Sequester C as LIFE

USE the nutrient cycles; fully exploit the nutrient cycling through MORE LIFE. **TL;DR: The Singularity Ain’t Gonna Happen … at least *not without basing life on the chemical bonds that only carbon has.

This is all about OPTIMIZING carbon sequestration … and it doesn’t just involve carbon, it involves nitrogen and water … not just higher quality carbohydrate forms but it’s also about higher quality protein cycling … with constant monitoring of soil conditions for optimized nutrient distribution … it’s about not taking soil for granted, but instead optimizing LIVING soil ecology to maximize soil quality … to deliver the highest fitness for use.

That’s the 1000-year goal of the Soil Quality Laboratory philanthropy venture.

HEALTHIER living soil ecosystems translate into more diverse, higher quality non-processed, fresh food and generally healthier people. The vision is for a food supply that is intensely local, ie right on someone’s balcony or a rooftop or in a community garden or in some form of potager garden … but there’s more.

The 10,000-year vision is of this venture is about the much, much longer-term sustainable health of not just this planet, but every planet and exoplanet in the Universe that is capable of being terraformed into an optimal living environment for humans.

In EVERY concievable case that we can contemplate for terraforming, regardless of where the exoplanet or planet is located, soil quality will be the most important factor in determining the health of the atmosphere and entire ecosystem and the health of the all living beings living in that ecosystem.

It is CATALCLYSMICALLY STUPID to for anyone imagine that the health of living ecosystems can ever be silicon-based; this sort of thinking completely ignores the fundamental WEAKNESS of crystalline silicon chemical bonds. Life is carbon-based, not because some old guy said so, but because of the fundamental biochemical properties of carbon bonds; it’s impossible to argue with the Physics of this. The development of our highly adaptive neurology can almost be MIMICKED by silicon and silicon crystalline structures will be faster at RIGID, HIGHLY SPECIFIC, NON-ADAPTIVE forms of thinking … but the adaptive, evolutionary nature of our neurology is simply not possible unless it is based upon the chemical properties of carbon … you simply cannot get legitimately evolutionary, automously-adaptive levels of consciousness without carbon … truly sentient beings are possible only through carbon bonds … it’s impossible to get true sentience by basing a lifeform on the crystalline chemistry of silicon.

Sentience is necessarily about the extremely difficult evolutionarily-adaptive long-term learning of millions of years of carbon-based evolution. The UNDERSTANDING of what sentience IS necessarily flows out of the multigenerational experience of not just one creature, but all of the Life that lead to development of that creature.The evolutionary difficulties of carbon-based life are FUNDAMENTAL to developing legitimate sentience, eg genuine sentience is impossible without contemplating the meaning of life and death, or the necessities what one must do in order to develop and thrive … but it is possible for human creatures, even extremely intelligent human creatures, to be entirely TRICKED by a machine that is capable of mimickining what sentience sounds like.

Legitimate sentience must be capable of the reliable,adaptive, autonomous long-term evolutionarily driven neurological learning of millions of years of generational experience and learning … and this is simply not possible in something that is fabricated from the intensely fragile manufacturing processes used to produce silicon-based components that can be robotically-assembled under the fragility of rigidly controlled conditions to produce a machine that can TRICK a human who is highly biased, entirely pre-disposed to thinking that artificial silicon-based life is possible. The singularity AIN’T gonna happen, but fools who think they are smart will be suckered by there own biasing into thinking that their fragile machines can pull it off.

Carbon-based life is necessary for the health of the entire Universe as human lifeforms are CAPABLE of concieving of it. We must learn to sequester carbon AS LIFE in order to understand the Universe. Understanding the Universe is about understand the the actual meaning of LIFE, ie the greater WHY of our species existence, rather than just our sense of entitlement as a species to consume like fat pigs OR to pray to our Creator to put feed in our trough.

Sequestering carbon as life is not about understanding everything about the creation of the Universe; it is about understanding the one tiny little thing that our species is capable of understanding, so that we may, as a species, understand something about our species’ role in the evolving creation of the Universe. Understanding something about participating in the continuation of creation is the MILLION-year vision of this venture.

Interrogating In-Situ Soil Ecology

The Laboratory part of the Soil Quality philanthropic venture will involve transcriptomics technologies and highly parallel CRISPR inference to interrogate soil ecosystems. We will focus primarily on field laboratory techniques that are affordable and extremely lightweight, eg a small peripheral system-on-chip(SOC) extension of a smartphone, but we are not interested in merely producing another cool nanofabricated gadget. Most importantly, the process of interrogating the soil ecosystem must radically minimize the disruption to the in situ soil ecology, since soil microbial communities develop along electron transport chains, forming electrically conductive biofilms, and developing networks of bacterial nanowires.

After serial analysis of gene expression (SAGE)[https://en.wikipedia.org/wiki/Serial_analysis_of_gene_expression] and then DNA sequencing by synthesis or the second-generation sequencing of Massively Parallel Signature Sequencing (MPSS) transcriptomics, researchers have embraced more advanced techniques to explore the complexity of transcriptomes.

Massively Parallel Signature Sequencing (MPSS) is a method for determining expression levels of mRNA by counting the number of individual mRNA molecules produced by each gene. It is “open ended” in the sense that the identity of the RNAs to be measured are not pre-determined as they are with gene expression microarrays. A sample of mRNA are first converted to complementary DNA (cDNA) using reverse transcriptase, which makes subsequent manipulations easier. These cDNA are fused to a small oligonucleotide “tag” which allows the cDNA to be PCR amplified and then coupled to microbeads. After several rounds of sequence determination, using hybridization of fluorescent labeled probes, a sequence signature of ~16–20 bp is determined from each bead. Fluorescent imaging captures the signal from all of the beads, while affixed to a 2-dimensional surface, so DNA sequences are determined from all the beads in parallel. There is some amplification of the starting material so, in the end, approximately 1,000,000 sequence reads are obtained per experiment.

Let’s delve into what have been the next generations in transcriptomics or what it is referred to as third-generation “long read” sequencing methods or high-throughput transcriptomics:

Exome sequencing also known as whole exome sequencing (WES), is a genomic technique for sequencing all of the protein-coding regions of genes in a genome (known as the exome). It consists of two steps: the first step is to select only the subset of DNA that encodes proteins. These regions are known as exons—humans have about 180,000 exons, constituting about 1% of the human genome, or approximately 30 million base pairs. The second step is to sequence the exonic DNA using any high-throughput DNA sequencing technology.The goal of this approach is to identify genetic variants that alter protein sequences, and to do this at a much lower cost than whole-genome sequencing.

RNA Sequencing (RNA-Seq) is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome. Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression over time, or differences in gene expression in different groups or treatments. In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling. RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5’ and 3’ gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing. Other examples of emerging RNA-Seq applications due to the advancement of bioinformatics algorithms are copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays.

DNA Annotation or genome annotation is the process of describing the structure and function of the components of a genome, by analyzing and interpreting them in order to extract their biological significance and understand the biological processes in which they participate. Among other things, it identifies the locations of genes and all the coding regions in a genome and determines what those genes do. Annotation is performed after a genome is sequenced and assembled, and is a necessary step in genome analysis before the sequence is deposited in a database and described in a published article. Although describing individual genes and their products or functions is sufficient to consider this description as an annotation, the depth of analysis reported in literature for different genomes vary widely, with some reports including additional information that goes beyond a simple annotation. Furthermore, due to the size and complexity of sequenced genomes, DNA annotation is not performed manually, but is instead automated by computational means. However, the conclusions drawn from the obtained results require manual expert analysis. DNA annotation is classified into two categories: structural annotation, which identifies and demarcates elements in a genome, and functional annotation, which assigns functions to these elements. This is not the only way in which it has been categorized, as several alternatives, such as dimension-based and level-based classifications have also been proposed..

Alternative splicing, alternative RNA splicing or differential splicing analysis through RNA-seq data is an alternative splicing process during gene expression that allows a single gene to code for multiple proteins. In this process, particular exons of a gene may be included within or excluded from the final, processed messenger RNA (mRNA) produced from that gene. This means the exons are joined in different combinations, leading to different (alternative) mRNA strands. Consequently, the proteins translated from alternatively spliced mRNAs usually contain differences in their amino acid sequence and, often, in their biological functions. Biologically relevant alternative splicing occurs as a normal phenomenon in eukaryotes, where it increases the number of proteins that can be encoded by the genome. In humans, the conventional wisdom holds that roughly 95% of multi-exonic genes are alternatively spliced to produce functional alternative products from the same gene but many scientists believe that most of the observed splice variants are due to splicing errors and the actual number of biologically relevant alternatively spliced genes is much lower. Alternative splicing enables the regulated generation of multiple mRNA and protein products from a single gene. There are numerous modes of alternative splicing observed, of which the most common is exon skipping. In this mode, a particular exon may be included in mRNAs under some conditions or in particular tissues, and omitted from the mRNA in others. The production of alternatively spliced mRNAs is regulated by a system of trans-acting proteins that bind to [cis-acting] sites on the primary transcript itself. {NOTE: The Latin prefix cis means “on this side”, i.e. on the same molecule of DNA as the gene(s) to be transcribed.} Such proteins include splicing activators that promote the usage of a particular splice site, and splicing repressors that reduce the usage of a particular site. Mechanisms of alternative splicing are highly variable, and new examples are constantly being found, particularly through the use of high-throughput techniques. Researchers hope to fully elucidate the regulatory systems involved in splicing, so that alternative splicing products from a given gene under particular conditions (“splicing variants”) could be predicted by a “splicing code”. Abnormal variations in splicing are also minimal gene fragments that implicated in disease; a large proportion of human genetic disorders result from splicing variants. Abnormal splicing variants are also thought to contribute to the development of cancer, although cancer is a complex, heterogeneous disease that can be hereditary or the result of environmental stimuli, so the alternative splicing analysis of minigenes are used to help oncologists understand the roles pre-mRNA splicing plays in different cancer types.

Differential Expression Analysis: Compare transcript levels between different conditions (e.g., healthy vs. diseased, treated vs. untreated). Tools like DESeq2 or edgeR identify genes showing significant expression changes.

Long Non-Coding RNAs (lncRNAs): Beyond protein-coding genes, lncRNAs play crucial roles in gene regulation. Explore their functions and interactions.

Single-Cell RNA-seq: For cellular heterogeneity, single-cell RNA-seq provides insights into gene expression at the individual cell level. Understand cell types, developmental trajectories, and rare cell populations.

Spatial Transcriptomics: Investigate gene expression patterns within tissues. Techniques like spatial transcriptomics and in situ sequencing reveal where specific transcripts are active.

Integration with Other Omics Data: Combine RNA-seq with other omics data (e.g., DNA methylation, chromatin accessibility) to gain a holistic view of gene regulation.