diff --git a/GoogleCloud/Module2_proteomic_secondary_analysis.ipynb b/GoogleCloud/Module2_proteomic_secondary_analysis.ipynb
index 6ecc788..201c236 100644
--- a/GoogleCloud/Module2_proteomic_secondary_analysis.ipynb
+++ b/GoogleCloud/Module2_proteomic_secondary_analysis.ipynb
@@ -469,12 +469,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873980/bin/f1000research-9-30844-g0000.jpg\" alt=\"model\" width=\"800\" height=\"800\">\n",
+    "<img src=\"https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74eb/7873980/c86fab854e14/f1000research-9-30844-g0000.jpg\" alt=\"model\" width=\"800\" height=\"800\">\n",
     "\n",
     "### Basic models for covariate and factor explanatory variables.\n",
     "LEFT: The basic model for covariates is referred to as a regression model, which is a line defined by the model parameters β 0 the y-intercept, and β 1 the slope. CENTER: One of two basic models for factors is referred to as a means model, where model parameters are calculated as the mean gene expression of each level of the factor e.g. β 1 represents the mean expression for wildtype and β 2 represents the mean of mutant. RIGHT: The other basic model we refer to for factors is a mean-reference model, where the first model parameter is calculated as the mean gene expression of the reference level, and subsequent parameters are calculated relative to the reference level e.g. β 1 represents the mean expression for wildtype and β 2 represents the difference between mutant and wildtype. In each plot, the points represent the original data; coloured lines are used to represent expected gene expression, where dashed lines are specifically used to represent expected gene expression for non-reference levels in the mean-reference model e.g. mutant.\n",
     "\n",
-    "Reference: Charity Law: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873980/#!po=6.25000 "
+    "Reference: Charity Law: https://pmc.ncbi.nlm.nih.gov/articles/PMC7873980/ "
    ]
   },
   {