JAMF thanks for joining us. Thank you very much Dan. We're going to talk about Zia biosciences its efforts to US plants to produce biologics and nutraceutical, and the absence taken to turn this into a predictable and reliable manufacturers process. Maybe you can begin with making the case for using plants to produce biologics. Sure. Well plants around for over five thousand years medicinal purposes over in Asia and in India plants actually have been part of our human population since the beginning of time and they actually have been proven many many times over and there's numerous a much research about it. To deliver the address, the issues of disease And have the ability to Help. Our population in a much better way than our synthetic counterparts parts do. You grow these plants in a clean room. I imagine people you know envision fields upon fields of of plants but why use a clip rim? Right right. Well, it goes back to I. Think what we want to start with is The it's not just that we grown cleanroom it's a technology platform and the cleanroom is only one part of that. Specific purpose is to answer your question about the cleanroom is when you grow plant in a pathogenic. Free Environment. You have the ability to turn those into plant based medicinal drugs whereas plants they're grown say in a greenhouse or in a warehouse or in the open land run the risk of pathogen clearly Pathogens with them that probably would not be able to be filtered out and it runs a risk to the general public. That's why we grow inside a game. You mentioned the technology platform, you build a platform. Rather. Data intensive. What's the range of data collected and how to use this produce plants that produce biologics? Well. So let's back up and talk about the platform. There's two parts of the platform and I'll answer that question in in in the second part. The first part is the physical part. The plants are grown inside and ice. Oh, seven cleanroom. The second part is the data science side where we. Hook Up. Over thirty parameters thirty centers. Inside that room that collect everything. Some of them are normal that you would think of, which would be Ph temperature humidity but some you may not under a would never think about the. The amount of parts per million a of Co two across the plant the airflow crossed the plan the. Megahertz, of electricity going through the hydroponic water, and so we take all that data collected on average every. Fifteen seconds to one minute. So we have millions and millions upon data points stork with. What are those data points allow you to do? We actually can generate a formula because our. Our. Whole reason for being at Zia. is to optimize claimed growth. So we are creating a formula. That are customer comes to us and says, I would like you to grow this plant and I would like you to optimize or express this certain enzyme protein some sort of substance in the plant itself. Such that on, it actually is expressed in a way that can be used in some sort of downstream pharmaceutical drug. Now. That being said That being said, what we would do then is we have to figure out so to speak a recipe and all that data allows us to optimize the plan to optimize that certain protein or substance in the plant. Such that it would be then. It would allow us to go downstream and give the best value for our customer. And how consistent is the output? Well, that's That's what amazing about our platform is. So in any type of Pharmaceutical product you are focused on. Two major things as an ingredient supplier to the pharmaceutical companies. Minimal variation that is he must have very little variation batch to batch and you must have maximum produce ability that is that I'm delivering ninety, six percent of what I say every time I'm producing a batch of the equipment.