This document presents a report in APA format written by Pedro Perez and Ramiro Ramirez and directed by William Suarez for the Universidad Distrital Francisco Jose de Caldas faculty of XXXXXXXX. The report includes an introduction, two title levels, tables and figures, and references. It follows the standard APA format requirements for structure, citations, and references.
Exponentials, Radical Longevity, and YouSteven Tucker
Designing healthy babies. Monitoring real-time molecular changes. Re-writing your own DNA. Bio-printing human organs from stem cells. Digestible sensors, mobile platforms, and predictive analytics.
Simply put, never getting sick again.
This may all sound like science fiction, but these ideas are as real today as driverless cars, private space flights, and augmented reality. In 2001, the cost of sequencing a single human genome was 100 million USD. Today that cost has fallen exponentially to under a 1000 USD. Take a moment to consider, not just how genome-based technologies will alter your future, but how they could synergistically and radically transform our definitions of health and wellness.
You may plan on being financially secure at 80, but I expect you to live to 140? Assuming I am right, how can you, and your extended family, remain mentally sharp and physically fit, and disease-free?
Exponentials, Radical Longevity, and YouSteven Tucker
Designing healthy babies. Monitoring real-time molecular changes. Re-writing your own DNA. Bio-printing human organs from stem cells. Digestible sensors, mobile platforms, and predictive analytics.
Simply put, never getting sick again.
This may all sound like science fiction, but these ideas are as real today as driverless cars, private space flights, and augmented reality. In 2001, the cost of sequencing a single human genome was 100 million USD. Today that cost has fallen exponentially to under a 1000 USD. Take a moment to consider, not just how genome-based technologies will alter your future, but how they could synergistically and radically transform our definitions of health and wellness.
You may plan on being financially secure at 80, but I expect you to live to 140? Assuming I am right, how can you, and your extended family, remain mentally sharp and physically fit, and disease-free?
Using High-Density Electrophysiological Recordings to Investigate Neural Mech...InsideScientific
In this webinar, Dr. Michael Long from the NYU Grossman School of Medicine and Dr. Kari Hoffman from Vanderbilt University present their work investigating the neural mechanisms of learning, memory, and behavior using high-density silicon probes from Diagnostic Biochips in small and large animals.
The ability to record network activity using emerging high-density electrophysiological arrays has revolutionized understanding of the link between brain function and behavior. In the first portion of the webinar, Dr. Long discusses how his laboratory has used custom-built (and now standard) probes to investigate the neural mechanisms of vocal production in two model systems: the zebra finch (a songbird) and a newly characterized Costa Rican singing mouse. In ongoing work, they have begun to apply these approaches to the study of human speech. His team has collaborated with Diagnostic Biochips and the University of Iowa Department of Neurosurgery to develop a recording electrode for measuring population activity in the human brain. Through these combined efforts, they have advanced understanding of the neural mechanisms of vocal production that can inform therapeutic approaches intended to combat a range of communication disorders.
In the second portion of the webinar, Dr. Hoffman introduces work in her lab investigating the neural mechanisms of learning and memory in freely-behaving macaques. This includes recording neural signatures from animals in a rich environment to examine how experiences shape new learning. Using spatially resolved units, distinct waveforms, and interactions with local currents and fields, they hope to identify the role of functional cell types in network states and network plasticity. Dr. Hoffman also describes the features and current limitations of the probe, and presents preliminary wireless recording results from her lab. She concludes with a discussion of factors that may make it more or less suitable for other users, and best practices for generating high quality data.
My talk at BASF Science Symposium: sustainable food chain - from field to table, Jun 23-24, 2015, Chicago.
Notes and acknowledgements at http://kamounlab.tumblr.com/post/122151022390/plant-pathology-in-the-post-genomics-era
The climbing vine kudzu, a member of the leguminous
pea family (Fabaceae), was introduced into the USA
from its native Asia in the 1800s. It was initially lauded
for efficacy in erosion control along highways and as a
high-quality grazing crop for livestock. P. montana var.
lobata has since become a truculent invasive, spreading
via vegetative runners and seed dispersal. Seven
million acres of the American southeast are now
plagued by this vine.
"Keeping up with the plant destroyers." My talk at The Royal Society, 7 March...Sophien Kamoun
Tackling emerging threats to animal health, food security and ecosystem resilience, The Royal Society, Monday 7 – Tuesday 8 March 2016. https://royalsociety.org/events/2016/03/emerging-fungal-threats/
Bio 240 Education Organization / snaptutorial.comBaileya121
For more classes visit
www.snaptutorial.com
• how forensic scientists take advantage of genomic variations in noncoding regions of DNA
• the techniques of polymerase chain reaction (PCR) and gel electrophoresis
CLA 2 Presentation
BUS 606 Advanced Statistical Concepts And Business Analytics
Agenda
Introduction
Multiple linear regression is the most appropriate statistical technique in predicting the outcome of a dependent variable at different values (Keith, 2019).
The study assessed the relationship between the cost of constructing an LWR Plant and the three predictor variables S, N, and CT.
We assessed the association between the two-test used to examine the employee performance.
Assumption of Regression Analysis
Multicollinearity
Multicollinearity is the condition where the predictor variables are highly correlated (Alin, 2010).
Correlation Analysis
4
Assumption of Regression Analysis Cont’
Normality test
The normality assumption is not violated after transforming the outcome variable C, using natural log (C) (Shapiro-Wilk = 0.967, p = 0.414).
5
Results and Discussion – Regression Analysis
Use Residual Analysis and R2 to Check Your Model
The R-Squared of 0.232 indicates that the model can explain about 23.2% of ln(C)
The low R-Square indicated that the model does not fit the data well (Brown, 2009).
6
Results and Discussion Cont’
State which Variables are Important in predicting the cost of constructing an LWR plant?
S is a significant contributing factor in predicting ln(C)(p = 0.021), but N and CT have no significant effect in predicting (p > 0.05)
7
Results and Discussion Cont’
State a prediction equation that can be used to predict ln(C).
After dropping N and CT from the model since they do not have a significance effect in predicting ln(C), the prediction equation is given by:
Does adding CT improve R2? If so, by what amount?
Adding CT in the model changes R-Square by 0.001 from 0.232 to 0.234 which is not significant different from zero (p > 0.05).
8
Results and Discussion Cont’ - Correlational Analysis
Evaluate the correlation between the two scores and state if there seems to be any association between the two.
There was a weak positive correlation between the two tests (r = 0.187). This suggested that the two test scores were not correlated.
9
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the Bayes’ theorem.
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
10
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the naïve version of the Bayes’ theorem
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
11
Results and Discussion Cont’
Compare your results in parts b and c and explain the difference or indifference based on observed probabilities
Naïve version and Bayes theorem have similar probabilities.
We have only one predictor in each sample division
This is because Naïve is a ...
Using High-Density Electrophysiological Recordings to Investigate Neural Mech...InsideScientific
In this webinar, Dr. Michael Long from the NYU Grossman School of Medicine and Dr. Kari Hoffman from Vanderbilt University present their work investigating the neural mechanisms of learning, memory, and behavior using high-density silicon probes from Diagnostic Biochips in small and large animals.
The ability to record network activity using emerging high-density electrophysiological arrays has revolutionized understanding of the link between brain function and behavior. In the first portion of the webinar, Dr. Long discusses how his laboratory has used custom-built (and now standard) probes to investigate the neural mechanisms of vocal production in two model systems: the zebra finch (a songbird) and a newly characterized Costa Rican singing mouse. In ongoing work, they have begun to apply these approaches to the study of human speech. His team has collaborated with Diagnostic Biochips and the University of Iowa Department of Neurosurgery to develop a recording electrode for measuring population activity in the human brain. Through these combined efforts, they have advanced understanding of the neural mechanisms of vocal production that can inform therapeutic approaches intended to combat a range of communication disorders.
In the second portion of the webinar, Dr. Hoffman introduces work in her lab investigating the neural mechanisms of learning and memory in freely-behaving macaques. This includes recording neural signatures from animals in a rich environment to examine how experiences shape new learning. Using spatially resolved units, distinct waveforms, and interactions with local currents and fields, they hope to identify the role of functional cell types in network states and network plasticity. Dr. Hoffman also describes the features and current limitations of the probe, and presents preliminary wireless recording results from her lab. She concludes with a discussion of factors that may make it more or less suitable for other users, and best practices for generating high quality data.
My talk at BASF Science Symposium: sustainable food chain - from field to table, Jun 23-24, 2015, Chicago.
Notes and acknowledgements at http://kamounlab.tumblr.com/post/122151022390/plant-pathology-in-the-post-genomics-era
The climbing vine kudzu, a member of the leguminous
pea family (Fabaceae), was introduced into the USA
from its native Asia in the 1800s. It was initially lauded
for efficacy in erosion control along highways and as a
high-quality grazing crop for livestock. P. montana var.
lobata has since become a truculent invasive, spreading
via vegetative runners and seed dispersal. Seven
million acres of the American southeast are now
plagued by this vine.
"Keeping up with the plant destroyers." My talk at The Royal Society, 7 March...Sophien Kamoun
Tackling emerging threats to animal health, food security and ecosystem resilience, The Royal Society, Monday 7 – Tuesday 8 March 2016. https://royalsociety.org/events/2016/03/emerging-fungal-threats/
Bio 240 Education Organization / snaptutorial.comBaileya121
For more classes visit
www.snaptutorial.com
• how forensic scientists take advantage of genomic variations in noncoding regions of DNA
• the techniques of polymerase chain reaction (PCR) and gel electrophoresis
CLA 2 Presentation
BUS 606 Advanced Statistical Concepts And Business Analytics
Agenda
Introduction
Multiple linear regression is the most appropriate statistical technique in predicting the outcome of a dependent variable at different values (Keith, 2019).
The study assessed the relationship between the cost of constructing an LWR Plant and the three predictor variables S, N, and CT.
We assessed the association between the two-test used to examine the employee performance.
Assumption of Regression Analysis
Multicollinearity
Multicollinearity is the condition where the predictor variables are highly correlated (Alin, 2010).
Correlation Analysis
4
Assumption of Regression Analysis Cont’
Normality test
The normality assumption is not violated after transforming the outcome variable C, using natural log (C) (Shapiro-Wilk = 0.967, p = 0.414).
5
Results and Discussion – Regression Analysis
Use Residual Analysis and R2 to Check Your Model
The R-Squared of 0.232 indicates that the model can explain about 23.2% of ln(C)
The low R-Square indicated that the model does not fit the data well (Brown, 2009).
6
Results and Discussion Cont’
State which Variables are Important in predicting the cost of constructing an LWR plant?
S is a significant contributing factor in predicting ln(C)(p = 0.021), but N and CT have no significant effect in predicting (p > 0.05)
7
Results and Discussion Cont’
State a prediction equation that can be used to predict ln(C).
After dropping N and CT from the model since they do not have a significance effect in predicting ln(C), the prediction equation is given by:
Does adding CT improve R2? If so, by what amount?
Adding CT in the model changes R-Square by 0.001 from 0.232 to 0.234 which is not significant different from zero (p > 0.05).
8
Results and Discussion Cont’ - Correlational Analysis
Evaluate the correlation between the two scores and state if there seems to be any association between the two.
There was a weak positive correlation between the two tests (r = 0.187). This suggested that the two test scores were not correlated.
9
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the Bayes’ theorem.
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
10
Results and Discussion Cont’
Find the probability of upgrading for each division of the sample by the naïve version of the Bayes’ theorem
P(Up/T1) = P (T1/Up) P(Up) ÷ P(T1)
= (23/46*46/86) ÷43/86
= 23/43
P(Up/T2) = P (T2/Up) P(Up) ÷ P(T2)
= (23/46*46/86) ÷43/86
= 23/43
11
Results and Discussion Cont’
Compare your results in parts b and c and explain the difference or indifference based on observed probabilities
Naïve version and Bayes theorem have similar probabilities.
We have only one predictor in each sample division
This is because Naïve is a ...
1. LOREM IPSUM DOLOR SIT AMET, CONSECTETUR ADIPISICING ELIT, SED DO
EIUSMOD TEMPOR INCIDIDUNT UT LABORE ET DOLORE MAGNA ALIQUA.
Presentado por:
PEDRO PEREZ
RAMIRO RAMIREZ
Director:
WILLIAM SUAREZ
TRABAJO QUE EXPONE EL FORMATO APA
UNIVERSIDAD DISTRITAL FRANCISCO JOSÉ DE CALDAS
FACULTAD DE XXXXXXXX
PROYECTO CURRICULAR
COLOMBIA
2014
2. TABLA DE CONTENIDO
1. Titulo nivel 1 .....................................................................................................................4
2. Titulo nivel 1 .....................................................................................................................4
2.1 Titulo nivel 2..............................................................................................................4
3. Tablas y figuras .................................................................................................................5
3.1 Figuras........................................................................................................................5
3.2 Tablas .........................................................................................................................6
Referencias ...............................................................................................................................7
3. TABLA DE ILUSTRACIONES
Figura 1. Logo APA (American Psychological Association)...................................................5
4. 1. Titulo nivel 1
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt
ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco
laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in
voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non
proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
2. Titulo nivel 1
2.1 Titulo nivel 2
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt
ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco
laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in
voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non
proident, sunt in culpa qui officia deserunt mollit anim id est laborum (Kahvejian, Quackenbush
& Thompson, 2008).
Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque
laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto
beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut
odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.
Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed
quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat
voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit
laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit
qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat
quo voluptas nulla pariatur? (Hogeweg, 2011).
5. Titulo nivel 3
Praesent id luctus massa. Duis tincidunt interdum ipsum id suscipit. Vivamus lectus mauris,
sagittis eget libero vitae, sollicitudin condimentum mi. Quisque eget faucibus diam. Praesent
interdum dui ac leo volutpat, vitae eleifend turpis luctus. Aenean ac pulvinar leo. Vestibulum
iaculis vestibulum tortor, eget rhoncus mi congue in. Donec eleifend nisl eleifend blandit
vehicula. Etiam vitae velit et quam iaculis ornare. Ut mattis pretium sapien, vitae viverra tortor
fermentum quis. Maecenas ultricies elementum dapibus. In quis risus et tortor suscipit gravida.
Donec at ipsum ac turpis lobortis ullamcorper. Ut at molestie nisi, eu semper purus..
3. Tablas y figuras
3.1 Figuras
En la figura 3 se muestra el logo de APA.
Figura 1. Logo APA (American Psychological Association).
6. 3.2 Tablas
En la tabla 1 se muestra un ejemplo de tabla.
Tabla 1
El título debe ser breve y descriptivo.
Categoría Categoría Categoría Categoría
Variable 1 Xx Xx Xx
Variable 2 XX XX XX
Esta es la nota de la tabla.
7. Referencias
Andrews, S. Fastqc, (2010). A quality control tool for high throughput sequence data.
Augen, J. (2004). Bioinformatics in the post-genomic era: Genome, transcriptome, proteome, and
information-based medicine. Addison-Wesley Professional.
Blankenberg, D., Kuster, G. V., Coraor, N., Ananda, G., Lazarus, R., Mangan, M., ... & Taylor, J.
(2010). Galaxy: a web‐based genome analysis tool for experimentalists. Current protocols
in molecular biology, 19-10.
Bolger, A., & Giorgi, F. Trimmomatic: A Flexible Read Trimming Tool for Illumina NGS Data.
URL http://www. usadellab. org/cms/index. php.
Giardine, B., Riemer, C., Hardison, R. C., Burhans, R., Elnitski, L., Shah, P., ... & Nekrutenko,
A. (2005). Galaxy: a platform for interactive large-scale genome analysis. Genome
research, 15(10), 1451-1455.
Goecks, J., Nekrutenko, A., & Taylor, J. (2010). Galaxy: a comprehensive approach for
supporting accessible, reproducible, and transparent computational research in the life
sciences. Genome Biol, 11(8), R86.
Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., ... & Regev,
A. (2013). De novo transcript sequence reconstruction from RNA-seq using the Trinity
platform for reference generation and analysis. Nature protocols, 8(8), 1494-1512.
HÁJKOVÁ, P., Zemanová, B., BRYJA, J., Hájek, B., Roche, K., TKADLEC, E., & ZIMA, J.
(2006). Factors affecting success of PCR amplification of microsatellite loci from otter
faeces. Molecular Ecology Notes, 6(2), 559-562.
Mardis, E. R. (2008). The impact of next-generation sequencing technology on genetics. Trends
in genetics, 24(3), 133-141.
Martin, J. A., & Wang, Z. (2011). Next-generation transcriptome assembly. Nature Reviews
Genetics, 12(10), 671-682.
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads.
Michigan State University. (2013). Annotation pipeline [Imagen]. Recuperada de
http://cpgr.plantbiology.msu.edu/training/workshop_mar07/Lecture3_GenomeAnnotation.
pdf
Miller, D. J., Ball, E. E., Forêt, S., & Satoh, N. (2011). Coral genomics and transcriptomics—
ushering in a new era in coral biology. Journal of Experimental Marine Biology and
Ecology, 408(1), 114-119.
8. Miller, J. R., Koren, S., & Sutton, G. (2010). Assembly algorithms for next-generation
sequencing data. Genomics, 95(6), 315-327.
Wilhelm, B. T., & Landry, J. R. (2009). RNA-Seq-quantitative measurement of expression
through massively parallel RNA-sequencing. Methods (San Diego, Calif.), 48(3), 249.