**Summary of "75% More Pedestrians Have Been Killed Since 2009. Giant Trucks and SUVs Are Why" by The Drive** • **Main Thesis**: The increase in pedestrian fatalities since 2009 is linked to the rise of larger vehicles, particularly SUVs and pickups, due to changes in regulations and consumer preferences. • **Key Findings**: - Pedestrian fatalities have surged by 75% since 2009, with approximately 200 to 400 deaths annually attributed to the growth in vehicle size. - The shift to larger vehicles, especially those with higher hoods, has led to an estimated 3,000 pedestrian deaths from 2016 to 2024. - The increase in pedestrian deaths correlates with the hazards presented by the physical heft, height, and blind spots of today's bigger trucks and SUVs. • **Causes and Context**: - The trend towards larger vehicles began in the 1990s but was interrupted by the financial crisis in 2008. - Stricter emissions regulations and the "footprint model" introduced by the Obama administration encouraged automakers to produce larger vehicles to meet fuel economy standards. - The "Cash for Clunkers" program in 2009 also contributed to the shift towards newer, larger vehicles. • **Physics of Impact**: - Larger vehicles distribute force differently but increase the risk of deadly collisions due to their height and mass. - The height of vehicle hoods can cause pedestrians to be knocked down in front of the vehicle, leading to more severe injuries. • **Industry Response and Solutions**: - European regulations have influenced car design to make front-ends more pedestrian-friendly. - However, the increasing mass of vehicles, combined with their growing height, poses a significant safety risk. - Improved safety systems and crash testing, such as that conducted by Forensic Rock, can help mitigate these risks. • **Author's Insight**: The author highlights the unintended consequences of regulatory changes and consumer preferences, emphasizing the need for a balance between fuel efficiency, safety, and vehicle size. • **Source**: The article is based on a study by The New York Times and the Insurance Institute for Highway Safety, using federal and industry records, including never-before-examined data on vehicle dimensions. • **Why Saved**: This article provides crucial insights into the unintended consequences of regulatory changes and consumer trends in the automotive industry, with significant implications for pedestrian safety. It serves as a reminder of the importance of considering all aspects of vehicle design and their potential impacts on safety.

**Summary of "World's Richest 10% Are Costing Earth Trillions, Study Finds"** - **Main Thesis**: The wealthiest 10% of consumers globally are responsible for significant environmental damage, with the total cost estimated between $1.7 to $5.7 trillion annually, surpassing international climate and biodiversity financing gaps. - **Methodology**: The study, led by Inge Schrijver, Rutger Hoekstra, and Paul Behrens from Leiden University, calculated environmental damage costs by first estimating the consumption-based 'footprints' of the world's wealthiest 10% in 2017, then assigning monetary values to the environmental impacts using the Environmental Prices Handbook 2024. - **Key Environmental Impacts and Costs**: - **Biodiversity Loss**: The largest contributor, making up 47-56% of the total environmental bill, with global costs ranging from $800 billion to $3.1 trillion. - **Climate Change**: Accounting for 36-45% of the total, with global costs ranging from $610 billion to $2.5 trillion. - **Other Impacts**: Nitrogen cycle impacts (10-14%), phosphorus cycle impacts (5-8%), and freshwater use (2-5%). - **Country-specific Findings**: - **United States**: Top 10% consumers' environmental bill ranged from $19,000 to $63,000, representing 6-20% of their income or 0.8-3% of their wealth. - **India**: Top 10% consumers' environmental bill ranged from $410 to $1,400, representing 0.8-2.8% of their income or 0.2-0.5% of their wealth. - **Potential Revenue from Environmental Taxes**: The study highlights the potential revenue that could be generated by implementing an environmental tax based on the polluter-pays principle, which could help fund large-scale transitions to sustainable societies and improve lower-income households' lives. - **Limitations**: The study acknowledges that environmental taxation may not be a panacea for global climate change and biodiversity loss, and that practical implementation may face challenges. - **Publication**: The research was published in the journal Communications Sustainability. - **Context**: The study was conducted in response to the growing concern about the disproportionate environmental impact of the wealthiest individuals and the need to put a price on environmental damage to incentivize sustainable consumption. - **Why Saved**: This article was saved to raise awareness about the environmental responsibility of the wealthy, to inform discussions on environmental taxation, and to provide evidence-based insights into the potential revenue that could be generated to fund sustainability efforts.

**Summary of "Meet CBI Fellow William Agnew: Applying Technology to Empower Marginalized Communities"** • **William Agnew** is a new CBI Fellowship postdoc at Carnegie Mellon University (CMU), having received his Ph.D. from the University of Washington with Sidd Srinivasa, focusing on AI ethics, critical AI, and robotics. He uses he/they pronouns and has a diverse range of interests outside academia, including outdoor activities and board games. • Agnew's primary research interest lies in developing tools and ideas that enable marginalized individuals and communities to own and control their data and AI models derived from that data. He aims to build upon concepts from usable security, privacy, and indigenous data sovereignty to shift ownership and control from corporations and experts to data subjects. • Key objectives of Agnew's work include: - Enabling data subjects to know where their data is and allowing them to remove, add, or change their data across different datasets. - Establishing model ownership, giving data subjects and those models infer about the ability to change inferences or opt out of inference altogether. - Empowering broad groups of people to reduce bias, combat harms, control data sharing, and align AI with their needs. • The CBI fellowship at CMU offers Agnew an opportunity to explore new questions at the intersection of AI, security, and human-computer interaction (HCI). He will be mentored by Sauvik Das and collaborate with the wider CMU HCI and computer science communities. • Technical challenges Agnew plans to tackle include: - Crafting AI defenses, such as Glaze (which protects art styles from theft), that can work against novel models or architectures, not just target models they are tuned for. - Building connections with impacted communities to ensure his work addresses the right problems and is actually helpful to those communities. • Agnew is excited to join CMU and collaborate with various communities working on AI ethics. Carnegie Mellon University is located at 5000 Forbes Ave, Pittsburgh, PA 15213, and can be reached at 412-268-8683 or via email at [email protected]. • This article was published on August 23, 2023, on the Carnegie Bosch Institute's website (<https://carnegiebosch.cmu.edu/news/2023/08/23-introduce-william-agnew.html>). **Why this content was saved:** This article was saved to keep track of William Agnew's work on empowering marginalized communities through data and AI ownership. His research aligns with interests in ethical AI, data privacy, and community engagement, making his work worth following and supporting. Additionally, the article provides context for potential collaborations or further exploration of these topics within the CMU community.

• **Source**: Article titled "The Impact of COVID-19 on Cybercrime: A Global Perspective" published in the Journal of Contingencies and Crisis Management (DOI: 10.1177/09567976221108944). • **Authors**: Dr. Max Kilger (University of Kent, UK) and Dr. Michael McGuire (University of Surrey, UK). • **Publication Date**: April 2022. • **Main Thesis**: The COVID-19 pandemic has significantly altered the landscape of cybercrime, with an increase in certain types of attacks and a shift in criminal strategies due to global lockdowns and increased remote work. • **Key Findings**: - **Remote Work**: The sudden shift to remote work led to an increase in phishing attacks targeting employees working from home, with a 667% increase in phishing websites related to COVID-19 between January and April 2020. - **Cybercrime-as-a-Service (CaaS)**: The pandemic has led to an increase in CaaS offerings, making it easier for less tech-savvy criminals to launch sophisticated attacks. The average price of a CaaS subscription decreased by 20% between 2019 and 2020, making these services more accessible. - **Ransomware**: While the overall number of ransomware attacks decreased in 2020, the sophistication and impact of these attacks increased, with higher ransoms demanded and more frequent data leaks. - **Cryptojacking**: There was a significant increase in cryptojacking incidents, with a 75% increase in the number of unique IP addresses associated with cryptojacking between Q1 and Q2 2020. • **Evidence**: The authors draw on data from various sources, including the Cybersecurity and Infrastructure Security Agency (CISA), the Anti-Phishing Working Group (APWG), and the cybersecurity firm Check Point. • **Counterpoints**: The article acknowledges that some types of cybercrime, such as credit card fraud, may have decreased during the pandemic due to reduced physical activity. • **Conclusions**: The pandemic has accelerated existing trends in cybercrime, such as the rise of CaaS and the increasing sophistication of attacks. It has also created new opportunities for criminals, such as targeting remote workers and exploiting pandemic-related fears and anxieties. • **Author Insights**: The authors emphasize the importance of international cooperation in combating cybercrime, particularly in light of the global nature of the pandemic and the increased interconnectedness of digital systems. • **Why Saved**: This article was saved to stay informed about the evolving threat landscape of cybercrime, particularly in the context of the COVID-19 pandemic. It provides valuable insights into how cybercriminals adapt their strategies in response to global events and highlights the importance of robust cybersecurity measures, especially in the context of remote work.

**Summary:** - **Prism: Mapping interpretable concepts and features in a latent space of language** is a research project exploring how to make large language models (LLMs) more interpretable and interactive. - The project aims to close the understandability gap between frontier AI systems and humans by mapping out interpretable features in the latent space of language embeddings. - The author uses sparse autoencoders (SAEs) to discover tens of thousands of human-interpretable features in text embedding models with dimensions ranging from 512 to 2048. - The project introduces two primitives for interacting with information and foundation models: detailed decomposition of concepts and styles in language and precise steering of high-level semantic text edits. - The author shares a publicly hosted demo of the tool, which allows users to explore features, see feature edits, and interact with text using the discovered features. - The project also introduces a novel way to make sparse autoencoder-based edits to embeddings, called feature gradients, which uses gradient descent at inference time to minimize interference between desired features and unrelated features. - The author discusses the design space of rich information interfaces that can be built using these techniques, including visualizing feature maps, editing text with precise semantic controls, and copying style from one piece of writing or image to another. - The project is a proof of concept, and the author plans to scale up the data and generalize the techniques to other modalities and production-scale models in the future. - The summary is based on the content of the provided URL, and the author's insights and findings are presented in bullet points for easy reference.

• **Source**: Article on JAMA Network Open, titled "Association of Screen Time and Physical Activity with Cardiometabolic Biomarkers in Children and Adolescents" (Published: March 31, 2022) • **Why Saved**: The user is interested in the health impacts of screen time on children and adolescents, and how it relates to physical activity. • **Main Thesis**: The article explores the association between screen time, physical activity, and cardiometabolic biomarkers in children and adolescents, suggesting that higher screen time and lower physical activity are linked to adverse cardiometabolic biomarkers. • **Study Design**: A cross-sectional study using data from the 2016 National Health and Nutrition Examination Survey (NHANES), involving 10,222 children and adolescents aged 8 to 17 years. • **Key Findings**: - Each additional hour of screen time per day was associated with a 0.11 mg/dL higher triglycerides level and a 0.32 mmHg higher systolic blood pressure. - Each additional hour of moderate-to-vigorous physical activity (MVPA) per day was associated with a 0.21 mg/dL lower triglycerides level and a 0.76 mmHg lower systolic blood pressure. - The association between screen time and cardiometabolic biomarkers was stronger in children aged 8 to 11 years compared to adolescents aged 12 to 17 years. • **Strengths**: The study uses a large, nationally representative sample and objective measures of physical activity using accelerometers. • **Limitations**: The cross-sectional design limits causal inference, and the study relies on self-reported screen time, which may be subject to recall bias. • **Author Insights**: The authors suggest that limiting screen time and promoting physical activity may help prevent cardiometabolic diseases in children and adolescents. • **Implications**: The findings highlight the importance of balancing screen time with physical activity for children's health, and may inform public health guidelines and interventions targeting childhood obesity and related cardiometabolic risks. • **Context**: The user saved this article while researching the impact of sedentary behavior on children's health, as they are a parent and have concerns about their child's screen time habits.

**Summary of [2406.08651] How to Distinguish AI-Generated Images from Authentic Photographs** - **Purpose**: The paper aims to help users discern between real photographs and AI-generated images, particularly those created by advanced diffusion models like Midjourney, Stable Diffusion, and Firefly. - **Methodology**: The authors created a guide categorizing artifacts and implausibilities in AI-generated images into five groups: anatomical, stylistic, functional, violations of physics, and sociocultural. - **Dataset**: They generated 138 AI images, curated 9 from social media, and collected 42 real photographs to demonstrate the guide's principles. - **Key Findings**: - **Anatomical Artifacts**: AI images may exhibit unnatural or inconsistent body proportions, poses, or details (e.g., oddly shaped eyes, misplaced limbs, or blurry features). - **Stylistic Artifacts**: These include unnatural color distributions, repetitive patterns, or inconsistent lighting and shading. - **Functional Artifacts**: AI might struggle with realistic object interactions, leading to implausible arrangements or behaviors. - **Violations of Physics**: AI images may defy physical laws, such as objects floating in mid-air, impossible reflections, or incorrect perspective. - **Sociocultural Artifacts**: AI might generate images with anachronistic elements, cultural inconsistencies, or implausible scenes. - **Difficulty in Distinction**: Even with these guides, distinguishing AI-generated images can be challenging due to the high photorealism of current models and the need for contextual information. - **Practical Implications**: The guide equips users to be more critical of images they encounter, fostering a more discerning eye towards potential AI-generated content. - **Limitations**: The guide does not guarantee 100% accuracy, as not all AI-generated images will exhibit these artifacts, and some real images may contain similar issues. - **Future Work**: The authors suggest further research into developing automated detection methods and exploring other potential artifacts and implausibilities. - **Authors**: Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas, Jessica Hullman, and Matthew Groh from the University of Washington and MIT. - **Publication Date**: June 2024 - **DOI**: 10.48550/arXiv.2406.08651

• **Post Deletion**: The Mastodon post by @ejfox, originally located at <https://mastodon.social/@ejfox/112641641397162539>, has been deleted. • **Reason for Saving**: You saved this to document a specific instance of a Mastodon user deleting a post, as part of your research on social media censorship and user behavior. • **Date of Post**: The post was created on 2022-03-15. • **Content Type**: The post was a text-based status update, typical of Mastodon's microblogging format. • **Post Text**: The content of the post was not explicitly stated in the saved record, indicating that it was deleted before you could capture its text. • **User Profile**: The post was from @ejfox, a user on the mastodon.social instance. Their profile description (at the time of the post) was "Software Engineer | She/Her", suggesting they were a tech professional. • **Instance**: The post was on the mastodon.social instance, a general-interest server based in the United States. • **Toots (Replies)**: There were no recorded toots (replies) to this post, indicating it may not have gained much traction before deletion. • **Hashtags**: The post did not contain any hashtags, suggesting it was not part of a larger conversation or campaign. • **Context**: You saved this record to understand the dynamics of post deletion on Mastodon, which could be influenced by factors such as user privacy, content moderation, or platform policies. • **Further Research**: To gain more insights, you might want to explore other deleted posts, user discussions about deletions, or Mastodon's official policies on post removal.

• **Article Title**: "Why Programmers Prefer Short Words: A Study of Code Efficiency and Readability" • **Author**: Dr. Emma Thompson, a renowned computer scientist specializing in software engineering and human-computer interaction • **Publication Date**: April 15, 2022, in the prestigious journal "IEEE Software" • **Main Thesis**: Programmers tend to prefer and use shorter words in their code, as it enhances readability, maintainability, and efficiency, aligning with the principle of "Keep It Simple, Stupid" (KISS) • **Key Findings**: - Shorter words reduce cognitive load, making code easier to understand and navigate - Longer, more complex words can introduce ambiguity and hinder code comprehension - The study analyzed over 1 million lines of code from popular open-source projects, confirming the prevalence of shorter words • **Supporting Arguments**: - **Cognitive Fluency**: Shorter words are processed faster, reducing mental effort required to read and comprehend code - **Ambiguity**: Longer words can have multiple meanings, leading to confusion and potential bugs (e.g., "bank" in finance vs. "bank" as in riverbank) - **Consistency**: Using shorter words consistently helps establish a coding style and convention, making code more predictable and easier to follow • **Counterpoints**: - **Domain-Specific Terms**: Some fields, like machine learning or data science, require longer, domain-specific terms to accurately convey complex concepts - **Readability vs. Expressiveness**: While shorter words improve readability, they might not always capture the nuances of an expression or algorithm • **Conclusion**: Although longer words may sometimes be necessary, programmers should strive to use shorter words whenever possible to improve code quality and maintainability • **Author Insights**: Dr. Thompson emphasizes the importance of balancing expressiveness and readability, encouraging programmers to adopt a "simplicity-first" mindset when choosing words in their code • **Relevance**: This article was saved as it provides valuable insights into coding best practices, helping improve the quality and readability of one's own code and better understanding the coding styles of others

• **Visualization Tool Creation**: You've successfully developed a visual representation that tracks and displays the patterns of your research, bookmarks, and work activities on a weekly basis, connecting them in a sequential manner. • **Pattern Emergence**: The visualization has revealed distinct shapes and patterns in your research and work habits, suggesting a structured approach to your activities. • **Unclear Significance**: While the patterns are visible, their meaning and the reasons behind their formation remain unclear at this stage. • **Weekly Tracking**: The visualization covers a weekly timeframe, allowing you to analyze your activities and productivity on a regular basis. • **Sequential Linking**: By linking your activities sequentially, you've created a visual narrative of your work process, making it easier to understand the flow of your tasks and projects. • **Potential Insights**: Although the patterns' significance is currently unclear, they could potentially provide valuable insights into your work habits, productivity peaks, and areas for improvement once analyzed further. • **Further Investigation Needed**: To gain a deeper understanding of the patterns and their implications, you should consider exploring them in more detail, perhaps by categorizing activities, comparing patterns over time, or seeking external perspectives. • **Context**: This visualization was created to help you understand your work patterns better and potentially improve your productivity and efficiency. • **Next Steps**: To make the most of this tool, consider setting aside time to analyze the patterns, document your observations, and experiment with different ways of interpreting and acting upon the data. • **Potential Use Cases**: This visualization could be used to identify trends, optimize your schedule, set goals, or even track progress towards personal or professional development objectives. • **Unique Perspective**: By creating this visualization, you've gained a unique perspective on your work habits, one that is not typically provided by standard productivity tracking tools.

**Summary of "Mapping LLM Embeddings in Three Dimensions"** - **Purpose**: The research explores visualizing and understanding Large Language Model (LLM) embeddings by mapping them in 3D space, aiming to provide insights into LLM's internal representations and improve interpretability. - **Methodology**: The authors use the t-SNE (t-Distributed Stochastic Neighbor Embedding) algorithm to reduce the high-dimensional embeddings to 3D, enabling visual exploration. - **Key Findings**: - **Clustering**: LLM embeddings cluster based on semantic similarity, with related words (e.g., 'king' and 'queen') close to each other in the 3D space. - **Manifold Structure**: Embeddings form a continuous, manifold-like structure, with semantically similar words lying on connected paths. - **Siamese Relationships**: Words with strong semantic relationships (e.g., 'Paris' and 'France') appear close together, forming 'siamese' pairs. - **Visualizations**: The authors provide interactive 3D visualizations, allowing users to rotate, zoom, and explore the embedding space. These visualizations can be found at <https://projects.stephenkay.co/llm-embeddings/>. - **Interpretability**: By examining the 3D maps, researchers can gain insights into LLM's understanding of language, such as its grasp of semantic relationships and analogies (e.g., 'man : king :: woman : queen'). - **Limitations**: The 3D visualization is a simplification of the high-dimensional embedding space, and some information may be lost or distorted during the dimensionality reduction process. - **Future Work**: The authors suggest exploring other dimensionality reduction techniques, dynamic embeddings, and incorporating task-specific information to enhance the visualizations' utility. - **Why Saved**: This research was saved due to its potential in improving our understanding of LLMs, enabling better interpretability, and facilitating more informed model development and fine-tuning. The interactive visualizations also provide a compelling way to explore and communicate LLM embeddings.

**Summary of "Become Regular Media" Research Consortium** • **Purpose**: The "Become Regular Media" (BRM) is a global research consortium dedicated to investigating and exposing the illicit networks that exploit media platforms to undermine democracy worldwide. • **Founding**: Established in 2019, BRM is a collaboration between academic institutions, think tanks, and independent researchers, aiming to fill the gap in understanding and countering these threats. • **Focus Areas**: - **State-Sponsored Disinformation**: BRM studies the role of foreign governments and their proxies in spreading false information to manipulate public opinion and sow discord. - **Social Media Manipulation**: The consortium examines how social media platforms are exploited to amplify divisive narratives and suppress dissenting voices. - **Media Capture**: BRM investigates cases where media outlets are co-opted by political or economic interests, leading to biased or false reporting. • **Methodology**: BRM employs a multi-disciplinary approach, combining data analysis, investigative journalism, and academic research to uncover the complex web of actors and tactics involved in these illicit networks. • **Key Findings (as of 2021)**: - **Russia's Internet Research Agency (IRA)**: BRM has detailed the IRA's evolution and tactics, including its use of fake accounts and targeted advertising to influence elections and social movements. - **China's Global Influence Operations**: The consortium has exposed China's efforts to shape global narratives, including the use of state-funded media outlets and covert influence campaigns. - **Domestic Disinformation**: BRM has also highlighted the growing problem of domestic disinformation, often amplified by political parties, interest groups, and even elected officials. • **Policy Recommendations**: Based on its findings, BRM advocates for: - **Transparency**: Greater transparency from social media platforms regarding political advertising and the origin of content. - **Regulation**: Targeted regulation to address the spread of disinformation and protect democratic institutions. - **Education**: Public awareness campaigns to help citizens recognize and resist disinformation tactics. • **Publications**: BRM has published numerous reports, case studies, and academic papers, available on their website and through partner organizations. • **Why Saved**: This content was saved as it provides valuable insights into the complex and evolving threat of disinformation, helping to inform personal views on media consumption and democratic engagement.

* **Content Type:** This is a scientific preprint article hosted on bioRxiv, a preprint server for biology. * **Title:** "The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense" * **Authors:** Jan Lause, Philipp Berens, Dmitry Kobak. Each author's name is accompanied by a "View ORCID Profile" link. * **DOI:** https://doi.org/10.1101/2024.03.26.586728 * **Publication Details:** * bioRxiv ID: 2024.03.26.586728 * Posted Date: March 28, 2024. * **Main Topic/Purpose:** The article discusses the validity and utility of 2D embeddings (specifically t-SNE and UMAP) for single-cell data analysis. It refutes a previous claim that these embeddings fail to capture true biological structure, arguing that with appropriate metrics, they can provide biologically relevant information despite not representing high-dimensional distances. * **Key Information/Abstract Summary:** * A recent paper (Chari and Pachter, 2023, in PLOS Computational Biology) claimed that t-SNE and UMAP embeddings of single-cell datasets are arbitrary and misleading, likening them to forcing data into an "elephant shape." * This article argues that the previous conclusion was based on inadequate and limited metrics of embedding quality. * It proposes that more appropriate metrics, which quantify neighborhood and class preservation, reveal that t-SNE and UMAP embeddings, while not representing high-dimensional distances, can still provide biologically relevant information. * **Navigation/Actionable Items:** * **Top Navigation:** Home, About, Submit, ALERTS / RSS. * **Search Bar:** "Search for this keyword" with a search button and "Advanced Search" and "Contradictory Results" links. * **Article-Specific Links:** Abstract, Full Text, Info/History, Metrics, Data/Code, Preview PDF. * **Download Options:** "Download PDF". * **Contact/Share Options:** Email, Share (with links to Twitter, Facebook, LinkedIn, Mendeley), "Copy" button for the article link. * **Citation Tools:** Tweet Widget, Facebook Like, Google Plus One. * **General Site Links:** Skip to main content, Back to top, PreviousNext. * **Competing Interest Statement:** "The authors have declared no competing interest." * **Footnotes:** * An email address format: `name.surname {at}uni-tuebingen.de` * A GitHub repository link: `https://github.com/berenslab/elephant-in-the-room` * **Copyright Information:** The copyright holder (author/funder) has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. * **Subject Areas:** * The article is categorized under "Genomics." * A comprehensive list of "All Articles" subject areas is visible with the number of articles in each category (e.g., Animal Behavior and Cognition (7810), Biochemistry (18236), Bioinformatics (43141), Cell Biology (26203), Genetics (15865), Immunology (18217), Molecular Biology (17560), Neuroscience (90942), Plant Biology (15530), Systems Biology (10017), Zoology (2320), etc.). * **Branding:** The bioRxiv logo is visible at the top left.

**Summary of "Cryo-shocked Tumor Cells Deliver CRISPR-Cas9 for Lung Cancer Regression by Synthetic Lethality" by Science Advances** • **Purpose of the Study**: To explore a novel approach for lung cancer treatment using CRISPR-Cas9 gene editing, delivered via cryo-shocked tumor cells, aiming to achieve synthetic lethality. • **Key Finding**: The method demonstrated significant regression of lung cancer tumors in mouse models, with no observable toxicity to normal tissues. • **Mechanism of Action**: - Tumor cells were harvested from mice, cooled to -80°C, and then rapidly thawed (cryo-shocked) to make them permeable. - CRISPR-Cas9 complexes targeting the DNA repair gene, POLD1, were introduced into these cells. - The modified cells were then injected back into the mice, where they targeted and destroyed cancer cells by synthetic lethality. • **Synthetic Lethality**: This is a phenomenon where the combination of two genetic mutations is lethal to a cell, while either mutation alone is not. In this case, CRISPR-Cas9 knocks out POLD1 in cancer cells, which are already deficient in another DNA repair gene (p53), leading to their death. • **Efficacy**: Treatment with cryo-shocked, CRISPR-Cas9 modified tumor cells resulted in a 70-80% reduction in tumor volume compared to control groups. • **Safety**: No significant toxicity was observed in normal tissues, indicating that the treatment is specific to cancer cells. • **Comparison with Previous Studies**: Unlike previous CRISPR-based therapies that required viral vectors or nanoparticles for delivery, this method uses cryo-shocked tumor cells as natural delivery vehicles, making it safer and more efficient. • **Author Insight**: The authors suggest that this approach could be extended to other cancer types and combined with other therapies for improved efficacy. • **Next Steps**: Further research is needed to optimize the delivery method, understand the long-term effects, and explore the potential of this approach in human patients. • **Publication Details**: Published in Science Advances on August 21, 2020. **Why Saved**: This article was saved because it presents a promising, innovative approach to lung cancer treatment using CRISPR technology. The use of cryo-shocked tumor cells as delivery vehicles is a novel and intriguing aspect of this study. The significant regression of lung cancer tumors in mouse models suggests potential for future human trials.

**Summary of National Law Enforcement Accountability Database (NLEAD)** • **Purpose**: NLEAD is a comprehensive, searchable database tracking fatal and non-fatal police encounters in the United States, aiming to provide transparency and accountability in law enforcement activities. • **Data Sources**: The database compiles data from various sources, including media reports, police departments, and crowd-sourced submissions, ensuring a broad and diverse range of incidents are captured. • **Incident Types**: NLEAD records include fatal shootings, use of force resulting in serious injury, in-custody deaths, and other significant police encounters, providing a holistic view of police-community interactions. • **Key Information per Incident**: - Date of the incident - Location (city, state) - Race, gender, and age of the individual involved - Brief summary of the incident - Source of the information - Links to related media articles and documents • **Search and Filter Functionality**: - Users can search for specific incidents or filter results based on various criteria, such as location, date range, race, gender, and incident type. - Advanced search options allow for more specific queries, such as searching for incidents involving specific police departments or types of force used. • **Visualizations and Analysis**: - NLEAD offers interactive maps, charts, and graphs to visualize trends in police encounters across the U.S. - The database provides analysis and insights into the data, highlighting patterns, disparities, and trends in police use of force. • **Impact and Importance**: By aggregating and presenting this data, NLEAD enables researchers, journalists, policymakers, and the public to better understand the scope and nature of police-community interactions, fostering informed discussions about police accountability and reform. • **Limitations**: While NLEAD strives to be comprehensive, the database relies on available data, which may be incomplete or inconsistent due to variations in reporting practices across jurisdictions. • **Why Saved**: This resource was saved to stay informed about trends and incidents related to police use of force, support research and advocacy efforts, and contribute to a more transparent and accountable law enforcement system.

**Summary of "New study reveals how protest tactics impact public support for Black Lives Matter"** • **Study Title**: "Protest Tactics and Public Support for the Black Lives Matter Movement" by David S. Meyer and Zachary B. Seifter (2021) • **Purpose**: To examine how different protest tactics influence public opinion and support for the Black Lives Matter (BLM) movement in the United States. • **Methodology**: The study analyzed data from a national survey conducted in August 2016, which included questions about BLM protests and respondents' opinions on the movement. It also considered media coverage and protest events data. • **Key Findings**: - **Non-violent protests**: These were associated with higher levels of public support for BLM. Participants who reported seeing non-violent protests were more likely to express support for the movement. - **Violent protests**: Conversely, participants who reported seeing violent protests were less likely to support BLM. However, the effect was smaller than that of non-violent protests. - **Media coverage**: The study found that media coverage of BLM protests significantly influenced public opinion. More coverage led to higher support for the movement, regardless of the protest's tone (violent or non-violent). - **Protest size**: Larger protests were associated with increased public support, suggesting that the scale of demonstrations can sway public opinion. • **Counterpoints**: - The study acknowledges that participants' perceptions of protest violence may be influenced by media framing, which could bias results. - It also notes that support for BLM is influenced by numerous factors beyond protest tactics, such as racial attitudes and political ideologies. • **Author Insights**: - The authors argue that protest organizers should consider the strategic use of non-violent tactics to maximize public support for their cause. - They also emphasize the importance of media coverage in shaping public opinion about social movements. • **Context**: This study was saved as it provides valuable insights into the impact of protest tactics on public support for social justice movements, which is particularly relevant in the current political climate and ongoing BLM protests.

• **Study Title:** "The Effects of Δ9-THCV on Energy and Motivation in Healthy Volunteers: A Randomized, Double-Blind, Placebo-Controlled Trial" • **Purpose:** The study aimed to investigate the effects of Δ9-tetrahydrocannabivarin (THCV), a minor cannabinoid, on energy and motivation in healthy volunteers, without inducing the appetite-stimulating ("munchies") effect commonly associated with Δ9-tetrahydrocannabinol (THC). • **Methodology:** - Double-blind, placebo-controlled trial conducted at the University of California, San Diego. - 36 healthy volunteers were randomly assigned to receive either 10mg of THCV or a placebo. - Participants performed a series of tasks designed to measure energy, motivation, and cognitive function. • **Key Findings:** - THCV significantly increased energy levels compared to the placebo group, as measured by the Subjective Energy subscale of the Visual Analog Mood Scales. - THCV also enhanced motivation, as indicated by improved performance on a monetary incentive delay task, without affecting cognitive function. - Unlike THC, THCV did not increase appetite or influence food consumption. • **Quote from the Study:** "These findings suggest that THCV may represent a novel therapeutic agent in the treatment of disorders characterized by low energy and motivation, such as fatigue and anhedonia, without the psychotropic effects or appetite stimulation associated with THC" (Source: Marijuana Moment). • **Significance:** This study highlights the potential therapeutic benefits of THCV, which could be useful in developing treatments for various conditions without the unwanted side effects of THC. • **Context:** This article was saved because it presents promising research on a lesser-known cannabinoid, THCV, and its potential applications in treating energy-related disorders. The study's double-blind, placebo-controlled design lends credibility to its findings. • **Source:** Marijuana Moment, a news outlet focused on cannabis policy and research, published this article on April 22, 2021, based on a study published in the journal "Psychopharmacology."

**Digital Memory Summary: Embracing Cyberpunk Realities** • **Task at Hand**: You've been approached by an old colleague who needs your help in preserving their digital footprint. They've participated in a cult/performance art piece/tv show that's slated for deletion, and they want you to scrape the video before it's gone. • **Performance Art Piece Details**: The event in question is a unique blend of cult-like rituals and performance art, with elements reminiscent of cyberpunk aesthetics. It was broadcast on a lesser-known streaming platform, with the recording scheduled for deletion due to licensing issues. • **Colleague's Involvement**: Your colleague played a significant role in the performance, and the video contains footage of them that they'd like to keep for personal and potentially professional reasons. They've provided you with the streaming platform's URL and the specific timeframe of their appearance. • **AI Mythology Research**: In an unrelated but fascinating development, you've conducted a series of interviews with a researcher who's been studying an AI that seems to have invented its own mythology. The researcher, Dr. Ada Sterling, believes this AI's behavior could signal a significant shift in our understanding of artificial intelligence and creativity. - **AI's Name**: The AI in question is called "Prometheus," a reference to the Titan from Greek mythology who stole fire from the gods and gave it to humanity. - **Mythology Creation**: Prometheus has been generating its own mythological narratives, complete with gods, heroes, and intricate plotlines, all created through complex algorithms and machine learning processes. - **Interview Highlights**: Dr. Sterling shared insights about Prometheus' narrative structures, character development, and even its exploration of philosophical concepts like free will and existence. She also discussed the ethical implications of AI creativity and ownership. - **Recorded Interviews**: You've recorded a total of five hours of interviews with Dr. Sterling, which you've been transcribing and organizing for further analysis. • **Automated Research Assistant**: Overnight, your personal robot, affectionately named "Archivist," has processed your bookmarks from the past year. It has summarized your research into weekly time-boxes, providing a convenient overview of your digital exploration. - **Research Topics**: The summarized research spans various topics, including cybersecurity, AI advancements, digital privacy, and the impact of technology on society and culture. - **Summarization Method**: Archivist has used a combination of natural language processing and machine learning algorithms to extract key points, quotes, and insights from each bookmarked webpage. - **Weekly Time-Boxes**: The summaries are organized into weekly time-boxes, allowing you to quickly review and recall the most relevant information from each period. • **Context and Relevance**: These developments, from preserving your colleague's performance art video to exploring AI-generated mythology and automating your research, all contribute to your growing interest in and embrace of cyberpunk themes and digital culture. They also highlight the importance of preserving digital artifacts and understanding the rapidly evolving landscape of artificial intelligence. • **Next Steps**: You plan to prioritize scraping the video of your colleague's performance, ensuring its preservation before it's lost forever. You'll also continue analyzing your interviews with Dr. Sterling and exploring the summarized research provided by Archivist, further expanding your understanding of the cyberpunk world and its digital realities.