Developing the Procedure (Sampling, Validation and Protocols)
The document discusses sampling methods and procedures. It defines key terms like population and sample. It explains that sampling aims to obtain samples that are representative and homogeneous of the larger population. The appropriate sampling method depends on factors like the physical state and chemistry of the material. Common sampling techniques are discussed for solids, liquids, and gases. Key steps in sampling include obtaining a gross sample and reducing it to a lab sample. Proper storage and preservation of samples is also covered.
Industrial wastewater treatment describes the processes used for treating wastewater that is produced by industries as an undesirable by-product. After treatment, the treated industrial wastewater (or effluent) may be reused or released to a sanitary sewer or to a surface water in the environment. Some industrial facilities generate wastewater that can be treated in sewage treatment plants. Most industrial processes, such as petroleum refineries, chemical and petrochemical plants have their own specialized facilities to treat their wastewaters so that the pollutant concentrations in the treated wastewater comply with the regulations regarding disposal of wastewaters into sewers or into rivers, lakes or oceans.
This PPT will help students, researchers, Pollution Control Board officials, Environmentalists and Industrial fraternity to know about Sampling techniques of Hazardous waste. This is an academic PPT presented by me as a part of Post Graduate course in Environmental Science at Department of Environmental Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
Industrial wastewater treatment describes the processes used for treating wastewater that is produced by industries as an undesirable by-product. After treatment, the treated industrial wastewater (or effluent) may be reused or released to a sanitary sewer or to a surface water in the environment. Some industrial facilities generate wastewater that can be treated in sewage treatment plants. Most industrial processes, such as petroleum refineries, chemical and petrochemical plants have their own specialized facilities to treat their wastewaters so that the pollutant concentrations in the treated wastewater comply with the regulations regarding disposal of wastewaters into sewers or into rivers, lakes or oceans.
This PPT will help students, researchers, Pollution Control Board officials, Environmentalists and Industrial fraternity to know about Sampling techniques of Hazardous waste. This is an academic PPT presented by me as a part of Post Graduate course in Environmental Science at Department of Environmental Science, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
Water sampling Definition Sampling techniques water pollutants
outlines
What is sample?
Water sampling
Why water to be sampled?
Water sampling techniques
Methods of water sample collection
Sample collection and storage equipment
Quality assurance in water sampling
Safety for sample taker
Water sample collection
Sample is a small part or quantity that possibly show what the whole is like.
Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost effective and manageable.
The primary goal of water sampling is to observe and measure how water quality changes over time.
An analysis of a water supply may be required to find out either:
water is safe to drink or
it needs to be treated before consumption.
It is important to collect a sample of water representative of the whole supply to achieve the above purpose.
Care MUST be taken during sampling, transporting, and storing of sampled water to avoid accidental contamination.
A brief introduction on passive sampling, with explanation of the general processes and uses, disadvantages or advantages, and comparison to biomonitors and grab sampling.
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.
Water sampling Definition Sampling techniques water pollutants
outlines
What is sample?
Water sampling
Why water to be sampled?
Water sampling techniques
Methods of water sample collection
Sample collection and storage equipment
Quality assurance in water sampling
Safety for sample taker
Water sample collection
Sample is a small part or quantity that possibly show what the whole is like.
Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost effective and manageable.
The primary goal of water sampling is to observe and measure how water quality changes over time.
An analysis of a water supply may be required to find out either:
water is safe to drink or
it needs to be treated before consumption.
It is important to collect a sample of water representative of the whole supply to achieve the above purpose.
Care MUST be taken during sampling, transporting, and storing of sampled water to avoid accidental contamination.
A brief introduction on passive sampling, with explanation of the general processes and uses, disadvantages or advantages, and comparison to biomonitors and grab sampling.
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.
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.
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.
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 .
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
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.
2. SAMPLING AND EVALUATION OF
EXPERIMENTAL DATA
Population - The group of things, items or
units under investigation
Sample - Obtained by collecting
information only about some members
of a "population“
Sampling – Act of collecting sample to
produce meaningful information.
2
3. SAMPLING
3
Sampling is the process to get a representative and
homogeneous sample.
Representative means that content of analytical
sample reflects content of bulk sample.
Homogeneous means that the analytical sample
has the same content throughout.
4. SAMPLING
Deciding how to obtain a sample for analysis depend
on:
A. The size of the bulk to be sampled.
B. The physical state of the fraction to be analyzed (solid,
liquid, gas)
C. The chemistry of the material to be assayed.
(Nothing can be done that would destroy or alter the
identity or quantity of the analyte)
4
5. In a chemical analysis :
A chemical analysis is usually
performed on only small portion of
the material collected to be
characterized.
If the amount of material is very small
and it is not needed for further use,
then the entire samples may be
used for analysis.
5
9. 9
Obtaining a representative sample is the first step of an analysis.
The gross sample is several small portions of the sample.
This is reduced to provide a laboratory sample.
An aliquot of this sample is taken for the analysis sample.
10. 10
Identify the population from
which the sample is to be
obtained.
Collect a gross sample that
is truly representative of the
population being sampled.
Reduce the gross sample to
a laboratory sample that is
suitable for analysis.
Steps involved in sampling bulk material
11. SAMPLING GASES
Tend to be homogeneous.
Large volume of samples is required because of their
low density.
Liquid displacement method: The sample must has
little solubility in the liquid and does not react with
the liquid
Breath sample: The subject could blow into evacuated
bag.
11
12. SAMPLING SOLID
Inhomogeneity of the material, make sampling of solids
more difficult.
The easiest way to sample a material is grab sample –
the sample taken at random and assumed to be
representative.
The gross sample must be reduced in size to obtain a
laboratory sample.
Solid samples may need drying
12
13. CONING AND QUARTERING
This process is continued until the gross sample is
small enough to be transported to the laboratory.
13
Sampling Solids
15. 3. There is no specific technique that can be used for
taking the samples. Using an example, explain how
to sample either a solid, liquid, or gas sample. (5
marks)
i) Sampling solid
Using the method cone and quarter.
Divide a pile of material into quarter.
Take a sample from each quarter of the pile and
crush these sample and form into a smaller conical
pile.
Flatten the conical pile and cut into equal quarters.
Two opposite quarters are chosen at random.
Crush the quarter further.
The whole steps are repeated until a laboratory
samples obtain.
16. SAMPLING LIQUIDS
Liquid samples are homogeneous and are much easier
to sample.
The gross sample can be relatively small.
If liquid samples are not homogeneous, and have only
small quantity, they can be shaken and sampled
immediately.
Sampling depends on the types of liquids:
i) large volume of liquids (impossible to mix)
ii) large stationary liquids (lakes, rivers)
iii) biological fluids
16
17. F R E E P O W E R P O I N T T E M P L A T E :
W W W . B R A I N Y B E T T Y . C O M 17
• If water sample is taken from the river,
then the water samples is collected at
the SURFACE, MIDDLE and at the
BOTTOM of the river bed.
18. SAMPLE STORAGE AND
PRESERVATION
18
Samples storage purpose:
There is a time gap between when the sample
is taken and the actual analysis is being carried
out.
For liquids samples, make sure that it is kept in
bottles with stoppers.
Acidic liquid samples can be stored in glass container
whereas basic liquid samples in plastic container.
Solid samples is easier to keep and have less chance to be
adulterated by foreign matters.
Sometimes it can also get absorbed or adsorbed to the wall
of the container.
19. What are the problems encounter during storage of
samples?
The sample can be contaminated by foreign matter .
There is a lost of analyte during storage.
Decomposition of sample.
The sample should not react with the wall of the
container or get adulterated.
During storage of samples, for example liquid samples,
sometimes there is a lost of analyte if it is volatile .[so
the container should be closed tightly]
F R E E P O W E R P O I N T T E M P L A T E :
W W W . B R A I N Y B E T T Y . C O M 19
20. SAMPLE STORAGE AND
PRESERVATION
Preparing a laboratory sample
Converting the sample to a useful form:
Solids are usually ground to a suitable particulate size to get a
homogeneous sample.
Dry the samples to get rid of absorption water. 20
An important aspect of the sampling process
Samples are preserved to prevent from:
Decomposition
Precipitation of metals from water samples
Loss of water from hygroscopic material
Loss of volatile analytes from water samples
21. DEFINING REPLICATE SAMPLES
Replicate samples are always performed unless the
quantity of the analyte, expense or other factors
prohibit.
Replicate samples are portion of a material of
approximately the same size that is carried through
an analytical procedure at the same time and the
same way.
Obtaining replicate data on samples improves the
quality of the results and provides a measure of their
reliability.
21
22. PREPARING SOLUTIONS OF THE
SAMPLE
A solvent is chosen that dissolves the whole sample
without decomposing the analyte.
Sources of error :
i) Incomplete dissolution of the analyte.
ii) Losses of analyte by the volatilization.
iii) Introduction of analyte as a solvent
contamination.
iv) Contamination from the reaction of the solvent
with vessel walls.
22
23. THANK YOU!
F R E E P O W E R P O I N T T E M P L A T E :
W W W . B R A I N Y B E T T Y . C O M 27