In this tutorial we’ll describe how to calculate species distribution for different periods. To do that, we’ll use the 3D Niche capability of ModestR, taking time as 3th dimension
20. Calculating most probable distribution of a species with Niche of Occurr...modestrsoftware
In this tutorial we describe how to perform species distribution modelling (SDM) with Niche Of Occurrence (NOO) in ModestR. NOO is a new proposed SDM that offers a very good performance and it's easier to perform and understand than other existing SDM's
4.- How to use data cleaning, EOO estimation and environmental of occurrence ...modestrsoftware
How to use data cleaning, EOO estimation and environmental occurrence features in ModestR.
Describes data cleaning; EOO methods supported in ModestR (convex hull, alpha shape, kernel density); environmental occurrence in ModestR.
15. Locating species occupied river basins with ModestR (Version ModestR 5.3...modestrsoftware
In this tutorial we'll explain how to automatically find the river basins that are occupied by a species, using the species known occurences. River basins are very interesting because they can be considered a natural geographical extent for a species.
1. How to create a database (Version ModestR v6.5 or higher)modestrsoftware
In this tutorial we’ll describe how to create a new ModestR database, how to populate it using taxonomic data stored in a CSV file.
ModestR software can be freely downloaded from http://www.ipez.es/ModestR
17. An example using ModestR to assess species distribution in different cli...modestrsoftware
In this tutorial we describe how to use ModestR to model species distribution in different climatic change scenarios. To do that, we’ll use the 3D Niche capability of ModestR, taking time as 3th dimension.
7.- ModestR tools for taxonomy (Version ModestR v5.3 or higher)modestrsoftware
This tutorial describes some tools provided by ModestR to work with taxonomic data. ModestR can use ITIS taxonomic database to explore and export taxonomic data.
It can also use GBIF to check and complete taxonomic data from a list of species.
20. Calculating most probable distribution of a species with Niche of Occurr...modestrsoftware
In this tutorial we describe how to perform species distribution modelling (SDM) with Niche Of Occurrence (NOO) in ModestR. NOO is a new proposed SDM that offers a very good performance and it's easier to perform and understand than other existing SDM's
4.- How to use data cleaning, EOO estimation and environmental of occurrence ...modestrsoftware
How to use data cleaning, EOO estimation and environmental occurrence features in ModestR.
Describes data cleaning; EOO methods supported in ModestR (convex hull, alpha shape, kernel density); environmental occurrence in ModestR.
15. Locating species occupied river basins with ModestR (Version ModestR 5.3...modestrsoftware
In this tutorial we'll explain how to automatically find the river basins that are occupied by a species, using the species known occurences. River basins are very interesting because they can be considered a natural geographical extent for a species.
1. How to create a database (Version ModestR v6.5 or higher)modestrsoftware
In this tutorial we’ll describe how to create a new ModestR database, how to populate it using taxonomic data stored in a CSV file.
ModestR software can be freely downloaded from http://www.ipez.es/ModestR
17. An example using ModestR to assess species distribution in different cli...modestrsoftware
In this tutorial we describe how to use ModestR to model species distribution in different climatic change scenarios. To do that, we’ll use the 3D Niche capability of ModestR, taking time as 3th dimension.
7.- ModestR tools for taxonomy (Version ModestR v5.3 or higher)modestrsoftware
This tutorial describes some tools provided by ModestR to work with taxonomic data. ModestR can use ITIS taxonomic database to explore and export taxonomic data.
It can also use GBIF to check and complete taxonomic data from a list of species.
19. Calculating 3D species distribution in marine habitats with ModestR (Ver...modestrsoftware
In this tutorial we describe how to calculate species distribution along depth (3D) in marine habitats with ModestR. In this way, if you have occurrences with depth information from a species, and environmental/climatic data for different depths (such as those from NOAA or Copernicus), with ModestR you can calculate species distribution in 3D, for each depth where the species is present
24. Creating virtual species with ModestR (Version ModestR 6.4 or higher)modestrsoftware
This tutorial describes how to easily create virtual species and importing them directly in ModestR. Virtual species are a procedure increasingly used in ecology to improve species distribution models. This feature uses the R virtualspecies package from Leroy et al (https://cran.r-project.org/web/packages/virtualspecies/index.html)
25. Phylogenetics trees with ModestRr and bold (Version ModestR 6.5 or higher)modestrsoftware
This tutorial describes how to donwload DNA barcoding data from BOLD to ModestR for any species, and how to use those data to calculate phylogenetic distances and build phylogenetic trees
21.- Creating virtual species and calculating simple ensemble models with R ...modestrsoftware
In this tutorial we describe two simple scripts in R to generate random virtual species, and to calculate species distribution models (SDM's) using ensemble models.
Those scripts may be useful to test hypotesis, or to work with ModestR or other tools
18. Estimating species composition in one or more regions with KnowBr and Mo...modestrsoftware
In this tutorial we describe how to use ModestR with KnowBR results to estimate species composition by region. Using SDM results for several species and the expected richness estimated by KnowBR, ModestR can determine the most suitable composition of species of a region.
12.- Recommendations for marine environments and Economic Exclusive Zones (Ve...modestrsoftware
In this tutorial we’ll give you some tips to work with ModestR in marine environments, different sources of information and how to use the marine Economic Exclusive Zones.
7.- ModestR tools for taxonomy (Version ModestR v6.5 or higher)modestrsoftware
This tutorial describes some tools provided by ModestR to work with taxonomic data. ModestR can use ITIS taxonomic database to explore and export taxonomic data.
It can also use GBIF to check and complete taxonomic data from a list of species.
16. Importing different climatic change scenarios from WorldClim to ModestR ...modestrsoftware
In this tutorial we describe how to import different climatic change scenarios from WorldClim to ModestR. This is necessary for example to use the 3D Niche capability of ModestR to model species distribution along those scenarios.
Training materials developed with peers at Columbia University for Google, Inc. These materials illustrate methods to incorporate JavaScript into Google Earth Engine to generate relevant products for stakeholders using climate data.
Week 4 Project - STAT 3001Student Name Type your name here.docxmelbruce90096
Week 4 Project - STAT 3001
Student Name: <Type your name here>
Date:<Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
Part I. Analyze Data
Instructions
Answers
1. Open the file COTININE using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about a collection of movies. How many observations are there in this file?
In this file, there are three variables, labeled Smokers, ETS, and No ETS. The dataset was collected for a study of second-hand smoke. The sample data consists of the measured serum cotinine levels in three different groups of people.
· The NOETS group lists the cotinine levels for subjects who are nonsmokers and have no exposure to environmental tobacco smoke at home or work.
· The ETS group lists cotinine levels for subjects who are nonsmokers exposed to tobacco smoke at home or work.
· The SMOKERS group lists cotinine levels for subjects who report tobacco use.
Serum cotinine is a metabolite of nicotine, meaning that cotinine is produced when nicotine is absorbed by the body. Higher levels of cotinine correspond to higher levels of exposure to smoke that contains nicotine.
2. What results do you expect to find in this data?
Part II. Descriptive Statistics
3. Generate descriptive statistics for all three groups of people and complete the following table. Round all results to 2 decimal places.
Variable
Sample
Mean
Sample
Standard Deviation
Sample Size
4.
Smokers
5.
ETS
6.
No ETS
7. Did you get the results you expected here? Explain why.
8. In which of the three groups did we experience the MOST variation (highest deviation from the mean)?
Part III. Confidence Intervals
9. Generate a 95% interval for the mean of the SMOKERS group. Paste your results here.
10. Generate a 95% interval for the mean of the ETS group. Paste your results here.
11. Generate a 95% interval for the mean of the No ETS group. Paste your results here
12. Create a graph below by illustrating all three confidence intervals on one graph using the tools in your word processor (example below). Statdisk cannot do this for you. Creat your graph and turn the font red. For this process, I just use the dashes and wrote a scale below the axes.
Here is an example, but it is not based on the data you are analyzing:
Case 1 14---------------------42
Case 2 35-------------------------70
________________________________________
0 20 40 60
Your
Solution
:
11. Based on the confidence intervals shown above, does there appear to be some evidence to indicate that exposure to tobacco smoke corresponds to higher levels of cotinin.
Assignment 9/Assignment 9.docx
GIS 5103 – Week 9 Assignment – Remote Sensing & Raster Data
https://earthexplorer.usgs.gov/
For this weeks assignment we are going to download multi-spectral imagery from the USGS. First we will go onto the earthexplorer and create a free account. You will have to confirm the account in the email you choose to sign up with.
Once our account is created, we will begin to search for an area of interest. There are multiple ways to do this. Simply click on the map, type in a specific address or location, or create a box/square on the map. All of these methods will find any satellite image that contains part of the area of interest. Because of this, it is important to preview the image before selecting it.
Here I only typed in Boston, and set the date range from September 2017 until September 2019. (Areas within the U.S will have more consistent images.)
Once we found our area of interest, we will have to choose which satellite system and data level we want to use. For this exercise we want the most recent up-to-date data, so we will be using Landsat 8, on demand level 2 data. This has already been converted to Surface reflectance. It will give you a warning which you can ignore.
Once we have selected the satellite system and level we can simply click results. What will appear is the following.
As you can see many of the images have clouds and will have to be skipped over. By clicking the footprint, we can see where the image lies on a world map. Once you are logged in you can click the shopping cart to order the scene from the USGS. Click the shopping cart, and it will glow green, meaning it has been added to item basket.
Once you have chosen a clear scene over your area of interest, you can view item basket and proceed to checkout. It costs nothing to order scenes, but the USGS will take some time to process your order. Real people are converting the satellite images to surface reflectance, have patience.
You will get a confirmation email and then another email with a link to download the data you selected. You should save this to your local machine or to the D Drive on the server. Make sure to delete any files in your downloads on the server.
The file should come as a .tiff or a geotiff, both can open in QGIS, ArcMap, and ArcPro.
When adding each band individually, it will prompt this box, click yes and continue adding each band. (7 total bands) Ignore any .tif file that does not have the word band in it. Once all the bands have been successfully added to ArcMap use the Composit bands tool to combine all the bands into one file.
By using the composite band tool we can reorder the bands to the correct color schemes and get a true color image of our location. The order I used below will create a true color image (4-3-2) (Red – Green – Blue)
We are going to take our multispectral data and calculate 5 indices. Indices are used to enhance the data we currently have to highlight c ...
Name:____________________________________________
Date:__________________________________________
Real world Situations
Directions:Write and solve each equation from the given word problem on a blank sheet of paper. Write your answers in a complete sentence on this page.
Show Your Work!!!!!
Level One: One/Two Step Real-World Application
1.) Maria combines her 39 seashells with Jacob’s seashells for a total of 173 seashells. Find how many seashells Jacob had before the collections were combined.
2.) The gym teacher divided a class into four teams with 7 students per team. How many students are in the class?
3.) An emergency plumber charges $65 as a call-out fee plus an additional $75 per hour. He arrives at a house at 9:30 and works to repair a water tank. If the total repair bill is $196.25, at what time was the repair completed?
4.) To convert temperatures in Fahrenheit to temperatures in Celsius, take the temperature in degrees Fahrenheit, subtract 32, and then divide the result by 1.8. This gives the temperature in degrees Celsius. Write an equation that shows the conversion process.
4a.) Convert 50 degrees Fahrenheit to degrees Celsius.
4b.) Convert 25 degrees Celsius to degrees Fahrenheit.
5.) Jasmin’s dad, Andrew, is planning a surprise birthday party for her. He will hire a bouncy castle, and will provide party food for all the guests. The bouncy castle costs $150 for the afternoon, and the food will cost $3 per person. Andrew has a budget of $300. Write an equation and use it to determine the maximum number of guests he can invite.
Name:____________________________________________
Date:__________________________________________
Real world Situations
Directions:Write and solve each equation from the given word problem on a blank sheet of paper. Write your answers in a complete sentence.
Show Your Work!!!!!
Level Two: Multiple-Step Real-World Application
6.) The speed of a body is the distance it travels per unit of time. That means that we can also find out how far an object moves in a certain amount of time if we know its speed: we use the equation “distance = time x speed” Shanice’s car is traveling 10 miles per hour slower than twice the speed of Brandon’s car. She covers 93 miles in 1 hour 30 minutes. How fast is Brandon driving?
7.) The electrical current, I (amps), passing through an electronic component varies directly with the applied voltage, V (volts), according to the relationship V=I⋅R where R is the resistance measured in Ohms (Ω).
a. A scientist is trying to deduce the resistance of an unknown component. He labels the resistance of the unknown component x Ω. The resistance of a circuit containing a number of these components is (5x+20) Ω. If a 120 volt potential difference across the circuit produces a current of 2.5 amps, calculate the resistance of the unknown component.
8.) A factory manager is packing engine components into wooden crates.
19. Calculating 3D species distribution in marine habitats with ModestR (Ver...modestrsoftware
In this tutorial we describe how to calculate species distribution along depth (3D) in marine habitats with ModestR. In this way, if you have occurrences with depth information from a species, and environmental/climatic data for different depths (such as those from NOAA or Copernicus), with ModestR you can calculate species distribution in 3D, for each depth where the species is present
24. Creating virtual species with ModestR (Version ModestR 6.4 or higher)modestrsoftware
This tutorial describes how to easily create virtual species and importing them directly in ModestR. Virtual species are a procedure increasingly used in ecology to improve species distribution models. This feature uses the R virtualspecies package from Leroy et al (https://cran.r-project.org/web/packages/virtualspecies/index.html)
25. Phylogenetics trees with ModestRr and bold (Version ModestR 6.5 or higher)modestrsoftware
This tutorial describes how to donwload DNA barcoding data from BOLD to ModestR for any species, and how to use those data to calculate phylogenetic distances and build phylogenetic trees
21.- Creating virtual species and calculating simple ensemble models with R ...modestrsoftware
In this tutorial we describe two simple scripts in R to generate random virtual species, and to calculate species distribution models (SDM's) using ensemble models.
Those scripts may be useful to test hypotesis, or to work with ModestR or other tools
18. Estimating species composition in one or more regions with KnowBr and Mo...modestrsoftware
In this tutorial we describe how to use ModestR with KnowBR results to estimate species composition by region. Using SDM results for several species and the expected richness estimated by KnowBR, ModestR can determine the most suitable composition of species of a region.
12.- Recommendations for marine environments and Economic Exclusive Zones (Ve...modestrsoftware
In this tutorial we’ll give you some tips to work with ModestR in marine environments, different sources of information and how to use the marine Economic Exclusive Zones.
7.- ModestR tools for taxonomy (Version ModestR v6.5 or higher)modestrsoftware
This tutorial describes some tools provided by ModestR to work with taxonomic data. ModestR can use ITIS taxonomic database to explore and export taxonomic data.
It can also use GBIF to check and complete taxonomic data from a list of species.
16. Importing different climatic change scenarios from WorldClim to ModestR ...modestrsoftware
In this tutorial we describe how to import different climatic change scenarios from WorldClim to ModestR. This is necessary for example to use the 3D Niche capability of ModestR to model species distribution along those scenarios.
Training materials developed with peers at Columbia University for Google, Inc. These materials illustrate methods to incorporate JavaScript into Google Earth Engine to generate relevant products for stakeholders using climate data.
Week 4 Project - STAT 3001Student Name Type your name here.docxmelbruce90096
Week 4 Project - STAT 3001
Student Name: <Type your name here>
Date:<Enter the date on which you began working on this assignment.>
Instructions: To complete this project, you will need the following materials:
· STATDISK User Manual (found in the classroom in DocSharing)
· Access to the Internet to download the STATDISK program.
Part I. Analyze Data
Instructions
Answers
1. Open the file COTININE using menu option Datasets and then Elementary Stats, 9th Edition. This file contains some information about a collection of movies. How many observations are there in this file?
In this file, there are three variables, labeled Smokers, ETS, and No ETS. The dataset was collected for a study of second-hand smoke. The sample data consists of the measured serum cotinine levels in three different groups of people.
· The NOETS group lists the cotinine levels for subjects who are nonsmokers and have no exposure to environmental tobacco smoke at home or work.
· The ETS group lists cotinine levels for subjects who are nonsmokers exposed to tobacco smoke at home or work.
· The SMOKERS group lists cotinine levels for subjects who report tobacco use.
Serum cotinine is a metabolite of nicotine, meaning that cotinine is produced when nicotine is absorbed by the body. Higher levels of cotinine correspond to higher levels of exposure to smoke that contains nicotine.
2. What results do you expect to find in this data?
Part II. Descriptive Statistics
3. Generate descriptive statistics for all three groups of people and complete the following table. Round all results to 2 decimal places.
Variable
Sample
Mean
Sample
Standard Deviation
Sample Size
4.
Smokers
5.
ETS
6.
No ETS
7. Did you get the results you expected here? Explain why.
8. In which of the three groups did we experience the MOST variation (highest deviation from the mean)?
Part III. Confidence Intervals
9. Generate a 95% interval for the mean of the SMOKERS group. Paste your results here.
10. Generate a 95% interval for the mean of the ETS group. Paste your results here.
11. Generate a 95% interval for the mean of the No ETS group. Paste your results here
12. Create a graph below by illustrating all three confidence intervals on one graph using the tools in your word processor (example below). Statdisk cannot do this for you. Creat your graph and turn the font red. For this process, I just use the dashes and wrote a scale below the axes.
Here is an example, but it is not based on the data you are analyzing:
Case 1 14---------------------42
Case 2 35-------------------------70
________________________________________
0 20 40 60
Your
Solution
:
11. Based on the confidence intervals shown above, does there appear to be some evidence to indicate that exposure to tobacco smoke corresponds to higher levels of cotinin.
Assignment 9/Assignment 9.docx
GIS 5103 – Week 9 Assignment – Remote Sensing & Raster Data
https://earthexplorer.usgs.gov/
For this weeks assignment we are going to download multi-spectral imagery from the USGS. First we will go onto the earthexplorer and create a free account. You will have to confirm the account in the email you choose to sign up with.
Once our account is created, we will begin to search for an area of interest. There are multiple ways to do this. Simply click on the map, type in a specific address or location, or create a box/square on the map. All of these methods will find any satellite image that contains part of the area of interest. Because of this, it is important to preview the image before selecting it.
Here I only typed in Boston, and set the date range from September 2017 until September 2019. (Areas within the U.S will have more consistent images.)
Once we found our area of interest, we will have to choose which satellite system and data level we want to use. For this exercise we want the most recent up-to-date data, so we will be using Landsat 8, on demand level 2 data. This has already been converted to Surface reflectance. It will give you a warning which you can ignore.
Once we have selected the satellite system and level we can simply click results. What will appear is the following.
As you can see many of the images have clouds and will have to be skipped over. By clicking the footprint, we can see where the image lies on a world map. Once you are logged in you can click the shopping cart to order the scene from the USGS. Click the shopping cart, and it will glow green, meaning it has been added to item basket.
Once you have chosen a clear scene over your area of interest, you can view item basket and proceed to checkout. It costs nothing to order scenes, but the USGS will take some time to process your order. Real people are converting the satellite images to surface reflectance, have patience.
You will get a confirmation email and then another email with a link to download the data you selected. You should save this to your local machine or to the D Drive on the server. Make sure to delete any files in your downloads on the server.
The file should come as a .tiff or a geotiff, both can open in QGIS, ArcMap, and ArcPro.
When adding each band individually, it will prompt this box, click yes and continue adding each band. (7 total bands) Ignore any .tif file that does not have the word band in it. Once all the bands have been successfully added to ArcMap use the Composit bands tool to combine all the bands into one file.
By using the composite band tool we can reorder the bands to the correct color schemes and get a true color image of our location. The order I used below will create a true color image (4-3-2) (Red – Green – Blue)
We are going to take our multispectral data and calculate 5 indices. Indices are used to enhance the data we currently have to highlight c ...
Name:____________________________________________
Date:__________________________________________
Real world Situations
Directions:Write and solve each equation from the given word problem on a blank sheet of paper. Write your answers in a complete sentence on this page.
Show Your Work!!!!!
Level One: One/Two Step Real-World Application
1.) Maria combines her 39 seashells with Jacob’s seashells for a total of 173 seashells. Find how many seashells Jacob had before the collections were combined.
2.) The gym teacher divided a class into four teams with 7 students per team. How many students are in the class?
3.) An emergency plumber charges $65 as a call-out fee plus an additional $75 per hour. He arrives at a house at 9:30 and works to repair a water tank. If the total repair bill is $196.25, at what time was the repair completed?
4.) To convert temperatures in Fahrenheit to temperatures in Celsius, take the temperature in degrees Fahrenheit, subtract 32, and then divide the result by 1.8. This gives the temperature in degrees Celsius. Write an equation that shows the conversion process.
4a.) Convert 50 degrees Fahrenheit to degrees Celsius.
4b.) Convert 25 degrees Celsius to degrees Fahrenheit.
5.) Jasmin’s dad, Andrew, is planning a surprise birthday party for her. He will hire a bouncy castle, and will provide party food for all the guests. The bouncy castle costs $150 for the afternoon, and the food will cost $3 per person. Andrew has a budget of $300. Write an equation and use it to determine the maximum number of guests he can invite.
Name:____________________________________________
Date:__________________________________________
Real world Situations
Directions:Write and solve each equation from the given word problem on a blank sheet of paper. Write your answers in a complete sentence.
Show Your Work!!!!!
Level Two: Multiple-Step Real-World Application
6.) The speed of a body is the distance it travels per unit of time. That means that we can also find out how far an object moves in a certain amount of time if we know its speed: we use the equation “distance = time x speed” Shanice’s car is traveling 10 miles per hour slower than twice the speed of Brandon’s car. She covers 93 miles in 1 hour 30 minutes. How fast is Brandon driving?
7.) The electrical current, I (amps), passing through an electronic component varies directly with the applied voltage, V (volts), according to the relationship V=I⋅R where R is the resistance measured in Ohms (Ω).
a. A scientist is trying to deduce the resistance of an unknown component. He labels the resistance of the unknown component x Ω. The resistance of a circuit containing a number of these components is (5x+20) Ω. If a 120 volt potential difference across the circuit produces a current of 2.5 amps, calculate the resistance of the unknown component.
8.) A factory manager is packing engine components into wooden crates.
Quantitative Analysis of 3D Refractive Index MapsMathieuFRECHIN
Nanolive’s 3D Cell Explorer allows for the creation of high-content 3D images of living cells with very
high spatio-temporal resolution (x, y:180nm; z:400nm; t:1.7sec). Being able to extract quantitative
data describing the objects (population of cells, cells, organelles) contained in such images is at
the core of leading-edge cell biology research. This application note will start where application
note 2 (AppNote2017-2) stopped by looking at how meaningful numbers can be extracted from
segmented objects using FIJI. We will then bring some cellular features in perspective to help you to
start elegant quantitative studies with the 3D Cell Explorer.
YARCA (Yet Another Raycasting Application) Projectgraphitech
The scope of this project is to extend NASA’s World Wind to make it possible to visualize ray casting not only in intersection with the terrain, but also to consider 3D objects, which we call barriers, that will be hit by rays emitted by other objects which we call transmitters, calculating the coverage area and field of view of the transmitters and showing how the transmission signal is reflected onto the objects’ surfaces.
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.
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.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
(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.
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.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
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.
11.- Calculating species distribution for different periods with ModestR (Versión ModestR 5.3 or higher)
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Step by step tutorial:
Calculating species
distribution for different
periods with ModestR
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What do you need for this tutorial:
1. ModestR v.5.3 or higher installed
2. Internet connection
3. About 25 minutes
ModestR software can be freely downloaded from http://www.ipez.es/ModestR
3. MODESTR QUICK TUTORIALS HTTP://WWW.IPEZ.ES/MODESTR/
We’ll describe how to calculate species
distribution for different periods. To do
that, we’ll use the 3D Niche capability
of ModestR, taking time as 3th
dimension. Follow the next steps!
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To calculate species distribution in different periods in time, we’ll need environmental data for those periods.
We have sets of those variables for 3 different years, that we just
number as year 0, 1 and 2 (you can number them at your
convenience). Of course, Altitude may be the same for all three
years; but in this case, we’ll need to have 3 files, one for each year,
even if they contain the same data.
We need to have those data in separated files ESRI ASC formats, for
example, and name them using this format:
name[year].ASC
For this example, let’s suppose we have data for:
Anual mean temperature
Anual mean precipitation
Altitude
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2. Next step will be to arrange those files in folders; one
folder for each variable, to obtain a structure like this:
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3. Run ModestR MapMaker and go to Layers/Manage environmental variables to open variable manager window.
1. Go to Layers / Manage enviromental variables
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2. Select the root node on the tree
3. Right-Click to show context menu
and select “Add new folder”
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4. Type a name
(For this example we’ll enter “Niche3DExample”)
5. Click OK
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4. Now, open a Windows Explorer (also called File Explorer), select the three folders that contain the environmental data.
Then drag them and drop them over the new folder.
1. Select the three folders
2. Drag them
3. And drop them on the new folder
Windows Explorer
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The environmental variables will be imported and added to the tree.
ModestR will automatically assign a Z value
to each variable using the number in square
brackets from the corresponding filename.
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5. Now we’ll have to convert each variable folder to a 3D dataset, which is a type of dataset containing several data for
the same variable, for different Z values (where Z can be for example depth, or time, as in this case).
1. Select the Niche3DExample node
and right-click
2. Select Convert all subfolders to 3D
datasets in the contextual menú
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Done! Now we have three 3D datasets. We can close this window.
3D icon identifies a 3D dataset
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6. Next step is creating a 3D CEL (Compounded Environmental Layer) that will bind together the variables added
before in a single multidimensional variable.
1. Go to menu Mapping/Niche of
occurrence/Create new compounded
environmental layer (CEL)/3D CEL
More details about what are compounded
environmental layers can be found in ModestR
User’s Manual on ModestR website.
A tutorial about compounded environmental layers
creation can also be found at
http://www.ipez.es/ModestR/Manual_Tutorial.html
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When creating a 3D CEL, only 3D datasets will be displayed among the candidate variables.
Note: unlike in this example you’ll usually employ more
than just three variables to calculate species niche, to
obtain more significant results.
2. For this example, in the dialog box
that will be shown, select the three
3D datasets added before
3. Click on Continue button.
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1
For the sake of simplicity, we’ll just use default settings. Thus, we’ll simply click Continue in each step until last one.
2
3
Of course, displayed graph will
depend on the data you used,
and won’t have to match the
one shown here.
4. Click Continue button
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In the last step, we’ll save this 3D CEL to be used later. Finally, we’ll click Close to go back to MapMaker main window.
5. Enter a name for the CEL (here
“Modestr 3D CEL example”)
6. Click Save 3D Layer
7. Click Close
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Example of how the environmental layer would be displayed depending on the data used.
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Now we’ll test this 3D CEL to calculate species distribution at several periods of time.
8. Click on Clear raster
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7. Then we’ll first download species occurrences from GBIF (you can also import them from a CSV file)
2. Then select Samples from online GBIF database
1. Go to File/Import
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Enter the scientific name of a species. Of course, select a species that lives in the habitats corresponding to the
environmental data imported before (i.e. select a marine species if you imported environmental variables for marine
areas)
3. Species name
4. Click Accept
For test purposes, we suggest to use a common (not too
rare) species, in order to have a number of occurrences
high enough to calculate a niche. Then click on Accept.
Download task will start immediately.
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Once data downloaded, you have to select the correct habitat for the species. Typically land or sea.
5. Select the correct habitat for the species
6. Click Accept
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8. Once we have occurrences from a species, we can calculate its niche for different time periods using the previously
created 3D CEL. We’ll start by Density map.
1. Go to menu Mapping/Niche of
occurrence/ Density map
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You may have to select the 3D CEL to be used (depending on it was already loaded or not).
2. Select the 3D CEL example created
3. Click Accept
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4. Set that the Z axis is
time
6. Click Continue
5. Assign a Z value to
the current occurrences
This Z value indicates here which time (thus, which
environmental data) corresponds to current species
occurrences. For example, if we assign them the 0, that
means that the occurrences should be considered as
corresponding to the environmental data assigned to Z
(time)=0 in the 3D CEL we are using.
Then we’ll have to set that the Z axis is time and have to assign a Z value to the current occurrences
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Results will be displayed both in the world map, showing geographic density, and in the dialog, showing polar density map.
8. You can see the density for each Z
(time) value of the 3D CEL using the
button bar
9. You can also open the 3D view
using the 3D View button
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If you clicked the 3D View button you’ll see a 3D viewer with the niche density data of the species.
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9. Going back to MapMaker, we’ll now calculate Distribution map.
1. Go to menu Mapping/Niche
of occurrence/ Distribution map
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2. Set that the Z axis is
time
4. Click Continue
3. Assign a Z value to
the current occurrences
This Z value indicates here which time (thus, which
environmental data) corresponds to current species
occurrences. For example, if we assign them the 0, that
means that the occurrences should be considered as
corresponding to the environmental data assigned to Z
(time)=0 in the 3D CEL we are using.
Then we’ll have to set that the Z axis is time and have to assign a Z value to the current occurrences
31. MODESTR QUICK TUTORIALS HTTP://WWW.IPEZ.ES/MODESTR/
Then select Add area option, and Run. You’ll be asked to select a folder to save results. Then calculation will start.
5. Select Add area option
6. Click Run
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Once ended, results will include a file that can be opened with MRMapping.
MRMapping is another application of ModestR
that can show several distribution maps at the
same time, while MapMaker is intended to
work with a single distribution map
7. Click to Open MRMapping
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10. The file generated for MRMapping contains a distribution map for each Z value of the 3D CEL used to calculate
niche. In this case, this means a distribution map for each period for which we added environmental data to the 3D CEL.
Each one of those distribution maps
is displayed with a different color
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1. Go to menu Edit / Distribution list editor
2. Modify colors
3. Show / Hide individual distribution maps
4. Change their display order
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This way you can see how species distribution will change in time.
5. Go to View / Slideshow A floating toolbar will be displayed, to easily move around the distribution
maps for each Z value, much like moving from a slide to another in a slideshow
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It was the Step by step tutorial:
Calculating species distribution for different
periods with ModestR
Thank you for your interest.
You can find this one and other tutorials in http://www.ipez.es/ModestR
By the ModestR team
Colaborators:
Estefanía Isaza Toro