the various measures of morbidity and mortality such as prevalence, incidence, and mortality rate.
mapping and application of measures of diseases.
rate, ratio, and proportion which are the basic tools to measure the disease in a a population.
Relationship between prevalence and incidence rate.
Frequency measures of health is an important aspect in the planing of the type of services required in a specific population. This is due to the fact that they are able to indicate the type and level of health problems being faced In that population during a specified period of time.
tHESE SLIDES ARE PREPAREED TO UNDERSTAND about DISPOSAL OF WASTE IN EASY WAY Important links- NOTES- https://mynursingstudents.blogspot.com/ youtube channel https://www.youtube.com/c/MYSTUDENTSU... CHANEL PLAYLIST- ANATOMY AND PHYSIOLOGY-https://www.youtube.com/playlist?list=PL93S13oM2gAPM3VTGVUXIeswKJ3XGaD2p COMMUNITY HEALTH NURSING- https://www.youtube.com/playlist?list=PL93S13oM2gAPyslPNdIJoVjiXEDTVEDzs CHILD HEALTH NURSING- https://www.youtube.com/playlist?list=PL93S13oM2gANcslmv0DXg6BWmWN359Gvg FIRST AID- https://www.youtube.com/playlist?list=PL93S13oM2gAMvGqeqH2ZTklzFAZhOrvgP HCM- https://www.youtube.com/playlist?list=PL93S13oM2gAM7mZ1vZhQBHWbdLnLb-cH9 FUNDAMENTALS OF NURSING- https://www.youtube.com/playlist?list=PL93S13oM2gAPFxu78NDLpGPaxEmK1fTao COMMUNICABLE DISEASES- https://www.youtube.com/playlist?list=PL93S13oM2gAOWo4IwNjLU_LCuhRN0ZLeb ENVIRONMENTAL HEALTH- https://www.youtube.com/playlist?list=PL93S13oM2gAPkI6LvfS8Zu1nm6mZi9FK6 MSN- https://www.youtube.com/playlist?list=PL93S13oM2gAOdyoHnDLAoR_o8M6ccqYBm HINDI ONLY- https://www.youtube.com/playlist?list=PL93S13oM2gAN4L-FJ3s_IEXgZCijGUA1A ENGLISH ONLY- https://www.youtube.com/playlist?list=PL93S13oM2gAMYv2a1hFcq4W1nBjTnRkHP facebook profile- https://www.facebook.com/suresh.kr.lrhs/ FACEBOOK PAGE- https://www.facebook.com/My-Student-S... facebook group NURSING NOTES- https://www.facebook.com/groups/24139... FOR MAKING EASY NOTES YOU CAN ALSO VISIT MY BLOG – BLOGGER- https://mynursingstudents.blogspot.com/ Instagram- https://www.instagram.com/mystudentsu... Twitter- https://twitter.com/student_system?s=08 #PEM, #ASHA,#EPIDEMIOLOGY,#ICDS,#nurses,#ASSESSMENT, #APPEARENCE,#PULSE,#GRIMACE,#REFLEX,#RESPIRATION,#RESUSCITATION,#NEWBORN,#BABY,#VIRGINIA, #CHILD, #OXYGEN,#CYANOSIS,#OPTICNERVE, #SARACHNA,#MYSTUDENTSUPPORTSYSTEM, #rashes,#nursingclasses, #communityhealthnursing,#ANM, #GNM, #BSCNURING,#NURSINGSTUDENTS, #WHO,#NURSINGINSTITUTION,#COLLEGEOFNURSING,#nursingofficer,#COMMUNITYHEALTHOFFICE
Morbidity has been defined as any departure, subjective or objective, from a state of physiological or psychological well-being. In practice, morbidity encompasses disease, injury, and disability.
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.
Frequency measures of health is an important aspect in the planing of the type of services required in a specific population. This is due to the fact that they are able to indicate the type and level of health problems being faced In that population during a specified period of time.
tHESE SLIDES ARE PREPAREED TO UNDERSTAND about DISPOSAL OF WASTE IN EASY WAY Important links- NOTES- https://mynursingstudents.blogspot.com/ youtube channel https://www.youtube.com/c/MYSTUDENTSU... CHANEL PLAYLIST- ANATOMY AND PHYSIOLOGY-https://www.youtube.com/playlist?list=PL93S13oM2gAPM3VTGVUXIeswKJ3XGaD2p COMMUNITY HEALTH NURSING- https://www.youtube.com/playlist?list=PL93S13oM2gAPyslPNdIJoVjiXEDTVEDzs CHILD HEALTH NURSING- https://www.youtube.com/playlist?list=PL93S13oM2gANcslmv0DXg6BWmWN359Gvg FIRST AID- https://www.youtube.com/playlist?list=PL93S13oM2gAMvGqeqH2ZTklzFAZhOrvgP HCM- https://www.youtube.com/playlist?list=PL93S13oM2gAM7mZ1vZhQBHWbdLnLb-cH9 FUNDAMENTALS OF NURSING- https://www.youtube.com/playlist?list=PL93S13oM2gAPFxu78NDLpGPaxEmK1fTao COMMUNICABLE DISEASES- https://www.youtube.com/playlist?list=PL93S13oM2gAOWo4IwNjLU_LCuhRN0ZLeb ENVIRONMENTAL HEALTH- https://www.youtube.com/playlist?list=PL93S13oM2gAPkI6LvfS8Zu1nm6mZi9FK6 MSN- https://www.youtube.com/playlist?list=PL93S13oM2gAOdyoHnDLAoR_o8M6ccqYBm HINDI ONLY- https://www.youtube.com/playlist?list=PL93S13oM2gAN4L-FJ3s_IEXgZCijGUA1A ENGLISH ONLY- https://www.youtube.com/playlist?list=PL93S13oM2gAMYv2a1hFcq4W1nBjTnRkHP facebook profile- https://www.facebook.com/suresh.kr.lrhs/ FACEBOOK PAGE- https://www.facebook.com/My-Student-S... facebook group NURSING NOTES- https://www.facebook.com/groups/24139... FOR MAKING EASY NOTES YOU CAN ALSO VISIT MY BLOG – BLOGGER- https://mynursingstudents.blogspot.com/ Instagram- https://www.instagram.com/mystudentsu... Twitter- https://twitter.com/student_system?s=08 #PEM, #ASHA,#EPIDEMIOLOGY,#ICDS,#nurses,#ASSESSMENT, #APPEARENCE,#PULSE,#GRIMACE,#REFLEX,#RESPIRATION,#RESUSCITATION,#NEWBORN,#BABY,#VIRGINIA, #CHILD, #OXYGEN,#CYANOSIS,#OPTICNERVE, #SARACHNA,#MYSTUDENTSUPPORTSYSTEM, #rashes,#nursingclasses, #communityhealthnursing,#ANM, #GNM, #BSCNURING,#NURSINGSTUDENTS, #WHO,#NURSINGINSTITUTION,#COLLEGEOFNURSING,#nursingofficer,#COMMUNITYHEALTHOFFICE
Morbidity has been defined as any departure, subjective or objective, from a state of physiological or psychological well-being. In practice, morbidity encompasses disease, injury, and disability.
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.
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.
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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
3. RATIO
● The value obtained by dividing one
quantity by other - X/Y.
● relations between two quantities.
● Numerator is not a part of denominator.
Ex. - Male : Female ratio
WBC : RBC ratio
4. RATE
● expresses a change in one quantity (the numerator) with respect to
another quantity (the denominator).
● ‘Time’ is included in the denominator.
● Ex. - Velocity (e.g., 10 m per second) is a rate.
5. Proportion
● Relation between two quantities.
● Numerator always parts of denominator.
Ex. - prevalence, case fatality are proportions.
8. Prevalence
number of individuals having a disease at a particular point in time
P = ——————————————————————————————
number of individuals in the population at risk at that point in time
9. Prevalence
Point prevalence
● the amount of disease in a population at a
particular point in time.
Period prevalence
● number of cases that are known to have
occurred during a specified period of time.
● Ex. - a year (annual prevalence).
11. Cumulative incidence Incidence Rate
● Proportion
number of individuals that become diseased during a particular period
———————————————————————————————
number of healthy individuals in population at beginning of that period
● Rate
number of new cases of disease that occur in a population during a
particular period of time
—————————————————————————————————
the sum, over all individuals, of the length of time at risk of developing
disease
12. The relationship between prevalence and
incidence rate
● P = I x D
● This means that a change in prevalence can be due to:
1 - a change in incidence rate.
2 - a change in the average duration of the disease.
3 - a change in both incidence rate and duration.
13. Attack rate Secondary attack rate
● describe the proportion of animals that develop
the disease, when period of risk is brief.
● Rate at which disease is spreading.
● Expressed in %.
● proportion of cases of a transmissible disease
that develop when contact with the primary
case.
14. Mortality
● When the relevant outcome is death.
● Rather than new case of a specific
disease.
15. Cumulative mortality
● estimated same as cumulative incidence.
● number of individuals that die during a particular period
number of individuals in the population at the beginning of the period
16. Mortality rate
● Calculated same as incidence rate.
number of deaths due to a disease that occur in a population particular
period of time
M=
the sum over all individuals, of the length of time at risk of dying
17. Death rate
● The death rate is the total mortality rate for all diseases
● rather than one specific disease, in a population.
18. Case fatality rate
● The tendency for a condition to cause the death of affected animals in a
specified time is the case fatality.
● proportion of diseased animals that die.
number of deaths
number of diseased animals
19. Measures
CRUDE
● Crude prevalence, incidence and mortality
values are an expression of the amount of
disease and deaths in a population as a whole.
● no account of the structure of the population
affected.
SPECIFIC
● describe disease occurrence in specific
categories of the population.
● related to host such as age, sex, breed and
method of husbandry, and, in man, occupation
and socio-economic group.
● convey more information than crude measures
on the pattern of disease.
● can indicate categories of animal that are
particularly at risk of disease, and can provide
evidence on its cause.
20. Measures
Adjusted (standardised)
● Remove confounding of different age structures.
● Adjusting the crude values.
● reflect the values that would be expected if the potentially confounding
characteristics were similarly distributed in the two study populations.
21. Mapping
● displaying the geographical (spatial) distribution of disease by
drawing maps (cartography).
● recording of areas where diseases exist but also in investigating
the mode and direction of transmission of infectious diseases.
22. POINT/DOT MAP
● illustrate outbreaks of disease in
discrete locations, by circles, squares,
dots or other symbols.
● Qualitative.
● Direction of spread of disease.
24. PROPORTIONAL
CIRCLE MAP
● Morbidity and mortality can be
depicted using circles whose area is
proportional to the amount of disease
or deaths.
● Shading is used to give the
impression of spheres on a two-
dimensional map.
25. CHOROPLETHIC
MAP
● Greek: choros = ‘an area’, ‘a region’;
plethos = ‘a throng’, ‘a crowd’, ‘the
population.
● display quantitative information as
discrete shaded units of areas.
● graded in intensity to represent the
variability of the mapped data.
26. ISO PLETHIC
MAP
● True boundaries between
different values is depicted by
joining all points of equal value
by a line.
27. Why should we
measure it?
● Describe the extent and nature of
disease n community - assist in
establishing priorities.
● Provide essential data for research.
● Starting point for etiological disease,
help in PREVENTION.
● Monitoring and evaluation of disease
control activities.
