Grants

Austin Community Foundation

Austin Community Foundation is an active grantmaking foundation based in Austin, TX. Focus: philanthropy. Contact the foundation directly for current funding opportunities.

Department of Commerce

Austin FY 2021 – FY 2023 EDA Planning and Local Technical Assistance

NSF

The project aims to serve the national interest by addressing student retention and success at a two-year college through the development of a multidisciplinary network of course-based undergraduate research experiences (CUREs) in introductory STEM courses. In collaboration with community partners, Delgado Community College seeks to engage students in authentic, place-based research projects that introduce STEM research early in their academic journey and support transitions to four-year institutions or the workforce. Students contribute to their communities by investigating research questions along the Lafayette Greenway, a field location in New Orleans, Louisiana. By embedding research opportunities in early course work, the project fosters technical skill development and responds to the national demand for a skilled STEM workforce. As part of a broader movement to expand early research experiences, this effort offers a replicable model for other two-year colleges. The goals of the project are to: 1) improve student success and retention in STEM by embedding CUREs into STEM pathways, 2) foster a learning context that promotes students' development as scientists, and 3) build relationships with community partners to support students' transition to four-year institutions. To meet these goals, the project creates a multidisciplinary network of research experiences integrated into courses such as biology, chemistry, and geology. Faculty from across STEM disciplines in collaboration with partners from academia, nonprofits, and industry will design research experiences and implement innovative instructional practices using data from the Greenway. A mixed-methods approach examines the impact of the intervention on faculty professional growth, as well as on students STEM knowledge, interest, and educational aspirations. Dissemination includes student and faculty presentations and publications in relevant journals. By integrating place-based research with evidenced-based teaching practices, the initiative positions two-year colleges as key contributors to advancing innovation in STEM education. The NSF IUSE: Innovation in Two-Year College STEM Education (ITYC) Program seeks to accelerate the impact of and advance knowledge about emerging and evidence-based practices in undergraduate STEM education at two-year colleges. 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.

Open Doors for Multicultural Families

Provide training to culturally and linguistically diverse families with Special Education students with Autism Spectrum Disorders in Seattle Public Schools on using iPads & iPad apps for communication needs.

Town of North Beach

Autism House Project

Town of North Beach

Autism House Project

Autism Science Foundation

Autism Science Foundation is an active grantmaking foundation based in Scarsdale, NY. Focus: health. Contact the foundation directly for current funding opportunities.

ACE AUTO BODY INC

Transportation and Storage and Mail Services

KERRYS AUTO BODY INC

Transportation and Storage and Mail Services

General Services, Non-Commodity

SS - Sole Source

Knight Body Shop

Transportation and Storage and Mail Services

NIOSH - National Institute for Occupational Safety and Health

Project Summary: In 2021, chemical manufacturing industries accounted for roughly 529,000 workers, and “Extraction” accounted for 226,840 workers in the U.S. oil & gas industry. With projected growth rate of 3.8% annually in the U.S. oil and gas industries, worker exposures to harmful levels of petrochemicals are likely to increase. From 2003-2015, this industry reported 148 fatal injuries from exposures to “chemicals and chemical products.” Long-term worker exposures are not well tracked, and monitoring only occurs after a problem or illness is identified. To help address this deficiency, Seacoast proposes to develop a fully automated indoor air quality (IAQ) analyzer with speciation capability. Seacoast's analyzer, the SeaPort AQ, combines a low-cost detector, two trap-and-purge, thermal desorption collectors for signal amplification, and the separation and resolving power of gas chromatography to (1) collect and analyze air in its immediate environment, (2) provide wirelessly networked data to industrial hygienists to track workplace exposure, (3) reduce the per-sample analytical costs by automatically including calibrations without user intervention. Remote programmability will provide low per-sample costs and the ability to track chemicals around the clock. In Phase I, Seacoast assembled and demonstrated a low-cost, chromatographic analyzer to speciate benzene, toluene, ethylbenzene, and xylene (BTEX) at levels well below the OSHA 8-hr (TWA) action level (500ppb) for benzene. We demonstrated automated field-blanks, calibration, and ambient air sample collection. We integrated an oven-less gas chromatography (GC) column specifically tailored for high resolution separation of BTEX from interfering compounds common to petrochemical and related industries. The analyzer uses a high-sensitivity chemical detector for hydrocarbons, which is insensitive to humidity and CO2. Miniature valves on custom designed manifolds eliminated the need for a bulky, expensive rotary valve, allowing the analyzer to cycle through collection, purges, and re-calibrations autonomously to verify analyzer performance. The Phase I prototype exceeded our proposed goal to sample and detect benzene at 50 ppb in under 30 minutes, demonstrating collection and detection of ~5ppb of benzene in less than 30 minutes. Seacoast's air quality analyzer can finally provide hourly vapor concentration tracking data, a vast improvement over weekly or monthly data to improve exposure or pollution tracking models at a per-sample cost of ~$1. In Phase II Seacoast will complete materials and system optimization and construct robust prototypes for field testing by two experts in the two primary industries interested in air monitoring: industrial hygiene and environmental pollution mitigation. These experts will perform side-by-side comparisons of our prototype and conventional systems, and field tests at contaminated environmental sites where BTEX and other chemical pollution is known to be present at elevated concentrations in the air.

NINDS - National Institute of Neurological Disorders and Stroke

PROJECT SUMMARY Up to 25% of individuals in the United States have low serum vitamin B12 levels, leading to hematologic impairment (megaloblastic anemia) and/or neurologic deficits (loss of coordination, memory loss, psychosis). Comorbid B12 deficiency is associated with worse neurologic outcomes in patients with Alzheimer’s disease (AD) and Parkinson’s disease. Therefore, improvements in the diagnosis and treatment of B12 deficiency, either as a primary disease or a comorbid condition, have the potential to substantially improve brain health at a population level. I recently discovered an autoimmune cause of B12 deficiency restricted to the central nervous system (CNS), termed autoimmune B12 central deficiency (ABCD). Using unbiased antigen discovery technology, I identified autoantibodies targeting the transcobalamin receptor (CD320) which inhibit cellular uptake of B12, an essential cofactor for hematopoiesis and myelination. Anti-CD320 is highly predictive (96% specificity) of low B12 in the CSF despite normal B12 in the blood and remarkably prevalent (~10%) in patients with dementia. However, the mechanism by which anti-CD320 impairs B12 transport across the blood-brain barrier (BBB), its effect on neurologic function, and its comorbid contribution to neurodegeneration remain unknown. In Aim 1 of this proposal, I will elucidate the mechanism of action of anti-CD320 at atomic resolution. In Aim 2, I will determine the effects of anti-CD320 on myelination and neurologic function. Finally, in Aim 3, I will measure the prevalence and penetrance of anti-CD320 in AD. A mechanistic understanding of ABCD may lead to substantial improvements in brain health, ameliorate the burden of unexplained neurologic disease, and identify a modifiable contributor to cognitive dysfunction in patients with comorbid neurodegenerative disorders like AD. With the support of my mentors and advisors at the University of California San Francisco, this K08 proposal will allow me to fill training gaps and acquire new technical, communication, and management skills necessary for my transition to an independent investigator role at the interface of autoimmune neurology and neurodegeneration.

NSF

This project aims to serve the national interest by improving curricula in computer science education. Computing professionals need to understand the possibilities and limitations of computation in order to design efficient algorithms for problems that can be solved in practice, or to avoid large investments in attempts to implement solutions for problems which have been proven to require unreasonable amounts of time or other resources. Modeling computation is an important building block for this understanding, however, students often struggle with abstract modeling and visualization. A prior Level 1 Engaged Student Learning project resulted in a prototype tool which provides immediate feedback on the computational models designed by students. This Level 2 Engaged Student Learning project aims to add features to the tool, improve its usability and adaptability, and investigate its impact on student problem-solving at a larger scale, in different educational settings. The existing Automated Feedback for Computing Theory (AFCT) prototype tool was built on the widely used Java Formal Languages and Automata Package (JFLAP) visualization tool that aids students in learning the basic concepts of formal languages and automata theory. The enhanced tool developed in this project will initially be deployed and outcomes assessed in theoretical computer science courses at the five collaborating institutions. It will be made available under an opensource license to enable others to use and modify the software to suit their needs. The research questions are focused on understanding the impacts of the tool on students' behavior, performance, and learning of computing theory; whether students from different types of institutions are impacted in significantly different ways; and the effects of various types of feedback on students' learning. The tool's added functionality, improved usability, and availability as opensource software will encourage its adoption at other institutions and increase its educational benefits. The project, including the upgraded feedback tool and the associated research study, will provide new insights into pedagogical approaches for improving student learning and will help students to be better prepared to develop high-quality software. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.

NSF

This project aims to serve the national interest by improving curricula in computer science education. Computing professionals need to understand the possibilities and limitations of computation in order to design efficient algorithms for problems that can be solved in practice, or to avoid large investments in attempts to implement solutions for problems which have been proven to require unreasonable amounts of time or other resources. Modeling computation is an important building block for this understanding, however, students often struggle with abstract modeling and visualization. A prior Level 1 Engaged Student Learning project resulted in a prototype tool which provides immediate feedback on the computational models designed by students. This Level 2 Engaged Student Learning project aims to add features to the tool, improve its usability and adaptability, and investigate its impact on student problem-solving at a larger scale, in different educational settings. The existing Automated Feedback for Computing Theory (AFCT) prototype tool was built on the widely used Java Formal Languages and Automata Package (JFLAP) visualization tool that aids students in learning the basic concepts of formal languages and automata theory. The enhanced tool developed in this project will initially be deployed and outcomes assessed in theoretical computer science courses at the five collaborating institutions. It will be made available under an opensource license to enable others to use and modify the software to suit their needs. The research questions are focused on understanding the impacts of the tool on students' behavior, performance, and learning of computing theory; whether students from different types of institutions are impacted in significantly different ways; and the effects of various types of feedback on students' learning. The tool's added functionality, improved usability, and availability as opensource software will encourage its adoption at other institutions and increase its educational benefits. The project, including the upgraded feedback tool and the associated research study, will provide new insights into pedagogical approaches for improving student learning and will help students to be better prepared to develop high-quality software. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.

NSF

Soft materials, including hydrogels, elastomers, and biological tissues, play an essential role in biomedical applications, flexible electronics, and soft robotics. However, understanding their fracture behavior remains a fundamental challenge due to nonlinear deformation and strain localization near crack tips. Current experimental techniques, such as methods based on full-field deformation measurements, provide valuable data but lack the resolution needed to capture localized damage. Additionally, existing computational tools to model fracture fail to efficiently integrate these full-field deformation measurements, leading to predictive inaccuracies. This award supports fundamental research that looks to overcome these limitations by developing an integrated experimental and computational framework for analyzing soft material fracture behavior. The outcomes of this research seek to advance the design of more reliable soft materials, enhancing the durability of medical implants, the resilience of soft robotic components, and the efficiency of energy-absorbing materials. Furthermore, the project looks to contribute to workforce development by integrating research findings into engineering courses, engaging students in interdisciplinary research, and supporting outreach programs to inspire the next generation of scientists and engineers. The objective of this award is to understand and model soft material fracture mechanics from full-field deformation measurements by resolving crack-tip inaccuracies. This research will develop high-resolution experimental methods for tracking crack propagation using improved digital image correlation and digital volume correlation techniques, combined with state-of-the-art machine learning algorithms. These measurements will be used to inversely calibrate phase-field fracture models, enabling precise calibration of material parameters such as stiffness and fracture toughness. The approach will also include development of an automated constitutive modeling framework based on physics-augmented machine learning to refine phenomenological models and minimize model form errors introduced by manual model selection. The modeling scheme will be validated through experiments on brittle hydrogels, demonstrating the effectiveness in capturing complex fracture behavior. By combining experimental and computational approaches, this effort looks to provide a robust framework for soft material fracture analysis, thereby establishing a foundation for future advancements in soft materials and their uses. 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.

NSF

The objective of this project is to update and advance an instrument necessary to conduct the trusted measurement of the concentration of dissolved oxygen in seawater by US and other international hydrographic programs. The Scripps Oceanographic Data Facility, a technical group operating under the Scripps Ship Operations and Marine Technical Support division and organized under Shipboard Technical Support, is responsible annually for thousands of measurements of dissolved oxygen performed on research vessels, while at sea, on bottles collected using classic hydrographic sampling techniques and measurement protocols. The majority of these measurements are for NSF funded programs and projects and constitute the reference-quality oxygen data for virtually all of the major NSF-funded global hydrography efforts that are the foundation of widely used databases. This award will allow continuation and improvement of these efforts through critical updates to a system that is ageing into obsolescence. Over the past 35 years Scripps has replicated and supported these oxygen titration systems for other oceanographic laboratories around the world. Many of these groups intend to either replicate the new design or hire Scripps to construct it through a service agreement. Leading the new design will allow Scripps to continue its role as an international leader in the collection and quality control of hydrographic oxygen data. As done for the past five years, technical experts will continue to interact with undergraduates, where students are given the opportunity to visit the laboratory and observe and, under supervision, operate the oxygen titration system. The methods employed to reach the project objective include the engineering of a new optical and control system, new component qualification, and extended testing both on-shore and at-sea. Full system qualification requires operation of the new system alongside an operational legacy system for extended periods. The project will modernize the hardware and software used to perform the classic Winkler titration method for determining dissolved oxygen concentration in seawater. In the process of updating an obsolete (but historically very well performing) system, this effort will capitalize on modern components and also merge two well-established methods (determination by direct absorbance and titration) into one system. The resulting system will maintain the outstanding measurement accuracy and precision achieved through titration by the previous generation instrument. The new system will be capable of a rapid direct measurement of dissolved oxygen concentration across the full oceanographic range (by direct absorbance), before the titration is performed. With its updated optical components, the system will require less user intervention and improve reliability during regular operation. 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.

Air Force -- Research Lab

Automated Processes for Knowledge Discovery and Information Retrieval

DEPT OF DEFENSE.DEPT OF THE AIR FORCE.AIR FORCE MATERIEL COMMAND.AIR FORCE RESEARCH LABORATORY.FA8750 AFRL RIK

Automated Processes for Knowledge Discovery and Information Retrieval

NIH

ABSTRACT Significance to VA: Knee osteoarthritis is a major health problem in Veterans and accounts for approximately one-half of all joint replacement surgeries in the VA system. Total knee arthroplasty is the current standard of care to treat advanced knee osteoarthritis and is effective in reducing pain. However, nearly 1 in 4 patients report not being satisfied with their surgery. While the reasons for this high number of unsatisfied patients is multifactorial, poor knee function is a major driver behind poor outcomes. Clinical research studies often characterize functional deficits by evaluating Arthrogenic Muscle Inhibition (AMI). But these evaluations typically require the patient to maximally contract their muscles, which often causes pain and leads to unreliable results. Additionally, specialized equipment and highly trained investigators are essential for reliable measurements. Innovation and Impact: This project addresses these challenges in quantifying AMI by developing, validating, and implementing a novel nerve stimulating platform called the Smart Nerve Stimulator to quantify Arthrogenic Muscle Inhibition (AMI) in Veterans before and after undergoing total knee arthroplasty surgery. This is an impactful innovation because it addresses an unmet clinical need by leveraging state-of-the-art electrode technology and a custom-developed user interface to establish the first-of-its-kind neuromechanical database in Veterans with knee pain. We expect that this will serve as a novel biomarker of knee health and support future VA research aimed at improving therapeutics for Veterans with knee osteoarthritis. Specific Aims: This device development Merit application has 3 aims. Aim 1 will optimize a fully automated nerve conduction testing tool using custom developed software and high-density stimulating and recording electrode arrays. Aim 2 will establish the first-of-its-kind Neuromechanical Database to Advance Veteran Health. Aim 3 will develop a novel AMI biomarker using machine learning. Methodology: This study uses an iterative design cycle in Aim 1 to optimize a working prototype that leverages our automated software interface and designs, fabricates, and deploys high-density stimulating and recording electrodes. Our Smart Nerve Stimulator will quantify AMI as the ratio H-reflex:M-max that represents the spinal and muscle excitability. Aim 2 uses a cross-sectional study design to evaluate arthrogenic muscle inhibition in 3 Veteran groups: 1) Veterans with knee osteoarthritis who are scheduled to undergo total knee arthroplasty surgery, 2) Veterans who are age/sex-matched but without any knee symptoms or pathology, and 3) Veterans who are 25-45 years of age and sex-matched without any knee symptoms or pathology. Aim 3 uses our innovative biomechanically informed latent neuromechanical dynamics learning architecture to automatically differentiate between Veterans with knee osteoarthritis, total knee arthroplasty, and healthy controls. We will quantify arthrogenic muscle inhibition before and after total knee arthroplasty to evaluate the effects of surgery on neuromechanical function. We will also quantify arthrogenic muscle inhibition with young and age-matched Veterans without knee pain to establish test-retest reliability. Path to Translation/Implementation: Our Smart Nerve Stimulator has strong paths towards translation towards commercialization as well as implementation into knee osteoarthritis research conducted at the CReATE Motion Center at the CMCVAMC and Atlanta VAMC. Our team will work with the University of Pennsylvania and CMCVAMC to protect our intellectual property and evaluate a path towards commercialization.

NIGMS - National Institute of General Medical Sciences

Project Summary/Abstract In this application, Eastern Washington University (EWU) proposes purchase of a Leica Microsystems THUNDER Imager Live Cell, an automated brightfield, phase contrast, and inverted fluorescence microscope with on-stage environmental chamber for endpoint and time- lapse imaging. Research projects in bone cell biology, neurobiology, and microbiology conducted by major users will leverage the instrument’s programmable automated imaging protocols, multi-color fluorescence imaging, and computational clearing to interrogate cultured to better understand mechanisms of bone homeostasis, brain function, and anti-microbial agents. In addition to supporting current and future research projects, the system will support undergraduate- and graduate-level courses in histology, microscopy, and cellular biology. Consisting of the microscope, motorized stage, objective, filter, and environmental chamber components, the Thunder Imager and its controlling computer will be housed in EWU’s newly renovated microscopy shared-use lab, an interdisciplinary facility that will host research and education activities from multiple departments including biology, geosciences, and chemistry. This facility will provide preparation areas for specimen processing for scanning electron microscopy and fluorescent and live-cell imaging with the proposed system. As part of a shared facility, the Thunder Imager will be available for research and educational use to all members of the EWU community and other institutions within the greater eastern Washington region, with training and supervision provided by a committee composed of the project’s principal investigator and major users. Acquisition of this system will enhance EWU’s research capacity and contribute to a richer training experience for our students.

NLM - National Library of Medicine

Abstract National Center of Biotechnology (NCBI) and International Nucleotide Sequence Database Collaboration (INSDC) databases have been cornerstones of public sharing of pathogen genomic data for basic science and epidemiological investigations. To ensure the integrity of sequence databases, all sequences must be validated and curated prior to deposition into GenBank. A major bottleneck in the rapid sharing of viral sequencing data is the requirement for inclusion of gene and/or protein annotations along with curation of sequences prior to deposition to NCBI GenBank. Annotations are critical for cross-referencing other NCBI databases, while curation is required to ensure the accuracy and usability of the databases. However, correctly annotating and performing appropriate quality control can be challenging to non-specialist submitters. Notably, this limitation is restricted to viral sequences, as prokaryotic and eukaryotic genome annotation and quality control has been automated via the NCBI's Prokaryotic and Eukaryotic Genome Annotation Pipelines. Recently, NCBI has created an open-source viral annotation tool called VADR (Viral Annotation DefineR). VADR validates and annotates viral sequences using RefSeq-based models. VADR is currently limited to a select number of human viruses, including SARS-CoV-2, influenza virus, monkeypox virus, norovirus, dengue virus, and respiratory syncytial virus. The implementation of VADR has reduced manual reviews by NCBI indexers by >95% for these viruses, illustrating its critical role in prescreening submitted viral sequences. However, for most viruses relevant to clinical infectious diseases and public health, there is no way to rapidly submit unannotated consensus sequences to open databases. Here, we propose to accelerate the public sharing of viral sequences by building, validating, and implementing sustainable, open-source VADR models for human viruses relevant to public health. Specifically, we will build, validate, and implement appropriate open-source models for VADR for respiratory viruses, viruses associated with vaccine preventable diseases, and hepatitis viruses, taking advantage of the moderate numbers of viral sequences available for these viruses. Our group has significant knowledge of viral genome quality control, annotation, and submission, including their associated challenges as currently constructed in NCBI. Using viral genome data from other sequencing projects, we will validate the accuracy and sustainability of our newly generated VADR models on sequences not yet present in NCBI GenBank. Working together with NCBI, we will deploy these open-source models to automate analysis for GenBank by the end of the grant period. The proposed work will both ease submission and increase open genomic data for viruses of public health importance. This work will and ensure that we are ready for increasing amounts of routine public health sequencing and help overall preparedness for the next pandemic. 1

NIBIB - National Institute of Biomedical Imaging and Bioengineering

Abstract With the progress in vascular bioengineering, three-dimensional (3D) bioprinting has emerged as a potential approach for generating physiologically relevant blood vessels with the ability to grow, remodel, and repair vascular structures in vitro and in vivo. Different 3D bioprinting techniques and strategies have been investigated and established over the past years to fabricate biomimetic blood vessels and are constantly being refined and improved. To this end, sacrificial bioprinting technologies developed in the past few years have provided a convenient solution for this problem due to its capability to fabricate interconnected microchannels of arbitrary geometries and connectivity. However, although sacrificial bioprinting provides a versatile method to create vessel-like structures, there are two major obstacles preventing its further growth, i.e., the patterns attainable are relatively simple, and the resolutions attainable for the perfusable vessels are insufficient in most cases, both due to the extrusion bioprinting method that has been exclusively utilized in sacrificial bioprinting so far. We accordingly propose to develop an innovative hybrid multi-material and multi- method autonomous printing (HM2AP) technology integrating aerosol jet sacrificial printing and extrusion bioprinting, which enables facile construction of 3D complex and hierarchical vascular structures with feature sizes ranging from 10-100 µm embedded within tissue microenvironments. Successful completion of this project will build a paradigm-shifting bioprinting technology using which complex and hierarchical vessels of varying dimensions can be readily generated.

NSF

Coral reefs are a critical nexus of many food systems, as a primary source of protein and micronutrients for ~1 billion people, and they are highly sensitive to changing ocean and land conditions, which can deplete their nutritional capacity. Agriculture production is also negatively impacted, particularly in areas experiencing higher levels of drought. Thus, when drought and reef decline occur in tandem, it signals a particularly grave threat to communities, making resilient food systems an immediate and critical challenge. This project will investigate different approaches to addressing these shocks to food systems. The project team will work with local communities to develop new food sources, systems to retain nutrients locally and a decision framework to enable communities to meet their nutritional needs. The project will rely on a continuous data collection program to assemble precise and up to date information. Temporal data will increase the resolution of the decision framework, thereby testing the ecosystem and food resource enhancement approaches on longer timescales. Data collection will leverage cutting-edge environmental monitoring techniques, including environmental DNA and image and video-based mapping and monitoring, to obtain a unique and holistic view on the functioning of the marine and terrestrial ecosystems. The project team will build on previous work in the test area of Southwest Madagascar, and the resulting framework with be directly transferable to other areas where drought and reef decline are impacting coastal communities in the United States such as Florida, Hawaii, the U.S. islands in the Caribbean and the Pacific. This project will enable U.S. communities to continually assess the health of the marine and terrestrial human-environment ecosystem and continually update the decision framework to bolster the food system and will be used as an early warning system for food shocks. 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.