Update README.md

Author: RXGottlieb

README.md

@@ -28,7 +28,7 @@ Differential(t)(x[1](t)) ~ x[1](t) + p[1]
 Differential(t)(x[2](t)) ~ x[2](t) + p[2]
 ```
 
-and generates a new ODE system (`new_syst') with equations:
+and generates a new ODE system (`new_syst`) with equations:
 ```
 Differential(t)(x_1_lo(t)) ~ p_1_lo + x_1_lo(t)
 Differential(t)(x_1_hi(t)) ~ p_1_hi + x_1_hi(t)
@@ -40,4 +40,4 @@ Differential(t)(x_2_cv(t)) ~ p_2_cv + x_2_cv(t)
 Differential(t)(x_2_cc(t)) ~ p_2_cc + x_2_cc(t)
 ```
 
-where `x_lo < x_cv < x < x_cc < x_hi`. This new system of ODEs is generated using GPU-compatible language--i.e., any decision points in the form of the resulting equation based on some terms being positive or negative are handled by IfElse.ifelse statements and/or min/max evaluations. By using only these types of expressions, multiple trajectories of the resulting ODE system can be solved simultaneously on a GPU, such as by using `DiffEqGPU` in the SciML ecosystem.
+where `x_lo < x_cv < x < x_cc < x_hi`. This new system of ODEs is generated using GPU-compatible language--i.e., any decision points in the form of the resulting equation based on some terms being positive or negative are handled by IfElse.ifelse statements and/or min/max evaluations. By using only these types of expressions, multiple trajectories of the resulting ODE system can be solved simultaneously on a GPU, such as by using `DiffEqGPU` in the SciML ecosystem.