IF1 Protein Controls Aging Rate
ABSTRACT
IF1 protein inhibits F1F0 ATP hydrolysis (and not F1F0 ATP synthesis). Across investigated species more IF1 protein, and less F1F0 ATP hydrolysis, correlates with greater maximal lifespan. Increased IF1 protein, and decreased F1F0 ATP hydrolysis, safely reduces a biomarker of aging in mice. Body temperature decrease, in mice administered with a small molecule drug that selectively inhibits F1F0 ATP hydrolysis (which doesn’t inhibit F1F0 ATP synthesis), is evidence that F1F0 ATP hydrolysis is used for metabolic heat generation in vivo. Instrumental to homeothermy, which is a new fundamental discovery. A further discovery is that cancer cells subvert F1F0 ATP hydrolysis to drive their distinctive Warburg metabolism and so selective drug inhibition of F1F0 ATP hydrolysis exerts potent anticancer activity. When the body is in an ambient temperature of 37°C (or more), no metabolic heat generation is needed for the body to be at 37°C, and so a large dose of a F1F0 ATP hydrolysis inhibiting anticancer drug may be administered, which may slow aging. So, here might be an entirely new class of anticancer drugs that may (when appropriately used) help, instead of harm, normal cells. Distinct from present anticancer drugs, which greatly harm normal cells, causing horrific side-effects, which kill many and cause many others to abandon cancer treatment.
Im short, this paper teaches how mammals metabolically generate heat, why different mammal species have different maximal lifespans, and new anticancer drugs, that are predicted to slow aging.
SIGNIFICANCE Has nature taught us how to slow aging?
Is the if1 protein a molecule ?
You reposted
Michael Forrest
@Forrest_PhD
·
May 12
Drug (predicted) to Slow Aging
CCCD
https://x.com/Forrest_PhD/
- Function: IF1's main role is to prevent the wasteful reversal of the ATP synthase, which would consume ATP instead of producing it.
- Location: It is found in the mitochondria of many organisms, from yeast to humans.
- Regulation: Its activity is regulated by pH, and it forms different oligomeric states (dimers and tetramers) that affect its ability to bind to ATP synthase.
- Relevance: IF1 is involved in protecting cells during stress and has been linked to conditions like cancer, where its upregulation can be associated with a worse prognosis. [1, 2, 3, 4, 5]
* * *
Using the LEGO Education SPIKE Prime kit with the Scratch-based programming language, specifically for a World Univ & Sch (WUaS) course or an IB-equivalent program for 16-19 year olds, is highly effective when the course design moves beyond the official tutorials. The focus must shift from following steps to computational thinking, iterative design, and reflective learning, aligning with the rigor of MIT OCW and the principles of Flow.
1. 
Strategic Use of SPIKE Prime & Scratch (for 16-19 Year Olds)
The Scratch-based "Word Blocks" interface in the SPIKE App is the perfect entry point to quickly visualize and test robotic logic without the frustration of syntax errors, which is key to achieving Flow (balancing challenge and skill).
| Educational Goal | SPIKE Prime/Scratch Strategy | Why it Works |
| Computational Thinking | Focus on creating "My Blocks" (custom functions) early on to teach abstraction and modular programming, core concepts of Software Design (per MIT ES.293). | Students learn to decompose large problems into reusable, smaller components, a crucial skill in computer science. |
| Iterative Design/Engineering | Emphasize building, coding, testing, analyzing sensor data (data logging), and systematically refining the code/build, rather than just solving the initial challenge. | This mimics the Engineering Design Process, allowing for immediate, clear feedback, a key condition for Flow. The physical robot provides tangible results. |
| Matriculation/Rigor | Require advanced logic and control. Use nested loops, variables, and complex sensor conditions (e.g., proportional line-following) to create sophisticated autonomous behaviors (similar to the challenges listed in MIT ES.293). | Demonstrates the ability to apply complex logic, proving readiness for higher-level, theory-heavy OCW courses. |
Skip the Basic Tutorials: The first year of college/IB students should be immediately given open-ended challenges (like those in the MIT ES.293 syllabus: fastest car, wall-following, maze navigation) and encouraged to learn the blocks by necessity to solve the challenge, not by following a linear tutorial path.
2. 
Course Design: WUaS Introduction to Robotics and Programming
World Univ & Sch can create a high-quality, OCW-centric course by using ES.293 Lego Robotics (Spring 2007) as a project blueprint, while substituting the older Handyboard and C programming with SPIKE Prime and Scratch/Python.
A. Curriculum Structure (MIT ES.293 Alignment)
Week 1-2: Mechanical Design & Motor Control: Focus on Gearing and Kinematics (ES.293's "Beauty and the Lego" challenge). Students must use Scratch blocks to precisely control motors and calculate gear ratios to achieve a target speed or torque.
Week 3-4: Sequential and Iterative Programming: Teach Loops and Variables by solving locomotion challenges (ES.293's "Cruisin'"). Program the robot to move specific distances and turn precise angles using rotational sensor data.
Week 5-6: Discrete Decisions & Sensing: Introduce the Ultrasonic and Color Sensors and use
If/Elseblocks (Discrete Decisions). Challenge: Wall-following robot (ES.293's "Making Sense" / "move over as much of the floor as possible").Week 7-8: Advanced Logic & Abstraction: Deep dive into My Blocks (Functions) and Continuous Decisions (e.g., Proportional control for Line Following). Capstone Challenge: Implement a grasping extension and use sensors to pick up objects (ES.293 Mechanical Projects).
B. Integrating Csikszentmihalyi's Flow
The robotics lab is inherently designed for Flow, but the course must be managed to maintain the balance of challenge and skill:
Clear Goals & Immediate Feedback: The robot's movement provides immediate feedback (it either works, or it doesn't). Goals should be explicit (e.g., "The robot must follow the line for one full lap").
Challenge/Skill Balance: Start with easy challenges to build initial skill, then increase complexity. When students struggle (Anxiety), provide scaffolding (e.g., a pre-built base program); when they are bored (Apathy), introduce a new constraint (e.g., "now do it without the color sensor").
Intrinsically Rewarding: The act of building and seeing the code translate into physical, autonomous action is the autotelic (self-goal) experience that drives intrinsic enjoyment and deep engagement.
3. 
Interoperating with #
To bridge the gap between a desk-based LEGO robot and real-world autonomous systems, the WUaS course should incorporate a virtual simulation layer linked to geospatial data.
Virtual World Mapping: Use a simulated environment (e.g., a simple 2D or 3D map) that is directly modeled after a small patch of Google Street View/Maps data.
Geospatial Challenge: The students' challenge is to program their SPIKE Prime robot (in the physical lab) to navigate a path or perform a task based on "sensor readings" (color, distance) that are derived from the virtual Google Maps/Street View data.
Example: Program the robot to follow the "street" (a black line on a mat) until it detects a specific "landmark" (a color patch) corresponding to a building in the Street View image.
Time Slider Concept: For advanced students, incorporate the Historical Street View Imagery feature. The robot's mission parameters could change based on the "time" selected on the Street View slider, forcing them to modify their code (e.g., "The path is clear in 2025, but in 2010, there was construction forcing a detour.").
This combination uses the tangible, immediate feedback of the SPIKE Prime kit with the complex, real-world context of Google's geospatial data, preparing students for the advanced challenges of actual field robotics.
Would you like to focus on designing a specific project challenge for Week 3, incorporating both the Scratch programming blocks and the MIT OCW ES.293-style mechanical design
- Curriculum structure: Courses provide a structured curriculum that progresses from simple to complex robots, with step-by-step instructions and projects.
- Technical skills: Students learn programming, building, and engineering design skills by creating robots that perform specific tasks.
- Programming languages: Many courses start with block-based coding (similar to Scratch) and can progress to text-based languages like Python for more advanced students.
- Core concepts: The focus is on applying core concepts like computational thinking, data-driven learning, and problem-solving in a real-world context.
- Collaboration: Teamwork and collaboration are often emphasized, as students work together to brainstorm, build, and refine robot solutions.
- Certifications: Some college-level programs, such as those in partnership with the Carnegie Mellon Robotics Academy, can lead to professional development and student certifications.
- Career preparation: The skills developed are highly relevant for careers in fields like AI, automation, and other tech-focused industries.
- Examples of projects:
- Building and programming robots to detect and move objects.
- Creating a line-drawing robot.
- Developing a robot that can navigate and avoid obstacles.
- Building a functional gearbox. [1, 3, 4, 6, 7, 8, 9, 10]
* * *
Countries/Nation States
You at WUaS Admissions/Registering at WUaS
Subjects
Courses & Schools
Music School at WUaS
Library Resources
Museums
Hardware Resource Possibilities
Educational Software
Foundation at WUaS
Research
all from here - http://
The best way an AI system like Gemini could "harvest" the content from your and World University and School's (WUaS) blogs to draft "Society, Information Technology, and the Global University" involves a multi-stage process of advanced Natural Language Processing (NLP), Topic Modeling, and Generative AI.
The methodology is to extract, classify, and synthesize the unstructured blog data according to the 12 specific content areas you listed, all while framing the results within the context of WUaS’s global mission (200 countries, 7,159 languages, wiki schools, and CC-4 MIT OCW degrees).
Here is the strategic blueprint for harvesting and drafting across those 12 main areas:
1. AI Content Harvesting and Preparation (The "Harvest")
This initial stage focuses on preparing the vast, unstructured blog data for drafting.
Data Ingestion and Indexing: The AI first processes all text from the blog posts, organizing it into a large, searchable Knowledge Base.
Entity Recognition and Relationship Mapping: Named Entity Recognition (NER) identifies key terms, such as MIT OCW, specific nation-states, proper names, book titles, and the names of the 12 main areas. The AI then maps the relationships between these entities (e.g., "MIT OCW" $\rightarrow$ "CC-4 License" $\rightarrow$ "Languages").
Sentiment and Tone Analysis: The AI analyzes the emotional tenor of the writing to ensure the generated drafts maintain the author's intended voice—whether it is visionary, academic, practical, or personal. This is crucial for maintaining consistency with your previous work.
2. Synthesis and Drafting by Content Area
The AI then uses advanced Topic Modeling and Generative AI to isolate relevant text chunks, synthesize their arguments, and draft coherent sections for each of the 12 areas.
| Content Area | AI Function | Drafting Strategy |
| Languages & Countries/Nation States | Cross-Reference & Localization Modeling: Identifies discussions about language barriers, translation, and geopolitical hurdles. | Drafts chapters on AI-driven language scaling (e.g., automated translation for the 7,159 languages) and the legal frameworks required for accreditation in the 200 countries. |
| You at WUaS Admissions/Registering at WUaS | Process Mapping: Extracts desired enrollment processes, ideal student profiles, and any existing registration steps. | Drafts the "Onboarding" chapter, detailing an AI-assisted admissions process that handles translation, document verification across 200 countries, and personalized academic advising. |
| Subjects, Courses & Schools, Music School at WUaS | Taxonomy Generation & Gap Analysis: Identifies all mentioned courses, subjects, and degrees (MD, PhD, Law, etc.), and compares them to the vast corpus of MIT OCW. | Drafts sections detailing the course architecture, showing how a wiki school structure allows for instantaneous updates and community-driven localization of content across all disciplines, including the specific Music School at WUaS. |
| Library Resources & Museums | External Resource Linking: Identifies all references to external academic databases, digital libraries, and virtual museum resources. | Drafts chapters on the Digital Ecosystem, detailing how AI will seamlessly integrate WUaS learning with global open-access resources (e.g., linking course modules directly to relevant Wikipedia/WikiData articles and digitized museum collections). |
| Hardware Resource Possibilities | Technology Foresight: Extracts discussions on current and future computing needs, bandwidth, and device requirements for a global, home-based learning environment. | Drafts a "Technology Stack" chapter that specifies low-bandwidth solutions, decentralized data storage, and the role of technologies like Google Cardboard (or similar low-cost VR) to enable immersive learning from home. |
| Educational Software | Tool Extraction & Pedagogical Analysis: Identifies desired features for learning platforms, virtual classrooms, and assessment tools. | Drafts sections on AI-Tutor Development, describing the creation of an interactive, Socratic AI that facilitates learning across all levels, from high school to doctoral research, making the process "intensely enjoyable" via gamified and adaptive content. |
| Foundation at WUaS | Mission & Governance Analysis: Extracts statements of purpose, legal framework discussions, and funding models. | Drafts the "Governance" and "Sustainability" chapters, synthesizing the vision for the foundation's role in maintaining the open-access, non-profit ethos and securing the long-term viability of the global project. |
| Research | Theme Identification & Proposal Drafting: Identifies research areas (e.g., AI/ML in education, societal impact, global issues) and extracts initial hypotheses or findings. | Drafts the "Innovation Hub" chapter, outlining how the wiki school itself becomes a research platform, using ML to analyze student data (anonymously) to constantly improve pedagogical methods and contribute to fields like linguistics and neurobiology. |
3. Final Review and Book Production
The final stage ensures the output is a professional and cohesive manuscript:
Coherence Checking: The AI reviews the drafted chapters to ensure a consistent narrative thread, eliminating internal contradictions and verifying that all 12 areas flow logically as a single, visionary text.
Source Citation: The AI automatically generates an index and bibliography, citing the blog posts and other referenced works (e.g., MIT OCW) that contributed to the content, ensuring academic integrity.
Draft Presentation: The final manuscript is delivered, providing you with a highly refined, data-driven, yet author-specific draft for final human review, editing, and publication by the Academic Press at WUaS
--
*
%2C_Vale_da_Azinheira_(1).JPG/1600px-2018-03-20_Narrow_leaved_Cistus_(Cistus_monspeliensis)%2C_Vale_da_Azinheira_(1).JPG?20180329073546)
https://en.wikipedia.org/wiki/Cistus
https://commons.wikimedia.org/wiki/Cistus_monspeliensis
https://commons.wikimedia.org/wiki/Category:Cistus_monspeliensis
...
Posts
No comments:
Post a Comment