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]


1. Introduction {#sec1-cells-09-00048}
===============

DNA methylation of cytosine residues is a key epigenetic mechanism in mammals \[[@B1-cells-09-00048]\]. Almost all scattered cytosines within CpG dinucleotides are methylated in human cells. In contrast, regions known as CpG islands, which contain relatively dense clusters of CpGs are often protected from DNA methylation. CpG islands are mostly found in promoter regions of many house-keeping and other highly expressed genes. Once methylated, the corresponding genes become inactivated. In addition, a genome-wide survey of DNA methylation of enhancers in healthy and cancerous cell types revealed a strong association between enhancer methylation and suppression of expression of the corresponding gene \[[@B2-cells-09-00048]\]. In addition to its fundamental role in regulating gene expression, DNA methylation also regulates imprinting and the silencing of transposable elements and other repetitive and low-complexity regions of the genome. Aberrant DNA methylation is associated with diseases including many types of cancer. Therefore, DNA methylation contains a crucial layer of information on genome activity, function, and phenotype \[[@B3-cells-09-00048]\].

As the interplay between DNA methylation and transcription regulation may work through modulating the effects of remote elements such as enhancers, in order to link specific enhancers with their target genes, the higher order 3D structure of the genome should be considered \[[@B4-cells-09-00048]\]. The human genome is partitioned into local domains formed by genomic loops, of which around 10,000 have currently been identified. These loops frequently link promoters and enhancers, correlate with gene expression, or can act as insulators for gene regulation \[[@B5-cells-09-00048]\]. Interestingly, such higher-order domains show a very high degree of conservation across cell types and species. Loop anchors typically occur at domain boundaries and can bind the CCCTC-binding factor (CTCF) insulator protein \[[@B6-cells-09-00048],[@B7-cells-09-00048]\].

In an evolutionary context, DNA methylation might help explain some of the molecular basis for phenotypic diversity, as it is widely accepted that many phenotypic differences between closely related species are attributed to changes in gene regulation \[[@B8-cells-09-00048]\]. Nevertheless, the evolution of the human methylome and the processes driving such changes are poorly understood \[[@B9-cells-09-00048]\].

Recently, we developed a novel method to infer genome-wide pre-mortem DNA methylation patterns from archaic genomes, opening the possibility of studying the methylomes of extinct species. Using this method, we identified thousands of differentially methylated regions (DMRs) separating humans, Neanderthals, and Denisovans \[[@B10-cells-09-00048],[@B11-cells-09-00048]\]. Of those, 873 are modern human-derived DMRs (MH-derived DMRs), where the methylation change occurred along the modern human lineage after the split from Neanderthals and Denisovans. Five hundred and eighty-five of these human-derived DMRs are associated with 520 unique MH-derived differentially methylated genes (DMGs), defined as genes whose body or promoter overlap with MH-derived DMRs. Using Gene ORGANizer, a tool that links genes to body parts that they phenotypically affect \[[@B11-cells-09-00048]\], we reported that the list of MH-derived DMGs is significantly enriched with genes affecting vocal and facial anatomy \[[@B10-cells-09-00048]\].

Here, we attempted to link some of the remaining 288 MH-derived DMRs to protein-coding genes \[[@B12-cells-09-00048]\], based on the 3D organization of the human genome. We used 3D genomic information from 4 different cell lines to obtain a consensus higher-order organization. We then analyzed our DMR data in the context of the consensus map to obtain potential long-distance interactions between genes and DMRs. Using this method, we were able to assign additional 70 MH-derived DMRs to genes, reporting 69 new DMGs. We further show that these DMGs not only strengthen the association of methylation changes with the anatomy of the voice and face, but also highlight additional body parts which are significantly enriched with DMGs, including the chin and spinal column.

2. Materials and Methods {#sec2-cells-09-00048}
========================

In order to identify topologically associating domains (TADs) preserved between four different cell types, we downloaded Hi-C data for Human Mammary Epithelial Cells (HMEC), Human Umbilical Vein Endothelial Cells (HUVEC), Human fetal lung cells (IMR90), and Normal Human Epidermal Keratinocytes (NHEK) cell types \[[@B6-cells-09-00048]\]. We chose IMR90 as the basis cell type, having the highest number of identified TADs ([Supplementary Table S1](#app1-cells-09-00048){ref-type="app"}). For each TAD in the IMR90 cell type, we calculated a score between 0 and 1 representing the tendency of this domain to be also present in the other cell types. A score of 1 is obtained when a TAD appears in the exact same coordinates in all cell types, whereas a score of 0 is obtained when a TAD does not appear in any of the other cell types. To compute the score, let us represent the IMR90 cell type by subscript $A$, and the other three cell types by subscripts $B$, $C$, and $D$. Let $T_{X}~$ be the set of all TADs in cell type $X$. Then, for every TAD in IMR90, $T_{i} \in T_{A}~$, the score would be:$$S_{i} = {median}\left. \left( {\max\limits_{T_{j} \in T_{B}}\frac{T_{i} \cap T_{j}}{T_{i} \cup T_{j}},\max\limits_{T_{k} \in T_{C}}\frac{T_{i} \cap T_{k}}{T_{i} \cup T_{k}},\max\limits_{T_{l} \in T_{D}}\frac{T_{i} \cap T_{l}}{T_{i} \cup T_{l}}} \right. \right).$$

Here, a TAD is viewed as an interval and $\cap$ and $\cup$ represent the length of the intersection or union of the intervals of two TADs.

In order to identify TADs that are conserved across all cell types, we performed a permutation test, where we replaced each $T_{i} \in T_{A}$ (with score $S_{i} > 0.5$) by a random interval of the same length. We used these data to recompute scores for all TADs, which yielded a *p*-value per TAD. The corresponding adjusted *q*-values were then computed using FDR.

3. Results {#sec3-cells-09-00048}
==========

3.1. Identifying Conserved Topologically Associating Domains (TADs) {#sec3dot1-cells-09-00048}
-------------------------------------------------------------------

In order to study methylation changes in the human skeletal system in the context of 3D genomic architecture, we needed data on topologically associating domains (TADs) in modern human bones. As TAD maps in human bone are not available, we used data from four diverse non-bone cell types, and generated consensus TAD maps (see Materials and Methods), assuming that TADs that are conserved across these cell types are likely to appear in bone as well. TADs are highly conserved both between different cell types as well as between different species: of the 3331 domains annotated in mouse CH12-LX cells (murine lymphoma), 1649 (50%) are orthologous to domains in the human lymphoblastoid cells GM12878 \[[@B6-cells-09-00048]\]. In addition, the four cell lines we used in our analysis include epithelial cells (HMEC), endothelial cells (HUVEC), fetal lung fibroblasts (IMR90), and epidermal keratinocytes (NHEK), representing all three embryonic germ layers. Therefore, a consensus TAD map of these cell lines would, in all probability, represent TADs that are very likely to be found in bone as well. The data were produced by the same laboratory, minimizing experimental variations \[[@B6-cells-09-00048]\]. As the location of the TAD boundaries (called anchors) in these experiments cannot be determined precisely, conserved TADs might display different boundaries in different cell types. We have therefore defined the boundaries of conserved TADs in two ways: (1) the TAD interval is taken as the section that is shared across all cell types (hereinafter denoted intersection-TADs), and (2) the TAD interval is taken as the section that is the union of all the intervals in the different cell types (hereinafter denoted union-TADs) ([Figure 1](#cells-09-00048-f001){ref-type="fig"}A). We therefore defined highly conserved TADs as those TADs that meet the following three criteria: i) A minimum TAD length of 100 kb, ii) Their conservation score $S_{i}$ (see Materials and Methods) satisfies $S_{i} > 0.5,$ and iii) Adjusted *q*-value in permutation test \< 0.05 (see Materials and Methods).

Overall, we identified 2902 conserved TADs, which are 37.8% of the total number of TADs detected in IMR90. The intersection-TADs and union-TADs span 28% and 32% of the modern human genome, respectively.

3.2. MH-Derived DMRs Tend to Reside Inside TADs {#sec3dot2-cells-09-00048}
-----------------------------------------------

In order to study the spatial organization of the DMRs with respect to the consensus TADs, we first tested whether MH-derived DMRs tend to reside, at least partially, within conserved TADs. To this end, we used a Monte Carlo simulation, randomly allocating DMRs while keeping their length, GC content, and CpG density similar. Using 200 simulations, we found that for both intersection-TADs and union-TADs the fraction of DMRs that reside inside TADs is much higher than expected by chance (*p* \< 0.005, [Table S2](#app1-cells-09-00048){ref-type="app"}). From the simulations we have also computed the expected fraction of DMRs inside TADs, which we compared to the observed number using a χ^2^-test (*p* \< 10^−12^, [Table S2](#app1-cells-09-00048){ref-type="app"}). Together, these analyses show that DMRs tend to reside inside conserved TADs much more than expected by chance.

3.3. MH-Derived DMRs Inside TADs Tend to Reside Closer to the Conserved TAD Ends {#sec3dot3-cells-09-00048}
--------------------------------------------------------------------------------

Loop domains are thought to affect gene regulation by bringing remote regulatory elements to the proximity of their targets if both reside inside the TAD and near the loop anchor. Therefore, we wanted to test if MH-derived DMRs inside a TAD are closer to the TAD boundary than expected by chance. For this, we defined a DMR residing up to 50 kb from a TAD boundary as proximal to the boundary, as enhancers are typically closer than 50 kb to their target \[[@B11-cells-09-00048]\]. Using a Monte Carlo simulation with 10,000 repetitions, we found that MH-derived DMRs are indeed much closer to TAD boundaries than expected by chance (*p* \< 10^−4^, [Table S3](#app1-cells-09-00048){ref-type="app"}). A similar conclusion was obtained using a χ^2^-test on the expected value of the simulations (*p* = 1.2 × 10^−54^ and *p* = 1.4 × 10^−58^ for intersection- and union-TADs, respectively, [Table S3](#app1-cells-09-00048){ref-type="app"}). Together, these results suggest that MH-derived DMRs reside within 50 kb of a TAD boundary much more frequently than expected by chance ([Figure 1](#cells-09-00048-f001){ref-type="fig"}B).

3.4. Mapping DMRs to Genes {#sec3dot4-cells-09-00048}
--------------------------

The 288 MH-derived DMRs that do not reside in a promoter or a gene body, and hence could not be linked to genes based on their genomic location, are hereinafter called non-genic DMRs (ngDMRs). To test if 3D genomic organization data may help in linking ngDMRs to genes, we redefined distances between a DMR and a gene in a way that accounts for topological information. For each pair of MH-derived ngDMR and a gene, we calculated two distances: a linear distance, measured directly along the DNA strand, but computed only if the ngDMR and the gene reside in the same TAD; and a spatial distance that is computed when the DMR and the gene are in different ends of the same TAD ([Figure 1](#cells-09-00048-f001){ref-type="fig"}C). We then associated ngDMRs with the closest gene if the distance between them (either linear or spatial) was smaller than 50 kb.

We performed this procedure for both intersection-TADs and union-TADs. For intersection-TADs we found 70 ngDMRs that could be linked to 69 DMGs. Of them, 61 are new DMGs, and 8 already appeared in our previous list, as they also overlap DMRs in their body or promoter. Using union-TADs, we could link 69 ngDMRs with 69 DMGs, of which 61 are new.

In order to identify body parts that are expected to be affected by changes in expression level of these genes, we used Gene ORGANizer \[[@B11-cells-09-00048]\] on the combined list of new and previously identified DMGs. We found that the previously reported enriched body parts (mostly related to the face and larynx) become even more significant, and that new body parts are now significantly enriched with the DMGs, including the chin, hair, scalp, and spinal-column ([Figure 2](#cells-09-00048-f002){ref-type="fig"}, [Table 1](#cells-09-00048-t001){ref-type="table"}, [Supplementary Figure S1](#app1-cells-09-00048){ref-type="app"}).

4. Discussion {#sec4-cells-09-00048}
=============

In this study, we used 3D genomic organization data in order to seek possible impacts of non-genic DMRs on gene expression in modern humans. We first identified TADs that are likely to be present in bone tissue, and showed that MH-derived DMRs tend to reside in TADs, and to be closer to their anchor points. We then redefined the definition of a distance between a DMR and a gene, and were able to link \~70 of the non-genic DMRs to genes. In a previous work we showed that genic-DMRs are linked to genes that tend to affect vocal and facial anatomy, and suggested that these genes might have played a role in forming the phenotypic changes in these organs throughout human evolution \[[@B13-cells-09-00048],[@B14-cells-09-00048]\]. Our current results reinforce these previous results, and identify additional body parts (the chin, spinal column, hair, and scalp) that are associated with differential methylation between modern and archaic humans. This work sets a proof of concept that using 3D genomic information may enhance our ability to identify regulatory changes.

Our redefinition of a distance between a DMR and a gene uses information on TADs in two ways. First, we use a relatively large linear distance (50 kb) between a DMR and a gene in order to offer a link between them, as we do it only if we know that the two reside within the same TAD. Second, we define a spatial distance connecting a DMR and a gene at two ends of a TAD. Seven and four of the ngDMRs in intersection TAD and union TAD analyses, respectively, were found to be close to a gene only based on spatial distance. For example, TBX3, a gene responsible for the ulnar mammary syndrome \[[@B15-cells-09-00048]\], lies directly close to its associated ngDMR ([Figure 3](#cells-09-00048-f003){ref-type="fig"}).

A well-characterized difference between the modern human and the Neanderthal is the chin, which is present only in modern humans, likely as a result of face flattening \[[@B16-cells-09-00048]\]. In fact, the presence of a chin has long been recognized as unique to *Homo sapiens* among mammals \[[@B17-cells-09-00048]\]. Our results show a significant enrichment of DMGs that are known to phenotypically affect the chin, including FIG4, AXIN2, FZD2, SMARCA2, and TBX3. These genes affect the entire skeletal system, and likely affect the chin by controlling the level of facial protrusion \[[@B18-cells-09-00048],[@B19-cells-09-00048],[@B20-cells-09-00048],[@B21-cells-09-00048]\].

Another known phenotypic difference between modern humans and Neanderthals is in the spinal column. It was suggested that Neanderthals had a natural lumbar kyphosis (i.e., their back was bent), suffered from less degenerative changes in their spinal column, and presumably did not suffer from lower back pain \[[@B22-cells-09-00048]\].

As ancient methylation is reconstructed in archaic bones, our DMGs are expected to be enriched within the skeletal system. It is therefore of note that we have identified scalp and hair as tissues that might be affected by differential methylation. As these tissues are not preserved in the fossil record, these predictions are difficult to test.

One important weakness of our analyses is the absence of TAD maps in human bone. Although highly conserved, the consensus TAD maps we obtained using four different cell lines represent only a fraction of the actual bone TADs. Our analysis therefore represents an underestimate of the potential 3D contacts between DMRs and genes. Once Hi-C data will become available in human bone, a more complete picture can be obtained, and we will likely be able to assign an even larger number of DMRs to function.

The following are available online at <https://www.mdpi.com/2073-4409/9/1/48/s1>, Figure S1: A heat map representing the level of enrichment of each anatomical part within the list of old and new DMGs based on union-TADs, Table S1: Number and median length of identified TADs in four different cell types, Table S2: MH-derived DMRs tend to reside inside TADs, Table S3: MH-derived DMRs tend to reside close to TAD boundaries.

###### 

Click here for additional data file.

Conceptualization, L.C. and E.M.; methodology, D.B., E.L., L.C. and E.M.; software and code, D.B. and E.L.; formal analysis, D.B. and E.L.; investigation, D.B., E.L., L.C. and E.M.; data curation, D.B. and E.L.; writing---original draft preparation, E.L., L.C. and E.M.; writing---review and editing, D.B., L.C. and E.M.; supervision, L.C. and E.M.; funding acquisition, L.C. and E.M. All authors have read and agreed to the published version of the manuscript.

This work was supported by the Israel Science Foundation \[1140/17 to E.M. and 1009/17 to L.C.\]. E.M. is the Arthur Gutterman Chair for Stem Cell Research.

The authors declare no conflict of interest.

![(**A**) Definition of a conserved Topologically Associating Domain (TAD) boundaries, as either the intersection of all intervals, or as their union. (**B**) Histograms of the distances between Differentially Methylated Regions (DMRs) inside TADs and their closest TAD boundary in intersection-TADs and union-TAD, respectively. Red vertical line represents the 50 kb cut-off value. (**C**) Definitions of spatial and linear distances between a DMR and a gene. The spatial distance of the DMR from gene 1 is $d_{1} + d_{2}$. The linear distance of the DMR from gene 2 is $d_{3}$.](cells-09-00048-g001){#cells-09-00048-f001}

![A heat map representing the level of enrichment of each anatomical part within the list of old and new DMGs based on intersection-TADs. Only body parts that are significantly enriched (FDR \< 0.05) are colored. Notice the enrichment of genes connected to the chin, hair, scalp, and spinal-column.](cells-09-00048-g002){#cells-09-00048-f002}

![An illustration of TBX3 and its spatially associated DMR. The black arrows represent the distance between the DMR and a ChIA-PET validated CTCF binding site and TBX3 and a ChIA-PET validated CTCF binding site \[[@B23-cells-09-00048]\]. The linear distance between TBX3 and the DMR is 1.2 Mb.](cells-09-00048-g003){#cells-09-00048-f003}

cells-09-00048-t001_Table 1

###### 

Anatomical parts significantly enriched in any of the analyses. Previous results that did not use topological information are compared to the results obtained here. FDR values in the current analyses decreased, and new anatomical parts became significant: spinal column, chin, hair, and scalp.

  Organ Name      Results Reported in Gokhman, et al., (2017) \[[@B11-cells-09-00048]\]   Intersection-TAD   Union-TAD                                                                                       
  --------------- ----------------------------------------------------------------------- ------------------ ----------- ------- ------- ------- ---- -------- ------- ------- ------- ---- -------- ------- -------
  face            1.291                                                                   80                 61.975      0       0.02    1.315   89   67.656   0       0.006   1.315   89   67.656   0       0.006
  forehead        1.568                                                                   48                 30.61       0       0.017   1.586   53   33.416   0       0.006   1.586   53   33.416   0       0.006
  vocal cords     2.109                                                                   25                 11.852      0       0.017   2.087   27   12.939   0       0.006   2.009   26   12.939   0       0.011
  lips            1.465                                                                   46                 31.4        0.002   0.046   1.517   52   34.278   0       0.012   1.546   53   34.278   0       0.008
  eyelid          1.386                                                                   52                 37.515      0.003   0.046   1.416   58   40.954   0.001   0.021   1.416   58   40.954   0.001   0.023
  teeth           1.528                                                                   40                 26.178      0.002   0.046   1.54    44   28.578   0.001   0.021   1.505   43   28.578   0.002   0.033
  hair            1.316                                                                   49                 37.24       0.012   0.1     1.377   56   40.654   0.002   0.029   1.353   55   40.654   0.004   0.04
  maxilla         1.301                                                                   65                 49.951      0.003   0.046   1.302   71   54.53    0.002   0.029   1.284   70   54.53    0.003   0.04
  jaws            1.291                                                                   65                 50.329      0.004   0.046   1.292   71   54.943   0.002   0.029   1.274   70   54.943   0.004   0.04
  nails           1.602                                                                   30                 18.723      0.004   0.048   1.615   33   20.439   0.002   0.029   1.566   32   20.439   0.005   0.04
  larynx          1.682                                                                   29                 17.246      0.002   0.046   1.647   31   18.827   0.003   0.029   1.593   30   18.827   0.005   0.04
  pelvis          1.335                                                                   58                 43.458      0.003   0.046   1.328   63   47.442   0.003   0.029   1.349   64   47.442   0.002   0.033
  spinal column   1.296                                                                   57                 43.974      0.008   0.069   1.312   63   48.005   0.004   0.032   1.333   64   48.005   0.002   0.033
  mandible        1.276                                                                   64                 50.157      0.006   0.055   1.278   70   54.755   0.004   0.032   1.26    69   54.755   0.006   0.049
  nose            1.325                                                                   55                 41.5        0.006   0.055   1.324   60   45.304   0.004   0.032   1.346   61   45.304   0.002   0.033
  scalp           1.811                                                                   14                 7.73        0.02    0.134   2.015   17   8.438    0.004   0.032   2.015   17   8.438    0.004   0.04
  urethra         1.365                                                                   22                 16.112      0.067   0.22    1.592   28   17.589   0.007   0.049   1.535   27   17.589   0.012   0.08

[^1]: These authors contributed equally to this paper.