1. ALPHA FOLD Presented by NAME : Raghavendra Reddy K USN : 3BR19CS119 Under the guidance of Dr. Anita S Patil

2. Abstract  Alpha Fold is an artificial intelligence (AI) system developed by DeepMind, a UK- based AI research company.  The system is designed to predict the three-dimensional (3D) structure of proteins, which are essential molecules for life and play a key role in many biological processes.  Alpha Fold uses deep learning algorithms to predict the 3D structure of a protein from its amino acid sequence, which is the basic building block of a protein.  The development of Alpha Fold has been hailed as a major breakthrough in the field of protein structure prediction.

3. Introduction  Alpha Fold was developed by researchers at the University of Washington in collaboration with the European Molecular Biology Laboratory (EMBL) and the University of Oxford.  Alpha Fold is a deep learning-based system designed for protein structure prediction.  Proteins are the building blocks of life. They are vital to our existence with roles in almost all the biochemical processes.  A protein is made up of one or more linear chains of amino acids.  There are 20 types of amino acids commonly found in proteins.

4. WHAT IS PROTEIN FOLDING ?  Incorrect folding can lead to protein malfunction and diseases such as Alzheimer’s and cystic fibrosis.  Proteins can fold into several levels of structure, including primary, secondary, tertiary, and quaternary structures.  The folding process is guided by the protein's amino acid sequence and various environmental factors, including temperature, pH, and the presence of other molecules.  Protein folding is the process by which a protein acquires its functional three-dimensional (3D) structure.

5. WHAT IS PROTEIN FOLDING ?  Understanding protein folding is important for developing new treatments for diseases caused by protein misfolding and for designing new proteins with specific functions.  Tertiary structure describes the overall 3D structure of the protein, while quaternary structure refers to the arrangement of multiple protein subunits.  Primary structure refers to the sequence of amino acids in the protein, while secondary structure describes the local folding patterns of the polypeptide chain.

6. BRAIN CELLS AND SILICON CHIPS LINKED ELECTRONICALLY  These electrodes can be used to record neural activity or to deliver electrical stimulation to the brain

7. BENEFITS OF IMPLANTABLE BRAINS  Improved medical treatment: Brain chips can be used to treat a variety of neurological and psychiatric conditions, including Parkinson's disease, epilepsy, depression, and obsessive- compulsive disorder.  Enhanced precision and control: Brain chips can provide a high degree of precision and control over neural activity, allowing researchers and clinicians to target specific areas of the brain and manipulate neural circuits with a high degree of accuracy.  Real-time monitoring and feedback: Brain chips can be used to monitor neural activity in real-time, providing valuable feedback on brain function and allowing for more precise and personalized treatment.  Improved prosthetic devices: Brain chips can be used to develop more advanced prosthetic devices, such as robotic limbs or exoskeletons, that are controlled directly by the user's thoughts.

8. DRAWBACKS IN BRAIN CHIPS  Invasive surgery: Implanting a brain chip typically requires invasive surgery, which carries a significant risk of complications, including infection, bleeding, and damage to surrounding brain tissue.  Potential for side effects: Brain chips can cause a range of side effects, including headaches, seizures, and changes in mood or behavior. These side effects can be unpredictable and may vary depending on the individual.  Ethical and social implications: The use of brain chips raises a range of ethical and social questions, including issues related to informed consent, privacy, and the potential for misuse or abuse of this technology.  Cost and accessibility: Brain chips are currently expensive and complex to manufacture, which limits their accessibility to a relatively small number of patients and researchers.

9. Related work RETINOMORPHIC CHIPS

10.  Retinomorphic chips, also known as neuromorphic retinas or artificial retinas, are a type of brain chip that mimics the structure and function of the retina, the part of the eye that processes visual information.  These chips consist of an array of photodetectors and processing elements that work together to detect light and generate visual signals.  These chips are a promising technology for the treatment of blindness and other visual impairments.  These chips has the ability to process visual information in a way that is similar to the natural functioning of the retina

11. Related work THE LAB-RAT AND THE MONKEY

12.  The use of laboratory animals such as rats and monkeys in research involving brain chips has been a longstanding practice. These animals are often used to study the safety and efficacy of brain chips, as well as to better understand the underlying mechanisms of brain function.  Rats, for example, have been used in studies to test the efficacy of brain chips for the treatment of neurological disorders such as Parkinson's disease and epilepsy.  Similarly, monkeys have been used in studies to investigate the potential of brain chips for restoring motor function in individuals with spinal cord injuries.  These studies have shown that brain chips can enable monkeys to control robotic limbs with their thoughts, demonstrating the potential of this technology for the development of prosthetic devices..

13. Future scope  Treatment of neurological disorders: Brain chips could be used to treat a wide range of neurological disorders, such as Parkinson's disease, epilepsy, and chronic pain. The chips could be programmed to deliver targeted electrical or chemical stimulation to specific areas of the brain, helping to alleviate symptoms and improve quality of life for patients.  Brain-computer interfaces: Brain chips could be used to create direct interfaces between the human brain and computers or other devices. This could enable people to control devices, communicate, or even navigate virtual reality environments using their thoughts alone.  Memory enhancement: Brain chips could be used to enhance memory by recording and replaying neural activity associated with specific memories. This could help people with memory impairments due to conditions such as Alzheimer's disease.  Augmentation of human abilities: Brain chips could be used to augment human abilities beyond their natural limits. For example, the chips could be used to enhance vision, hearing, or other senses, or to allow people to communicate telepathically.

14.  Brain chips are a promising and rapidly evolving technology that have the potential to revolutionize our understanding and treatment of neurological and psychiatric disorders.  Brain chips can provide precise and personalized control over neural activity, real-time monitoring and feedback on brain function  However, the development and implementation of brain chips also raise a range of significant challenges and drawbacks, including issues related to safety, efficacy, compatibility with the human body, data processing and analysis, and ethical and regulatory considerations.  As this technology continues to evolve, it is important to approach it with caution, and to ensure that its development and use are guided by rigorous scientific and ethical principles.  By doing so, we can maximize the potential benefits of brain chips while minimizing their risks and drawbacks. Conclusion

15. REFERENCES [ 1] Lebedev MA, Nicolelis MA. Brain–machine interfaces: past, present and future. Trends Neurosci. 2006;29(9):536-546. [2] Kim Y-T, Hitchcock RW, Bridge MJ, Tresco PA. Chronic response of adult rat brain tissue to implants anchored to the skull. Biomaterials. 2004;25(12):2229-2237. [3] Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature. 2006;442(7099):164-171. [4] Wise KD, Sodagar AM, Yao Y, et al. Microelectrodes, microelectronics, and implantable neural microsystems. Proc IEEE. 2008;96(7):1184-1202. [5] Chen Z, Aghajan H, Movassaghi S, et al. Towards practical, high-performance brain–computer interfaces using scalable spiking neural networks. Front Neurosci. 2021;15:621832. [6] Sheth SA, Neal J, Tangherlini F, et al. Brain-machine interfaces for neuroscience and clinical applications. Cold Spring Harb Perspect Med. 2014;5(10):a024007.

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