This document discusses key concepts in physics related to speed, velocity, and acceleration. It defines speed and velocity, explaining that velocity includes both magnitude and direction. It describes how to calculate average speed, acceleration, and deceleration. Graphs of speed versus time and velocity versus time are examined, including how to determine acceleration from gradients and distance from areas. Free fall under gravity and the effects of air resistance on terminal velocity are also summarized.
This presentation covers vertical motion under gravity, effect of air resistance on free fall & graphs of free fall. I hope this PPT will be helpful for instructors as well as students.
Learn about various motion graphs through interesting graphics.This ppt also includes questions from past papers.It is ideal for educators and students alike who can learn the concepts and their application at the ame time.
This presentation covers vertical motion under gravity, effect of air resistance on free fall & graphs of free fall. I hope this PPT will be helpful for instructors as well as students.
Learn about various motion graphs through interesting graphics.This ppt also includes questions from past papers.It is ideal for educators and students alike who can learn the concepts and their application at the ame time.
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.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
(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.
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...!
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. LEARNING
OBJECTIVES
1.2 Motion
Core
• Define speed and calculate average
speed from total time / total distance
• Plot and interpret a speed-time graph or
a distance- time graph
• Recognise from the shape of a speed-
time graph when a body is
– at rest
– moving with constant speed
– moving with changing speed
• Calculate the area under a speed-time
graph to work out the distance travelled
for motion with constant acceleration
• Demonstrate understanding that
acceleration and deceleration are related
to changing speed including qualitative
analysis of the gradient of a speed-time
graph
• State that the acceleration of free fall
for a body near to the Earth is constant
Supplement
• Distinguish between speed and velocity
• Define and calculate acceleration using
time taken change of velocity
• Calculate speed from the gradient of a
distance-time graph
• Calculate acceleration from the gradient
of a speed-time graph
• Recognise linear motion for which the
acceleration is constant
• Recognise motion for which the
acceleration is not constant
• Understand deceleration as a negative
acceleration
• Describe qualitatively the motion of
bodies falling in a uniform gravitational field
with and without air resistance (including
reference to terminal velocity)
17. Acceleration is the rate at which an
object increases speed or velocity.
Acceleration = change in velocity
time taken
18. Acceleration is the rate at which an
object increases speed or velocity.
Acceleration = change in velocity
time taken
Also written as: a = v - u
t
19. Acceleration is the rate at which an
object increases speed or velocity.
Acceleration = change in velocity
time taken
Velocity measured in m/s
Time measured in s
Acceleration measured in m/s/s or m/s2
20. Example: a drag car increases its
velocity from zero to 60m/s in 3s.
a = v - u
t
21. Example: a drag car increases its
velocity from zero to 60m/s in 3s.
a = v - u
t
a = 60 – 0
3
22. Example: a drag car increases its
velocity from zero to 60m/s in 3s.
a = v - u
t
a = 60 – 0
3
a = 60 = 20m/s-2
3
23. Example: a drag car increases its
velocity from zero to 60m/s in 3s.
a = v - u
t
a = 60 – 0
3
a = 60 = 20m/s-2
3
Don’t forget that
acceleration is a vector
– it has size and
direction
25. Constant acceleration example
A B
6s
Car passes point A with a velocity of 10m/s. It has a steady (constant) acceleration of
4m/s2. What is the velocity when it passes point B?
26. Constant acceleration example
A B
6s
Car passes point A with a velocity of 10m/s. It has a steady (constant) acceleration of
4m/s2. What is the velocity when it passes point B?
Solution: car gains 4m/s of velocity every second. In 6s
it gains an extra 24m/s.
27. Constant acceleration example
A B
6s
Car passes point A with a velocity of 10m/s. It has a steady (constant) acceleration of
4m/s2. What is the velocity when it passes point B?
Solution: car gains 4m/s of velocity every second. In 6s
it gains an extra 24m/s.
Final velocity = initial velocity + extra velocity
28. Constant acceleration example
A B
6s
Car passes point A with a velocity of 10m/s. It has a steady (constant) acceleration of
4m/s2. What is the velocity when it passes point B?
Solution: car gains 4m/s of velocity every second. In 6s
it gains an extra 24m/s.
Final velocity = initial velocity + extra velocity
Final velocity = 10 + 24 = 34m/s
45. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
Acceleration can be calculated by the gradient of a velocity:time graph. (Remember
gradient is the difference up divided by the difference across)
Calculate the acceleration for each
of the 4 sections of the graph.
46. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
Acceleration can be calculated by the gradient of a velocity:time graph. (Remember
gradient is the difference up divided by the difference across)
Calculate the acceleration for each
of the 4 sections of the graph.
Acceleration = V - U
t
47. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
Acceleration can be calculated by the gradient of a velocity:time graph. (Remember
gradient is the difference up divided by the difference across)
Calculate the acceleration for each
of the 4 sections of the graph.
Acceleration = 40 - 0 = 4m/s2
10
48. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
Acceleration can be calculated by the gradient of a velocity:time graph. (Remember
gradient is the difference up divided by the difference across)
Calculate the acceleration for each
of the 4 sections of the graph.
Acceleration = 0 (no change in
velocity)
49. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
Acceleration can be calculated by the gradient of a velocity:time graph. (Remember
gradient is the difference up divided by the difference across)
Calculate the acceleration for each
of the 4 sections of the graph.
Acceleration = 20 - 0 = 2m/s2
10
50. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
Acceleration can be calculated by the gradient of a velocity:time graph. (Remember
gradient is the difference up divided by the difference across)
Calculate the acceleration for each
of the 4 sections of the graph.
Acceleration = 0 - 60 = -3m/s2
20
51. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
52. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
53. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
Area =
200m2
54. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
Area =
200m2
Area =
400m2
55. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
Area =
200m2
Area =
400m2
Area =
400m2
56. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
Area =
200m2
Area =
400m2
Area =
400m2
Area =
100m2
57. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
Area =
200m2
Area =
400m2
Area =
400m2
Area =
100m2
Area =
600m2
58. Velocity-time graphs
80
60
40
20
0
10 20 30 40 50
Velocity
m/s
Time/s
On a velocity – time (or speed – time) graph, the area under the line is numerically
equal to the distance travelled.
Remember that the area of a
triangle is ½ x base x height.
Area =
200m2
Area =
400m2
Area =
400m2
Area =
100m2
Area =
600m2
The total distance travelled = 200 + 400 + 400 + 100 + 600 = 1700m
62. Acceleration of free fall (g)
Which object
will hit the
ground first?
Obviously the
brick (because the
feather is slowed
much more by the
air)
63. Acceleration of free fall (g)
No air
resistance,
objects both
fall with the
same downward
acceleration.
In air
In a
vacuum
64. Acceleration of free fall (g)
No air
resistance,
objects both
fall with the
same downward
acceleration.
In air
In a
vacuum
Acceleration of
free fall =
9.8m/s2
Given the
symbol ‘g’
65. Acceleration of free fall (g)
No air
resistance,
objects both
fall with the
same downward
acceleration.
In air
In a
vacuum
Acceleration of
free fall =
9.8m/s2
Given the
symbol ‘g’
67. Acceleration and gravity
Falling objects
accelerate towards
the ground at
10m/s2 due to
gravity. The force
of gravity always
acts towards the
centre of the
Earth.
The atmosphere
creates an upward
force that slows
down falling
objects. This is
known as air
resistance or drag.
68. Acceleration and gravity
Falling objects
accelerate towards
the ground at
10m/s2 due to
gravity. The force
of gravity always
acts towards the
centre of the
Earth.
The atmosphere
creates an upward
force that slows
down falling
objects. This is
known as air
resistance or drag.
69. Acceleration and gravity
Falling objects
accelerate towards
the ground at
10m/s2 due to
gravity. The force
of gravity always
acts towards the
centre of the
Earth.
The atmosphere
creates an upward
force that slows
down falling
objects. This is
known as air
resistance or drag.
The larger the surface area of the
object, the larger the drag force
72. Acceleration and gravity
Speed
(m/s)
Time (s)
Drag
Weight
C
When drag equals the
force due to gravity there
is no resultant force and
the acceleration is zero.
The object continues at
terminal velocity.
Terminal velocity
73. LEARNING
OBJECTIVES
1.2 Motion
Core
• Define speed and calculate average
speed from total time / total distance
• Plot and interpret a speed-time graph or
a distance- time graph
• Recognise from the shape of a speed-
time graph when a body is
– at rest
– moving with constant speed
– moving with changing speed
• Calculate the area under a speed-time
graph to work out the distance travelled
for motion with constant acceleration
• Demonstrate understanding that
acceleration and deceleration are related
to changing speed including qualitative
analysis of the gradient of a speed-time
graph
• State that the acceleration of free fall
for a body near to the Earth is constant
Supplement
• Distinguish between speed and velocity
• Define and calculate acceleration using
time taken change of velocity
• Calculate speed from the gradient of a
distance-time graph
• Calculate acceleration from the gradient
of a speed-time graph
• Recognise linear motion for which the
acceleration is constant
• Recognise motion for which the
acceleration is not constant
• Understand deceleration as a negative
acceleration
• Describe qualitatively the motion of
bodies falling in a uniform gravitational field
with and without air resistance (including
reference to terminal velocity)