science olympia

science olympia

anatomy and physiology: 50 minutes. one page note, 8.5x11 inches two sided, 
 Integumentary system; 

 skeleton system: 

 muscular system: 

 

heredity: 50 minutes.  one page note, 8.5x11 inches two sided, 

  Monohybrid cross, dihybrid cross, dominant and recessive alleles, sex-linked traits, genotype vs phenotype; human sex determination; multiple alleles; gene-protein relationship, DNA structure & replication, mutation; mitosis, meiosis, and gamete formation, transcription and translation; human kayotypes analysis for nondisdjunction disorders; co-dominance & incomplete dominance; 

bird: 

SARS-COV-2 entry to cell lines

 Cell Line and Plasmids. HEK293T, HeLa, Calu-3, and MRC-5 cells were obtained

from the American Type Culture Collection and cultured in Dulbecco’s

Cell entry mechanisms of SARS-CoV-2

Jian Shanga,1, Yushun Wana,1, Chuming Luoa,1, Gang Yea, Qibin Genga, Ashley Auerbacha, and Fang Lia,2

aDepartment of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN 55108

Estimating the genome-wide contribution of selection to temporal allele frequency change

 

https://www.pnas.org/content/117/34/20672

Estimating the genome-wide contribution of selection to temporal allele frequency change

Vince Buffalo and Graham Coop

PNAS August 25, 2020 117 (34) 20672-20680; first published August 12, 2020;

https://github.com/vsbuffalo/cvtk

DNA methylation, aging, and tissue and cell types

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017047/
Understanding the Relevance of DNA Methylation Changes in Immune Differentiation and Disease, Carlos de la Calle-Fabregat,† Octavio Morante-Palacios,† and Esteban Ballestar*

Immune cells are one of the most complex and diverse systems in the human organism. Such diversity implies an intricate network of different cell types and interactions that are dependently interconnected. The processes by which different cell types differentiate from progenitors, mature, and finally exert their function requires an orchestrated succession of molecular processes that determine cell phenotype and function. The acquisition of these phenotypes is highly dependent on the establishment of unique epigenetic profiles that confer identity and function on the various types of effector cells. These epigenetic mechanisms integrate microenvironmental cues into the genome to establish specific transcriptional programs. Epigenetic modifications bridge environment and genome regulation and play a role in human diseases by their ability to modulate physiological programs through external stimuli. DNA methylation is one of the most ubiquitous, stable, and widely studied epigenetic modifications. Recent technological advances have facilitated the generation of a vast amount of genome-wide DNA methylation data, providing profound insights into the roles of DNA methylation in health and disease.


What does "stable" methylation means? 
DNA methylation, particularly cytosine methylation, is the best-studied epigenetic modification
It consists of the addition of a methyl group to the carbon 5 (5meC) of cytosine-followed-by-guanine dinucleotides (CG or CpG sites). It is characterized by its stability and heritability, 

CpG are often found in vertebrate promoters.
DNA methylation regulation is essential for differentiation of human stem cells. 
CpG islands is pivotal in long-term gene silencing, x-chromosome inactivation, genomic imprinting and pre-mRNA alternative splicing. 


important reviews:
Goldberg, A.D.; Allis, C.D.; Bernstein, E. Epigenetics: A Landscape Takes Shape. Cell 2007, 128, 635–638.
Jones, P.A. Functions of DNA methylation: Islands, start sites, gene bodies and beyond. Nat. Rev. Genet.
2012, 13, 484–492. 

 

Peer-led Team Learning (PLTL)

 Eric Voss, SIU Edwardsville.  

A Little Help from My Friends: Peer Led Team Learning Before and After COVID-19

Friday, November 13, 2020

3:00 – 4:30 pm:  Active Learning Strategies in STEM Education

Peer-led Team Learning (PLTL) is a model of active learning that introduces peer-led workshops as an integral part of undergraduate STEM courses.  Students who have done well in the course are recruited and trained to become peer-leaders.  The peer-leaders meet with small groups of six to ten students each week for one hour to discuss, debate, and engage in problem solving related to the course material.  PLTL originated in a General Chemistry course at the City College of New York in the 1990s.  Early evidence of improved student attitudes and performance led to further study and development of PLTL by a national team, which resulted in more widespread adoption of PLTL in a variety of science, mathematics, and engineering courses.  Very early on, several SIUE chemistry faculty members attended PLTL training sessions sponsored by the National Science Foundation and subsequently implemented PLTL workshops into the SIUE on-sequence General Chemistry courses.  Since then, implementation has expanded into all first-year chemistry courses and several biology courses.  Due to COVID‑19, PLTL workshops have transitioned from face-to-face to online synchronous sessions, with new challenges and opportunities.  Student performance data, student attitudes, peer-leader training methods, workshop material development, scheduling, space allocation, institutionalization, and sustainability of PLTL will be discussed in this webinar. Questions prior to the webinar? 

college board

 

https://www.collegeboard.org/

ggplot themes cutomerize

 

https://themockup.blog/posts/2020-12-26-creating-and-using-custom-ggplot2-themes/

heritability 201

 

http://www.nealelab.is/blog/2017/9/13/heritability-201-types-of-heritability-and-how-we-estimate-it

aging and COVID19 reading list

List provided by Dr. J Choy. 

papers that look at methylation changes as a function of age - this might be a good place to start to see if any genes related to the SARS infection are methylated in old cells or vice versa?

https://www.nature.com/articles/s41586-020-03065-y

https://www.nature.com/articles/s41467-019-12293-4

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067378

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1824-y

https://link.springer.com/article/10.1186/gb-2013-14-10-r115

https://www.sciencedirect.com/science/article/pii/S0163725818301980#bb0155

genome-wide screen for host factors required for SARS2 replication

https://www.cell.com/cell/fulltext/S0092-8674(20)31394-5

https://www.cell.com/cell/fulltext/S0092-8674(20)31676-7

https://www.cell.com/cell/fulltext/S0092-8674(20)31626-3#supplementaryMaterial

https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(20)30671-5#%20

https://www.cell.com/cell/fulltext/S0092-8674(20)31392-1#supplementaryMaterial

Transcriptome

https://www.cell.com/iscience/fulltext/S2589-0042(20)31144-5

USA 2020 election results by counties

MIT election night 2020

https://github.com/MEDSL/election_night2020

2020 results by counties

https://github.com/tonmcg/US_County_Level_Election_Results_08-20/blob/master/2020_US_County_Level_Presidential_Results.csv

https://github.com/openelections

2004, 2008, 2012 

https://github.com/helloworlddata/us-presidential-election-county-results

covid19-age-stratified-ifr

 

https://github.com/mbevand/covid19-age-stratified-ifr

commuting operator model of entanglement

 

commuting operator model of entanglement

https://www.quantamagazine.org/landmark-computer-science-proof-cascades-through-physics-and-math-20200304/

MIP = RE, computer science and math in 2020

 

https://www.quantamagazine.org/quantas-year-in-math-and-computer-science-2020-20201223/

Laplacian matrix

 

Laplacian mixture modeling for network analysis and unsupervised learning on graphs

Daniel Korenblum 

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204096

Laplacian spectrum in chemistry

liquid wrapping GAN

 

https://graspcoding.com/giveaway/

https://github.com/agermanidis/Liquid-Warping-GAN 

@misc{liu2020liquid,
      title={Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis}, 
      author={Wen Liu and Zhixin Piao, Zhi Tu, Wenhan Luo, Lin Ma and Shenghua Gao},
      year={2020},
      eprint={2011.09055},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@InProceedings{lwb2019,
    title={Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis},
    author={Wen Liu and Zhixin Piao, Min Jie, Wenhan Luo, Lin Ma and Shenghua Gao},
    booktitle={The IEEE International Conference on Computer Vision (ICCV)},
    year={2019}
}

Blokh and Stambler, 2016, information theory for aging

 The application of information theory for the research of aging and aging-related diseases 
David Blokha, Ilia Stamblerb,2016. 

p8, BS16: 

It argues for entropy conservation. It does not appear possible to speak about entropy increase or decrease with aging generally. Hong noticed that this is seems to be discussed in the realm of physiology. 

David Sinclair's information theory of aging

lifespan - Why we age and why don't have to. David Sinclair 

Sinclair claimed that he had the idea of the Information Theory of Aging on October 28, 1996, page 35, with a hand-written title provided therein. 

Sinclair presented the arguments that DNA mutation is not a cause of aging, but the chaos air of epigenome such as DNA methylation is the major cause of aging. 

Sinclair claims that "aging is a loss of information". 

Hong think this is NOT right, at least based on Shannon's information formula,  H = - sum( p* log(p)) , because in chaotic old aging, there would be more states and more randomness, so information entropy would increase not decrease. So, it seems that Shanon's H would increase during aging. 

blogs on Sinclair's lifespan book: 

https://hplus.club/blog/a-summary-of-david-sinclairs-information-theory-of-aging/

https://medium.com/intuitionmachine/deep-learning-and-solving-aging-21eaaa6eec8b#:~:text=David%20Sinclair's%20%E2%80%9Cinformation%20theory%20of,an%20organism%20eventually%20stops%20growing.

A good contradictory riddle in Sinclair's information theory of aging and reprogramming for youth:  

https://hplus.club/blog/david-sinclairs-level-3-reversing-aging-with-the-yamanaka-factors/

freek book, dive into deep learning

 

https://d2l.ai/d2l-en.pdf

cellular aging models, references

 
C. Lopez-Otin, M. Blasco, L. Partridge, M. Serrano, G. Kroemer, The hallmarks of aging. Cell 153, 1194–1217 (2013).
T. Kirkwood, Understanding the odd science of aging. Cell 120, 437–447 (2005).
A. Kowald, T. Kirkwood, A network theory of ageing: The interactions of defective mitochondria, aberrant proteins, free radicals and scavengers in the ageing process. Mutat. Res. 316, 209–236 (1996).
C. Soti, P. Csermely, Aging and molecular chaperones. Exp. Gerontol. 38, 1037–1040 (2003).

6. J. Hoeijmakers, DNA damage, aging, and cancer. N. Engl. J. Med. 361, 1475–1485 (2009).

7. R. Bahar et al., Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 441, 1011–1014 (2006). 

CDC pulse survey, anxiety

 

https://data.cdc.gov/NCHS/Indicators-of-Anxiety-or-Depression-Based-on-Repor/8pt5-q6wp/data

https://data.cdc.gov/NCHS/Indicators-of-Anxiety-or-Depression-Based-on-Repor/8pt5-q6wp/data

jianxin wang, dynamic protein network

 Construction and application of dynamic protein interaction network based on time course gene expression data

Wang, Yi Pan, 2013, Proteomics. 

Euler's formula

 https://en.wikipedia.org/wiki/Euler%27s_formula

This can be proved by Taylor's series. 

Feymanb called this "our jewel" and "the most remarkable formula in mathematics" in his physics lecture notes. 

Laplace transformation to solve a differential equation

 

Laplace transform to solve a differential equation

Laplace transform

 

https://en.wikipedia.org/wiki/Laplace_transform

"In particular, it transforms differential equations into algebraic equations and convolution into multiplication.^[1][2][3] "

https://mathvault.ca/laplace-transform/

In fact, it takes a time-domain function, where $t$ t is the variable, and outputs a frequency-domain function, where s$s$ is the variable

It seems to me that Fourier transformation can be viewed as a special form of Laplace transformation when the alpha is zero. 

Laplace transformation is not Lapalce operator

https://en.wikipedia.org/wiki/Laplace_operator

canonical correlation analysis

 

https://en.wikipedia.org/wiki/Canonical_correlation#Hypothesis_testing

single cell seq, threshold

 basepairs

https://www.basepairtech.com/knowledge-center/determining-filtering-thresholds-for-single-cell-rna-seq-data/

Ayalew SIR model script

 

https://www.glowscript.org/#/user/mayalew/folder/MyPrograms/program/EpiModeling/edit

proteostasis collapase is a driver of cell aging and death

 Santra, Deill, and de Graff, PNAS 2019

use folded and unfoled protein, damage to model aging. 

 

journals for software publication

 

https://www.software.ac.uk/which-journals-should-i-publish-my-software

sino biolgical, List of SARS-CoV-2 Spike Mutants

 

An unprecedented mink cull has been ordered in Denmark amid the outbreak of SARS-CoV-2 in these farmed animals. The plan was announced after scientists discovered a widespread Y453F mutation in the spike protein, that has been passed from animal to humans.

This mutation is of particular concern, because it occurs at a conservative domain of the receptor binding domain (RBD) directly involved in ACE2 binding. Results from some preliminary studies suggest the Y453F mutation affects the ability of the Spike protein to bind with ACE2, while others demonstrate that the mutated spike can escape from detection from a commercial anti-S antibody.

Although there is still no clear evidence indicating this mutation, or any other mutation like the popular D614G, has any clinical significance, the characteristics of the mutations need to be thoroughly investigated in the context of vaccine and antibody therapy.

Sino Biological has launched the recombinant Y453F RBD protein. This product is the newest addition to a large library of recombinant spike variants (full list here). These proteins can be used to evaluate the efficacy of the antibodies and vaccination.

Full List of SARS-CoV-2 Spike Mutants

P337S   F338L   V341I   F342L   A344S   A348S   N354D   A352S   S359N   V367F   N370S   A372T   A372S   F377L   K378N   K378R   P384L   T385A   T393P   V395I   E406Q   R408I   Q409E   Q414R   Q414E   K417N   A435S   W436R   N439K   N440K   K444R   V445F   G446V   G446S   L452R   Y453F   F456L   F456E   K458R   K458Q   E471Q   I472V   G476S   S477R   S477I   S477N   T478I   P479S   N481D   G482S   V483A   V483I   G485S   F486S   F490S   S494P   P499R   V503F   Y505C   Y508H   A520V   A520S   P521S   P521R   A522V   A522S   D614G   D405V, Q414A

biological clocks, Forger,

Biological Clocks, Rhythms, and Oscillations

The Theory of Biological Timekeeping

Daniel B. Forger.

https://www.ncbi.nlm.nih.gov/books/NBK544607/ 

https://www.ncbi.nlm.nih.gov/books/NBK544607/pdf/Bookshelf_NBK544607.pdf

Forger said that many bio clock use the same mathematic equations (models). convergent evolution. Forger said that Hoff bifurcation seems to be the common cause in all biological clocks. 

J Tyson seems to have strong reservation on Forger's argument of evolution of the general math equation. 

General structure of clocks. 

not all bacteria has circadian clocks. 

Dynamo: Mapping Vector Field of Single Cells

 

Dynamo: Mapping Vector Field of Single Cells

Inclusive model of expression dynamics with metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and potential landscape mapping.

https://github.com/aristoteleo/dynamo-release

linear dynamic systems, laplace transform

 

https://see.stanford.edu/Course/EE263 

https://see.stanford.edu/materials/lsoeldsee263/13-lin-sys.pdf

advantages of temporal network, continued

 Li, ..., Barabasi, Science, 2017,

temporal network advantages.

Energy needed from state vector x0 to final state xf
  E(x0, xf) = 1/2 d^T x W^01_eff  x d

where Weff encode the energy structure of the network.

I did not follow S1.1 method. 

After reading Laplace transformation, and watched YouTube movies on solving differential equations with Laplace transformation, it seems the Li17 presented a general solution. 

Li17 cited a yeast dynamic protein network: 

27. J. Wang, X. Peng, M. Li, Y. Pan, Construction and application of dynamic protein interaction

network based on time course gene expression data. Proteomics 13, 301–312 (2013).

doi:10.1002/pmic.201200277 Medline

In Figure S6, a digram is presented to describe the construct of a yeast dynamic PPI. An interaction is consider as active when both protein are active at that time point. based on  X. Tang, J. Wang, B. Liu, M. Li, G. Chen, Y. Pan, A comparison of the functional modules identified from time course and static PPI network data. BMC Bioinformatics 12, 339

(2011). doi:10.1186/1471-2105-12-339 Medline

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-339#Sec17

In Fig 2. Protein networks used contain only 84, 74, and 85 nodes. So, it seems Li17 only used a small subset of PPI. 

Li17 fixed the number of driver nodes to 20% of the nodes. Hong did not find the criteria on the reasons behind this choice. 

draw a graph diagram on latex

 

https://www.baeldung.com/cs/latex-drawing-graphs

PH525x series - Biomedical Data Science

 

http://genomicsclass.github.io/book/

https://github.com/genomicsclass/labs

geometric multiplicity == algebraic multiplicity-for-a-symmetric-matrix

 good post to prove that geometric and algebraic multiplicity are the same for a symmetric matrix.

https://math.stackexchange.com/questions/393149/geometric-multiplicity-algebraic-multiplicity-for-a-symmetric-matrix

yeast double strand breaks, γ-H2AX and γ-H2B

 

Dynamics of yeast histone H2A and H2B phosphorylation in response to a double-strand break

https://www.nature.com/articles/nsmb.2737

In budding yeast, a single double-strand break (DSB) triggers extensive Tel1 (ATM)- and Mec1 (ATR)-dependent phosphorylation of histone H2A around the DSB, to form γ-H2AX

In Saccharomyces cerevisiae, histone H2A comprises the great majority of H2A isoforms and is phosphorylated on S129; we will refer to this modification also as γ-H2AX. Both in yeast and in mammals, γ-H2AX rapidly spreads on large chromatin domain (on more than a megabase in mammals and about 50 kb in yeast)

Both γ-H2AX and γ-H2B are strongly diminished over highly transcribed regions.

coding theory

 

https://en.wikipedia.org/wiki/Coding_theory

this is related to encryption and error detection. 

Is this related to how DNA use quaternary code and basepairing? 

Neuronal Dynamics

neuronal dynamics, from single neurons to networks and models of cognition 

https://neuronaldynamics.epfl.ch/online/index.html

AI notes

 
https://hai.stanford.edu/blog/what-computations-role-neuroscience
AI, and what I call NI — natural intelligence — going to converge at some point and really be use
our brain contains about 100 billion of neurons. 
 
"One individual neuron ­— and our brain contains about 100 billion of them — is incredibly complex: incredibly complex shapes and incredibly complex biophysics, and different types of neurons in our brain have different types of physics. They’re profoundly non-linear, and they are hooked together in these synapses and ways that form circuits, and understanding and mapping those circuits is a big fundamental problem in neuroscience.
But something that should give all of us great pause is that there are these substances that are released locally in the brain called neuromodulator substances, and they actually diffuse to thousands of synapses in the space around them in the brain, and they can completely change that circuitry. This is beautiful, beautiful work by Eve Marder, who spent her career studying this neuromodulation. You take one group of neurons that are hooked up in a particular way, spritz on this neuromodulator, and suddenly they’re a different circuit, literally."

Newsome: And another feature of brain architecture, that you and I have talked about offline together, is that brain architecture is almost universally recurrent. So area A of the brain has a projection to area B. You can kind of imagine that as one layer in the deep convolutional network to another layer. But inevitably, B projects back to A. And you can’t understand the activity of either area without understanding both, and the non-linear actions, the dynamical interactions that occur to produce a state that involves multiple layers simultaneously.

Dynamics are, again, another universal feature of brain operation. They reflect the dynamics in the world around them, and the input but also the dynamics in the output. You’ve got to have dynamical output in order to drive muscles to move arms from one place to the other, right? So the brain is much richer, in terms of dynamics.

latexdiff

 

  latexdiff 

$  latexdiff    first_version.tex  second version .tex  >  marked_up_file.tex 

CpG density and lifespan correlation in vertebrates

Mayne B 2019, a genomic predictor of lifespan in vertebrates, Sci Rep, 9, 17866

McLain and Faulk, 2018. Evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging, 10, 561-572. 

 

online biology RCN

 

https://www.nsf.gov/pubs/2021/nsf21026/nsf21026.jsp?WT.mc_id=USNSF_25&WT.mc_ev=click

relative entropy, cross entropy

 

https://www.iitg.ac.in/cseweb/osint/slides/Anasua_Entropy.pdf

Al-hasmi talk, conformational penalty

Conformational penalties: The other half of molecular recognition

At the most fundamental level, living organisms are the product of biomolecules interacting with one another through a process commonly referred to as molecular recognition.  To understand how biomolecule interact with one another, we need a framework that describes those properties of the biomolecules that determine their binding affinities and specificities.  Our current understanding dates back six decades ago when Linus Pauling proposed that specificity is achieved through the structural complementarity of the binding partners.  This concept has been reinforced over the decades thanks to advances in the determination of high-resolution structures of biomolecules by X-ray crystallography and cryoEM.  Static structures only carry information regarding one half of the molecular recognition equation, which I will refer to as income.  This half describes the favorable contacts formed upon complex formation.  The second half, which has received much less attention, I will refer to as income tax.  It represents the energetic cost associated with changing the structure of a biomolecule from one form to another when binding a partner molecule.  Unlike income, the income tax half of the molecular recognition equation can only be determined experimentally through an ensemble description of biomolecules as a probability distribution of many different conformations.  I will argue that income tax, and mechanisms for tax evasion, are ubiquitous in biology and disease, drawing on DNA replication as a primary example. 

https://www.nature.com/articles/s41586-020-2843-2

https://sites.duke.edu/alhashimilab/research/

free energy tax

distribution of the sub-states really affect taxation of free energy. Mentioned to use MD to run and get distribution of molecules, keep the caveat that it might not reflect experimental results. 

TF factors and motifs:: specificity and affinity are generally correlated, but can be altered. 

free engery and probability landscape is connected by logrithm

Tax impairs DNA replication forks and increases DNA breaks in specific oncogenic genome regions

Hassiba Chaib-Mezrag, Delphine Lemaçon, Hélène Fontaine, Marcia Bellon, Xue Tao Bai, Marjorie Drac, Arnaud Coquelle, and Christophe Nicot. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168069/

"Free energy minimisation is equivalent to maximising the mutual information between sensory states and internal states that parameterise the variational density (for a fixed entropy variational density).^{[11][better source needed]} This relates free energy minimization to the principle of minimum redundancy^[25] and related treatments using information theory to describe optimal behaviour." 

This is related to our cross-entropy work on aging analysis 

Kullback-Leibler divergence, or relative entropy

https://en.wikipedia.org/wiki/Relative_entropy

active inference by Karl Friston

Re Friston: a good intro talk: https://www.santafe.edu/events/me-and-my-markov-blanket

what’s the authors name of the self replicating machine?

von Neumann

 The book that was cited is called "The theory of self-reproducing automata"

Here’s Art Burk’s review of von Neumann: http://walterfontana.zone/wp-content/uploads/2020/12/Burks-1969.pdf

https://www.sciencedirect.com/science/article/pii/S2405471218300577

 This paper does exactly that on the Lac repressor