Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&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=1424&sort=funder_divisions", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=funder_divisions", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=funder_divisions" }, "data": [ { "type": "Grant", "id": "2548", "attributes": { "award_id": "2014934", "title": "SBIR Phase I: Development of a Novel Diagnostic Test for Pulmonary Embolism Based on Artificial Intelligence and Spectral Analysis of Blood", "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": 7321, "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": "2020-09-01", "end_date": "2021-08-31", "award_amount": 209881, "principal_investigator": { "id": 7322, "first_name": "Artur", "last_name": "Adib", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 886, "ror": "", "name": "BIOCOGNIV INC.", "address": "", "city": "", "state": "VT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 886, "ror": "", "name": "BIOCOGNIV INC.", "address": "", "city": "", "state": "VT", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a fast, non-invasive, and highly accurate test to diagnose pulmonary embolism in the emergency department. In the United States, pulmonary embolism (PE) affects up to 1 million patients per year and is responsible for nearly 100,000 yearly deaths. Its diagnosis is challenging due to the presentation of nonspecific symptoms and the lack of high-accuracy screening methods. While the current standard of care is to rule out PE with an established blood test (D-Dimer), approximately 90% of those results are false positives, causing the test to be used with restraint in the clinic, and leading to both the underdiagnosis of the disease and the overuse of strongly radiative imaging methods like CT pulmonary angiograms. A new, highly specific test for PE could increase patient safety, standardize clinical care processes, reduce costs and save lives.\n\nThis Small Business Innovation Research (SBIR) Phase I project will develop and validate a new diagnostic tool for PE based on the combination of fast blood spectroscopy and modern machine learning (ML) algorithms. A key aim of the research is demonstrating that ML combined with blood spectroscopy can substantially outperform the D-Dimer biomarker test, which has notoriously low specificity (~40%). An important Phase I milestone will be to show that the specificity of the resulting PE test either (a) already surpasses that of the D-Dimer test when trained on the relatively small dataset used in this Phase I proposal, or (b) substantially increases with the size of the training dataset, so that the test can outperform D-Dimer simply by procuring a larger pool of blood samples. The technical challenges addressed in this phase include evaluating different spectroscopic methods and modalities, minimizing the coefficient of variation for spectra acquisition, as well as designing and optimizing ML models for one-dimensional spectral data.\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": "2600", "attributes": { "award_id": "2026135", "title": "SBIR Phase I: Using Automation to Deliver Photo-Realistic Clothing Simulations for Virtual Fittings", "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": 7550, "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": "2020-08-01", "end_date": "2021-09-30", "award_amount": 256000, "principal_investigator": { "id": 7551, "first_name": "Marcelino", "last_name": "Rodriguez Cancio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 900, "ror": "", "name": "COUTURE TECHNOLOGIES LLC", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 900, "ror": "", "name": "COUTURE TECHNOLOGIES LLC", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to demonstrate the feasibility of the virtual garment creation and try-on system. As businesses increase their e-commerce presence, they face a major challenge: the high rate of returns in e-commerce. The return rate for online purchases exceeds that of in-store purchases by roughly 4 to 1, with customers (52-74%) citing dissatisfaction with the garments’ fit as the primary reason for returns. A reduction in returns as small as 1% could keep over 50 million pounds of goods out of the landfill and return $2.3 B to fashion retailers. This Phase I project is aimed at developing a sophisticated process using 3D modeling and fabric simulation technologies to enable customized fit and sizing visualizations prior to purchase. \n\nThis Small Business Innovation Research (SBIR) Phase I project will demonstrate the feasibility of the virtual garment creation system by using machine learning, numerical simulations and 3D graphic rendering to generate virtual garments based on (i) images and text that describe the garment and (ii) a minimum set of measurements of the customer's body. This process will advance the translation of novel approaches to combining artificial intelligence and 3D representation of a deformable shape in a computationally efficient manner.\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": "2605", "attributes": { "award_id": "2026010", "title": "SBIR Phase I: Volition With An App", "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": 7573, "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": "2020-08-01", "end_date": "2021-04-30", "award_amount": 255880, "principal_investigator": { "id": 7574, "first_name": "Elizabeth", "last_name": "La Rue", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 902, "ror": "", "name": "HERA GLOBAL TECH INC.", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 902, "ror": "", "name": "HERA GLOBAL TECH INC.", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project aims to decrease the number of overdose deaths in the U.S. by making an AI-driven mobile app to those suffering from substance use disorder (SUD). There were more than 67,000 drug overdose deaths in the U.S. in 2018. This project will potentially alter the existing behavioral health marketplace through its ability to save lives and reduce the $740 billion spent annually treating SUDs. The app, freely available to anyone with a mobile phone, uses an expert artificial intelligence (AI) system to suggest an appropriate evidence-based recovery plan to those in need. Medically recognized contingency-based therapy processes are incorporated to help individuals follow their recovery plans. \n\nThis Small Business Innovation Research (SBIR) Phase I project will develop an expert system to develop an evidence-based personalized recovery program to individuals affected with SUD. To do this, the company is developing SUD self-directed recovery while enhancing compliance and motivation through a novel, salient reward system. Increasing recovery plan compliance is a major addition to current healthcare industry functions. The project will utilize a positive feedback loop via the novel use of value-based payment for individuals with substance use disorder with or without clinician oversight.\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": "2625", "attributes": { "award_id": "2026152", "title": "SBIR Phase I: Fetal Monitoring During Exercise", "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": 7650, "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": "2020-09-01", "end_date": "2022-02-28", "award_amount": 255762, "principal_investigator": { "id": 7651, "first_name": "Ann", "last_name": "Holder", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 906, "ror": "", "name": "MARANI HEALTH, INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 906, "ror": "", "name": "MARANI HEALTH, INC.", "address": "", "city": "", "state": "MN", "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 fetal heart rate monitoring technology as part of the wearable fitness technology market, empowering women and clinicians to manage maternal health during pregnancy. The proposed system can disrupt the traditional fetal monitoring methods as the only wireless solution enabling real-time, continuous monitoring when a pregnant woman is exercising. This technology will potentially improve the quality of health and wellness of women and their babies during pregnancy, having the potential to offer new evidence-based guidance. \n\nThis Small Business Innovation Research (SBIR) Phase I project addresses the technical challenge of developing a wearable fetal monitoring device for pregnant women, specifically tailored for use during exercise. The project will use dry electrodes designed to minimize motion artifacts and improve the quality of the recorded signal, embedded in an abdominal compression garment for ease of use and comfort. The project will also develop machine learning algorithms to separate the confounded maternal and fetal electrocardiogram (ECG) signals and movement artifacts. The project objectives include: (1) optimize spatial density of sensors for accurate measurements; (2) identify the best sensor-incorporating smart clothing design based on functionality; (3) develop and refine signal processing algorithms for accurate filtering and data analysis; and (4)validate the performance of the resulting prototype for accurate fetal monitoring during exercise.\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": "2638", "attributes": { "award_id": "2026281", "title": "SBIR Phase I: Stabilization of the desired epitopes of hRSV-F protein for efficient absorption through the gut", "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": 7703, "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": "2020-12-01", "end_date": "2021-11-30", "award_amount": 256000, "principal_investigator": { "id": 7704, "first_name": "Swarnamali", "last_name": "Rupassara", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 910, "ror": "", "name": "FruitVaccine Incorporation", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 910, "ror": "", "name": "FruitVaccine Incorporation", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to provide global populations with a safe, affordable and viable edible vaccine against the human respiratory syncytial virus (hRSV). hRSV is a critical global health problem, annually affecting 64 million people and causing over 160,000 death, motivating a vaccine. Natural food-derived vaccines offer advantages over traditional injections by providing a safe, affordable, non-invasive, non-egg-based (reduced allergenic risk), vegan-friendly, effective and efficient antigen-delivery system. The project addresses this need by developing a cherry-tomato-based oral vaccine that can be administered painlessly in a chewable gummy-like pill formulation or in oral drops. The proposed fruit-vaccines will minimize the global incidence of hRSV through scalable production and distribution, increasing global accessibility and vaccination rates. Furthermore, with fewer disposables it is more environmentally friendly. This method could apply broadly to other oral-delivery platforms.\n\nThis Phase I project will advance peptide-based oral-delivery platforms. The project will improve the yield and consistent expression of the immunogenic antigen (hRSV-F protein) in the plant, both pre- and post-harvest, as well as before and after delivery into the body. The approach involves bioengineering the plant-optimized gene for the hRSV-F protein to improve its stability by blocking undesirable cleavage sites while retaining the desirable protective epitopes of the hRSV-F immunogen. Transgenic plants expressing the stabilized hRSV-F in the tomato fruits will be grown in a greenhouse. The hRSV-F-containing cherry-tomatoes will be harvested, homogenized (pureed), and then lyophilized (freeze-dried) to formulate innovative vaccine purees or pills, respectively. Tasks include conducting qualitative and quantitative analyses, such as color, consistency, pH, concentration of intact hRSV-F protein, its potential degradation products and desired epitope/s, analyzed using enzyme-linked immunosorbent assay (ELISA), western-blot and high-performance liquid chromatography. These tests will help characterize the persistence and effectiveness of the proposed changes on hRSV-F protein expression, yield, and stability in fresh-fruits, tomato-puree and in the freeze-dried fruit-pills. Similar assessments will be performed also post-ingestion, in the gut contents and in blood of mice.\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": "2641", "attributes": { "award_id": "2035934", "title": "SBIR Phase I: Gentle: A Smart, Affordable, Soft Gripper for Robotic Food Picking and Packaging", "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": 7719, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2021-12-31", "award_amount": 256000, "principal_investigator": { "id": 7720, "first_name": "Tugba", "last_name": "Efendigil", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 914, "ror": "", "name": "UBIROS INC.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 914, "ror": "", "name": "UBIROS INC.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the improvement in throughput of fresh fruit, vegetable, baked item picking and packing, agriculture, and tasks that require physically handling or grasping a wide range of delicate objects. The electrically-actuated soft robotic gripper technology is inherently safe to work in human-occupied environments and collaborate with human workers since its soft fingers can deform and absorb impacts in case of a collision. In addition, with the data generated from the research and development, new use cases will be opened for robotic hands in the long term such as helping farmers pick fruits from trees. Working in the robotics technology area and targeting items that are difficult to pick up by traditional robotic grippers and algorithms, the research will help automate and increase the efficiency of traditionally manual processes that suffer from a lack of enough workers to ultimately support American manufacturing. This research also has longer-term benefits to expand into other sectors such as collaborative robotics, healthcare, and assistive robotics areas because of the inherent safety and adaptability of soft robotics in human interaction. \n\nThis Small Business Innovation Research (SBIR) Phase I project focuses on technical research and development of electrically actuated soft grippers with a modular approach, sensory integration, and feedback control, as well as the study of new algorithms and user interfaces to endow these systems with intelligence for improved performance. When there is variation in object size, weight, shape, and fragility, robotic system integrators find it difficult to use traditional rigid and expensive grippers to automate mundane, low-value tasks. Soft grippers can provide a solution, adapting to variations in objects and other conditions. Enabling electrical operation of soft fingers enables direct integration with robotic arms eliminating complex infrastructure requirements and reducing cost, which will help improve the return on investment and reduce barriers of entry for small businesses to utilize this latest technology. The research will undertake testing and validation under common, realistic conditions encountered in fresh produce picking and packing and e-commerce order fulfillment applications. The research goals are to: optimize the design and fabrication techniques and quantify grasping behavior for improved adaptability, provide feedback control, and integrate intelligence to the grippers by incorporating data and user input to determine optimal grasp parameters.\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": "2644", "attributes": { "award_id": "1843074", "title": "SBIR Phase I: Non-Chromatographic Method for the Purification of Monoclonal 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": 7730, "first_name": "Kaitlin", "last_name": "Bratlie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-02-01", "end_date": "2021-02-28", "award_amount": 225000, "principal_investigator": { "id": 7731, "first_name": "Kelli", "last_name": "Luginbuhl", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 915, "ror": "", "name": "Isolere Bio, Inc", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 915, "ror": "", "name": "Isolere Bio, Inc", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to improve manufacturing technology for the purification of monoclonal antibodies, which are important therapeutics as well as valuable tools in research and diagnostics. Approximately four new antibody drugs are approved every year, and while they can have tremendous clinical outcomes and are sometimes heralded as \"magic bullets,\" they often come with a significant price tag. This puts a strain on patients and insurance companies, limiting the accessibility of antibody-based drugs. Furthermore, antibodies are critical research tools that enhance the understanding of biology. The technology developed in this research project will provide a completely novel method for the purification of antibodies from cell culture that will lower cost, increase manufacturing throughput, and accelerate the time to market for new therapeutics. Commercially, this technology will disrupt the current gold standard - Protein A resin - making antibody purification simpler, faster, and cheaper at all scales: research, clinical, and industrial. \n\nThe intellectual merit of this SBIR Phase I project is to develop technology for improved purification of monoclonal antibodies. Although upstream production of antibodies in cell culture has improved dramatically, downstream purification has not kept pace, resulting in a production bottleneck and a major market opportunity. The objectives of this SBIR Phase I project are to demonstrate technical and commercial feasibility of a new technology that combines affinity with liquid-liquid phase separation to separate antibodies from cell culture contaminants. It involves an antibody-binding domain fused to a biopolymer with stimulus responsive phase behavior. When this fusion protein is added to cell culture harvest, it binds the antibody and, after triggering the phase separation with salt, pulls the antibody out of solution. The purified antibody can then be eluted from the fusion by lowering the pH. This project aims to 1) optimize regeneration conditions so that the fusion can be reused, 2) evaluate long-term stability, and 3) validate the technology at scale and conduct a head-to-head comparison to the industry gold standard, Protein A resin. The goal is to identify storage conditions that provide a long shelf life for a product that can be reused 10-100 times without compromising antibody yield or purity. The focus of the project is to demonstrate promising capabilities of the technology for use in industrially manufactured monoclonal antibodies, replacing conventional chromatography steps with a simpler and more cost-effective method.\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": "2784", "attributes": { "award_id": "1913609", "title": "SBIR Phase I: COWculator: Automated Cattle Counting and Bovine Temperature Screening from Aerial Feedlot Images", "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": 8256, "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": "2019-07-01", "end_date": "2021-02-28", "award_amount": 225000, "principal_investigator": { "id": 8257, "first_name": "Shoshana", "last_name": "Ginsburg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 952, "ror": "", "name": "IMAGINAG TECH, LLC", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 952, "ror": "", "name": "IMAGINAG TECH, LLC", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a quick, easy, and accurate way to count cattle and detect bovine illnesses on feedlots and ranches via Unmanned Aerial Vehicles (UAVs). Current methods for counting cattle are extremely time-consuming or inaccurate, and sometimes both. Additionally, bovine illnesses are often diagnosed too late, leading to 50% of cattle mortalities on feedlots and yielding a $1.9 billion economic loss to the cattle industry. The proposed technology will leverage aerial images to (a) count cattle accurately and efficiently and (b) identify ill cows up to one week before clinical symptoms appear without the need to install expensive health-monitoring equipment on each cow. Ultimately, the proposed technology promises to more broadly impact the way wildlife and endangered species are tracked by automating wildlife counting on aerial images. \n\nThis Small Business Innovation Research (SBIR) Phase I project proposes to develop an imaging-based solution for feedlot accountants, nutritionists, and auditors to monitor cattle. The project will leverage aerial photos of feedlot pens to automatically count all cattle breeds - regardless of season and ground conditions - using a combination of deep learning and traditional image processing tools. Additionally, this project will leverage aerial thermography to measure bovine temperatures; machine learning tools will be developed to differentiate between elevated body temperatures associated with illness and those associated with normal confounding factors. The goals of this Phase I project are to develop and fully validate the technology for cattle counting on feedlots and to establish the technical feasibility of leveraging aerial thermographic imaging for prediction of cattle health.\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": "2797", "attributes": { "award_id": "1913545", "title": "SBIR Phase I: A novel, active acoustic wearable for real-time deterioration assessment in Chronic Obstructive Pulmonary Disease (COPD)", "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": 8309, "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": "2019-07-01", "end_date": "2020-10-31", "award_amount": 225000, "principal_investigator": { "id": 8310, "first_name": "Maria", "last_name": "Artunduaga", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 523, "ror": "", "name": "Respira Labs, 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 will result from the development of a next-generation product that will revolutionize COPD management by empowering patients, providers and caregivers to monitor the disease, prevent respiratory attacks, and receive timely care at home. Currently, COPD kills one American every four minutes and costs nearly $72B a year - almost half of that cost is attributable to ER visits and hospitalization. Projected benefits include early detection of lung deterioration, which will facilitate preventive interventions at home and thereby reduce the $36B/year spent on ER and hospital visits. A recent CMS regulatory change (CMS-1689-FC) permits reimbursements for remote monitoring of COPD patients, indicating market readiness by recognizing the success of remote monitoring in reducing admissions and long-term acute care use. Our solution has a validated business model (through over 220 I-Corps customer interviews) that drives value for patients, physicians, provider networks and payers. Successful development of this product is forecast to create 42 new jobs (2024) with an annual payroll exceeding $6.5M. As a direct result of this award, this innovative product can reach the U.S. market in 2023, with $100M in projected revenue by 2027. \n \nThis Small Business Innovation Research (SBIR) Phase I project aims to develop a new gold-standard for detecting COPD deterioration. The product is based on low-cost audio sensors paired with AI algorithms on a Smartphone platform that track lung resonance and flag any changes in lung volume. Current methods for tracking lung function at home are sub-optimal. Home spirometers are difficult to use, and not reimbursable. Pulse-oximeters are highly inaccurate and only provide data at discrete points, leading to late diagnosis. This lack of timely information manifests in excess hospitalizations because detection often occurs too late to prevent an attack. This product creates a fundamental shift in the technology employed at home by identifying air trapping, a more sensitive biomarker for lung deterioration. The novelty is affirmed with two pending patent applications and a freedom to operate analysis that found no prior art of concern. Currently lung resonance measurement is not used in other respiratory monitoring devices, providing the company with a strong competitive differentiation. The goal of this project is to create a Minimum Viable Product that can be used for human testing. By the end of this award the company will test and validate the concept in a small cohort of patients and controls.\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": "2799", "attributes": { "award_id": "1913598", "title": "SBIR Phase I: Anti-Microbial Graphene Oxide Nanofiltration Membrane", "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": 8319, "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": "2019-07-01", "end_date": "2021-05-31", "award_amount": 225000, "principal_investigator": { "id": 8320, "first_name": "Shelby", "last_name": "Foster", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 959, "ror": "", "name": "CatalyzeH2O LLC", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 959, "ror": "", "name": "CatalyzeH2O LLC", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project results from the ability to design a reusable nanofiltration membrane platform for wastewater treatment. Energy-efficient and effective wastewater treatment for water purification and reuse remains a tremendous challenge because safe and reliable approaches are often capital and energy intensive. The production of clean water from wastewater for municipal or industrial reuse requires the removal of a wide range of organic and inorganic contaminants, including many hazardous and toxic substances (e.g., pesticides, heavy metals, pharmaceuticals, etc.). Energy costs are driven even higher by the high fouling propensity of polymeric membranes with the wide array of water contaminants. Preventing fouling and enabling high contaminant rejection with low energy requirements remain the two core challenges of membrane filtration for wastewater treatment. The proposed technology will address the two core challenges through the use of an anti-fouling surface chemistry. The low energy requirements, contaminant rejection, and anti-fouling properties of the proposed membrane make it a disruptive innovation that can easily penetrate the market, providing a cost-effective solution that is lacking in current membrane purification systems.\n\nThis SBIR Phase I project proposes to develop a nanofiltration technology utilizing surface chemistry modification for the creation of an anti-fouling membrane for the rejection of pesticides. The United States spends nearly $9 billion a year on pesticides, which account for 16% of the world pesticide market. Out of the 25 most common active ingredients in pesticides, 76% are water soluble, which leads to contaminated soil, groundwater, and nearby bodies of water. The objectives of this project are to remove common commercial pesticides from water while investigating the advantageous effects of an anti-fouling membrane surface. Performance of the membrane will be investigated through cross flow filtration experiments to identify rejection, stability, and anti-fouling properties. The vision is to create a nanofiltration membrane with a broad contaminant rejection while decreasing energy requirements and fouling.\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 } } ], "meta": { "pagination": { "page": 1392, "pages": 1424, "count": 14236 } } }