This document summarizes a study examining how the Quasi-Biennial Oscillation (QBO) can modulate the atmospheric response to projected Arctic sea ice loss. The study uses the Whole Atmosphere Community Climate Model to simulate different phases of the QBO and responses to historical and future sea ice conditions. It finds that a weaker early winter polar vortex occurs during the QBO's easterly phase, leading to constructive wave interference. A North Atlantic Oscillation-like response is seen in the QBO's westerly phase, while Siberian cold extremes occur in the easterly phase. The results suggest the QBO can influence both stratospheric and tropospheric/surface responses through the Holton-
The Pan-Arctic Impacts of Thinning Sea IceZachary Labe
The Arctic is rapidly changing. However, long-term observations of trends in Arctic sea-ice thickness are still quite limited. In this presentation, Zachary will discuss the different methods (satellite instruments and climate model simulations) of observing sea-ice thickness in order to understand changes in the recent Arctic amplification era. He will also highlight the far-reaching environmental and societal impacts from a thinning Arctic sea-ice cover.
Communicating Arctic climate change through data-driven storiesZachary Labe
Arctic Science Summit Week 2021 (Session 2: “The 4 Essential Cs - Coordination, Communication, Community, and Collaboration”):
In this presentation, I will discuss the power of sharing Arctic climate change information through accessible and engaging data visualizations. In particular, I will focus on using social media (Twitter) as one tool for communicating science to broad audiences.
The Pan-Arctic Impacts of Thinning Sea IceZachary Labe
The Arctic is rapidly changing. However, long-term observations of trends in Arctic sea-ice thickness are still quite limited. In this presentation, Zachary will discuss the different methods (satellite instruments and climate model simulations) of observing sea-ice thickness in order to understand changes in the recent Arctic amplification era. He will also highlight the far-reaching environmental and societal impacts from a thinning Arctic sea-ice cover.
Communicating Arctic climate change through data-driven storiesZachary Labe
Arctic Science Summit Week 2021 (Session 2: “The 4 Essential Cs - Coordination, Communication, Community, and Collaboration”):
In this presentation, I will discuss the power of sharing Arctic climate change information through accessible and engaging data visualizations. In particular, I will focus on using social media (Twitter) as one tool for communicating science to broad audiences.
Improving scientific graphics of climate (change) dataZachary Labe
Creating visuals of data is a critical part of our jobs as scientists. We use figures for journal publications, presentations, posters, and science communication. This week we'll discuss a framework for making better figures, particularly in the climate sciences. I will also give examples of what not to do, and how we can improve these figures moving forward. e
Improving science (communication) through data visualizationZachary Labe
Creating visuals of data is an important part of our jobs as scientists. We use figures for journal publications, presentations, posters, lab group meetings, science communication, and more. In this workshop, we'll use examples from climate science to discuss a framework and network of resources available for making accessible figures. I will also share examples of what not to do and how to improve these figures moving forward.
Substantial disagreement continues between modeling studies in attributing midlatitude climate extremes to Arctic sea-ice anomalies. This is a result of uncertainties due to internal variability, nonlinear interactions, model biases, or more likely a combination of these effects. In this study, we use large ensembles from two high-top atmospheric general circulation models (SC-WACCM4 and E3SM) to separate the sea ice-forced signal from atmospheric internal variability (noise). Following protocol for the Polar Amplification Model Intercomparison Project (PAMIP), each simulation is prescribed with either pre-industrial, present-day, or future levels of sea-ice concentration, which are associated with global warming projections of 2°C. We use 300 ensemble members per simulation to obtain large sample sizes for robust statistics in the context of internal variability.
While an equatorward shift of the eddy-driven jet is found in boreal winter, the response to future sea-ice loss is small relative to climatology and highly sensitive to the number of ensemble members considered. On average, a sea ice-forced signal in the large-scale circulation cannot be distinguished from atmospheric internal variability in our simulations. A low signal-to-noise ratio is also demonstrated in the stratosphere, where the sign of the polar vortex response can be interpreted differently depending on the ensemble size. However, the local thermodynamic effects are statistically significant with strong surface warming and increases in precipitation found in the vicinity of newly ice-free areas. This warming is generally confined to the Arctic, and there is little response in the midlatitudes. Our results highlight the important role of internal variability in the extratropics and emphasize the need for especially large ensembles (>150-200 members) when assessing the dynamical response to both present-day and future Arctic sea-ice loss. (from https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/367289)
A talk about the climate history of earth, and what may have effected it. Given as part of the exam in Climate Physics course at the University of Aarhus
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
Reexamining future projections of Arctic climate linkagesZachary Labe
10 May 2024…
Atmospheric and Oceanic Sciences Student/Postdoc Seminar (Presentation): Reexamining future projections of Arctic climate linkages, Princeton University, USA.
References...
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss,
Geophysical Research Letters, DOI:10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation.
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
Revisiting projections of Arctic climate change linkagesZachary Labe
16 November 2023…
Department Seminar (Presentation): Revisiting projections of Arctic climate change linkages, Tongji University, Shanghai, China. Remote Presentation.
References:
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI: 10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI: 10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?
Journal of Climate, DOI: 10.1175/JCLI-D-20-0613.1
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI: 10.1029/2022EA002348
Improving scientific graphics of climate (change) dataZachary Labe
Creating visuals of data is a critical part of our jobs as scientists. We use figures for journal publications, presentations, posters, and science communication. This week we'll discuss a framework for making better figures, particularly in the climate sciences. I will also give examples of what not to do, and how we can improve these figures moving forward. e
Improving science (communication) through data visualizationZachary Labe
Creating visuals of data is an important part of our jobs as scientists. We use figures for journal publications, presentations, posters, lab group meetings, science communication, and more. In this workshop, we'll use examples from climate science to discuss a framework and network of resources available for making accessible figures. I will also share examples of what not to do and how to improve these figures moving forward.
Substantial disagreement continues between modeling studies in attributing midlatitude climate extremes to Arctic sea-ice anomalies. This is a result of uncertainties due to internal variability, nonlinear interactions, model biases, or more likely a combination of these effects. In this study, we use large ensembles from two high-top atmospheric general circulation models (SC-WACCM4 and E3SM) to separate the sea ice-forced signal from atmospheric internal variability (noise). Following protocol for the Polar Amplification Model Intercomparison Project (PAMIP), each simulation is prescribed with either pre-industrial, present-day, or future levels of sea-ice concentration, which are associated with global warming projections of 2°C. We use 300 ensemble members per simulation to obtain large sample sizes for robust statistics in the context of internal variability.
While an equatorward shift of the eddy-driven jet is found in boreal winter, the response to future sea-ice loss is small relative to climatology and highly sensitive to the number of ensemble members considered. On average, a sea ice-forced signal in the large-scale circulation cannot be distinguished from atmospheric internal variability in our simulations. A low signal-to-noise ratio is also demonstrated in the stratosphere, where the sign of the polar vortex response can be interpreted differently depending on the ensemble size. However, the local thermodynamic effects are statistically significant with strong surface warming and increases in precipitation found in the vicinity of newly ice-free areas. This warming is generally confined to the Arctic, and there is little response in the midlatitudes. Our results highlight the important role of internal variability in the extratropics and emphasize the need for especially large ensembles (>150-200 members) when assessing the dynamical response to both present-day and future Arctic sea-ice loss. (from https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/367289)
A talk about the climate history of earth, and what may have effected it. Given as part of the exam in Climate Physics course at the University of Aarhus
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
Reexamining future projections of Arctic climate linkagesZachary Labe
10 May 2024…
Atmospheric and Oceanic Sciences Student/Postdoc Seminar (Presentation): Reexamining future projections of Arctic climate linkages, Princeton University, USA.
References...
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss,
Geophysical Research Letters, DOI:10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation.
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
Revisiting projections of Arctic climate change linkagesZachary Labe
16 November 2023…
Department Seminar (Presentation): Revisiting projections of Arctic climate change linkages, Tongji University, Shanghai, China. Remote Presentation.
References:
Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI: 10.1029/2018GL078158
Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI: 10.1029/2019GL083095
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583
Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?
Journal of Climate, DOI: 10.1175/JCLI-D-20-0613.1
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI: 10.1029/2022EA002348
Southern Hemisphere atmospheric circulation: impacts on Antarctic climate and...Andrew Russell
Presentation given at the PAGES symposium in Chile in October 2010. (NB I gave this talk before O'Donnell et al. was published so I'd probably do it differently now.)
Climate change is a significant and lasting change in the statistical distribution of weather patterns over periods ranging from decades to millions of years. It may be a change in average weather conditions, or in the distribution of weather around the average conditions (i.e., more or fewer extreme weather events). Climate change is caused by factors such as biotic processes, variations in solar radiation received by Earth, plate tectonics, and volcanic eruptions. Certain human activities have also been identified as significant causes of recent climate change, often referred to as "global warming"
Scientists actively work to understand past and future climate by using observations and theoretical models. A climate record — extending deep into the Earth's past — has been assembled, and continues to be built up, based on geological evidence from borehole temperature profiles, cores removed from deep accumulations of ice, floral and faunal records, glacial and periglacial processes, stable-isotope and other analyses of sediment layers, and records of past sea levels. More recent data are provided by the instrumental record. General circulation models, based on the physical sciences, are often used in theoretical approaches to match past climate data, make future projections, and link causes and effects in climate change.
Kim Cobb's Borneo stalagmite talk - AGU 2015Kim Cobb
This talk presents the latest results from the Borneo stalagmite project that seeks to reconstruct Western tropical Pacific hydrology over the last half million years. We discuss our results in the context of climate forcing, the El Nino-Southern Oscillation, and climate modeling studies.
Similar to Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to Stratospheric Variability in Early Winter (20)
Techniques and Considerations for Improving Accessibility in Online MediaZachary Labe
3 April 2024…
United States Association of Polar Early Career Scientists (USAPECS) IDEA Training Course (Presentation): Accessibility and disability in online spaces. Remote Presentation.
An intro to explainable AI for polar climate scienceZachary Labe
26 March 2024…
GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ.
References:
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464
Using accessible data to communicate global climate changeZachary Labe
25 March 2024…
Climate Communication Workshop: Learn How To Make Your Research Matter (Keynote Presentation): Using accessible data to communicate global climate change, Temple University, Philadelphia, PA.
Water in a Frozen Arctic: Cross-Disciplinary PerspectivesZachary Labe
14 March 2024…
United States Association of Polar Early Career Scientists (USAPECS) Webinar (Host): Water in a Frozen Arctic: Cross-Disciplinary Perspectives. Remote Panel.
Event Page: https://www.usapecs.org/post/webinar-water-frozen-arctic
Explainable AI approach for evaluating climate models in the ArcticZachary Labe
27 March 2024…
IARPC Collaborations, Modelers’ Community of Practice (Presentation): Explainable AI approach for evaluating climate models in the Arctic. Remote Presentation.
References...
Labe, Z. M., & Barnes, E. A. (2022). Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, 9(7), e2022EA002348, https://doi.org/10.1029/2022EA002348
Explainable neural networks for evaluating patterns of climate change and var...Zachary Labe
12 March 2024…
Sharing Science – North American Webinar, Young Earth System Scientists (YESS) Community (Presentation): Explainable neural networks for evaluating patterns of climate change and variability. Remote Presentation.
References...
Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a
Applications of machine learning for climate change and variabilityZachary Labe
23 February 2024…
Department of Environmental Sciences Seminar (Presentation): Applications of machine learning for climate change and variability, Rutgers University, New Brunswick, NJ.
References:
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464
Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173
Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981
data-driven approach to identifying key regions of change associated with fut...Zachary Labe
Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431300
Distinguishing the regional emergence of United States summer temperatures be...Zachary Labe
Labe, Z.M., N.C. Johnson, and T.L. Delworth. Distinguishing the regional emergence of United States summer temperatures between observations and climate model large ensembles, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431288
Researching and Communicating Our Changing ClimateZachary Labe
6 December 2023…
Mercer County Community College (Presentation): Meet the climate scientists: our journey and our science, West Windsor Township, NJ, USA.
Visualizing climate change through dataZachary Labe
18 November 2023…
NJ State Museum Planetarium (Presentation): Visualizing climate change through data, Trenton, NJ.
References...
Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1, https://journals.ametsoc.org/view/journals/clim/36/20/JCLI-D-22-0716.1.xml
Using explainable machine learning to evaluate climate change projectionsZachary Labe
5 October 2023…
Atmosphere and Ocean Climate Dynamics Seminar (Presentation): Using explainable machine learning to evaluate climate change projections, Yale University, New Haven, CT. Remote Presentation.
References...
Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a, https://iopscience.iop.org/article/10.1088/1748-9326/acc81a
Contrasting polar climate change in the past, present, and futureZachary Labe
28 September 2023…
Guest lecture for “Observing and Modeling Climate Change (EES 3506/5506)” (Presentation): Contrasting polar climate change in the past, present, and future, Temple University, Philadelphia, PA. Remote Presentation.
Climate change extremes by season in the United StatesZachary Labe
11 September 2023…
Hershey Horticulture Society (Presentation): Climate change extremes by season in the United States, Hershey, PA, USA.
References...
Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1
Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2016), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, R. Clim Dyn, DOI:10.1007/s00382-016-3313-2
Labe, Z.M., N.C. Johnson, and T.L Delworth (2023). Changes in United States summer temperatures revealed by explainable neural networks. Preprint. DOI: 10.22541/essoar.168987129.98069596/v1
Guest Lecture: Our changing Arctic in the past and futureZachary Labe
22 August 2023…
Guest lecture for “Introduction to Global Climate Change (ESS 15)” (Invited): Our changing Arctic in the past and future, University of California, Irvine, CA. Remote Presentation.
References...
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J. H., Dunne, K. A., ... & Zhao, M. (2020). SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001895
Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348
Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088583
Monitoring indicators of climate change through data-driven visualizationZachary Labe
19 June 2023…
La Uni Climática - IV Edition (Presentation): Monitoring indicators of climate change through data-driven visualization. Remote Presentation.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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.
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.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to Stratospheric Variability in Early Winter
1. Linking the Quasi-Biennial Oscillation and
projected Arctic sea-ice loss to
stratospheric variability in early winter
Zachary M. Labe
Gudrun Magnusdottir, Yannick Peings
Department of Earth System Science at UC Irvine
10 January 2019
@ZLabe
2. Atmospheric response to
sea-ice loss may be
sensitive to background
state and stratospheric
pathway
Polar Amplification Model
Intercomparison Project
(PAMIP; Smith et al. 2018,
Geosci. Model Dev.)
SMITH ET AL. 2017, JCLI
MOTIVATION
3. 1. Composite atmospheric responses by QBO phase (westerly,
neutral, easterly) – approximately 67 years per phase
2. Assess Holton-Tan effect (QBO-E minus QBO-W)
3. Does the QBO modulate the atmospheric response to sea-ice
loss? (Future minus Historical)
Assess the role of the Quasi-biennial Oscillation
(QBO) on the atmospheric response to Arctic sea-
ice loss
4. WACCM4
Whole Atmosphere
Community Climate
Model version 4 –
Specified Chemistry
“high top”
chemistry-climate
atmosphere
model
Physical
parameterizations
from CAM4
• 66 vertical levels – extending to 5
x 10-6 hPa (140 km)
• 1.9° latitude x 2.5° longitude
• QBO prescribed from radiosonde
observations
• Improved representation of
sudden stratospheric warming
(SSW) events
• fixed radiative forcings from year
2000
5. NAME SEA ICE FORCING DURATIO
N
Historical Average 1976-2005 LENS SIT
Average 1976-2005 LENS SIC
ONDJFM;
200 members
Future Future 2051-2080 LENS SIT
Future 2051-2080 LENS SIC
ONDJFM;
200 members
All experiments have average 1976-2005 LENS SST*
Atmospheric General Circulation Model
Experiments