Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=funder_divisions
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=funder_divisions", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=funder_divisions", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=funder_divisions", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=funder_divisions" }, "data": [ { "type": "Grant", "id": "9637", "attributes": { "award_id": "2014266", "title": "SBIR Phase I: Novel drug delivery system using engineered exosomes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 936, "first_name": "Henry", "last_name": "Ahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2022-01-31", "award_amount": 225000, "principal_investigator": { "id": 25453, "first_name": "Souvenir", "last_name": "Tachado", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1850, "ror": "", "name": "SOUVIE BIODELIVERY LLC", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "he broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase 1 project entails developing an novel drug delivery platform to deliver drug into poorly accessible druggable targets. Drugs targeting inflammation are encapsulated in an new way for treatment of chronic inflammatory diseases such as psoriasis, lupus, rheumatoid arthritis. Biological therapies remain the standard for autoimmune diseases, but patients can have immune system problems due to the production of autoantibodies. This project addresses this challenge with a novel encapsulation technology that delivers biological therapies to hard-to-reach inflammation sites. This technology could be advantageous because it reduces immunosuppression and anti-drug antibodies production. This project will improve patient treatment outcomes and reduce treatment reduce cost. \n\nThis Small Business Innovation Research (SBIR) Phase 1 project will test a proof-of-concept that entails demonstrating that an exosome-based delivery vehicle for biologics will potentially inhibit inappropriate TLR-driven inflammation. Induction of autoimmune diseases including psoriasis, rheumatoid arthritis and lupus strongly correlate with chronic inflammation driven by inflammatory signaling pathways mediated by endosomal TLRs. Our goal is to develop an exosome-based therapy that can inhibit poorly accessible endosomal TLR7-driven inflammation. To test this concept, our plan involves genetically modifying human cell lines to produce engineered exosomes that contain anti-TLR7 antibody to inhibit endosomal TLR7-driven inflammation. We will develop a drug encapsulation platform in which engineered exosomes bear an anti-TLR7 antibody fused with an exosome transmembrane protein called tetraspanin. This complex delivers an antibody payload to TLR7 targets located in on endosomal membrane inner surface. This anti-TLR7 antibody will bind to dimer-interfaces of TLR7 monomer and thereby block TLR7 receptor dimerization, which in turn blocks proinflammatory signaling. This work is important because blocking TLR7 receptor activation, which is situated upstream in the signaling cascade, will presumably diminish immunosuppression and lead to a better outcome that current anti-TNF therapies that are more broad spectrum because their site of action is downstream in a signaling pathway axis.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9644", "attributes": { "award_id": "2051419", "title": "SBIR Phase I:Development of a Multi-Robot System to Reduce End-to-End Labor Automation Costs in Local Food Production", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-05-01", "end_date": "2022-04-30", "award_amount": 255147, "principal_investigator": { "id": 25463, "first_name": "David", "last_name": "Ashton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1852, "ror": "", "name": "CANOPII INC.", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this SBIR Phase I project lie in the greater availability of healthy, local, and affordable produce for all levels of society through new methods of end-to-end labor automation and energy management in urban agriculture. Through a low-footprint vertical greenhouse design, this technology will enable individuals to grow produce on small areas of available space for their community. The produce will be an attractive alternative to large-scale industrial agriculture and foreign food imports, not only for sustainability concerns, but through its superior freshness, quality, and taste. This project will have an impact on the advancement of local farming technologies to further the viability of commercial food production in urban communities. Creating an economically viable urban farm will have lasting impacts on the U.S. food system and environment as it will provide a means for sustainable food production.\n\nThe Phase I effort will be used to design, build, and demonstrate a subscale vertical farm prototype that demonstrates cost-effective methods for all aspects of the farm’s end-to-end labor automation. This will advance the implementation of robotics in food production by addressing the labor and energy barriers that local controlled environmental agriculture systems currently face. To achieve this objective, key challenges include (1) automation to completely remove human labor over extended durations, (2) low-cost design for setup and ongoing operations, and (3) the ability to adjust product outputs in real-time to market demands. Human interaction with the grow process will be limited through high degree of system automation, including computer vision for plant inspections and self-cleaning processes. Novel plant growth and handling processes will allow for virtually any type of produce. A variety of sensors are used to carefully monitor conditions and adjust the system, allowing fresh produce in areas without suitable agricultural opportunities. The prototype robotic farming system being developed in the Phase I effort will demonstrate the end-to-end labor automation and energy management technologies necessary to the expansion of urban agriculture.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9646", "attributes": { "award_id": "2100092", "title": "SBIR Phase I: A Transformational Method to Extract Polychlorinated Biphenyls (PCBs) from Building Masonry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-05-15", "end_date": "2022-04-30", "award_amount": 256000, "principal_investigator": { "id": 6451, "first_name": "Martha", "last_name": "Inglese", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 794, "ror": "", "name": "MARLEY ENVIRONMENTAL INC", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase 1 project is solving the legacy polychlorinated biphenyls (PCBs) problem impacting our aging schools and infrastructure. The U.S. Environmental Protection Agency (EPA) estimates as many as 55,000 schools and 800,000 government and non-government buildings may have been constructed with PCB-laden paints, caulks, mastics and adhesives before the 1979 PCBs ban. Simply removing the PCB-laden source in hopes it will eliminate the hazard has proven futile as a growing body of data is revealing PCBs from weathered caulk can leach as deep as 6-inches into adjacent porous masonry (e.g., concrete, brick, and mortar). Currently, total demolition and select removal (i.e., partial demolition) are the only EPA-approved PCB removal options and both are quickly filling up the handful of landfills willing to take it. The proposed technical innovation will transform a dormant government patent that extracts PCBs in paint, into a non-destructive treatment method that penetrates and extracts PCBs absorbed in building masonry. Such an innovation will have a direct and beneficial impact on the government agencies and school renovation commissions who cannot afford to demolish the old and rebuild new. \n\nThis SBIR Phase 1 project proposes to demonstrate the feasibility of two proprietary solvent-paste formulations at extracting PCBs from different masonry types after the source (e.g., caulk) has been removed. The solvent-paste is applied directly to the contaminated masonry surface and scraped off after a pre-determined treatment period. Once applied, the lipophilic alcohol in the solvent-paste penetrates the masonry’s open pore spaces, and solubilizes the PCB molecules it encounters along the way. The process of desorbing the PCBs from the inorganic masonry particles and into the applied paste is aided – via capillary action – by the lipophilic alcohol drawing the hydrophobic PCBs toward the paste. Technical challenges include desorbing the stickier spectrum of hydrophobic Aroclors (e.g., 1248, 1254, 1260) added to paints, caulks and adhesives in seasonally cool (< 50°F), wet weather. Since successful commercialization of an alternative PCB treatment technology requires approval from EPA in accordance with the PCB regulations (40 CFR 761), the performance of both solvent-pastes will be evaluated against the regulation’s stringent 1 ppm high occupancy cleanup criterion.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10058", "attributes": { "award_id": "2151374", "title": "SBIR Phase I: Airborne Contagion Mapping through Visual Exhale Monitoring", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2023-08-31", "award_amount": 255870, "principal_investigator": { "id": 25933, "first_name": "Shane", "last_name": "Transue", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1877, "ror": "", "name": "STRIVISION LLC", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to create a method for monitoring and evaluating exposure risks from airborne viral contaminants to reduce public health risks from respiratory disease transmission such as COVID-19. The aim of this project is to advance our understanding of how airborne contagions are spread within confined interior spaces through exhaling visualization, to understand transmission and potentially reduce workspace respiratory disease transmission. By developing an anonymized vision-based network, this work provides an effective data-driven method for modeling and analysis of how respiratory behaviors contribute to viral transmission within real-world workspaces. The proposed technology aims to identify potentially effective mitigations to reduce communicable disease costs. The resulting platform will provide real-time analysis and AI-driven feedback for exhaled contagion risks for populated interior spaces in educational settings and new forms of data-driven evaluations of open-air exhale behaviors for high-risk populations in healthcare facilities.\n\nThis Small Business Innovation Research (SBIR) Phase I project aims to address the open problem of how to effectively and quantitatively evaluate transmission risks associated with airborne viral contaminants as they are spread through respiratory behaviors within real-world workspaces. While aerosolized viral contaminant transmission is well-studied, idealized models provide a limited effective analysis of the complex interplay between turbulent exhale behaviors, indoor traffic patterns, and environmental factors that contribute to erratic airborne contaminant transmissions. This project will present a stochastic method for modeling the potential transmission of airborne viral contaminants through vision-based analysis of expiratory flows within 3D reconstructed workspaces. Through spectral filtered thermal imaging, we isolate and track exhaled CO2 within the 3-5um spectral range to model exhale behaviors within 3D mapped point-cloud models of interior spaces obtained through networked depth cameras. The innovation in our system is the adoption of the measurements of exhaling flow in open air into a quantitative metric that evaluates flow and volume per exhale and models potential airborne contamination spread, providing a quantitative foundation for measuring and tracking exhale exposure regions. The expected outcome of this project is a platform for data-driven modeling of multi-subject respiratory behavioral analysis for potential contagion exposure mitigation.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10083", "attributes": { "award_id": "2112403", "title": "SBIR Phase I: Restoring natural sleep for adolescents: circadian clock advancement at night assisted with mobile application-delivered cognitive behavioral therapy", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2023-08-31", "award_amount": 256000, "principal_investigator": { "id": 25974, "first_name": "Biquan", "last_name": "Luo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1881, "ror": "", "name": "LUMOSTECH, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to address chronic sleep deprivation among American teenagers, which has only been made worse by the recent pandemic. Adolescents' natural internal circadian clocks typically run 1-3 hours later than adults, leading to the tendency to stay up late, difficulty falling asleep, insufficient total sleep time, and morning grogginess. This project is designed to improve sleep for adolescents and young adults by advancing their circadian clocks leading to healthier sleep. Today, there are almost 42 million adolescents in the United States aged 11-19, and another 18.9 million young adults aged 20-24, making the commercial potential of this project to be a $10 Billion market opportunity in the United States alone.\n\nThis Small Business Innovation Research (SBIR) Phase I project is commercializing an innovation in sleep therapy. Approximately half of all American adolescents and young adults in the United States do not get enough sleep. Cognitive behavioral therapy (CBT) for insomnia has been somewhat effective when administered by a therapist in person, but the in-person model is not scalable. Lack of sleep can negatively impact physical and neurocognitive development, leading to inability to concentrate, poor grades, higher risk of diabetes and long-term cardiovascular problems, and increased risk of mental health issues, depression, and suicide. The proposed solution applies recent sleep research and innovates it into a product worn at night and plans to go to market with an integrated solution of both the product hardware and telehealth support. Healthier sleep brought by this solution may lead to better recovery, improved memories and concentration, and enhanced neurocognitive and physical development. It may also reduce the risks for depression and other mental health issues. The enhanced physical and mental health may have other positive impacts in society, for example, reducing healthcare costs and preventing accidents caused by sleep deprivation. Short light pulses may alter circadian phases of human subjects during sleep. Based on light flash technology, the team proposes creating a smart sleep mask that emits light flashes at night during sleep to effectively advance teenagers’ circadian phases without disrupting their daytime activities. This light flash technology will be assisted with cognitive behavioral therapy (CBT) delivered via a mobile application to promote earlier bedtime and better sleep hygiene.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10131", "attributes": { "award_id": "2036161", "title": "SBIR Phase I: Development of a novel technology to manufacture animal-free muscle proteins in bacteria and yeasts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2022-05-31", "award_amount": 256000, "principal_investigator": { "id": 26033, "first_name": "Chenfeng", "last_name": "Lu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1888, "ror": "", "name": "FYBRAWORKS FOODS INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial impact of this Small Business Innovation Research (SBIR) Phase I project includes improved food security, environmental benefits, and human health benefits. This project will develop a meat alternative that more closely mimics the taste and texture of animal meat. The technology developed from the proposed project will be used to build a vertically integrated food manufacturing platform that can withstand supply chain disruptions from natural disasters and pandemics. These meat alternatives will reduce and replace traditional meat consumption, with many environmental benefits - lowered greenhouse gas emissions and aquatic pollution; lower use of energy, water and land; and reduced antibiotics usage. There are benefits to human health for reduced meat consumption. Furthermore, production costs will be eventually be comparable to that of mushroom farming.\n\nThe proposed project aims to develop a fermentation-based meat alternative that more closely mimics the taste and texture of animal meal through recombinant protein technologies. Additionally, the project aims to leverage the texture and flavor of mushroom mycelia, and supplement this with recombinant muscle proteins to further enhance the taste and nutritional profiles and overcome many of the shortcomings of existing plant-based meat products. To date the concept of combining recombinant muscle protein and single cell protein is novel and has not been reported. Large gaps persist between plant-based and lab-grown meat, with regards to cost and consumer experience. Plant-based meat is more affordable but faces consumer resistance due to sub-optimal texture and nutritional profiles, while cultivated meat offers a consumer experience similar to that of animal meat but at a much higher cost. Towards this goal, muscle protein genes will be expressed in a microbial host and enzymatically cross-linked with vegetable protein to produce protein fibers that can be formulated into synthetic meat. The technical objectives also include demonstration the feasibility of crosslinking muscle fiber proteins extracted from meat with mycoprotein from mushroom to produce desired textural and flavor properties.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10154", "attributes": { "award_id": "2036664", "title": "SBIR Phase I: Viral inactivation in air and on surfaces across large areas by safe, non-thermal, non-ionizing electromagnetic radiation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 670, "first_name": "Anna", "last_name": "Brady", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2022-06-30", "award_amount": 254392, "principal_investigator": { "id": 26067, "first_name": "Luke", "last_name": "Raymond", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1889, "ror": "", "name": "AIRITY TECHNOLOGIES, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is a new and safe non-contact method and device to inactivate viruses in air or on surfaces and objects. The non-chemical method proposed is scalable and can be quickly adjusted to target novel pathogens and therefore prevent or mitigate future epidemics of novel viral pathogens. The solution enabled by the proposed project is a relatively low-cost portable or ceiling-mounted device to be operated in health care settings, offices, schools, public transit, or other locations where the risk of disease transmission is high. The device would provide a continuous antiviral effect in thousands or millions of locations while operating within safe limits for human exposure. \n\nThis SBIR Phase I project proposes to validate the concept of a non-contact viral inactivation method that relies on resonant energy transfer from microwaves, and to demonstrate the efficacy in viral assays under operation parameters that are safe for humans and within regulatory guidelines. To accomplish this, a miniaturized and cost-effective high power pulsed microwave source and antenna is required. To this end, a prototype device will be developed that consists of a high-power vacuum electronics device, such as a magnetron or a traveling-wave tube, a custom and novel high voltage power source, and firmware and controls. The power supply will be designed to drive an existing vacuum device, by means of a regulated high-voltage output in the kilowatt range and a floating low-power auxiliary power source. To control pulsed operation, a control algorithm will be implemented in a microcontroller. The prototype will be characterized and used to expose viral samples in common viral growth assays, followed by a quantitative assessment of the antiviral effect. The outcome expected is a functional prototype that achieves significant reduction, e.g., 2 logs, within safe limits that will serve as a blueprint for a novel class of antiviral devices.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10158", "attributes": { "award_id": "2126731", "title": "SBIR Phase I: Leveraging machine learning to enable generalized phage therapy for pulmonary infections", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2022-08-31", "award_amount": 256000, "principal_investigator": { "id": 26073, "first_name": "Robert", "last_name": "McBride", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1890, "ror": "", "name": "FELIX BIOTECHNOLOGY, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a new therapy for bacterial infections, especially those resistant to current antibiotics, which have generated antibiotic-resistant “super-bug” bacterial infections that cannot be treated easily. Bacteriophages (‘phages’) are viruses that only infect specific bacteria and cannot infect humans. Phages kill harmful bacteria, but they currently do not work well as general solutions that can be prescribed broadly because each phage only kills a subset of bacteria; therefore a unique phage may be required for different people with the same infection. This project develops new technology to understand how phages target bacteria. It uses machine learning to determine the parts of each phage responsible for killing specific bacteria, in order to make phages for broad use in treating infections. This innovation is a key competitive advantage, and helps both national health and defense by creating new treatments for antibiotic-resistant infections, which cost >$64 billion annually and may become the next major pandemic. \n\nThis Small Business Innovation Research (SBIR) Phase I project will develop machine learning algorithms that identify genetic determinants of host range in phages in order to engineer phage to have expanded host range. The widespread evolution of multidrug-resistant infections is a major threat to global health, and traditional antibiotics have significant adverse effects on patients and their microbiomes. Phages can solve this global health challenge, but the inability to expand and tune phage host-range to create a generalizable therapeutic remains a key barrier to commercial success. This project will leverage machine learning and proprietary high throughput phage characterization methods to generate maps of phage-host interactions to identify genes that determine phage host range, and use novel engineering techniques to validate these genetic determinants of host range. The expected outputs are twofold: 1) a machine learning model for predicting variants, genes, or genomic regions that determine phage host range and 2) an engineered phage with expanded host range. This work will further scientific understanding of phage biology and phage-host interactions, while also providing a platform to develop phages with tunable host range for therapeutic, agricultural, and environmental applications.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15117", "attributes": { "award_id": "2418011", "title": "SBIR Phase I:Combinatorial Platform for the Discovery of Improved Molecular Recognition Components for Use in Therapeutic and Diagnostic Antibodies", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 273550, "principal_investigator": { "id": 31668, "first_name": "Christopher", "last_name": "Szent-Gyorgyi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2510, "ror": "", "name": "BIOCOGNON LLC", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project is the fundamental improvement of crucial antibody components that recognize and bind therapeutic or diagnostic targets. Modern antibodies are usually engineered as protein chimeras comprised of different parts, including one to several molecular recognition domains that mediate binding. The proposed research will integrate breakthroughs in next generation DNA sequencing and synthetic and computational biology to create a combinatorial high throughput platform for generating better recognition domains. The core aim is to creatively and efficiently use genetic information from patients, pathogens and antibodies for the advancement of therapeutics and diagnostics across a spectrum of diseases. The platform could expedite the design and discovery of current antibody-based therapeutics to reduce the enormous costs and time required to bring these drugs to market. The platform is ideally suited for the development of new classes of therapeutics where very rapid, adaptable and inexpensive response is required, such as in truly personalized treatments of continuously changing tumors or in rapidly evolving viral pandemics where passive vaccines need to be generated at scale.<br/><br/>The proposed project will demonstrate that a novel yeast-based high throughput screening platform is able to efficiently generate molecular recognition domains that specifically recognize clinically important targets. The proof-of-concept target antigens are a human receptor/ligand pair important for the immunosuppression of certain cancers and a coronavirus surface protein that mediates infection by binding a human receptor. In these screens, the use of yeast cells that surface display antibody recognition domains, and secrete these target antigens from the same cell, enables next generation sequencing to identify the genetic information encoding both the domain and the target. This dual detection capability is made possible by innovative fluorescent biosensors and is unique to this screening platform. The project will utilize synthetic biology to construct a library with a rich variety of recognition domains that will be screened simultaneously against several target antigens of varying design. Next generation sequencing analysis will show that it is practical to implement combinatorial screens using engineered recognition domains and antigens to identify recognition domains with desired binding specificity and affinity.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14397", "attributes": { "award_id": "2038067", "title": "SBIR Phase I: Open Machine Learning Competitions with Private Data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1211, "first_name": "Peter", "last_name": "Atherton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": null, "award_amount": 256000, "principal_investigator": { "id": 31002, "first_name": "Peter", "last_name": "Bull", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2460, "ror": "", "name": "DRIVENDATA, INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to expand access to artificial intelligence (AI) talent and spur innovation to solve hard problems while protecting privacy. Machine learning and AI are bringing transformational change to governments, private companies, and social sector organizations. Yet in the coming years, innovation will be hamstrung by limited access to AI talent. Open innovation, such as machine learning (ML) competitions, provides governments and firms the ability to tap into a global talent pool to solve some of their most pressing and vexing challenges. Yet there is currently an immense barrier to running these competitions: the data must be made available to participants, which can preclude running a competition if the associated data are too sensitive to release due to concerns about privacy, security, or confidentiality. With data talent in increasingly high demand, government agencies, companies, and others have demonstrated a willingness to invest in this fashion. The proposed project develops a method to maintain data privacy at scale. <br/><br/>This Small Business Innovation Research (SBIR) Phase I project will develop an end-to-end competition system that provides privacy guarantees for data used to build crowdsourced algorithmic solutions. Open ML challenges typically work by providing participants with training data to learn underlying patterns, then evaluating resulting predictions on unlabeled test data. For many important problems, making training data available in this way violates concerns about privacy or enables abuse. The critical gap is preserving the privacy of training data while enabling participants to build models that can learn from it. This project will bring together recent advances in three of the most promising approaches in privacy-preserving data analysis: homomorphic encryption, federated learning, and differential privacy. Each technique will be developed and tested in a dedicated challenge structure with two core properties: 1) to preserve the privacy of sensitive data; and 2) to ensure competitors are able to get feedback on submitted models during the competition to inform algorithm improvements. Each competition system will result in a set of performance measures, including benchmarked algorithm performance and data privacy guarantees, to assess system feasibility.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1419, "count": 14184 } } }