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
============

Major histocompatibility complex (MHC) II DRB3 gene is known to be extremely polymorphic and functions in the adaptive immune responsiveness and disease resistance by the animal. The high structural polymorphism of class II molecules is accounted for the differences in the immune response among individuals to infectious agents. Polymorphism studies of MHC class II DRB3 show association with resistance to disease such as Bovine leukemia virus infection, Mastitis and with sheep, resistance to parasitic diseases \[[@ref1]\].

In cattle, two alleles of DRB3 of exon 2 were associated with the increased risk with the occurrence of mastitis \[[@ref2]\]. Using sequence-based typing, Yoshida et al. \[[@ref3]\] reported two allelic differences of MHC DRB3.2 that are related to the susceptibility of Japanese Holstein cow with mastitis and three alleles were associated with the resistance to the infection. A similar study by Ibrahim et al. \[[@ref4]\] in Egyptian cows showed three different alleles of the gene. DRB3.2\*11 allele occurring 22% as compared to 47% of DRB3.2\*16 and 31% of DRB3.2\*24 allele. Animals having the DRB3.2\*16 were found to be susceptible to *Staphylococcus aureus* infection while resistant to *Streptococcus agalactiae*infection, whereas DRB3.2\*16 individuals were found to be more unaffected by *S. aureus* infection. The overall finding showed that allele \*11 was significantly correlated with vulnerability to mastitis, while allele\*24 was found to have epidemiological activity in maintaining a source of infection in dairy farm. Dietz et al. \[[@ref5]\] reported that DRB3.2\*16 allele was associated with the acutely elevated somatic cell count (\>500,000 cells) in lactating Holstein cows. Baltian et al. \[[@ref6]\], on the other hand, reported allele \*23 and \*27 as having protective or susceptibility effects, respectively.

A genetic marker is a DNA sequence with a known position on a chromosome and are found to be associated with a particular gene or trait. Most studies on genetic markers are focused on the immunologic gene essential for innate and adaptive immunity. MHC II DRB3 is one of the genes studied in cattle \[[@ref6]--[@ref8]\].

There is inadequate information about the genetic characterization of MHC II DRB3 in dairy buffaloes. Although initial reports have been published in other countries, no specific finding has been mentioned about the Philippine carabao or swamp buffaloes (*Bubalus carabanensis*). It is vital to compare the genetic similarities of the riverine (*Bubalus bubalis*) and swamp buffaloes as most breeding practices promote the upgrading of the Philippine swamp buffaloes. Incidence of subclinical mastitis in riverine buffaloes was reportedly high; hence, there is also a need to identify possible genetic markers that can be associated with this condition which has been vastly studied in dairy cattle.

The objective of this study is to contribute additional information on the genetic characteristics of the MHC II DRB3 gene in riverine and swamp buffaloes. Future identification of genetic polymorphism of these genes can be used to select genetic markers associated with resistance or susceptibility to subclinical mastitis. Establishing the contribution of these genes to the occurrence of resistance or susceptibility in subclinical mastitis can assist farmers in their selection of dairy animals through breeding and selection.

Materials and Methods
=====================

Sample collection
-----------------

For riverine buffalo, RNA samples were extracted from milk of 10 animals on their second month of lactation and above. This prevented collecting high somatic cell count that normally occurs on the first month of lactation \[[@ref9],[@ref10]\]. The sample animals were manually milked to collect 30 ml of milk sample. The milk samples were collected in a 50-ml conical tube and were placed in a cooler. For the swamp buffaloes, RNA was extracted from blood as these 10 animals were non-lactating. Using anticoagulated vials, 5 ml of blood were collected from the jugular vein of the animals. These were processed a day after the collection.

RNA extraction
--------------

RNA from milk and blood samples were extracted using TRIzol^®^ reagent (Invitrogen, Carlsbad, CA) following the manufacturer's protocol. One thousand microliters of TRIzol^®^ was used to dissolve the isolated cells then added with 200 μl of chloroform. The resulting mixture was initially centrifuged at 14,000 rpm for 15 min at 4˚C. The clear part at the top of the mixture was pipetted and then placed to a 1.5 microcentrifuge tube already containing 500-μl isopropanol. It was followed by incubation for 10 min at room temperature and centrifugation at 10,000 rpm for 10 min at 4˚C. The supernatant was then discarded then followed by washing with 1 ml 75% ethanol. Final centrifugation at 8,000 rpm was done for 5 min at 4˚C. Pellets were dried and reconstituted with 30 μl DEPC treated water. Quality and quantity of RNA were checked using spectrophotometer before reverse transcription-polymerase chain reaction (RT-PCR).

RT-PCR of RNA
-------------

Power cDNA Synthesis Kit (iNtRON ^™^, South Korea) was used to synthesize the cDNAs from the total RNA samples extracted. One microliter of dT random primers was mixed with 1 μl dNTP, 5 μl of RNAse free water, and 3 μl of RNA template. The solution was incubated at 65°C for 5 min. The prepared RNA primer mix was then mixed with 4 μl 5× buffer, 1 μl reverse transcriptase, 0.5 μl RNAse inhibitor, and 4.3 μl RNAse free water. It was then subjected to another incubation (segment 1; 10 min for 30°C, segment 2; 45 min for 50°C and segment 3; 5 min at 95°C). The cDNA was then kept in a --20°C freezer before it was subjected to ß-actin amplification (using primers 5'-CGCACCACCGGCATGCTGAT-3' and 5'-TCCAGGGCCACGTAGCAGAG-3') for verification.

Primer design
-------------

MHC II DRB3 gene primers were designed using *Bos taurus* MHC DRB3 coding sequence (NM 001012680) downloaded from the National Center for Biotechnology Information (NCBI). Candidate-specific primers for MHC II DRB3 were analyzed using OligoAnalyzer \[[@ref11]\] for the presence of hairpins and self-annealing. Thereafter, selected primers were checked using Primer-Blast of the NCBI for its specificity.

Polymerase chain reaction (PCR)
-------------------------------

MHC II DRB3 gene having expected amplicon size of 801 bp was amplified using MHC~F~ (5'-CATTGGTGTGCCTGTATTTC-3') and MHC~R~ (5'-CTACACTTCAGCCAACAGGG-3') using the following PCR thermal profile: (stage 1) initial denaturation at 94°C for 5 min; (stage 2) denaturation at 94°C for 30 sec, annealing at 64°C for 30 sec, and extension at 72°C for 45 sec (35 cycles); and (stage 3) final elongation at 72°C for 8 min. All PCR assays were performed in 10 μl reaction volume containing 4.0 μl SdH~2~O, 3.0 μl 10X PCR Buffer, 0.5 μl 25 mM MgCl~2~, 0.5 μl 25 mM dNTPs, 0.2 μl 10 nM of each primers, 0.1 μl Go*Taq* ^™^ polymerase (Promega, Madison, USA), and 1.5 μl cDNA template. PCR assay was performed using a thermocycler (SimpliAmp, Amplified Biosystems). After amplification, 3 μl of the PCR product was electrophoresed in 1.5% agarose gel containing 1× TAE (Trisacetate-EDTA) buffer at 100 V for 30 min and visualized under ultraviolet light using UV transillumination advance imaging system (Flour Chem E by Protein simple). Three microliter 1 kb ladder was placed in the first well of the gel.

Gene sequencing and analysis
----------------------------

Samples for sequencing reactions were sent to first BASE (Malaysia). Sequences assembly and Square matrix based on the number of differences mode were also made using MEGA 7.1 software \[[@ref8]\]. The gene sequences generated in this study were compared with the previously reported MHC II DRB3 sequences from *Bubalus bubalis*, *B. taurus*, *Ovis aries*, *Capra hircus*, and *Sus scrofa* (outgroup) from GenBank ([Table 1](#table1){ref-type="table"}). The estimation of genetic distance and Neighbor-Joining (NJ) analyses from the MHC II DRB3 gene was carried out using the number of differences mode (complete deletion, MEGA 7.1). Nucleotide sequences were submitted at the GenBank under accession numbers LC210724--LC210733. The functional domains of the genes were predicted using the SignalP4.1(<http://www.cbs.dtu.dk>) server and simple modular architecture analysis tool (SMART) (<http://smart.embl-heidelberg.de/>).

3.. Results
===========

Polymerase Chain Reaction
-------------------------

Before using the cDNA template for MHC II DRB3 amplification, samples were subjected to ß-actin gene amplification. [Fig. 1](#figure1){ref-type="fig"} shows the agarose gel images positive with ß-actin gene. After checking the quality of the cDNA, samples were then subjected to MHC II DRB3 gene amplification. All 20 samples were successfully amplified.

Sequence analysis of MHC II DRB3
--------------------------------

Ten sequences from each type of water buffalo were successfully generated. Amplified MHC II DRB3 gene produced 801-bp ([Fig. 2](#figure2){ref-type="fig"}) translating to 201 amino acids. Comparing the aligned sequences of the 20 water buffaloes and four other ruminants' MHC sequences from NCBI show that there was an average of 705 identical pairs, with 22 transitional pairs and 30 transversional pairs with a ratio of 0.7. Comparing riverine from swamp buffalo MHC nucleotide sequences, there was an average of 766 identical pairs, with 17 transitional pairs and 18 transversional pairs with a ratio of 1.0.

###### Nucleotide and amino acid percentage similarity of MHC II DRB3 in ruminant species with reference to swamp and riverine type water buffaloes.

  Species                                      Nucleotide sequences   Amino acid sequence          
  -------------------------------------------- ---------------------- --------------------- ------ -----
  *B. bubalis*(LOC 102408905, XM006047519.2)   99%                    95%                   100%   99%
  B. *Taurus*(AY125893.1, D45357.1)            96%                    94%                   93%    92%
  *C. hircus*(AB008346.1, NM001314217.2)       95%                    94%                   91%    91%
  *O. aries*(KY860768.1, KY80648.1)            95%                    \-                    \-     \-
  *S. scrofa*(EU432075.1, AF464034.1)                                                              

![Agarose gel image of ß-actin gene amplified from the samples.](JAVAR-6-308-g001){#figure1}

![Agarose gel image of MHC II DRB3 gene amplified from the samples.](JAVAR-6-308-g002){#figure2}

The nucleotide BLAST of MHC coding sequences of riverine and swamp ([Table 1](#table1){ref-type="table"}) showed similarity with *B. bubalis* at 99% and 95%, respectively. In general, riverine has higher similarity with other ruminant species than that of the swamp. Furthermore, a lower similarity of nucleotide sequences was seen when riverine samples were compared with *B. taurus*, *O. aries*, and *C. hircus* at 96%, 95%, and 94% similarity, respectively. No data were seen to compare swamp MHC sequence to that of *C. hircus*; however, there was a lower sequence similarity when compared to *B. taurus* (94%) and *O. aries* (94%). Consequently, the lower similarity was seen in the translated amino acid sequence of the water buffaloes as compared to other ruminants' MHC sequences from NCBI. Riverine and swamp buffalo samples had 100% and 99% similarity with *B. bubalis*, respectively. Riverine buffalo MHC sequences were slightly higher in similarity with *B. taurus* at 93%, as compared to the 92% similarity with swamp buffalo samples. Both breeds have 91% similarity of amino acid sequences when compared to *O. aries*. No data were found to compare *C. hircus* with the two water buffaloes.

MHC II DRB3 nucleotide sequences of the samples and other ruminants were subjected to phylogenetic analysis following the NJ algorithm ([Fig. 3](#figure3){ref-type="fig"}). The riverine and swamp nucleotide sequences are clustered with the *B. bubalis* samples from GenBank (LOC 102408905, XM006047519.2) with 95% bootstrap value. In [Fig. 3](#figure3){ref-type="fig"}, the phylogenetic tree yielded four distinct clades and 13 groups with bootstrap values ranging from 20% to 100%. The roots of the tree showed that riverine and swamp buffaloes may have originated with similar descendants that their similarities in their sequences showed that swamp and riverine can be placed in one clade such that of riverine 3 and swamp 6. The high bootstrap value within the clade represents a conserved sequence of the gene in riverine and swamp buffaloes.

Predicted functional domains of MHC II DRB3
-------------------------------------------

The translated amino acid of the MHC II DRB3 nucleotide sequence is shown in [Fig. 4](#figure4){ref-type="fig"}. The signal peptide was found to be at 29--30 location, the transmembrane region started at 7 and ended at 29 positions. MHC II beta started at 42 and ended at 116; IGc1 started at 141 and ended at 212, and another transmembrane region started at 228 and ended at 250 positions.

The schematic representation of the predicted domains of the MHC II DRB3 gene is shown in [Fig. 5](#figure5){ref-type="fig"}.

Discussion
==========

Genetic characterization of MHC II DRB3 gene was conducted to compare this gene in riverine and swamp buffaloes. MHC II DRB3 presents processed exogenous antigens to CD4^+^T-lymphocytes. This gene is important in innate and adaptive immune responses. Characterization of this gene had provided evidence of their possible role in resistance, tolerance, and susceptibility to diseases of water buffaloes specifically to subclinical mastitis.

Sequence analysis of MHC II DRB3
--------------------------------

Several reports about MHC states that this gene is highly polymorphic due to the fact that the protein receptor functions in the detection of various external pathogens \[[@ref12]--[@ref14]\], and it has been imputed for the differences in the resistance to infectious diseases \[[@ref15]\]. The phylogenetic tree clearly depicted complex parsimony in the MHC II DRB3 gene between riverine and swamp but shows a high degree of similarity between buffalo and other ruminants. This means that there are conserved regions in the sequences for all the animal species. Consequently, it appears that MHC II DRB3 gene of buffalo had the highest degree of similarity when compared to *B. taurus* (93% and 92% for riverine and swamp, respectively) which coincides with the earlier claims of Abdel-Rahman and Elsayed \[[@ref16]\] that there was a high nucleotide similarity between cattle and buffalo and was linked to their molecular markers.

Predicted functional domains of MHC II DRB3
-------------------------------------------

This study showed the transmembrane region, MHC_II beta and IGc1 domains were present in all ruminant sequences except for *O. aries* which had an incomplete sequence. This was in consonance with the findings of \[[@ref1]\], who reported the three regions to be conserved among mammalian species. Class II region of the gene was vastly studied as this contained the polymorphic region. Class II genes contained the DR allele. The DR gene is further subdivided into DRA and DRB. DRB encodes the ß-chain molecule. Accordingly, only DRB3 was found functional \[[@ref1],[@ref17]\] and it was found to be highly polymorphic \[[@ref18]\].

![Phylogenetic tree showing the evolutionary relationship of MHC II DRB3 of water buffaloes and other ruminant species.](JAVAR-6-308-g003){#figure3}

MHC II beta domain is an important site for peptide binding while IGc1 is responsible for cell-to-cell recognition and cell-surface receptors \[[@ref19]\]. Both have been found to be variable in sequence in this study which can possibly be attributed to its function in the immune system. Likewise, De et al. \[[@ref15]\] also found high variability in amino acid sequence in Indian cattle and buffalo at the peptide-binding site. He also explained that high heterozyzgosities in DRB alleles in cattle, sheep, and goat accounted for the great genetic variability among these animals. This inferred their better adaptableness in different geographical regions. Similarly, this study showed riverine and swamp buffaloes MHC II DRB3 sequences were clustered in various clades inferring to the highly polymorphic property of the target gene in isolated buffalo samples.

MHC is an essential component of the immune system of all animals. Defining the structures and diversity of the system is important in understanding the immune response in an individual. Characterization of DRB3 locus had been thoroughly studied in cattle and the polymorphism identified in this region had been related to the differences of immune responsiveness of different animals to infectious agents.

![Predicted functional domains in MHC II DRB3 of riverine and swamp buffaloes. Vertical bars represent TM domain, and horizontal dark gray bars represent a simple sequence. MHC_II beta- Class II histocompatibility antigen, beta domain; IGc1- Immunoglobulin C-Type.](JAVAR-6-308-g004){#figure4}

![Predicted functional domains in MHC II DRB3 of riverine and swamp buffaloes. Vertical bars represent TM domains, and horizontal dark gray bars represent a simple sequence. MHC_II beta- Class II histocompatibility antigen, beta domain; IGc1-Immunoglobulin C-type.](JAVAR-6-308-g005){#figure5}

Conclusion
==========

Characterization of riverine and swamp buffalo MHC II DRB3 provided inferences on the variation of the two types of buffaloes as well as in comparison with other ruminants. And this also explained the variability of immune responsiveness between the two types of water buffaloes. These findings have potential application in the possible selection of genetic marker and study the possible association of such in the disease resistance of a particular animal. The polymorphic nature of the genes merits further investigation for other nucleotide loci and their significance to diseases resistance or susceptibility. Expression analysis can also help in the understanding of how the gene progresses in disease protection. Since subclinical mastitis is considered as a multifactorial disease, identification of microbial cause can also be associated with the genotype of the genes.

The authors would like to thank DA-BIOTECH for providing financial assistance to this research project. The authors would also like to thank the Philippine Carabao Center for their support in this study. Moreover, due credits to the Biosafety and Environment Section staff or their technical assistance that we are greatly indebted to.

Conflict of interest
====================

The authors declare that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Author's contribution
=====================

Dr. Noraine P. Medina is the main proponent of the project. Mr. Arren Christian M. de Guia is the research assistant, while Drs. Claro N. Mingala and Virginia M. Venturina were the co-researchers involved in checking the veracity of the protocols.