This is a colloquium that I presented on 4/22/21: Stockholm University, Nordic Institute for Theoretical Physics (NORDITA), WINQ–AlbaNova Colloquium
Here is a video of my talk: http://video.albanova.se/ALBANOVA20210422/video.mp4
Hypothesis -Concept Sources Types
Hypothesis
It is a tentative prediction about the nature of the relationship between two or more variables.
It is a tentative explanation of the research problem
Hypotheses are always in declarative sentence form
An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved
Sources of hypothesis
Experience of researcher
Review of literature
Findings of the pilot study
Interaction with knowledgeable persons of the concerned field
Knowledge of culture and society
Creative thinking and imagination of researcher
Types of Hypotheses
Directional Hypotheses / One tailed Hypothesis
Non-Directional Hypotheses / Two tailed Hypothesis
Null Hypotheses
Directional Hypotheses / One Tailed Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables /two groups or conditions
directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.
It is often symbolized as H1
Non-Directional Hypotheses / Two Tailed Hypothesis
A non-directional simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc.
non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.
Null Hypotheses
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables.
null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).
It is the hypothesis that the researcher is trying to disprove.
the null hypothesis is a statement of
-‘no effect’ or ‘no difference’
It is often symbolized as H0.
Examples
“ In a clinical trial of a new drug with the current drug ”
We would write Null Hypotheses (H0):
H0 : there is no difference between the two drugs.
We would write Directional Hypotheses (H1):
H1 : the new drug is better than the current drug.
We would write Non-Directional Hypothesis:
the two drugs have different effects, on average.
Theory building, What Is a Theory? , What Are the Goals of Theory?, Research Concepts, Constructs, Propositions, Variables, and Hypotheses, Research Concepts and Constructs, Research Propositions and Hypotheses, Understanding Theory, Verifying Theory, Theory Building, The Scientific Method
Opinion Dynamics on Generalized NetworksMason Porter
This is a talk on opinion dynamics (especially bounded-confidence models) on generalized networks.
It is part of the MIX-NEXT III (Multiscale & Integrative compleX Networks: EXperiments & Theories) satellite at NetSci 2022.
(Thursday 14 July 2022)
This is a colloquium that I presented on 4/22/21: Stockholm University, Nordic Institute for Theoretical Physics (NORDITA), WINQ–AlbaNova Colloquium
Here is a video of my talk: http://video.albanova.se/ALBANOVA20210422/video.mp4
Hypothesis -Concept Sources Types
Hypothesis
It is a tentative prediction about the nature of the relationship between two or more variables.
It is a tentative explanation of the research problem
Hypotheses are always in declarative sentence form
An hypothesis is a statement or explanation that is suggested by knowledge or observation but has not, yet, been proved or disproved
Sources of hypothesis
Experience of researcher
Review of literature
Findings of the pilot study
Interaction with knowledgeable persons of the concerned field
Knowledge of culture and society
Creative thinking and imagination of researcher
Types of Hypotheses
Directional Hypotheses / One tailed Hypothesis
Non-Directional Hypotheses / Two tailed Hypothesis
Null Hypotheses
Directional Hypotheses / One Tailed Hypothesis
A directional hypothesis is a prediction made by a researcher regarding a positive or negative change, relationship, or difference between two variables /two groups or conditions
directional hypothesis predicts the nature of the effect of the independent variable on the dependent variable.
It is often symbolized as H1
Non-Directional Hypotheses / Two Tailed Hypothesis
A non-directional simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc.
non-directional hypothesis predicts that the independent variable will have an effect on the dependent variable, but the direction of the effect is not specified.
Null Hypotheses
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables.
null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).
It is the hypothesis that the researcher is trying to disprove.
the null hypothesis is a statement of
-‘no effect’ or ‘no difference’
It is often symbolized as H0.
Examples
“ In a clinical trial of a new drug with the current drug ”
We would write Null Hypotheses (H0):
H0 : there is no difference between the two drugs.
We would write Directional Hypotheses (H1):
H1 : the new drug is better than the current drug.
We would write Non-Directional Hypothesis:
the two drugs have different effects, on average.
Theory building, What Is a Theory? , What Are the Goals of Theory?, Research Concepts, Constructs, Propositions, Variables, and Hypotheses, Research Concepts and Constructs, Research Propositions and Hypotheses, Understanding Theory, Verifying Theory, Theory Building, The Scientific Method
Opinion Dynamics on Generalized NetworksMason Porter
This is a talk on opinion dynamics (especially bounded-confidence models) on generalized networks.
It is part of the MIX-NEXT III (Multiscale & Integrative compleX Networks: EXperiments & Theories) satellite at NetSci 2022.
(Thursday 14 July 2022)
This presentation discusses about content analysis, its use, Types, Advantages, Issues of Reliability & Validity, Problems, Quantitative content analysis, coding, Qualitative content analysis, Creative synthesis, Data reduction and Constant comparison.,
Exploratory research - Research Methodology - Manu Melwin Joymanumelwin
Exploratory research is research conducted for a problem that has not been clearly defined. It often occurs before we know enough to make conceptual distinctions or posit an explanatory relationship. Exploratory research helps determine the best research design, data collection method and selection of subjects.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
This is a presentation I gave in a workshop on "Language, concepts, history" organized by historian Joanna Innes. It took place on Friday 4/22/16 in Somerville College, Oxford.
I was one of the only people present who was not from the humanities, so it was a rather different-than-usual audience and set of participants for me.
I drew some of these slides from other presentations to rather different audiences. I emphasized rather different parts of some of those slides, so I am not sure if the slides on their own give an accurate reflection of the difference between this presentation and some of my other ones.
I thought the presentation went rather well.
This presentation discusses about content analysis, its use, Types, Advantages, Issues of Reliability & Validity, Problems, Quantitative content analysis, coding, Qualitative content analysis, Creative synthesis, Data reduction and Constant comparison.,
Exploratory research - Research Methodology - Manu Melwin Joymanumelwin
Exploratory research is research conducted for a problem that has not been clearly defined. It often occurs before we know enough to make conceptual distinctions or posit an explanatory relationship. Exploratory research helps determine the best research design, data collection method and selection of subjects.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
This is a presentation I gave in a workshop on "Language, concepts, history" organized by historian Joanna Innes. It took place on Friday 4/22/16 in Somerville College, Oxford.
I was one of the only people present who was not from the humanities, so it was a rather different-than-usual audience and set of participants for me.
I drew some of these slides from other presentations to rather different audiences. I emphasized rather different parts of some of those slides, so I am not sure if the slides on their own give an accurate reflection of the difference between this presentation and some of my other ones.
I thought the presentation went rather well.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
The Complexity of Data: Computer Simulation and “Everyday” Social ScienceEdmund Chattoe-Brown
Although the existence of various forms of complexity in social systems is now widely recognised, this approach to explanation faces two major challenges that turn out to be intimately connected. The first is the existing conflict in social science between “micro” and “macro” styles of social explanation. The second is the relationship of complexity to the kind of data routinely collected in social science. In order to be accepted, complexity approaches need simultaneously to dodge the first conflict while making much better use of existing forms of data.
The first part of the talk will provide an introduction to the simulation approach and a discussion of various concepts in complexity with reference to simulation as a distinctive theory-building tool and methodology. The second part of the talk will develop these ideas in more depth using simulations by the author as case studies.
Big data, new epistemologies and paradigm shiftsrobkitchin
This presentation examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines.
Introduction to Systemics with focus on Systems BiologyMrinal Vashisth
The core content discusses the terminology used in Systems Sciences, the systems thinking/approach or Systemics. Focus is kept on Systems Biology for the most part of the presentations where it is compared with other disciplines and examples of Systems Biology approach and challenges of systems science are also discussed.
The sad thing about uploading this to Slide Share is that animations don't work.
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Presented at Kean University Research Days April 2019: The use of social media information to examine and model student's civic engagement. Trans-disciplinary effort of Kean Faculty.
Presentation at the New Zealand Association of Gerontology conference in 2014. Focus on the utility of spatial and visual methods in ageing science and policy domains.
Presented at IZEAfest in Orlando, FL
Social network and sharing analysis including:
+Document analysis at scale: Meme tracking combined with other variables like sentiment and bias
+Social network at scale: Information cascades and virality, inference of social networks given meme-like information as contagions
+The node level perspective and its effects on what an individual sees and shares: Illusions, effort and overload, topics, personality and demographics
+Personas and segmentation: Grouping based on demographics and interests
Explainable AI is not yet Understandable AIepsilon_tud
Keynote of Dr. Nava Tintarev at RCIS'2020. Decision-making at individual, business, and societal levels is influenced by online content. Filtering and ranking algorithms such as those used in recommender systems are used to support these decisions. However, it is often not clear to a user whether the advice given is suitable to be followed, e.g., whether it is correct, whether the right information was taken into account, or if the user’s best interests were taken into consideration. In other words, there is a large mismatch between the representation of the advice by the system versus the representation assumed by its users. This talk addresses why we (might) want to develop advice-giving systems that can explain themselves, and how we can assess whether we are successful in this endeavor. This talk will also describe some of the state-of-the-art in explanations in a number of domains (music, tweets, and news articles) that help link the mental models of systems and people. However, it is not enough to generate rich and complex explanations; more is required in order to understand and be understood. This entails among other factors decisions around which information to select to show to people, and how to present that information, often depending on the target users and contextual factors
Introduction to Topological Data AnalysisMason Porter
Here are slides for my 3/14/21 talk on an introduction to topological data analysis.
This is the first talk in our Short Course on topological data analysis at the 2021 American Physical Society (APS) March Meeting: https://march.aps.org/program/dsoft/gsnp-short-course-introduction-to-topological-data-analysis/
Topological Data Analysis of Complex Spatial SystemsMason Porter
These are slides from a seminar I gave in "Cardiff" (for the mathematics department at University of Cardiff) on 4/15/20.
You can also find a recording of a similar talk that I gave in March 2020 for MBI (Mathematical Biosciences Institute): https://mbi.osu.edu/events/online-colloquium-mason-porter-spatial-systems-and-topological-data-analysis
Here are my slides (though the animated gifs on a couple of them are stills in this version) of my talk on an introduction to the science of "chaos" at WorldCon 77 in Dublin, Ireland.
This is my attempt to give a gentle introduction to the notion of chaos to a science-fiction audience.
Paper Writing in Applied Mathematics (slightly updated slides)Mason Porter
Here are my slides (which I have updated very slightly) in writing papers in applied mathematics.
There will be an accompanying oral presentation and discussion on Friday 20 April. I am recording the video for that and plan to post it along with these (or a further updated version of these) slides.
Tutorial on Paper-Writing in Applied Mathematics (Preliminary Draft of Slides)Mason Porter
These are preliminary slides for a tutorial and discussion on "Writing Papers in Applied Mathematics" that I'll be giving at UCLA, first for a few of my own PhD students on 4/6 and later (on 4/20 ?) in a recorded session to a larger UCLA group.
Several people have expressed interest, so I will post the recorded session online and circulate it.
My talk at the 2017 SIAM "Snowbird" conference on applications of dynamical systems (#SIAMDS17).
I spoke in a session on topological data analysis (TDA). My talk concerned persistent homology and its application to Brexit data (including voting data) and "functional networks" from coupled time series from both experiments and output of dynamical systems.
Eventually, a version of these slides that is synchronized with the audio of my talk is supposed to be posted online.
This is my attempt at an introduction to data ethics for mathematicians. Mathematicians increasingly need to deal with these kinds of issues, but we don't have the tradition of ethics training from other disciplines.
I welcome comments on how to improve these slides. Did I miss any salient points? Do you want to offer a different perspective on any of these? Do you want to offer any counterpoints? (Please e-mail me directly with comments and suggestions.)
Eventually, I hope to develop these slides further into an article for a venue aimed at mathematical scientists, and of course I would love to have knowledgeable coauthors who can offer a different perspective from mine.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
Networks in Space: Granular Force Networks and BeyondMason Porter
This is my talk for the Network Geometry Workshop (http://ginestra-bianconi-6flt.squarespace.com) at QMUL on 16 July 2015.
(A few of the slides are adapted from slides by my coauthors Dani Bassett and Karen Daniels.)
These are slides for my tutorial talk on network dynamics. (The colors are fine in the downloaded version, though there seem to be color issues if you view the slides directly in slideshare.)
Slides from my talk on a systems-level investigation of long-term human migration in Korea. Our paper is available at the following page: http://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.041009
I adapted these slides from the ones created by my coauthor Sang Hoon Lee.
These are the slides for a tutorial talk about "multilayer networks" that I gave at NetSci 2014.
I walk people through a review article that I wrote with my PLEXMATH collaborators: http://comnet.oxfordjournals.org/content/2/3/203
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx
Social Dynamics on Networks
1. Social Dynamics on Networks
Mason A. Porter (@masonporter)
Department of Mathematics, UCLA
2. Some Review Articles
•Hossein Noorazar, Kevin R. Vixie, Arghavan Talebanpour, & Yunfeng Hu
[2020], “From classical to modern opinion dynamics”, International
Journal of Modern Physics C, Vol. 31, No. 07: 2050101
•Claudio Castellano, Santo Fortunato, & Vittorio Loreto [2009], “Statistical
physics of social dynamics”, Reviews of Modern Physics, Vol. 81, No. 2:
pp. 591–646
•Sune Lehmann & Yong-Yeol Ahn [2018], Complex Spreading Phenomena
in Social Systems: Influence and Contagion on Real-World Social
Networks, Springer International Publishing
3. Some Things that People Study
in Models of Social Dynamics
• Notes:
• Researchers focus on different things in different types of models
• I’mbringing up what comes to mind. I amrelying on the audience to bring up other examples.
• Consensus vs Polarization vs Fragmentation
• How do you measure polarization and fragmentation?
• What is the convergence time to a steady state (if one reaches one)?
• Cascades and virality
• How far and how fast do things (e.g., a meme) spread? When do things go viral, and when do they not?
• Measuring virality in theory (e.g., percolation and giant components) versus in practice
• Incorporating behavior into models of the spread of diseases
• Just concluding that model social dynamics is impossible to do well and giving up on it isn’t an option for studying
certain problems
• More general: investigate effects of network structure on dynamical processes (and vice versa)
• Making good choices of synthetic networks to consider is often helpful for obtaining insights
4. Some Challenges in Modeling
Social Dynamics
• How “correct” can these models ever be?
• But maybe they can be insightful or helpful?
• How does one connect the models and the behavior of those models with real life and real data?
• Example: Can one measure somebody’s opinion as some scalar in the interval [–1,1] based on their
online “fingerprints” or survey answers?
• Comparing outputs like spreading trees of tweets from a model and reality, rather than comparing
node states themselves?
• Juan Fernández-Gracia, Krzysztof Suchecki, José J. Ramasco, Maxi San Miguel, & Víctor M. Eguíluz
[2014], “Is the voter model a model for voters?”, Physical Review Letters, Vol. 112, No. 15: 158701
• Ethical considerations in measurements in attempts to evaluate models of social dynamics with
real data
• More general: complexity of models versus mathematical analysis of them?
5. Types of Social-Dynamics Models
• Compartmental models (hijacked from disease dynamics), threshold models
(percolation-like), voter models, majority-vote models, DeGroot models, bounded-
confidence models, games on networks, …
• Discrete states versus opinion states
• Deterministic update rules versus stochastic update rules
• Dynamical systems versus stochastic processes
• Synchronous updating of node states versus asynchronous updating
• Note: Some of the different types of models can be related to each other
• Example: certain threshold models have been written in game-theoretic terms
6. Researchers Study Different Types of
Phenonema in the Different Types of Models
•Examine cascades, virality, and influence maximization in threshold
models
•Examine consensus vs polarization in voter models
•Examine consensus vs polarization vs fragmentation in bounded-confidence
models
7. Different Mathematical Approaches
in Different Types of Models
• What mean-field theories looks like can be rather different in different types of
models
• For example, bounded-confidence models (kinetic theories, like in studies of
collective behavior, but with different kernels) vs degree-based mean-field theories,
pair approximations, etc. in threshold models
• Branching-process calculations and percolation-based methods are often useful for
threshold models.
• Approximate master equations
• Dynamical-systems approaches vs probabilistic approaches
8. Generalizing Network Structures
• Multilayer networks, temporal networks, adaptive networks, hypergraphs (and, more generally,
polyadic interactions), etc.
• How do such more general structures affect dynamics?
• What new phenomena occur that cannot arise in simpler situations?
• Multiple choices for how to do the generalizations, and they matter significantly
• When is consensus more likely, and when is it less likely?
• When is convergence to a steady state sped up and when is it slowed down?
• When is virality more likely, and when is it less likely?
• If you do the “same type of generalization” on different types of models (e.g., a voter model vs a
bounded-confidence model), when does the same type of generalization have a similar effect on the
qualitative dynamics?
• Example: Under what conditions do polyadic interactions promote consensus and when do they make it harder?
How does this answer differ —does it? —in different types of social-dynamics models?
9. Some Application-Related Questions
• Spread and mitigation of misinformation, disinformation, and “fake news”
• Formation of echo chambers
• Spread of extremist opinions
• Measuring and forecasting viral posts?
• Distinguishing internal effects from external ones (e.g., something gets popular enough from
retweets that it then shows up on mainstream media sources)
• Inverse problems
• Example: determining “patient 0” in the spread of content
• “Majority illusion” and “minority illusion”
10. Other Things
•Using ideas like text analysis and sentiment analysis to infer opinions from
textual data
• Perhaps helpful for model evaluation but also to e.g. inspire inputs (such as
ideological values of “media nodes” that influence other nodes) into models of
social dynamics?
•Other connections with tools from machine learning, statistics, natural-
language processing (NLP), etc.
• Topic modeling, etc.
11. Social Networks
• Typically (but not always), nodes represent individuals
• Depending on the application, edges can represent one (or more) of various types
of social connections: offline interactions, phone calls, Facebook ‘friendships’,
Twitter followership, etc.
• Notions of actual social ties, but also notions of communication
• Different things propagate on different types of networks
• For example: information spreading versus disease spreading
• Complicated mixture of regular and ‘random’ structures
• Good random-graph models provide baselines for comparison
12. Dynamical Processes on Networks
•Incorporate which individuals (nodes) interact with which other
individuals via their ties (edges).
•This yields a dynamical system on a network.
•A fundamental question: How does network structure affect
dynamics (and vice versa)?
•MAP & J. P Gleeson [2016], “Dynamical Systems on Networks: A
Tutorial”, Frontiers in Applied Dynamical Systems: Reviews and
Tutorials, Vol. 4
13. A General Note About Time Scales and Modeling
Dynamical Systems on Dynamical Networks
• Relative time scales of evolution of states versus evolution of network structure
• States change much faster than structure?
• Faster: Dynamical systems on static networks (“quenched”)
• MUCH faster (too rapidly): Can only trust statistical properties of states
• Structure changes much faster than states?
• Faster: Temporal networks
• MUCH faster (too rapidly): Can only trust statistical properties of network structure (“annealed”)
• Comparable time scales?
• “Adaptive” networks (aka “coevolving” networks)
• Dynamics of states of network nodes (or edges) coupled to dynamics of network structure
14. Spreading and Opinion Models
•There are many types of models. Some examples:
• Compartmental models (hijacked from disease dynamics)
• Convenient because of a long history of work on analyzing them
• Threshold models
• A type of model with discrete states (usually two of them) that models social
reinforcement in contagious spreading processes in a minimalist way
• Voter models
• Discrete-valued opinions, although not really a model for “voters”
• Bounded-confidence models
• Continuous-valued opinions
15. Coupling the Spread of Opinions/Behavior
with the Spread of a Disease
• Jamie Bedson et al. [2021], “A review and agenda for integrated disease models
including social and behavioural factors”, Nature Human Behaviour, Vol. 5, No. 7:
834–846
• In a compartmental model, nodes have different states (i.e., “compartments”) and there
are rules for how to transition between states
• For example, in a stochastic SIR (susceptible–infected–recovered) model, nodes in S change to I
with some probability if they have a contact with a node in I. Nodes in I recover and change to
R with some probability.
• A rich history of work on mean-field theories (both homogeneous and heterogeneous
ones), pair approximations, and other approximations.
• István Z. Kiss, Joel C. Miller, & Péter L. Simon [2017], Mathematics of Epidemics on
Networks: From Exact to Approximate Models, Springer International Publishing
16. Coupling the Spread of Opinions/Behavior
with the Spread of a Disease
• Kaiyan Peng, Zheng Lu, Vanessa Lin, Michael R. Lindstrom, Christian Parkinson, Chuntian
Wang, Andrea L. Bertozzi, & Mason A. Porter [2021], “A Multilayer Network Model of the
Coevolution of the Spread of a Disease and Competing Opinions”, Mathematical Models and
Methods in Applied Sciences, Vol. 31, No. 12: 2455–2494
• Opinions (no opinion, pro-physical-distancing, and anti-physical-distancing) spread on one layer
of a multilayer network.
• An infectious disease spreads on the other layer. People who are anti-physical-distancing are
more likely to become infected.
• It is crucial to develop models in which human behavior is coupled to disease spread. Models of
disease spread need to incorporate behavior.
• For simplicity (e.g., the same type of mathematical form in the right-hand sides for both layers), we
used compartmental models for each layer (SIR/SIR and SIR/SIRS). It is important to develop more
realistic models.
17.
18. Some of the Equations for the
Evolution of Pairs
19. Threshold Models
Example: Watts Threshold Model
• D. J.Watts, PNAS, 2002
• Each node j has a (frozen) threshold Rj drawn from some distribution and can be in one of two states (0 or 1)
• Choose a seed fraction ρ(0) of nodes (e.g. uniformly at random) to initially be in state 1 (“infected”,“active”,
etc.)
• Updating can be either:
• Synchronous: discrete time; update all nodes at once
• Asynchronous:“continuous” time; update some fraction of nodes in time step dt (e.g., using a Gillespie
algorithm)
• Update rule: Compare fraction of infected neighbors (m/kj) to Rj. Node j becomes infected if m/kj ≥ Rj.
Otherwise no change.
• Variant (Centola–Macy): Look at number of active neighbors (m) rather than fraction of active neighbors
• Monotonicity: Nodes in state 1 stay there forever.
J. P. Gleeson, PRX,Vol. 3, 021004 (2013): has a table of more than 20 binary-state models (WTM, percolation models, etc.)
21. A Threshold Model with Hipsters
• J. S. Juul & MAP [2019], “ Hipsters on Networks: How a Minority Group of Individuals Can Lead to an
Antiestablishment Majority”, Physical Review E, Vol. 99: 022313
• WTM rules to adopt some product (A or B)
• Conformist node: Adopts majority opinion from local neighborhood
• Hipster node: Adopts minority opinion (from full network, like a best-seller list) from ! times ago
24. “The” Voter Model
• S. Redner [2019], “Reality Inspired Voter Models: A Mini-Review”, Comptes
Rendus Physique, Vol. 20:275–292
• In an update step, an individual updates their opinion based on the opinion of a
neighbor
• One choice: asynchronous versus synchronous updating
• Select a random node (e.g., uniformly at random) and then a random neighbor
• Another choice: node-based models versus edge-based models
• Select a random edge (or perhaps a random “discordant” edge)
• In Kureh & Porter (2020), we use asynchronous, edge-based updates.
25. A Nonlinear Coevolving Voter Model
• Y. Kureh & MAP [2020], “Fitting In
and Breaking Up: A Nonlinear Version
of Coevolving Voter Models”, Physical
Review E, Vol. 101, No. 6: 062303
• We consider versions of the model with
three types of changes in network
structure.
• Each step: probability !q of rewiring
step and complementary probability 1 –
!q of opinion update
• q = nonlinearity parameter
30. Majority Illusion and Echo Chambers
• “Liberal Facebook” versus
“Conservative Facebook”:
http://graphics.wsj.com/blue-feed-
red-feed/
• “Majority illusion”: K. Lerman, X.
Yan, & X.-Z. Wu, PLoS ONE, Vol.
11, No. 2: e0147617 2016
• Such network structures form
naturally from homophily and are
exacerbated further by heated
arguments in contentious times.
32. Bounded-Confidence Models
• Continuous-valued opinions on some space, such as [–1,1]
• When two agents interact:
• If their opinions are sufficiently close, they compromise by some amount
• Otherwise, their opinions don’t change
• Two best-known variants
• Deffuant–Weisbuch (DW) model: asynchronous updating of node states
• Hegselmann–Krause (HK) model: synchronous updating of node states
• Most traditionally studied without network structure (i.e., all-to-all coupling of agents) and with a
view towards studying consensus
• By contrast, early motivation — but has not been explored much in practice — of bounded-confidence
models was to examine how extremist ideas, even when seeded in a small proportion of a population,
can take root in a population
33. Bounded-Confidence Model on Networks
• X. Flora Meng, Robert A. Van Gorder, & MAP [2018], “Opinion Formation and Distribution in a Bounded-
Confidence Model on Various Networks”, Physical Review E, Vol. 97, No. 2: 022312
• Network structure has a major effect on the dynamics, including how many opinion groups form and how long they take to form
• At each discrete time, randomly select a pair of agents who are adjacent in a network
• If their opinions are close enough, they compromise their opinion by an amount proportional to the difference
• If their opinions are too far apart, they don’t change
• Complicated dynamics
• Does consensus occur? How many opinion groups are there at steady state? How long does it take to converge to steady state?
How does this depend on parameters and network structure?
• Example: Convergence time seems to undergo a critical transition with respect to opinion confidence bound (indicating
compromise range) on some types of networks
36. Influence of Media
• Heather Z. Brooks & MAP [2020], “A Model for the Influence of Media on the Ideology of
Content in Online Social Networks”, Physical Review Research, Vol. 2, No. 2: 023041)
• Discrete events (sharing stories), but the probability to share them (and thereby influence
opinions of neighboring nodes) is based on a bounded-confidence mechanism
• Distance based both on location in ideology space and on the level of quality of the content that is
being spread
• Include “media nodes” that have only out-edges
• How easily can media nodes with extreme ideological positions influence opinions in a network?
• Future considerations: can also incorporate bots, sockpuppet accounts, cyborg accounts, etc.
39. Conclusions
• Many different types of models of social dynamics
• Examples include threshold models, voter models, bounded-confidence models, and others.
• Interactions between social dynamics and disease dynamics
• How does network structure affect dynamics?
• Is there a consensus? How many opinion groups? How long does it take to converge to a steady state? Etc.
• How can we tell when one of these models is “good”?
• Recent papers and some works in progress
• A. Hickok, Y. H. Kureh, H. Z. Brooks, M. Feng, & MAP [2022]: “A Bounded-Confidence Model on Hypergraphs”, SIAM Journal on
Applications of Dynamical Systems, Vol. 21, No. 1: 1–32
• U. Kan, M. Feng, & MAP [2021]: “An Adaptive Bounded-Confidence Model”, arXiv: 2112.05856
• H. Z. Brooks & MAP, “Spreading Cascades in Bounded-Confidence Dynamics on Networks”, in preparation
• P. Chodrow, H. Z. Brooks, & MAP, “Bifurcations in Bounded-Confidence Models with Smooth Transition Functions”, in preparation
• G. Li & MAP, “Bounded-Confidence Models of Opinion Dynamics with Heterogeneous Node-Activity Levels”, in preparation
• K. Peng & MAP, “Bifurcations in a Multiplex Majority-Vote Model”, in preparation