diff --git a/.gitignore b/.gitignore
index 5777d674c486dada283e38b2948a0c9399533f20..448df5855e840e880baf8f8fb2704111180dc6ee 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,4 @@
-.ipynb_checkpoints/
+*/.ipynb_checkpoints/*
 #LaTeX & TeXmaker
 *.aux
 *.log
diff --git a/README.md b/README.md
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index 0000000000000000000000000000000000000000..6025dec3425bbe49a155b63524aa066204259b75
--- /dev/null
+++ b/README.md
@@ -0,0 +1,27 @@
+# FM-tarea-02
+
+|Curso|Tarea|Fecha de lÃmite|
+|:---:|:---:|:---:|
+|FÃsica MÃ©dica|RadiobiologÃa|2021-07-05 23:59:59 -5 UTC|
+
+Ver directorio ```materiales```
+
+Instrucciones: 
+
+* Copie el repositorio haciendo un *fork* a su espacio de trabajo personal.
+
+* Cambie la opciÃ³n de visibilidad de su proyecto a ```Private```.
+
+* Use el directorio ```miTrabajo``` para guardar las soluciones a los ejercicios propuestos. Actualice el repositorio para incluir el material a entregar, haciendo un *commit* nuevo, antes de la **fecha lÃmite**
+
+* Las soluciones a los ejercicios propuestos se entregaran digitalizadas en un documento pdf, que deberÃ¡ reunir todo el desarrollo en forma ordenada y legible. Algunos anÃ¡lisis pueden requerir que se suministre un cÃ³digo o un algoritmo reproducible.
+
+* Al finalizar, la entrega su repositorio debe ser visible y editable para el equipo docente encargado de la revisiÃ³n. Para ello deberÃ¡ agregar al repositorio al profesor JosÃ© Antonio LÃ³pez como ```Developer```.
+
+![Agregar miembros](anexos/Members.png)
+
+![@jal](anexos/invitaJAL.png)
+
+![developer](anexos/developer.png)
+
+* Si tiene dudas sobre la resoluciÃ³n de los ejercÃcios explore la conversaciÃ³n el canal **FM FÃsica MÃ©dica** en **Mattermost Edu LA-CoNGA**. Si tiene problemas tÃ©cnicos con el repositorio, los permisos, etc, explore el canal de **Soporte**. Si no encuentra nada que le ayude, pregunte.
diff --git a/anexos/Members.png b/anexos/Members.png
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diff --git a/1/Natarajan et al 1991-Mutation Research_Fundamental and Molecular Mechanisms of Mutagenesis.pdf b/material/1/Natarajan et al 1991-Mutation Research_Fundamental and Molecular Mechanisms of Mutagenesis.pdf
similarity index 100%
rename from 1/Natarajan et al 1991-Mutation Research_Fundamental and Molecular Mechanisms of Mutagenesis.pdf
rename to material/1/Natarajan et al 1991-Mutation Research_Fundamental and Molecular Mechanisms of Mutagenesis.pdf
diff --git a/1/Sakamoto-Hojo 2018- Lessons from the accident with 137 Cesium in Goiania.pdf b/material/1/Sakamoto-Hojo 2018- Lessons from the accident with 137 Cesium in Goiania.pdf
similarity index 100%
rename from 1/Sakamoto-Hojo 2018- Lessons from the accident with 137 Cesium in Goiania.pdf
rename to material/1/Sakamoto-Hojo 2018- Lessons from the accident with 137 Cesium in Goiania.pdf
diff --git "a/1/Tarea Radiobiolog\303\255a.pdf" "b/material/1/Tarea Radiobiolog\303\255a.pdf"
similarity index 100%
rename from "1/Tarea Radiobiolog\303\255a.pdf"
rename to "material/1/Tarea Radiobiolog\303\255a.pdf"
diff --git a/material/2/.ipynb_checkpoints/Supervivencia-checkpoint.ipynb b/material/2/.ipynb_checkpoints/Supervivencia-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e0d0b12f82e6fa7fb51b540a6cb3c56d844c1074
--- /dev/null
+++ b/material/2/.ipynb_checkpoints/Supervivencia-checkpoint.ipynb
@@ -0,0 +1,486 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"img/isotipo-RGB-pequeno.png\" alt=\"\" width=\"15%\" />\n",
+    "\n",
+    "# Mezcla de poblaciones celulares y resistencia a la radiaciÃ³n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pandas import read_csv\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ejercicio\n",
+    "Se dispone de un archivo de texto (poblacion.csv) con valores en un arreglo de dimensiÃ³n 101,101.\n",
+    "Los valores posibles del arreglo son los enteros del 0 al 3. Cada valor corresponderÃ¡ con un estado de una cÃ©lula presente en el sitio. \n",
+    "\n",
+    "Este arreglo podrÃa estar relacionado con una simulaciÃ³n usando un modelo de Potts celular.\n",
+    "\n",
+    "En el caso de interÃ©s los valores tienen la siguiente interpretaciÃ³n:\n",
+    "\n",
+    "* 0: no hay cÃ©lula\n",
+    "* 1: cÃ©lula aireada\n",
+    "* 2: cÃ©lula hipÃ³xica\n",
+    "* 3: cÃ©lula muerta\n",
+    "\n",
+    "El objetivo de este ejercicio es simular el efecto de la radiaciÃ³n sobre este conjunto de cÃ©lulas, usando un modelo de blanco simple.\n",
+    "\n",
+    "### Modelo:\n",
+    "La probabilidad de daÃ±o a una determinada cÃ©lula es $p=1-e^{-D/Do}$, donde $Do=1$ Gy para las cÃ©lulas tipo 1 y $Do=5$ Gy para las cÃ©lulas tipo 2.\n",
+    "\n",
+    "### Instrucciones\n",
+    "* Represente grÃ¡ficamente los datos del archivo, de forma que se pueda visualizar en que zona se encuentra cada tipo de cÃ©lula.\n",
+    "* Simule la aplicaciÃ³n de diferentes dosis a rÃ©plicas de su poblaciÃ³n de prueba en un rango de 1 a 25 Gy. Utilice el modelo proporcionado para decidir si cada cÃ©lula sobrevive o muere. \n",
+    "    * \n",
+    "* Calcule la fracciÃ³n de supervivencia en cada caso, tabule y grafique $\\ln(S)$ en funciÃ³n de $D$\n",
+    "* Tabule y grafique la proporciÃ³n de cÃ©lulas sobrevivientes tipo 2 con respecto al total de cÃ©lulas sobrevivientes tipo 1 y 2 en funciÃ³n de la dosis.\n",
+    "* Indique quÃ© propiedad de la curva resultante evidencia la resistencia diferencial de las sub poblaciones 1 y 2.\n",
+    "* Usted puede usar esta hoja para desarrollar la respuesta o cualquier otra herramienta a su alcance, siempre que el resultado sea razonablemente reproducible.\n",
+    "\n",
+    "Este ejercicio estÃ¡ inspirado ligerÃsimamente en el caso de irradiaciÃ³n _in vivo_ estudiado en:  \n",
+    "POWERS, W., TOLMACH, L. A Multicomponent X-ray Survival Curve for Mouse Lymphosarcoma Cells irradiated in vivo. Nature 197, 710â€“711 (1963). https://doi.org/10.1038/197710b0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "r una dosis uniforme para todo el conjunto y calcular las probabilidades para las cÃ©lulas tipo 1 y 2 para obtener luego la fracciÃ³n de sobrevivencia? Creo que debe redactarse mÃ¡s allÃ¡ de toda duda. Debe ir algo asÃ... \"para simular la aplicaciÃ³n de la dosis vamos a...\" y das los detalles de la simulaciÃ³n, "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = read_csv('poblacion.csv',header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
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+       "     94   95   96   97   98   99   100  \n",
+       "0      0    0    0    0    0    0    0  \n",
+       "1      0    0    0    0    0    0    0  \n",
+       "2      0    0    0    0    0    0    0  \n",
+       "3      0    0    0    0    0    0    0  \n",
+       "4      0    0    0    0    0    0    0  \n",
+       "..   ...  ...  ...  ...  ...  ...  ...  \n",
+       "96     0    0    0    0    0    0    0  \n",
+       "97     0    0    0    0    0    0    0  \n",
+       "98     0    0    0    0    0    0    0  \n",
+       "99     0    0    0    0    0    0    0  \n",
+       "100    0    0    0    0    0    0    0  \n",
+       "\n",
+       "[101 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(df, cmap='viridis')\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.6",
+   "language": "python3.6",
+   "name": "python3.6"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/material/2/README.md b/material/2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..09d18879d27395d3747eab5296265ddc86f2b8ca
--- /dev/null
+++ b/material/2/README.md
@@ -0,0 +1,30 @@
+# Mezcla de poblaciones celulares y resistencia a la radiaciÃ³n
+Se dispone de un archivo de texto (poblacion.csv) con valores en un arreglo de dimensiÃ³n 101,101.
+Los valores posibles del arreglo son los enteros del 0 al 3. Cada valor corresponderÃ¡ con un estado de una cÃ©lula presente en el sitio. 
+
+Este arreglo podrÃa estar relacionado con una simulaciÃ³n usando un modelo de Potts celular.
+
+En el caso de interÃ©s los valores tienen la siguiente interpretaciÃ³n:
+
+* 0: no hay cÃ©lula
+* 1: cÃ©lula aireada
+* 2: cÃ©lula hipÃ³xica
+* 3: cÃ©lula muerta
+
+El objetivo de este ejercicio es simular el efecto de la radiaciÃ³n sobre este conjunto de cÃ©lulas, usando un modelo de blanco simple.
+
+### Modelo:
+La probabilidad de daÃ±o a una determinada cÃ©lula es $P(D)=1-e^{-D/Do}$, donde $Do=1$ Gy para las cÃ©lulas tipo 1 y $Do=5$ Gy para las cÃ©lulas tipo 2.
+
+### Instrucciones
+* Represente grÃ¡ficamente los datos del archivo, de forma que se pueda visualizar en que zona se encuentra cada tipo de cÃ©lula.
+* Simule la aplicaciÃ³n de diferentes dosis a rÃ©plicas de su poblaciÃ³n de prueba en un rango de 1 a 25 Gy. Utilice el modelo proporcionado para decidir si cada cÃ©lula sobrevive o muere. Tome en cuenta:
+    * Suponga que la dosis es uniforme sobre todo el arreglo. Cada sitio recibe una dosis idÃ©ntica.
+    * El estado final de cada cÃ©lula luego de la irradiaciÃ³n con una dosis $D$ es el resultado de un ensayo de Bernoulli con probabilidades $P(D)$ (cÃ©lula muere) y $1-P(D)$ (cÃ©lula sobrevive).
+* Calcule la fracciÃ³n de supervivencia en cada caso, tabule y grafique $\ln(S)$ en funciÃ³n de $D$
+* Indique quÃ© propiedad de la curva resultante evidencia la resistencia diferencial de las sub poblaciones 1 y 2.
+* Tabule y grafique la proporciÃ³n de cÃ©lulas sobrevivientes tipo 2 con respecto al total de cÃ©lulas sobrevivientes tipo 1 y 2 en funciÃ³n de la dosis.
+* Usted puede usar la hoja [Supervivencia.ipynb](Supervivencia.ipynb) para desarrollar la respuesta o cualquier otra herramienta a su alcance, siempre que el resultado sea razonablemente reproducible.
+
+Este ejercicio estÃ¡ inspirado ligerÃsimamente en el caso de irradiaciÃ³n _in vivo_ estudiado en:  
+POWERS, W., TOLMACH, L. A Multicomponent X-ray Survival Curve for Mouse Lymphosarcoma Cells irradiated in vivo. Nature 197, 710â€“711 (1963). https://doi.org/10.1038/197710b0
diff --git a/2/Supervivencia.ipynb b/material/2/Supervivencia.ipynb
similarity index 99%
rename from 2/Supervivencia.ipynb
rename to material/2/Supervivencia.ipynb
index 0092b18c6237637f4949c346f777bb8ab2d161e9..e8b380ee5798d5ddbfb7aae572bc3f0c2a8e675d 100644
--- a/2/Supervivencia.ipynb
+++ b/material/2/Supervivencia.ipynb
@@ -41,20 +41,29 @@
     "El objetivo de este ejercicio es simular el efecto de la radiaciÃ³n sobre este conjunto de cÃ©lulas, usando un modelo de blanco simple.\n",
     "\n",
     "### Modelo:\n",
-    "La probabilidad de daÃ±o a una determinada cÃ©lula es $p=1-e^{-D/Do}$, donde $Do=1$ Gy para las cÃ©lulas tipo 1 y $Do=5$ Gy para las cÃ©lulas tipo 2.\n",
+    "La probabilidad de daÃ±o a una determinada cÃ©lula es $P(D)=1-e^{-D/Do}$, donde $Do=1$ Gy para las cÃ©lulas tipo 1 y $Do=5$ Gy para las cÃ©lulas tipo 2.\n",
     "\n",
     "### Instrucciones\n",
     "* Represente grÃ¡ficamente los datos del archivo, de forma que se pueda visualizar en que zona se encuentra cada tipo de cÃ©lula.\n",
-    "* Simule la aplicaciÃ³n de diferentes dosis a rÃ©plicas de su poblaciÃ³n de prueba en un rango de 1 a 25 Gy. Utilice el modelo proporcionado para decidir si cada cÃ©lula sobrevive o muere. \n",
+    "* Simule la aplicaciÃ³n de diferentes dosis a rÃ©plicas de su poblaciÃ³n de prueba en un rango de 1 a 25 Gy. Utilice el modelo proporcionado para decidir si cada cÃ©lula sobrevive o muere. Tome en cuenta:\n",
+    "    * Suponga que la dosis es uniforme sobre todo el arreglo. Cada sitio recibe una dosis idÃ©ntica.\n",
+    "    * El estado final de cada cÃ©lula luego de la irradiaciÃ³n con una dosis $D$ es el resultado de un ensayo de Bernoulli con probabilidades $P(D)$ (cÃ©lula muere) y $1-P(D)$ (cÃ©lula sobrevive).\n",
     "* Calcule la fracciÃ³n de supervivencia en cada caso, tabule y grafique $\\ln(S)$ en funciÃ³n de $D$\n",
-    "* Tabule y grafique la proporciÃ³n de cÃ©lulas sobrevivientes tipo 2 con respecto al total de cÃ©lulas sobrevivientes tipo 1 y 2 en funciÃ³n de la dosis.\n",
     "* Indique quÃ© propiedad de la curva resultante evidencia la resistencia diferencial de las sub poblaciones 1 y 2.\n",
+    "* Tabule y grafique la proporciÃ³n de cÃ©lulas sobrevivientes tipo 2 con respecto al total de cÃ©lulas sobrevivientes tipo 1 y 2 en funciÃ³n de la dosis.\n",
     "* Usted puede usar esta hoja para desarrollar la respuesta o cualquier otra herramienta a su alcance, siempre que el resultado sea razonablemente reproducible.\n",
     "\n",
     "Este ejercicio estÃ¡ inspirado ligerÃsimamente en el caso de irradiaciÃ³n _in vivo_ estudiado en:  \n",
     "POWERS, W., TOLMACH, L. A Multicomponent X-ray Survival Curve for Mouse Lymphosarcoma Cells irradiated in vivo. Nature 197, 710â€“711 (1963). https://doi.org/10.1038/197710b0"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ejemplo para editar"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/2/img/isotipo-RGB-pequeno.png b/material/2/img/isotipo-RGB-pequeno.png
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diff --git a/3/Problemas de Tarea - Semana 2.docx b/material/3/Problemas de Tarea - Semana 2.docx
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diff --git a/material/README.md b/material/README.md
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+# Materiales
+La tarea consta de 3 ejercicios. Los materiales e instrucciones para cada uno estÃ¡n almacenados en sendas secciones 1, 2 y 3.
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+En este directorio se dejarÃ¡ el documento digitalizado con las respuestas a la tarea.