This document discusses methods for measuring greenhouse gas exchange at the landscape scale. It describes using aircraft instrumentation to measure carbon dioxide fluxes over multiple fields to assess the greenhouse gas budget of an entire landscape mosaic. Specific techniques discussed include eddy covariance measurements, simplified flux measurement methods, and using aircraft to map fluxes and extrapolate measurements across a region. Results are presented on measuring the effects of submerged drainage on peatland emissions and using aircraft data to validate flux tower measurements. Developing methods to estimate nighttime ecosystem respiration and complete 24-hour carbon budgets is an area of ongoing work.
Pre-Injection Assessment of Time-Lapse Seismic Repeatability at the Aquistore CO2 Storage Site - presentation by Lisa Roach of the University of Leeds at the UKCCSRC meeting Monitoring of the deep subsurface: leakage pathways – understanding and monitoring the mechanics of CO2 storage, 23 October 2014
Pre-Injection Assessment of Time-Lapse Seismic Repeatability at the Aquistore CO2 Storage Site - presentation by Lisa Roach of the University of Leeds at the UKCCSRC meeting Monitoring of the deep subsurface: leakage pathways – understanding and monitoring the mechanics of CO2 storage, 23 October 2014
Sentinel-1 satellites, ESA’s Synthetic Aperture Radar (SAR) mission, provide continuous data from the Earth surface in weekly to biweekly time intervals. This data availability provides an unprecedented opportunity to continuously monitor the Earth surface motion in areas prone to geohazards; such as regions of high seismic and volcanic activities, with the end goal of supporting the Early Warning Systems. However, the great challenge is to derive insights from Terabytes of satellite image sequences, in a computationally-efficient and time-critical manner. We’ve risen to this challenge by designing innovative signal processing and deep learning algorithms to efficiently mine this invaluable wealth of data. This talk gives on overview of our designed solutions, as well as a demonstration of these solutions in the Tectonic and Volcanic monitoring of South America (TecVolSA) project.
GPR systems work by sending a tiny pulse of energy into a material via an antenna. An integrated computer records the strength and time required for the return of any reflected signals. Subsurface variations will create reflections that are picked up by the system and stored on digital media. These reflections are produced by a variety of material such as geological structure differences and man-made objects like pipes and wire.
Retrieval & monitoring of atmospheric green house gases (gh gs) through remot...debasishagri
Climate change is one of the most important global environmental challenges of this century. Green House Gases (GHGs) are the main culprit for this problem. Though much of research has already been done about the distribution and sources (and sinks) of GHGs , still much more uncertainties are present. Currently, there are only a few satellite instruments in orbit which are able to measure atmospheric GHGs. The High Resolution Infrared Radiation Sounder (HIRS), the Atmospheric InfraRed Sounder (AIRS), and the Infrared Atmospheric Sounding Interferometer (IASI) perform measurements in the thermal infrared (TIR) spectral region. But these are having low sensitivity to lower troposphere. In contrast to this, the sensitivity of instruments measuring reflected solar radiation in the near-infrared (NIR)/shortwave infrared (SWIR) spectral region is much more constant (with height) and shows maximum values near the surface. At present, SCIAMACHY aboard ENVISAT launched in 2002 and TANSO (Thermal And Near infrared Sensor for carbon Observation) aboard GOSAT (Greenhouse gases Observing SATellite) launched in 2009 are the only orbiting instruments measuring in NIR region. Among all the algorithms the WFM-DOAS algorithm (Weighting Function Modified Differential Optical Absorption Spectroscopy) developed at the University of Bremen for the retrieval of trace gases from SCIAMACHY (Buchwitz et al.2005) is mostly used. This is based on the principle of differential detection of radiance in gaseous absorption channels with respect to neighboring atmospheric transparent spectral channels (not influenced by gas) to detect the conc. of desired gas. But scattering at aerosol and/or cloud particles remains a major source of uncertainty for SCIAMACHY XCO2 retrievals(Houweling 2005, Schneising 2008).Of late with the use of new merged fit window approach scientists have come up with less than 0.5 ppm error in the estimation of CO2 in the presence of thin cirrus cloud(Reuter, Buchwitz et. al. 2010). Schneising et. al.,2007,retrieved d three year’s column-averaged CO2 dry air mole fraction from the SCIAMACHY instrument using the retrieval algorithm WFM-DOAS version 1.0, with precision of about 2 ppm. In India a study was undertaken to compare the atmospheric methane concentration pattern from SCIAMACHY with the vegetation dynamics from SPOT, showed fairly good correlation of methane emission with the rice cultivation(Goroshi et. al.).
Sentinel-1 satellites, ESA’s Synthetic Aperture Radar (SAR) mission, provide continuous data from the Earth surface in weekly to biweekly time intervals. This data availability provides an unprecedented opportunity to continuously monitor the Earth surface motion in areas prone to geohazards; such as regions of high seismic and volcanic activities, with the end goal of supporting the Early Warning Systems. However, the great challenge is to derive insights from Terabytes of satellite image sequences, in a computationally-efficient and time-critical manner. We’ve risen to this challenge by designing innovative signal processing and deep learning algorithms to efficiently mine this invaluable wealth of data. This talk gives on overview of our designed solutions, as well as a demonstration of these solutions in the Tectonic and Volcanic monitoring of South America (TecVolSA) project.
GPR systems work by sending a tiny pulse of energy into a material via an antenna. An integrated computer records the strength and time required for the return of any reflected signals. Subsurface variations will create reflections that are picked up by the system and stored on digital media. These reflections are produced by a variety of material such as geological structure differences and man-made objects like pipes and wire.
Retrieval & monitoring of atmospheric green house gases (gh gs) through remot...debasishagri
Climate change is one of the most important global environmental challenges of this century. Green House Gases (GHGs) are the main culprit for this problem. Though much of research has already been done about the distribution and sources (and sinks) of GHGs , still much more uncertainties are present. Currently, there are only a few satellite instruments in orbit which are able to measure atmospheric GHGs. The High Resolution Infrared Radiation Sounder (HIRS), the Atmospheric InfraRed Sounder (AIRS), and the Infrared Atmospheric Sounding Interferometer (IASI) perform measurements in the thermal infrared (TIR) spectral region. But these are having low sensitivity to lower troposphere. In contrast to this, the sensitivity of instruments measuring reflected solar radiation in the near-infrared (NIR)/shortwave infrared (SWIR) spectral region is much more constant (with height) and shows maximum values near the surface. At present, SCIAMACHY aboard ENVISAT launched in 2002 and TANSO (Thermal And Near infrared Sensor for carbon Observation) aboard GOSAT (Greenhouse gases Observing SATellite) launched in 2009 are the only orbiting instruments measuring in NIR region. Among all the algorithms the WFM-DOAS algorithm (Weighting Function Modified Differential Optical Absorption Spectroscopy) developed at the University of Bremen for the retrieval of trace gases from SCIAMACHY (Buchwitz et al.2005) is mostly used. This is based on the principle of differential detection of radiance in gaseous absorption channels with respect to neighboring atmospheric transparent spectral channels (not influenced by gas) to detect the conc. of desired gas. But scattering at aerosol and/or cloud particles remains a major source of uncertainty for SCIAMACHY XCO2 retrievals(Houweling 2005, Schneising 2008).Of late with the use of new merged fit window approach scientists have come up with less than 0.5 ppm error in the estimation of CO2 in the presence of thin cirrus cloud(Reuter, Buchwitz et. al. 2010). Schneising et. al.,2007,retrieved d three year’s column-averaged CO2 dry air mole fraction from the SCIAMACHY instrument using the retrieval algorithm WFM-DOAS version 1.0, with precision of about 2 ppm. In India a study was undertaken to compare the atmospheric methane concentration pattern from SCIAMACHY with the vegetation dynamics from SPOT, showed fairly good correlation of methane emission with the rice cultivation(Goroshi et. al.).
Near surface gas monitoring at the CO2 Field Lab, Norway - presentation by Sarah Hannis in the Test Injection Sites session at the UKCCSRC Cardiff Biannual Meeting, 10-11 September 2014
From Weather Dwarfs to Kilometre-Scale Earth System Simulationsinside-BigData.com
In this deck from PASC18, Nils P. Wedi from ECMWF presents: From Weather Dwarfs to Kilometre-Scale Earth System Simulations.
"The increasingly large amounts of data being produced b weather and climate simulations and earth system observations is sometimes characterised as a deluge. This deluge of data is both a challenge and an opportunity. The main opportunities are to make use of this wealth of data to 1) improve knowledge by extracting additional knowledge from the data and 2) to improve the quality of the models themselves by analysing the accuracy, or lack thereof, of the resultant simulation data. An example of the former case is improved prediction of large scale phenomena such as El Nino. An example of the latter is the improvement of a Physics parameterisation scheme through detailed analysis of the errors in a large number of datasets.
"One way to realize these opportunities is to use machine learning approaches. As machine learning in weather and climate is a relatively new topic this minisymposium introduces the audience to how machine learning could be used in weather and climate and outlines its implications in terms of computing costs. To ground the ideas in concrete examples it also illustrates the use of machine learning in the weather and climate domain with practical examples."
Watch the video: https://wp.me/p3RLHQ-iPB
Learn more: https://pasc18.pasc-conference.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
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.
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.
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.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
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.
Nutraceutical market, scope and growth: Herbal drug technology
Assessing GHG exchange at landscape scale
1. Assessing GHG exchange at landscape
scale
Bart Kruijt, Ronald Hutjes, Cor Jacobs, Merit van den Berg,
Christain Fritz, Torsten Sachs, Andrei Serafimovich, Stefan
Weideveld, Wilma Jans
Wageningen University&Research; Radboud University
Nijmegen; GeoForschungs Centrum Potsdam
5. Measuring at landscape scale
Effects of management
GHG exchange of the landscape mosaic
Today:
- Peatland emission measures and monitoring
- Simpler flux measurement on small fields
- Integral landscape and fluxes an inventories: aircraft
fluxes
5
6. Drained peatlands
Emission Netherlands: 4.2
million ton CO2
Submerged drains (SMD) a
solution?
Peat
CO2
Peat
Degraded peat
CO2
-60 cm
Ditch
-60 cm
Ditch
Submerged drains
6 m
Winter (wet) Summer (dry)
Business as usual
7. Effect of submerged drains on water
table and Reco
Translucent soil chamber measurements:
Take light response curves to derive
dark flux
PhD research work, Stefan Weideveld
8. Pair of eddy covariance sets on peat
meadows with and without submerged drains
8
14. Polar 5 in NE Germany
14
Andrej Serafimovich preliminary results
15. 2008 data PH-WUR
Flight Strategy
• Full seasonal cycle, mar08-feb09, weekly flights
• Three routes, representative landscapes
Unique data set
• ~215 flight hours, 40 days, nobs=6102 (after QA)
Stratification
• Temporal m, bm, s
• Soil regions: 5
• 13 LUC types
turbulence probe
Novatel GPS antenna
laser altimeter
infra-red thermometer
net radiation
PAR sensors
LICOR 7500
thermocouple
operator
display
C-Migits
IGPS
antenna
turbulence probe
Novatel GPS antenna
laser altimeter
infra-red thermometer
net radiation
PAR sensors
LICOR 7500
thermocouple
operator
display
C-Migits
IGPS
antenna
16. Footprint of airborne measurements
Ede
is footprint model (Kljun, 2004 BLM)
Method Dis-aggregation (DFMR)
footprint analysis: fractional areas cover class in footprint
of airborne flux observation (right)
find cover class-specific fluxes FK by multiple regression or
other (constrained) linear solvers (left)
p x 1 vector of airborne
flux observations
p x n matrix of coverclass
fractional areas,
footprint weighted
n x 1 vector of errors
n x 1 vector of coverclass-
specific fluxes
17. Extrapolate and map fluxes
17
𝑭𝒄 𝒍𝒐𝒄𝒂𝒍
𝑷𝑨𝑹 𝒍𝒐𝒄𝒂𝒍
× PAR 𝒂𝒓𝒆𝒂 = 𝑵𝑬𝑬 𝒂𝒓𝒆𝒂, (DAY!)
(Hutjes & Kruijt in prep)
19. So far: daytime only. How get 24-hour NEE?
Aircraft can only fly in DAYTIME
What is lacking is nighttime/ Ecosystem respiration
● Get it from zero-intercept of NEE light response?
● Need to fit non-linear light response on
sparse data – difficult!
● Response is linear on LONGER time scales
● But is that useful?
19
20. Is night-time NEE predictable from day-
time NEE?
Daytime or GPP fluxes are ‘easy’:
- good turbulence for EC
- Aircraft can fly
- Remote sensing works
- Photosynthesis well understood
Is NEEnight / NEEday
predictable?
20
21. A ‘quick look’ at
(NEEnight / NEEday)/night hr
Analysed a few EC tower data data sets
21
22. A ‘quick look’ at
abs [(NEEnight / NEEday)/night hr]
Analysed a few EC tower data data sets
22
(low values)
Vegetated,
MINERAL soil gives
reasonably
consistent ratio.
Peat soil and bare
soil give higher
values
23. Still some work
to do!
- Comparative flux
measurements at small
fields
- Develop satisfactory
routine measurement
methods
- Extend aircraft flux
inventories to include
nighttime/dark flux
23