Big Data, Patents, and the Future of Medicine

Big data has tremendous potential to improve health care. Unfortunately, intellectual property law isn’t ready to support that leap. In the next wave of data- driven medicine, black-box medicine, researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. Black-box medicine offers potentially immense benefits, but also requires substantial high investment. Firms must develop new datasets, models, and validations, which are all nonrivalrous information goods with significant spillovers, requiring incentives for welfare-optimizing investment. Current intellectual property law fails to provide adequate incentives for black- box medicine. The Supreme Court has sharply restricted patentable subject matter in the recent Prometheus, Myriad, and Alice cases, and what might still be patentable is limited by the statutory requirements of written description and enablement. Other incentives for investment, such as trade secrecy or prizes, fail to fill the gaps. These limits push firms away from using big data in medicine to solve big problems, and push firms toward small-scale incremental innovation. Small tweaks to doctrine will help, but are not enough. Instead, the big data needed to support transformative medical innovation should be considered as infrastructure for innovation and should be the focus of substantial public effort

Black-Box Medicine

Personalized medicine, where Big Data meets Big Health, has been hailed as the next leap forward in health care, but that leap raises tremendous challenges for our current policy landscape. This Article is the first to label the phenomenon of black-box medicine, a version of personalized medicine in which researchers use sophisticated algorithms to examine huge troves of health data, finding complex, implicit relationships and making individualized assessments for patients. This new form of medicine offers potentially immense benefits but faces major hurdles both in development and in application. Development requires high investment; firms must develop new datasets, models, and validations, which are all nonrivalrous information goods that require incentives for welfare-optimizing levels of development. However, current innovation policy lacks the necessary incentives and instead pushes firms in socially suboptimal directions. Black-box medicine also raises significant challenges with respect to privacy, regulation, and commercialization. This Article describes black-box medicine, explains its differences-in-kind from current forms of medicine, and briefly explores the landscape of policy challenges ahead

Sustainable design approach underpinned with Life Cycle Impact Assessment(LCIA) and ontology

Sustainable development has been a subject of global interest when people shift the focus from the economy and productivity only to the economy with consideration of the environment and resources on the earth. Manufacturing industry is one of the most crucial sectors that people focused on to make it more sustainable. However, the sustainability for current existing products are not enough to satisfy the requirement of sustainable development within the modern society. Therefore, an approach to design and to optimise product considering ecological impact is to be developed by this research. After review and comparison of popular LCIA methods and tools, the three-tier sustainable design approach considering human labour ecological impact is developed. Design optimisation with eco-constraints using genetic algorithm is followed. Moreover, from a product life cycle point of view, production may not be the least sustainable section. Use and disposal also play important roles in the whole product life cycle. In this case, Ontology is proposed in the research. It is a powerful tool to collect and exchange data of products and manage the relationships among different parts, properties of products, and suppliers in one specific area such as a factory or an industrial estate. Afterwards, The approach is validated by case study. Finally, the sustainable design approach underpinned with life cycle impact assessment (LCIA) and ontology is developed

Recent Advances in the Development of Biomimetic Materials

: In this review, we focused on recent efforts in the design and development of materials with biomimetic properties. Innovative methods promise to emulate cell microenvironments and tissue functions, but many aspects regarding cellular communication, motility, and responsiveness remain to be explained. We photographed the state-of-the-art advancements in biomimetics, and discussed the complexity of a "bottom-up" artificial construction of living systems, with particular highlights on hydrogels, collagen-based composites, surface modifications, and three-dimensional (3D) bioprinting applications. Fast-paced 3D printing and artificial intelligence, nevertheless, collide with reality: How difficult can it be to build reproducible biomimetic materials at a real scale in line with the complexity of living systems? Nowadays, science is in urgent need of bioengineering technologies for the practical use of bioinspired and biomimetics for medicine and clinics

MICRO$EC: Cost Effective, Whole-Genome Sequencing

While the feasibility of whole human genome sequencing was proven by the success of the Human Genome Project several years ago, the prevalence of personal genome sequencing in the medical industry is still elusive due to its unrealistic cost and time requirements. Micro$eq is a startup company with the goal of overcoming these limitations by sequencing a minimum of 12 complete human genomes per day at an error rate less than ten parts in million at a profitable market price of less than US$1000 per genome. To overcome the technology bottlenecks hindering current biotech companies from achieving these target throughput, error rate, and market price goals, Micro$eq has developed an innovative sequencing technique that uses shortread fragments with high coverage on a microfluidics platform. Short, amplified DNA fragments are generated from an input of customer saliva. 6 base pair(bp) sequence hybridization is used for sequencing each of the DNA fragments individually. The results are these hydridization reads are then assembled via de Bruijn graph theory and the graphical reconstructions of each fragment’s sequence are then assembled to a complete genome via shotgun sequencing with an expected error rate less than 1 in 100,000bp. Upon the completion of financial analysis, both a small-scale business model producing 72 genomes per day at US$999 per genome, and a largescale business model producing 52.2 genomes per year at a market price of US$299 per genome were found to be profitable, yielding Micro$eq investors return margins of ~90% and 300% for the small and large scale models, respectively. With a market price Micro$eq offers personal genome sequencing at one-tenth of its nearest potential competitor’s cost. Additionally, its ability for bulk-sequencing allows it to profitably venture into the previously untapped Pharmaceutical Industry market sector, enabling the creation of large-scale genome databases which are the next step forward in the quest for truly personalized

Personalized Medicine: the Future of Health Care

BACKGROUND: Most medical treatments have been designed for the “average patients”. As a result of this “one-size-fits-all-approach”, treatments can be very successful for some patients but not for others. The issue is shifting by the new innovation approach in diseases treatment and prevention, precision medicine, which takes into account individual differences in people\u27s genes, environments, and lifestyles. This review was aimed to describe a new approach of healthcare performance strategy based on individual genetic variants.CONTENT: Researchers have discovered hundreds of genes that harbor variations contributing to human illness, identified genetic variability in patients\u27 responses to different of treatments, and from there begun to target the genes as molecular causes of diseases. In addition, scientists are developing and using diagnostic tests based on genetics or other molecular mechanisms to better predict patients\u27 responses to targeted therapy.SUMMARY: Personalized medicine seeks to use advances in knowledge about genetic factors and biological mechanisms of disease coupled with unique considerations of an individual\u27s patient care needs to make health care more safe and effective. As a result of these contributions to improvement in the quality of care, personalized medicine represents a key strategy of healthcare reform

Computer detection of spatial visualization in a location-based task

An untapped area of productivity gains hinges on automatic detection of user cognitive characteristics. One such characteristic, spatial visualization ability, relates to users’ computer performance. In this dissertation, we describe a novel, behavior-based, spatial visualization detection technique. The technique does not depend on sensors or knowledge of the environment and can be adopted on generic computers. In a Census Bureau location-based address verification task, detection rates exceeded 80% and approached 90%

The Role of Mutations in Protein Structural Dynamics and Function: A Multi-scale Computational Approach

abstract: Proteins are a fundamental unit in biology. Although proteins have been extensively studied, there is still much to investigate. The mechanism by which proteins fold into their native state, how evolution shapes structural dynamics, and the dynamic mechanisms of many diseases are not well understood. In this thesis, protein folding is explored using a multi-scale modeling method including (i) geometric constraint based simulations that efficiently search for native like topologies and (ii) reservoir replica exchange molecular dynamics, which identify the low free energy structures and refines these structures toward the native conformation. A test set of eight proteins and three ancestral steroid receptor proteins are folded to 2.7Å all-atom RMSD from their experimental crystal structures. Protein evolution and disease associated mutations (DAMs) are most commonly studied by in silico multiple sequence alignment methods. Here, however, the structural dynamics are incorporated to give insight into the evolution of three ancestral proteins and the mechanism of several diseases in human ferritin protein. The differences in conformational dynamics of these evolutionary related, functionally diverged ancestral steroid receptor proteins are investigated by obtaining the most collective motion through essential dynamics. Strikingly, this analysis shows that evolutionary diverged proteins of the same family do not share the same dynamic subspace. Rather, those sharing the same function are simultaneously clustered together and distant from those functionally diverged homologs. This dynamics analysis also identifies 77% of mutations (functional and permissive) necessary to evolve new function. In silico methods for prediction of DAMs rely on differences in evolution rate due to purifying selection and therefore the accuracy of DAM prediction decreases at fast and slow evolvable sites. Here, we investigate structural dynamics through computing the contribution of each residue to the biologically relevant fluctuations and from this define a metric: the dynamic stability index (DSI). Using DSI we study the mechanism for three diseases observed in the human ferritin protein. The T30I and R40G DAMs show a loss of dynamic stability at the C-terminus helix and nearby regulatory loop, agreeing with experimental results implicating the same regulatory loop as a cause in cataracts syndrome.Dissertation/ThesisPh.D. Physics 201

Visual AI and Linguistic Intelligence Through Steerability and Composability

This study explores the capabilities of multimodal large language models (LLMs) in handling challenging multistep tasks that integrate language and vision, focusing on model steerability, composability, and the application of long-term memory and context understanding. The problem addressed is the LLM's ability (Nov 2023 GPT-4 Vision Preview) to manage tasks that require synthesizing visual and textual information, especially where stepwise instructions and sequential logic are paramount. The research presents a series of 14 creatively and constructively diverse tasks, ranging from AI Lego Designing to AI Satellite Image Analysis, designed to test the limits of current LLMs in contexts that previously proved difficult without extensive memory and contextual understanding. Key findings from evaluating 800 guided dialogs include notable disparities in task completion difficulty. For instance, 'Image to Ingredient AI Bartender' (Low difficulty) contrasted sharply with 'AI Game Self-Player' (High difficulty), highlighting the LLM's varying proficiency in processing complex visual data and generating coherent instructions. Tasks such as 'AI Genetic Programmer' and 'AI Negotiator' showed high completion difficulty, emphasizing challenges in maintaining context over multiple steps. The results underscore the importance of developing LLMs that combine long-term memory and contextual awareness to mimic human-like thought processes in complex problem-solving scenarios