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Introduction to Statistics -
Sampling Techniques, Types of Statistics, Descriptive Statistics,
Inferential Statistics,
Variables and Types of Data: Qualitative, Quantitative, Discrete,
Continuous, Organizing and Graphing Data: Qualitative Data, Quantitative Data
Introduction to Statistics -
Sampling Techniques, Types of Statistics, Descriptive Statistics,
Inferential Statistics,
Variables and Types of Data: Qualitative, Quantitative, Discrete,
Continuous, Organizing and Graphing Data: Qualitative Data, Quantitative Data
This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
Formulating Hypothesis
Hypothesis Formulation is –
-the process of creating possible, tentative explanations for a given set of information.
-the whole Process of creating and formulating the hypothesis
How is Hypothesis Formulated
Reichenbach (1938) made a distinction between the two processes found commonly in any hypothesis formulation -
-Context of Discovery:
--Hypotheses is ‘discovered’ from earlier research findings, existing theories and personal observations, and experience.
-Context of justification:
--When a Researcher reconstructs his thoughts and communicates them in the form of a hypothesis to others, he uses the context of justification –
Steps in Formulation of Hypothesis
-Understand the area of problem
-Consider goal
-Identify variables
-identify the relationship between the variables.
-Think critically about hypothesis
-Express the idea as own hypothesis
Process of Hypothesis Formulation
-Understand the area of problem
Understand the problem that is being worked on.
-Consider goal
After selecting the problem & understanding the problem, objectives have to be selected according to the problem
-Identify variables
Must be define the variables.
Variables in hypothesis are testable not ?
Specify dependent and independent & others variables.
-Identify the relationship between the variables.
Variables are influence each other or not?
-Think critically about hypothesis
Hypothesis are testable, verifiable or not ? Which will make able to confirm the hypothesis.
-Express the idea as own hypothesis
Here researcher made the hypothesis in a Tentative Solution Statement manner
This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
Formulating Hypothesis
Hypothesis Formulation is –
-the process of creating possible, tentative explanations for a given set of information.
-the whole Process of creating and formulating the hypothesis
How is Hypothesis Formulated
Reichenbach (1938) made a distinction between the two processes found commonly in any hypothesis formulation -
-Context of Discovery:
--Hypotheses is ‘discovered’ from earlier research findings, existing theories and personal observations, and experience.
-Context of justification:
--When a Researcher reconstructs his thoughts and communicates them in the form of a hypothesis to others, he uses the context of justification –
Steps in Formulation of Hypothesis
-Understand the area of problem
-Consider goal
-Identify variables
-identify the relationship between the variables.
-Think critically about hypothesis
-Express the idea as own hypothesis
Process of Hypothesis Formulation
-Understand the area of problem
Understand the problem that is being worked on.
-Consider goal
After selecting the problem & understanding the problem, objectives have to be selected according to the problem
-Identify variables
Must be define the variables.
Variables in hypothesis are testable not ?
Specify dependent and independent & others variables.
-Identify the relationship between the variables.
Variables are influence each other or not?
-Think critically about hypothesis
Hypothesis are testable, verifiable or not ? Which will make able to confirm the hypothesis.
-Express the idea as own hypothesis
Here researcher made the hypothesis in a Tentative Solution Statement manner
Basic statistical & pharmaceutical statistical applicationsYogitaKolekar1
This is knowledge sharing PPT specially designed for Non-statisticians to understand basic fundamentals regarding statistics & related to pharmaceutical statistics.
How statistics involve in daily life as well as pharmaceutical industry etc., not limited.
#WhatisMeanByStatistics? #WhyStatistics? #HowStatisticsEssentialtoEverydayLife? #StatisticalApplicationsinDailyLife #Toothpaste
#IndependentDependentVariables #Tea #TypesofData #ClassificationofDiscreteVariableContinuousVariables #TypesofDataMeasurementScale
#StatisticalMethodsforAnalyzingData #ConceptofPopulationSampleandPointEstimate
#DescriptiveStatistics #InferentialStatistics
#MeasuresofCentralTendency #MeasuresofDispersion #RealLifeApplications #DataPresentation #PictorialView
#PharmaceuticalStatistics #ResearchDevelopment #Statistician
Notes of BBA /B.Com as well as BCA. It will help average students to learn Business Statistics. It will help MBA and PGDM students in Quantitative Analysis.
7 excellent reasons why statistics are important statsworkStats Statswork
Statistics are used to analyze what's happening within the world around us. In this data-driven world, all activities of ours are monitored by someone else every time. Statistics help us to convert whatever occurs in the past can be used in predicting the future. Statswork Is A Premier Statistics Consulting Company That Spearheaded Online Statistics Consultancy Service With Clientele Ranging From Educational Institutions, Academics, Corporations And Ngos. We Provide End-To-End Service And Assistance For Your Statistical Research And Analytical Needs From Data Collection, Data Mining, Data Analysis To Research Framework And Research Methodology.
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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.
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 .
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.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
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.
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.
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.
Lateral Ventricles.pdf very easy good diagrams comprehensive
Statistics in different fields of life
1. In today's world we are faced with situations everyday where
statistics can be applied. Statistics can be used to determine the
potential outcome of thousands of things where the human mind
alone wouldn't be able to. Statistics benefits all of us because we
are able to predict the future based on data we have previously
gathered. Being able to predict the future not only changes our
lifestyle but also helps us be more efficient and effective. With
statistics we can determine how we will live a year from now, ten
years from now, and so on. This is important because if we didn't
have data from the past to look upon, we wouldn't be able to
prolong our existence by avoiding recurring climate changes like
hurricanes and tsunamis and such. Things like these make our
lives easier and help us make educated decisions based on what
we already know because of statistics.
A good example is that based on data we have
gathered from the past, we know that it is extremely likely that in
specific parts of the world it will snow during winter and because
we know this, we can prepare by having warm clothes ready and
the proper equipment to deal with the snow. Statistics are used
all over the world. They can be applied in almost any situation and
can always help. They are used in explaining group behavior of
organisms, marketing research, and the list goes on and on. A
good example is how scientists observe the behavior of groups of
animals. Scientists can record data from a group of elephants and
determine that a certain percentage of elephant herds will defend
themselves from predators while the other percentage may run
2. away. This kind of data can help scientists predict the elephant's
lifestyle and culture. Statistics affects our daily life every day.
Researchers use statistics to advertise their products which in turn
we the consumer purchase. The price of the products we buy are
determined upon statistics which show the demand for the
product at that point in time and because of these statistics, we
the consumers pay a certain amount of money to buy the
product. Another example of how statistics affect me is in school.
Every year statistics are looked over and the curriculums for the
classes I take are based on data collected in the past. The
curriculums are modified and help the learning process. With
these statistics we are able to modify things to make them more
effective. This is why statistics is important in the first place - we
can improve our lifestyle with statistics. If we know how people
have lived in the past and how we have evolved, we can prepare
for the future and live longer and evolve more effectively. We can
tell from data gathered in the 90's that cigarette smoking in the
10th grade has been slowly declining over the years (1). From this
we can assume that something is being done correctly to bring
the statistics down. Another example is that in 1975, the USA
started paying more attention to the spousal homicide rates. The
USA took the proper precautions to help lower these rates and it
worked (2). Since 1975, the yearly spousal homicide rate has gone
down from 2300 to 800. Because of statistics, people were able to
make predictions and help save lives. In conclusion, statistics are a
major staple of our world today. They are used in practically any
situation and help improve our overall lifestyle. Statistics change
the way we think about tomorrow and the way we live today and
without them.
3. It is a fact of life that experimental results always show some
degree of random variation, and a chemist needs to be able to
handle and quantify the resultant uncertainties. For example, if
we measure the concentration of a solution to be 23.00 mg dm-3,
it is very unlikely that the true value is exactly 23.00 mg dm-3 - so
how do we describe the result in a way that conveys the inherent
uncertainty? The best that we can do might be to say that we are
95% confident that the true value lies between 22.86 mg dm-3
and 23.14 mg dm-3 - this is an example of a 95% confidence
interval. To calculate this ‘confidence interval’ we need some
simple mathematics to handle the random variation in
experimental data - this is a function of the branch of
mathematics called ‘statistics’.
Statistics also allows a chemist to make decisions, based on
probabilities, in a way that is understood and accepted universally
by other scientists. This is the area of statistics called hypothesis
testing, which provides a range of tests suitable for different
problems. For example, a number of replicate measures of a
pollutant in industrial waste water might suggest that a regulatory
level has been exceeded - but could the difference be due only to
random uncertainty in the measurement itself? A t-test could be
used to decide, with a defined level of confidence, whether the
regulation has been broken.
-------------------------
The sources of experimental variation fall into TWO main
categories:
4. • Variations in the measurement process itself, e.g. variations in
the output from a spectrophotometer when exactly the same
measurement is repeated.
• Variations in the system being measured, e.g. the emission
from a radioactive isotope shows random fluctuations in addition
to its long-term exponential decay.
1. Design of Experiments (DOE) uses statistical techniques to test
and construct models of engineering components and systems.
2. Quality control and process control use statistics as a tool to
manage conformance to specifications of manufacturing
processes and their products.
3. Time and methods engineering uses statistics to study
repetitive operations in manufacturing in order to set standards
and find optimum (in some sense) manufacturing procedures.
4. Reliability engineering uses statistics to measures the ability of
a system to perform for its intended function (and time) and has
tools for improving performance.
5. Probabilistic design uses statistics in the use of probability in
product and system design.
The most obvious answer runs as follows:
Understanding the physical world, from large scale processes
(e.g., the orbit of planets around the sun), to small scale processes
(e.g., the behavior of sub-atomic particles) involves
experimentation. The experiments physicists carry out produce
5. data, and statistics is required to make sense of the data. Usually
results are inductive. The physicist can use the observations in the
experiment to make general statements about the nature of the
universe (this is called "inferential" statistics).
Economics largely depends upon statistics. National income
accounts are multipurpose indicators for economists and
administrators, and statistical methods are used to prepare these
accounts. In economics research, statistical methods are used to
collect and analyze the data and test hypotheses. The relationship
between supply and demand is studied by statistical methods;
imports and exports, inflation rates, and per capita income are
problems which require a good knowledge of statistics.
Statistics plays an important role in banking. Banks
make use of statistics for a number of purposes. They work on the
principle that everyone who deposits their money with the banks
does not withdraw it at the same time. The bank earns profits out
of these deposits by lending it to others on interest. Bankers use
statistical approaches based on probability to estimate the
number of deposits and their claims for a certain day.
7. 0
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Pakistan India UK Egypt Japan
Birth Rate
Death Rate
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no. of goats
no. of sheep
MULTIPLE BAR CHART :-
Death & Birth Rate In different Countries
COMPONENT BAR CHART:-
No. Of Sheep & Goats In Different Cities Of Pak.