This document discusses the differences between living and non-living things. It states that living things can move, grow, breathe, need food, feel changes, and reproduce, while non-living things cannot do these things. The document provides examples of living things like plants and animals and non-living things like books. It then explores each of these characteristics in more detail for both living and non-living things.
Basic presentation of the parts of a plant and of the life cycle of plants. Pitched at about the 2nd, 3rd or 4th grade level. Lots of descriptive pictures and diagrams.
This is a Science unit about plants for elementary students.
Unit index:
- Plants are living things.
The needs of a plant.
Plant parts
- Tree, bush and grass.
- We eat plants.
- Wild and cultivated plants.
- We need plants.
Basic presentation of the parts of a plant and of the life cycle of plants. Pitched at about the 2nd, 3rd or 4th grade level. Lots of descriptive pictures and diagrams.
This is a Science unit about plants for elementary students.
Unit index:
- Plants are living things.
The needs of a plant.
Plant parts
- Tree, bush and grass.
- We eat plants.
- Wild and cultivated plants.
- We need plants.
Living and non living things for national science olympiadhemacolours
Colours Innovation Academy ( Pune - India ) is glad to share study material for NSO to promote science. Let's learn together.
Regards,
Team Colours
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THE LIVING ORGANISMS -CHARACTERISTICS AND HABITATS-3 CBSE-V CHAPTER-9BIOLOGY TEACHER
Organisms
An organism is simply defined as any living thing, ranging from microscopic bacteria to large elephants and everything in between.
Different types of plants and animals are found in different areas.
E.g. deserts have camel and cacti as plants. Beaches show coconut trees and crabs. Fishes and other marine animals inhabit the sea
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.
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.
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.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
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.
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 .
1. LIVING AND NON-LIVING THINGS
By ANIRUDDH KOMMAJOSYULA
III C
Delhi public school, nerul
2. Living Things Vs Non-living Things
Things that move,
grow, breathe, need
food, feel changes,
and reproduce are
called living things.
Things that do not
move, grow, breathe,
need food, feel
changes, or
reproduce are called
non-living things.
3.
4. Differences
Living Things Non-living Things
1. Living things can move on
their own.
2. Living things grow with
time.
3. Living things need air to
breathe.
4. Living things need food to
grow.
5. Living things feel changes
around them.
6. Living things reproduce.
7. Human beings, plants, and
animals are examples of
living things.
1. Non-living things cannot move
on their own.
2. Non-living things do not grow
with time.
3. Non-living things do not
breathe.
4. Non-living things do not need
food.
5. Non-living things do not feel
changes around them.
6. Non-living things do not
reproduce.
7. Book, pencil, bat etc. are
examples of non-living things.
5. MOVEMENT
Living things can move on their own.
Animals move from one place to another in search of
food and shelter. Dogs, cats, and human beings walk with
the help of their legs. Birds and bees fly with the help of
their wings. Fish move with the help of fins.
Plants do not move from place to place because they do
not have to look for food. Green plants make their own
food. Some plants move in a special way. For example, a
lotus flower opens out at sunrise and closes at night. The
leaves of Mimosa (Touch-me-not) plant close when
touched. Thus, plants show movement.
Non-living things like books, toys, and chairs do not
move on their own. They move only when someone
moves them.
6. GROWTH
Living things grow.
Plants and animals grow
with time.
A child grows into an adult.
A seed grows into a plant.
Non-living things like dolls,
cricket bats, tables etc.
do not grow.
7. ACTIVITY
Aim: To watch plants grow
Materials needed: Bean seeds, soil, a glass jar, and
water.
Method:
1. Take soil in a glass jar. Place bean seeds in it, and water
it.
2. Keep it out in the sun.
3. Water it regularly and watch the plants grow!
Observation: After a few days, you will see baby plants
growing from the seeds.
Conclusion: Plants are living things and so they grow.
8. BREATHE
All living things need air to breathe.
We breathe through our nose. Several other animals
breathe through their noses, too. Cockroaches,
butterflies, and mosquitoes breathe through air holes
in their body.
Plants breathe through tiny pores called stomata
present in their leaves.
Non-living things do not breathe.
9. FOOD
Living things need food to grow.
Food gives us energy to work and move.
Animals eat plants or flesh of other animals as food.
Plants do not need to go in search of food. They
make their own food in the presence of air, water,
and light.
Non-living things do not need food.
10. FEEL CHANGES
Living things feel changes.
We feel changes around us like hot and cold weather.
Animals: Most animals feel or sense changes around
them with the help of their sense organs, i.e., eyes (see),
ears (hear), nose (smell), tongue (taste), and skin
(touch). Animals like cockroach, grasshopper, and
butterfly have special body parts called antennae,
which help them to feel changes around them.
Plants: Plants also feel changes around them. For
example, Mimosa (Touch-me-not) plant ‘feels’ a touch. A
lotus flower opens out at sunrise and closes at night.
Plants can also feel light and grow towards it.
Non-living things do not feel changes.
11. REPRODUCE
The process by which living things produce more of their
own kind is called reproduction.
Animals: Animals reproduce by either laying eggs or
giving birth to young ones. A woman gives birth to a
baby, while a pigeon and a hen lay eggs from which
chicks come out.
Plants: Most plants reproduce with the help of seeds.
These seeds grow into new plants. Some plants also give
rise to new plants with the help of their roots, stems or
leaves. For example, rubber plant, money plant, and
sugar cane.
Non-living things do not reproduce.
