Those who are familiar with my recent condition already understand the situation: over the past several days, I have been almost entirely bedridden. Last night, I attempted to walk as a form of rehabilitation, but I cannot clearly remember whether it lasted an hour or only thirty minutes.
Those who have supported me over the years are aware of the following facts.
My purpose in life has already passed the stage of “health first.” As one proof of having lived, I chose to release my research results openly. While maintaining a level suitable for submission to top journals such as Nature, I intentionally relinquished that right and made the work publicly accessible, following a philosophy similar to the early days of Linux—contributing to the advancement of humanity.
For many years, I worked between 20 and nearly 24 hours a day. I also ended all contractual relationships with companies that had kindly entrusted me with work, expressing my gratitude to each of them.
I no longer have material desires or financial motivations. I live solely for the purpose described above. Those close to me also understand that “Take care of your health” is a forbidden phrase, as they respect my own view that I have already lived enough.
While being bedridden, I attempted to make an appointment with the neurologist I specifically trust—because this is a brain‑related issue, it is not a matter of “any doctor will do.” However, I learned that the schedule is completely full, and securing an appointment is not easy. This situation made me think once again that my intention is not to speak about momentary life‑or‑death matters, but rather to leave meaningful hints for humanity, so that I can say my life had no regrets. With that in mind, I decided to publish the following article on this blog:
“The Modern Da Vinci Code Uncovered by Ken Theory”
Furthermore, I began to consider the genes related to my own brain and requested computational analysis of experimental datasets, including RNA. Although I only provided the theoretical framework and mathematical instructions, it appears that the computer may have failed during the process.
[Part II]
Execution Dynamics in Residual Geometry: Hysteresis, Persistence, and the Stability of Realization
🔵 Data Sources, Processing Pipeline, and Auxiliary Analyses (excluding non‑public experimental data)
The primary dataset used in this study consists of ChIP‑seq peak calls in narrowPeak format obtained from publicly available ENCODE resources. Each entry contains genomic coordinates and peak‑calling signal measures, from which only the start coordinate and the signalValue field were used. These quantities were treated not as explanatory variables but as structural carriers defining nodes in the genomic residual space. Each peak was mapped to a node , with representing the genomic position and the associated signal intensity. A simplified directional parameter was assigned a neutral constant, as the analysis focused on spatial topology rather than directional asymmetry. The resulting dataset was stored as a table containing genomic position, signal intensity, and a placeholder delta_k parameter.
Local structural condensation was evaluated through a threshold‑dependent cluster scan. Nodes were filtered by minimum signal thresholds and grouped into local clusters based on spatial proximity. Observables such as maximum local node count, largest connected local cluster, and total retained nodes were computed. Surrogate datasets generated by randomizing node positions while preserving signal distributions enabled Z‑score estimation. The results showed statistically significant local condensation under specific thresholds, but these local structures alone did not produce global connectivity, indicating that local concentration is necessary but insufficient for higher‑order realization.
To assess non‑local geometric structure, distance‑dependent connectivity graphs were constructed. For each genomic distance , edges were defined between nodes satisfying . Graphs were evaluated using edge counts, largest connected component size, number of connected components, and isolated node ratios, with surrogate comparisons providing Z‑scores. A pronounced peak emerged near a genomic band of approximately 530 kb, where the largest connected component expanded and Z‑scores exceeded random expectations. This behavior indicates a topology ignition condition, marking the emergence of non‑random geometric connectivity at a critical scale.
Auxiliary RNA‑derived signals were incorporated as a negative control. Although RNA data exhibited local fluctuations and occasional apparent clusters, they failed to produce stable distance‑dependent connectivity or reproducible surrogate separation. While locally structured patterns appeared, RNA‑based representations did not generate robust non‑local topological structures under identical analytical conditions.
The RNA analysis served as a structural control by demonstrating that local signal variation alone can create apparent clustering without yielding genuine topology, that density‑driven artifacts cannot account for the observed structures, and that only specific representations of genomic residual space support topology‑bearing configurations. The inability of RNA data to reproduce topology ignition reinforces that the observed connectivity is not a generic property of genomic signals but a geometry‑dependent phenomenon.
Interpretations attributing topology to increased node density, core structures to high‑intensity regions, or connectivity to trivial graph‑theoretic artifacts are excluded. Instead, the results support a multi‑stage structural organization in which statistically significant local condensation emerges, global connectivity remains suppressed at short distances, and a finite genomic band around ~500 kb triggers non‑random connectivity. RNA‑based controls confirm that this structure is not driven by signal density. All analyses are reproducible using narrowPeak inputs, node construction from genomic coordinates and signal values, distance‑based graph construction with fixed tolerance, and surrogate generation via spatial randomization, without normalization or smoothing, ensuring that the observed structures arise directly from the underlying data.
Note: This section is an edited version of Appendix A of Part I prepared for the blog.
If my health allows, I hope we can meet again on this blog.
