Taxonomic Collections, Preservation and Curating of InsectsKamlesh Patel
Taxonomy: Taxonomy is the science of defining and naming groups of biological organisms on the basis of shared characteristics.
The classification of organisms is according to hierarchal system or in taxonomic ranks (eg; domain, kingdom, phylum class, order, family, genus and species) based on phylogenetic relationship established by genetic analysis.
Taxonomic Collection : Biological collection are typically preserved plant or animals specimens along with specimen documentations such as labels and notations.
Dry Collection - Dry collections consist of those specimens that are preserved in a dry state.
Wet Collection - Wet collections are specimens kept in a liquid preservative to prevent their deterioration.
Molecular evolution, four class of chromosomal mutation, Negative Selection and Positive Selection, Mutations in DNA and protein, Neutral Theory of Molecular Evolution, Evidence supporting neutral evolution, Phylogenetic trees, Methods of Tree reconstruction
This document will help you and will clear your concepts about the terms of Orthogenesis, Allometry & Adaptive Radiations, which are usually studied in evolution.
Taxonomic Collections, Preservation and Curating of InsectsKamlesh Patel
Taxonomy: Taxonomy is the science of defining and naming groups of biological organisms on the basis of shared characteristics.
The classification of organisms is according to hierarchal system or in taxonomic ranks (eg; domain, kingdom, phylum class, order, family, genus and species) based on phylogenetic relationship established by genetic analysis.
Taxonomic Collection : Biological collection are typically preserved plant or animals specimens along with specimen documentations such as labels and notations.
Dry Collection - Dry collections consist of those specimens that are preserved in a dry state.
Wet Collection - Wet collections are specimens kept in a liquid preservative to prevent their deterioration.
Molecular evolution, four class of chromosomal mutation, Negative Selection and Positive Selection, Mutations in DNA and protein, Neutral Theory of Molecular Evolution, Evidence supporting neutral evolution, Phylogenetic trees, Methods of Tree reconstruction
This document will help you and will clear your concepts about the terms of Orthogenesis, Allometry & Adaptive Radiations, which are usually studied in evolution.
http://hrst.mit.edu/hrs/evolution/public/profiles/king.html
http://hrst.mit.edu/hrs/evolution/public/profiles/jukes.html
http://hrst.mit.edu/hrs/evolution/public/papers/simpson1964/simpson1964.pdf
http://hrst.mit.edu/hrs/evolution/public/kimura1968/kimura1968.pdf
http://hrst.mit.edu/hrs/evolution/public/papers/kimura1968/kimura1968.pdf
http://hrst.mit.edu/hrs/evolution/public/papers/simpson1964/simpson1964.pdf
DISREGARD:
Evolutionary Rate at the Molecular Level
b y
M O T 0 0 KIMURA
National Institute of Genetics,
Japan
Calculating the rate of evolution in terms of nucleotide substitutions
seems t o give a value so high that many of the mutations involved
must be neutral ones.
COMPARATIVE studies of haemoglobin molecules among change in for a chain consisting of some amino-
different groups of animals suggest that, during the acids. For example, by comparing the and chains of
evolutionary history of mammals, amino-acid substitution man with those of horse, pig, cattle and rabbit, the
has taken place roughly at the rate of one amino-acid figure of one amino-acid change in x was obtained'.
http://hrst.mit.edu/hrs/evolution/public/profiles/kimura.html
http://hrst.mit.edu/hrs/evolution/public/papers/zuckerkandlpauling1965/zuckerkandlpauling1965.pdf
This is roughly equivalent to the rate of one amino-acid
substitution in for a chain consisting of
amino-acids.
A comparable value has been derived from the study
of the haemoglobin of primates. The rate of amino-acid
substitution calculated by comparing mammalian and
avian cytochrome c (consisting of about 100 amino-acids)
turned out to be one replacement in 48 x 106 yr (ref. 3).
Also by comparing the amino-acid composition of human
triosephosphate dehydrogenase with that of rabbit and
figure of a t least one amino-acid substitution
for every X yr can be obtained for the chain con-
sisting of about amino-acids. This figure is roughly
equivalent to the rate of one amino-acid substitution in
x yr for a chain consisting of amino-acids.
Averaging those figures for haemoglobin, cytochrome c
and triosephosphate’ dehydrogenase gives an evolutionary
rate of approximately one substitution in 28 x 108 yr for
a polypeptide chain consisting of 100 amino-acids.
I intend to show that this evolutionary rate, although
appearing to be very low for each polypeptide chain of a
size of cytochrome c, actually amounts t o a very high
rate for the entire genome.
First, the DNA content in each nucleus is roughly the
same among different species of mammals such as man,
cattle and rat (see, for example, ref. 5 ) . Furthermore, we
note that the G-C content of DNA is fairly uniform among
mammals, lying roughly within the range of 40-44 per
These two facts suggest that nucleotide substitution
played a principal part in mammalian evolution.
I n t h e following calculation, I shall assume that the
haploid c ...
what are the consequences of HWE on allele and genotype frequencies.pdfnishadvtky
what are the consequences of HWE on allele and genotype frequencies?
Solution
The Hardy-Weinberg law states that the proportions or frequencies of the alleles in gene pool of
a population remain constant or at equilibrium from generation to generation unless acted upon
by agents other than sexual recombination. The genotypic frequencies stabilize after one
generation in proportions determined by allelic frequencies. The Hardy-Weinberg equation
provides a standard to measure the changes in allele frequencies occurring in the natural
populations without this equation the change in the frequency of alleles the magnitude and
direction of the alleles and the forces responsible for change in allele characteristics can not be
detected.
Apart from the natural selection the change in the composition of the gene pool of a population is
influenced by other conditions as follows:-
Mutations alter the gene pool by changing one allele in to another.
Mutations are the heritable changes in the genotype and can be caused by addition or deletion of
few nucleotides in a DNA molecule or segments of chromosomes or whole set of chromosomes.
Mutations may occur spontaneously and randomly. The rate of spontaneous mutations is
generally low but can bring about the evolutionary change because the variation is acted upon by
evolutionary forces.
Movement of individuals leads to the transfer of alleles in to the population and can change the
gene pools.
The gene flow or movement of alleles occurs due to immigration or emigration of individuals.
Incase of plants the gene flow is possible through the movement of pollen between populations.
The gene flow can introduce new alleles in to the populations or can change the frequency of
existing alleles. The overall effect of gene flow is to decrease the difference the populations.
Large population size can alter the frequencies or relative proportions of alleles in a gene pool.
Population size is more important to estimate the frequency of alleles in the gene pool. A
defective allele with 1% frequency in a population containing 1 million individuals the affected
population would be 20,000. If few individuals of this population with the defective gene are
destroyed before producing the progeny the effect on the frequency on defective allele would be
negligible. In a population of 50 individuals the frequency of defective allele would be 1 in 80.
Thus, the change in the gene pool takes place by chance is known as genetic drift and it plays a
major role in determination of evolution of small populations.
The gene pool is altered when the population is deviated by the random mating.
Random mating results in the mating of population with the close neighbors and large
populations contain the closely related individuals. Inbreeding is resulted due to non random
mating of closely related individuals. The non random mating does not change the frequencies of
alleles in the gene pool but affects the genotypic frequencies..
This presentation elaborates the economic crisis in Sri Lanka. It explains the causes of economic instability in Sri Lanka and the factors worsening it. Such miserable economic situation is presenting valuable lessons for other sister asian countries to counter their economic instability. Pakistan, a sister country of Sri Lanka is facing severe political and economic instability these days. Pakistan is learning from the Sri Lankan economic situation and tending to improve its economy but the extreme political instability is hurdling and exacerbating the economic crisis. However, policies are underway to counter the economic crisis and more probably Pakistan will escape the Sri Lankan experience.
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.
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 .
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
1. Mutation pressure
Mutation pressure is the change in allele frequencies due to the repeated occurrence of the same mutations.
There are not many biologically realistic situations where mutation pressure is the most important
evolutionary process while random drift will usually be more important. However, sometimes the mutation
rate is high enough that mutation pressure need to be considered; in addition, it provides a simple illustration
of a population genetic equilibrium.
Mutation pressure theory
A quantitative theory of directional mutation pressure proposed in 1962 explained the wide variation of
DNA base composition observed among different bacteria and its small heterogeneity within individual
bacterial species. The theory was based on the assumption that the effect of mutation on a genome is not
random but has a directionality toward higher or lower guanine-plus-cytosine content of DNA, and this
pressure generates directional changes more in neutral parts of the genome than in functionally significant
parts. Now that DNA sequence data are available, the theory allows the estimation of the extent of neutrality
of directional mutation pressure against selection. Newly defined parameters were used in the analysis, and
two apparently universal constants were discovered. Analysis of DNA sequence has revealed that practically
all organisms are subject to directional mutation pressure. The theory also offers plausible explanations for
the large heterogeneity in guanine-plus-cytosine content among different parts of the vertebrate genome.
Explaination
Imagine a population in which all individuals have the same allele ("red"), but there is a high rate of
mutation to a second allele ("blue"). At each generation, some red alleles will mutate and will become blue
alleles. The frequency of the blue alleles will therefore increase over time.
This process, under which allele frequencies change solely due to the same mutations occurring over and
over, is known as mutation pressure. For most kinds of genetic variation in most populations, random drift is
more important than mutation pressure; the changes in allele frequency from one generation to the next due
to random drift will be much larger than the changes due to mutation pressure.
In order for mutation pressure to play an important role in changing allele frequencies, the mutation rate
has to be relatively high. Some organisms, such as RNA viruses (including HIV), have extremely high
mutation rates. In other organisms, some categories of mutations have mutation rates that are high enough
that mutation pressure becomes important. For example, microsatellites are stretches of short sequence
repeated over and over, such as GAGAGAGAGAGAGA. Through a process known as strand slippage,
mutations that increase or decrease the number of repeats in a microsatellite occur often enough that you
would have to take mutation pressure into account when modeling the evolution of a microsatellite.
This simulation models mutation in a population of 20 haploid individuals. Each individual has exactly
one offspring, so there is no random drift or selection; this is unlikely in the real world, but possible for
some organisms in laboratory experiments. The empty red squares represent individuals with one allele, and
the filled blue squares are a different allele. The population starts out with all red alleles. Set the red-to-blue
mutation rate greater than 0 and less than 1, and the blue-to-red mutation rate from 0 to less than 1. If the
blue-to-red mutation rate is 0, you should see that the population will eventually consist of all blue alleles,
because sooner or later, each lineage will have a red-to-blue mutation. If the blue-to-red mutation rate is
greater than 0, the population should reach an equilibrium, with a mixture of red and blue alleles.
It is possible to calculate what the equilibrium allele frequency should be. At equilibrium, you would
expect the allele frequency to remain the same from one generation to the next, on average. In other words,
the average change in allele frequency from one generation to the next should be 0. Another way of stating
2. this is that at equilibrium, the proportion of alleles that mutate from red to blue is equal to the proportion that
mutate from blue to red.
The average proportion of red-to-blue mutations in the population is given by the proportion of alleles
that are red, pr, times the proportion of red alleles that mutate to blue (the red-to-blue mutation rate), μrb. The
proportion of alleles that are blue is 1−pr, so the average proportion of blue-to-red mutations is (1−pr)×μbr.
At equilibrium,
In other words, the expected proportion of red alleles is equal to the proportion of all mutations that are blue-
to-red mutations. For example, if the blue-to-red mutation rate is 0.002 and the red-to-blue rate is 0.005, the
expected proportion of red alleles is