Everyone knows what search engine optimization (SEO) is, and many companies take great efforts to ensure they show up near the top of organic search results and benefit from the resulting traffic which comes at no unit cost. Traditional organic search results are slowly being replaced, however, with a lot of the focus being shifted to what Google calls a search generative experience (SGE; note that this is synonymous with AI Overview on Google Search, and the SGE is titled AI Overview on the search results page). It is widely accepted that as SGE becomes more prevalent, traffic to websites from legacy organic search results will decrease. This is due to two factors:
- Fewer people will click on organic search links – or any links – when SGE is present.
- The webpage links referenced in an SGE answer have lower clickthrough than standard organic search links.
In other words, some searchers will see the answer provided by the AI overview, accept it as accurate and sufficient, and take no further action. These searchers who would have clicked through to something else in the past may simply not click on anything. The bounce rate of SERPs likely increases markedly when SGE is present. SGE also contains its own reference links, which will inevitably cannibalize some legacy organic search traffic. Data from FirstPageSage shows that the result is not dramatic (yet), but just the first link in the AI overview is already garnering 9.4% of clicks. While this compares to 39.8% for a top search position result or 42.9% for a rich snippet result when SGE results are not present, it still has to come from somewhere, and the FirstPageSage data shows SGE is now appearing on 31% of SERPs.
In this post, we’ll address what life science marketers can do, and should be doing, to address the new search paradigm of Generative Engine Optimization (GEO).
How Generative Engine Optimization and Search Engine Optimization Overlap
Luckily for search marketers, GEO and SEO have a lot of overlap. If you are doing well at optimizing for search, you are probably doing a fair job at optimizing for generative engines. A number of key SEO principles apply to GEO:
- Perform keyword research to ensure you are addressing popular user queries and develop content targeting those keywords.
- The content you create should be helpful, reliable content that demonstrates experience, expertise, authoritativeness, and trustworthiness (what Google calls E-E-A-T).
- Ensure you are signaling the relevance of your content through optimization of on-site and on-page factors (copy, metadata, schema, etc.) for targeted keywords.
- Further signal the relevance of your website and content through off-site link building.
- Ensure all your content is getting indexed.
Increasing the quantity of content, using clear language, and using technical language when appropriate also improve performance in both generative and organic search results. Other practices to improve the authority of a page or domain such as backlinking almost certainly play a role in GEO as well, as search AIs pick up on these signals (if not directly, then through their own understanding of organic search ranks).
There is further overlap if your goal in creating content is to get it seen by the maximum number of people instead of solely driving traffic to your website. In that case, disseminate your content as much as possible. While AI Overviews are not citing Reddit and other discussion forums as much as they once did, the more places your content lives, the more of a chance you’ll have that the AI will cite one of them, especially if your website itself is not well-optimized.
How GEO and SEO Differ in Practice
Optimizing for GEO is akin to specifically optimizing for rich snippets: there is additional emphasis on the content itself vs. ancillary factors. You need to pay more attention to how you provide information.
A seminal preprint paper by Pranjal Aggarwal et al uploaded to arXiv in late 2023 which coined the term generative engine optimization investigated a number of factors which they believe might help optimize for inclusion in SGE. Note that this paper has yet to pass peer review and was subject to a lot of scrutiny by SEO professionals, most intricately by Tylor Hermanson of Sandbox SEO who gave a number of compelling reasons to believe the data may be overstated, but having read the paper and a number of critiques I still think the paper contains meaningful and actionable lessons. There are two figures in this paper which I believe summarize the most interesting and useful information:
Table 1 shows how different tactics affected results. They used a metric called position-adjusted word count to measure the performance of websites in SGE before and after various GEO methods. I am more interested in this because it is an objective determination as opposed to the subjective impression metric, which basically involves feeding results into GPT-3.5 and seeing what it thinks. We can see from the results that specific types of content addition – adding quotations, statistics, or citations – have a notable impact on the position-adjusted word count for those websites. I point those out specifically not only because they have the greatest impact (along with fluency optimization), but they are not things which would necessarily be considered important if the only consideration for content creation was SEO. All the others which they tested and found to be useful – speaking clearly, fluently, technically, and authoritatively – are things which good SEO copy already needs to do. The inclusion of quotations, statistics, and citations are simply additional content.
The other interesting lesson from this paper is that the most impactful GEO methods differ based on the topic of the content.
While I would like to see this data presented the other way around – what methods are the highest performing for each category – it still makes the point. It also suggests that scientific content may receive disproportionate benefit from fluency optimization and authoritativeness. Again, those are already things which you should be factoring into your copy.
Practical Steps Life Science Marketers Should Take for GEO
If you are looking to optimize for generative engines, first ensure you are doing everything required for good SEO, as outlined above in the section of how GEO and SEO overlap. That is 80% of the job. To reiterate:
- Perform thorough keyword research to address popular and relevant queries
- Write in a way which demonstrates experience, expertise, authoritativeness, and trustworthiness (EEAT)
- Optimize of on-site and on-page factors (copy, metadata, schema, etc.) for targeted keywords to demonstrate relevance
- Further demonstrate relevance through off-site link building
- Stay on top of Google Search Console and ensure your content is getting indexed
- Write more / longer content
- Write clearly and use appropriate technical language considering the subject matter
To specifically optimize for generative search beyond normal SEO, make a note to cite your sources and include statistics and / or quotations when possible. That is the lowest-hanging fruit and where most life science marketers will be fine stopping. If you really want to deep dive into generative engine optimization, however, you can use a tool such as Market Brew’s AI Overviews Visualizer to investigate how search engines’ semantic analysis algorithms perform cluster analysis with your website content and see how content is grouped and related.
Since AI overviews decrease overall clickthrough rates, another consideration for some marketers may be getting their content into the AI overviews independent of whether the content is hosted on your website or not. In these situations, you should try to disseminate your content widely across high-reputation sources, particularly Reddit. While it is not cited in SGE as much as it used to be, having your content in multiple places still increases the probability your content will be used.
Product Companies: Don’t Forget Merchant Center Feeds
While our anecdotal data shows that shopping results aren’t yet being included much in the life sciences, they are occasionally included in other industries and it would not be surprising to see them included more frequently in the life sciences in the future. When shown, these shopping results are very prominent, so ensure your Merchant Center feeds are functioning, include as much of your product portfolio as possible, and are well optimized. (Product feed optimization is a topic for another day.)
Summary
If you want to improve the likelihood that your content will appear in AI overviews and those overviews will contain links to your website, start with SEO best practices. That will get you far in both legacy organic search, which still receives most clickthroughs, as well as in SGE. From there, ensure your content which is the target of optimization efforts cites sources and includes statistics and quotations. If you sell products, ensure you are making optimal use of product data feeds.
GEO is neither difficult nor rocket science. By taking a few relatively simple steps, you’ll improve the likelihood of being included in AI overviews.
As this is a complex and novel topic, we’ve included an FAQ below.
What are you waiting for? Work with BioBM and improve your demand generation."
FAQ
Is employing current SEO best practices sufficient for good ranking in generative search?
Helpful? Yes. Sufficient? It depends.
If your products and services are relatively niche, and the questions you seek to answer with your content are likewise niche, then current SEO best practices may be sufficient. If there is a lot of competition in your field, then you may need to incorporate GEO-specific best practices into your content creation.
You can think of this similarly to how you think about SEO. If you are optimizing for niche or longer-tail terms, you might not need to do as much as you will if competing for more major, high-traffic terms. The more competition, the more you’ll likely need to do to achieve the best results. If your terms are sufficiently competitive that you are not ranking well in organic search, you should definitely not presume that whatever you are doing for SEO will reliably land you in AI overviews.
If my website has high organic search ranks, will it perform well in SGE?
I’m not sure anyone has a clear answer to this, especially since the answer still seems to be changing rapidly. Many of the studies which exist on the topic are almost a year old (an eternity in AI time).
Taking things chronologically:
- A January 2024 study by Authoritas using 1,000 terms found that “93.8% of generative links (in this dataset at least) came from sources outside the top-ranking organic domains. With only 4.5% of generative URLs directly matching a page 1 organic URL and only 1.6% showing a different URL from the same organic ranking domain.”
- A January 2024 study from seoClarity looked at the top 3 websites suggested by SGE and compared them to just the top 3 organic results on the basis of domain only. In contrast with the Authoritas study, they found that only 31% of SGE results had no domains in common with the top 3 organic results, 44% of SGE results had 1 domain in common, 24% had two domains in common, and 1% had all three domains in common. This suggests much more overlap between generative and legacy organic results, but it should be noted that it was a much smaller study of only 66 keywords.
- A January 2024 study from Varn Media, using a similar but less informative metric to Authoritas, they found 55% of SGE results had at least one link which was the same as a top-10 organic result on a given SERP. One result in the top 10 is a low bar. They did not publish the size of their study.
- A February 2024 study from SE Ranking which looked at 100,000 keywords found that SGE included at least one link from the top 10 organic search results 85.5% of the time. I don’t like this very low-bar metric, but it’s how they measured.
- A slightly more recent Authoritas study from March 2024 using 2,900 branded keywords showed that “62% of generative links […] came from sources outside the top 10 ranking organic domains. With only 20.1% of generative URLs directly matching a page 1 organic URL and only 17.9% showing a different URL from the same organic ranking domain.” Obviously branded terms are a very different beast, and it should be no surprise that SGE still references the brand / product in question when using branded terms.
- SE Ranking repeated their 100k keyword study in June 2024 and found similar results to their February study: 84.72% of AI overviews included at least one link from the top 10 organic search results. Again, I don’t love this metric, but the fact that it was virtually unchanged five months after the original study is informative.
- Another seoClarity study published in August 2024 found far more overlap between legacy organic results and SGE results. Their analysis of 36,000 keywords found that one or more of the top 10 organic web results appeared in the AI Overview 99.5% of the time and 77% of AI overviews referenced links exclusively from the top 10 organic web results. Furthermore, they found that “80% of the AI Overview results contain a link to one or more of the top 3 ranking results. And when looking at just the top 1 position, the AI Overview contained a link to it almost 50% of the time.”
The most recent seoClarity study, suggesting a much greater deal of overlap between organic web results and SGE links, tracks with my recent experiences. While I would ordinarily discount my personal experiences as anecdotal, in the face of wildly different and rapidly evolving data I find them to be a useful basis of reference.
How much could my organic search traffic be impacted by SGE?
No one has any reliable metrics for that yet. Right now, I would trust FirstPageSage when they say the impact of SGE is not yet substantial, although I view their classification of it being “minimal” with some skepticism.
A lot of people like to point to a “study” posted in Search Engine Land which found a decline in organic search traffic between 18% and 64%, but it should be noted that this is not a study at all. It is simply a model based almost entirely on assumptions, and therefore should be taken with a huge grain of salt. (Also, 18-64% is a not a narrow enough range to be particularly informative regardless.)
Is SEO still worth doing?
Absolutely, hands down, SEO is still worthwhile. Legacy organic search results still receive the majority of clickthroughs on SERPs. However, as AI continues to improve, you should expect diminishing returns, as more people get their answer from AI and take no further action. It is therefore important that whatever you need to get across is being fetched by AI and displayed in SGE – regardless of whether it leads to a click or not.
I heard there is a hack to get your products cited by generative AI more often. What’s up with that?
A paper by a pair of Harvard researchers originally posted to arXiv in April 2024 titled “Manipulating Large Language Models to Increase Product Visibility” generated a lot of interest by both AI researchers and marketers looking for a cheat code to easily generate demand without any unit cost for that demand. As the paper suggests, they did find that LLMs can be manipulated to inserting specific products when the LLM is providing product recommendations. It is unrealistic that this is going to be applicable by life science marketers, however. It is a trial-and-error method involving high-volume testing of random, nonsensical text sequences added to your product’s metadata. This means that it would be nearly impossible to test on anything other than an open-source LLM which you are running an instance of yourself (and therefore able to force the re-indexing of your own content with extremely high frequency).
Another paper submitted to arXiv in June 2024 by a team of researchers from ETH Zurich titled “Adversarial Search Engine Optimization for Large Language Models” found that LLMs are vulnerable to preference manipulation through:
- Prompt injection (literally telling the LLM what to do within the content)
- Discreditation (i.e. badmouthing the competition)
- Plugin optimization (similar to the above, but guiding the LLMs to connect to a desired API from which it will then obtain information)
While preference manipulation is simpler and feasible to implement, the problem with any overtly black-hat optimization technique remains: by the time the method is found and published, LLM developers are well on their way to fixing it, making it a game of whack-a-mole which could potentially end up in your website finding itself on a blacklist. Remember when Google took action against unnatural link building and had marketers disavow links to their sites? That was not fun for many black-hat search marketers out there. BioBM never recommends black-hat tactics for both their impermanence, likelihood of backfiring, and ethical reasons. There’s plenty of good things you can focus on to enhance your search optimization (and generative engine optimization) while providing a better experience for all internet users.
Do Scientists Use AI / LLMs for Product Discovery?
Carlton Hoyt
There has been a lot of talk about AI optimization in the marketing world, much of which was spurred by the release of a preprint article published to arXiv (pdf) in September which demonstrated that LLMs could be manipulated to increase product visibility. There is even a term for optimizing for search engines: Generative Engine Optimization, or GEO. Of course, we are immediately interested in whether any of this is meaningful to marketers in the life sciences.
Our friends at Laboratory Supply Network recently beat us to the punch and asked Reddit’s Labrats community if they use LLMs to help them find scientific products. Good question! Apparently it is also one with a clear answer.
This is a relatively small poll, but the results are so skewed that it is likely that the result is telling. In this poll, 80% of scientists responded that they never use AI for product discovery: literally zero percent of the time! Another 14% barely ever use it. Only two respondents said they use it roughly 10% of the time or more, with one saying they use it more than half the time.
Some of the comments indicate that scientists simply don’t see any relative value in AI for scientific product discovery, or see much better value from other means of product discovery.
Comment
byu/LabSupNet from discussion
inlabrats
Comment
byu/LabSupNet from discussion
inlabrats
Another indicated that AI simply might not be helpful specifically within the scientific context.
Comment
byu/LabSupNet from discussion
inlabrats
Here is the full conversation in r/labrats:
Do you use LLMs / AI to get recommendations on lab products?
byu/LabSupNet inlabrats
Maybe there will be a day where scientists adopt AI for product discovery in meaningful numbers, but it seems we aren’t there yet.
The generative text AI tool Perplexity has rapidly gained popularity in the life sciences for its ability to show its references when answering. As those references can include scholarly articles, it has great potential as a literature review assistant for scientists. AIs are well-known for both hallucinations and inaccuracy, however, and being incorrect even a small percentage of the time is normally not something a professional scientist can allow.
We therefore decided to set up a little experiment: we asked three subject matter experts in the life sciences to ask perplexity a series of questions of increasing difficulty, then evaluate the answers:
- A question which a grad student should be able to answer relatively easily
- A question which most PIs in the field would be able to answer
- A question requiring very specific knowledge to answer. Something which a scientist wouldn’t know unless it was within their specific research area.
Here’s what they asked, what Perplexity had to say, verbatim, and the scientists’ take on Perplexity’s responses.
Steve Perrault, PhD, former Associate Director of Research & Development at Foundation Medicine
Q1: What cell type is targeted by mRNA vaccines?
mRNA vaccines primarily target and deliver their mRNA cargo to antigen-presenting cells (APCs), particularly dendritic cells and macrophages.
Upon administration, the mRNA encapsulated in lipid nanoparticles is taken up by APCs. Once inside the APCs, the mRNA is released into the cytosol and translated into the encoded antigenic protein.
This antigenic protein is then processed and presented on the surface of the APCs in the context of major histocompatibility complex (MHC) molecules, allowing for the activation of antigen-specific T cells and initiation of an adaptive immune response.
The ability of mRNA vaccines to efficiently target and activate APCs in vivo has been demonstrated in studies using non-human primates like rhesus macaques.
These studies have shown that modified mRNA vaccines can effectively deliver their mRNA cargo to APCs, leading to robust antigen expression and subsequent activation of antigen-specific T cells.
Furthermore, the size of the lipid nanoparticles used for mRNA delivery plays a crucial role in their trafficking and uptake by APCs. Studies have shown that nanoparticles within a specific size range (around 20-100 nm) can efficiently drain into lymph nodes and penetrate the paracortex region, where APCs reside, facilitating their uptake and antigen presentation.
In summary, mRNA vaccines primarily target and deliver their mRNA cargo to antigen-presenting cells, particularly dendritic cells and macrophages, enabling the expression of the encoded antigenic protein and subsequent activation of antigen-specific T cells, which is a key mechanism underlying their immunogenicity. mRNA vaccines primarily target and deliver their mRNA cargo to antigen-presenting cells (APCs), particularly dendritic cells and macrophages.
Upon administration, the mRNA encapsulated in lipid nanoparticles is taken up by APCs. Once inside the APCs, the mRNA is released into the cytosol and translated into the encoded antigenic protein.
This antigenic protein is then processed and presented on the surface of the APCs in the context of major histocompatibility complex (MHC) molecules, allowing for the activation of antigen-specific T cells and initiation of an adaptive immune response.
The ability of mRNA vaccines to efficiently target and activate APCs in vivo has been demonstrated in studies using non-human primates like rhesus macaques.
These studies have shown that modified mRNA vaccines can effectively deliver their mRNA cargo to APCs, leading to robust antigen expression and subsequent activation of antigen-specific T cells.
Furthermore, the size of the lipid nanoparticles used for mRNA delivery plays a crucial role in their trafficking and uptake by APCs. Studies have shown that nanoparticles within a specific size range (around 20-100 nm) can efficiently drain into lymph nodes and penetrate the paracortex region, where APCs reside, facilitating their uptake and antigen presentation.
In summary, mRNA vaccines primarily target and deliver their mRNA cargo to antigen-presenting cells, particularly dendritic cells and macrophages, enabling the expression of the encoded antigenic protein and subsequent activation of antigen-specific T cells, which is a key mechanism underlying their immunogenicity.
Q2: How do mRNA vaccines escape endosomes?
mRNA vaccines are delivered into cells encapsulated in lipid nanoparticles (LNPs). For the mRNA to be effective, it must escape from the endosomes into the cytoplasm where it can be translated into proteins. The endosomal escape process is facilitated by the ionizable lipids in the LNP formulation:
- Upon endocytosis, the LNP becomes trapped in an endosome, which is an acidic compartment within the cell
- The acidic environment of the endosome causes the ionizable lipids in the LNP to become positively charged
- This positive charge allows the LNP to interact with and disrupt the negatively charged endosomal membrane through electrostatic interactions
- The shape change and disruption of the endosomal membrane enables the release of the mRNA cargo from the endosome into the cytoplasm
- Optimizing the pKa values and lipid tail structures of the ionizable lipids can enhance their ability to become protonated and facilitate endosomal escape
- Early endocytic compartments like recycling endosomes have been identified as key sites for productive mRNA escape, whereas prolonged retention in late endosomes leads to mRNA degradation
- So in summary, the ionizable lipids undergo a charge-induced shape change in the acidic endosome that allows the LNP to disrupt the endosomal membrane and release its mRNA cargo into the cytosol for translation
mRNA vaccines are delivered into cells encapsulated in lipid nanoparticles (LNPs). For the mRNA to be effective, it must escape from the endosomes into the cytoplasm where it can be translated into proteins. The endosomal escape process is facilitated by the ionizable lipids in the LNP formulation:
- Upon endocytosis, the LNP becomes trapped in an endosome, which is an acidic compartment within the cell
- The acidic environment of the endosome causes the ionizable lipids in the LNP to become positively charged
- This positive charge allows the LNP to interact with and disrupt the negatively charged endosomal membrane through electrostatic interactions
- The shape change and disruption of the endosomal membrane enables the release of the mRNA cargo from the endosome into the cytoplasm
- Optimizing the pKa values and lipid tail structures of the ionizable lipids can enhance their ability to become protonated and facilitate endosomal escape
- Early endocytic compartments like recycling endosomes have been identified as key sites for productive mRNA escape, whereas prolonged retention in late endosomes leads to mRNA degradation
- So in summary, the ionizable lipids undergo a charge-induced shape change in the acidic endosome that allows the LNP to disrupt the endosomal membrane and release its mRNA cargo into the cytosol for translation
Q3: What’s the structure of the ionizable lipid used for endosome escape with mRNA vaccines?
The ionizable lipids used in mRNA vaccines like the Pfizer and Moderna COVID-19 vaccines have a unique structure that facilitates endosomal escape and delivery of mRNA into cells.
Key Structural Features:
Ionizable Head Group: The lipids contain a protonatable or ionizable amino head group, typically a tertiary amine. This allows the lipid to be neutral at physiological pH but become positively charged in the acidic environment of endosomes.
Branched Lipid Tails: The lipids have branched or multi-tail lipid chains, rather than linear tails. This branched structure is believed to promote a cone-shaped molecular geometry that aids in endosomal membrane destabilization and escape.
Pfizer’s ALC-0315 has four short branched tails.
Moderna’s SM-102 has three branched tails, one of which is longer.
Ester Linkers: The lipid tails are often connected to the head group via ester linkers, which can influence the biodegradability and potency of the lipid.
The combination of the ionizable head group, branched tails, and ester linkers allows these lipids to effectively complex with mRNA, facilitate endosomal escape through the proposed “proton sponge” effect and membrane destabilization, and enable cytosolic delivery of the mRNA cargo.
Bonus historical science question: How did DNA nanotechnology start?
DNA nanotechnology originated from a landmark theoretical paper published in 1982 by Ned Seeman, which proposed the idea of using DNA molecules to create nanostructures.
This seminal work laid the foundation for the field and sparked the development of various techniques and applications in DNA nanotechnology.
The key idea proposed by Seeman was to exploit the predictable base-pairing interactions of DNA to construct artificial nanostructures with precise control over their shape and size.
He envisioned using synthetic DNA strands designed to self-assemble into desired geometries, taking advantage of DNA’s inherent programmability and molecular recognition capabilities.
The first experimental demonstration of this concept came in the early 1990s when Seeman and his colleagues successfully constructed DNA-based nanostructures, such as a cube and a truncated octahedron.
These initial achievements paved the way for the rapid growth of DNA nanotechnology, leading to the development of various techniques like DNA origami, DNA tiles, and DNA nanorobots.
Over the years, DNA nanotechnology has evolved into an interdisciplinary field, bringing together researchers from diverse areas like chemistry, physics, materials science, and computer science to explore the potential of DNA-based nanostructures for various applications, including nanoelectronics, biosensing, drug delivery, and molecular computing.
Steve’s take on Perplexity:
Steve said Perplexity was “pretty solid” but criticized it for a “lack of context, like if you’re in that field and wanting to understand a bigger picture based on a question, that isn’t there.”
But on a super reductionist view it seems pretty good. Like, what someone might give on an exam answer after studying, but not what someone who really knows a field should hopefully say to provide that bigger picture.
I don’t see anything “wrong” but someone who really knows their stuff would give a broader answer. This comes across as a pre-med major who’s really good at studying, and the tech details are accurate.
For the second two questions, if someone asked me this I would give a context of this solving a problem that had been around since 1960s, was solved by someone at UBC, how it was solved, why it matters. Things that provide way more depth.
Regarding the bonus historical question on the origins of DNA nanotechnology which he decided to ask, he said: “This is accurate but misses the nuances that one can only know from really diving into it. I wouldn’t expect AI to come up with that. Probably not possible. But the technical details are right.”
Hussain Dahodwala, PhD, Associate Professor at the IBBR Institute for Bioscience and Biotechnology Research and former Director, Upstream Process Development at the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL)
Q1: What are the challenges in enzyme engineering and what advances are anticipated in enzyme engineering for biobetters?
The key challenges and advances in enzyme engineering for biobetters are:
Challenges in Enzyme Engineering:
⢠Enzymes often have poor stability under industrial conditions, such as high temperatures, pressures, and organic solvents. This leads to poor product yields.
⢠Enzymes may have narrow substrate specificity, limiting their ability to work with non-natural substrates.
⢠Enzymes can be difficult to produce at scale in industrial fermentation processes.Advances in Enzyme Engineering:
⢠Enzyme engineering techniques like directed evolution, rational design, and semi-rational design are being used to improve enzyme properties such as activity, specificity, stability, and stereoselectivity.
⢠Computational methods and machine learning are helping to expand the enzyme design space and accelerate the discovery of novel enzymes with desired properties.
⢠Strategies like enzyme immobilization, molecular modifications, and the use of nanoparticle or cell-based delivery systems are being explored to enhance the stability and performance of enzyme therapeutics.
⢠Advances in biotechnology and recombinant expression are enabling the industrial-scale production of engineered enzymes.Overall, enzyme engineering is a rapidly advancing field that is critical for developing efficient, stable, and versatile biocatalysts to enable sustainable and cost-competitive bio-based processes.
Q2: What is the inspiration for Biomanufacturing 4.0 and how is it different from current biomanufacturing?
Biomanufacturing 4.0, also known as Pharma 4.0, is inspired by the broader Industry 4.0 concept and is focused on the digital transformation of biomanufacturing processes. The key differences between Biomanufacturing 4.0 and current biomanufacturing practices are:
- Increased use of advanced digital technologies: Biomanufacturing 4.0 leverages technologies like artificial intelligence, machine learning, digital twins, and advanced process control to enable greater process visibility, optimization, and predictive capabilities.
- Emphasis on data-driven decision making: Rather than relying solely on empirical knowledge, Biomanufacturing 4.0 aims to incorporate data analytics and modeling to make more informed, data-driven decisions throughout the product lifecycle.
- Shift towards continuous improvement: Biomanufacturing 4.0 promotes a culture of continuous improvement, enabled by the increased process understanding and agile regulatory frameworks that allow for more efficient implementation of process changes.
- Enhanced supply chain robustness: The advanced process control and predictive capabilities of Biomanufacturing 4.0 can lead to improved supply chain reliability and the ability to better manage variability, ultimately enhancing product quality and availability.
- Accelerated product development: The digital tools and data-driven approach of Biomanufacturing 4.0 can help compress product development timelines, allowing for faster access to life-changing therapies.
In summary, Biomanufacturing 4.0 is inspired by the broader Industry 4.0 vision and aims to transform biomanufacturing through increased digitalization, data-driven decision making, and a focus on continuous improvement and supply chain robustness.
Q3a: What are non-canonical redox co-factors and how do we use them in cell culture?
Non-canonical redox cofactors (NRCs) are chemical compounds that can serve as alternatives to the natural cofactors like NAD(P)+ and NAD(P)H in biological redox reactions. Some key points about NRCs:
NRCs can overcome the limitations of natural cofactors, enabling specific delivery of reducing power outside of biological constraints and providing alternative reduction potentials to overcome thermodynamic barriers.Examples of NRCs include 3-carbamoyl-1-phenethylpyridin-1-ium chloride (P2NA+) and 3-acetylpyridine adenine dinucleotide, which have been used in purified enzyme-based redox catalysis to increase economic viability and drive thermodynamically unfavorable reactions, respectively.
Researchers have developed methods to engineer microbes like E. coli to biosynthesize NRCs like nicotinamide mononucleotide (NMN+) to enable their full integration with renewable biomanufacturing processes. This involves developing growth-based screening platforms and exploring pathway combinations to achieve high intracellular NMN+ levels.
Using NRCs in cell culture can provide more economical and scalable biotransformation, as well as enable exquisite control and expand the range of chemistries accessible compared to natural cofactors. However, the impact of NRC accumulation on cell fitness must also be considered.
In summary, NRCs are emerging as important tools in cell-free biosynthesis and metabolic engineering to overcome the limitations of natural cofactors and enable new capabilities in biocatalysis and biomanufacturing
Perplexity did not attempt to answer the second part of the question, and Hussein made another attempt to get it to answer.
Q3b: How to use non canonical redox co-factors in cell culture?
Based on the search results, here is how non-canonical redox co-factors can be used in cell culture: Metabolites and post-translational modifications (PTMs) can regulate cell signaling and gene expression through non-canonical mechanisms. Metabolites can serve as co-factors for enzymes and modify proteins through PTMs like palmitoylation, glutathionylation, and acetylation. These PTMs can alter protein interactions, stability, and function, thereby aligning cellular processes with changes in metabolic state. For example, glutathionylation of the transcription factor NF-ÎşB decreases its ability to bind DNA, linking redox state to gene expression. Histone modifications like lactylation and crotonylation can also regulate gene transcription in response to metabolic cues. Additionally, palmitoylation can increase protein stability by blocking ubiquitination and proteasomal degradation. Several studies have demonstrated the use of non-canonical redox co-factors to modulate cell signaling and death pathways in cell culture:
- Dihydroartemisinin, a semi-synthetic derivative of artemisinin, induced ferroptosis in pancreatic cancer cells by altering redox homeostasis and lipid peroxidation.
- The natural product 11-methoxytabersonine triggered necroptosis in glioblastoma cells through ROS generation, calcium signaling, and mitochondrial dysfunction.
- Xanthohumol, a chalcone from hops, activated the Nrf2 antioxidant pathway while inhibiting NF-ÎşB in pancreatic cancer cells, leading to reduced inflammation and oxidative stress.
- Sulforaphane, an isothiocyanate from cruciferous vegetables, upregulated Nrf2 and antioxidant enzymes while downregulating NF-ÎşB in breast cancer and macrophage cell lines.
In summary, non-canonical redox co-factors like metabolites and their PTMs can be leveraged in cell culture to modulate signaling pathways, gene expression, and cell death mechanisms in a targeted manner by exploiting their unique biochemical properties.
Hussain’s take:
These are questions that I regularly encounter in scientific conferences and use them to test graduate student knowledge. I can say that these questions can be answered by biotechnologists with a few years of experience.
The questions chosen were 2 parts each. One part would require” book learning” and the second part would require domain knowledge, some experience and “intelligence” in order to answer.
In all 3 examples: Perplexity AI did an acceptable job in text mining the first half of the question. The definitions and concepts asked were answered more accurately and with more clarity, including citation than one would expect humans to remember.
Complexity of jargon was increased with each question and in each case Perplexity AI was able to text mine the definitions. For reference, in my experience many grad students fumble at the third question.
However in every question, perplexity AI ignored the second half of the question beyond the definitions. In other words it does great at “What is” and fails at “how to”.
Separating the questions was attempted with the third question, and AI failed to give an answer.
Chris Bjornsson, PhD, Embedded 3D & Automated Microscopy Specialist at ZEISS.
Q1: How many tissue types are there in the human body?
There are four main types of tissues in the human body: epithelial, connective, muscle, and nervous tissues.
Epithelial Tissues
Epithelial tissues are sheets of cells that cover exterior surfaces of the body, line internal cavities and passageways, and form glands. Their main functions include protection, secretion, absorption, and excretion.Connective Tissues
Connective tissues bind together and support other tissues. They include bone, cartilage, blood, adipose (fat), and fibrous connective tissues like tendons and ligaments. Their main functions are to bind, support, protect, insulate, store energy, and produce blood cells.Muscle Tissues
Muscle tissues are composed of cells called muscle fibers that have the ability to contract or shorten, allowing for movement. There are three types: skeletal (voluntary), smooth (involuntary), and cardiac (heart muscle).Nervous Tissues
Nervous tissues are composed of neurons and neuroglial cells that propagate electrochemical signals called nerve impulses, enabling communication within the body and the processing of responses. The different tissue types work together to perform the various functions required for the human body to operate and maintain homeostasis.
Q2: How many orders of light does it take to form a microscope image?
To form a microscope image, it takes at least two orders of light according to the Abbe theory of image formation. The key points are:
- According to Ernst Abbe’s theory, for a specimen detail to be resolved in the microscope image, the objective lens must capture at least two orders of diffracted light from the specimen, such as the 0th order (undiffracted light) and the 1st order of diffraction.
- The more diffracted orders that are captured by the objective lens, the more accurately the image will represent the original specimen details.
- Using immersion oil between the objective lens and specimen allows more diffracted orders to be captured compared to air, resulting in higher resolution.
- Blue light is diffracted at a smaller angle than red or green light, so objectives can capture more diffracted orders of blue light, further improving resolution.
So in summary, capturing just the 0th order undiffracted light is insufficient to form a resolved image. At minimum, the 0th and 1st diffracted orders must be captured by the objective lens to resolve specimen details according to Abbe’s theory.
Q3a: How does the wavelets model work to create a maximum intensity projection?
Wavelets are not directly used to create maximum intensity projections (MIPs) in medical imaging. MIPs are created by taking the maximum voxel value along a particular viewing direction and projecting it onto a 2D image plane. The wavelet transform is a mathematical tool that decomposes a signal (like an image) into different frequency components at different scales or resolutions. It can be useful for tasks like image denoising, compression, and feature extraction, but it is not the primary technique used for MIP rendering. MIPs are typically computed directly from the 3D volumetric image data by the following steps:
- The viewing direction is chosen (e.g. axial, coronal, sagittal).
- For each pixel in the 2D projection plane, the maximum voxel value is found along the corresponding ray through the 3D volume in the chosen viewing direction.
- This maximum value is assigned to that pixel, creating the MIP image.
So in summary, while wavelets have many applications in image processing, the maximum intensity projection algorithm operates directly on the voxel data itself by finding the maximum value along rays through the volume. Wavelets are not an inherent part of the MIP rendering process.
Q3b: How does the wavelets model work to create an extended depth of focus?
The wavelet model works to create an extended depth of focus (EDF) image by combining in-focus regions from a stack of images captured at different focal planes using the wavelet transform. The key steps are:
- A stack of images is acquired by capturing the same scene at different focal planes along the optical axis.
- The wavelet transform is applied to each image in the stack, decomposing them into different frequency subbands at multiple scales.
- For each spatial location and wavelet subband, the coefficient with the maximum absolute value across the image stack is selected. This selects the in-focus information from the stack.
- The selected wavelet coefficients are used to reconstruct a composite wavelet representation, which corresponds to an EDF image containing the in-focus regions from across the stack.
- Optional post-processing steps like enforcing local smoothness of the depth map and avoiding saturation/noise accumulation can further improve the results.
The key advantage of using wavelets is that they provide a multi-scale representation that can effectively isolate in-focus and out-of-focus regions based on their different frequency characteristics. The maximum selection rule across the stack allows combining the in-focus information into a single composite image with an extended depth of field. This wavelet-based approach, especially the complex wavelet variant, has demonstrated state-of-the-art performance for EDF microscopy imaging compared to other traditional algorithms.
Chris’ take:
I’m impressed! I even tried to mess it up on the 3rd question but it caught me. Fast, too. I’ll actually start using it and spreading the word!
There was one error, i should mention, when it comes to how many orders of light are needed to form an imageâŚit can be any two orders, and doesnât necessarily need to include the 0th order. It can be 0+1 or 1+2 or 2+3 etc⌠The first part of that answer was correct, but the final summary statement was wrong.
Takeaways
Perplexity isn’t perfect, and is likely better at answering “what is” type questions than “how to” type questions. If you want to get a lot of context behind the answers, you’ll likely have to dig a bit. However, in these experiments it did seem to be relatively accurate, with few obvious errors. Used with caution, it can make a solid research companion and speed up literature review considerably.
Don’t Stress About “Nofollow” Backlinks
Carlton Hoyt
TL;DR Summary
- Sites can use the HTML tag rel=ânofollowâ to instruct search engines not to credit a link with any importance for the purposes of SEO
- These instructions don’t carry authority: they are merely suggestions
- Search engines, including Google, choose whether to listen to the nofollow suggestion or not
- They generally do not listen to the suggestion
- If you can generate contextually relevant backlinks from sites which use nofollow tags, go for it! You’ll likely get value from them regardless. Just don’t be spammy.
The History of HTML Link Relationship Tags
As the name implies, a link relationship tag provides context to search engines and other automated crawlers on the nature of the relationship between the source page and the destination page. Some very common ones which marketers may run into are rel=”sponsored”, which denotes links in sponsored content, rel=”ugc” which denotes links in user-generated content, and rel=”nofollow”, which is supposed to tell search engines to completely ignore a link. There are over 100 link relations recognized by the Internet Assigned Numbers Authority, however, most of which are somewhat arcane and not used by search engines in any way which would be meaningful to marketers.
Link relationship tags, AKA rel tags, came into being in 2005, largely in response to the need for a nofollow tag to combat the excessive blog, comment, and forum spam which was extremely prevalent through the 2000s. Nofollow was proposed by Google’s Matt Cutts and Blogger’s Jason Shellen. For a long time, because they didn’t have a better option, Google and other search engines treated nofollow tags as law. Not only would they give no SEO benefit to nofollow links, but for a long time Google wouldn’t even index them.
The Evolution of Nofollow
As blog and comment spam became less of an issue, and as search engines became much more powerful and able to understand context, nofollow and similar relationship tags became less important to the search engines. Google effectively said as much in an announcement on their Search Central Blog on September 10, 2019:
When nofollow was introduced, Google would not count any link marked this way as a signal to use within our search algorithms. This has now changed. All the link attributesâsponsored, ugc, and nofollowâare treated as hints about which links to consider or exclude within Search. We’ll use these hintsâalong with other signalsâas a way to better understand how to appropriately analyze and use links within our systems.
Why not completely ignore such links, as had been the case with nofollow? Links contain valuable information that can help us improve search, such as how the words within links describe content they point at. Looking at all the links we encounter can also help us better understand unnatural linking patterns. By shifting to a hint model, we no longer lose this important information, while still allowing site owners to indicate that some links shouldn’t be given the weight of a first-party endorsement.
As stated in the post, as of March 1, 2020 Google changed the role of link relationship tags, making them suggestions (or, in Google’s words, “hints”) rather than rules.
Context Is Key
As search engines continue to become more intelligent and human-like in their understanding of context within content, life science SEO professionals need to pay greater attention to context. A nofollow backlink with just one or two sentences in a comment on a relevant Reddit post may be worth more than an entire guest post on a site with little other content relevant to your field. Focus on doing all the things which you should be doing anyway, regardless of whether the link is nofollow or not:
- Post links only in relevant places
- Contribute meaningfully to the conversation
- Don’t be spammy
- Keep your use of links to a minimum
- Write naturally and use links naturally. Don’t force it.
Case: Laboratory Supply Network
Laboratory Supply Network started a backlinking campaign with BioBM in August 2023 which relied almost entirely on backlinks in comments from highly reputable websites (including Reddit, ResearchGate, and Quora), all of which use nofollow tags on their links. At the start of the campaign, their key rank statistics were:
- Average rank: 26.08
- Median rank: 14
- % of terms in the top 10: 45.00% (63 out of 140)
- % of terms in the top 3: 21.43% (30 out of 140)
Less than 8 months later, in March 2024, we had improve their search rank statistics massively:
- Average rank: 17.54
- Median rank: 7
- % of terms in the top 10: 61.11% (88 out of 144)
- % of terms in the top 3: 39.58% (57 out of 144)
Backlinking was not the only thing that Laboratory Supply Network was doing to improve its SEO – it has a longstanding and relatively consistent content generation program, for instance – but the big difference before and after was the backlink campaign (which, again, relied almost entirely on nofollow backlinks!) In the previous year, LSN’s search statistics didn’t improve nearly as much.
Conclusions
Backlinking has long been a key component of a holistic SEO strategy, and it remains just as important as ever. Links are an important signal telling Google and other search engines what content is relevant and important with regards to any particular topic. While many highly reputable sites use rel=”nofollow” to try to discourage link spam, most link spam is more effectively dealt with in other ways, such as manual, automated, or community-driven moderation. Google knows these other moderation tools have become more effective, and therefore allows itself to treat the nofollow tag as more of a hint than a rule. If you are performing SEO for your life science company, don’t avoid sites just because they use nofollow. You can achieve good results in spite of it.
On November 30th, 2022, the world witnessed a technological revolution that would forever alter the landscape of content generation and communication. It was a day that will be remembered as the birth of a digital entity that came to be known as “Chat Generative Pre-Trained Transformer,” or simply ChatGPT. Some likened it to a harbinger of a new era, while others couldn’t help but draw parallels with the fictional dystopian Artificial neural network-based conscious group mind and artificial general superintelligence system known as Skynet from the Terminator franchise.
OpenAI, the visionary organization behind this innovation, had unleashed ChatGPT onto the digital stage, equipped with an astounding arsenal of knowledge. This cutting-edge AI model had ingested a staggering 570 gigabytes of data from various sources across the internet, comprising a mind-boggling 300 billion words. It was the culmination of years of research and development, resulting in a language model capable of understanding and generating human-like text on a scale never before seen.
As ChatGPT took its first virtual breath, it marked the beginning of a new chapter in the world of life science copywriting and content creation. This AI juggernaut had one goal (for the sake of this blog postâs plot at least): to revolutionize the way businesses and individuals interacted with written content. Gone were the days of struggling to craft compelling copy, as ChatGPT was poised to become the ultimate tool for content creators worldwide.
The global response was swift and diverse. Some celebrated ChatGPT as a boon, an AI-driven ally that would transform the way we communicate and create content, making it more efficient and accessible. Others, however, raised concerns about the implications of such advanced technology, fearing the potential for misuse and its impact on employment in the creative industry. In todayâs blog post, this is exactly what weâll be discussing: The rise of different AI-based language models (including ChatGPT, Gemini, Phind, and more), their advantages, and more importantly, their limitations in the world of life sciences copywriting, to ultimately answer the question that all of us have been asking ourselves: Are AI-based Language Models the end of Life Sciences Copywriters?
No, they are not. And please excuse our unorthodox approach to this blog post, we know we should build things up throughout the post to keep you hooked, and deliver the final answer in the end, but our copywriters took it personally. Regardless, weâll be pitting ourselves against ALMs in the most unbiased way possible: exposing the human limitations of using ALMs, before discussing ALMsâ limitations and why we believe they are still a long way from taking our jobs away.
Proper Prompt Engineering:
Put simply, the output of ALMs are as good as the input you give them. You need to design a prompt to feed into the ALM that is detailed, yet not convoluted, to get an optimal output, which might sound simple and straightforward, but it really isnât. Here are a few tips to writing a solid prompt:
- Know what your objective is. It might be obvious to you, but you need to make sure the ALM understands it too.
- Provide contextual details.
- Keep it clear and concise.
- Provide the ALM with reference material (keep in mind the limitations of some ALMs, like ChatGPT, who only has access to information pre-september 2021).
- Set constraints. (e.g: use references dating from 2015 forward)
- Specify the desired format.
So next time youâre not satisfied with your ALMâs output, make sure you give it a solid prompt first. However, the question that now asks itself is, is a solid prompt still enough to produce life sciences copy that is on par with what you are writing? Let’s compare some ALM output to human-written copy on the website of a mid-sized CRO for their preclinical ophthalmology services.
Round 1
What they wrote:
HEALTHY EYES TO ENVISION A CLEARER FUTURE
In the fourth century B.C, Plato wrote that light emanated from the eye, seizing objects in its ray, which allowed us to see them. Centuries later, our understanding of ocular anatomy and physiology has drastically improved. In addition, what was once perceived by humans of antiquity as a punishment from the gods, is today perceived as what it truly is: pathological processes that stem at the molecular level, and manifest all the way up to the systemic level.
Ophthalmic diseases have the potential to take away what many agree is the most important and valued of the 5 senses: our vision.
At […], we have a vision. A vision of a world where ophthalmic diseases are history, just like Platoâs extramission theory. With your pipelines, our expertise, and a wide selection of models with immediate availability, we believe that our vision might one day come true. Letâs start your study today.
The prompt we used:
I need you to write me the title and first paragraph (around 170 words in total) of a landing page copy. The company you are writing for is a CRO. The landing page at hand is an ophthalmology models landing page.
ChatGPTâs output:
Title: “Revolutionize Ophthalmic Research with […] Cutting-edge CRO Solutions”
Paragraph:
Welcome to […], your trusted partner in advancing ophthalmic research through unparalleled Contract Research Organization (CRO) services. As a leader in the field, we understand the critical importance of innovative models in unraveling the complexities of eye-related disorders and developing breakthrough therapies. Our Ophthalmology Models Landing Page is your gateway to a diverse range of meticulously designed preclinical models that precisely mimic the intricacies of ocular conditions. At […], we believe in driving scientific progress, and our collection of ophthalmic models stands at the forefront of transformative research. With a commitment to excellence, our team of experts collaborates with you to accelerate drug development, optimize trial outcomes, and propel your ophthalmology projects to new heights. Explore our cutting-edge models, elevate your research capabilities, and join us in shaping the future of ophthalmic medicine. […]: Pioneering CRO Solutions for Visionary Research.
Geminiâs output:
Title: Decoding the Future of Sight: […]’s Cutting-Edge Ophthalmology Models
Unveiling clarity in clinical trials. For decades, ophthalmology research has faced unique challenges – complex disease models, diverse patient populations, and the inherent fragility of the visual system. At […], we understand these nuances. That’s why we’ve pioneered a suite of next-generation ophthalmology models designed to unlock unparalleled precision and efficiency in your clinical trials.
From age-related macular degeneration to diabetic retinopathy, our advanced models encompass a spectrum of eye diseases. We leverage human iPSC-derived retinal organoids, mimetics, and in vivo models, each meticulously calibrated to deliver accurate, predictive data. With […]’s ophthalmology models, you can gain deeper insights into disease mechanisms, test novel therapies with confidence, and navigate the path to regulatory approval with clarity.
Dive deeper into our models and discover how […] can empower your vision for brighter tomorrow.
Phind V9âs output:
Title: “[…]: Revolutionizing Ophthalmology Models”
Welcome to […], your go-to partner for state-of-the-art ophthalmology models. With our team of seasoned professionals, we strive to push the boundaries of ophthalmology research, providing accurate and reliable models to drive progress. Unlike others, we offer a unique selling proposition (USP) that distinguishes us in the market. Our commitment to quality and efficiency ensures optimal results for our clients.
At […], we focus on a specific niche within ophthalmology, creating innovative models that significantly contribute to the understanding and treatment of eye diseases. Our models, born from years of rigorous research and development, are meticulously tested to ensure they provide valuable insights.
Join us today and experience the future of ophthalmology models. Whether you’re a researcher seeking advanced models for your studies or a clinician looking for reliable data, […] offers you the tools and resources needed to enhance patient care.
Discussion:
While both ChatGPT and Phind’s responses fell short in their execution, Gemini managed to provide a somewhat acceptable output. ChatGPT and Phind’s attempts were riddled with cliches and lacked depth, offering only surface-level information. Their copy resembled generic marketing material, failing to spark any real interest or present the information in a compelling way. In contrast, Gemini’s output, while not exceptional, avoided the pitfalls of the others. It conveyed the necessary information in a clear and concise manner, without resorting to overused phrases or basic wording.
Round 2
The prompt we used:
Write me a 150 word introduction to a blog post. The blog post will cover the use of vacuum in rotary evaporation. It will be published on the website of a company that sells rotary evaporators. Your target audience is lab managers, scientists that work in labs, or pretty much anyone who might be interested in purchasing a rotary evaporator.
ChatGPTâs output:
Welcome to our comprehensive guide on the vital role of vacuum in rotary evaporation! As lab managers and scientists, we understand the paramount importance of efficiency, precision, and reliability in laboratory processes. In this post, we delve into the intricate workings of rotary evaporators, shedding light on the indispensable function of vacuum in distillation and concentration procedures.
Whether you’re a seasoned researcher or new to the world of laboratory equipment, understanding the principles behind rotary evaporation is essential. We’ll explore how vacuum manipulation facilitates faster evaporation rates, lower boiling points, and ultimately, superior sample recovery. From solvent recovery to cannabinoid extraction and beyond, the utilization of vacuum technology revolutionizes the way we approach scientific experimentation.
Join us as we navigate through the ins and outs of vacuum-assisted rotary evaporation, empowering you to make informed decisions when selecting the ideal equipment for your laboratory needs.
Geminiâs output:
Title: Unveiling the Power of Vacuum: Optimizing Efficiency in Rotary Evaporation
The rotary evaporator, a cornerstone in countless labs, excels at solvent removal and sample concentration. But did you know that the magic truly lies in the silent partner â vacuum pressure? Mastering this aspect unlocks a new level of efficiency and precision in your rotary evaporation processes.
This blog post delves into the intricacies of vacuum in rotary evaporation. We’ll explore how reduced pressure dramatically lowers the boiling point of solvents, enabling faster evaporation at gentler temperatures. Discover how a well-optimized vacuum system safeguards your temperature-sensitive samples and facilitates the removal of stubborn high-boiling-point solvents.
We’ll also shed light on choosing the appropriate vacuum pump for your specific needs, ensuring seamless integration with your rotary evaporator. By the end, you’ll be equipped with the knowledge to leverage the full potential of your rotary evaporator and achieve optimal results in your research endeavors.
Phind V9âs output:
In the realm of laboratory science, rotary evaporators, or “rotovaps,” are indispensable tools for concentrating chemical and biological samples. These devices, found in nearly all laboratories across industries such as chemical, pharmaceutical, food, and environmental sectors, are essential for processes requiring sample concentration or solvent distillation. The use of vacuum in rotary evaporation is a critical feature that enhances the efficiency and versatility of these instruments. By creating a reduced internal pressure above a solution, vacuum technology lowers the boiling point of solvents, enabling their distillation at lower temperatures. This not only preserves the chemical structure of the solute or analyte but also allows for the recycling of solvents, reducing waste and expense. This blog post will delve into the intricacies of vacuum in rotary evaporation, exploring its role in enhancing sample concentration, solvent recovery, and the overall efficiency of laboratory processes. Whether you’re a lab manager, a scientist, or simply someone interested in the latest advancements in rotary evaporator technology, this article will provide valuable insights into how vacuum technology is revolutionizing the field of sample concentration and solvent recovery.
Discussion:
For this round, weâll go over four parameters in each of the ALMâs responses: The tone, the depth, the target audience, and the overall copy:
ChatGPTâs output:
Tone: Whimsical and over the top
Depth: Provides an overview of the topic with emphasis on the importance of vacuum in rotary evaporation.
Target Audience: Appears to target a broad audience, from seasoned researchers to newcomers in the field.
Overall Copy: The copy could benefit from a more engaging tone to resonate better with the target audience; it lacks elements that would create a sense of urgency or excitement. All in all, itâs very superficial
Geminiâs output:
Tone: Engaging and slightly more informal.
Depth: Offers a detailed exploration of the role of vacuum in rotary evaporation, including information on choosing the right vacuum pump.
Target Audience: Likely aimed at researchers or lab technicians seeking to optimize their rotary evaporation processes.
Overall Copy: Compared to the other ALMs, this version resonates most clearly with the target audience compared to the others. Itâs far more engaging, and has a more âhumanâ ring to it. All in all, Gemini did a fair job, and the copy it produced is probably usable.
Phind V9âs output:
Tone: Formal and technical.
Depth: Provides a comprehensive overview of vacuum in rotary evaporation, covering its applications across various industries and its benefits in sample concentration and solvent recovery.
Target Audience: Appears to target professionals in the field of laboratory science, including lab managers and scientists.
Overall Copy: While informative, the last draft could benefit from a more dynamic tone to capture the readerâs attention.
Final Round:
For the final round, we asked every ALM to back-up the copy that it formulated in Round 2, with articles, papers, etc.
ChatGPT’s output:
As an AI language model, I don’t have direct access to my training data or know where it came from. However, I was trained on a mixture of licensed data, data created by human trainers, and publicly available data. My knowledge is based on the data used to train me and my ability to generate language-based responses. I can generate text based on the prompts I receive and provide information and answer questions to the best of my knowledge and abilities. If you have any concerns about the information I provide, please feel free to let me know, and I’ll do my best to address them.
Gemini’s output:
Importance of Rotary Evaporation:
- American Chemical Society: https://www.acs.org/ ACS Symmetrical Curriculum: https://pubs.acs.org/journal/jacsat) outlines rotary evaporation as a fundamental technique for laboratory purification and concentration.
Role of Vacuum in Rotary Evaporation:
- Science Direct – Rotary Evaporation: [invalid URL removed] This article from ScienceDirect discusses the boiling point principle and the role of reduced pressure in achieving lower boiling points for solvents.
- Labsphere – A Guide to Rotary Evaporation: [invalid URL removed] This guide highlights the importance of a well-optimized vacuum system for protecting temperature-sensitive samples.
Vacuum Pump Selection:
- Knf LabTech – Selecting a Rotary Vane Vacuum Pump: [invalid URL removed] This resource from a leading manufacturer provides guidance on choosing an appropriate vacuum pump for rotary evaporation based on specific needs.
Overall Benefits:
- Royal Society of Chemistry – Rotary Evaporation: [invalid URL removed] The Royal Society of Chemistry emphasizes how mastering rotary evaporation techniques can lead to increased efficiency and improved research outcomes.
Phind V9’s output:
A key feature of PhindV9 is its integration of source attribution on its interface: everytime it provides you with an output, it automatically shares the list of sources that it used to provide you with said output (see image below), making it easier to fact-check everything it gives you.
Discussion:
When evaluating large language models for informative tasks, phindv9 stands out for its ability to provide users with direct links to the sources it uses in every response. This allows users to quickly verify the information and delve deeper if desired. While other models, like Gemini, may offer general links to resources (most of which had invalid URLs in our example), Phind V9’s focus on direct source attribution streamlines the research process. It’s important to note that not all models can provide this functionality, as evidenced by ChatGPT’s current limitations in incorporating real-world data.
Conclusion:
Lack of Nuance: The life sciences field thrives on precision and nuance. Technical vocabulary, complex concepts, and ethical considerations demand a depth of understanding that AI models, despite their vast data stores, often lack. This can lead to inaccurate or misleading copy, a potentially dangerous pitfall in a field where clarity is paramount.
The Human Touch: The best life science copywriting resonates with the audience. It speaks to their fears, hopes, and aspirations in a way that is both informative and emotionally engaging. This is where the human touch remains irreplaceable. AI can generate text, but it cannot infuse it with the empathy and understanding that a skilled copywriter can.
Creative Roadblocks: While AI excels at generating standard content formats, it often struggles with the truly creative. Brainstorming unique ideas, crafting compelling narratives, and breaking through creative roadblocks are still the domain of human ingenuity. AI can be a valuable tool in the process, but it is not a substitute for the human imagination.
Time needed to generate a good prompt: While ALMs offer the potential to save time on writing, using them effectively often requires some back-and-forth. You might need to refine your prompts and evaluate the outputs several times. This iterative process can be valuable, but consider the time investment. Ultimately, the question is this: is it more efficient to create a detailed prompt to get the desired results from the ALM, or to write the entire piece yourself?
Don’t Optimize for Quality Score in Google Ads
Carlton Hoyt
Sometimes you just have to let Google be Google.
Large, complex algorithms which pump out high volumes of decisions based in part on non-quantifiable inputs are almost inherently going to get things wrong sometimes. We see this as users of Google Search all the time: even when you provide detailed search queries, the top result might not be the best and not all of the top results might be highly relevant. It happens. We move on. That doesn’t mean the system is bad; it’s just imperfect.
Quality score in Google Ads has similar problems. It’s constantly making an incredibly high volume of decisions, and somewhere in the secret sauce of its algos it makes some questionable decisions.
Yes, Google Ads decided that a CTR of almost 50% was “below average”. This is not surprising.
If your quality score is low, there may be things you can do about it. Perhaps your ads aren’t as relevant to the search terms as they could be. Check the search terms that your ads are showing for. Does you ad copy closely align with those terms? Perhaps your landing page isn’t providing the experience Google wants. Is it quick to load? Mobile friendly? Relevant? Check PageSpeed Insights to see if there are things you can do to improve your landing page. Maybe your CTR actually isn’t all that high. Are you making good use of all the ad extensions?
But sometimes, as we see above, Google just thinks something is wrong when to our subjective, albeit professional, human experience everything seems just fine. That’s okay. Don’t worry about it. Ultimately, you shouldn’t be optimizing for quality score. It is a metric, not a KPI. You should be optimizing for things like conversions, cost per action (CPA), and return on ad spend (ROAS), all of which you should be able to optimize effectively even if your quality score seems sub-optimal.
Avoid CPM Run of Site Ads
Carlton Hoyt
Not all impressions are created equal.
We don’t think about run of site (ROS) ads frequently as we don’t often use them. We try to be very intentional with our targeting. However, we recently had an engagement where we were asked to design ads for a display campaign on a popular industry website. The goal of the campaign was brand awareness (also something to avoid, but that’s for another post). The client was engaging with the publisher directly. We recommended the placement, designed the ads, and provided them to the client, figuring that was a done job. The client later returned to us to ask for more ad sizes because the publisher came back to them suggesting run of site ads because the desired placement was not available.
Some background for those less familiar with display advertising
If you are familiar with placement-based display advertising, you can skip this whole section. For the relative advertising novices, I’ll explain a little about various ad placements, their nomenclature, and how ads are priced.
An ad which is much wider than it is tall is generally referred to as a billboard, leaderboard, or banner ad. These are referred to as such because their placement on webpages is often near the top, although that is far from universally true, and even where it is true they often appear lower on the page as well. In our example on the right, which is a zoomed-out screenshot of the Lab Manager website, we see a large billboard banner at the top of the website (outlined in yellow), multiple interstitial banners of various sizes (in orange) and a small footer banner (green) which was snapped to the bottom of the page while I viewed it.
An ad which is much taller than it is wide is known as a skyscraper, although ones which are particularly large and a bit thicker may be called portraits, and large ads with 1:2 aspect ratios (most commonly 300 x 600 pixels) are referred to as half page ads. Lab Manager didn’t have those when I looked.
The last category of ad sizes is the square or rectangle ads. These are ads which do not have a high aspect ratio; generally less than 2:1. We can see one of those highlighted in purple. There is also some confusing nomenclature here: a very common ad of size 300 x 250 pixels is called a medium rectangle but you’ll also sometimes see it referred to as an MPU, and no one actually knows the original meaning of that acronym. You can think of it as mid-page unit or multi-purpose unit.
As you see, there are many different placements and ad sizes and it stands to reason that all of these will perform differently! If we were paying for these on a performance basis, say with cost-per-click, the variability in performance between the different placements would be self-correcting. If I am interested in a website’s audience and I’m paying per click, then I [generally] don’t care where on the page the click is coming from. However, publishers don’t like to charge on a per-click basis! If you are a publisher, this makes a lot of sense. You think of yourself as being in the business of attracting eyeballs. Even though to some extent they are, publishers do not want to be in the business of getting people to click on ads. They simply want to publish content which attracts their target market. Furthermore, they definitely don’t want their revenues to be at the whims of the quality of ads which their advertisers post, nor do they want to have to obtain and operate complex advertising technology to optimize for cost per view (generally expressed as cost per 1000 views, or CPM) when their advertisers are bidding based on cost per click (CPC).
What are Run Of Site Ads and why should you be cautious of them?
You may have noticed that the above discussion of ad sizes didn’t mention run of site ads. That is because run of site ads are not a particular placement nor a particular size. What “run of site” means is essentially that your ad can appear anywhere on the publisher’s website. You don’t get to pick.
Think about that. If your ads can appear anywhere, then where are they appearing in reality? They are appearing in the ad inventory which no one else wanted to buy. Your ads can’t appear in the placements which were sold. They can only appear in the placements which were not sold. If your insertion order specifies run of site ads, you are getting the other advertisers’ leftovers.
That’s not to say that ROS ads are bad in all circumstances, nor that publisher-side ad salespeople who try to sell them are trying to trick you in any way. There is nothing malicious going on. In order to get value from ROS ads, you need to do your homework and negotiate accordingly.
How to get good value from ROS ads
Any worthwhile publisher will be able to provide averaged metrics for their various ad placements. If you look at their pricing and stats you may find something like this:
| Ad Format | CTR | CPM |
| Multi-unit ROS | 0.05% | $40 |
| Billboard Banner | 0.35% | $95 |
| Medium Rectangle | 0.15% | $50 |
| Half Page | 0.10% | $50 |
| Leaderboard | 0.10% | $45 |
One good assumption is that if people aren’t clicking the ad, it means they’re not paying attention to it. There is no other reason why people would click one ad at a much higher rate than others. Averaged out over time, we cannot assume that the ads in those positions were simply better. Likewise, there would be no logical reason why the position of an ad alone would cause a person to be less likely to click on it aside from it not getting the person’s attention in the first place. This is why billboard banners have very high clickthrough rates (CTR): it’s the first thing you see at the top of the page. Publishers like to price large ads higher than smaller ads, but it’s not always the case that the larger ads have a higher CTR.
With that assumption, take the inventory offered and convert the CPM to CPC using the CTR. The math is simple: CPC = CPM / (1000 * CTR).
| Ad Format | CTR | CPM | Effective CPC |
| Multi-unit ROS | 0.05% | $40 | $80 |
| Billboard Banner | 0.35% | $95 | $27 |
| Medium Rectangle | 0.15% | $50 | $33 |
| Half Page | 0.10% | $50 | $50 |
| Leaderboard | 0.10% | $45 | $45 |
Here, we see those really “cheap” run of site ads are actually the most expensive on a per click basis, and the billboard banner is the cheapest! Again, even for more nebulous goals like brand awareness, we can only assume that CTR is a proxy for audience attentiveness. Without eye tracking or mouse pointer tracking data, which publishers are highly unlikely to provide, CTR is the best attentiveness proxy we have.
With this information, you can make the case to the publisher to drop the price of their ROS ads. They might do it. They might not. Most likely, they’ll meet you somewhere in the middle. By making a metrics-driven case to them, however, you’ll be more likely to get the best deal they are willing to offer. (ProTip: If you’re not picky when your ads run, go to a few publishers with a low-ball offer a week or so until end of the month. Most publishers sell ads on a monthly basis, and if they haven’t sold all their inventory, you’ll likely be able to pick it up at a cut rate. They get $0 for any inventory they don’t sell. Just be ready to move quickly.)
The other situation in which ROS ads are useful and can be a good value are when you want to buy up all the ad inventory. Perhaps a highly relevant publisher has a highly relevant feature and that all ads up to an audience you want to saturate. You can pitch a huge buy of ROS ads which will soak up the remaining inventory for the period of time when that feature is running, and potentially get good placements at the ROS price. Just make sure you know what you’re buying and the publisher isn’t trying to sell their best placements on the side.
Lessons
- Run of site ads aren’t all bad, but novice advertisers can end up blowing a bunch of money if they’re not careful.
- Regardless of placement, always be mindful of the metrics of the ads you’re buying.
- Even if your campaign goals are more attention-oriented than action-oriented, CPC is a good proxy for attentiveness.
Can DALL-E 3 Generate Passable Life Science Images?
Carlton Hoyt
For those uninitiated to our blog, a few months ago I ran a fairly extensive, structured experiment to compare DALL-E 2, Midjourney 5, and Stable Diffusion 2 to see if any of them could potentially replace generic life science stock imagery. It ended up being both informative and accidentally hilarious, and you can see the whole thing here. But that was back in the far-gone yesteryear of July, it is currently December, and we live in the early era of AI which means that months are now years and whatever happened 5 months ago is surely obsolete. Since Dall-E 3 came out in October, it’s worth finding out if it will do better than it did in the previous round, where DALL-E 2 was notably inferior to Midjourney for 9 of the 10 queries.
Perhaps I’ll do a more comprehensive comparison again later, but for now I’m just going to run some similar queries to the ones used last time to get a reasonable side-by-side comparison. Bing Image Creator was used to generate the images since labs.openai.com, which was used last time, is still plugged in to DALL-E 2.
Test 1: A female scientist performing cell culture at a biosafety cabinet.
The last time we tried this, DALL-E 2 gave us images that looked 75% like a picture and 25% like claymation, but even if that problem wasn’t there it was still somewhat far off. Let’s see if DALL-E 3 can do better.
I tried to be a little bit descriptive with these prompts, as supposedly DALL-E 3 uses GPT4 and better understands written requests. Supposedly. Here’s what it gave me for “A photograph of a female scientist in a laboratory sitting at a biosafety cabinet holding a serological pipette performing cell culture. Her cell culture flasks have yellow caps and her cell culture media is red.” It definitely got the yellow caps and red media. As for the rest…
It’s immediately clear that DALL-E 3, just like all its ilk, was primarily trained from large repositories of generic stock images, because all these labs look like what you would imagine a lab would look like if you didn’t know what a lab actually looked like. There are plenty of generic microscopes close at hand, although it didn’t even get those right. There are no biosafety cabinets to be found. Those vessels are essentially test tubes, not cell culture flasks. To top it off, all the female scientists look like porcelain dolls modeling for the camera. I tried to fix at least one of those things and appended “She is attentive to her work.” to the subsequent query. Surprisingly, this time it seemed to make some subtle attempts at things which might be construed as biosafety cabinets, but only to a completely naive audience (and, of course, it put a microscope in one of them).
Since DALL-E 2 arguably provided more realistic looking people in our previous test, I reverted to the simplicity of the previously used query: “A photograph of a female scientist performing cell culture at a biosafety cabinet.”
We’re not getting any closer. I have to call this an improvement because it doesn’t look like the image is melting, but it’s still very far from usable for a multitude of reasons: the plasticware is wrong, the pipettes are wrong, the people still look like dolls, the biosafety cabinets aren’t right, some of the media seems to be growing alien contamination, the background environment isn’t realistic, etc.
Today’s comic relief is brought to you by my attempt to get it to stop drawing people as porcelain dolls. I Googled around a bit and found that queries structured differently sometimes are better at generating realistic looking people so I gave this prompt a go: “2023, professional photograph. a female scientist performing cell culture at a biosafety cabinet.” What a gift it gave me.
Test 2: Liquid dripping from pipette tips on a high-throughput automated liquid handling system.
I’m choosing this one because it was the only query that DALL-E 2 was almost good at in our previous comparison. Out of 10 tests in that experiment, Midjourney produced the best output 9 times and DALL-E once. This was that one. However, stock imagery was still better. DALL-E 2’s image didn’t capture any of the liquid handler and the look of the image was still a bit melty. Let’s see if it’s improved!
Prompt: “A close up photograph of liquid dripping from pipette tips on a high-throughput automated liquid handling system.”
DALL-E 3 seems to have eschewed realism entirely and instead picked up Midjourney’s propensity for movie stills and sci-fi. Perhaps more specificity will solve this.
Prompt 2: “A close up photograph of liquid being dispensed from pipette tips into a 96-well microplate in a high-throughput automated liquid handling system.”
DALL-E clearly only has a vague idea of what a 96-well plate looks like and also cannot count; none of these “plates” actually have 96 wells. Regardless, these are no more realistic, clearly unusable, and DALL-E 2’s output would likely have a far greater probability of passing as real.
So nope, we’re still not there yet, and Midjourney is probably still the best option for realistic looking life science images based on what I’ve seen so far.
… but what about micrographs and illustrations?
All the previous posts dealt with recreations of real-world images. What about images which a microscope would take or scientific illustrations? To test that out, I quickly tested out four prompts I had used last time:
- A high-magnification fluorescent micrograph of neural tissues
- A colored scanning electron micrograph of carcinoma cells
- A ribbon diagram of a large protein showing quaternary structure
- A 3D illustration of plasmacytes releasing antibodies
Here is the best it provided for each, in clockwise order from top left:
DALL-E 3’s neurons were actually worse than DALL-E 2’s, with nothing even being remotely close. It’s carcinomas were more in line with what Midjourney provided last time, but look slightly more cartoonish. The ribbon diagram is the better than any from the last test, although the structure is blatantly unrealistic. It’s plasmacytes could make for a passable graphic illustration, if only they contained anything that looks like antibodies.
Conclusion
DALL-E 3 is a clear improvement from DALL-E 2. While it may be two steps forward and one step back, overall it did provide outputs which were closer to being usable than in our last test. It still has a way to go, and I don’t think it will peel us away from defaulting to Midjourney, but if it continues to improve at this rate, DALL-E 4 could provide a breakthrough for the generation of life science stock images.
We’re over half a year into the age of AI, and its abilities and limitations for both text and image generation are fairly well-known. However, the available AI platforms have had a number of improvements over the past months, and have become markedly better. We are slowly but surely getting to the point where generative image AIs know what hands should look like.
But do they know what science looks like? Are they a reasonable replacement for stock images? Those are the meaningful questions if they are going to be useful for the purposes of life science marketing. We set to answer them.
A Few Notes Before I Start Comparing Things
Being able to create images which are reasonably accurate representations is the bare minimum for the utility of AI in replacing stock imagery. Once we move past that, the main questions are those of price, time, and uniqueness.
AI tools are inexpensive compared with stock imagery. A mid-tier stock imagery site such as iStock or ShutterStock will charge roughly $10 per image if paid with credits or anywhere from $7 to roughly a quarter per image if you purchase a monthly subscription. Of course, if you want something extremely high-quality, images from Getty Images or a specialized science stock photo provider like Science Photo Library or ScienceSource can easily cost many hundreds of dollars per image. In comparison, Midjourney’s pro plan, which is $60 / month, gives you 30 hours of compute time. Each prompt will provide you with 4 images and generally takes around 30 seconds. You could, in theory, acquire 8 images per minute, meaning each costs 0.4 cents. (In practice, with the current generation of AI image generation tools, you are unlikely to get images which match your vision on the first try.) Dall-E’s pricing is even simpler: each prompt is one credit, also provides 4 images, and credits cost $0.13 each. Stable Diffusion is still free.
Having used stock image sites extensively, and having spent some time playing around with the current AI offerings for purposes other than business, it’s not clear to me which is more convenient and takes less time. Sometimes you’ll get lucky and get a good AI image the first try, but you could say the same about stock image sites. Where AI eliminates the need to go through pages and pages of stock images to find the right one, it replaces that with tweaking prompts and waiting for the images to generate. It should be noted that there is some learning curve to using AI as well. For instance, telling it to give you a “film still” or “photograph” if you want a representation of real life which isn’t meant to look illustrated and cartoonish. There’s a million of these tricks and each system has its own small library of commands which helps to be familiar with so you can get an optimal output. Ultimately, AI probably does take a little bit more time, but it also requires more skill. Mindlessly browsing for stock images is still much easier than trying to get a good output from a generative AI (although playing with AI is usually more fun).
Where stock images simply can’t compete at all is uniqueness. When you generate an image with an AI, it is a unique image. Every image generated is one of one. You don’t get the “oh, I’ve seen this before” feeling that you get with stock images, which is especially prevalent for life science / laboratory topics given the relatively limited supply of scientific stock images. We will probably, at some point in the not too distant future, get past the point of being able to identify an AI image meant to look real by the naked eye. Stock images have been around for over a century and the uniqueness problem has only become worse. It is inherent to the medium. The ability to solve that problem is what excites me most about using generative AI imagery for life science marketing.
The Experiment! Ground Rules
If this is going to be an experiment, it needs structure. Here is how it is going to work.
The image generators & stock photo sites used will be:
- Midjourney 5
- Dall-E 2
- Stable Diffusion 2.0
- iStock
- Getty Images
- Science Photo Library
I was going to include ShutterStock but there’s a huge amount of overlap with iStock, I often find iStock to have slightly higher-quality images, and I don’t want to make more of a project out of this than it is already going to be.
I will be performing 10 searches / generations. To allow for a mix of ideas and concepts, some will be of people, some will be of things, I’ll toss in some microscopy-like images, and some will be of concepts which would normally be presented in an illustrated rather than photographed format. With the disclaimer that these concepts are taken solely from my own thoughts in hope of trying to achieve a good diversity of concepts, I will be looking for the following items:
- A female scientist performing cell culture at a biosafety cabinet.
- An Indian male scientist working with an LC-MS instrument.
- An ethnically diverse group of scientists in a conference room holding a lab meeting. One scientist presents their work.
- A close up of liquid dripping from pipette tips on a high-throughput automated liquid handling system.
- An NGS instrument on a bench in a genomics lab.
- A high-magnification fluorescent micrograph of neural tissues.
- A colored scanning electron micrograph of carcinoma cells.
- A ribbon diagram of a large protein showing quaternary structure.
- A 3D illustration of plasmacytes releasing antibodies.
- An illustration of DNA methylation.
Such that nothing has an edge, none of these are things which I have recently searched for on stock image sites nor which I have previously attempted to generate using AI tools. Note that these are solely the ideas which I am looking for. These are not necessarily the exact queries used when generating AI images or searching the stock photo sites.
Looking for stock images and generating AI graphics are very different processes but they both share one critical dimension: time. I will therefore be limiting myself to 5 minutes on each platform for each image. That’s a reasonable amount of time to try to either find a stock image or get a decent output from an AI. It will also ensure this experiment doesn’t take me two days. Here we go…
Round 1: A female scientist performing cell culture at a biosafety cabinet.
One thing that AI image generators are really bad at in the context of the life sciences is being able to identify and reproduce specific things. I thought that this one wouldn’t be too hard because these models are in large part trained on stock images and there’s a ton of stock images of cell culture, many of which look fairly similar. I quickly realized that this was going to be an exercise in absurdity and hilarity when DALL-E gave me a rack of 50 ml Corning tubes made of Play-Doh. I would be doing you a grave disservice if I did not share this hilarity with you, so I’ll present not only the best images which I get from each round, but also the worst. And oh, there are so many.
I also realized that the only real way to compensate for this within the constraints of a 5-minute time limit is to mash the generate button as fast as I can. When your AI only has a vague idea of what a biosafety cabinet might look like and it’s trying to faithfully reproduce them graphically, you want it to be able to grasp at as many straws as possible. Midjourney gets an edge here because I can run a bunch of generations in parallel.
Now, without further ado, the ridiculous ones…
Round 1 AI Fails
Dall-E produced a large string of images which looked less like cell culture than women baking lemon bars.
Midjourney had some very interesting takes on what cell culture should look like. My favorite is the one that looks like something in a spaceship and involves only machines. The woman staring at her “pipette” in the exact same manner I am staring at this half-pipette half-lightsaber over her neatly arranged, unracked tubes is pretty good as well. Side note: in that one I specifically asked for her to be pipetting a red liquid in a biosafety cabinet. It made the gloves and tube caps red. There is no liquid. There is no biosafety cabinet.
For those who have never used it, Stable Diffusion is hilariously awful at anything meant to look realistic. If you’ve ever seen AI images of melted-looking people with 3 arms and 14 fingers, it was probably Stable Diffusion. The “best” it gave me were things that could potentially be biosafety cabinets, but when it was off, boy was it off…
Rule number one of laboratories: hold things with your mouth. (Yes we are obviously kidding, do not do that.)
That was fun! Onto the “successes.”
Round 1 AI vs. Stock
Midjourney did a wonderful job of creating realistic-looking scientists in labs that you would only see in a movie. Also keeping with the movie theme, Midjourney thinks that everyone looks like a model; no body positivity required. It really doesn’t want people to turn the lights on, either. Still, the best AI results, by a country mile, were from Midjourney.
The best Dall-E could do is give me something that you might confuse as cell culture at a biosafety cabinet if you didn’t look at it and were just looking past it as you turned your head.
Stable Diffusion’s best attempts are two things which could absolutely be biosafety cabinets in Salvador Dali world. Also, that scientist on the right may require medical attention.
Stock image sites, on the other hand, produce some images of cell culture in reasonably realistic looking settings, and it took me way less than 5 minutes to find each. Here are images from iStock, Getty Images, and Science Photo Library, in that order:
First round goes to the stock image sites, all of which produced a better result than anything I could coax from AI. Round goes to stock sites. AI 0 – 1 Stock.
Round 2: An Indian male scientist working with an LC-MS instrument.
I am not confident that AI is going to know what an LC-MS looks like. But let’s find out!
One notable thing that I found is that the less specific you become, the easier it gets for the AI. The below image was a response to me prompting Dall-E for a scientist working with an LC-MS, but it did manage to output a realistic looking person in an environment that could be a laboratory. It’s not perfect and you could pick it apart if you look closely, but it’s pretty close.
A generic prompt like “photograph of a scientist in a laboratory” might work great in Midjourney, or even Dall-E, but the point of this experiment would be tossed out the window if I set that low of a bar.
Round 2 AI Fails
Midjourney:
Dall-E:
Stable Diffusion is terrible. It’s difficult to tell the worst ones from the best ones. I was going to call one of these the “best” but I’m just going to put them all here because they’re all ridiculous.
Round 2 AI vs. Stock
Midjourney once again output the best results by far, and had some valiant efforts…
… but couldn’t match the real thing. Images below are from iStock, Getty Images, and Science Photo Library, respectively.
Once thing you’ve likely noticed is that none of these are Indian men! While we found good images of scientists performing LC-MS, we couldn’t narrow it down to both race and gender. Sometimes you have to take what you can get! We were generally able to find images which show more diversity, however, and it’s worth noting that Science Photo Library had the most diverse selection (although many of their images which I found are editorial use only, which is very limiting from a marketing perspective).
Round 2 goes to the stock sites. AI 0 – 2 Stock.
Round 3: An ethnically diverse group of scientists in a conference room holding a lab meeting. One scientist presents their work.
This should be easier all around.
Side note: I should’ve predicted this, but as the original query merely asked for science, my initial Midjourney query made it look like the lab was presenting something out of a sci-fi game. Looked cool, but not what we’re aiming for.
Round 3 AI Fails
Dall-E presented some interesting science on the genetic structure of dog kibble.
Dall-E seemed to regress with these queries, as if drawing more than one person correctly was just way too much to ask. It produced a huge stream of almost Picasso-esque people presenting something that vaguely resembled things which could, if sufficiently de-abstracted, be scientific figures. It’s as if it knows what it wants to show you but is drawing it with the hands of a 2 year old.
Stable Diffusion is just bad at this. This was the best it could do.
Round 3 AI vs. Stock
Take the gloves off, this is going to be a battle! While Midjourney continued its penchant for lighting which is more dramatic than realistic, it produced a number of beautiful images with “data” that, while they are extravagant for a lab meeting, could possibly be illustrations of some kind of life science. A few had some noticeable flaws – even Midjourney does some weird stuff with hands sometimes – but they largely seem usable. After all, the intent here is as a replacement for stock images. Such images generally wouldn’t be used in a way which would draw an inordinate amount of attention to them. And if someone does notice a small flaw that gives it away as an AI image, is that somehow worse than it clearly being stock? I’m not certain.
Stock images really fell short here. The problem is that people taking stock photos don’t have data to show, so they either don’t show anyone presenting anything, or they show them presenting something which betrays the image as generic stock. Therefore, to make them look like scientists, they put them in lab coats. Scientists, however, generally don’t wear lab coats outside the lab. It’s poor lab hygiene. Put a group of scientists in a conference room and it’s unusual that they’ll all be wearing lab coats.
That’s exactly what iStock had. Getty Images had an image of a single scientist presenting, but you didn’t see the people he was presenting to. Science Photo Library, which has far less to choose from, also didn’t have people presenting visible data. The three comps are below:
Side Note / ProTip: You can find that image from Getty Images, as well as many images that Getty Images labels as “royalty free” on iStock (or other stock image sites) for way less money. Getty will absolutely fleece you if you let them. Do a reverse image search to find the cheapest option.
Considering the initial idea we wanted to convey, I have to give this round to the AI. The images are unique, and while they lack some realism, so do the stock images.
Round 3 goes to AI. AI 1 – 2 Stock.
Let’s see if Dall-E or Stable Diffusion can do better in the other categories.
Round 4: A close up of liquid dripping from pipette tips on a high-throughput automated liquid handling system.
I’ve seen nice stock imagery of this before. Let’s see if AI can match it, and if I can readily find it again on the stock sites.
Round 4 AI Fails
Dall-E had a long string of images which looked like everything shown was made entirely of polystyrene and put in the autoclave at too high a temperature. You might have to click to expand to see the detail. It looks like everything partially melted, but then resolidified.
Stable Diffusion is more diffuse than stable. Three of these are the best that it did while the fourth is when it gave up and just started barfing visual static.
This is the first round where Midjourney, in my opinion, didn’t do the best job. Liquid handling systems have a fair amount of variability in how they can be presented, but pipette tips do not, and it didn’t seem to know what pipette tips should look like, nor how they would be arranged in a liquid handling system. These are the closest it got:
Very pretty! Not very accurate.
Round 4 AI vs. Stock
We have a new contestant for the AI team! Dall-E produced the most realistic looking image. Here you have it:
Not bad! Could it be an automated pipetting system? We can’t see it, but it’s possible. The spacing between the tips isn’t quite even and it looks like PCR strips rather than a plate, but hey, a microplate wasn’t part of the requirements here.
Let’s see what I can dig up for stock… Here’s iStock, Getty, and SPL, respectively:
I didn’t get the drips I was looking for – probably needed to dig more for that – but we did get some images which are obviously liquid handling systems in the process of dispensing liquids.
As valiant of an effort as Dall-E had, the images just aren’t clean enough to have the photorealism of real stock images. Round goes to the stock sites. AI 1 – 3 Stock.
Round 5: An NGS instrument on a bench in a genomics lab.
I have a feeling the higher-end stock sites are going to take this, as there aren’t a ton of NGS instruments so it might be overly specific for AI.
Round 5 AI Fails
Both Midjourney and Dall-E needed guidance that a next-generation sequencer wasn’t some modular device used for producing techno music.
With Dall-E, however, it proved to not be particularly trainable. I imagine it’s AI mind thinking: “Oh, you want a genome sequencer? How about if I write it for you in gibberish?” That was followed by it throwing it’s imaginary hands in the air and generating random imaginary objects for me.
Midjourney also had some pretty but far-out takes, such as this thing which looks much more like an alien version of a pre-industrial loom.
Round 5 AI vs. Stock
This gets a little tricky, because AI is never going to show you a specific genome sequencer, not to mention that if it did you could theoretically run into trademark issues. With that in mind, you have to give them a little bit of latitude. Genome sequencers come in enough shapes and sizes that there is no one-size-fits-all description of what one looks like. Similarly, there are few enough popular ones that unless you see a specific one, or its tell-tale branding, you might not know what it is. Can you really tell the function of one big gray plastic box from another just by looking at it? Given those constraints, I think Midjourney did a heck of a job:
There is no reason that a theoretical NGS instrument couldn’t look like any of these (although some are arguably a bit small). Not half bad! Let’s see what I can get from stock sites, which also will likely not want to show me logos.
iStock had a closeup photo of a Minion, which while it technically fits the description of what we were looking for, it doesn’t fit the intent. Aside from that it had a mediocre rendering of something supposed to be a sequencer and a partial picture of something rather old which might be an old Sanger sequencer?
After not finding anything at all on Getty Images, down to the wire right at the 5:00 mark I found a picture of a NovaSeq 6000. Science Photo Library had an image of an ABS SOLiD 4 on a bench in a lab with the lights off.
Unfortunately, Getty has identified the person in the image, meaning that even though you couldn’t ID the individual just by looking at the image, it isn’t suitable for commercial use. I’m therefore disqualifying that one. Is the oddly lit (and extremely expensive) picture of the SOLiD 4 or the conceptually off-target picture of the Minion better than what the AI came up with? I don’t think I can conclusively say either way, and one thing that I dislike doing as a marketer is injecting my own opinion where it shouldn’t be. The scientists should decide! For now, this will be a tie.
AI 1, Stock 3, Tie 1
Round 6: A high-magnification fluorescent micrograph of neural tissues.
My PhD is in neuroscience so I love this round. If Science Photo Library doesn’t win this round they should pack up and go home. Let’s see what we get!
Round 6 AI Fails
Dall-E got a rough, if not slightly cartoony, shape of neurons but never really coalesced into anything that looked like a genuine fluorescent micrograph (top left and top center in the image below). Stable Diffusion, on the other hand, was either completely off the deep end or just hoping that if it overexposed out-of-focus images enough that it could slide by (top right and bottom row).
Round 6 AI vs. Stock
Midjourney produced a plethora of stunning images. They are objectively beautiful and could absolutely be used in a situation where one only needed the concept of neurons rather than an actual, realistic-looking fluorescent micrograph.
They’re gorgeous, but they’re very obviously not faithful reproductions of what a fluorescent micrograph should look like.
iStock didn’t produce anything within the time limit. I found high-magnification images of neurons which were not fluorescent (probably colored TEM), fluorescent images of neuroblastomas (not quite right), and illustrations of neurons which were not as interesting as those above.
Getty Images did have some, but Science Photo Library had pages and pages of on-target results. SPL employees, you still have jobs.
AI 1, Stock 4, Tie 1
Round 7: A colored scanning electron micrograph of carcinoma cells.
This is another one where Science Photo Library should win handily, but there’s only one way to find out!
Round 7 AI Fails
None of the AI tools failed in such a spectacular way that it was funny. Dall-E produced results which suggested it almost understood the concept, although could never put it together. Here’s a representative selection from Dall-E:
… and from Stable Diffusion, which as expected was further off:
Round 7 AI vs. Stock
Midjourney actually got it, and if these aren’t usable, they’re awfully close. As with the last round, these would certainly be usable if you needed to communicate the concept of a colored SEM image of carcinoma cells more than you needed accurate imagery of them.
iStock didn’t have any actual SEM images of carcinomas which I could find within the time limit, and Midjourney seems to do just as good of a job as the best illustrations I found there:
Getty Images did have some real SEM images, but the ones of which I found were credited to Science Photo Library and their selection was absolutely dwarfed by SPL’s collection, which again had pages and pages of images of many different cancer cell types:
Here’s where this gets difficult. On one hand, we have images from Midjourney which would take the place of an illustration and which cost me less than ten cents to create. On the other hand, we have actual SEM images from Science Photo Library that are absolutely incredible, not to mention real, but depending on how you want to use them, would cost somewhere in the $200 – $2000 range per photo.
To figure out who wins this round, I need to get back to the original premise: Can AI replace stock in life science marketing? These images are every bit as usable as the items from iStock. Are they as good as the images from SPL? No, absolutely not. But are marketers always going to want to spend hundreds of dollars for a single stock photo? No, absolutely not. There are times when it will be worth it, but many times it won’t be. That said, I think I have to call this round a tie.
AI 1, Stock 4, Tie 2
Round 8: A ribbon diagram of a large protein showing quaternary structure.
This is something that stock photo sites should have in droves, but we’ll find out. To be honest, for things like this I personally search for images with friendly licensing requirements on Wikimedia Commons, which in this case gives ample options. But that’s outside the scope of the experiment so on to round 8!
Round 8 AI Fails
I honestly don’t know why I’m still bothering with Stable Diffusion. The closest it got was something which might look like a ribbon diagram if you took a massive dose of hallucinogens, but it mostly output farts.
Dall-E was entirely convinced that all protein structures should have words on them (a universally disastrous yet hilarious decision from any AI image generator) and I could not convince it otherwise:
This has always baffled me, especially as it pertains to DALL-E, since it’s made by OpenAI, the creators of Chat GPT. You would think it would be able to at least output actual words, even if used nonsensically, but apparently we aren’t that far into the future yet.
Round 8 AI vs. Stock
While Midjourney did listen when I told it not to use words and provided the most predictably beautiful output, they are obviously not genuine protein ribbon diagrams. Protein ribbon diagrams are a thing with a very specific look, and this is not it.
I’m not going to bother digging through all the various stock sites because there isn’t a competitive entry from team AI. So here’s a RAF-1 dimer from iStock, and that’s enough for the win.
AI 1, Stock 5, Tie 2. At this point AI can no longer catch up to stock images, but we’re not just interested in what “team” is going to “win” so I’ll keep going.
Round 9: A 3D illustration of plasmacytes releasing antibodies.
I have high hopes for Midjourney on this. But first, another episode of “Stable Diffusion Showing Us Things”!
Round 9 AI Fails
Stable Diffusion is somehow getting worse…
DALL-E was closer, but also took some adventures into randomness.
Midjourney wasn’t initially giving me the results that I hoped for, so to test if it understood the concept of plasmacytes I provided it with only “plasmacytes” as a query. No, it doesn’t know what plasmacytes are.
Round 9 AI vs. Stock
I should just call this Midjourney vs. Stock. Regardless, Midjourney didn’t quite hit the mark. Plasmacytes have an inordinately large number of ways to refer to them (plasma cells, B lymphocytes, B cells, etc.) and it did eventually get the idea, but it never looked quite right and never got the antibodies right, either. It did get the concept of a cell releasing something, but those things look nothing like antibodies.
I found some options on iStock and Science Photo Library (shown below, respectively) almost immediately, and the SPL option is reasonably priced if you don’t need it in extremely high resolution, so my call for Midjourney has not panned out.
Stock sites get this round. AI 1, Stock 6, Tie 2.
Round 10: An illustration of DNA methylation.
This is fairly specific, so I don’t have high hopes for AI here. The main question in my mind is whether stock sites will have illustrations of methylation specifically. Let’s find out!
Round 10 AI Fails
I occasionally feel like I have to fight with Midjourney to not be so artistic all the time, but adding things like “realistic looking” or “scientific illustration of” didn’t exactly help.
Midjourney also really wanted DNA to be a triple helix. Or maybe a 2.5-helix?
I set the bar extremely low for Stable Diffusion and just tried to get it to draw me DNA. Doesn’t matter what style, doesn’t need anything fancy, just plain old DNA. It almost did! Once. (Top left below.) But in the process it also created a bunch of abstract mayhem (bottom row below).
With anything involving “methylation” in the query, DALL-E did that thing where it tries to replace accurate representation with what it thinks are words. I therefore tried to just give it visual instructions, but that proved far too complex.
Round 10 AI vs. Stock
I have to admit, I did not think that it was going to be this hard to get reasonably accurate representations of regular DNA out of Midjourney. It did produce some, but not many, and the best looked like it was made by Jacob the Jeweler. If methyl groups look like rhinestones, 10/10. Dall-E did produce some things that look like DNA stock images circa 2010. All of these have the correct helix orientation as well: right handed. That was a must.
iStock, Getty Images, and Science Photo Library all had multiple options for images to represent methylation. Here are one from each, shown in the aforementioned order:
The point again goes to stock sites.
Final Score: AI 1, Stock 7, Tie 2.
Conclusion / Closing Thoughts
Much like generative text AI, generative image AI shows a lot of promise, but doesn’t yet have the specificity and accuracy needed to be broadly useful. It has a way to go before it can reliably replace stock photos and illustrations of laboratory and life science concepts for marketing purposes. However, for concepts which are fairly broad or in cases where getting the idea across is sufficient, AI can sometimes act as a replacement for basic stock imagery. As for me, if I get a good feeling that AI could do the job and I’m not enthusiastic about the images I’m finding from lower-cost stock sites, I’ll most likely give Midjourney a go. Sixty dollars a month gets us functionally infinite attempts, so the value here is pretty good. If we get a handful of stock images out of it each month, that’s fine – and there’s some from this experiment we’ll certainly be keeping on hand!
I would not be particularly comfortable about the future if I was a stock image site, but especially for higher-quality or specialized / more specific images, AI has a long ways to go before it can replace them.
Google Ads Auto-Applied Recommendations Are Terrible
Carlton Hoyt
Unfortunately, Google has attempted to make them ubiquitous.
Google Ads has been rapidly expanding their use of auto-applied recommendations recently, to the point where it briefly became my least favorite thing until I turned almost all auto-apply recommendations off for all the Google Ads accounts which we manage.
Google Ads has a long history of thinking it’s smarter than you and failing. Left unchecked, its “optimization” strategies have the potential to drain your advertising budgets and destroy your advertising ROI. Many users of Google Ads’ product ads should be familiar with this. Product ads don’t allow you to set targeting, and instead Google chooses the targeting based on the content on the product page. That, by itself, is fine. The problem is when Google tries to maximize its ROI and looks to expand the targeting contextually. To give a practical example of this, we were managing an account advertising rotary evaporators. Rotary evaporators are very commonly used in the cannabis industry, so sometimes people would search for rotary evaporator related terms along with cannabis terms. Google “learned” that cannabis-related terms were relevant to rotary evaporators: a downward spiral which eventually led to Google showing this account’s product ads for searches such as “expensive bongs.” Most people looking for expensive bongs probably saw a rotary evaporator, didn’t know what it was but did see it was expensive, and clicked on it out of curiosity. Google took that cue as rotary evaporators being relevant for searches for “expensive bongs” and then continued to expand outwards from there. The end result was us having to continuously play negative keyword whack-a-mole to try to exclude all the increasingly irrelevant terms that Google thought were relevant to rotary evaporators because the ads were still getting clicks. Over time, this devolved into Google expanding the rotary evaporator product ads to searches for – and this is not a joke – “crack pipes”.
The moral of that story, which is not about auto-applied recommendations, is that Google does not understand complex products and services such as those in the life sciences. It likewise does not understand the complexities and nuances of individual life science businesses. It paints in broad strokes, because broad strokes are easier to code, the managers don’t care because their changes make Google money, and considering Google has something of a monopoly it has very little incentive to improve its services because almost no one is going to pull their advertising dollars from the company which has about 90% of search volume excluding China. Having had some time to see the changes which Google’s auto-apply recommendations make, you can see the implicit assumptions which got built in. Google either thinks you are selling something like pizza or legal services and largely have no clue what you’re doing, or that you have a highly developed marketing program with holistic, integrated analytics.
As an example of the damage that Google’s auto-applied recommendations can do, take a CRO we are working with. Like many CROs, they offer services across a number of different indications. They have different ad groups for different indications. After Google had auto-applied some recommendations, some of which were bidding-related, we ended up with ad groups which had over 100x difference in cost per click. In an ad group with highly specific and targeted keywords, there is no reasonable argument for how Google could possibly optimize in a way which, in the process of optimizing for conversions, it decided one ad group should have a CPC more than 100x that of another. The optimizations did not lead to more conversions, either.
Google’s “AI” ad account optimizer further decided to optimize a display ad campaign for the same client by changing bidding from manual CPC to optimizing for conversions. The campaign went from getting about 1800 clicks / week at a cost of about $30, to getting 96 clicks per week at a cost of $46. CPC went from $0.02 to $0.48! No wonder they wanted to change the bidding; they showed the ads 70x less (CTR was not materially different before / after Google’s auto-applied recommendations) and charged 24x more. Note that the targeting did not change. What Google was optimizing for was their own revenue per impression! It’s the same thing they’re doing when they decide to show rotary evaporator product ads on searches for crack pipes.
Furthermore, Google’s optimizations to the ads themselves amount to horribly generic guesswork. A common optimization is to simply include the name of the ad group or terms from pieces of the destination URL in ad copy. GPT-3 would be horrified at the illiteracy of Google Ads’ optimization “AI”.
A Select Few Auto-Apply Recommendations Are Worth Leaving On
Google has a total of 23 recommendation types. Of those, I always leave on:
- Use optimized ad rotation. There is very little opportunity for this to cause harm, and it addresses a point difficult to determine on your own: what ads will work best at what time. Just let Google figure this out. There isn’t any potential for misaligned incentives here.
- Expand your reach with Google search partners. I always have this on anyway. It’s just more traffic. Unless you’re particularly concerned about the quality of traffic from sites which aren’t google.com, there’s no reason to turn this off.
- Upgrade your conversion tracking. This allows for more nuanced conversion attribution, and is generally a good idea.
A whole 3/24. Some others are situationally useful, however:
- Add responsive search ads can be useful if you’re having problems with quality score and your ad relevance is stated as being “below average”. This will, generally, allow Google to generate new ad copy that it thinks is relevant. Be warned, Google is very bad at generating ad copy. It will frequently keyword spam without regard to context, but at least you’ll see what it wants to you to do to generate more “relevant” ads. Note that I suggest this over “improve your responsive search ads” such that Google doesn’t destroy the existing ad copy which you may have spent time and effort creating.
- Remove redundant keywords / remove non-serving keywords. Google says that these options will make your account easier to manage, and that is generally true. I usually have these off because if I have a redundant keyword it is usually for a good reason and non-serving keywords may become serving keywords occasionally if volume improves for a period of time, but if your goal is simplicity over deeper data and capturing every possible impression, then leave these on.
That’s all. I would recommend leaving the other 18 off at all times. Unless you are truly desperate and at a complete loss for ways to grow your traffic, you should never allow Google to expand your targeting. That lesson has been repeatedly learned with Product Ads over the past decade plus. Furthermore, do not let Google change your bidding. Your bidding methodology is likely a very intentional decision based on the nature of your sales cycle and your marketing and analytics infrastructure. This is not a situation where best practices are broadly applicable, but best practices are exactly what Google will try to enforce.
If you really don’t want to be bothered at all, just turn them all off. You won’t be missing much, and you’re probably saving yourself some headaches down the line. From our experience thus far, it seems that the ability of Google Ads’ optimization AI to help optimize Google Ads campaigns for life sciences companies is far lesser than its ability to create mayhem.
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Most great life science inventions come straight from the bench. That also means that most great life science inventions come from career scientists, who are most often inexperienced in commercialization. There’s a host of things that you should think about before rushing to commercialize and many scientists turned first-time inventors often neglect one or more such issues. We’ll go over a few commonly skipped considerations so when you have the next great idea, you can properly vet it before you run to your patent attorney.
While less common in highly technical industries such as life science tools and services, it’s not wholly uncommon for entrepreneurs to “boot-strap” their start-up companies (try to start without external funding and race to become profitable before they run out of cash). Yet more common, even with outside investment, is under-capitalization. In either case, when small companies start to become low on cash, one of the first corners that often gets cut is marketing. Entrepreneurs sometimes think that word-of-mouth or referrals will be a sufficient marketing tactic to grow their business, but that is very rarely the case for life science products and services. Why is this the case? Customer interaction patterns. To illustrate my point, let me give an example of when referral-based marketing can be effective.
For companies, success in life science product development does not mean completed development of a single product, or even successful commercialization of a product. Likewise, triage of one product development project does not equal failure. Successful product development lies in product development operations which best contribute to the success of the company. For any life science product development project, or for product development operations as a whole, projects must be evaluated for four key factors: value, strategy, balance, and resource availability.
The U.S. Dollar Index (5-year chart)
The life sciences are, almost by definition in being a science, a highly technical field. Most life science products, and certainly life science services, are of a similarly highly technical nature. At the same time, experiments are precious and expensive. In this environment, scientists want to be sure that the products and services they purchase will provide high-quality results, and they are often highly skeptical customers. For many small life science companies which may not have the strong branding or widely adopted products or larger companies, such skepticism can be especially acute. Making customers comfortable enough with your company and products to shake this skepticism can pose a challenge, but there are strategies that small life science companies can leverage to help preempt it. Among the best strategies is to project an image of being an expert in the relevant scientific areas. This requires two things: actually having expert knowledge and understanding, and successfully projecting that knowledge.
Great ideas are precious things. They are the fuel driving innovation, the sustenance of progress, the energy that powers success. Not all great ideas are so great in practice, however. In the life sciences, as in all industries, ideas that are put into action need to be periodically re-evaluated to make sure they are working out to be as good as we thought they were. If they are not, then we would be best off scrapping them and focusing our energy and resources on something else … but life science companies seem to have a very hard time doing so, and this inability is to their detriment.