lunes, 7 de octubre de 2013

8vo SOLABIMA - Cronogramas

8vo Congreso Latinoamericano de Biomatemática –  2013

Luján – Argentina

15 al 18 de octubre de 2013



Debido a que el sitio web del encuentro no se encuentra disponible, en los siguientes links se pueden consultar el cronograma definitivo y el cronograma detallado de ponencias:


Disculpen las molestias ocasionadas, cualquier consulta se puede enviar al mail fmomo @ ungs.edu.ar (borrar espacios entre arroba y texto)

 

martes, 16 de abril de 2013

SOLABIMA

8vo Congreso Latinoamericano de Biomatemática –  2013

Luján – Argentina

15 al 18 de octubre de 2013

PRIMERA CIRCULAR



Estimados y estimadas colegas:
Nos complace anunciar la realización de nuestro congreso que este año contará nuevamente con la presencia de especilistas de renombre y una serie de actividades de gran interés.
La reunión se realizará en la ciudad de Luján, provincia de Buenos Aires, Argentina. Un lugar hospitalario, con buena hotelería y gastronomía y una intensa actividad cultural; además, se encuentra a sólo 60 kilómetros de la ciudad de Buenos Aires lo que le permite un fácil acceso desde cualquier lugar del país o del extranjero. Desde 1994, cuando se realizó en esta ciudad en III Encuentro Latinoamericano de Ecología Matemática, Luján ha sido sede de varios cursos internacionales en el tema y recientemente (en septiembre de 2012) ha sido sede de la Reunión Argentina de Ecología con gran éxito.
Pasemos a detallar algunas de las actividades previstas para nuestro congreso:

Conferencistas


Ignacio Barradas (CIMAT, México)
María Josefina Hernández (Universidad Central de Venezuela)
Luis Hernando Hurtado (Universidad del Quindío, Colombia)
Carlos Condat (Universidad Nacional de Córdoba, Argentina)
Eduardo Gonzalez Olivares (Universidad Católica de Valparaíso, Chile)
Juan Aparicio (Universidad Nacional de Salta, Argentina)
Gustavo Sibona (Universidad Nacional de Córdoba, Argentina)
Fernando Córdova Lepe (Universidad Católica del Maule, Chile)

Talleres y cursos


  • Aplicaciones de Ecuaciones Diferenciales Ordinarias a la modelación matemática de problemas biológicos
  • Modelos matemáticos de poblaciones contínuas para la interacción de dos especies
  • Análisis de modelos matemáticos contínuos
  • Series temporales multivariadas con aplicaciones al análisis de modelos biológicos
  • Modelado matemático del cáncer
  • Modelos espaciales de movimiento con Ecuaciones Diferenciales Parciales y sus tratamientos numéricos

Aranceles (en pesos argentinos)


Profesores-investigadores formados: $ 500.-
Estudiantes de doctorado: $ 350.-
Estudiantes de grado: $ 200.-

El cobro del arancel se realizará el primer día de congreso para evitar engorrosos trámites y costos extra por las transferencias de dinero o depósitos bancarios. Por esta razón, solicitamos que confirmen su asistencia lo antes posible.
La inscripción incluye el acceso libre a todas las actividades académicas, a la recepción de apertura, el material documental y los materiales de los cursillos o talleres en que se inscriba cada uno.

Presentación de trabajos y publicación


Los resúmenes, en formatos Word u Open Office, serán recibidos hasta el 6 de septiembre. En breve habilitaremos la página de internet del congreso y el envío de resúmenes se hará a través de ella. La extensión máxima es de dos carillas A4, a espacio y medio, con todos los márgenes de 2,5 cm. En la página se podrá bajar una plantilla.
Aquellos interesados en publicar los trabajos completos podrán entregar los manscritos terminados en el Congreso. Estamos en tratativas para que la publicación se realice en una revista internacional ISI.

Temáticas


Aplicación de modelos matemáticos a:
  • Ecología
  • Epidemiología
  • Medicina
  • Fisiología
  • Etología
  • Bioeconomía
  • Agronomía
  • Neurociencias
  • Genética
  • Biología Celular
  • Biofísica
  • Otras ramas de las ciencias biológicas

Comisión organizadora


  • Fernando Momo (Universidad Nacional de General Sarmiento)
  • Leonardo Saravia (Universidad Nacional de General Sarmiento)
  • Santiago Doyle (Universidad Nacional de General Sarmiento)
  • Andrés Duhour (Universidad Nacional de Luján)
  • Macarena Rionda (Universidad Nacional de General Sarmiento)
  • Luciana Torre (Universidad Nacional de Córdoba – Conicet)

Comité científico


  • Ignacio Barradas (CIMAT, México)
  • María Josefina Hernández (Universidad Central de Venezuela)
  • Irene Duarte Gandica (Universidad del Quindío, Colombia)
  • Gerard Olivar Tost (Universidad de Manizales, Colombia)
  • João Frederico Da Costa Meyer (Universidad de Campinas, Brasil)
  • Eduardo González Olivares (Universidad Católica de Valparaíso, Chile)
  • Fernando Córdova Lepe (Universidad Católica del Maule, Chile)
  • Juan Aparicio (Universidad Nacional de Salta, Argentina)





domingo, 13 de enero de 2013

In tribute to Aaron Swartz's death, my peer-reviewed papers


2012

Saravia LA, Giorgi A, Momo FR (2012) Multifractal Spatial Patterns and Diversity in an Ecological Succession. PLoS ONE 7: e34096. doi:10.1371/journal.pone.0034096.

Saravia LA, Giorgi A, Momo F (2012) Multifractal growth in periphyton communities. Oikos 121: 1810–1820. doi:10.1111/j.1600-0706.2011.20423.x. [PDF]

2009


Saravia LA (2009) Algunas cuestiones sobre el espacio en ecología. Modelos, datos y aplicaciones. Ph.D. Thesis. Universidad Nacional de Buenos Aires. Argentina. [PDF]

2000

Saravia LA, Ruxton GD, Coviella CE (2000) The importance of transients’ dynamics in spatially extended populations. Proceedings of the Royal Society London Series B 267: 1781–1785. doi:10.1098/rspb.2000.1210. [PDF]

1999

Saravia LA, Giorgi A, Momo FR (1999) A photographic method for estimating chlorophyll in periphyton on artificial substrata. Aquatic Ecology 33: 325–330. doi:10.1023/A:1009934626188. [PDF]


1998

Ruxton GD, Saravia LA (1998) The need for biological realism in the updating of cellular automata models. Ecological Modelling 107: 105–112. doi:10.1016/S0304-3800(97)00179-8. [PDF]

Saravia LA, Momo F, Boffi Lissin LD (1998) Modelling periphyton dynamics in running water. Ecological Modelling 114: 35–47. doi:10.1016/S0304-3800(98)00113-6. [PDF]

viernes, 26 de octubre de 2012

Making slides without powerpoint/impress reloaded


After doing html slides what's next?

Beamer

Beamer is a LaTeX class for creating slides for presentations.

But I know nothing about Beamer neither about LaTeX.

I wanted to convert the html presentations to Beamer, because the output of Beamer/LaTex is pdf, and pdfs shows nicely in figshare.

Ok, pandoc comes to rescue us, I already have the files I've done with Rstudio and knitr which I described in the previous post so it's very easy, with pandoc you can generate html slides or Beamer slides:

pandoc -t beamer RAE2012_DivPatron_charla.md -V theme:Warsaw -o RAE2012_DivPatron_charla.pdf

but the result is not very good, the images were BIG, very BIG.

Thanks to Jeromy Anglim's post about Beamer/Markdown I learn to use some simple LaTex formating commands for images:

\centerline{\includegraphics[height=2in]{my_image.pdf}}

The problem is that images dynamically generated with R, can't be formated this way. Knitr chunk options have the posibility to change size: fig.width and fig.height.
The images get smaller but the fonts dont' change it's size, the images are awfullThe other chunk options to control output size:out.width and out.height didn't work with Beamer.

Thus I choose the option to modify the Markdown file generated by knitr with the previous LaTex command. It was not a very elegant solution but worked.
UPDATE: A python script to replace <center>![](figure)</center> with LaTeX code

Then I ended with two files: the Rstudio markdown with all the R commands to generate the plots and a modified markdown file used for generate the beamer output by pandoc.


And I uploaded the presentation to figshare, but the presentation can be showed in your blog/web page using the google docs viewer

https://docs.google.com/viewer

Updated: 
Copy the link from the download button on figshare article's web page

http://files.figshare.com/101487/RAE2012_DivPatron_charla.pdf


Paste the link to google viewer, generate the link, include the resulting code in your web page and voila:





I like the slides generated by pandoc/Beamer,  so I think that I am prepared to say goodbye to powerpoint/impress now.



domingo, 30 de septiembre de 2012

How to make slides without powerpoint (or LlbreOffice Impress)



I started to use R Markdown with RStudio, but what is Markdown? :

"Markdown is a simple markup language designed to make authoring web content easy for everyone"

So it's easy to do a report of the millions of figures and analysis  while experimenting with a dataset or developing a manuscript.

A good stating point is the post of Jeromy Anglim's Blog about that.

Then I found that you could produce slides for presentations with only an additional step pandoc. The Markdown generated by RStudio can be converted to html slides and there are different "kinds" of slides you can convert to, and they look nice.
I have to prepare two talks for the RAE2012, so why not?

I was happy because I thought I would end with the curse of Powerpoint/Impress

But it was not so easy, things were not always as they should be. I was pressed to finish and tables don't get formated, figures don't get in the places I wanted, and the videos don't work at all. 

After endless try and error cycles  I finished, I have to learn a little of html and the posts of Markus Gesmann and Christopher Gandrud  were invaluable. I decided to use slidy and as there was no internet connection at the conference room everything must be included in the html file so I used the following pandoc command:

pandoc --self-contained -s -S -i -t slidy -V slidy-url=slidy  RAE2012_Charla.md -o RAE2012_Charla_slidy.html

for use this you have to download the Slidy code in a local folder called "slidy" 


 The source of one of talks is here and the slides are
 here




One drawback is that if I upload talk to figshare the html code is displayed and not like when uploading a pdf, it shows the formated talk ...

I think it's a matter of experience, things get easier after some work,  but I'm not sure if I get rid of powerpoint/impress... next time we'll see.





lunes, 3 de septiembre de 2012

Los 10 puntos para revisar antes de enviar un manuscrito


Bueno esto lo extraje del muy recomendable blog de  Mike Kaspari asi que lo podemos llamar un afano, será un afano pero es util de todas formas.

Well this is extracted from the highly recommended blog of Mike Kaspari so we can call it a steal, but it will be a useful theft anyway.
  1. Get rid of every adjective modifying a relationship. Was x larger than y? Just say so. Saying it was much larger, or especially tiny, or amazingly huge adds no information.
  2. Replace long words with short words. Good writing maximizes the content of the message per number of letters used. Replace long words with short words of equal meaning. Replace utilization with use.
  3. Replace every “differed” or “was different” with the actual, quantitative relationship. Compare the content per letters used for the following two sentences:
    Plants fertilized with nitrogen differed in height from controls.
    Plants fertilized with nitrogen were 2.5 x taller than controls.
    Not only have you conveyed that nitrogen increased growth, you’ve given a vivid word picture as to how much. In fewer words!

     
  4. Make sure your Discussion has a caveat paragraph. Every study is flawed or makes simplifying assumptions; every study has a method or result that may be misinterpreted. Grad students often attempt to hide these flaws. But, like an untreated cut, such problems can fester in the mind of a reviewer. Consider inserting a caveat paragraph somewhere in the middle of the Discussion that thoughtfully addresses at least two topics.
    The first is a plausible mistake a harried reader might make and why it is incorrect (look for patterns in your friendly reviews to identify likely candidates). Good writing is good teaching, and good teachers anticipate the problems of their students.

    The second should confront the biggest weakness of the study, how you tried to ameliorate it, and perhaps how future work could better tackle it (in other words, ending on a positive note). Do you want to be the first person to raise this issue, or would you rather your reviewers do so?
    A caveat paragraph depicts a thoughtful author who is after the truth, not someone who is trying to sell something.

  5. If your Discussion is more than 2x longer than your results, cut it down. Discussions are not brain dumps, nor are they opportunities to lay claim in print to every idea you have on the subject. Careful topical reviewers, by the time they reach the Discussion, want to know how your results relate to your hypotheses, the strengths and weaknesses of your results, and perhaps one or two implications of your results. Focus on these three tasks, and leave your reviewer wanting more, not flipping ahead to see when the bibliography begins.
  6. Market test your title and abstract. More and more editors are rejecting papers before they send them out for review. Reviewers typically accept or decline to review papers on the basis of the title and abstract. The title and abstract are the front door to your study. They are the most important parts of the paper. Craft them carefully and show them to your friendly reviewers.
  7. Spell check everything. Natch.
  8. Even your bibliography. Your scientific career is built on a bedrock of trust. Reviewers want to believe that you have carefully collected and analyzed your data. However, to a large degree, your reviewer’s ability to see just how meticulous you are is limited. This is why typos in the manuscript loom far larger than many beginning scientists think. And, similarly, why care in constructing your bibliography–that Latinate names are italicized, that the journal’s formatting is followed to the letter, that authors names are spelled correctly–also reflects your ability to conscientiously manage detail. Will reviewers give you the benefit of the doubt? Often it’s the little things that decide.
  9. Read it aloud. There is no better way to gauge the flow and logic of a manuscript than to read it aloud, effectively using your whole brain in the enterprise. Beginning scientists should do this in three steps:
    Read the first sentence of every paragraph, in sequence, from the Introduction through the Discussion. If the paper is well written, it should sound like you are explaining the study to a colleague, albeit in a rather stilted way. If the paper doesn’t make much sense, it needs work on its paragraph structure.

    Next do it again, but this time read the first and last sentence of every paragraph. This should result in greater logical flow–the final sentence of one paragraph leading into, and often introducing, the first sentence of the next paragraph. If you find little difference between this reading and the previous, spend a day or two fixing the ends of your paragraphs.

    Now, after a cup of coffee, a long walk, or a nice bout of screaming into a pillow, read the whole paper aloud. Listen for any awkward phrasing, which will sound like your car engine misfiring. For some reason, reading the whole manuscript aloud allows you to see it in a new light, or, more aptly, with fresh ears.
  10. The following is from  the comentaries of the original post:
    Arrange the tables and figures on your desk in the order they appear in your manuscript, but don’t look at the text. Do they tell your story, even with the text removed? Does the progression of figures have a natural feel to it, or do they need to be rearranged to flow better? Are there any figures that don’t seem to belong and potentially could be removed to streamline the paper? Is there consistency in style? If the figures are shrunk by 50%, can you still read the text? (Ben)
  11. Here there are more: "Fourteen steps to a clearly written paper"
  12. UPDATE: a great post with more writing advice: some well known tricks for clear writing

domingo, 19 de agosto de 2012

The neutral bomb

This is not a sex bomb, but who knows. A couple of weeks ago I was giving a talk at the Escuela Argentina de Matemática y Biología about neutral and non-neutral models in ecology. I was pointed to the paper of  Clark 2012 The coherence problem with the Unified Neutral Theory of Biodiversity with the commentary: the neutral theory is uninformative. And the paper  basically says that neutral models or neutral theory is based on ignorance and that they are incoherent.
I think this is very interesting, but I disagree, here I review the last four points of the article:

(i) Incoherence: Neutral models assume all individuals are equal then go on and predict things like SADs and SARs, what is this telling us about the niche? The individuals are all equal, the species can have different effective probabilities to reach a place, different densities, but there is no niche. In this sense it can be thought as a null model for niches, other kinds of null models for niche can be constructed and maybe they are better, but I don't see incoherence.  Competition and niche are not the only things relevant to coexistence of species.

(ii) Ignorance: I am sure we are ignorant about nature, but assuming that all individuals are equal is not equal probability and equal probability is not a null model for sameness. Anyway the equivalence of individuals is certainly not ignorance is an assumption.


(iii)Victory: There are no victories here, but models or theories to explain nature patterns and we need to confront them with data in several ways, and much better in several ways at the same time.

(iv)Applying neutral models in conservation: you have to be careful to apply a model to conservation practices, neutral or not neutral does not matter at all. Models are not reality, always have simplifications, aproximations, or assumptions and we can select which is better for our problem (see Model selection in ecology and evolution).


The real issue I think is a matter of scale: which are the microscale processes that determine macroscale patterns. If niche models predicts the same patterns that neutral models and both are consistent with real data (see Niche and neutral models predict asymptotically equivalent...) a parsimonious interpretation is that we don't need niche. But if real data does not match the model we do need to add more complexities.

I think that neutral models are usefull both intellectually and practically, more about this in Neutrality without incoherence: a response to Clark