This document summarizes a study on the genetic architecture of developmental traits in gypsy moth populations. The study established 7 gypsy moth populations in common gardens and sequenced 188 individuals to identify 11,021 SNPs. Three phenotypes - pupal duration, mass, and total development time - were measured. Population structure was corrected using PCA. Several SNPs were significantly associated with each trait, though effect sizes were small. Multilocus models explained over 50% of trait variation. Future work could involve refining the genome assembly and studying additional populations to detect smaller genetic effects.
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Investment Insights from NIFCU$: Standard & Poor's Downgrades U.S. Government...NAFCU Services Corporation
Standard & Poor’s (S&P) has downgraded their U.S. Government long-term AAA debt rating to AA+ for the first time since granting it in 1917. While forewarned, it still seems to have taken investors by surprise. Fitch and Moody’s recently re-affirmed their top-tier rankings of U.S. long-term debt, however. We do not expect S&P’s downgrade will have much impact on interest rates or the sale of Treasuries to finance U.S. borrowing, particularly if both Moody’s and Fitch continue to maintain their current top-tier ratings. The consequences of S&P's U.S. Government downgrade will likely have a greater emotional impact on investor sentiment, exacerbated by the European chaos, than any longer term impact on the economy or fixed income liquidity.
Learn more from NIFCU$ at http://www.nafcu.org/nifcus
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The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.
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Investment Insights from NIFCU$: Standard & Poor's Downgrades U.S. Government...NAFCU Services Corporation
Standard & Poor’s (S&P) has downgraded their U.S. Government long-term AAA debt rating to AA+ for the first time since granting it in 1917. While forewarned, it still seems to have taken investors by surprise. Fitch and Moody’s recently re-affirmed their top-tier rankings of U.S. long-term debt, however. We do not expect S&P’s downgrade will have much impact on interest rates or the sale of Treasuries to finance U.S. borrowing, particularly if both Moody’s and Fitch continue to maintain their current top-tier ratings. The consequences of S&P's U.S. Government downgrade will likely have a greater emotional impact on investor sentiment, exacerbated by the European chaos, than any longer term impact on the economy or fixed income liquidity.
Learn more from NIFCU$ at http://www.nafcu.org/nifcus
Using text mining to inform genetic variant interpretationKarin Verspoor
There are ongoing large-scale efforts to catalog genomic variation related to disease in structured databases. Much of the relevant information is available only from unstructured sources, including the scientific literature. In our work, we have explored the ability of text mining tools to recover the mutations catalogued in curated databases based on the article text, specifically examining the recovery of mutations in the COSMIC and InSiGHT databases. We demonstrate that there are excellent tools for extraction of mutation mentions from the literature, but that the recovery of the information in databases is far less than what would be expected based on that tool performance, even when full text articles are available. I will present an analysis in which we explore the impact of processing tables and supplementary material associated to relevant literature, demonstrating that the coverage of variants improves dramatically, from 2% to over 50%. I will further present the Variome corpus, a small collection of full text publications annotated with relationships such as gene-disease and mutation-disease relationships, and introduce our recent efforts to develop strategies to extract this relational information from the literature. Joint work with Antonio Jimeno Yepes (IBM Research) and Min Song (Yonsei University).
Exploring temporal graph data with Python: a study on tensor decomposition o...André Panisson
Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures.
The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.
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Abstract: Ontologies are used in numerous research disciplines and commercial applications to uniformly and semantically annotate real-world objects. Due to a rapid development of application domains the corresponding ontologies are changed frequently to include up-to-date knowledge. These changes dramatically influence dependent data as well as applications/systems, for instance, ontology mappings, that semantically interrelate ontologies. The talk will give an overview on evolution of ontologies and ontology-based mappings.
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Workshop/other materials available at DOI:10.5281/zenodo.49447
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Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
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.
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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 .
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Genetic architecture of developmental traits in populations of male gypsy moths
1. Genetic architecture of
developmental traits in
populations of male gypsy moths
Christopher J. Friedline, Ph.D.!
Virginia Commonwealth University
@noituloveand @cfriedline
Evolution 2014!
Raleigh, NC!
6.21.2014
14. Population Structure
Correction by PCA
• Price et al. (2006) ”Principal Components Analysis
Corrects for Stratification in Genome-wide Association
Studies." Nat Genet 38.8
• First principal component approximates FST
• Number of axes chosen using a Tracy-Widom test,
described in Eckert et al. (2010). Genetics 185:
969-982.
• Correlation of genotype vs. phenotype residuals to X2
to p-values
15. Population Structure
Correction by PCA
• Price et al. (2006) ”Principal Components Analysis
Corrects for Stratification in Genome-wide Association
Studies." Nat Genet 38.8
• First principal component approximates FST
• Number of axes chosen using a Tracy-Widom test,
described in Eckert et al. (2010). Genetics 185:
969-982.
• Correlation of genotype vs. phenotype residuals to X2
to p-values
40 20 0 20 40 60 80
PC1 (0.021%)
60
50
40
30
20
10
0
10
20
30
PC2(0.015%)
PCA of n=7 populations on 11021 loci
16. Population Structure
Correction by PCA
• Price et al. (2006) ”Principal Components Analysis
Corrects for Stratification in Genome-wide Association
Studies." Nat Genet 38.8
• First principal component approximates FST
• Number of axes chosen using a Tracy-Widom test,
described in Eckert et al. (2010). Genetics 185:
969-982.
• Correlation of genotype vs. phenotype residuals to X2
to p-values
17. Top SNP
C/C C/T T/T
Locus 14103 (ctg7180001511349/152)
0.2
0.3
0.4
0.5
0.6
0.7
Mass (p = 0.000004, FST = 0.008543)
T/T T/C C/C
Locus 14908 (ctg7180001527347/31)
65
70
75
80
85
90
95
Total Dev Time (p = 0.000111, FST = 0.016849)
A/A A/T T/T
Locus 10529 (ctg7180001452692/364)
7
8
9
10
11
12
13
Pupual Duration (p = 0.000022, FST = 0.024810)
18. 468
444
44
444
73 29
9
Total Dev Time Pupual Duration
Mass
• Corrected for population structure (Price et al. 2006), binned by MAF
• By p value (p < 0.05):
• Mass: n = 555
• Pupual duration: n = 526
• Total development time: n = 524
• By q value (Storey and Tibshirani, 2003)
• Mass: n = 3 (14103
(*,10)
, 27843
(40)
, 9023
(40)
)
• Pupual duration: n = 1 (S10529
(40)
)
• Total development time: n = 0
Significant SNPs
20. Blast results
Mass
reverse transcriptase*
non-LTR
retrotransposon
predicted craniofacial
development protein
sulfotransferase
(amine, estrogen)
Pupual duration
endonuclease-reverse
transcriptase
phosphatidylinositol 3-
kinase
Development time
reverse transcriptase
endonuclease-reverse
transcriptase
transcription initiation factor
TFIID subunit 2-like protein
chosen by p+q*!
chosen by q+
21. Conclusions/Future Work
• Assembly curation is likely necessary for more
robust biological conclusion
• Small effect sizes difficult to detect with small
sample size and populations
• High degree of multilocus effects
• Additional replicate gardens with related material
• Probabilistic genotype calling with full set
22. Acknowledgments
Johnson Lab!
Derek Johnson
Kristine Grayson
Trevor Faske
NPGI: NSF Postdoctoral Fellowship in Biology FY 2013
Rodney Dyer, VCU
Dylan Parry, SUNY-ESF
Eckert Lab!
Andrew Eckert
Brandon Lind
Erin Hobson
Ethan Harwood
VCU NARF
VCU CHiPC