Question about DDPM: Meaning of L_{0} (in loss) , and how it gets simplified into L_{simple}?

What does the last term of the loss, -log p(x_0 | x_1) mean? It seems similar to the the log-likelihood of a single data point from VAE's ELBO. It that's what it is, I'm puzzled how to interpret the conditional, | x_1.

Also, the paper's authors mention that this L_{0} term is included in the L_{simple} loss that they used, saying:

The t = 1 case corresponds to L0 with the integral in the discrete decoder definition (13) approximated by the Gaussian probability density function times the bin width, ignoring σ21 and edge effects.

How does this correspondence work?