training and test set

  set        Observation     CD class   Healthy class
  ---------- --------------- ---------- ---------------
  Training   CD class        18         0
             Healthy class   0          21
  Test       CD class        7          1
             Healthy class   0          8

A summary of the classification parameters are shown in [Table 3](#T0003){ref-type="table"}. These results show that PLS-DA classification model has great chance in the diagnosis of CD. Area under ROC curve is often used as a measure of quality of the classification models. A random classifier has an area under the curve of 0.5, while AUC for a perfect classifier is equal to 1. In practice, most of the classification models have an AUC between 0.5 and 1. The obtained values of AUC for training and test set are 1 and 0.94, respectively. The high AUC score of the proposed model for the samples in the external test set is another evidence for capability of PLS-DA model in CD detection.

###### 

The calculated error and non-error rates of the classification index and the classification performances of training and test sets

                 Error rate   Non-error rate   specificity   sensitivity   accuracy
  -------------- ------------ ---------------- ------------- ------------- ----------
  Training set   0            1                1             1             1
  Test set       0.06         0.94             0.89          1             0.94

Discussion {#S0006}
==========

Investigation of the selected variable revealed that the selected chemical shift correspond to the NMR spectrum of lipid ([@CIT0014]--[@CIT0016]). Consequently it can be stated that the discrimination of CD and control samples by the PLS_DA model, based on NMR data, is on the basis of different amounts of lipid in the two groups. This result shows the reduction of lipid level in blood serum of patients compared to healthy individuals. Using PLS_DA method, the most important metabolite lipid was identified. The results of Kuroki and Hrabovsky et al. for lipid level in CD are similar to our study. They found that the serum levels of total lipids and total cholesterol were reduced in patients with CD ([@CIT0018], [@CIT0019]).

Lipids are molecules that include fat-soluble vitamins (such as vitamins A, D, E and K), monoglycerides, diglycerides, phospholipids, and others. They are necessary elements which acting as structural components of cell membranes. Main energy supply in cells and tissues is role of lipids. Also it has been proved that the serum vitamin E levels associates almost with serum lipid levels ([@CIT0020], [@CIT0021]). In the study by Fernandez-Banares et al. and Kuroki et al. ([@CIT0022], [@CIT0023]), serum concentrations of vitamin E have also been found to be lower in CD patient.

Fernandez-Banares et al. stated that the pathophysiological and clinical implications of the suboptimal vitamin status observed in acute CD are unknown. Kuroki and colleagues suggested that there is a variety of vitamin deficiencies in CD prior to treatment that may reflect the severity of the disease ([@CIT0023]).

Vitamin E is as a fat-soluble antioxidant; it can be effective at scavenge free radicals at the cellular level and can be also prevented a great deal of the resultant scarring during CD. The result of inflammation in the gut mucosa and decreased oral intake can be cause risk for vitamin and mineral deficiencies in CD. Probably the reduce of lipid levels may relate to the remission of body level of Vitamin E. Classification model prediction procedure permits demonstrating that the classification obtained by the PLS-DA technique is good enough to execute classification of unknown samples.

In the present work, a ^1^H NMR based metabonomics approach was gave an evidence for the existence of clear metabolic differentiation between two groups (CD and control group).

Since ^1^H NMR based metabonomics is effective to monitor the progression of disease, and helpful to discover biomarkers of CD, we can suggest that, NMR based on metabonomics can provide the possibility for assisting in early of CD. Therefore, further investigations are required to establish its real usefulness in clinical practice.

The authors would like to express their thanks to Gastroenterology and Liver Diseases Research Center and school of paramedical science, Shahid Beheshti University of Medical Sciences, for their valuable collaboration in this study. We gratefully acknowledge financial support from Iran National Science Foundation (INSF), Sharif University of Technology and Shahid Beheshti University of Medical Sciences. This article was part of PhD thesis of Fariba Fathi.