I have taken a picture of this before... it was one of my best taken from my DSLR + simple star tracker setup.
However, you can see that the detail with the telescope+guided mount is much sharper.
With hydrogen & oxygen filters, I can separate the data cleanly, too.
/2
Incredibly, the photo is taken from my balcony in Vancouver, where there is a tremendous amount of light pollution, AND it was taken pretty much directly pointing at a 3/4 moon. It was not a DARK sky at all.
/3
But, it's an incredible application of physics and science. The photons from the nebula are there, but they are in specific wavelengths of light. I purchased a filter that very narrowly allows those wavelengths through. @antlia_filter
/4
Each five minute photo i take lets through an incredibly faint amount of data. In fact, this is what 1 five minute exposure looks like:
if you look very closely, you can faintly see some stars.
/5
However, there is data there! in the darkest areas of the picture, the camera is receiving photons of light. if we streeeettttttchhhhh the data as far as we can (exaggerated here for effect), there is a signal in the noise!!
/6
By "stacking" (repeating this signal>noise and adding the images together), what happens is that the random noise stays random, but the faint signal stays a signal, so the computer can start working out what is noise and what is signal even better!
/7
When I get this image I have incredible processing tools (Pixinsight, Photoshop), that allow me to remove the colour cast (this is because of the moonlight and the filter itself).
/8
From this point, its just a ton of practice and technique! I separate the colour layers so that i can work on Hydrogen alpha and Oxygen III data separately (with stars removed using machine learning!)
/9
There are many processes I use, but mostly it's bending the curves of light to boost signal while minimizing background (space) noise.
Voila!!! The final image is 6500x4000, more than enough for me to print!
total data: 3.5 hours. As I add more data, it will get better.
Point 1: "Disease-targeting" is an invented criterion
1a. You demand drugs show "disease-targeting effects" or be presumed harmful. This is never necessary. The actual claim: reliable symptom change across replicated RCTs.
/2
Point 1: "Disease-targeting" is an invented criterion
1b. Cardiology doesn't know the molecular lesion driving most post-MI mortality benefit from beta-blockers. We use them anyway because they work. "No known mechanism, therefore presume harm" would gut most of medicine.
/3
The core trick: he treats prescription prevalence as self-evidently bad. But high rates only signal a problem if the meds don't work, are given to people who don't need them, or cause net harm. He establishes none of this. He just gestures at numbers.
/2
The same rhetorical structure would indict insulin prescribing, or asthma inhalers. Prevalence is not pathology. The question is whether treatment matches need — and whether the alternative (untreated illness) is better or worse.
/3
It makes no sense the way we treat our people with disabilities in Canada. Canada has the full apparatus to implement adjusted payments, yet we typically support disabled people WELL under the poverty line.
/1
Canada has an official poverty line: the Market Basket Measure. It's regionally calibrated, methodologically sound, and updated by StatCan.
A single person on BC PWD receives ~$18.4k/year. The Vancouver MBM is ~$29k.
That's not a rounding error. It's a structural choice.
PWD recipients in Vancouver sit at roughly 47% of the poverty line and below the Deep Income Poverty threshold (75% of MBM), which is the level StatCan uses to flag the worst material deprivation in the country.
/3
To be clear, my first answer is "well we know they are supposed to block serotonin reuptake, but it's not that simple and we don't really know."
But, if you want the best 2026 science...
/1
For a few particularly science-interested patients, I walk them through what we currently have for the 'best evidence' even though we're still not sure.
This is the "best story" I can tell about SSRI's right now.
(nb, this is NOT locked in, this is MY best synthesis)
/2
1) SSRIs BLOCK the Serotonin Transporter
The protein that pulls serotonin back into the neuron after its released is blocked. Serotonin lingers longer in the synapse, the gap where neurons signal each other.
This is very well established, & how SSRIs were designed.
The Ihben story is making the rounds. "Judge forced 18 vaccines, child got autism." It's being treated as a smoking gun. It is not a smoking gun. It is barely a story.
Sourcing: one father, one advocacy org (CHD), one GiveSendGo. Records sealed. No filings. No named physicians. Every outlet repeating it cites the same Defender article. This is a closed loop, not corroboration.
/2
"18 vaccines in one day" is not a thing. That number counts antigens as doses to make the headline scream. Real catch-up schedules don't work this way and you can verify that in five minutes on the CDC site.
/3
Ask any person who has been even suggested to have BPD; they will uniformly tell you that they have been told to try DBT (Dialectical Behavioural Therapy). Reflexively recommended. "Gold standard."
This is not science-supported.
/1
Quick history: Marsha Linehan developed DBT in the late 1980s, published the foundational manual in 1993. She drew on CBT, Zen Buddhism, and dialectical philosophy. Brilliant clinician, brilliant marketer. Her institute has trained tens of thousands of therapists worldwide.
/2
That marketing machine is the reason DBT is "the BPD treatment." It is not the reason DBT works better than alternatives, because it does not.
The faint superiority signals in older trials evaporate once you adjust for allegiance bias (DBT researchers studying DBT).
/3