There are three main classes of RNA in organisms: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). mRNA comprises 5% of cellular RNA and carries genetic information from genes to the protein synthesis machinery. mRNA has a 5' cap and 3' poly-A tail that aid in its stability and recognition during translation. tRNA transfers amino acids during protein synthesis and has a distinctive cloverleaf secondary structure. rRNA makes up the ribosomal subunits and is necessary for ribosome assembly and translation functions like mRNA binding and peptidyl transferase activity.
RNA is a ribonucleic acid that helps in the synthesis of proteins in our body. This nucleic acid is responsible for the production of new cells in the human body. It is usually obtained from the DNA molecule.
RNA is a ribonucleic acid that helps in the synthesis of proteins in our body. This nucleic acid is responsible for the production of new cells in the human body. It is usually obtained from the DNA molecule.
Titration curve of amino acid by KK Sahu sirKAUSHAL SAHU
Introduction of amino acid
Structure of amino acid
Classification of amino acid
Non-polar aliphatic ‘R’ group
Aromatic ‘R’ group
Polar uncharged ‘R’ group
Positively charged ‘R’ group
Negatively charged ‘R’ group
What is titration curve?
Amino acid act as acid and base
Curve of amino acid
Conclusions
References
The flow of information in the cell starts at DNA, which replicates to form more DNA. Information is then ‘transcribed” into RNA, and then it is “translated” into protein.
Information does not flow in the other direction.
A few exceptions to the Central Dogma exist
some RNA viruses, called “retroviruses”.
Amino acids are biologically important organic compounds composed of amine (-NH2) and carboxylic acid (-COOH) functional groups, along with a side-chain specific to each amino acid. The key elements of an amino acid are carbon, hydrogen, oxygen, and nitrogen, though other elements are found in the side-chains of certain amino acids. About 500 amino acids are known and can be classified in many ways. They can be classified according to the core structural functional groups' locations as alpha- (α-), beta- (β-), gamma- (γ-) or delta- (δ-) amino acids; other categories relate to polarity, pH level, and side-chain group type (aliphatic, acyclic, aromatic, containing hydroxyl or sulfur, etc.). In the form of proteins, amino acids comprise the second-largest component (water is the largest) of human muscles, cells and other tissues.Outside proteins, amino acids perform critical roles in processes such as neurotransmitter transport and biosynthesis.
It contain more information about Amino acids and their structure. Then , contain both physical and chemical properties. Next Classification of amino acids based on nutritional requirements, based on metabolic fate, Position of NH2 group, etc.,
Titration curve of amino acid by KK Sahu sirKAUSHAL SAHU
Introduction of amino acid
Structure of amino acid
Classification of amino acid
Non-polar aliphatic ‘R’ group
Aromatic ‘R’ group
Polar uncharged ‘R’ group
Positively charged ‘R’ group
Negatively charged ‘R’ group
What is titration curve?
Amino acid act as acid and base
Curve of amino acid
Conclusions
References
The flow of information in the cell starts at DNA, which replicates to form more DNA. Information is then ‘transcribed” into RNA, and then it is “translated” into protein.
Information does not flow in the other direction.
A few exceptions to the Central Dogma exist
some RNA viruses, called “retroviruses”.
Amino acids are biologically important organic compounds composed of amine (-NH2) and carboxylic acid (-COOH) functional groups, along with a side-chain specific to each amino acid. The key elements of an amino acid are carbon, hydrogen, oxygen, and nitrogen, though other elements are found in the side-chains of certain amino acids. About 500 amino acids are known and can be classified in many ways. They can be classified according to the core structural functional groups' locations as alpha- (α-), beta- (β-), gamma- (γ-) or delta- (δ-) amino acids; other categories relate to polarity, pH level, and side-chain group type (aliphatic, acyclic, aromatic, containing hydroxyl or sulfur, etc.). In the form of proteins, amino acids comprise the second-largest component (water is the largest) of human muscles, cells and other tissues.Outside proteins, amino acids perform critical roles in processes such as neurotransmitter transport and biosynthesis.
It contain more information about Amino acids and their structure. Then , contain both physical and chemical properties. Next Classification of amino acids based on nutritional requirements, based on metabolic fate, Position of NH2 group, etc.,
RNA- A polymer of ribonucleotides, is a single stranded structure. There are three major types of RNA- m RNA,t RNA and r RNA. Besides that there are small nuclear,micro RNAs, small interfering and heterogeneous RNAs. Each of them has a specific structure and performs a specific function.
Ribonucleic acid (RNA) is a polymeric molecule essential in various biological roles in coding, decoding, regulation, and expression of genes. RNA and DNA are nucleic acids, and, along with proteins and carbohydrates, constitute the four major macromolecules essential for all known forms of life. Like DNA, RNA is assembled as a chain of nucleotides, but unlike DNA it is more often found in nature as a single-strand folded onto itself, rather than a paired double-strand.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Richard's aventures in two entangled wonderlandsRichard 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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
2. In all prokaryotic and eukaryotic
organisms,
three main classes of RNA molecules
exist-
1) Messenger RNA(m RNA)
2) Transfer RNA (t RNA)
3) Ribosomal RNA (r RNA)
3. Comprises only 5% of the RNA in the cell
All members of the class ,function as
messengers carrying the information in a
gene to the protein synthesizing machinery
4. The 5’ terminal end is capped
by 7- methyl guanosine
triphosphate cap.
The cap is involved in the
recognition of mRNA by the
translating machinery
It stabilizes m RNA by
protecting it from 5’
exonuclease
5.
6. The 3’end of most m-RNAs have a
polymer of Adenylate residues
The tail prevents the attack by 3’
exonucleases
On both 5’ and 3’ end there are non
coding sequences which are not
translated
The intervening region between non
coding sequences present between 5’
and 3’ end is called coding region. This
region encodes for the synthesis of a
protein.
7. The m- RNA molecules are formed with
the help of DNA template during the
process of transcription.
The sequence of nucleotides in m RNA
is complementary to the sequence of
nucleotides on template DNA.
The sequence carried on m -RNA is
read in the form of codons.
A codon is made up of 3 nucleotides
The m-RNA is formed after processing
of heterogeneous nuclear RNA
9. Transfer RNA are the smallest of three major
species of RNA molecules
They are synthesized by the nuclear processing of
a precursor molecule
They transfer the amino acids from cytoplasm to
the protein synthesizing machinery, hence the
name t RNA.
They are easily soluble , hence called “Soluble
RNA or s RNA
They are also called Adapter molecules, since
they act as adapters for the translation of the
sequence of nucleotides of the m RNA in to specific
amino acids
9
10. 1) Primary structure- The nucleotide
sequence of all the t RNA molecules
allows extensive intrastand
complimentarity that generates a
secondary structure.
2) Secondary structure- Each single t-
RNA shows extensive internal base
pairing and acquires a clover leaf like
structure. The structure is stabilized by
hydrogen bonding between the bases and
is a consistent feature.
Structure of t-rna
11.
12. Secondary structure (Clover leaf
structure)
All t-RNA contain 5 main arms or
loops which are as follows-
a) Acceptor arm
b) Anticodon arm
c) D HU arm
d) TΨ C arm
e) Extra arm
13. The L shaped tertiary
structure is formed by further
folding of the clover leaf due
to hydrogen bonds between T
and D arms.
The base paired double
helical stems get arranged in
to two double helical
columns, continuous and
perpendicular to one another.
14. •The mammalian ribosome contains
two major nucleoprotein subunits—a
larger one with a larger subunit
(60S) and a smaller subunit (40S).
The 60S subunit contains a 5S
ribosomal RNA (rRNA), a 5.8S rRNA,
and a 28S rRNA; there are also
probably more than 50 specific
polypeptides.
14
15. The 40S subunit is smaller and
contains a single 18S rRNA and
approximately 30 distinct
polypeptide chains.
All of the ribosomal RNA
molecules except the 5S rRNA are
processed from a single 45S
precursor RNA molecule in the
nucleolus .
5S rRNA is independently
transcribed
16.
17. The functions of the ribosomal
RNA molecules in the ribosomal
particle are not fully understood,
but they are necessary for
ribosomal assembly and seem to
play key roles in the binding of
mRNA to ribosomes and its
translation
Recent studies suggest that an
rRNA component performs the
peptidyl transferase activity and
thus is an enzyme (a ribozyme).