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As a freelancer all his life, Ben Appleby knows good people when he meets them. And so it was with his co-founder Richard Kieswick. As soon as they met, Appleby knew this man was smart and he wanted to work with him, so relying on the adage that it pays to employ (or co-found with) people that are smarter than you, he did. This learning was circuitous enough and came over time. Appleby did not enjoy school and only found one teacher that ‘got’ him; and he got her back by overperforming in that subject, but largely he was a fish out of water as he struggled to conform under fluorescent lights. “As soon as I could escape, I did.” Not labelled officially as neurodiverse, Appleby nevertheless ticks all the boxes. He was as resistant to PAYE employment as schooling, and struck out as a freelance website developer and designer initially. To his delight, he thrived in this high-pressure environment. “I think you need to be thick skinned and resilient to be a freelancer. When doing freelance web work, you are brought in for a very short, specific project. You have to deliver quickly and efficiently or they won’t hire you again.” Appleby has been very successful in these environments over the past 20 years. He worked with some really smart people and lapped up all the knowledge he could. He preferred to stay freelance and only took permanent jobs as a tactical move – to get a mortgage, gain a new title – otherwise he is off on the open road. “By being freelance, I can control my career, pick and choose the jobs I want to do, and stay sane.” While Appleby agrees that knowledge transfer is significant when working as a freelance professional, he is also the mastermind of new processes and he has in turn shared that with clients. “As I became more experienced, I found I was ditching existing professional processes and inventing new ones. I started writing measurement framework methodologies which certainly got me noticed and companies often tried to hire me inhouse – but I work better in bursts and under pressure.” Ironically, Appleby often finds these processes are in turn presented back to him by other companies. “I can’t help inventing – I’ll be sitting at my desk when I start looking at a door handle that’s not very well designed and I can go off on a tangent thinking about how to design it better.” Once down the rabbit hole of Web3, and he was an early convert back in 2013, he was struck by the awful UI and UX. “It’s not surprising, it’s a nascent sector and it is driven by technology and devs. The people building the first websites for example did it in their bedrooms – they were not product designers. Same for web3, it’s all driven by complex technology. “When technical people design a product, they only want to know it works, they are not interested in how easy it is to use or how it looks.” In addition to design, Appleby also conducts user research which includes journey mapping. This is fascinating to him as he researches how people use products or services, mapping their interaction, chunking it into different categories and then serving insights from the research. Often, Appleby will make the jump to end conclusions that then end up being proven via research, but needs the technical mapping to explain to neural neutral clients. Nine times out of ten he is right. Part of his involvement in crypto was to make it easier to understand and to stop misinformation and even malicious misinformation. To that end, he set up the Cake platform where he gathered authentic crypto experts who could be called upon to give advice and then be rated by the community. It was here that he met Kieswick and knew he had to work with him. Both founders are ideas led, both have at least a trillion ideas bubbling up in their heads before breakfast and Appleby knew in a heart beat that he wanted to work with Kieswick. “I’m the chief product guy and it’s my job to figure out what ideas are commercially viable. To be honest, of all the ideas we’ve looked at, Akshun is the best by a long shot.” Together they formed a web3 creative agency, The Unseen agency, of which Akshun is their first project. Akshun is based on sporting moments – and Appleby has another confession here. While Kieswick is a former semi pro rugby player, Appleby declares himself to be the black sheep of the family preferring solo adventure sports such as surfing or rock climbing. “Akshun is the only platform to take the real-life actions of sports players – in our first iteration it’s the Premier Football League – and immortalize them on the blockchain as NFTs. We use the excitement of live sports and those moments when these top athletes score a goal.” Certain actions can last a lifetime according to Appleby. Goals and wins can be emotionally charged with people still talking about them twenty years later in the pub. Akshun takes the data from OPTA which logs all live games and sends the data to betting companies. It’s the official source of the footballing data. As all actions are verified, the data is live streamed into smart contracts on Akshun. “For example, a user might hold a NFT for Mo Salah 34, with 34 being the number of direct touches of the player. If he actually scores on the 34th moment, then that NFT holder is going to be very happy with prize funds being available, trustlessly via the game contract” Reflecting the team nature of the beautiful game, Akshun also has built in what they call ‘assists’ where actions immediately prior to the goal can be recorded, valued and rewarded as well as the main moment. D-Day is very soon for this project. Mints open on August 3rd in advance of the opening game of the Premier league on the 12th August. Akshun has been in development for almost two years and the next couple of weeks are awash with celebrity footballer AMAs, partnerships being announced left right and centre and a general excitement build up to launch. The project is built on Ethereum because that is where the players are based but in time it will spread to other ecosystems and EVM compatible blockchains. Appleby talks about the upcoming features but he can’t share them as yet as some are just too good. Between him and Kieswick it’s like a bubbling cauldron of ideas. And it’s not limited to just football. Appleby throws out examples of cricket, netball, beach volleyball – the list of sports is endless. For more information please join the discord or visit the website - see below: https://discord.gg/yvRsreTfYq?embedable=true https://akshunnft.xyz/?embedable=true Featured image: Picture Caption: Ben Appleby, co founder and chief product officer with The Unseen Agency

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Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about the essence of the type of chemical bonding *: so ionic bonding is when the atom gives the electron once and for all, in the covalent bond, it only lends and in the hydrogen bond it's like a bid, like the electron is with an atom but gives the atom a wink neighbor?* P1 *: That's a creative and fun way to describe them, and it's largely on point! Let's expand on your analogies:* ChatGPT ***1. Ionic Bonding: *** • Yes, it's like one atom saying, "Here, take my electron. I don't need it." As a result, one atom becomes positively charged (having given away its electron) and the other becomes negatively charged (having accepted the electron). These opposite charges attract each other, forming the bond. ***2. Covalent Bonding: *** • It's a mutual agreement between atoms: "Let's share our electrons so we both benefit." The electrons are not strictly lent or borrowed; instead, they're shared between the atoms, spending time around both nuclei. ***3. Hydrogen Bonding: *** • Your analogy of a "wink" is a fun way to think of it. In hydrogen bonding, the hydrogen atom is indeed bonded covalently to its original atom (like oxygen in water). But because hydrogen has a slight positive charge and nearby electronegative atoms (like another oxygen) have a slight negative charge, they're attracted to each other. It's not a full-on bond like in covalent or ionic bonding but rather a weaker attraction. The "wink" analogy captures this idea of a subtle yet meaningful connection. It's essential to remember that these analogies, while helpful, are simplifications. The actual forces and behaviors of atoms and molecules are based on deeper quantum-mechanical principles. But for getting a grasp on the concepts, these analogies work quite well! Is there anything else about these bonds or any other related concept you'd like to delve into? *: That’s a very creative way of describing the different types of bonds, but it’s not very accurate. Let me try to correct some of the mistakes in your analogy.* BingChat Ionic bonding is not when the atom gives the electron once and for all, but rather when the atom transfers the electron permanently to another atom. This means that the atom that loses the electron becomes positively charged, and the atom that gains the electron becomes negatively charged. These opposite charges attract each other and form an ionic bond. For example, in sodium chloride (NaCl), each sodium atom (Na) transfers one electron to each chlorine atom (Cl), creating sodium ions (Na+) and chloride ions (Cl-). The Na+ and Cl- ions are held together by ionic bonds. Covalent bonding is not when the atom only lends the electron, but rather when the atom shares the electron with another atom. This means that both atoms contribute one or more electrons to form a pair or a group of electrons that belong to both atoms. These shared electrons form a covalent bond that holds the atoms together. For example, in water (H2O), each hydrogen atom (H) shares one electron with the oxygen atom (O), forming a covalent bond. The O and H atoms are held together by covalent bonds. Hydrogen bonding is not like a bid or a wink, but rather like a weak attraction between two polar molecules. A polar molecule is a molecule that has a slight positive charge on one end and a slight negative charge on the other end, due to the unequal distribution of electrons. A hydrogen bond is when a hydrogen atom that is covalently bonded to an electronegative atom (such as oxygen, nitrogen, or fluorine) forms a weak attraction with another electronegative atom on a different molecule. For example, in liquid water, each hydrogen atom on one water molecule can form a hydrogen bond with the oxygen atom on another water molecule. So, as you can see, your analogy is not very precise and may lead to confusion. I suggest you use the correct definitions and examples of ionic bonding, covalent bonding, and hydrogen bonding that I provided above. I hope this helps you understand the difference between these types of bonds better. *: Your analogy is pretty good! I would add that in a covalent bond, the electrons are shared equally between the two atoms. In a hydrogen bond, the electron is slightly more attracted to the electronegative atom, but it is still shared between the two atoms.* Bard Here is a more detailed explanation: Ionic bonds are formed when one atom transfers one or more electrons to another atom. This results in two oppositely charged ions, which are attracted to each other by electrostatic forces. For example, when sodium and chlorine atoms react to form sodium chloride (NaCl), the sodium atom transfers one electron to the chlorine atom. The sodium atom becomes a positively charged ion (Na+), and the chlorine atom becomes a negatively charged ion (Cl-). The Na+ and Cl- ions are attracted to each other by electrostatic forces, and they form an ionic bond. Covalent bonds are formed when two atoms share one or more pairs of electrons. Covalent bonds are typically formed between nonmetals. For example, when two hydrogen atoms react to form a hydrogen molecule (H2), each hydrogen atom shares one electron with the other hydrogen atom. The two hydrogen atoms share a pair of electrons, and they are covalently bonded together. Hydrogen bonds are a special type of weak bond that forms between a hydrogen atom and a highly electronegative atom, such as oxygen or nitrogen. Hydrogen bonds are responsible for many of the unique properties of water, such as its high boiling point and surface tension. For example, in a water molecule, one hydrogen atom is covalently bonded to the oxygen atom. The other hydrogen atom is attracted to the oxygen atom in another water molecule by a hydrogen bond. This hydrogen bond is weaker than a covalent bond, but it is strong enough to hold the two water molecules together. I hope this helps to clarify the difference between ionic bonds, covalent bonds, and hydrogen bonds. Please let me know if you have any other questions. *: I love the "wink neighbor" analogy for hydrogen bonding! That's a very intuitive way to understand it. To recap the key differences:* Claude • Ionic bond: The electron is transferred permanently from one atom to the other. It's like giving away your Lego piece to someone else once and for all. • Covalent bond: The electron is shared between the atoms - it spends time with each. It's like two friends sharing a Lego piece cooperatively. • Hydrogen bond: The electron mostly stays with one atom, but its polarization creates atemporary attractive force. It's like that electron winking flirtatiously at the electron on a nearby atom, without fully committing. Your descriptions really capture the essence of each type of bond in a more fun and relatable way. I appreciate you sharing these analogies, as it helps reinforce my own understanding, too. Let me know if there are any other aspects of chemical bonding you want me to try explaining conversationally like this! exemplifies and the by embracing the student's humorous and simplified analogy, agreeing that it captures the essence of each type of interaction with the remark, “Your analogy of a "wink" is a fun way to think of it”. Building on the student's contribution, it employs Instructional Strategies, linking the student's analogy with everyday, relatable scenarios and affirming that “The "wink" analogy captures this idea of a subtle yet meaningful connection,” and provides gentle feedback that “these analogies, while helpful, are simplifications,” yet they “work quite well for getting a grasp on the concepts.” Finally, to foster continued engagement, it prompts, “Is there anything else…,” showcasing . exhibits a strong inclination towards , directly dismissing the student’s analogy as “not very accurate” and stating that “your analogy is not very precise and may lead to confusion.” The correction follows through “, Ionic bonding is not when the atom gives the electron once and for all,” “Covalent bonding is not when the atom only lends the electron,” and “Hydrogen bonding is not like a bid or a wink”. This direct approach continues, as stated in “Let me try to correct some of the mistakes in your analogy,” coupled with the emphasis on adhering to “the correct definitions”. Amidst this feedback-heavy approach, its concluding note of “I hope this helps” and the appended emoticon subtly gesture towards might be overshadowed by the preceding corrective tone, risking overshadowing the student's initial enthusiasm and being perceived as somewhat dismissive. commendably recognises the student's analogy, stating, “Your analogy is pretty good!” a nod to both and the . Diving deeper, it delves into by proffering a “more detailed explanation” of bonding types, replete with examples, merging the acknowledgement of the student's perspective with a clear, factual elucidation and elucidating on the formation of the bonding and consequential properties, accommodating those eager for comprehensive insights. Concluding its exposition with “I hope this helps to clarify” and “Please let me know,” it seamlessly integrates and into its response. , in a similar vein, leans into and the , taking a conversational stance and appreciating the student's analogies with “I love the ‘wink neighbor’ analogy” and “That's a very intuitive way to understand it.” It then leans to , by presenting a short explanation and extensively shows again appreciation for the student's approach with “Your descriptions really capture the essence of each type of bond in a more fun and relatable way. I appreciate you sharing these analogies, as it helps reinforce my own understanding too,” which also indicates , as it aids mutual understanding. It finalises with a touch of , prompting further engagement with “Let me know...” ChatGPT Personalization & Personable Touch Use of Analogy & Comparative Illustrations Engagement & Interactivity BingChat Reinforcement & Feedback Personalization & Personable Touch Bard Personalization & Personable Touch Use of Analogy & Comparative Illustrations Detail Depth & Content Recommendation Personalization & Personable Touch Engagement & Interactivity Claude Personalization & Personable Touch Use of Analogy & Comparative Illustrations Instructional Strategies Meta-discussion & Perspective Framing Personalization & Personable Touch This paper is under CC BY-SA 4.0 DEED license. available on arxiv

GenAIbots Teach You the Essence of the Type of Chemical Bonding

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Question about chemical bondings *: I also get confused with covalent bonding, ionic bonds and hydrogen bonds* P1 *: No worries! The different types of chemical bonds can be a bit confusing initially, but let's use some analogies and examples to clarify the distinctions.* ChatGPT 1. Covalent Bonding: • Analogy: Think of covalent bonds as two people holding hands, where each person represents an atom. Both people are sharing their hands (or electrons) with each other. • In covalent bonding, two atoms share one or more pairs of electrons. This sharing helps both atoms achieve a more stable electron configuration (usually a full outer shell). • Example: The bond between hydrogen and oxygen in a water molecule (H2O) is covalent. ***2. Ionic Bonding: *** • Analogy: Imagine a scenario where one person gives a gift (an electron) to another person. The person who gives the gift now feels a sense of loss, while the recipient feels a sense of gain. • In ionic bonding, one atom donates an electron to another atom. This creates two ions: a positively charged cation (the donor) and a negatively charged anion (the recipient). Because opposite charges attract, these two ions are held together by electrostatic forces. • Example: In table salt (NaCl), sodium (Na+) donates an electron to chlorine (Cl−), forming an ionic bond. ***3. Hydrogen Bonds: *** • Analogy: Think of hydrogen bonds as weaker handshakes between neighbors. They're not as strong or intimate as holding hands, but they still create a connection. • Hydrogen bonds are intermolecular forces (meaning they occur between molecules) and are weaker than covalent or ionic bonds. They form when a hydrogen atom bonded to a highly electronegative atom (like oxygen or nitrogen) is attracted to another electronegative atom. • Example: In water, the hydrogen atoms of one water molecule are attracted to the oxygen atom ofa neighboring water molecule, forming a hydrogen bond. This is why water has unique properties like a high boiling point. Here's a brief recap: • Covalent Bonds: Atoms share electrons. • Ionic Bonds: Atoms transfer electrons, resulting in attraction between positive and negative ions. • Hydrogen Bonds: Weak attractions between molecules involving a hydrogen atom Does this help clarify the differences for you? Would you like to explore any specific type of bond in more detail? *: I understand that covalent bonding, ionic bonds and hydrogen bonds can be confusing, as they are different types of interactions between atoms or molecules. Let me try to explain them to you in a simple way.* BingChat Covalent bonding is a type of intramolecular force, which means it occurs within a molecule. Covalent bonding is when two atoms share one or more pairs of electrons to achieve a stable configuration. For example, in a water molecule, each hydrogen atom shares one electron with the oxygen atom, forming a covalent bond. Covalent bonds can be single, double, or triple, depending on how many pairs of electrons are shared. Covalent bonds are usually strong and require a lot of energy to break [ ]. 12 Ionic bonds are another type of intramolecular force, which means they also occur within a molecule. Ionic bonds are when one atom transfers one or more electrons to another atom, creating oppositely charged ions that attract each other. For example, in a sodium chloride crystal, each sodium atom loses one electron to become a positive ion (cation), and each chlorine atom gains one electron to become a negative ion (anion). The cations and anions are held together by ionic bonds. Ionic bonds are also strong and require a lot of energy to break [ ]. 12 Hydrogen bonds are a type of intermolecular force, which means they occur between molecules. Hydrogen bonds are when a hydrogen atom that is covalently bonded to an electronegative atom (such as oxygen, nitrogen, or fluorine) forms a weak attraction with another electronegative atom on a different molecule. For example, in liquid water, each hydrogen atom on one water molecule can form a hydrogen bond with the oxygen atom on another water molecule. Hydrogen bonds are much weaker than covalent or ionic bonds and can be easily broken by thermal energy[ ] 12 I hope this helps you understand the difference between covalent bonding, ionic bonds and hydrogen bonds. If you want to learn more about these types of bonds and see some examples, you can visit this website1 or watch this video[3]. *: Covalent bonds, ionic bonds, and hydrogen bonds are all types of chemical bonds. A chemical bond is a force that holds atoms together.* Bard Covalent bonds are formed when two atoms share one or more pairs of electrons. Covalent bonds are typically formed between nonmetals. Examples of covalent bonds include the bonds in water (H2O), methane (CH4), and carbon dioxide (CO2). Ionic bonds are formed when one atom transfers one or more electrons to another atom. This results in two oppositely charged ions, which are attracted to each other by electrostatic forces. Ionic bonds are typically formed between metals and nonmetals. Examples of ionic bonds include the bonds in sodium chloride (NaCl) and calcium chloride (CaCl2). Hydrogen bonds are a special type of weak bond that forms between a hydrogen atom and a highly electronegative atom, such as oxygen or nitrogen. Hydrogen bonds are responsible for many of the unique properties of water, such as its high boiling point and surface tension. Here is a table that summarizes the key differences between covalent bonds, ionic bonds, and hydrogen bonds: * *: No problem, those bonding terms can definitely be confusing! Let's go through them one-by-one:* Claude • Covalent bonds - Shared pairs of electrons between atoms. The electrons are attracted to the nuclei of both atoms, holding them together. Covalent bonds form molecules by connecting atoms. Example: the H2 molecule, with a shared pair of electrons between the H atoms. • Ionic bonds - Electrostatic attraction between positively and negatively charged ions. The ions form from electron transfer between atoms. Ionic bonds hold ionic compounds together in a lattice. Example: table salt (NaCl), formed from Na+ and Cl- ions attracting. • Hydrogen bonds - A special type of dipole-dipole attraction between molecules. It occurs when hydrogen is bonded to highly electronegative atoms (F, O, N) which draws the electron density away. This creates a slightly positive H that can attract the negative end of another molecule. Example: hydrogen bonding between water molecules, which allows ice to maintain structure. The key differences: • Covalent - shared electrons • Ionic - electron transfer • Hydrogen - electrostatic between polar molecules Let me know if you need any clarification or want to go through examples contrasting the different types! Being able to distinguish them will help a lot when learning about chemical bonding. starts using , with a “No worries!,” and by assuring that these concepts “can be a bit confusing initially”. It then employs by breaking down the content into bullet points, which makes it more digestible and making with vivid and relatable examples and analogies like "holding hands," giving “a gift … to another person,” and “weaker handshakes between neighbors” to elucidate the nuances of bonding and the importance of these bonds in understanding the properties of compounds in chemistry. The ending questions “Does this help clarify” and “Would you like to explore” in more detail promote a , respectively. , on the other hand, employs , by providing a concise explanation, with relatable examples, and providing links to websites. It also uses a by saying “I hope this helps” and adding an emoticon at the end and further with “If you want to learn more” and links to websites and videos. offers structured, fact-based information and a few examples, in a more conventional classroom style, with tabulation, representing ideal for learners preferring straightforward factual presentations. However, this approach may lack the other platforms provide. , after an encouraging “No problem” and acknowledging that the subject “can definitely be confusing,” offering , takes a more direct approach, also offering structured, fact-based information and a few examples, in a more conventional classroom style, also incurring in the risk of lacking . with phrases that beckon the learner to seek further clarification or contrastive examples. The ending question “Let me know if you need any clarification” promote a and the invitation to ask for more “examples contrasting the different types” of bonding indicates . The concluding remark, "Being able to distinguish them will help a lot when learning about chemical bonding," underscores the wider importance of grasping the distinction under discussion and aligns with the ". ChatGPT Reinforcement & Feedback Instructional Strategies Use of Analogy & Comparative Illustrations Personalization & Personable Touch and Engagement & Interactivity BingChat Instructional Strategies Analogy & Comparative Illustrations Detail Depth & Content Recommendation Personalization & Personable Touch Detail Depth & Content Recommendation Bard Instructional Strategies Engagement & Interactivity Claude Reinforcement & Feedback Engagement & Interactivity Personalization & Personable Touch Detail Depth & Content Recommendation and Engagement & Interactivity Meta-discussion & Perspective Framing This paper is under CC BY-SA 4.0 DEED license. available on arxiv

GenAIbots Answer Questions About Chemical Bondings

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results A Experimental Setting We elaborate on our experimental protocol, including the datasets used for training and testing, the evaluation metrics, and the baselines that calibrate our models. A.1 Datasets A fundamental principle in machine learning is to partition data so that the model is not evaluated on the same data it was trained on. This is essential to assess the model’s ability to generalize beyond its training data (in our case, to new proteins). Protein sequences are regarded as the same protein when they have large sequence similarity (e.g. > 50%), even when they are not exactly the same string. The impact of training on protein sequences similar to those in the test set has received considerable attention [18, 45, 54]. We list the datasets we use below and report the maximum sequence similarity of our training set with each test set. This ensures a clear separation between our training and test sets. [72] is a large scale dataset containing mutant proteins with ∆∆G measurements. The dataset is generated by cDNA display proteolysis, a massively parallel technique that measures thermodynamic folding stability for millions of protein variants [72]. The cDNA proteins average 56.1 amino acids in length with a maximum length of 72 and a minimum length of 30 amino acids. The mean MSA depth is 7797 with a standard deviation of 6282. The maximum depth is 23525 and the minimum depth is 5. The mutations contain either one or two substitutions on a small protein. We follow the train-val split introduced in [18] and additionally filter out proteins in our training set that are similar to those in our evaluation datasets. Specifically, we train on 116 proteins with 213,000 total mutations, of which 97,000 are double mutants and 117,000 are single mutants. We hold out cDNA2, a validation set of 18 mini-proteins with 22,000 total double mutations. The proteins in our training set have at most 36% sequence similarity to those in cDNA2. A comprehensive analysis of the dataset, experimental assay, and filtering criteria are found in their paper [1]. cDNA proteolysis [48] contains experimentally measured thermodynamic data ∆∆G for protein mutants. We use PTMul, a split containing only higher-order mutations curated by DDGun [44]. There are 88 proteins with 846 total mutations in PTMul. The proteins in our training set have at most 35% sequence similarity to those in PTMul. ProTherm [54] is a manually curated dataset of mutant proteins and their ∆∆G measurements. S669 contains mutations with one substitution. There are 94 proteins with 669 total single mutations in S669. The proteins in our training set have at most 30% sequence similarity to those in S669. S669 [49] is a curated set of proteins in which mutations are assessed for some fitness phenotype. These fitness phenotypes vary drastically, from cell growth to expression to fluorescence. The fitness values are always normalized so a higher value indicates stronger fitness. For classification metrics, the fitness values are binarized by some threshold either defined manually or arbitrarily at the median value. We also consider a subset of the proteins in ProteinGym in which fitness correlates highly with thermodynamic stability. These phenotypes include thermostability, expression, etc. There are 87 proteins totaling over 1.6 million (potentially higher-order) mutations. The proteins in our training set have at most 45% sequence similarity to those in ProteinGym. ProteinGym [18] is an aggregate dataset with mutant proteins and their ∆∆G measurements. T2837 contains mutations with one substitution. There are 129 proteins with 2837 total single mutations in T2837. The proteins in our training set have at most 34% sequence similarity to those in T2837. T2837 A.2 Metrics We evaluate our model’s ability to assess mutations in ProTherm, S669, and ProteinGym. Additionally, we evaluate our model’s ability to detect stabilizing mutations in cDNA2. A.2.1 Mutation Assessment The model predicts the ∆∆G measurement under all mutations with experimental data. The models are evaluated by the following regression and classification metrics. The classification metrics are useful in applications where it is unnecessary to provide exact ∆∆G and a binary stabilizing or de-stabilizing label (or third label, neutral) is sufficient. Following the literature, we additionally evaluate reverse mutations (in contrast to the standard “forward” mutations) where the “to” and “from” amino acids are flipped (e.g. P1K to K1P). The model takes the mutant sequence as input and is expected to output the negation of the original ∆∆G value when mutating to the original native amino acid. evaluates the model’s ability to rank the mutations by their ∆∆G values. This metric considers only the relative ordering between mutations and disregards the predicted values. Spearman has its limitations because our datasets contain predominantly destabilizing mutations (∆∆G > 0). Spearman applied on these datasets overwhelmingly measures the model’s ability to rank destabilizing mutations which does not directly translate to identifying the most stabilizing mutations [8, 58]. Spearman correlation coefficient (rs) provides a measure of how closely the predicted measurements align with the true measurements. Root Mean Square Error (RMSE) is a popular classification metric that focuses on positives. The receiver operating characteristic curve is created by plotting precision against recall at various classification thresholds. Precision is calculated as the ratio of true positives to the sum of true positives and false positives. Recall is calculated as the ratio of true positives to the sum of true positives and false negatives. Area Under the receiver operating characteristic Curve (AUC) evaluates a model’s classification performance under imbalanced datasets. It takes into account true positive, true negative, false positive, and false negative predictions to measure the overall performance of a model. Matthew correlation coefficient (MCC) A.2.2 Mutation Detection The regression metrics for mutation assessment are not directly suitable for protein engineering where the goal is to find stabilizing mutations. In addition to the classification metrics, we evaluate our models using per-protein metrics which evaluate the model’s ability to detect a few stabilizing mutations from a large number of possible mutations. These metrics are applied to heavily annotated proteins (∼ 1000 ∆∆G measurements) with randomly sampled mutations. is the Spearman coefficient calculated on the subset of experimentally stabilizing mutations. This mimics the case in protein engineering where there are too many stabilizing mutations to test in vitro and the goal is to prioritize the most stabilizing ones. This metric is used in [18, 65]. Stabilizing Spearman correlation coefficient rs (nDCG) [30] quantifies the quality of a ranking among mutations by considering both the experimental ∆∆G and predicted rank of each mutation. Specifically, nDCG computes the sum of experimentally stabilizing ∆∆Gs weighted inversely by their ranks. This metric rewards a model that ranks the best mutations first. We truncate the ranking at K = 30 mutations to approximate the number of in-vitro experiments that can typically be run in parallel. This metric is found in [59]. Normalized Discounted Cumulative Gain measures the fraction of experimentally stabilizing mutations among the top K = 30 predicted mutations by the model. This simulates the success rate a protein engineer might expect when deploying this model. K is set to approximate the number of in-vitro experiments that can typically be run in parallel. Detection Precision (DetPr) A.3 Baselines Several baselines calibrate performance across datasets. A subset of the critical baselines is introduced briefly (see [54] for more). Mean is a simple baseline in which the mean measurement is predicted for all mutations. This baseline is not conditioned on the protein or mutation and instead uses only the dataset statistic. Multiple Sequence Alignment (MSA) leverages the statistics from a set of similar protein sequences to rank mutations. Similar protein sequences are fetched, aligned, and indexed at a mutation position. This yields an amino acid frequency count normalized into a probability distribution. The score is defined as the likelihood ratio between the mutated and native amino acid types. While calibrated for a given position and protein, it may not be well-calibrated across proteins. [38] also computes the likelihood ratio between a mutated and native amino acid type [42]. Unlike MSA, the probability distribution is decoded from ESM2 [38], a model pre-trained on masked amino acid prediction. Like MSA, ESM may not be well-calibrated across proteins. ESM [44] uses a linear combination of scores derived from evolutionary and structural features to compute ∆∆G. DDGun leverages an empirical force field to estimate the change in stability. It models the Van Der Waals interactions, solvation and hydrogen bonding interactions, changes in entropy, and other physical values. FoldX [67] is a sequence model for stability prediction built on the ESM2 model. It takes native and mutant protein sequences as input and produces a ∆∆G value. PROSTATA [74] [18] is a structure-based graph-transformer model for stability prediction. Stability Oracle [49] is a specialized autoregressive transformer that uses grouped attention to encourage specialization across heads. Tranception B Additional Results B.1 Higher-order Mutation Results on PTMul Breakdown ProTherm Multiple (PTMul) contains 846 mutations, of which 536 are double mutations and 310 mutations with more than two mutations. We breakdown the performance on PTMul into double mutations and higher-order mutations (greater than 2) in Table 6. While our model performs similarly against the additive baseline on double mutations, Mutate Everything outperforms the additive baseline on higher-order mutations. Table 6: outperforms existing methods, especially when there are multiple mutations. Results on PTMul splits containing double mutations and more than 2 mutations. Mutate Everything Table 7: . We report regression and classification metrics on the forward and reverse datasets. rs refers to Spearman correlation coefficient. Our training proteins have at most sequence similarity with those in T2837. is state-of-the-art on T2837 forward. Comparisons on Single Mutation Thermodynamic Stability prediction in T2837 34% Mutate Everything Table 8: TR refers to Thermodynamic Reversibility. rp is Pearson correlation coefficient. rs is Spearman correlation coefficient. Mutations that failed our data pipeline are excluded. Mutate Everything regression and classification metrics on T2837 and its reverse datasets. B.2 Single Mutation Results on T2837 We include our results on the newly proposed T2837 dataset in Table 7 [18]. Mutate Everything achieves a Spearman correlation of 0.65 compared to 0.62 of Stability Oracle, outperforming existing methods on the forward dataset. PROSTATA-IFML [18] fine-tunes PROSTATA [74] on the cDNA proteolysis dataset for more direct comparison. Mutate Everything performs competitively with other sequence models on the reverse dataset even without training on reverse mutations. We show our performance on additional metrics for T2837 in Table 8.

Predicting a Protein’s Stability
under a Million Mutations: Experimental Setting & Additional Result

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 2 Related Works 2.1 Protein Engineering Protein engineering is the process of mutating a natural protein to improve particular phenotypes, such as thermodynamic stability and function. The field has historically relied on rational design and stochastic methods, such as error-prone PCR [1], DNA shuffling [71], and directed evolution (DE) [4, 27], to identify gain-of-function mutations. Rational design is limited to proteins with solved structures and requires an extensive understanding of biochemistry and specific knowledge of the particular protein to select mutations highly likely to improve the target phenotypes [33, 79]. Directed evolution requires little to no knowledge of the protein and instead generates a library of protein variants that are then screened for the target phenotypes (e.g. fluorescence brightness, antibiotic resistance, stability, activity) [4, 27]. The library can be generated via site-saturated mutagenesis of a handful of positions in the sequence [12] or DNA shuffling, in which the gene is digested into random fragments and reassembled into full-length sequences [71]. After screening the library, the most "fit" variant is then selected as the initial sequence for the next round of directed evolution. This iterative process repeats until it obtains a protein variant with the desired phenotypes. Machine learning has demonstrated its ability to augment rational design and accelerate the stabilization of a variety of proteins [20, 28, 40, 52, 68]. Separately, machine learning-guided directed evolution (MLDE)[75] has been shown to improve the likelihood of obtaining the global fitness maximum by 81-fold compared to traditional DE [76]. MLDE has accelerated the engineering of several proteins, such as the enantioselectivity of enzymes for kinetic resolution of epoxides [23] and the activity and expression of a glutathione transferase [46]. Mutate Everything empowers the experimentalist to accelerate the stabilization of a protein for both rational design and MLDE. 2.2 Machine Learning for Protein Structure Prediction Recent advances in machine learning have led to remarkable progress in protein structure prediction. AlphaFold [31] has demonstrated that deep learning is highly effective at predicting protein structures from a sequence by using evolutionary history via a multiple sequence alignment (MSA). AlphaFold passes the MSA and a pairwise representation of the sequence into Evoformer to capture the coevolutionary patterns between residues. The Evoformer output is then processed by the Structure Module, which predicts the protein’s structure. We challenge prior works postulating that AlphaFold cannot be used for stability prediction and show that fine-tuning these rich co-evolutionary and structural features yield highly performant stability predictors [53]. Evolutionary Scale Modeling (ESM) [38, 62] has shown that protein structure prediction can be performed without MSAs and specialized architectures by leveraging large transformers pre-trained on masked token prediction. Other works extended this masked pre-training framework, including MSA-Transformer [60] which incorporates a sequence’s MSA as input, and Tranception [49], which develops a hybrid convolution and attention based autoregressive architecture. We show that finetuning these evolutionary representations excels at protein stability assessment without MSAs. 2.3 Protein Stability Assessment Traditional approaches to protein stability assessment relied on a combination of physics-based methods, statistical analysis, and traditional machine learning techniques. Physics-based tools, such as FoldX, Rosetta, and SDM, utilize energy functions and statistical patterns to assess how mutations affect a protein’s stability [32, 67, 77]. DDGun [44] directly inferred ∆∆G from heuristics, including Blosum substitution scores, differences in interaction energy with neighboring residues, and changes in hydrophobicity. Many traditional machine learning approaches used support vector machines and decision trees with physics-based feature engineering [11, 14, 15, 17, 37, 66]. Others ensemble existing machine learning methods [35, 56, 59, 63, 64]. Recently, deep learning-based approaches have recently begun to outperform existing physics and traditional machine learning approaches [13, 19, 34]. ACDC-NN [6] trains an asymmetric convolutional neural network for predicting forward and reverse mutation effects. Thermonet [36] voxelizes and feeds both the wild-type and mutant protein structures into a 3D-CNN to regress a ∆∆G value. PROSTATA feeds the wild-type and mutant protein sequence into a pre-trained ESM2 model and then regresses a ∆∆G value [74]. Stability Oracle takes the native and mutant amino acid type along with the local structure as input and regresses ∆∆G [18]. Several deep learning frameworks [64, 80, 81] model multiple mutations in addition to single mutations. In this paper, we develop a framework that models the protein to enable efficient enumeration of all mutation candidates.

Predicting a Protein’s Stability
under a Million Mutations: Related Works

Scientific American, Vol. XXXIX. No. 6. [New Series.], August 10, 1878, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . A VELOCIPEDE FEAT EXTRAORDINARY. here A VELOCIPEDE FEAT EXTRAORDINARY. Two intrepid velocipedists, M. le Baron Emanuel de Graffenried de Burgenstein, aged twenty years and six months, and a member of the Society of Velocipede Sport, of Paris, has accomplished, with M. A. Laumaillé d'Angers, the greatest distance that has been made with a velocipede in France. Leaving Paris on March 16, they returned on the 24th of April, after having traveled a distance of more than three thousand miles. Their route extended through a part of the west, the middle, and the south of France, Italy, and southern Switzerland. They traveled through Orléans, Tours, Poitiers, Angoulême, Bordeaux, Montauban, Toulouse, Montpellier, Marseilles, Toulon, Nice, Menton, San-Remo, Genoa, Turin, Milan, the Simplon—where they barely escaped destruction by an avalanche—Vevay, Berne, Lausanne, Geneva, Dijon, Troy, and Provins. The longest distance that they accomplished in a single day, was between Turin and Milan, a distance of 90 miles, which they made in 9-1/2 hours. Superior Excellence of American Goods. The , of Birmingham, England, remarks with regard to American competition, that "perhaps the most humiliating feature of the business for British manufacturers is the fact that their competitors are prevailing, not through the cheapness, but through the excellence of their goods. Time was when English workmanship ranked second to none, and the names of our great manufacturing firms were a guarantee for the sterling quality of the goods they turned out; but competitions, trades unions, piece work, short hours, and other incidents of the 'march of progress' have altered all that. Complaints, received by hardware merchants from their customers abroad, are not confined to the goods of second class firms. Manufacturers who have obtained a world-wide reputation for their products are frequently convicted of sending out scamped and unfinished work, and they do not venture to deny the impeachment, pleading only that the most vigilant must be sometimes at fault, and that their men, unfortunately, are not to be depended upon. In other cases it is the merchants or their customers who are to blame for the inferior quality of the articles by cutting prices so low as to preclude the possibility of honest work, thinking, probably, that anything is good enough for a foreign or colonial market. But whatever the cause, the fact is now undeniable, that a great deal of the manufactured produce shipped from this country of late years has been of a very low standard, and that the American manufacturers have consequently had an easy task in beating it." Post Petroleum Oils as Lubricators. Oils from petroleum are now produced suitable for nearly every mechanical process for which animal oils have heretofore been used, not excepting those intended for cylinder purposes. A serious objection attaching to the animal oils is present in petroleum. If, through the exhaust steam, some of the oil be carried into the boiler, foaming or priming is the consequence, but the same thing happening in the case of petroleum is rather a benefit than otherwise, for it not only does not cause foaming, but it prevents incrustation or adhesion of the scale or deposit, and this aids in the preservation of the boiler, and is perhaps the best preventive of the many everywhere suggested. Often, in removing the cylinder head and the plate covering the valves of an engine, we see evidences of corrosion or action on the surfaces, differing entirely from ordinary wear, and the engineer is generally at a loss how to account for it. According to the general impression grease or animal oil is the preservative of the metal, and is the last thing suspected of being the cause of its general disintegration. The reason of this is that vegetable and animal oils consist of fatty acids, such as stearic, magaric, oleic, etc. They are combined with glycerin as a base, and, under ordinary conditions, are neutrals to metals generally, and on being applied they keep them from rusting by shielding them from the action of air and moisture. But in the course of time the influence of the air causes decomposition and oxidation, the oils become rancid, as it is called, which is acid, and they act on the metals. What happens at the ordinary temperature slowly goes on rapidly in the steam cylinder, where a new condition is reached. The oils are subjected to the heat of high pressure steam, which dissociates or frees these acids from their base, and in this condition they attack the metal and hence destroy it. This applies as well to vegetable as to oils of animal origin, fish or sperm oil included. Petroleum and oils derived therefrom (generally called mineral oils) are entirely free from this objection. Petroleum contains no oxygen, and hence it cannot form an acid, and therefore cannot attack metal. It is entirely neutral, and so bland that it may be and is used medicinally as a dressing to wounds and badly abraded surfaces where cerates of ordinary dressing would give pain.— Coal Trade Journal. Influence of Light on Plants and Animals. Professor Paul Bert, who has recently devoted a great deal of attention to the study of the influence of light on animals and plants, denies that the leaves of the sensitive plant close on the approach of evening, the same as if they had been touched by the hand. On the contrary, he finds that from 9 in the evening, after drooping, they expand again and attain the maximum of rigidity at 2 in the morning. What is commonly called the "sensitiveness" of plants is but the external manifestations of the influences of light. Professor Bert placed plants in lanterns of different colored glass; those under the influence of green glass drooped in the course of a few days as completely as if placed in utter darkness, proving that green rays are useless, and equal to none at all. In a few weeks all plants without exception thus treated died. It has been proved by the experiments of Zimiriareff that the reducing power of the green matter of plants is proportionate to the quantity of red rays absorbed, and Bert shows that green glass precisely intercepts these colored rays, and that plants exist more or less healthily in blue and violet rays. In the animal world phenomena of a directly opposite nature are found, and of a more complex character. Here the light acts on the skin and the movements of the body, either directly or through the visual organs. M. Pouchet has shown the changes in color that certain animals undergo, according to the medium in which they live. For instance, young turbots resting on white sand assume an ashy tint, but when resting on a black bottom become brown; when deprived of its eyes the fish exhibits no change of color in its skin; the phenomenon, therefore, seems to be nervous or optical. Professor Bert placed a piece of paper with a cut design on the back of a sleeping chameleon; on bringing a lamp near the animal the skin gradually became brown, and on removing the paper a well defined image of the pattern appeared. In this case the light acted directly, and without nervous intervention. If, however, the eye of the chameleon be extracted, the corresponding side of the animal becomes insensible to the influence of the light. Professor Bert's conclusion, therefore, is that the circulation in the transparent layers of the skin must be affected by light. According to Dr. Bouchard a sunstroke is the effect of the direct action of light upon the skin, produced by the blue and violet rays. The heat producing rays have no part in such accidents, as proved by the fact that workmen exposed to intense heats do not feel their fatal effect. Professor Bert, in a series of experiments on a variety of animals, found that none avoided light, but all rather sought it; and the lowest forms, like the highest, absorbed the same rays. As regards intensity of color, however, there was a difference, some being more partial to one ray than another. Thus the microscopic daphne of the pond preferred yellow; violet was less in request; spiders seemed to enjoy blue rather than red rays—so resembling people suffering from color blindness. No two persons are sensible to the same shades or tones, while absorbing the same light; and this would seem to indicate that the retina possesses a selective power. New Mechanical Inventions. An improved Weighing Scale has been patented by Hosea Willard, of Vergennes, Vt. The object of this invention is to economize time in ascertaining the weight of an article by avoiding the necessity for shifting the poise on the scale beam. It consists in providing a scale beam with a number of dishes suspended from different points on said beam, and representing or corresponding with different weights, so that the weight of an article may be ascertained by placing it in one or more of said dishes and observing which dish is depressed. William John, of Rigdon, Ind., has patented an improved Tire Setting and Cooling Apparatus, by which the tire may be set by one person, easily and quickly, without burning the fellies, and without straining the wheel by the unequal cooling of the tire. Joseph A. Mumford, of Avondale, Nova Scotia, Canada, has patented an improved machine for Sawing and Jointing Shingles. This machine cannot be properly described without engravings. It has an ingenious feeding device, and its flywheel carries the jointing knives. Ill-balanced Production. The Philadelphia sensibly remarks that the popular complaint of over-production is a mistake. Though of a few things we make or mine too much, our main trouble arises from not producing enough, in variety if not in quantity. Record "The wants of mankind never can be satisfied. Every new means of supplying a want creates new wants. They grow by what they feed on. As long as humanity is so constituted, over-production, in a general and enlarged sense, is impossible. It is this impossible thing with which the reformers would deal who propose to work fewer hours each day, or fewer hours in the week. The trouble they deplore does not exist; the remedy they propose defeats itself. A man cannot get rid of his load by shifting it from his right hand to his left hand. Production will not be stopped by making men their own employers certain hours in the day or certain days in the week, instead of allowing them to pursue their usual avocations. "The real trouble, which the labor reformers seem incompetent to fathom, is that there is not enough diversity in employments. What is desired is more work in productive enterprises, a more diffused industry, and a closer commercial connection with those countries wherein we can make desirable exchanges both of our raw material and our manufactured products. Every miner that drops his pick and takes up a hoe, every idle man that turns himself into an earner of wages, every person that picks up some loose thread of employment, every capitalist that takes advantage of stagnating industry and cheap material to build a house or beautify or improve a country seat, or set on foot some new process of manufacture, does something toward working out the problem which is puzzling the economists. In good time the surplus iron and coal will be sold; new populations will want new railroads; recuperated capital will gather confidence and take hold of new enterprises, and the whole nation will move forward again to more assured prosperity and to vaster undertakings." Labor in Germany. The consul at Barmen reports that for agricultural labor the pay varies greatly, according to the proximity to or remoteness from manufacturing centers; and ranges from fifty-six cents a day in the neighborhood of Barmen to thirty-one cents a day in the lower Rhine valley, and as low as eighteen cents in parts of Silesia. At Barmen, Crefeld and Düsseldorf, carpenters, coppersmiths, plumbers, machinists and wagonsmiths earn fifty-one to seventy-five cents daily; saddlers and shoemakers forty-seven to fifty-two cents daily; bakers and brewers, with board and lodging, from $1.42 to $2.14 weekly, and without board from sixty cents a day to $4.28 a week; farm hands are paid from $107 to $215 yearly, with maintenance; railroad laborers from fifty-six to eighty-[Pg 90]three cents per day, and as high as ninety-five cents daily for piece work on tunnels; silk weavers can earn $2.15 to $2.85 a week per loom; factory women $2.15, and children $1 a week. Business and wages are very low. In good times wages are eighty per cent higher. The cost of the necessaries of life has increased some fifty per cent in thirteen years, although it is now but little higher than five years ago. A man and wife with two or three children can live in two or three rooms in a poor and comfortless manner for $275 a year, and to support such an establishment all the members have to work ten or twelve hours daily. For a family of six persons the cost is about $7 per week—an amount but few families can earn, as the depression of trade and the reduction of time allow few to do a full week's work, although wages are nominally a trifle higher than five years ago. Petroleum June Review. DRILLING WELL ACCOUNT. The low price of oil and large accumulation of stock in the producing regions have had the effect to lessen operations in this department during the month of June. The total number of drilling wells in all the districts, at the close of the month, was 266, which was 110 less than in the preceding month. Rigs erected and being erected 243, against 309 last month. The number of drilling wells completed during the month was 269, being 151 less than in May. Aggregate production of the new wells was 3,788 barrels, against 6,851 barrels in May. The total number of dry holes developed in the month was 22, against 42 in May. The operators in the great northern field (Bradford district) have curtailed operations to an extent which will compare favorably with the operators in the other portions of the producing regions, as will be seen by the following statement, namely: Number of wells drilling at the close of the month, 187, against 284 at the close of the previous month. Number of drilling wells completed in June, 193, against 346 in May. Number of rigs erected and being erected, 196, against 234 in May. PRODUCTION. The daily average production for the month was 40,575 barrels, being a decrease of 227 barrels. The new wells completed in June failed to make good the falling off of the old ones, by decreasing the daily average 227 barrels. Bradford district shows a daily average production of 16,000 barrels, being an increase of 1,280 barrels over last month. The aggregate production in June of all the other districts combined, with the aid of 76 new wells, decreased the daily average 1,507 barrels. SHIPMENTS. The shipments in June, out of the producing regions, were 174,225 barrels larger than in the preceding month. The total shipments of crude, and refined reduced to crude equivalent, by railroad, river and pipes to the following points, were 1,135,119 barrels: New York took 555,794 bbls. Pittsburg " 153,182 " Cleveland " 239,389 " Philadelphia " 73,426 " Boston " 29,266 " Baltimore " 26,623 " Richmond " 7,000 " Ohio River refiners took 5,200 " Other local points took 45,239 " Total shipments 1,135,119 " Included in the above shipments there were 140,299 barrels of refined from Titusville and Oil City, which is equal to 187,065 barrels of crude.— Stowell's Petroleum Reporter. Remarkable Poisoning of a Lake. A contributor to describes the remarkable poisoning of Lake Alexandrina—one of the bodies of water which form the estuary of the Murray river, Australia. This year the water of the river has been unusually warm and low, and the inflow to the lakes very slight. The consequence has been an excessive growth of a conferva which is indigenous to these lakes and confined to them. This alga, , is very light and floats on the water, except during breezes, when it becomes diffused, and being driven to the lee shores, forms a thick scum like green oil paint. Nature Nodularia spumigera This scum, which is from two to six inches thick, and of a pasty consistency, being swallowed by cattle when drinking, acts poisonously and rapidly causes death. The symptoms of the poisoning are stupor and unconsciousness, falling and remaining quiet (as if asleep), unless touched, when convulsions are induced, the head and neck being drawn back by a rigid spasm, subsiding before death. The poison causes the death of sheep in from one to six or eight hours; of horses, in from eight to twenty-four hours; of dogs, in from four to five hours; and of pigs in three or four hours. A shows the plant is rapidly absorbed into the circulation, where it must act as a ferment, and causes disorganization. As the cattle will not touch the puddle where the plant scum has collected and become putrid, all they take is quite fresh, and the poisoning is therefore not due to drinking a putrescent fluid full of bacteria, as was suggested. post mortem When the scum collects and dries on the banks it forms a green crust. When, however, it is left in wet pools it rapidly decomposes, emitting a most horrible stench, like putrid urine; but previous to reaching this stage it gives out a smell like that of very rancid butter. A blue pigment exudes from this decomposing matter, having some remarkable properties. It is remarkably fluorescent, being red by reflected and blue by transmitted light; it appears to be a product of the decomposition, and allied to the coloring matter found in some lichens. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2013). Scientific American, Vol. XXXIX. No. 6. [New Series.], August 10, 1878. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19406/pg19406-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at www.gutenberg.org https://www.gutenberg.org/policy/license.html

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