Let's ignore n for a moment, because it doesn't affect the chance of getting a more extreme result under the null hypothesis. And that's always a good thing to wonder about when you're looking at multiple comparisons.
They calculated 11 p-values, and got a significant result for two of them under α=0.1. We'd expect that that to happen about half the time if the null hypothesis is true.
(Realistically, they got a significant result on two numbuers that were both derived from the same measurement, which is less compelling than two unrelated ones. But I'm not sure how to model that, so, like any true armchair statistician, I'm going to handle it by ignoring it.)
I also did a hasty power calculation, and estimate that a study of this size could detect an effect as large as the biggest one they reported about half the time.
In summary: If there's no real effect, there's a 50/50 chance of getting a significant p-value out of this experimental design. If there is a real effect, still a 50/50 chance of getting a significant p-value.
Which hypothesis would I pick if I had to guess? Well, I'm sure this is also junk statistics, but the p-values sure look to me like they could have been drawn from a uniform distribution, which is what I would expect them to look like under the null hypothesis. Definitely not what I would expect them to look like under the alternative hypothesis, given that all these tests are trying to measure roughly the same thing.