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IMF Warns: CBDCs Could Enable State Surveillance Through Personal Data and Behavioral Insights

IMF সতর্ক করে: CBDCs ব্যক্তিগত ডেটা এবং আচরণগত অন্তর্দৃষ্টির মাধ্যমে রাষ্ট্রীয় নজরদারি সক্ষম করতে পারে

IWF warnt: CBDCs könnten staatliche Überwachung durch persönliche Daten und Verhaltenseinblicke ermöglichen

El FMI advierte: las CBDC podrían permitir la vigilancia estatal a través de datos personales y conocimientos sobre el comportamiento

Le FMI met en garde : les CBDC pourraient permettre la surveillance de l'État grâce aux données personnelles et aux informations comportementales

आईएमएफ ने चेतावनी दी: सीबीडीसी व्यक्तिगत डेटा और व्यवहार संबंधी अंतर्दृष्टि के माध्यम से राज्य निगरानी को सक्षम कर सकते हैं

IMFが警告：CBDCは個人データと行動分析を通じて国家監視を可能にする可能性がある

IMF 경고: CBDC는 개인 데이터와 행동 통찰력을 통해 국가 감시를 가능하게 할 수 있음

FMI alerta: CBDCs podem permitir vigilância estatal por meio de dados pessoais e insights comportamentais

МВФ предупреждает: CBDC могут позволить осуществлять государственный надзор с помощью персональных данных и поведенческих анализов

IMF Uyarıyor: CBDC'ler Kişisel Veriler ve Davranışsal İçgörüler Aracılığıyla Devlet Gözetimine Olanak Tanıyabilir

IMF cảnh báo: CBDC có thể cho phép giám sát nhà nước thông qua dữ liệu cá nhân và hiểu biết về hành vi

国际货币基金组织警告：CBDC 可能通过个人数据和行为洞察实现国家监控

Imagine you are eager to start using a brand new gadget you bought recently, maybe a sleek smartphone or the newest laptop. But you have to create a password before you can begin exploring its features. How many of us spend time creating a sophisticated, safe password that is challenging to figure out? Most people will choose something simple like "password123" or the name of their dog. And who could hold them responsible? Security sometimes seems like a barrier we are eager to cross in our hurry to enjoy the ease of technology. The foundation of modern technology, convenience, tends to entice us into inadequate security methods. But how does user behavior change with this inclination for simplicity? Let us explore the several ways convenience influences our decisions, often at the expense of security. The Temptation of Easy Choices with Weak Passwords Password creation is among the most instructive illustrations of how convenience compromises security. Remember the most recent occasion you had to create a new account? Did you recycle a known password or spend some time creating a distinctive, difficult-to-guess one? Many choose simple passwords such as birthdays, pet names, and even the dreaded "123456" because they are easy to remember. According to a National Cyber Security Centre (NCSC) study, 23 million users still rely on the password "123456." Why? Because many conveniences come before security. Remembering complicated, distinctive passwords for every account seems like a chore, thus users turn toward simplicity. It gets you to your intended activity faster, is quick, and painless. Weak passwords, however, make you a prime target for cybercriminals, so this trade-off has costs. The "Remind Me Later" Trap: Ignoring Updates Delaying software updates is another habit that partly shapes user behavior by convenience. Imagine yourself working on a crucial project when a pop-up alerts you it is time to update your system. Are you more likely to click "Remind Me Later," or "Update Now"? Of course, most people usually go with “Remind me Later.” Although they can be annoying, updates usually include crucial security fixes to protect your devices against vulnerabilities. While hackers quickly exploit out-of-date systems, many consumers put off updates to save their workflow from being disrupted. This is the ideal example of how quickly decisions motivated by convenience could jeopardize security. The WannaCry ransomware attack in 2017, which compromised practically 200,000 computers all around is an example of this. The attack used a flaw in previous Windows versions. Had people kept their systems updated, this horrible hack never would have happened. This reminds us powerfully of the truth that convenience should never come before security. Autofill: Convenient but Risky For people who want convenience, auto-fills included in web browsers and apps are a blessing. This is because they can save time and effort by logging in from your browser and memorizing your username and password. One-click will bring your shopping cart, social media profiles, and online bank account directly into your hands. The issue is that should a hacker obtain access to your device or browser, all the sensitive data kept using autofill can be accessed. On shared or public devices, where users sometimes overlook to log off or clean their data, this is especially dangerous. Autofill generates a possible gold mine for cybercriminals even while it saves time. Public Wi-Fi’s Convenience Comes with Hidden Risks We have all been there, sitting in a café or airport, appreciative of the free public Wi-Fi allowing us to check emails or browse social media without running through our data allotment. Unquestionably convenient public Wi-Fi is also one of the most often used tools for violating security. Unencrypted public Wi-Fi networks are well known for allowing hackers to easily access your information. Still, users of these networks connect without thinking since it is practical. This activity seriously compromises personal data including financial information and login credentials. A few years ago, a reputable cybersecurity researcher showed how simple public Wi-Fi hacking of devices is. In a crowded public area, he created a fake Wi-Fi network, and within minutes, several people connected without question. The lesson? In this case, convenience can cause a severe lapse in judgment. Skipping Two-Factor Authentication as the Easiest but Riskiest Path Requiring a second form of verification such as a text message or email code, which is two-factor authentication (2FA) gives your accounts even more security. Although 2FA greatly improves account security, many users ignore enabling it since it adds a login step. The same logic applies to the usage of security applications including password managers. Some users find these tools difficult even though they are meant to create strong, distinctive credentials for every account and securely save passwords. Rather, they turn to memorizing one password or, worse, using the same password across several sites. However, security should be considered as a long-term investment in your mental health. The little additional work needed for 2FA or a password manager is a price worth paying to protect your digital data. Like closing your front door is a no-brainer, so too should be protecting your internet accounts. The Cognitive Bias of "It Won’t Happen to Me" Convenience beats security in part because of the cognitive bias of optimism, many users think cyberattacks affect other people, not them. Poor practices follow from this false sense of security. Users often believe they will not be targeted whether it comes to password recycling, neglect of updates, or using unprotected Wi-Fi. However, cybersecurity hazards do not discriminate. From personal consumers to large companies, everyone is touched. By raising awareness and providing education, users can overcome their prejudice and acquire better knowledge of the risks they come across. Finding a Balance: Convenience and Security Can Coexist Fortunately, safety and comfort of use do not always contradict each other. Modern technology lets one discover a good balance between the two. For example, biometric authentication, which takes advantage of fingerprints or facial recognition offers both security and convenience. For hackers, it takes little time, is quick, easy to replicate. The growing user-friendliness of password managers allows users to safely create and save passwords without sacrificing convenience. Since updates can be automated, the "remind me later" option is outdated. These advances show that sometimes great security and simplicity of use are not mutually exclusive. Conclusion: A Call for Smarter User Behavior To grow in an environment going more and more digital, we have to understand how convenience shapes our security measures. Sometimes users choose convenience above security when deciding on weak passwords or skipping to install software updates. Knowing these tendencies and changing our behavior will help us to use modern technologies without endangering our safety. Little habits like keeping up-to-date software, turning on two-factor authentication, and using a password manager can have a significant impact. Remember the security implications the next time you find yourself inclined to follow the less complicated route. Little work now will help you to avoid significant problems tomorrow. Imagine you are eager to start using a brand new gadget you bought recently, maybe a sleek smartphone or the newest laptop. But you have to create a password before you can begin exploring its features. How many of us spend time creating a sophisticated, safe password that is challenging to figure out? Most people will choose something simple like "password123" or the name of their dog. And who could hold them responsible? Security sometimes seems like a barrier we are eager to cross in our hurry to enjoy the ease of technology. Imagine you are eager to start using a brand new gadget you bought recently, maybe a sleek smartphone or the newest laptop. But you have to create a password before you can begin exploring its features. How many of us spend time creating a sophisticated, safe password that is challenging to figure out? Most people will choose something simple like "password123" or the name of their dog. And who could hold them responsible? Security sometimes seems like a barrier we are eager to cross in our hurry to enjoy the ease of technology. The foundation of modern technology, convenience, tends to entice us into inadequate security methods. But how does user behavior change with this inclination for simplicity? Let us explore the several ways convenience influences our decisions, often at the expense of security. The foundation of modern technology, convenience, tends to entice us into inadequate security methods. But how does user behavior change with this inclination for simplicity? Let us explore the several ways convenience influences our decisions, often at the expense of security. The Temptation of Easy Choices with Weak Passwords Password creation is among the most instructive illustrations of how convenience compromises security. Remember the most recent occasion you had to create a new account? Did you recycle a known password or spend some time creating a distinctive, difficult-to-guess one? Many choose simple passwords such as birthdays, pet names, and even the dreaded "123456" because they are easy to remember. According to a National Cyber Security Centre (NCSC) study, 23 million users still rely on the password "123456." Why? Because many conveniences come before security. Remembering complicated, distinctive passwords for every account seems like a chore, thus users turn toward simplicity. It gets you to your intended activity faster, is quick, and painless. Weak passwords, however, make you a prime target for cybercriminals, so this trade-off has costs. The "Remind Me Later" Trap: Ignoring Updates The "Remind Me Later" Trap: Ignoring Updates Delaying software updates is another habit that partly shapes user behavior by convenience. Imagine yourself working on a crucial project when a pop-up alerts you it is time to update your system. Are you more likely to click "Remind Me Later," or "Update Now"? Of course, most people usually go with “Remind me Later.” Although they can be annoying, updates usually include crucial security fixes to protect your devices against vulnerabilities. While hackers quickly exploit out-of-date systems, many consumers put off updates to save their workflow from being disrupted. This is the ideal example of how quickly decisions motivated by convenience could jeopardize security. The WannaCry ransomware attack in 2017, which compromised practically 200,000 computers all around is an example of this. The attack used a flaw in previous Windows versions. Had people kept their systems updated, this horrible hack never would have happened. This reminds us powerfully of the truth that convenience should never come before security. Autofill: Convenient but Risky Autofill: Convenient but Risky For people who want convenience, auto-fills included in web browsers and apps are a blessing. This is because they can save time and effort by logging in from your browser and memorizing your username and password. One-click will bring your shopping cart, social media profiles, and online bank account directly into your hands. The issue is that should a hacker obtain access to your device or browser, all the sensitive data kept using autofill can be accessed. On shared or public devices, where users sometimes overlook to log off or clean their data, this is especially dangerous. Autofill generates a possible gold mine for cybercriminals even while it saves time. Public Wi-Fi’s Convenience Comes with Hidden Risks We have all been there, sitting in a café or airport, appreciative of the free public Wi-Fi allowing us to check emails or browse social media without running through our data allotment. Unquestionably convenient public Wi-Fi is also one of the most often used tools for violating security. Unencrypted public Wi-Fi networks are well known for allowing hackers to easily access your information. Still, users of these networks connect without thinking since it is practical. This activity seriously compromises personal data including financial information and login credentials. A few years ago, a reputable cybersecurity researcher showed how simple public Wi-Fi hacking of devices is. In a crowded public area, he created a fake Wi-Fi network, and within minutes, several people connected without question. The lesson? In this case, convenience can cause a severe lapse in judgment. Skipping Two-Factor Authentication as the Easiest but Riskiest Path Requiring a second form of verification such as a text message or email code, which is two-factor authentication (2FA) gives your accounts even more security. Although 2FA greatly improves account security, many users ignore enabling it since it adds a login step. The same logic applies to the usage of security applications including password managers. Some users find these tools difficult even though they are meant to create strong, distinctive credentials for every account and securely save passwords. Rather, they turn to memorizing one password or, worse, using the same password across several sites. However, security should be considered as a long-term investment in your mental health. The little additional work needed for 2FA or a password manager is a price worth paying to protect your digital data. Like closing your front door is a no-brainer, so too should be protecting your internet accounts. The Cognitive Bias of "It Won’t Happen to Me" The Cognitive Bias of "It Won’t Happen to Me" Convenience beats security in part because of the cognitive bias of optimism, many users think cyberattacks affect other people, not them. Poor practices follow from this false sense of security. Users often believe they will not be targeted whether it comes to password recycling, neglect of updates, or using unprotected Wi-Fi. However, cybersecurity hazards do not discriminate. From personal consumers to large companies, everyone is touched. By raising awareness and providing education, users can overcome their prejudice and acquire better knowledge of the risks they come across. Finding a Balance: Convenience and Security Can Coexist Finding a Balance: Convenience and Security Can Coexist Fortunately, safety and comfort of use do not always contradict each other. Modern technology lets one discover a good balance between the two. For example, biometric authentication, which takes advantage of fingerprints or facial recognition offers both security and convenience. For hackers, it takes little time, is quick, easy to replicate. The growing user-friendliness of password managers allows users to safely create and save passwords without sacrificing convenience. Since updates can be automated, the "remind me later" option is outdated. These advances show that sometimes great security and simplicity of use are not mutually exclusive. Conclusion: A Call for Smarter User Behavior Conclusion: A Call for Smarter User Behavior To grow in an environment going more and more digital, we have to understand how convenience shapes our security measures. Sometimes users choose convenience above security when deciding on weak passwords or skipping to install software updates. Knowing these tendencies and changing our behavior will help us to use modern technologies without endangering our safety. Little habits like keeping up-to-date software, turning on two-factor authentication, and using a password manager can have a significant impact. Remember the security implications the next time you find yourself inclined to follow the less complicated route. Little work now will help you to avoid significant problems tomorrow.

User Behavior: How Convenience Impacts Security Practices

During the final week of Aleph—Crecimiento’s pop-up city in the Web3 hub of LATAM, Buenos Aires—I attended a workshop that really pushed our thinking. Naty Shi, a graduate of the Token Engineering Academy, walked us through the complex layers of designing token economics for various use cases. A few weeks later, we caught up again to explore the subtle art of token design and its vital role in building balanced, sustainable Web3 ecosystems for the future. Let’s start with the basics. How would you describe token engineering? Token engineering is a relatively new discipline that pulls from many different areas. People often think it's just about finance, but actually, it’s much broader. It combines elements from finance, technology (like blockchain and smart contracts), psychology, economic behavior, AI, and even law. The goal of token engineering is to create systems that consider human behavior, incentives, and long-term sustainability. It’s about designing ecosystems where people, technology, and economics interact in a balanced way. What we’re trying to do is build systems that motivate people to engage with Web3 projects and stay involved long-term by aligning their motivations with the project’s goals. Great! Can you share more about the different roles tokens can play in communities? Tokens can serve many purposes beyond just being a currency; they can represent reputation, influence, or power within a community. They can be used to reward members who contribute value by mentoring others, sharing knowledge, or supporting various community initiatives. These tokens might then grant decision-making power or offer other benefits within the community. One example is clean energy communities. In these settings, tokens are used to incentivize energy sharing among members. If someone generates excess energy, they can earn tokens that could be traded for resources or used to access other services within the community. Another example could be carpooling, where people earn tokens for sharing rides. In all these cases, token engineers work behind the scenes to ensure the systems run smoothly and sustainably. What sparked your interest in token engineering? I’ve been an engineer for over 20 years, but my background is in chemistry, not coding or software. What drew me to token engineering was the process of creating value. I’ve always believed in starting with a clear problem, understanding what users need, and then developing a solution through a well-thought-out process. But in Web3, I noticed that many projects were launching quickly without enough due diligence or quality control. It made me wonder why we aren’t applying the same rigor that civil engineers use to build bridges—structures meant to last. That led me to explore token engineering, where I found a framework that applied the same thoughtful, process-driven approach I was used to. I started studying at Token Engineering Academy, and it felt like a natural extension of my experience. Now, it’s what I want to dedicate my future to! Let’s dive more into the connection between human behavior and token engineering. Sure! Token engineering focuses on creating systems that are sustainable and scalable, but it’s not just about the technical aspects. One of the key principles is designing incentives that align human behavior with the goals of a project. You’re not only assigning value to tokens—you’re building systems that influence how people engage with a community. The goal is to guide individuals toward positive actions and discourage negative ones. This is where psychology and game theory really come into play. Human behavior is unpredictable, shaped by experiences, culture, and emotions. Here in Latin America, for example, where people have faced economic instability more often, they might respond to financial volatility with a sense of resilience. In contrast, someone from a stable economy might show more concern in the same situations. As a token engineer, you have to consider these different reactions. While game theory helps predict certain behaviors, human psychology adds another layer of complexity. The most effective token systems are flexible enough to adapt when people act in unexpected ways. It’s all about creating frameworks that motivate positive contributions while keeping the system stable, even when things don’t go as planned. Is there a framework you’d recommend to someone starting out with token design? The first step is to define the purpose of your token. This means being clear about what problem the token is solving and how it uniquely adds value to the ecosystem. Are you addressing a gap in the market or creating incentives that weren’t possible before? Understanding this will help guide your decisions, from tokenomics to governance models. Without a clear purpose, the token is unlikely to deliver meaningful value. The second step is to ask yourself whether you even need a token. It can be tempting to create one just because it’s trendy, but tokens should only exist if they solve a real, critical problem for your project. Could you achieve the same results without a token? If a token isn’t adding clear utility or value, it’s often better to explore alternatives. Next, step three is about identifying your users and actors. Tokens don’t operate in isolation, so you need to understand who will interact with your system and their needs. Your users actively engage with your product, but you also need to consider external actors—investors or stakeholders who may influence the ecosystem without using the product directly. Knowing these groups and their motivations will shape how your token functions and the incentives you create. Step four involves deciding on the type of token. For most projects, utility tokens are the simplest and most flexible option, designed to provide access to a product or service within your ecosystem. Security tokens, on the other hand, come with stricter regulatory requirements and are better suited for projects with complex financial structures. Starting with a utility token allows you to focus on building the core of your project before diving into more legally complex options. Once the token type is clear, the fifth step is generating value. Your token should represent more than just financial value—it should reflect the expertise of your team, the vision behind the project, and its potential for long-term growth. Both investors and users will look for evidence that your token holds value beyond speculation, and trust will come from how well you articulate and deliver on that value over time. For step six, focus on distribution. This is one of the trickier aspects. Your tokens need to be distributed in a way that encourages long-term engagement, not just short-term speculation. Poor distribution can lead to a price crash, damaging the project’s credibility. Consider distributing tokens over time or tying them to key milestones to keep users invested in the project’s success. Finally, step seven is all about documentation. Properly documenting your token’s design and functionality is crucial for transparency. Good documentation builds trust with users and investors, showing that you’ve considered potential challenges and solutions. It’s not just about having a great idea—you also need to show that you’ve planned for the long term and are ready to adapt as the project evolves. When you analyze token ecosystems, what are some common red flags? The first thing I look at is the purpose of the project. If it feels like a copy-paste of something else without bringing anything new, that’s a big red flag. After that, I check the documentation—it always reveals whether the team has really planned their approach and how they’ll grow over time. If the documentation is missing or unclear, it’s usually a sign that the project might not be fully ready. I also focus on how the token is structured, especially when it comes to voting power. If a few whales have too much control, it can lead to centralization, which hurts the project in the long run. A good governance model should make sure things stay balanced. And finally, I have to ask—what’s your take on memecoins? Memecoins aren’t really my thing, haha! But I do think more people should learn about token engineering hands-on. It’s a powerful tool for bridging Web3 and traditional industries. In traditional businesses, there are set processes to ensure quality, but in Web3, things can move so fast that sometimes quality gets overlooked. The challenge is to find a balance between innovation and responsibility. There’s a lot of room for experimentation, but we also need to focus on building robust and sustainable systems. Especially here in Latin America, with so many projects taking off, it’s key to focus that growth on building something that lasts. During the final week of Aleph—Crecimiento’s pop-up city in the Web3 hub of LATAM, Buenos Aires—I attended a workshop that really pushed our thinking. Naty Shi, a graduate of the Token Engineering Academy, walked us through the complex layers of designing token economics for various use cases. A few weeks later, we caught up again to explore the subtle art of token design and its vital role in building balanced, sustainable Web3 ecosystems for the future. During the final week of Aleph —Crecimiento’s pop-up city in the Web3 hub of LATAM, Buenos Aires—I attended a workshop that really pushed our thinking. Naty Shi , a graduate of the Token Engineering Academy , walked us through the complex layers of designing token economics for various use cases. A few weeks later, we caught up again to explore the subtle art of token design and its vital role in building balanced, sustainable Web3 ecosystems for the future. Aleph Aleph Naty Shi Naty Shi Token Engineering Academy Token Engineering Academy Let’s start with the basics. How would you describe token engineering? Token engineering is a relatively new discipline that pulls from many different areas. People often think it's just about finance, but actually, it’s much broader. It combines elements from finance, technology (like blockchain and smart contracts), psychology, economic behavior, AI, and even law. The goal of token engineering is to create systems that consider human behavior, incentives, and long-term sustainability. It’s about designing ecosystems where people, technology, and economics interact in a balanced way. What we’re trying to do is build systems that motivate people to engage with Web3 projects and stay involved long-term by aligning their motivations with the project’s goals. Great! Can you share more about the different roles tokens can play in communities? Tokens can serve many purposes beyond just being a currency; they can represent reputation, influence, or power within a community. They can be used to reward members who contribute value by mentoring others, sharing knowledge, or supporting various community initiatives. These tokens might then grant decision-making power or offer other benefits within the community. One example is clean energy communities. In these settings, tokens are used to incentivize energy sharing among members. If someone generates excess energy, they can earn tokens that could be traded for resources or used to access other services within the community. Another example could be carpooling, where people earn tokens for sharing rides. In all these cases, token engineers work behind the scenes to ensure the systems run smoothly and sustainably. What sparked your interest in token engineering? I’ve been an engineer for over 20 years, but my background is in chemistry, not coding or software. What drew me to token engineering was the process of creating value. I’ve always believed in starting with a clear problem, understanding what users need, and then developing a solution through a well-thought-out process. But in Web3, I noticed that many projects were launching quickly without enough due diligence or quality control. It made me wonder why we aren’t applying the same rigor that civil engineers use to build bridges—structures meant to last. That led me to explore token engineering, where I found a framework that applied the same thoughtful, process-driven approach I was used to. I started studying at Token Engineering Academy, and it felt like a natural extension of my experience. Now, it’s what I want to dedicate my future to! Let’s dive more into the connection between human behavior and token engineering. Sure! Token engineering focuses on creating systems that are sustainable and scalable, but it’s not just about the technical aspects. One of the key principles is designing incentives that align human behavior with the goals of a project. You’re not only assigning value to tokens—you’re building systems that influence how people engage with a community. The goal is to guide individuals toward positive actions and discourage negative ones. This is where psychology and game theory really come into play. Human behavior is unpredictable, shaped by experiences, culture, and emotions. Here in Latin America, for example, where people have faced economic instability more often, they might respond to financial volatility with a sense of resilience. In contrast, someone from a stable economy might show more concern in the same situations. As a token engineer, you have to consider these different reactions. While game theory helps predict certain behaviors, human psychology adds another layer of complexity. The most effective token systems are flexible enough to adapt when people act in unexpected ways. It’s all about creating frameworks that motivate positive contributions while keeping the system stable, even when things don’t go as planned. Is there a framework you’d recommend to someone starting out with token design? The first step is to define the purpose of your token. This means being clear about what problem the token is solving and how it uniquely adds value to the ecosystem. Are you addressing a gap in the market or creating incentives that weren’t possible before? Understanding this will help guide your decisions, from tokenomics to governance models. Without a clear purpose, the token is unlikely to deliver meaningful value. The second step is to ask yourself whether you even need a token. It can be tempting to create one just because it’s trendy, but tokens should only exist if they solve a real, critical problem for your project. Could you achieve the same results without a token? If a token isn’t adding clear utility or value, it’s often better to explore alternatives. Next, step three is about identifying your users and actors. Tokens don’t operate in isolation, so you need to understand who will interact with your system and their needs. Your users actively engage with your product, but you also need to consider external actors—investors or stakeholders who may influence the ecosystem without using the product directly. Knowing these groups and their motivations will shape how your token functions and the incentives you create. Step four involves deciding on the type of token. For most projects, utility tokens are the simplest and most flexible option, designed to provide access to a product or service within your ecosystem. Security tokens, on the other hand, come with stricter regulatory requirements and are better suited for projects with complex financial structures. Starting with a utility token allows you to focus on building the core of your project before diving into more legally complex options. Once the token type is clear, the fifth step is generating value. Your token should represent more than just financial value—it should reflect the expertise of your team, the vision behind the project, and its potential for long-term growth. Both investors and users will look for evidence that your token holds value beyond speculation, and trust will come from how well you articulate and deliver on that value over time. For step six, focus on distribution. This is one of the trickier aspects. Your tokens need to be distributed in a way that encourages long-term engagement, not just short-term speculation. Poor distribution can lead to a price crash, damaging the project’s credibility. Consider distributing tokens over time or tying them to key milestones to keep users invested in the project’s success. Finally, step seven is all about documentation. Properly documenting your token’s design and functionality is crucial for transparency. Good documentation builds trust with users and investors, showing that you’ve considered potential challenges and solutions. It’s not just about having a great idea—you also need to show that you’ve planned for the long term and are ready to adapt as the project evolves. When you analyze token ecosystems, what are some common red flags? When you analyze token ecosystems, what are some common red flags? The first thing I look at is the purpose of the project. If it feels like a copy-paste of something else without bringing anything new, that’s a big red flag. After that, I check the documentation—it always reveals whether the team has really planned their approach and how they’ll grow over time. If the documentation is missing or unclear, it’s usually a sign that the project might not be fully ready. I also focus on how the token is structured, especially when it comes to voting power. If a few whales have too much control, it can lead to centralization, which hurts the project in the long run. A good governance model should make sure things stay balanced. And finally, I have to ask—what’s your take on memecoins? And finally, I have to ask—what’s your take on memecoins? Memecoins aren’t really my thing, haha! But I do think more people should learn about token engineering hands-on. It’s a powerful tool for bridging Web3 and traditional industries. In traditional businesses, there are set processes to ensure quality, but in Web3, things can move so fast that sometimes quality gets overlooked. The challenge is to find a balance between innovation and responsibility. There’s a lot of room for experimentation, but we also need to focus on building robust and sustainable systems. Especially here in Latin America, with so many projects taking off, it’s key to focus that growth on building something that lasts.

Technology, Economics, and Human Behavior: The Art of Token Engineering with Naty Shi

Τεχνολογία, Οικονομία και Ανθρώπινη Συμπεριφορά: Η Τέχνη του Token Engineering με τον Naty Shi

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បច្ចេកវិទ្យា សេដ្ឋកិច្ច និងអាកប្បកិរិយារបស់មនុស្ស៖ សិល្បៈនៃវិស្វកម្មសញ្ញាសម្ងាត់ជាមួយ Naty Shi

Technologijos, ekonomika ir žmogaus elgesys: žetonų inžinerijos menas su Naty Shi

टेक्नोलोजी, अर्थशास्त्र, र मानव व्यवहार: नाटी शिसँग टोकन इन्जिनियरिङको कला

Farsamada, Dhaqaalaha, iyo Dhaqanka Aadanaha: Farshaxanka Token Engineering ee Naty Shi

Технологија, економија и људско понашање: Уметност инжењеринга токена са Нати Ши

Teknolojia, Uchumi, na Tabia ya Kibinadamu: Sanaa ya Uhandisi wa Ishara pamoja na Naty Shi

தொழில்நுட்பம், பொருளாதாரம் மற்றும் மனித நடத்தை: நாட்டி ஷியுடன் டோக்கன் இன்ஜினியரிங் கலை

How Big Data and Analytics are Shaping Traveler Behavior: Insights from a Digital Marketer

Game Theory and Blockchain: Why DAG-Protocol Fails Against Greedy Behavior

Authors:
(1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France;
(2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France;
(3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Methods Results Model Discussion, Acknowledgements and References Supplementary Material V. DISCUSSION We have shown experimentally that the change in phototactic behavior of C. reinhardtii can be due to its recent history of illumination. When exposed to two closely spaced, identical stimuli, the algae act as if they were time-integrating the stimuli. This is visible when the stimulus is close to that delimiting positive and negative phototaxis. Then, the algae show two different behaviors in response to the two consecutive stimuli: first a backand-forth response, then a negative phototaxis. When the stimuli are sufficiently spaced apart, the algae have had time to relax to a basal state, and show the same qualitative response to the two stimuli, see Fig. 6. In our experiments, we find this time of complete relaxation to be approximately equal to 30 minutes. A simplified model of phototaxis explains this memory effect by the introduction of a time-scale of relaxation in the inner biochemistry of the cell. When the time-scale of relaxation is larger than the time between two consecutive stimuli, the alga integrates the signal and can display different behaviors in response to the two identical stimuli, see Fig. 8. The time needed to return to the basal state should be of order 3τ , which underlies our choice of τ = 10 min for the relaxation time constant. Our experimental data are consistent with an inner relaxation time τ in the range ≈ 5 to 20 minutes. What could be the biochemical species corresponding to the intracellular species S introduced in the model? It is very likely that our simplified model compounds the dynamics of multiple biochemical species, activated sequentially in a signalling cascade. Still, it is known that the photoreceptors for phototaxis in C. reinhardtii are channelrhodopsins, that become phosphorylated within milliseconds after the application of a light stimulus. A high level of phosphorylated channelrhodopsin-1 (ChR1) in the cell correlates with a change from positive to negative phototaxis [11], much like a high level of activated S corresponds to negative phototaxis in our model. Moreover, the time scale of dephosphorylation of ChR1 in C. reinhardtii has been shown to be of the order of ≈ 5 to 15 minutes [11], which is compatible with our value of τ = 10 min. A possibility is therefore to identify S with ChR1. A more physical model of phototaxis was recently introduced by Leptos et al. to describe the response of C. reinhardtii to light [22]. In their model, Leptos et al. take into account the rotation of the microalga around its axis while swimming, and the resulting oscillating light intensity seen by the eyespot. The consecutive step-up and step-down stimuli sensed by the eyespot during one body rotation lead to a realignment of the trajectory towards the light source. Our model averages out the body rotation of the microalgae and describes the behavior of the microorganism at a more coarse-grained time-scale. Another difference is that the model of Leptos et al. does not take into account the fact that phototaxis changes sign when the light intensity increases. This change of phototactic behavior requires the introduction of a threshold value in the model, below (resp. above) which phototaxis is positive (resp. negative). Such a threshold is incorporated in our simplified model. Note that the threshold in the model, introduced as a simple means to obtain a change in behavior between positive and negative phototaxis at different light intensities, leads to the back-and-forth behavior at intermediate light intensities. The existence of this threshold also enables to explain why the time scale of negative phototaxis is longer than that of positive phototaxis. Negative phototaxis occurs when the amount of active species s ⋆ overcomes a threshold sT ; this takes time, during which the alga exhibits positive phototaxis. Only when s ⋆ > sT do the algae show negative phototaxis, and accumulate at one boundary of the well. The small initial positive phototactic response predicted by the model is in agreement with experimental data. The model also predicts that it is possible to switch the phototactic behavior of algae from positive phototaxis to back-and-forth to negative phototaxis, using three consecutive identical stimuli, by adjusting the waiting times between the stimuli and choosing carefully the stimulus intensity, see Fig. 8d. While we were not able to find the experimental conditions corresponding to this theoretical prediction, we were able to experimentally observe the switch from back-and-forth to negative phototaxis, and from positive phototaxis to back-and-forth in a couple experiments, see Supp. Fig. 5. While the model successfully reproduces the phototactic behavior and the time scales at play, it fails to reproduce two experimental observations. First, in a couple experiments, multiple consecutive back-and-forth of the center of mass were observed at intermediate intensities, see Supp. Fig. 6. Second, in experiments with consecutive stimuli at intermediate light intensities, algae in the third experiment (after a waiting time of 30 minutes) display positive phototaxis instead of the expected backand-forth behavior observed during the first experiment, see Fig. 6b and Supp. Fig. 5. Both behaviors can be obtained in the model by supposing that the threshold sT increases with the time spent under a light stimulus, and so depends on the history of the microorganism. Such an increasing threshold would also enable to recover the traditional adaptative behavior at longer time scales [15]. This implies having a third equation describing the timeevolution of sT , thus introducing another time constant into the problem. In the lab, the memory effects reported here imply that it is not possible to repeat the same phototaxis experiments on a given batch of cells and expect similar outcomes. Such an effect was already known in the case of long-term adaptation [15]. The data reported here show that even in the case of short-term experiments, the behavior is not necessarily reproducible. To study phototaxis quantitatively, experimental repeats should be carried out with different batches of naive cells. Having identified this short time-scale, our experiments pave the way for the study of phototaxis in the presence of timevarying light stimuli, much as the algae are subject to in their natural environment. There, light is constantly fluctuating. The short-term memory could then be a beneficial way to integrate a light signal randomly interrupted by the shadow of other motile organisms or objects. ACKNOWLEDGMENTS We thank David Gonzalez-Rodriguez for insightful comments on an early version of the model, Maxime Simon for preliminary experiments, Salome Guttierez Ramos for help with microfabrication, and Caroline Frot for precious technical support. This work was supported by “Investissements d’Avenir” LabEx PALM (ANR-10- LABX-0039-PALM) and by PEPS “M´ecanique du futur”. References [1] G. J´ekely, Philosophical Transactions of the Royal Society B: Biological Sciences 364, 2795 (2009). [2] K. W. Foster and R. D. Smyth, Microbiological reviews 44, 572 (1980). [3] P. Hegemann and P. Berthold, in The Chlamydomonas Sourcebook (Elsevier, 2009) pp. 395–429. [4] U. R¨uffer and W. Nultsch, Cell motility and the cytoskeleton 18, 269 (1991). [5] M. E. H. Feinleib and G. M. Curry, Physiologia Plantarum 25, 346 (1971). [6] W. Nultsch, J. Pfau, and R. Dolle, Archives of microbiology 144, 393 (1986). [7] N. Morel-Laurens, Photochemistry and Photobiology 45, 119 (1987). [8] R. Dolle, J. Pfau, and W. Nultsch, Journal of plant physiology 126, 467 (1987). [9] T. Takahashi and M. Watanabe, FEBS Letters 336, 516 (1993). [10] K.-i. Wakabayashi, Y. Misawa, S. Mochiji, and R. Kamiya, Proceedings of the National Academy of Sciences 108, 11280 (2011). [11] M. B¨ohm, D. Boness, E. Fantisch, H. Erhard, J. Frauenholz, Z. Kowalzyk, N. Marcinkowski, S. Kateriya, P. Hegemann, and G. Kreimer, The Plant Cell 31, 886 (2019). [12] R. M. Macnab and D. E. Koshland Jr, Proceedings of the National Academy of Sciences 69, 2509 (1972). [13] H. C. Berg and P. Tedesco, Proceedings of the National Academy of Sciences 72, 3235 (1975). [14] P. Van Haastert, The Journal of cell biology 96, 1559 (1983). [15] A. Mayer, Nature 217, 875 (1968). [16] E. Harris, D. Stern, and G. Witman, The chlamydomonas sourcebook, vol. 1 san diego (2009). [17] Y. Xia and G. M. Whitesides, Annual review of materials science 28, 153 (1998). [18] N. Ueki, T. Ide, S. Mochiji, Y. Kobayashi, R. Tokutsu, N. Ohnishi, K. Yamaguchi, S. Shigenobu, K. Tanaka, J. Minagawa, et al., Proceedings of the National Academy of Sciences 113, 5299 (2016). [19] A. Ramamonjy, J. Dervaux, and P. Brunet, Physical Review Letters 128, 258101 (2022). [20] C. T. Kreis, M. Le Blay, C. Linne, M. M. Makowski, and O. B¨aumchen, Nature Physics 14, 45 (2018). [21] S. Till, F. Ebmeier, A. A. Fragkopoulos, M. G. Mazza, and O. B¨aumchen, Physical Review Research 4, L042046 (2022). [22] K. C. Leptos, M. Chioccioli, S. Furlan, A. I. Pesci, and R. E. Goldstein, Physical Review E 107 (2023). This paper is available on arxiv under CC 4.0 license. Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Authors: Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Model Model Discussion, Acknowledgements and References Discussion, Acknowledgements and References Supplementary Material Supplementary Material V. DISCUSSION We have shown experimentally that the change in phototactic behavior of C. reinhardtii can be due to its recent history of illumination. When exposed to two closely spaced, identical stimuli, the algae act as if they were time-integrating the stimuli. This is visible when the stimulus is close to that delimiting positive and negative phototaxis. Then, the algae show two different behaviors in response to the two consecutive stimuli: first a backand-forth response, then a negative phototaxis. When the stimuli are sufficiently spaced apart, the algae have had time to relax to a basal state, and show the same qualitative response to the two stimuli, see Fig. 6. In our experiments, we find this time of complete relaxation to be approximately equal to 30 minutes. A simplified model of phototaxis explains this memory effect by the introduction of a time-scale of relaxation in the inner biochemistry of the cell. When the time-scale of relaxation is larger than the time between two consecutive stimuli, the alga integrates the signal and can display different behaviors in response to the two identical stimuli, see Fig. 8. The time needed to return to the basal state should be of order 3τ , which underlies our choice of τ = 10 min for the relaxation time constant. Our experimental data are consistent with an inner relaxation time τ in the range ≈ 5 to 20 minutes. What could be the biochemical species corresponding to the intracellular species S introduced in the model? It is very likely that our simplified model compounds the dynamics of multiple biochemical species, activated sequentially in a signalling cascade. Still, it is known that the photoreceptors for phototaxis in C. reinhardtii are channelrhodopsins, that become phosphorylated within milliseconds after the application of a light stimulus. A high level of phosphorylated channelrhodopsin-1 (ChR1) in the cell correlates with a change from positive to negative phototaxis [11], much like a high level of activated S corresponds to negative phototaxis in our model. Moreover, the time scale of dephosphorylation of ChR1 in C. reinhardtii has been shown to be of the order of ≈ 5 to 15 minutes [11], which is compatible with our value of τ = 10 min. A possibility is therefore to identify S with ChR1. A more physical model of phototaxis was recently introduced by Leptos et al. to describe the response of C. reinhardtii to light [22]. In their model, Leptos et al. take into account the rotation of the microalga around its axis while swimming, and the resulting oscillating light intensity seen by the eyespot. The consecutive step-up and step-down stimuli sensed by the eyespot during one body rotation lead to a realignment of the trajectory towards the light source. Our model averages out the body rotation of the microalgae and describes the behavior of the microorganism at a more coarse-grained time-scale. Another difference is that the model of Leptos et al. does not take into account the fact that phototaxis changes sign when the light intensity increases. This change of phototactic behavior requires the introduction of a threshold value in the model, below (resp. above) which phototaxis is positive (resp. negative). Such a threshold is incorporated in our simplified model. Note that the threshold in the model, introduced as a simple means to obtain a change in behavior between positive and negative phototaxis at different light intensities, leads to the back-and-forth behavior at intermediate light intensities. The existence of this threshold also enables to explain why the time scale of negative phototaxis is longer than that of positive phototaxis. Negative phototaxis occurs when the amount of active species s ⋆ overcomes a threshold sT ; this takes time, during which the alga exhibits positive phototaxis. Only when s ⋆ > sT do the algae show negative phototaxis, and accumulate at one boundary of the well. The small initial positive phototactic response predicted by the model is in agreement with experimental data. The model also predicts that it is possible to switch the phototactic behavior of algae from positive phototaxis to back-and-forth to negative phototaxis, using three consecutive identical stimuli, by adjusting the waiting times between the stimuli and choosing carefully the stimulus intensity, see Fig. 8d. While we were not able to find the experimental conditions corresponding to this theoretical prediction, we were able to experimentally observe the switch from back-and-forth to negative phototaxis, and from positive phototaxis to back-and-forth in a couple experiments, see Supp. Fig. 5. While the model successfully reproduces the phototactic behavior and the time scales at play, it fails to reproduce two experimental observations. First, in a couple experiments, multiple consecutive back-and-forth of the center of mass were observed at intermediate intensities, see Supp. Fig. 6. Second, in experiments with consecutive stimuli at intermediate light intensities, algae in the third experiment (after a waiting time of 30 minutes) display positive phototaxis instead of the expected backand-forth behavior observed during the first experiment, see Fig. 6b and Supp. Fig. 5. Both behaviors can be obtained in the model by supposing that the threshold sT increases with the time spent under a light stimulus, and so depends on the history of the microorganism. Such an increasing threshold would also enable to recover the traditional adaptative behavior at longer time scales [15]. This implies having a third equation describing the timeevolution of sT , thus introducing another time constant into the problem. In the lab, the memory effects reported here imply that it is not possible to repeat the same phototaxis experiments on a given batch of cells and expect similar outcomes. Such an effect was already known in the case of long-term adaptation [15]. The data reported here show that even in the case of short-term experiments, the behavior is not necessarily reproducible. To study phototaxis quantitatively, experimental repeats should be carried out with different batches of naive cells. Having identified this short time-scale, our experiments pave the way for the study of phototaxis in the presence of timevarying light stimuli, much as the algae are subject to in their natural environment. There, light is constantly fluctuating. The short-term memory could then be a beneficial way to integrate a light signal randomly interrupted by the shadow of other motile organisms or objects. ACKNOWLEDGMENTS We thank David Gonzalez-Rodriguez for insightful comments on an early version of the model, Maxime Simon for preliminary experiments, Salome Guttierez Ramos for help with microfabrication, and Caroline Frot for precious technical support. This work was supported by “Investissements d’Avenir” LabEx PALM (ANR-10- LABX-0039-PALM) and by PEPS “M´ecanique du futur”. References [1] G. J´ekely, Philosophical Transactions of the Royal Society B: Biological Sciences 364, 2795 (2009). [2] K. W. Foster and R. D. Smyth, Microbiological reviews 44, 572 (1980). [3] P. Hegemann and P. Berthold, in The Chlamydomonas Sourcebook (Elsevier, 2009) pp. 395–429. [4] U. R¨uffer and W. Nultsch, Cell motility and the cytoskeleton 18, 269 (1991). [5] M. E. H. Feinleib and G. M. Curry, Physiologia Plantarum 25, 346 (1971). [6] W. Nultsch, J. Pfau, and R. Dolle, Archives of microbiology 144, 393 (1986). [7] N. Morel-Laurens, Photochemistry and Photobiology 45, 119 (1987). [8] R. Dolle, J. Pfau, and W. Nultsch, Journal of plant physiology 126, 467 (1987). [9] T. Takahashi and M. Watanabe, FEBS Letters 336, 516 (1993). [10] K.-i. Wakabayashi, Y. Misawa, S. Mochiji, and R. Kamiya, Proceedings of the National Academy of Sciences 108, 11280 (2011). [11] M. B¨ohm, D. Boness, E. Fantisch, H. Erhard, J. Frauenholz, Z. Kowalzyk, N. Marcinkowski, S. Kateriya, P. Hegemann, and G. Kreimer, The Plant Cell 31, 886 (2019). [12] R. M. Macnab and D. E. Koshland Jr, Proceedings of the National Academy of Sciences 69, 2509 (1972). [13] H. C. Berg and P. Tedesco, Proceedings of the National Academy of Sciences 72, 3235 (1975). [14] P. Van Haastert, The Journal of cell biology 96, 1559 (1983). [15] A. Mayer, Nature 217, 875 (1968). [16] E. Harris, D. Stern, and G. Witman, The chlamydomonas sourcebook, vol. 1 san diego (2009). [17] Y. Xia and G. M. Whitesides, Annual review of materials science 28, 153 (1998). [18] N. Ueki, T. Ide, S. Mochiji, Y. Kobayashi, R. Tokutsu, N. Ohnishi, K. Yamaguchi, S. Shigenobu, K. Tanaka, J. Minagawa, et al., Proceedings of the National Academy of Sciences 113, 5299 (2016). [19] A. Ramamonjy, J. Dervaux, and P. Brunet, Physical Review Letters 128, 258101 (2022). [20] C. T. Kreis, M. Le Blay, C. Linne, M. M. Makowski, and O. B¨aumchen, Nature Physics 14, 45 (2018). [21] S. Till, F. Ebmeier, A. A. Fragkopoulos, M. G. Mazza, and O. B¨aumchen, Physical Review Research 4, L042046 (2022). [22] K. C. Leptos, M. Chioccioli, S. Furlan, A. I. Pesci, and R. E. Goldstein, Physical Review E 107 (2023). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Short-term Memory Effects in the Phototactic Behavior of Microalgae: Discussion and References

Authors:
(1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France;
(2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France;
(3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Methods Results Model Discussion, Acknowledgements and References Supplementary Material IV. MODEL A prediction of the model is that algae can switch behavior successively from positive phototaxis, to backand-forth, to negative phototaxis before reverting back to positive phototaxis. Such is the case for three consecutive stimuli of I0 = 0.02 µmol · m−2 ·s −1 spaced by 90 s and a pause of 30 min before the fourth stimulus, see Fig. 8d. This switch through all phototactic behaviors requires a precise tuning of both the light intensity and the waiting times, which is experimentally tedious. We were however able to experimentally observe the switch from positive to back-and-forth behavior and from back-and-forth to negative behavior, see Supp. Fig. 5. This paper is available on arxiv under CC 4.0 license. Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Authors: Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Model Model Discussion, Acknowledgements and References Discussion, Acknowledgements and References Supplementary Material Supplementary Material IV. MODEL A prediction of the model is that algae can switch behavior successively from positive phototaxis, to backand-forth, to negative phototaxis before reverting back to positive phototaxis. Such is the case for three consecutive stimuli of I0 = 0.02 µmol · m−2 ·s −1 spaced by 90 s and a pause of 30 min before the fourth stimulus, see Fig. 8d. This switch through all phototactic behaviors requires a precise tuning of both the light intensity and the waiting times, which is experimentally tedious. We were however able to experimentally observe the switch from positive to back-and-forth behavior and from back-and-forth to negative behavior, see Supp. Fig. 5. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Short-term Memory Effects in the Phototactic Behavior of Microalgae: Model

(1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France;
(2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France;
(3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Methods Results Model Discussion, Acknowledgements and References Supplementary Material III. RESULTS A. First experimental results In between these two light intensities, there is a transition regime, where the phototactic behavior is hard to reproduce, despite keeping all experimental parameters identical: the microalgae sometimes exhibit a transient positive phototaxis followed by a negative phototaxis (see Fig. 2b), but can also display purely positive phototaxis, or purely negative phototaxis (see later in text). The aim of our work is to better understand the parameters responsible for this variety of algal behaviors in the transition regime. We investigate the effects of well diameter, algae concentration, light intensity, and finally history of the algae, on the phototactic behavior. B. Quantification of the phototactic behavior To quantify the phototactic behavior of a microalgae population constrained in its well, we binarize the experimental images using a simple Otsu threshold on pixel intensities. This leads to images where the algae are white, on a black background (see Supp. Fig. 1). In each image, we then calculate the center of mass zcm of the white pixels, corresponding to the center of mass of the population. The position of the center of mass is tracked over time, and eventually normalized to the radius of the well. A value of the center of mass zcm = 1 (resp. zcm = −1) corresponds to all algae accumulating at the boundary of the well facing (resp. opposite from) the light. In one experimental run, the phototactic response of 15 – 30 wells containing microalgae is quantified. The field of view contains wells with at least 3 different diameters, and there are at least 4 wells of each diameter, see Fig. 1b. We start with an experiment where the algae are at the same concentration in all wells, and do not find any impact of the well diameter on the phototactic behavior of populations of C. reinhardtii: for all diameters, in one given experiment, the algae exhibit the same behavior, as shown by the evolution of the center of mass of the population, see Fig. 3. Note that the center of mass never reaches ±1. This is mainly due to a fraction of the algae not responding to light, see Supp. Fig. 2. A second-order effect is that algae take space, and so the center of mass of the population can never reach ±1. The time scales for pure positive and pure negative phototaxis are different: positive phototaxis leads to an accumulation on the side of the light source within tpos ≈ 5.3 ± 0.9 s (mean ± std. deviation) after the stimulus is turned on. The time scale for negative phototaxis is slightly longer: tneg ≈ 9.1 ± 1.1 s. In the case where there is a back-and-forth between positive and negative phototaxis, the first accumulation occurs within tpos ≈ 5.1±0.3 s while the second accumulation is within tneg ≈ 34 ± 10 s. Negative phototaxis always occurs on a time scale longer than positive phototaxis, irrespective of the behavior (back-and-forth or not) of the population. C. Effect of cell concentration One possibility for the alternating phototactic behavior would be to invoke screening of the light by the algae. We would then expect that at high algae concentration, the algae closest to the light source screen the light intensity, so that the algae further away from the light source see a dimmer light. This could result in a seemingly biphasic behavior, with, at high light intensities, algae close to the light source showing negative phototaxis, and algae further away from the light source exhibiting positive phototaxis. To test this hypothesis, we enclosed microalgae at concentrations ϕ = 0.008 to ϕ = 0.6 in the microwells, and monitored the evolution of their center of mass. The concentration ϕ is defined as the surface fraction of algae in a microwell, see Methods. For ϕ > 0.5, the well is essentially packed with algae, which impedes their motion and so leads to the center of mass not moving even during the light stimulus, see Supp. Fig. 3. For ϕ ≤ 0.5, we do not see any effect of the concentration on the sign of phototaxis: the phototactic behavior depends solely on the light intensity, see Fig. 4. There is however an effect of concentration on the position of the center of mass zcm: when the concentration is higher, the algae take more space and the center of mass of the population gets closer to the center of the well, see Fig. 4a and c. The experiments were reproduced over a range of light intensities and algae concentrations, in more than 300 wells. In each well, the behavior was quantified as positive phototaxis, negative phototaxis, or back-and-forth. The resulting phase diagram of phototactic behavior as a function of light intensity and algae concentration shows no clear influence of the algae concentration on the phototactic behavior, see Fig. 5. D. Effect of history We finally investigated the effect of history on the behavior of the algae. To do so, a population of algae was kept in the dark for one hour and then exposed to a given light stimulus during 90 seconds. The light was then turned off during 90 seconds, after which the exact same stimulus was re-applied. At low light intensities, the qualitative behavior of algae does not change between the two stimuli: the algae always exhibit positive phototaxis, see the first two graphs in Fig. 6a. At high light intensities, the algae always exhibit negative phototaxis, see the first two graphs in Fig. 6c. At intermediate light intensities however, the behavior of the algae changes between the two stimuli: in the first stimulus, the algae exhibit a back-and-forth motion, first towards the light and then away from it. During the second stimulus, the algae show negative phototaxis, see the first two graphs in Fig. 6b. To understand whether the change in behavior was permanent, the algae were allowed to rest in the dark for 30 minutes after the end of the second stimulus. Then, the same protocol was followed: a third stimulus, of the same intensity as the two previous ones, was applied. After 90 seconds in the dark, the algae were restimulated with a fourth, identical stimulus. In the case of low and high light intensity, the qualitative response of the algae was the same for the third and fourth stimuli as for the two first ones, see the last two graphs in Fig. 6a,c. At intermediate light intensities, the algae showed positive phototaxis during the third stimulus, and then qualitatively changed their behavior during the fourth stimulus to exhibit slightly negative phototaxis, see the last two graphs in Fig. 6b. This last response to light, averaged over 31 wells, is quite subtle. Indeed, after several stimuli, most of the algae stick to the wells, a likely effect of light stimulation [20]. These stuck algae move very slowly by gliding [21], see Supp. Fig. 4. The center of mass of the population then largely reflects the position of the stuck algae. The motile algae however do exhibit negative phototaxis, see Supp. Fig. 4. Note that the quantitative response of the algae, as measured by the position of the center of mass zcm, differs between the four stimuli – even in the case of low and high light intensities. For example, at low light intensities (positive phototaxis), the shift in the center of mass is identical for stimuli 1 and 2. The shift is less pronounced for stimuli 3 and 4, see Fig. 6a. In contrast, in the case of negative phototaxis, the shift of the center of mass is identical for stimuli 1 and 3, as well as for stimuli 2 and 4, see Fig. 6c: there, resting in the dark for 30 minutes seems to reset the phototactic behavior. What is important in our case however is the qualitative response: the algae can change phototactic behavior at intermediate light intensities due to the stimuli they previously experienced. The change in behavior is not an adaptive response, which would make the algae show first negative phototaxis, then positive phototaxis in response to the same stimulus. It rather corresponds to an integration of the signal over time. This effect explains the variety of behaviors observed in the transition regime between positive and negative phototaxis: one cannot predict the phototactic response of an alga without knowing its history. This paper is available on arxiv under CC 4.0 license. (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Model Model Discussion, Acknowledgements and References Discussion, Acknowledgements and References Supplementary Material Supplementary Material III. RESULTS A. First experimental results In between these two light intensities, there is a transition regime, where the phototactic behavior is hard to reproduce, despite keeping all experimental parameters identical: the microalgae sometimes exhibit a transient positive phototaxis followed by a negative phototaxis (see Fig. 2b), but can also display purely positive phototaxis, or purely negative phototaxis (see later in text). The aim of our work is to better understand the parameters responsible for this variety of algal behaviors in the transition regime. We investigate the effects of well diameter, algae concentration, light intensity, and finally history of the algae, on the phototactic behavior. B. Quantification of the phototactic behavior To quantify the phototactic behavior of a microalgae population constrained in its well, we binarize the experimental images using a simple Otsu threshold on pixel intensities. This leads to images where the algae are white, on a black background (see Supp. Fig. 1). In each image, we then calculate the center of mass zcm of the white pixels, corresponding to the center of mass of the population. The position of the center of mass is tracked over time, and eventually normalized to the radius of the well. A value of the center of mass zcm = 1 (resp. zcm = −1) corresponds to all algae accumulating at the boundary of the well facing (resp. opposite from) the light. In one experimental run, the phototactic response of 15 – 30 wells containing microalgae is quantified. The field of view contains wells with at least 3 different diameters, and there are at least 4 wells of each diameter, see Fig. 1b. We start with an experiment where the algae are at the same concentration in all wells, and do not find any impact of the well diameter on the phototactic behavior of populations of C. reinhardtii: for all diameters, in one given experiment, the algae exhibit the same behavior, as shown by the evolution of the center of mass of the population, see Fig. 3. Note that the center of mass never reaches ±1. This is mainly due to a fraction of the algae not responding to light, see Supp. Fig. 2. A second-order effect is that algae take space, and so the center of mass of the population can never reach ±1. The time scales for pure positive and pure negative phototaxis are different: positive phototaxis leads to an accumulation on the side of the light source within tpos ≈ 5.3 ± 0.9 s (mean ± std. deviation) after the stimulus is turned on. The time scale for negative phototaxis is slightly longer: tneg ≈ 9.1 ± 1.1 s. In the case where there is a back-and-forth between positive and negative phototaxis, the first accumulation occurs within tpos ≈ 5.1±0.3 s while the second accumulation is within tneg ≈ 34 ± 10 s. Negative phototaxis always occurs on a time scale longer than positive phototaxis, irrespective of the behavior (back-and-forth or not) of the population. C. Effect of cell concentration One possibility for the alternating phototactic behavior would be to invoke screening of the light by the algae. We would then expect that at high algae concentration, the algae closest to the light source screen the light intensity, so that the algae further away from the light source see a dimmer light. This could result in a seemingly biphasic behavior, with, at high light intensities, algae close to the light source showing negative phototaxis, and algae further away from the light source exhibiting positive phototaxis. To test this hypothesis, we enclosed microalgae at concentrations ϕ = 0.008 to ϕ = 0.6 in the microwells, and monitored the evolution of their center of mass. The concentration ϕ is defined as the surface fraction of algae in a microwell, see Methods. For ϕ > 0.5, the well is essentially packed with algae, which impedes their motion and so leads to the center of mass not moving even during the light stimulus, see Supp. Fig. 3. For ϕ ≤ 0.5, we do not see any effect of the concentration on the sign of phototaxis: the phototactic behavior depends solely on the light intensity, see Fig. 4. There is however an effect of concentration on the position of the center of mass zcm: when the concentration is higher, the algae take more space and the center of mass of the population gets closer to the center of the well, see Fig. 4a and c. The experiments were reproduced over a range of light intensities and algae concentrations, in more than 300 wells. In each well, the behavior was quantified as positive phototaxis, negative phototaxis, or back-and-forth. The resulting phase diagram of phototactic behavior as a function of light intensity and algae concentration shows no clear influence of the algae concentration on the phototactic behavior, see Fig. 5. D. Effect of history We finally investigated the effect of history on the behavior of the algae. To do so, a population of algae was kept in the dark for one hour and then exposed to a given light stimulus during 90 seconds. The light was then turned off during 90 seconds, after which the exact same stimulus was re-applied. At low light intensities, the qualitative behavior of algae does not change between the two stimuli: the algae always exhibit positive phototaxis, see the first two graphs in Fig. 6a. At high light intensities, the algae always exhibit negative phototaxis, see the first two graphs in Fig. 6c. At intermediate light intensities however, the behavior of the algae changes between the two stimuli: in the first stimulus, the algae exhibit a back-and-forth motion, first towards the light and then away from it. During the second stimulus, the algae show negative phototaxis, see the first two graphs in Fig. 6b. To understand whether the change in behavior was permanent, the algae were allowed to rest in the dark for 30 minutes after the end of the second stimulus. Then, the same protocol was followed: a third stimulus, of the same intensity as the two previous ones, was applied. After 90 seconds in the dark, the algae were restimulated with a fourth, identical stimulus. In the case of low and high light intensity, the qualitative response of the algae was the same for the third and fourth stimuli as for the two first ones, see the last two graphs in Fig. 6a,c. At intermediate light intensities, the algae showed positive phototaxis during the third stimulus, and then qualitatively changed their behavior during the fourth stimulus to exhibit slightly negative phototaxis, see the last two graphs in Fig. 6b. This last response to light, averaged over 31 wells, is quite subtle. Indeed, after several stimuli, most of the algae stick to the wells, a likely effect of light stimulation [20]. These stuck algae move very slowly by gliding [21], see Supp. Fig. 4. The center of mass of the population then largely reflects the position of the stuck algae. The motile algae however do exhibit negative phototaxis, see Supp. Fig. 4. Note that the quantitative response of the algae, as measured by the position of the center of mass zcm, differs between the four stimuli – even in the case of low and high light intensities. For example, at low light intensities (positive phototaxis), the shift in the center of mass is identical for stimuli 1 and 2. The shift is less pronounced for stimuli 3 and 4, see Fig. 6a. In contrast, in the case of negative phototaxis, the shift of the center of mass is identical for stimuli 1 and 3, as well as for stimuli 2 and 4, see Fig. 6c: there, resting in the dark for 30 minutes seems to reset the phototactic behavior. What is important in our case however is the qualitative response: the algae can change phototactic behavior at intermediate light intensities due to the stimuli they previously experienced. The change in behavior is not an adaptive response, which would make the algae show first negative phototaxis, then positive phototaxis in response to the same stimulus. It rather corresponds to an integration of the signal over time. This effect explains the variety of behaviors observed in the transition regime between positive and negative phototaxis: one cannot predict the phototactic response of an alga without knowing its history. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Short-term Memory Effects in the Phototactic Behavior of Microalgae: Results

Authors:
(1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France;
(2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France;
(3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Methods Results Model Discussion, Acknowledgements and References Supplementary Material II. METHODS A. Culture preparation C. reinhardtii strain CC-125 (Chlamydomonas Resource Center, University of Minnesota, MN, USA) were cultured on a solid medium prepared with TrisAcetate-Phosphate (Gibco™ TAP, ThermoFischer Scientific, France) and agar every 4 weeks, to keep the strain motile, responsive to light and prevent as much as possible the cells from sticking to the walls of the devices. For experiments, colonies of C. reinhardtii were picked from solid cultures and recultured in liquid TAP. The cultures were placed in an agitating incubator at 176 rpm, under a day/light cycle of 14h/10h with an illuminance of 60 µmol · m−2 · s −1 and at a fixed temperature of 22◦C. The cells reached maximum motile cell density within 3 days [16], after which a solution of swimming C. reinhardtii was obtained. Before being used for experiments, the cultures underwent a series of centrifugation steps. This enabled to concentrate the algal solution, get rid of low-motility and dead algae, and of cellular debris. First, 45 mL of the liquid culture were centrifuged at 1057g for 10 minutes. Then, 39 mL of the supernatant was removed, and the bottom 6 mL were centrifuged at 73g for 2 minutes. The supernatant, containing the motile cells, was kept and centrifuged again at 285g for 5 minutes. The amount of supernatant was adjusted to obtain the desired algae final concentration. B. Experimental setup An array of shallow cylindrical wells was made in polydimethylsiloxane (PDMS, Dow-Corning Sylgard 184), using standard soft lithography techniques [17]. The wells had a depth of 32 µm and diameters ranging from 200 µm to 500 µm. The PDMS was rendered hydrophilic by plasma cleaning. Then, a drop of ≈ 10 µL of the algal solution was deposited on the PDMS. The device was closed with a plasma-activated glass slide by applying gentle pressure. The trapped algae were observed under a Nikon TI microscope, using a 4x objective. Images were recorded at 10 fps with a CMOS camera (Hamamatsu ORCAFlash4.0 LT, Hamamatsu Photonics, France). A longpass red filter with a 645 nm cut-on wavelength (Newport RG645) was placed between the microscope’s light source and the microwells. A 3 mm blue LED with a wavelength λ = 470 nm and a light intensity of 7000 mcd (Plan`ete Leds, France) was placed on the microscope’s plate, approximately 2 cm away from the microfluidic chip, on the same plane. The LED operating voltage range was between 2.4 V and 3.2 V and its operating current was 20 mA. The light intensity was tuned by varying the applied voltage. An illustration of the setup is shown in Fig. 1. The algae concentration ϕ was determined in each well from images captured at the beginning of the experiments, before exposition to a light stimulus. Initially, the algae swam randomly and were distributed homogeneously in each well. They were identified by thresholding the images, and the total area they occupied in each well was determined. The concentration in algae was then defined as the fraction of surface area occupied by the algae in the well. C. Measuring the local light intensity We estimated the local light intensity inside a well using the number of photons reaching the sensor of the camera. First, we determined the camera offset value by blocking off all the light to the camera and taking a 16-bit image. The spatial average intensity in grey value of all the pixels in the image was the camera’s offset. Then, a 16-bit setup image of the sample was taken at the current experimental condition, with the light of the microscope turned off and the blue LED light on. The camera offset value was then subtracted from each pixel in the setup image. The grey values were first converted to number of electrons by dividing each pixel in the image by the conversion gain of the camera. The electrons were converted to photons by dividing the number of electrons from each pixel by the quantum efficiency (QE) of the sensor at λ = 470 nm. Last, the local light intensity inside a well was computed as the average number of photons within the well. This paper is available on arxiv under CC 4.0 license. Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Authors: Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Model Model Discussion, Acknowledgements and References Discussion, Acknowledgements and References Supplementary Material Supplementary Material II. METHODS A. Culture preparation C. reinhardtii strain CC-125 (Chlamydomonas Resource Center, University of Minnesota, MN, USA) were cultured on a solid medium prepared with TrisAcetate-Phosphate (Gibco™ TAP, ThermoFischer Scientific, France) and agar every 4 weeks, to keep the strain motile, responsive to light and prevent as much as possible the cells from sticking to the walls of the devices. For experiments, colonies of C. reinhardtii were picked from solid cultures and recultured in liquid TAP. The cultures were placed in an agitating incubator at 176 rpm, under a day/light cycle of 14h/10h with an illuminance of 60 µmol · m−2 · s −1 and at a fixed temperature of 22◦C. The cells reached maximum motile cell density within 3 days [16], after which a solution of swimming C. reinhardtii was obtained. Before being used for experiments, the cultures underwent a series of centrifugation steps. This enabled to concentrate the algal solution, get rid of low-motility and dead algae, and of cellular debris. First, 45 mL of the liquid culture were centrifuged at 1057g for 10 minutes. Then, 39 mL of the supernatant was removed, and the bottom 6 mL were centrifuged at 73g for 2 minutes. The supernatant, containing the motile cells, was kept and centrifuged again at 285g for 5 minutes. The amount of supernatant was adjusted to obtain the desired algae final concentration. B. Experimental setup An array of shallow cylindrical wells was made in polydimethylsiloxane (PDMS, Dow-Corning Sylgard 184), using standard soft lithography techniques [17]. The wells had a depth of 32 µm and diameters ranging from 200 µm to 500 µm. The PDMS was rendered hydrophilic by plasma cleaning. Then, a drop of ≈ 10 µL of the algal solution was deposited on the PDMS. The device was closed with a plasma-activated glass slide by applying gentle pressure. The trapped algae were observed under a Nikon TI microscope, using a 4x objective. Images were recorded at 10 fps with a CMOS camera (Hamamatsu ORCAFlash4.0 LT, Hamamatsu Photonics, France). A longpass red filter with a 645 nm cut-on wavelength (Newport RG645) was placed between the microscope’s light source and the microwells. A 3 mm blue LED with a wavelength λ = 470 nm and a light intensity of 7000 mcd (Plan`ete Leds, France) was placed on the microscope’s plate, approximately 2 cm away from the microfluidic chip, on the same plane. The LED operating voltage range was between 2.4 V and 3.2 V and its operating current was 20 mA. The light intensity was tuned by varying the applied voltage. An illustration of the setup is shown in Fig. 1. The algae concentration ϕ was determined in each well from images captured at the beginning of the experiments, before exposition to a light stimulus. Initially, the algae swam randomly and were distributed homogeneously in each well. They were identified by thresholding the images, and the total area they occupied in each well was determined. The concentration in algae was then defined as the fraction of surface area occupied by the algae in the well. C. Measuring the local light intensity We estimated the local light intensity inside a well using the number of photons reaching the sensor of the camera. First, we determined the camera offset value by blocking off all the light to the camera and taking a 16-bit image. The spatial average intensity in grey value of all the pixels in the image was the camera’s offset. Then, a 16-bit setup image of the sample was taken at the current experimental condition, with the light of the microscope turned off and the blue LED light on. The camera offset value was then subtracted from each pixel in the setup image. The grey values were first converted to number of electrons by dividing each pixel in the image by the conversion gain of the camera. The electrons were converted to photons by dividing the number of electrons from each pixel by the quantum efficiency (QE) of the sensor at λ = 470 nm. Last, the local light intensity inside a well was computed as the average number of photons within the well. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Short-term Memory Effects in the Phototactic Behavior of Microalgae: Methods

Authors:
(1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France;
(2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France;
(3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Methods Results Model Discussion, Acknowledgements and References Supplementary Material Abstract Phototaxis, the directed motion in response to a light stimulus, is crucial for motile microorganisms that rely on photosynthesis, such as the unicellular microalga Chlamydomonas reinhardtii. It is well known that microalgae adapt to ambient light stimuli. On time scales of several dozen minutes, when stimulated long enough, the response of the microalga evolves as if the light intensity were decreasing [Mayer, Nature (1968)]. Here, we show experimentally that microalgae also have a shortterm memory, on the time scale of a couple of minutes, which is the opposite of adaptation. At these short time scales, when stimulated consecutively, the response of C. reinhardtii evolves as if the light intensity were increasing. Our experimental results are rationalized by the introduction of a simplified model of phototaxis. Memory comes from the interplay between an internal biochemical time scale and the time scale of the stimulus; as such, these memory effects are likely to be widespread in phototactic microorganisms. I. INTRODUCTION Phototaxis, the directed motion of organisms in response to a light stimulus, is widespread both in prokaryotes and single-cell eukaryotes [1]. One of the cellular models for eukaryotic phototaxis is the microalga Chlamydomonas reinhardtii, which responds to bluegreen light [2, 3]. When light hits the eyespot of the microalga, it induces photocurrents, whose amplitude depend on the light intensity. These photocurrents then induce flagellar currents which change the beating pattern of the flagella, leading to reorientation and eventually phototaxis [3, 4]. At low light intensities, wild-type C. reinhardtii cells swim towards the light, while they swim away from the light at high light intensities. This corresponds to positive and negative phototaxis, respectively [5]. What sets the change in phototactic behavior? The answer to this question is not fully settled [3]. Several biochemical parameters were found to affect the sign of phototaxis of C. reinhardtii, such as the amount of calcium ions in the surrounding medium [6–8], photosynthetic activity of the microalgae [9], the amount of intracellular reactive oxygen species [10], or the phosphorylation of channelrhodopsin-1, one of the photoreceptors of C. reinhardtii [11]. It is also known that the history of the alga plays a role in its phototactic response: like many unicellular organisms [12–14], C. reinhardtii adapt to their environment. A naive cell population, kept in the dark, exhibits negative phototaxis in response to an intense light stimulus; the same cell population, exposed to the same intense light stimulus, undergoes positive phototaxis when it has been previously exposed to light for a couple dozens minutes [15]. This change in phototactic behavior of a population depending on the history of irradiation is consistent with results obtained at the single-cell level by R¨uffer and Nultsch [4], who monitored the change in beating of the two flagella of C. reinhardtii in response to increasing and decreasing light-stimuli. Such a history-dependent change in the phototactic response occurs on long time scales, of the order of a couple dozen minutes. Here, we show experimentally that the change in phototactic behavior also depends on the recent history of the cell, where the time scale is of the order of a couple minutes. At these short time scales, the algae exhibit a behavior that is the exact opposite of the long-term adaptation: they integrate consecutive signals over time. When subjected to two consecutive, closely spaced identical stimuli, an alga essentially adds up the second stimulus to the first one, and acts as if the second stimulus were of higher intensity than the first one. Such a signal integration has, to the best of our knowledge, never been observed in the phototactic response of microalgae. Our results are rationalized by the introduction of a simplified model of phototaxis. The memory emerges in the model from the interplay between two time scales, the time between successive stimuli and the relaxation time of an inner biochemical process. Since the model is generic, similar short-term memory effects are likely to be widespread in other organisms that experience phototaxis. This paper is available on arxiv under CC 4.0 license. Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Authors: Authors: (1) Taha Laroussi, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France; (2) Mojtaba Jarrahi, Universit´e Paris-Saclay, CNRS, FAST, 91405 Orsay, France; (3) Gabriel Amselem, Laboratoire d’Hydrodynamique (LadHyX), CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France. Table of Links Abstract and Introduction Abstract and Introduction Methods Methods Results Results Model Model Discussion, Acknowledgements and References Discussion, Acknowledgements and References Supplementary Material Supplementary Material Abstract Phototaxis, the directed motion in response to a light stimulus, is crucial for motile microorganisms that rely on photosynthesis, such as the unicellular microalga Chlamydomonas reinhardtii. It is well known that microalgae adapt to ambient light stimuli. On time scales of several dozen minutes, when stimulated long enough, the response of the microalga evolves as if the light intensity were decreasing [Mayer, Nature (1968)]. Here, we show experimentally that microalgae also have a shortterm memory, on the time scale of a couple of minutes, which is the opposite of adaptation. At these short time scales, when stimulated consecutively, the response of C. reinhardtii evolves as if the light intensity were increasing. Our experimental results are rationalized by the introduction of a simplified model of phototaxis. Memory comes from the interplay between an internal biochemical time scale and the time scale of the stimulus; as such, these memory effects are likely to be widespread in phototactic microorganisms. I. INTRODUCTION Phototaxis, the directed motion of organisms in response to a light stimulus, is widespread both in prokaryotes and single-cell eukaryotes [1]. One of the cellular models for eukaryotic phototaxis is the microalga Chlamydomonas reinhardtii, which responds to bluegreen light [2, 3]. When light hits the eyespot of the microalga, it induces photocurrents, whose amplitude depend on the light intensity. These photocurrents then induce flagellar currents which change the beating pattern of the flagella, leading to reorientation and eventually phototaxis [3, 4]. At low light intensities, wild-type C. reinhardtii cells swim towards the light, while they swim away from the light at high light intensities. This corresponds to positive and negative phototaxis, respectively [5]. What sets the change in phototactic behavior? The answer to this question is not fully settled [3]. Several biochemical parameters were found to affect the sign of phototaxis of C. reinhardtii, such as the amount of calcium ions in the surrounding medium [6–8], photosynthetic activity of the microalgae [9], the amount of intracellular reactive oxygen species [10], or the phosphorylation of channelrhodopsin-1, one of the photoreceptors of C. reinhardtii [11]. It is also known that the history of the alga plays a role in its phototactic response: like many unicellular organisms [12–14], C. reinhardtii adapt to their environment. A naive cell population, kept in the dark, exhibits negative phototaxis in response to an intense light stimulus; the same cell population, exposed to the same intense light stimulus, undergoes positive phototaxis when it has been previously exposed to light for a couple dozens minutes [15]. This change in phototactic behavior of a population depending on the history of irradiation is consistent with results obtained at the single-cell level by R¨uffer and Nultsch [4], who monitored the change in beating of the two flagella of C. reinhardtii in response to increasing and decreasing light-stimuli. Such a history-dependent change in the phototactic response occurs on long time scales, of the order of a couple dozen minutes. Here, we show experimentally that the change in phototactic behavior also depends on the recent history of the cell, where the time scale is of the order of a couple minutes. At these short time scales, the algae exhibit a behavior that is the exact opposite of the long-term adaptation: they integrate consecutive signals over time. When subjected to two consecutive, closely spaced identical stimuli, an alga essentially adds up the second stimulus to the first one, and acts as if the second stimulus were of higher intensity than the first one. Such a signal integration has, to the best of our knowledge, never been observed in the phototactic response of microalgae. Our results are rationalized by the introduction of a simplified model of phototaxis. The memory emerges in the model from the interplay between two time scales, the time between successive stimuli and the relaxation time of an inner biochemical process. Since the model is generic, similar short-term memory effects are likely to be widespread in other organisms that experience phototaxis. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Short-term Memory Effects in the Phototactic Behavior of Microalgae: Abstract and Introduction

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