Accurate and precise estimation of discards is a major objective of data collection programs throughout the world. Discard reduction is also a major topic of the new Common Fisheries Policy (CFP) and the future Data Collection Multi-Annual Programme (DC-MAP). Using data from the Portuguese on-board observer programme that samples two otter trawl fisheries in ICES Division IXa, we compare two different approaches for estimating the sampling effort required to attain "assessment grade" discard estimates: a model-based approach (exponential-decay models) and a probability-based approach (based on classic sampling theory). We show that both approaches attain comparable sample size estimates and that the sample size required to attain precision objectives
varies across species and across fisheries being likely influenced by discard motifs. We demonstrate that sampling levels at least two fold higher than the present sampling levels would be required to attain the precision levels set in the current Data Collection Framework (DCF). We discuss the implications of these results in light of the future ability of European onboard sampling programs to detect, e.g., progressive reductions in discard levels.
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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.
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.
Astronomy Update- Curiosity’s exploration of Mars _ Local Briefs _ leadertele...
Portuguese Market and On-board Sampling Effort Review
1. Sampling
review
Jardim, E.,
Prista, N. &
Dias, M.
Introduction Portuguese Market and On-board
Data
Methods
Sampling Effort Review
Working document presented to PGCCDBS, 7-11 February 2011
Results
Conclusions
Jardim, E., Prista, N. & Dias, M.
February 5, 2011
2. Sampling
review
Jardim, E., Outline
Prista, N. &
Dias, M.
Introduction
Data
1 Introduction
Methods
Results
Conclusions 2 Data
3 Methods
4 Results
5 Conclusions
3. Sampling
review
Jardim, E., Introduction
Prista, N. &
Dias, M.
Introduction The implementation of the metier approach resulted in:
Data
a decrease in the precision of the length frequencies
Methods
estimates by species, due to the spread of sampling effort
Results
to new species and the reduction of trips sampled.
Conclusions
an increase in the number of strata to be sampled
on-board
The objective of this work is to optimize sampling effort by
computing the number of samples required to achieve the
precision levels defined by the DCF:
for length frequencies of the landings sampled at the
market
for total discards sampled on-board
4. Sampling
review
Jardim, E., Data
Prista, N. &
Dias, M.
Introduction
Data
Methods
On market:
Results number of individuals estimated by trip
Conclusions
data from 2009-2010,
by REGION, GEAR, SPECIES & QUARTER
On board:
weight discarded by trip
data from 2004-2010
by METIER (OTBDEF, OTBCRU) & QUARTER
Data is scarce and the breakdown by metier makes it
even scarcer, it was necessary to aggregate.
5. Sampling
review
Jardim, E., Methods
Prista, N. &
Dias, M.
Introduction
Data
Model CV = f (N) using exponential decay models (N
Methods
being number of samples)
Results
Conclusions
Compute N to achive 12.5% CV for market sampling or
20% for on-board
Compute 95% percentile of N as an indicator of a high
probability to achieve the objective and cover species with
more variability than average
Review the sampling plans
(Lots of technical details and statistical mambo-jambo to be
provided if requested)
6. Sampling
review
Jardim, E., Data aggregation for
Prista, N. &
Dias, M. market sampling
Introduction
Data
Methods Each pair used in model refers to
Results
the CV of the total number of individuals sampled
Conclusions the number of samples collected from which the CV above
was computed
Each pair was computed by GEAR (aggregation of
metiers), QUARTER, REGION & SPECIES
Each model was fit to distinct dimensions of the data
collapsing all other dimensions
for each REGION
for each GEAR
for each combination of REGION and GEAR
8. Sampling
review
Jardim, E., Models for on-board
Prista, N. &
Dias, M. sampling
Introduction
Data
Methods
Results
Conclusions
9. Sampling
review
Jardim, E., Preliminary conclusions
Prista, N. &
Dias, M. for market sampling
Introduction
Region Metier SampEff.2010 SampEff.2011
Data 1 North FPO MOL >=29 0 0 5 17
2 North GNS DEF 80-99 0 0 2 21
Methods 3 North GNS DEF 60-79 0 0 3 21
4 North GTR DEF >=100 0 0 8 12
Results
5 North LLS DEF 0 0 0 1 17
Conclusions 6 North OTB DEF 65-69 0 0 6 18
7 North PS SPF >=16 0 0 7 13
8 North TBB CRU >=20 0 0 1 17
9 Center FPO MOL >=29 0 0 5 17
10 Center GNS DEF 80-99 0 0 3 19
11 Center GTR DEF >=100 0 0 7 4
12 Center LLD LPF 0 0 0 2 2
13 Center LLS DEF 0 0 0 2 17
14 Center LLS DWS 0 0 0 2 17
15 Center OTB CRU >=70 0 0 6 4
16 Center OTB CRU 55-59 0 0
17 Center OTB DEF 65-69 0 0 1 4
18 Center PS SPF >=16 0 0 5 13
19 South FPO MOL >=29 0 0 5 17
20 South GNS DEF 80-99 0 0 2 21
21 South LLD LPF 0 0 0 1 1
22 South LLS DEF 0 0 0 1 17
23 South OTB CRU >=70 0 0 5 18
24 South OTB CRU 55-59 0 0
25 South OTB DEF 65-69 0 0 2 18
26 South PS SPF >=16 0 0 2 13
27 Total 84 338
10. Sampling
review
Jardim, E., Preliminary conclusions
Prista, N. &
Dias, M. for on-board sampling
Introduction
Data
Methods Model point estimate is 15 samples per quarter for both
Results
metiers
Conclusions
Sampling theory estimate is 18-20 samples per quarter
95 percentile is 48 samples per quarter
Increase sampling effort up to 192 trips per year for each
metier
The sampling effort is not applicable due to high costs
and lack of human resources. In 2011 on-board sampling
effort will be increased up to the maximum possible,
taking into account other metiers and resources available.
11. Sampling
review
Jardim, E., The End
Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
Conclusions
12. Sampling
review
Jardim, E., Details: codes
Prista, N. &
Dias, M.
Introduction
Data
Methods
Results N, C, S = Norte, Centro Sul
Conclusions
OTB, PS, GTR, GNS, FPO, LLS = trawl, purse seine,
trammel nets, gill nets, traps, longliners
Models: exp, strexp, exp log, strexp log = exponential,
streched exponential, exponential with log errors, streched
exponential with log errors.
13. Sampling
review
Jardim, E., Details: Methods
Prista, N. &
Dias, M.
Models are fit to distinct data breakdowns, All, by
Introduction
REGION, by GEAR, by REGION & GEAR = 60 models
Data
Methods
(only market)
Results Models are fit to both metiers merged (only on-board)
Conclusions Fits are analysed by visual inspection of residuals, AIC,
likelihood, precision of parameters, precision of the
estimated number of samples to achieve objective.
Fits selected are averaged considering the inverse of the
residuals variance (only market)
Number of samples are allocated considering the highest
number for each combination of GEAR & REGION (only
market).
Number of samples are estimated by the best model as
well as with sampling theory (only on-board).
14. Sampling
review
Jardim, E., Details CV (µ) = CV (τ )
Prista, N. &
Dias, M.
Introduction
Data
Methods
Results
τ =C ∗µ
ˆ ˆ
Conclusions
var (ˆ) = C 2 ∗ var (ˆ)
τ µ
C 2 ∗ var (ˆ)
µ var (ˆ)
µ
CV (ˆ) =
τ = = CV (ˆ)
µ
C ∗µ ˆ µ
ˆ
15. Sampling
review
Jardim, E., Details τ & var (τ )
Prista, N. &
Dias, M.
Introduction Consider N the number of individuals, i = 1 . . . l to index
Data length classes and j = 1 . . . s to index sampled trips.
Methods
Results N= Ni
Conclusions i
Σ = var (N) = var (Ni ) + 2 ∗ cov (Ni , Nj )
i i j=i+1
Ni = Nij
j
Nij ∗w
j ( wj − Ni )2
var (Ni ) =
s ∗ (s − 1)
16. Sampling
review
Jardim, E., Details DPUE & var (DPUE )
Prista, N. &
Dias, M.
Introduction
Data
Methods Let i be the index of the number of hauls sampled in trip j
Results (i = 1, 2, .., nj , j = 1, 2, .., nt ), d be total weight discarded (in
Conclusions
kg) and h be the haul duration (in hours)
nj di,j
i=1 hi,j
DPUE j = nj
and nt
DPUE j
DPUE = i=1
nt
nt
j=1 (DPUEj −DPUE )2
VAR(DPUE ) = nt (nt −1)
17. Sampling
review
Jardim, E., Details residuals of
Prista, N. &
Dias, M. on-board model
Introduction
Data
Methods
Results
Conclusions